Merge pull request #21 from fmcquillan99/1dot18dot0-jupyter-notebooks
1dot18dot0 jupyter notebooks
diff --git a/community-artifacts/Data-types-and-transformations/Encoding-categorical-variables-v2.ipynb b/community-artifacts/Data-types-and-transformations/Encoding-categorical-variables-v2.ipynb
index 5e4cb6f..9eada83 100644
--- a/community-artifacts/Data-types-and-transformations/Encoding-categorical-variables-v2.ipynb
+++ b/community-artifacts/Data-types-and-transformations/Encoding-categorical-variables-v2.ipynb
@@ -12,18 +12,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
@@ -32,27 +21,13 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.4.0 on GCP (demo machine)\n",
- "%sql postgresql://gpadmin@35.184.253.255:5432/madlib\n",
+ "# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib\n",
- "\n",
- "# Greenplum Database 4.3.10.0\n",
- "#%sql postgresql://gpdbchina@10.194.10.68:61000/madlib"
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
@@ -75,12 +50,12 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.14-dev, git revision: rc/1.13-rc1-21-g3af2d70, cmake configuration time: Mon Feb 26 18:00:54 UTC 2018, build type: release, build system: Linux-2.6.32-696.20.1.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-100-g4987e8f, cmake configuration time: Wed Mar 24 23:51:47 UTC 2021, build type: release, build system: Linux-3.10.0-1160.21.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.14-dev, git revision: rc/1.13-rc1-21-g3af2d70, cmake configuration time: Mon Feb 26 18:00:54 UTC 2018, build type: release, build system: Linux-2.6.32-696.20.1.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]"
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-100-g4987e8f, cmake configuration time: Wed Mar 24 23:51:47 UTC 2021, build type: release, build system: Linux-3.10.0-1160.21.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
"execution_count": 3,
@@ -103,7 +78,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -313,7 +288,7 @@
" (20, None, 0.45, 0.32, 0.1, 9)]"
]
},
- "execution_count": 28,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -366,7 +341,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -617,7 +592,7 @@
" (20, 0.45, 0.32, 0.1, 9, 0, 0, 0)]"
]
},
- "execution_count": 29,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -645,7 +620,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -917,7 +892,7 @@
" (20, 0.45, 0.32, 0.1, 9, 0, 0, 0, 1)]"
]
},
- "execution_count": 30,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -3732,7 +3707,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -3983,7 +3958,7 @@
" (20, None, 0.45, 0.32, 0.1, 9, 0, 0)]"
]
},
- "execution_count": 24,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -4018,7 +3993,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Data-types-and-transformations/Path-demo-4.ipynb b/community-artifacts/Data-types-and-transformations/Path-demo-4.ipynb
index 57cbae3..a8fa2cf 100644
--- a/community-artifacts/Data-types-and-transformations/Path-demo-4.ipynb
+++ b/community-artifacts/Data-types-and-transformations/Path-demo-4.ipynb
@@ -11,9 +11,8 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
- "collapsed": false,
"scrolled": true
},
"outputs": [],
@@ -23,35 +22,52 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 2,
"metadata": {
- "collapsed": false,
"scrolled": true
},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: fmcquillan@madlib'"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# %sql postgresql://gpdbchina@10.194.10.68:55000/madlib\n",
- "%sql postgresql://fmcquillan@localhost:5432/madlib"
+ "# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-100-g4987e8f, cmake configuration time: Wed Mar 24 23:51:47 UTC 2021, build type: release, build system: Linux-3.10.0-1160.21.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-100-g4987e8f, cmake configuration time: Wed Mar 24 23:51:47 UTC 2021, build type: release, build system: Linux-3.10.0-1160.21.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%sql select madlib.version();"
]
@@ -65,10 +81,8 @@
},
{
"cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 4,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -212,16 +226,23 @@
" </tr>\n",
" <tr>\n",
" <td>2015-04-15 02:16:00</td>\n",
+ " <td>101331</td>\n",
+ " <td>103</td>\n",
+ " <td>WINE</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2015-04-15 02:16:00</td>\n",
" <td>102201</td>\n",
" <td>107</td>\n",
" <td>BEER</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>2015-04-15 02:16:00</td>\n",
- " <td>101331</td>\n",
- " <td>103</td>\n",
- " <td>WINE</td>\n",
+ " <td>2015-04-15 02:17:00</td>\n",
+ " <td>103711</td>\n",
+ " <td>109</td>\n",
+ " <td>BEER</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
@@ -233,13 +254,6 @@
" </tr>\n",
" <tr>\n",
" <td>2015-04-15 02:17:00</td>\n",
- " <td>103711</td>\n",
- " <td>109</td>\n",
- " <td>BEER</td>\n",
- " <td>0.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2015-04-15 02:17:00</td>\n",
" <td>102201</td>\n",
" <td>107</td>\n",
" <td>WINE</td>\n",
@@ -247,13 +261,6 @@
" </tr>\n",
" <tr>\n",
" <td>2015-04-15 02:18:00</td>\n",
- " <td>102871</td>\n",
- " <td>108</td>\n",
- " <td>BEER</td>\n",
- " <td>0.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2015-04-15 02:18:00</td>\n",
" <td>103711</td>\n",
" <td>109</td>\n",
" <td>LANDING</td>\n",
@@ -261,15 +268,15 @@
" </tr>\n",
" <tr>\n",
" <td>2015-04-15 02:18:00</td>\n",
- " <td>101331</td>\n",
- " <td>103</td>\n",
- " <td>WINE</td>\n",
+ " <td>102871</td>\n",
+ " <td>108</td>\n",
+ " <td>BEER</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>2015-04-15 02:19:00</td>\n",
- " <td>103711</td>\n",
- " <td>109</td>\n",
+ " <td>2015-04-15 02:18:00</td>\n",
+ " <td>101331</td>\n",
+ " <td>103</td>\n",
" <td>WINE</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
@@ -282,16 +289,16 @@
" </tr>\n",
" <tr>\n",
" <td>2015-04-15 02:19:00</td>\n",
- " <td>102871</td>\n",
- " <td>108</td>\n",
+ " <td>103711</td>\n",
+ " <td>109</td>\n",
" <td>WINE</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>2015-04-15 02:22:00</td>\n",
- " <td>101443</td>\n",
- " <td>104</td>\n",
- " <td>BEER</td>\n",
+ " <td>2015-04-15 02:19:00</td>\n",
+ " <td>102871</td>\n",
+ " <td>108</td>\n",
+ " <td>WINE</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
@@ -302,10 +309,10 @@
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>2015-04-15 02:25:00</td>\n",
- " <td>102871</td>\n",
- " <td>108</td>\n",
- " <td>LANDING</td>\n",
+ " <td>2015-04-15 02:22:00</td>\n",
+ " <td>101443</td>\n",
+ " <td>104</td>\n",
+ " <td>BEER</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
@@ -316,6 +323,13 @@
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
+ " <td>2015-04-15 02:25:00</td>\n",
+ " <td>102871</td>\n",
+ " <td>108</td>\n",
+ " <td>LANDING</td>\n",
+ " <td>0.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
" <td>2015-04-15 02:29:00</td>\n",
" <td>101881</td>\n",
" <td>105</td>\n",
@@ -349,26 +363,26 @@
" (datetime.datetime(2015, 4, 15, 2, 9), 100821, 101, u'WINE', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 15), 102201, 107, u'WINE', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 15), 101331, 103, u'LANDING', 0.0),\n",
- " (datetime.datetime(2015, 4, 15, 2, 16), 102201, 107, u'BEER', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 16), 101331, 103, u'WINE', 0.0),\n",
- " (datetime.datetime(2015, 4, 15, 2, 17), 101331, 103, u'HELP', 0.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 16), 102201, 107, u'BEER', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 17), 103711, 109, u'BEER', 0.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 17), 101331, 103, u'HELP', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 17), 102201, 107, u'WINE', 0.0),\n",
- " (datetime.datetime(2015, 4, 15, 2, 18), 102871, 108, u'BEER', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 18), 103711, 109, u'LANDING', 0.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 18), 102871, 108, u'BEER', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 18), 101331, 103, u'WINE', 0.0),\n",
- " (datetime.datetime(2015, 4, 15, 2, 19), 103711, 109, u'WINE', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 19), 101331, 103, u'CHECKOUT', 16.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 19), 103711, 109, u'WINE', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 19), 102871, 108, u'WINE', 0.0),\n",
- " (datetime.datetime(2015, 4, 15, 2, 22), 101443, 104, u'BEER', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 22), 102871, 108, u'CHECKOUT', 21.0),\n",
- " (datetime.datetime(2015, 4, 15, 2, 25), 102871, 108, u'LANDING', 0.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 22), 101443, 104, u'BEER', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 25), 101443, 104, u'CHECKOUT', 12.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 25), 102871, 108, u'LANDING', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 29), 101881, 105, u'LANDING', 0.0),\n",
" (datetime.datetime(2015, 4, 15, 2, 30), 101881, 105, u'BEER', 0.0)]"
]
},
- "execution_count": 18,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -429,12 +443,74 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {
- "collapsed": false,
"scrolled": true
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "6 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>session_id</th>\n",
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+ " (103, 1.0, 16.0),\n",
+ " (104, 1.0, 12.0),\n",
+ " (108, 1.0, 21.0)]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%%sql\n",
"SELECT madlib.path(\n",
@@ -459,11 +535,55 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "5 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>session_id</th>\n",
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+ ]
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+ "execution_count": 6,
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+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%%sql\n",
"SELECT session_id, sum(checkout_rev) FROM path_output GROUP BY session_id ORDER BY session_id;"
@@ -478,11 +598,99 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "6 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
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+ " <th>revenue</th>\n",
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+ " <th>match_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2015-04-15 01:05:00</td>\n",
+ " <td>100821</td>\n",
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+ " <td>101443</td>\n",
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+ " <td>12.0</td>\n",
+ " <td>buy</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2015-04-15 02:22:00</td>\n",
+ " <td>102871</td>\n",
+ " <td>108</td>\n",
+ " <td>CHECKOUT</td>\n",
+ " <td>21.0</td>\n",
+ " <td>buy</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(datetime.datetime(2015, 4, 15, 1, 5), 100821, 100, u'CHECKOUT', 39.0, u'buy', 1.0),\n",
+ " (datetime.datetime(2015, 4, 15, 1, 17), 101121, 102, u'CHECKOUT', 15.0, u'buy', 1.0),\n",
+ " (datetime.datetime(2015, 4, 15, 1, 22), 101121, 102, u'CHECKOUT', 23.0, u'buy', 2.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 19), 101331, 103, u'CHECKOUT', 16.0, u'buy', 1.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 25), 101443, 104, u'CHECKOUT', 12.0, u'buy', 1.0),\n",
+ " (datetime.datetime(2015, 4, 15, 2, 22), 102871, 108, u'CHECKOUT', 21.0, u'buy', 1.0)]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%%sql\n",
"SELECT * FROM path_output_tuples ORDER BY session_id ASC, event_timestamp ASC;"
@@ -499,10 +707,8 @@
},
{
"cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 8,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -543,7 +749,7 @@
"[(100, 1.0, 39.0), (102, 1.0, 15.0), (102, 2.0, 23.0)]"
]
},
- "execution_count": 19,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -579,10 +785,8 @@
},
{
"cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "collapsed": false
- },
+ "execution_count": 9,
+ "metadata": {},
"outputs": [
{
"name": "stdout",
@@ -709,7 +913,7 @@
" (datetime.datetime(2015, 4, 15, 1, 22), 101121, 102, u'CHECKOUT', 23.0, u'buy', 2.0)]"
]
},
- "execution_count": 20,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -728,11 +932,55 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "3 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>session_id</th>\n",
+ " <th>match_id</th>\n",
+ " <th>elapsed_time</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0:02:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0:02:00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>2.0</td>\n",
+ " <td>0:04:00</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(100, 1.0, datetime.timedelta(0, 120)),\n",
+ " (102, 1.0, datetime.timedelta(0, 120)),\n",
+ " (102, 2.0, datetime.timedelta(0, 240))]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS path_output, path_output_tuples;\n",
@@ -763,11 +1011,61 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "3 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>date</th>\n",
+ " <th>user_id</th>\n",
+ " <th>session_id</th>\n",
+ " <th>revenue</th>\n",
+ " <th>avg_checkout_rev</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2015-04-15</td>\n",
+ " <td>100821</td>\n",
+ " <td>100</td>\n",
+ " <td>39.0</td>\n",
+ " <td>25.6666666667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2015-04-15</td>\n",
+ " <td>101121</td>\n",
+ " <td>102</td>\n",
+ " <td>15.0</td>\n",
+ " <td>25.6666666667</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2015-04-15</td>\n",
+ " <td>101121</td>\n",
+ " <td>102</td>\n",
+ " <td>23.0</td>\n",
+ " <td>25.6666666667</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(datetime.date(2015, 4, 15), 100821, 100, 39.0, 25.6666666666667),\n",
+ " (datetime.date(2015, 4, 15), 101121, 102, 15.0, 25.6666666666667),\n",
+ " (datetime.date(2015, 4, 15), 101121, 102, 23.0, 25.6666666666667)]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%%sql\n",
"SELECT DATE(event_timestamp), user_id, session_id, revenue,\n",
@@ -786,11 +1084,45 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <th>page_path</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[u'BEER', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(5L, [u'WINE', u'CHECKOUT']), (1L, [u'BEER', u'CHECKOUT'])]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS path_output, path_output_tuples;\n",
@@ -826,11 +1158,121 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "14 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>session_id</th>\n",
+ " <th>match_id</th>\n",
+ " <th>page_path</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'LANDING', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'LANDING', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>3.0</td>\n",
+ " <td>[u'LANDING', u'HELP', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>4.0</td>\n",
+ " <td>[u'HELP', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>5.0</td>\n",
+ " <td>[u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'LANDING', u'WINE', u'HELP', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[u'WINE', u'HELP', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>3.0</td>\n",
+ " <td>[u'HELP', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>4.0</td>\n",
+ " <td>[u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>104</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'BEER', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'BEER', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(100, 1.0, [u'LANDING', u'WINE', u'CHECKOUT']),\n",
+ " (100, 2.0, [u'WINE', u'CHECKOUT']),\n",
+ " (102, 1.0, [u'LANDING', u'WINE', u'CHECKOUT']),\n",
+ " (102, 2.0, [u'WINE', u'CHECKOUT']),\n",
+ " (102, 3.0, [u'LANDING', u'HELP', u'WINE', u'CHECKOUT']),\n",
+ " (102, 4.0, [u'HELP', u'WINE', u'CHECKOUT']),\n",
+ " (102, 5.0, [u'WINE', u'CHECKOUT']),\n",
+ " (103, 1.0, [u'LANDING', u'WINE', u'HELP', u'WINE', u'CHECKOUT']),\n",
+ " (103, 2.0, [u'WINE', u'HELP', u'WINE', u'CHECKOUT']),\n",
+ " (103, 3.0, [u'HELP', u'WINE', u'CHECKOUT']),\n",
+ " (103, 4.0, [u'WINE', u'CHECKOUT']),\n",
+ " (104, 1.0, [u'BEER', u'CHECKOUT']),\n",
+ " (108, 1.0, [u'BEER', u'WINE', u'CHECKOUT']),\n",
+ " (108, 2.0, [u'WINE', u'CHECKOUT'])]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS path_output, path_output_tuples;\n",
@@ -859,11 +1301,73 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "6 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>session_id</th>\n",
+ " <th>match_id</th>\n",
+ " <th>page_path</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'LANDING', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'LANDING', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>2.0</td>\n",
+ " <td>[u'LANDING', u'HELP', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'LANDING', u'WINE', u'HELP', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>104</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'BEER', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'BEER', u'WINE', u'CHECKOUT']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(100, 1.0, [u'LANDING', u'WINE', u'CHECKOUT']),\n",
+ " (102, 1.0, [u'LANDING', u'WINE', u'CHECKOUT']),\n",
+ " (102, 2.0, [u'LANDING', u'HELP', u'WINE', u'CHECKOUT']),\n",
+ " (103, 1.0, [u'LANDING', u'WINE', u'HELP', u'WINE', u'CHECKOUT']),\n",
+ " (104, 1.0, [u'BEER', u'CHECKOUT']),\n",
+ " (108, 1.0, [u'BEER', u'WINE', u'CHECKOUT'])]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS path_output, path_output_tuples;\n",
@@ -900,9 +1404,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.16"
}
},
"nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
}
diff --git a/community-artifacts/Deep-learning/Load-model-selection-table-v1.ipynb b/community-artifacts/Deep-learning/Load-model-selection-table-v1.ipynb
deleted file mode 100644
index 778d988..0000000
--- a/community-artifacts/Deep-learning/Load-model-selection-table-v1.ipynb
+++ /dev/null
@@ -1,955 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Load model selection table\n",
- "This utility function generates model selection tuples (model architecture, compile parameters, fit parameters) for both hyper-parameter search and model architecture search. The model selection table and associated summary table are used by the multiple model fit feature of MADlib. \n",
- "\n",
- "This utility was added in MADlib 1.17.\n",
- "\n",
- "## Table of contents\n",
- "\n",
- "<a href=\"#define_model_arch\">1. Define model architecture table</a>\n",
- "\n",
- "<a href=\"#load_model_arch\">2. Load model architecture</a>\n",
- "\n",
- "<a href=\"#load_model_selection\">3. Load model selection table</a>\n",
- "\n",
- "<a href=\"#load_model_selection_manual\">4. Create model selection table manually</a>\n",
- "\n",
- "<a href=\"#load_model_selection_auto\">5. Generate hyperparameters automatically</a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
- "source": [
- "%load_ext sql"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
- "# Greenplum Database 5.x on GCP - via tunnel\n",
- "%sql postgresql://gpadmin@localhost:8000/madlib\n",
- " \n",
- "# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>version</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql select madlib.version();\n",
- "#%sql select version();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"define_model_arch\"></a>\n",
- "# 1. Define model architecture table\n",
- "The model selection loader works in conjunction with the model architecture table, so we first create a model architecture table with two different models. See http://madlib.apache.org/docs/latest/group__grp__keras__model__arch.html for more details on the model architecture table.\n",
- "\n",
- "Import Keras libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "import keras\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Define model architecture with 1 hidden layer:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "dense_8 (Dense) (None, 10) 50 \n",
- "_________________________________________________________________\n",
- "dense_9 (Dense) (None, 10) 110 \n",
- "_________________________________________________________________\n",
- "dense_10 (Dense) (None, 3) 33 \n",
- "=================================================================\n",
- "Total params: 193\n",
- "Trainable params: 193\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model1 = Sequential()\n",
- "model1.add(Dense(10, activation='relu', input_shape=(4,)))\n",
- "model1.add(Dense(10, activation='relu'))\n",
- "model1.add(Dense(3, activation='softmax'))\n",
- " \n",
- "model1.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_8\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_9\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_10\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model1.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 2, \"batch_input_shape\": [null, 3], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"new_dense\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'\n",
- " "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Define model architecture with 2 hidden layers:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "dense_11 (Dense) (None, 10) 50 \n",
- "_________________________________________________________________\n",
- "dense_12 (Dense) (None, 10) 110 \n",
- "_________________________________________________________________\n",
- "dense_13 (Dense) (None, 10) 110 \n",
- "_________________________________________________________________\n",
- "dense_14 (Dense) (None, 3) 33 \n",
- "=================================================================\n",
- "Total params: 303\n",
- "Trainable params: 303\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model2 = Sequential()\n",
- "model2.add(Dense(10, activation='relu', input_shape=(4,)))\n",
- "model2.add(Dense(10, activation='relu'))\n",
- "model2.add(Dense(10, activation='relu'))\n",
- "model2.add(Dense(3, activation='softmax'))\n",
- " \n",
- "model2.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_11\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_12\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_13\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_14\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model2.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load_model_arch\"></a>\n",
- "# 2. Load model architecture\n",
- "\n",
- "Load both into model architecture table:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "2 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_id</th>\n",
- " <th>model_arch</th>\n",
- " <th>model_weights</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>__internal_madlib_id__</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
- " <td>None</td>\n",
- " <td>Sophie</td>\n",
- " <td>MLP with 1 hidden layer</td>\n",
- " <td>__madlib_temp_80732521_1576707528_41018934__</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
- " <td>None</td>\n",
- " <td>Maria</td>\n",
- " <td>MLP with 2 hidden layers</td>\n",
- " <td>__madlib_temp_54900499_1576707528_11190984__</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'MLP with 1 hidden layer', u'__madlib_temp_80732521_1576707528_41018934__'),\n",
- " (2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Maria', u'MLP with 2 hidden layers', u'__madlib_temp_54900499_1576707528_11190984__')]"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS model_arch_library;\n",
- "\n",
- "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
- " \n",
- "$$\n",
- "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
- "$$\n",
- "::json, -- JSON blob\n",
- " NULL, -- Weights\n",
- " 'Sophie', -- Name\n",
- " 'MLP with 1 hidden layer' -- Descr\n",
- ");\n",
- "\n",
- "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
- " \n",
- "$$\n",
- "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
- "$$\n",
- "::json, -- JSON blob\n",
- " NULL, -- Weights\n",
- " 'Maria', -- Name\n",
- " 'MLP with 2 hidden layers' -- Descr\n",
- ");\n",
- "\n",
- "SELECT * FROM model_arch_library ORDER BY model_id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load_model_selection\"></a>\n",
- "# 3. Load model selection table\n",
- "\n",
- "Select the model(s) from the model architecture table that you want to run, along with the compile and fit parameters. Unique combinations will be created for the set of model selection parameters:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "12 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>mst_key</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>12</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
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- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
- "\n",
- "SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table\n",
- " 'mst_table', -- model selection table output\n",
- " ARRAY[1,2], -- model ids from model architecture table\n",
- " ARRAY[ -- compile params\n",
- " $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,\n",
- " $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,\n",
- " $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$\n",
- " ],\n",
- " ARRAY[ -- fit params\n",
- " $$batch_size=4,epochs=1$$,\n",
- " $$batch_size=8,epochs=1$$\n",
- " ]\n",
- " );\n",
- " \n",
- "SELECT * FROM mst_table ORDER BY mst_key;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The name of the model architecture table is stored in the summary table:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_arch_table</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>model_arch_library</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'model_arch_library',)]"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM mst_table_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load_model_selection_manual\"></a>\n",
- "# 4. Create model selection table manually\n",
- "\n",
- "If you would like to have more control over the set of model selection parameters to run, you can manually create the model selection table and the associated summary table. Both must be created since they are needed by the multiple model fit module.\n",
- "\n",
- "For example, let's say we don't want all combinations but only want batch_size=4 for model_id=1 and batch_size=8 for model_id=2:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "Done.\n",
- "6 rows affected.\n",
- "6 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>mst_key</th>\n",
- " <th>model_arch_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
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- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS mst_table_manual;\n",
- "\n",
- "CREATE TABLE mst_table_manual(\n",
- " mst_key serial,\n",
- " model_arch_id integer,\n",
- " compile_params varchar,\n",
- " fit_params varchar\n",
- ");\n",
- "\n",
- "INSERT INTO mst_table_manual(model_arch_id, compile_params, fit_params) VALUES\n",
- "(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),\n",
- "(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),\n",
- "(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),\n",
- "(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'),\n",
- "(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'),\n",
- "(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=8,epochs=1');\n",
- "\n",
- "SELECT * FROM mst_table_manual ORDER BY mst_key; "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create the summary table which must be named with the model selection output table appended by \"_summary\":"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_arch_table</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>model_arch_library</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'model_arch_library',)]"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS mst_table_manual_summary;\n",
- "\n",
- "CREATE TABLE mst_table_manual_summary (\n",
- " model_arch_table varchar\n",
- ");\n",
- "\n",
- "INSERT INTO mst_table_manual_summary(model_arch_table) VALUES\n",
- "('model_arch_library');\n",
- "\n",
- "SELECT * FROM mst_table_manual_summary; "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load_model_selection_auto\"></a>\n",
- "# 5. Generate hyperparameters automatically\n",
- "\n",
- "You can use other libraries or methods to generate hyperparameters according to the tests that you want to run. For example, let's randomly generate batch size from powers of 2 and learning rate on a log scale.\n",
- "\n",
- "We use psycopg which is a PostgreSQL database adapter for the Python programming language."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "12 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>mst_key</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=32,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=64,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=32,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=64,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=32,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=64,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=32,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=64,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=32,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=64,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=32,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>12</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=64,epochs=1</td>\n",
- " </tr>\n",
- "</table>"
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- " (9, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=32,epochs=1'),\n",
- " (10, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=64,epochs=1'),\n",
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- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import numpy as np\n",
- "import psycopg2 as p2\n",
- "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
- "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
- "cur = conn.cursor()\n",
- "\n",
- "%sql DROP TABLE IF EXISTS mst_table_auto, mst_table_auto_summary;\n",
- "\n",
- "#compile params\n",
- "learning_rate = np.random.permutation([0.1,0.01,0.001,0.0001])[:3]\n",
- "compile_param1 = \"loss='categorical_crossentropy',optimizer='Adam(lr=\" + str(learning_rate[0]) + \")',metrics=['accuracy']\"\n",
- "compile_param2 = \"loss='categorical_crossentropy',optimizer='Adam(lr=\" + str(learning_rate[1]) + \")',metrics=['accuracy']\"\n",
- "compile_param3 = \"loss='categorical_crossentropy',optimizer='Adam(lr=\" + str(learning_rate[2]) + \")',metrics=['accuracy']\"\n",
- "compile_params = [compile_param1,compile_param2,compile_param3]\n",
- "\n",
- "#fit params\n",
- "batch_size = np.random.permutation([4,8,16,32,64])[:2]\n",
- "fit_param1 = \"batch_size=\" + str(batch_size[0]) + \",epochs=1\"\n",
- "fit_param2 = \"batch_size=\" + str(batch_size[1]) + \",epochs=1\"\n",
- "fit_params = [fit_param1,fit_param2]\n",
- "\n",
- "query = \"SELECT madlib.load_model_selection_table('model_arch_library', 'mst_table_auto', ARRAY[1,2], %s, %s);\"\n",
- "\n",
- "cur.execute(query,[compile_params, fit_params])\n",
- "conn.commit()\n",
- "\n",
- "#review model selection table\n",
- "%sql SELECT * FROM mst_table_auto ORDER BY mst_key;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The name of the model architecture table is stored in the summary table:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_arch_table</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>model_arch_library</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'model_arch_library',)]"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM mst_table_auto_summary;"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-MLP-v2.ipynb b/community-artifacts/Deep-learning/MADlib-Keras-MLP-v2.ipynb
deleted file mode 100644
index c86fb75..0000000
--- a/community-artifacts/Deep-learning/MADlib-Keras-MLP-v2.ipynb
+++ /dev/null
@@ -1,4057 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Multilayer Perceptron Using Keras and MADlib\n",
- "\n",
- "E2E classification example using MADlib calling a Keras MLP.\n",
- "\n",
- "Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this workbook we use the well known iris data set from https://archive.ics.uci.edu/ml/datasets/iris to help get you started. It is similar to the example in user docs http://madlib.apache.org/docs/latest/index.html\n",
- "\n",
- "For more realistic examples with images please refer to the deep learning notebooks at\n",
- "https://github.com/apache/madlib-site/tree/asf-site/community-artifacts\n",
- "\n",
- "## Table of contents\n",
- "\n",
- "<a href=\"#class\">Classification</a>\n",
- "\n",
- "* <a href=\"#create_input_data\">1. Create input data</a>\n",
- "\n",
- "* <a href=\"#pp\">2. Call preprocessor for deep learning</a>\n",
- "\n",
- "* <a href=\"#load\">3. Define and load model architecture</a>\n",
- "\n",
- "* <a href=\"#train\">4. Train</a>\n",
- "\n",
- "* <a href=\"#eval\">5. Evaluate</a>\n",
- "\n",
- "* <a href=\"#pred\">6. Predict</a>\n",
- "\n",
- "* <a href=\"#pred_byom\">7. Predict BYOM</a>\n",
- "\n",
- "<a href=\"#class2\">Classification with Other Parameters</a>\n",
- "\n",
- "* <a href=\"#val_dataset\">1. Validation dataset</a>\n",
- "\n",
- "* <a href=\"#pred_prob\">2. Predict probabilities</a>\n",
- "\n",
- "* <a href=\"#warm_start\">3. Warm start</a>\n",
- "\n",
- "<a href=\"#transfer_learn\">Transfer learning</a>\n",
- "\n",
- "* <a href=\"#load2\">1. Define and load model architecture with some layers frozen</a>\n",
- "\n",
- "* <a href=\"#train2\">2. Train transfer model</a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
- "source": [
- "%load_ext sql"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
- "# Greenplum Database 5.x on GCP - via tunnel\n",
- "%sql postgresql://gpadmin@localhost:8000/madlib\n",
- " \n",
- "# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>version</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql select madlib.version();\n",
- "#%sql select version();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"class\"></a>\n",
- "# Classification\n",
- "\n",
- "<a id=\"create_input_data\"></a>\n",
- "# 1. Create input data\n",
- "\n",
- "Load iris data set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "Done.\n",
- "150 rows affected.\n",
- "150 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>id</th>\n",
- " <th>attributes</th>\n",
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- " <td>1</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
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- " <td>2</td>\n",
- " <td>[Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
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- " <td>4</td>\n",
- " <td>[Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
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- " <td>6</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
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- " <tr>\n",
- " <td>7</td>\n",
- " <td>[Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
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- " <td>8</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>[Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
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- " <tr>\n",
- " <td>11</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
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- " <tr>\n",
- " <td>12</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>13</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>14</td>\n",
- " <td>[Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>15</td>\n",
- " <td>[Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>16</td>\n",
- " <td>[Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>17</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>18</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>19</td>\n",
- " <td>[Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>20</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>21</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>22</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>23</td>\n",
- " <td>[Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>24</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>25</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>26</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>27</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>28</td>\n",
- " <td>[Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>29</td>\n",
- " <td>[Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>30</td>\n",
- " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>31</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>32</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>33</td>\n",
- " <td>[Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>34</td>\n",
- " <td>[Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>35</td>\n",
- " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>36</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>37</td>\n",
- " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>38</td>\n",
- " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>39</td>\n",
- " <td>[Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>40</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>41</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>42</td>\n",
- " <td>[Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>43</td>\n",
- " <td>[Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>44</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>45</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>46</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>47</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>48</td>\n",
- " <td>[Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>49</td>\n",
- " <td>[Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>50</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>51</td>\n",
- " <td>[Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>52</td>\n",
- " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>53</td>\n",
- " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>54</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>55</td>\n",
- " <td>[Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>56</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>57</td>\n",
- " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>58</td>\n",
- " <td>[Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>59</td>\n",
- " <td>[Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>60</td>\n",
- " <td>[Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>61</td>\n",
- " <td>[Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>62</td>\n",
- " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>63</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>64</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>65</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>66</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>67</td>\n",
- " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>68</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>69</td>\n",
- " <td>[Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>70</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>71</td>\n",
- " <td>[Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>72</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>73</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>74</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>75</td>\n",
- " <td>[Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>76</td>\n",
- " <td>[Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>77</td>\n",
- " <td>[Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>78</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>79</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>80</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>81</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>82</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>83</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>84</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>85</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>86</td>\n",
- " <td>[Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>87</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>88</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>89</td>\n",
- " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>90</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>91</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>92</td>\n",
- " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>93</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>94</td>\n",
- " <td>[Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>95</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>96</td>\n",
- " <td>[Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>97</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>98</td>\n",
- " <td>[Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>99</td>\n",
- " <td>[Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>100</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>101</td>\n",
- " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>102</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>103</td>\n",
- " <td>[Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>104</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>105</td>\n",
- " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>106</td>\n",
- " <td>[Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>107</td>\n",
- " <td>[Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>108</td>\n",
- " <td>[Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>109</td>\n",
- " <td>[Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>110</td>\n",
- " <td>[Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>111</td>\n",
- " <td>[Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>112</td>\n",
- " <td>[Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>113</td>\n",
- " <td>[Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>114</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>115</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>116</td>\n",
- " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>117</td>\n",
- " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>118</td>\n",
- " <td>[Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>119</td>\n",
- " <td>[Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>121</td>\n",
- " <td>[Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>122</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>123</td>\n",
- " <td>[Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>124</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>125</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>126</td>\n",
- " <td>[Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>127</td>\n",
- " <td>[Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>128</td>\n",
- " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>129</td>\n",
- " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>130</td>\n",
- " <td>[Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>131</td>\n",
- " <td>[Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>132</td>\n",
- " <td>[Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>133</td>\n",
- " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>134</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>135</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>136</td>\n",
- " <td>[Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>137</td>\n",
- " <td>[Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>138</td>\n",
- " <td>[Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>139</td>\n",
- " <td>[Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>140</td>\n",
- " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>141</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>142</td>\n",
- " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>143</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>144</td>\n",
- " <td>[Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>145</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>146</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>147</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>148</td>\n",
- " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>149</td>\n",
- " <td>[Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>150</td>\n",
- " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
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- " (2, [Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (3, [Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (4, [Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (5, [Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (6, [Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (7, [Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (8, [Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (9, [Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (10, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (11, [Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (12, [Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (13, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (14, [Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (15, [Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (16, [Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (17, [Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (18, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (19, [Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (20, [Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (21, [Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (22, [Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (23, [Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (24, [Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')], u'Iris-setosa'),\n",
- " (25, [Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (26, [Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (27, [Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (28, [Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (29, [Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (30, [Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (31, [Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (32, [Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (33, [Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (34, [Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (35, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (36, [Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (37, [Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (38, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (39, [Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (40, [Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (41, [Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (42, [Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (43, [Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (44, [Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')], u'Iris-setosa'),\n",
- " (45, [Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (46, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (47, [Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (48, [Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (49, [Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (50, [Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (51, [Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (52, [Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (53, [Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (54, [Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (55, [Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (56, [Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (57, [Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')], u'Iris-versicolor'),\n",
- " (58, [Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (59, [Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (60, [Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (61, [Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (62, [Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (63, [Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (64, [Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (65, [Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (66, [Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (67, [Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (68, [Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (69, [Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (70, [Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')], u'Iris-versicolor'),\n",
- " (71, [Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')], u'Iris-versicolor'),\n",
- " (72, [Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (73, [Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (74, [Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (75, [Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (76, [Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (77, [Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (78, [Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')], u'Iris-versicolor'),\n",
- " (79, [Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (80, [Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (81, [Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')], u'Iris-versicolor'),\n",
- " (82, [Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (83, [Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (84, [Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')], u'Iris-versicolor'),\n",
- " (85, [Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (86, [Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')], u'Iris-versicolor'),\n",
- " (87, [Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (88, [Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (89, [Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (90, [Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (91, [Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (92, [Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (93, [Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (94, [Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (95, [Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (96, [Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (97, [Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (98, [Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (99, [Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')], u'Iris-versicolor'),\n",
- " (100, [Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (101, [Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')], u'Iris-virginica'),\n",
- " (102, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (103, [Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (104, [Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (105, [Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')], u'Iris-virginica'),\n",
- " (106, [Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (107, [Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')], u'Iris-virginica'),\n",
- " (108, [Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (109, [Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (110, [Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')], u'Iris-virginica'),\n",
- " (111, [Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (112, [Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (113, [Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (114, [Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (115, [Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')], u'Iris-virginica'),\n",
- " (116, [Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (117, [Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (118, [Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')], u'Iris-virginica'),\n",
- " (119, [Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (120, [Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')], u'Iris-virginica'),\n",
- " (121, [Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (122, [Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (123, [Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (124, [Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (125, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (126, [Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (127, [Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (128, [Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (129, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (130, [Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')], u'Iris-virginica'),\n",
- " (131, [Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (132, [Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (133, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')], u'Iris-virginica'),\n",
- " (134, [Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')], u'Iris-virginica'),\n",
- " (135, [Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')], u'Iris-virginica'),\n",
- " (136, [Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (137, [Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
- " (138, [Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (139, [Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (140, [Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (141, [Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
- " (142, [Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (143, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (144, [Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (145, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')], u'Iris-virginica'),\n",
- " (146, [Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (147, [Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (148, [Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (149, [Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (150, [Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')], u'Iris-virginica')]"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql \n",
- "DROP TABLE IF EXISTS iris_data;\n",
- "\n",
- "CREATE TABLE iris_data(\n",
- " id serial,\n",
- " attributes numeric[],\n",
- " class_text varchar\n",
- ");\n",
- "\n",
- "INSERT INTO iris_data(id, attributes, class_text) VALUES\n",
- "(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),\n",
- "(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),\n",
- "(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),\n",
- "(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),\n",
- "(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),\n",
- "(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),\n",
- "(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),\n",
- "(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),\n",
- "(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),\n",
- "(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
- "(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),\n",
- "(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),\n",
- "(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),\n",
- "(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),\n",
- "(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),\n",
- "(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),\n",
- "(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),\n",
- "(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),\n",
- "(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),\n",
- "(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),\n",
- "(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),\n",
- "(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),\n",
- "(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),\n",
- "(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),\n",
- "(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),\n",
- "(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),\n",
- "(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),\n",
- "(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),\n",
- "(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),\n",
- "(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),\n",
- "(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),\n",
- "(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),\n",
- "(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),\n",
- "(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),\n",
- "(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
- "(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),\n",
- "(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),\n",
- "(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
- "(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),\n",
- "(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),\n",
- "(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),\n",
- "(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),\n",
- "(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),\n",
- "(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),\n",
- "(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),\n",
- "(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),\n",
- "(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),\n",
- "(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),\n",
- "(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),\n",
- "(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),\n",
- "(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),\n",
- "(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),\n",
- "(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),\n",
- "(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),\n",
- "(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),\n",
- "(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),\n",
- "(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),\n",
- "(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),\n",
- "(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),\n",
- "(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),\n",
- "(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),\n",
- "(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),\n",
- "(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),\n",
- "(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),\n",
- "(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),\n",
- "(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),\n",
- "(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),\n",
- "(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),\n",
- "(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),\n",
- "(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),\n",
- "(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),\n",
- "(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),\n",
- "(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),\n",
- "(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),\n",
- "(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),\n",
- "(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),\n",
- "(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),\n",
- "(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),\n",
- "(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),\n",
- "(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),\n",
- "(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),\n",
- "(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),\n",
- "(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),\n",
- "(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),\n",
- "(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),\n",
- "(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),\n",
- "(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),\n",
- "(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),\n",
- "(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),\n",
- "(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),\n",
- "(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),\n",
- "(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),\n",
- "(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),\n",
- "(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),\n",
- "(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),\n",
- "(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),\n",
- "(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),\n",
- "(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),\n",
- "(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),\n",
- "(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),\n",
- "(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),\n",
- "(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
- "(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),\n",
- "(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),\n",
- "(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),\n",
- "(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),\n",
- "(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),\n",
- "(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),\n",
- "(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),\n",
- "(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),\n",
- "(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),\n",
- "(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),\n",
- "(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),\n",
- "(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),\n",
- "(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),\n",
- "(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),\n",
- "(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),\n",
- "(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),\n",
- "(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),\n",
- "(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),\n",
- "(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),\n",
- "(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),\n",
- "(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),\n",
- "(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),\n",
- "(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),\n",
- "(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),\n",
- "(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),\n",
- "(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),\n",
- "(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),\n",
- "(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),\n",
- "(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),\n",
- "(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),\n",
- "(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),\n",
- "(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),\n",
- "(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),\n",
- "(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),\n",
- "(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),\n",
- "(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),\n",
- "(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),\n",
- "(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),\n",
- "(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),\n",
- "(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),\n",
- "(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
- "(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),\n",
- "(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),\n",
- "(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),\n",
- "(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),\n",
- "(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),\n",
- "(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),\n",
- "(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');\n",
- "\n",
- "SELECT * FROM iris_data ORDER BY id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create a test/validation dataset from the training data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(120L,)]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_train, iris_test;\n",
- "\n",
- "-- Set seed so results are reproducible\n",
- "SELECT setseed(0);\n",
- "\n",
- "SELECT madlib.train_test_split('iris_data', -- Source table\n",
- " 'iris', -- Output table root name\n",
- " 0.8, -- Train proportion\n",
- " NULL, -- Test proportion (0.2)\n",
- " NULL, -- Strata definition\n",
- " NULL, -- Output all columns\n",
- " NULL, -- Sample without replacement\n",
- " TRUE -- Separate output tables\n",
- " );\n",
- "\n",
- "SELECT COUNT(*) FROM iris_train;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"pp\"></a>\n",
- "# 2. Call preprocessor for deep learning\n",
- "Training dataset (uses training preprocessor):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "2 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
- " <th>buffer_id</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[60, 4]</td>\n",
- " <td>[60, 3]</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[60, 4]</td>\n",
- " <td>[60, 3]</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[([60, 4], [60, 3], 0), ([60, 4], [60, 3], 1)]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;\n",
- "\n",
- "SELECT madlib.training_preprocessor_dl('iris_train', -- Source table\n",
- " 'iris_train_packed', -- Output table\n",
- " 'class_text', -- Dependent variable\n",
- " 'attributes' -- Independent variable\n",
- " ); \n",
- "\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM iris_train_packed ORDER BY buffer_id;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>output_table</th>\n",
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- " <th>independent_varname</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
- " <th>buffer_size</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>num_classes</th>\n",
- " <th>distribution_rules</th>\n",
- " <th>__internal_gpu_config__</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train</td>\n",
- " <td>iris_train_packed</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>character varying</td>\n",
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- " <td>60</td>\n",
- " <td>1.0</td>\n",
- " <td>3</td>\n",
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- ],
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- "[(u'iris_train', u'iris_train_packed', u'class_text', u'attributes', u'character varying', [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 60, 1.0, 3, 'all_segments', 'all_segments')]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_train_packed_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Validation dataset (uses validation preprocessor):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "2 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>independent_var_shape</th>\n",
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- " </tr>\n",
- " <tr>\n",
- " <td>[15, 4]</td>\n",
- " <td>[15, 3]</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[15, 4]</td>\n",
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- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;\n",
- "\n",
- "SELECT madlib.validation_preprocessor_dl('iris_test', -- Source table\n",
- " 'iris_test_packed', -- Output table\n",
- " 'class_text', -- Dependent variable\n",
- " 'attributes', -- Independent variable\n",
- " 'iris_train_packed' -- From training preprocessor step\n",
- " ); \n",
- "\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM iris_test_packed ORDER BY buffer_id;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
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- " <th>output_table</th>\n",
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- " <th>independent_varname</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
- " <th>buffer_size</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>num_classes</th>\n",
- " <th>distribution_rules</th>\n",
- " <th>__internal_gpu_config__</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_test</td>\n",
- " <td>iris_test_packed</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>character varying</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>15</td>\n",
- " <td>1.0</td>\n",
- " <td>3</td>\n",
- " <td>all_segments</td>\n",
- " <td>all_segments</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'iris_test', u'iris_test_packed', u'class_text', u'attributes', u'character varying', [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 15, 1.0, 3, 'all_segments', 'all_segments')]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_test_packed_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load\"></a>\n",
- "# 3. Define and load model architecture\n",
- "Import Keras libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Couldn't import dot_parser, loading of dot files will not be possible.\n"
- ]
- }
- ],
- "source": [
- "import keras\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Define model architecture"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "dense_1 (Dense) (None, 10) 50 \n",
- "_________________________________________________________________\n",
- "dense_2 (Dense) (None, 10) 110 \n",
- "_________________________________________________________________\n",
- "dense_3 (Dense) (None, 3) 33 \n",
- "=================================================================\n",
- "Total params: 193\n",
- "Trainable params: 193\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model_simple = Sequential()\n",
- "model_simple.add(Dense(10, activation='relu', input_shape=(4,)))\n",
- "model_simple.add(Dense(10, activation='relu'))\n",
- "model_simple.add(Dense(3, activation='softmax'))\n",
- " \n",
- "model_simple.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model_simple.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load into model architecture table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_id</th>\n",
- " <th>model_arch</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
- " <td>Sophie</td>\n",
- " <td>A simple model</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, u'Sophie', u'A simple model')]"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS model_arch_library;\n",
- "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
- " \n",
- "$$\n",
- "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
- "$$\n",
- "::json, -- JSON blob\n",
- " NULL, -- Weights\n",
- " 'Sophie', -- Name\n",
- " 'A simple model' -- Descr\n",
- ");\n",
- "\n",
- "SELECT model_id, model_arch, name, description FROM model_arch_library ORDER BY model_id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"train\"></a>\n",
- "# 4. Train\n",
- "Train the model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>madlib_keras_fit</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td></td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[('',)]"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_model, iris_model_summary;\n",
- "\n",
- "SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table\n",
- " 'iris_model', -- model output table\n",
- " 'model_arch_library', -- model arch table\n",
- " 1, -- model arch id\n",
- " $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params\n",
- " $$ batch_size=5, epochs=3 $$, -- fit_params\n",
- " 10 -- num_iterations\n",
- " );"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View the model summary:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>model</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>model_arch_table</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " <th>num_iterations</th>\n",
- " <th>validation_table</th>\n",
- " <th>metrics_compute_frequency</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>start_training_time</th>\n",
- " <th>end_training_time</th>\n",
- " <th>metrics_elapsed_time</th>\n",
- " <th>madlib_version</th>\n",
- " <th>num_classes</th>\n",
- " <th>class_values</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>metrics_type</th>\n",
- " <th>training_metrics_final</th>\n",
- " <th>training_loss_final</th>\n",
- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " <th>metrics_iters</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train_packed</td>\n",
- " <td>iris_model</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>model_arch_library</td>\n",
- " <td>1</td>\n",
- " <td> loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] </td>\n",
- " <td> batch_size=5, epochs=3 </td>\n",
- " <td>10</td>\n",
- " <td>None</td>\n",
- " <td>10</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>2019-12-18 18:09:06.678020</td>\n",
- " <td>2019-12-18 18:09:09.703493</td>\n",
- " <td>[3.02539992332458]</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>3</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>character varying</td>\n",
- " <td>1.0</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.958333313465</td>\n",
- " <td>0.619696557522</td>\n",
- " <td>[0.958333313465118]</td>\n",
- " <td>[0.61969655752182]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>[10]</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'iris_train_packed', u'iris_model', u'class_text', u'attributes', u'model_arch_library', 1, u\" loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] \", u' batch_size=5, epochs=3 ', 10, None, 10, None, None, u'madlib_keras', 0.7900390625, datetime.datetime(2019, 12, 18, 18, 9, 6, 678020), datetime.datetime(2019, 12, 18, 18, 9, 9, 703493), [3.02539992332458], u'1.17-dev', 3, [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], u'character varying', 1.0, [u'accuracy'], 0.958333313465, 0.619696557522, [0.958333313465118], [0.61969655752182], None, None, None, None, [10])]"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_model_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"eval\"></a>\n",
- "# 5. Evaluate\n",
- "\n",
- "Now run evaluate using model we built above:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>loss</th>\n",
- " <th>metric</th>\n",
- " <th>metrics_type</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0.627631247044</td>\n",
- " <td>0.899999976158</td>\n",
- " <td>[u'accuracy']</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(0.62763124704361, 0.899999976158142, [u'accuracy'])]"
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_validate;\n",
- "\n",
- "SELECT madlib.madlib_keras_evaluate('iris_model', -- model\n",
- " 'iris_test_packed', -- test table\n",
- " 'iris_validate' -- output table\n",
- " );\n",
- "\n",
- "SELECT * FROM iris_validate;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"pred\"></a>\n",
- "# 6. Predict\n",
- "\n",
- "Now predict using model we built. We will use the validation data set for prediction as well, which is not usual but serves to show the syntax. The prediction is in the estimated_class_text column:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "30 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>id</th>\n",
- " <th>estimated_class_text</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>14</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>18</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>28</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>44</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>48</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>54</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>56</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>69</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>80</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>83</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>85</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>88</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>89</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>90</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>94</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>97</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>103</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>105</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>111</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>128</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>131</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>132</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>133</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>136</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>138</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>149</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- "</table>"
- ],
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- " (44, u'Iris-setosa'),\n",
- " (48, u'Iris-setosa'),\n",
- " (54, u'Iris-virginica'),\n",
- " (56, u'Iris-versicolor'),\n",
- " (69, u'Iris-virginica'),\n",
- " (80, u'Iris-versicolor'),\n",
- " (83, u'Iris-versicolor'),\n",
- " (85, u'Iris-versicolor'),\n",
- " (88, u'Iris-virginica'),\n",
- " (89, u'Iris-versicolor'),\n",
- " (90, u'Iris-versicolor'),\n",
- " (94, u'Iris-versicolor'),\n",
- " (97, u'Iris-versicolor'),\n",
- " (103, u'Iris-virginica'),\n",
- " (105, u'Iris-virginica'),\n",
- " (111, u'Iris-virginica'),\n",
- " (120, u'Iris-virginica'),\n",
- " (128, u'Iris-virginica'),\n",
- " (131, u'Iris-virginica'),\n",
- " (132, u'Iris-virginica'),\n",
- " (133, u'Iris-virginica'),\n",
- " (136, u'Iris-virginica'),\n",
- " (138, u'Iris-virginica'),\n",
- " (149, u'Iris-virginica')]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_predict;\n",
- "\n",
- "SELECT madlib.madlib_keras_predict('iris_model', -- model\n",
- " 'iris_test', -- test_table\n",
- " 'id', -- id column\n",
- " 'attributes', -- independent var\n",
- " 'iris_predict' -- output table\n",
- " );\n",
- "\n",
- "SELECT * FROM iris_predict ORDER BY id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Count missclassifications"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
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- "<table>\n",
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- "</table>"
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- "[(3L,)]"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id) \n",
- "WHERE iris_predict.estimated_class_text != iris_test.class_text;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Percent missclassifications"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
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- },
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- "data": {
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- "<table>\n",
- " <tr>\n",
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- "execution_count": 19,
- "metadata": {},
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- }
- ],
- "source": [
- "%%sql\n",
- "SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from\n",
- " (select iris_test.class_text as actual, iris_predict.estimated_class_text as estimated\n",
- " from iris_predict inner join iris_test\n",
- " on iris_test.id=iris_predict.id) q\n",
- "WHERE q.actual=q.estimated;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"pred_byom\"></a>\n",
- "# 7. Predict BYOM\n",
- "The predict BYOM function allows you to do inference on models that have not been trained on MADlib, but rather imported from elsewhere. \n",
- "\n",
- "We will use the validation dataset for prediction as well, which is not usual but serves to show the syntax.\n",
- "\n",
- "See load_keras_model()\n",
- "http://madlib.apache.org/docs/latest/group__grp__keras__model__arch.html\n",
- "for details on how to load the model architecture and weights. In this example we will use weights we already have:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "UPDATE model_arch_library \n",
- "SET model_weights = iris_model.model_weights \n",
- "FROM iris_model \n",
- "WHERE model_arch_library.model_id = 1;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now train using a model from the model architecture table directly without referencing the model table from the MADlib training. \n",
- "\n",
- "Note that if you specify the class values parameter as we do below, it must reflect how the dependent variable was 1-hot encoded for training. In this example the 'training_preprocessor_dl()' in Step 2 above encoded in the order {'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'} so this is the order we pass in the parameter. If we accidently picked another order that did not match the 1-hot encoding, the predictions would be wrong."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "30 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
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- " <th>estimated_dependent_var</th>\n",
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- " <td>7</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>14</td>\n",
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- " <td>54</td>\n",
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- " </tr>\n",
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- " <td>56</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <td>69</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <td>80</td>\n",
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- " </tr>\n",
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- " <td>83</td>\n",
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- " </tr>\n",
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- " <td>85</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <td>88</td>\n",
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- " </tr>\n",
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- " <td>89</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
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- " <td>97</td>\n",
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- " </tr>\n",
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- " </tr>\n",
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- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>111</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>128</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>131</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>132</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>133</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
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- " <td>136</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>138</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>149</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- "</table>"
- ],
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- " (8, u'Iris-setosa'),\n",
- " (9, u'Iris-setosa'),\n",
- " (14, u'Iris-setosa'),\n",
- " (18, u'Iris-setosa'),\n",
- " (28, u'Iris-setosa'),\n",
- " (44, u'Iris-setosa'),\n",
- " (48, u'Iris-setosa'),\n",
- " (54, u'Iris-virginica'),\n",
- " (56, u'Iris-versicolor'),\n",
- " (69, u'Iris-virginica'),\n",
- " (80, u'Iris-versicolor'),\n",
- " (83, u'Iris-versicolor'),\n",
- " (85, u'Iris-versicolor'),\n",
- " (88, u'Iris-virginica'),\n",
- " (89, u'Iris-versicolor'),\n",
- " (90, u'Iris-versicolor'),\n",
- " (94, u'Iris-versicolor'),\n",
- " (97, u'Iris-versicolor'),\n",
- " (103, u'Iris-virginica'),\n",
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- " (131, u'Iris-virginica'),\n",
- " (132, u'Iris-virginica'),\n",
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- " (138, u'Iris-virginica'),\n",
- " (149, u'Iris-virginica')]"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_predict_byom;\n",
- "\n",
- "SELECT madlib.madlib_keras_predict_byom('model_arch_library', -- model arch table\n",
- " 1, -- model arch id\n",
- " 'iris_test', -- test_table\n",
- " 'id', -- id column\n",
- " 'attributes', -- independent var\n",
- " 'iris_predict_byom', -- output table\n",
- " 'response', -- prediction type\n",
- " FALSE, -- use GPUs\n",
- " ARRAY['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], -- class values\n",
- " 1.0 -- normalizing const\n",
- " );\n",
- "SELECT * FROM iris_predict_byom ORDER BY id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Count missclassifications:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
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- "data": {
- "text/html": [
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- "</table>"
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- ]
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- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT COUNT(*) FROM iris_predict_byom JOIN iris_test USING (id)\n",
- "WHERE iris_predict_byom.estimated_dependent_var != iris_test.class_text;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Percent missclassifications:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
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- "data": {
- "text/html": [
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- ],
- "source": [
- "%%sql\n",
- "SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from\n",
- " (select iris_test.class_text as actual, iris_predict_byom.estimated_dependent_var as estimated\n",
- " from iris_predict_byom inner join iris_test\n",
- " on iris_test.id=iris_predict_byom.id) q\n",
- "WHERE q.actual=q.estimated;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"class2\"></a>\n",
- "# Classification with Other Parameters\n",
- "\n",
- "<a id=\"val_dataset\"></a>\n",
- "# 1. Validation dataset\n",
- "Now use a validation dataset and compute metrics every 2nd iteration using the 'metrics_compute_frequency' parameter. This can help reduce run time if you do not need metrics computed at every iteration."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>madlib_keras_fit</th>\n",
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- " </tr>\n",
- "</table>"
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- "[('',)]"
- ]
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- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_model, iris_model_summary;\n",
- "\n",
- "SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table\n",
- " 'iris_model', -- model output table\n",
- " 'model_arch_library', -- model arch table\n",
- " 1, -- model arch id\n",
- " $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params\n",
- " $$ batch_size=5, epochs=3 $$, -- fit_params\n",
- " 10, -- num_iterations\n",
- " FALSE, -- use GPUs\n",
- " 'iris_test_packed', -- validation dataset\n",
- " 2, -- metrics compute frequency\n",
- " FALSE, -- warm start\n",
- " 'Sophie L.', -- name\n",
- " 'Simple MLP for iris dataset' -- description\n",
- " );"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View the model summary:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
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- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>model</th>\n",
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- " <th>independent_varname</th>\n",
- " <th>model_arch_table</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " <th>num_iterations</th>\n",
- " <th>validation_table</th>\n",
- " <th>metrics_compute_frequency</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>start_training_time</th>\n",
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- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " <th>metrics_iters</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train_packed</td>\n",
- " <td>iris_model</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>model_arch_library</td>\n",
- " <td>1</td>\n",
- " <td> loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] </td>\n",
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- " <td>10</td>\n",
- " <td>iris_test_packed</td>\n",
- " <td>2</td>\n",
- " <td>Sophie L.</td>\n",
- " <td>Simple MLP for iris dataset</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>2019-12-18 18:09:19.330964</td>\n",
- " <td>2019-12-18 18:09:21.010635</td>\n",
- " <td>[0.915475130081177, 1.10240316390991, 1.24091100692749, 1.37801814079285, 1.67959213256836]</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>3</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>character varying</td>\n",
- " <td>1.0</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.983333349228</td>\n",
- " <td>0.308444350958</td>\n",
- " <td>[0.949999988079071, 0.975000023841858, 0.975000023841858, 0.983333349227905, 0.983333349227905]</td>\n",
- " <td>[0.5235316157341, 0.450434356927872, 0.391158282756805, 0.344655215740204, 0.30844435095787]</td>\n",
- " <td>0.933333337307</td>\n",
- " <td>0.363271415234</td>\n",
- " <td>[0.866666674613953, 0.933333337306976, 0.933333337306976, 0.933333337306976, 0.933333337306976]</td>\n",
- " <td>[0.549321353435516, 0.490176409482956, 0.438665509223938, 0.397390514612198, 0.363271415233612]</td>\n",
- " <td>[2, 4, 6, 8, 10]</td>\n",
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- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_model_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Accuracy by iteration"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x12c58eb50>"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "import sys\n",
- "import os\n",
- "from matplotlib import pyplot as plt\n",
- "\n",
- "# get accuracy and iteration number\n",
- "iters_proxy = %sql SELECT metrics_iters FROM iris_model_summary;\n",
- "train_accuracy_proxy = %sql SELECT training_metrics FROM iris_model_summary;\n",
- "test_accuracy_proxy = %sql SELECT validation_metrics FROM iris_model_summary;\n",
- "\n",
- "# get number of points\n",
- "num_points_proxy = %sql SELECT array_length(metrics_iters,1) FROM iris_model_summary;\n",
- "num_points = num_points_proxy[0]\n",
- "\n",
- "# reshape to np arrays\n",
- "iters = np.array(iters_proxy).reshape(num_points)\n",
- "train_accuracy = np.array(train_accuracy_proxy).reshape(num_points)\n",
- "test_accuracy = np.array(test_accuracy_proxy).reshape(num_points)\n",
- "\n",
- "#plot\n",
- "plt.title('Iris validation accuracy by iteration')\n",
- "plt.xlabel('Iteration number')\n",
- "plt.ylabel('Accuracy')\n",
- "plt.grid(True)\n",
- "plt.plot(iters, train_accuracy, 'g.-', label='Train')\n",
- "plt.plot(iters, test_accuracy, 'r.-', label='Test')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Loss by iteration"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x12f1b1c10>"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# get loss\n",
- "train_loss_proxy = %sql SELECT training_loss FROM iris_model_summary;\n",
- "test_loss_proxy = %sql SELECT validation_loss FROM iris_model_summary;\n",
- "\n",
- "# reshape to np arrays\n",
- "train_loss = np.array(train_loss_proxy).reshape(num_points)\n",
- "test_loss = np.array(test_loss_proxy).reshape(num_points)\n",
- "\n",
- "#plot\n",
- "plt.title('Iris validation loss by iteration')\n",
- "plt.xlabel('Iteration number')\n",
- "plt.ylabel('Loss')\n",
- "plt.grid(True)\n",
- "plt.plot(iters, train_loss, 'g.-', label='Train')\n",
- "plt.plot(iters, test_loss, 'r.-', label='Test')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Accuracy by time"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x12f24bbd0>"
- ]
- },
- "execution_count": 28,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# get time\n",
- "time_proxy = %sql SELECT metrics_elapsed_time FROM iris_model_summary;\n",
- "\n",
- "# reshape to np arrays\n",
- "time = np.array(time_proxy).reshape(num_points)/60.0\n",
- "\n",
- "#plot\n",
- "plt.title('Iris validation accuracy by time')\n",
- "plt.xlabel('Time (min)')\n",
- "plt.ylabel('Accuracy')\n",
- "plt.grid(True)\n",
- "plt.plot(time, train_accuracy, 'g.-', label='Train')\n",
- "plt.plot(time, test_accuracy, 'r.-', label='Test')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Time to achieve a given accuracy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x12f344210>"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "#plot\n",
- "plt.title('Iris time by validation accuracy')\n",
- "plt.xlabel('Accuracy')\n",
- "plt.ylabel('Time (min)')\n",
- "plt.grid(True)\n",
- "plt.plot(train_accuracy, time, 'g.-', label='Train')\n",
- "plt.plot(test_accuracy, time, 'r.-', label='Test')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"pred_prob\"></a>\n",
- "# 2. Predict probabilities\n",
- "Predict with probabilities for each class:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "30 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>id</th>\n",
- " <th>prob_Iris-setosa</th>\n",
- " <th>prob_Iris-versicolor</th>\n",
- " <th>prob_Iris-virginica</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>0.89789814</td>\n",
- " <td>0.0880069</td>\n",
- " <td>0.014094983</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>0.90666765</td>\n",
- " <td>0.081442654</td>\n",
- " <td>0.011889744</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>0.8795763</td>\n",
- " <td>0.1017618</td>\n",
- " <td>0.018661851</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>14</td>\n",
- " <td>0.8874597</td>\n",
- " <td>0.095630445</td>\n",
- " <td>0.016909808</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>18</td>\n",
- " <td>0.9102227</td>\n",
- " <td>0.078691445</td>\n",
- " <td>0.011085836</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>28</td>\n",
- " <td>0.9124432</td>\n",
- " <td>0.077006854</td>\n",
- " <td>0.010549883</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>44</td>\n",
- " <td>0.90255314</td>\n",
- " <td>0.08451119</td>\n",
- " <td>0.012935703</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>48</td>\n",
- " <td>0.89486533</td>\n",
- " <td>0.09027297</td>\n",
- " <td>0.014861753</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>54</td>\n",
- " <td>0.026524143</td>\n",
- " <td>0.51825184</td>\n",
- " <td>0.45522407</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>56</td>\n",
- " <td>0.020466398</td>\n",
- " <td>0.538594</td>\n",
- " <td>0.4409396</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>69</td>\n",
- " <td>0.009856132</td>\n",
- " <td>0.38160574</td>\n",
- " <td>0.60853815</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>80</td>\n",
- " <td>0.088389054</td>\n",
- " <td>0.68402624</td>\n",
- " <td>0.22758465</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>83</td>\n",
- " <td>0.04700892</td>\n",
- " <td>0.6974011</td>\n",
- " <td>0.25559002</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>85</td>\n",
- " <td>0.02379873</td>\n",
- " <td>0.53655416</td>\n",
- " <td>0.4396471</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>88</td>\n",
- " <td>0.014446292</td>\n",
- " <td>0.48625696</td>\n",
- " <td>0.4992967</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>89</td>\n",
- " <td>0.045492876</td>\n",
- " <td>0.6929876</td>\n",
- " <td>0.26151955</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>90</td>\n",
- " <td>0.032893542</td>\n",
- " <td>0.57819253</td>\n",
- " <td>0.38891393</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>94</td>\n",
- " <td>0.078232706</td>\n",
- " <td>0.6571468</td>\n",
- " <td>0.26462048</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>97</td>\n",
- " <td>0.036127776</td>\n",
- " <td>0.6550248</td>\n",
- " <td>0.30884746</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>103</td>\n",
- " <td>0.0017510698</td>\n",
- " <td>0.222155</td>\n",
- " <td>0.7760939</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>105</td>\n",
- " <td>0.001789655</td>\n",
- " <td>0.1864191</td>\n",
- " <td>0.81179124</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>111</td>\n",
- " <td>0.010464086</td>\n",
- " <td>0.46730253</td>\n",
- " <td>0.52223337</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " <td>0.0034266405</td>\n",
- " <td>0.21947922</td>\n",
- " <td>0.7770942</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>128</td>\n",
- " <td>0.011816905</td>\n",
- " <td>0.4490503</td>\n",
- " <td>0.5391328</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>131</td>\n",
- " <td>0.0009711725</td>\n",
- " <td>0.17797765</td>\n",
- " <td>0.82105124</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>132</td>\n",
- " <td>0.0024771853</td>\n",
- " <td>0.395437</td>\n",
- " <td>0.6020858</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>133</td>\n",
- " <td>0.0019595844</td>\n",
- " <td>0.18066718</td>\n",
- " <td>0.8173732</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>136</td>\n",
- " <td>0.0012088934</td>\n",
- " <td>0.20632832</td>\n",
- " <td>0.7924628</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>138</td>\n",
- " <td>0.0045178244</td>\n",
- " <td>0.3225387</td>\n",
- " <td>0.6729434</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>149</td>\n",
- " <td>0.006746102</td>\n",
- " <td>0.3544818</td>\n",
- " <td>0.6387721</td>\n",
- " </tr>\n",
- "</table>"
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- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_predict;\n",
- "\n",
- "SELECT madlib.madlib_keras_predict('iris_model', -- model\n",
- " 'iris_test', -- test_table\n",
- " 'id', -- id column\n",
- " 'attributes', -- independent var\n",
- " 'iris_predict', -- output table\n",
- " 'prob' -- response type\n",
- " );\n",
- "\n",
- "SELECT * FROM iris_predict ORDER BY id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"warm_start\"></a>\n",
- "# 3. Warm start\n",
- "Next, use the warm_start parameter to continue learning, using the coefficients from the run above. Note that we don't drop the model table or model summary table:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>madlib_keras_fit</th>\n",
- " </tr>\n",
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- " </tr>\n",
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- "[('',)]"
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- "execution_count": 31,
- "metadata": {},
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- "source": [
- "%%sql\n",
- "SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table\n",
- " 'iris_model', -- model output table\n",
- " 'model_arch_library', -- model arch table\n",
- " 1, -- model arch id\n",
- " $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params\n",
- " $$ batch_size=5, epochs=3 $$, -- fit_params\n",
- " 10, -- num_iterations\n",
- " FALSE, -- use GPUs\n",
- " 'iris_test_packed', -- validation dataset\n",
- " 2, -- metrics compute frequency\n",
- " TRUE, -- warm start\n",
- " 'Sophie L.', -- name \n",
- " 'Simple MLP for iris dataset' -- description\n",
- " );"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the summary table and plots below note that the loss and accuracy values pick up from where the previous run left off:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {
- "scrolled": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
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- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>model</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>model_arch_table</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " <th>num_iterations</th>\n",
- " <th>validation_table</th>\n",
- " <th>metrics_compute_frequency</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>start_training_time</th>\n",
- " <th>end_training_time</th>\n",
- " <th>metrics_elapsed_time</th>\n",
- " <th>madlib_version</th>\n",
- " <th>num_classes</th>\n",
- " <th>class_values</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>metrics_type</th>\n",
- " <th>training_metrics_final</th>\n",
- " <th>training_loss_final</th>\n",
- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " <th>metrics_iters</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train_packed</td>\n",
- " <td>iris_model</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>model_arch_library</td>\n",
- " <td>1</td>\n",
- " <td> loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] </td>\n",
- " <td> batch_size=5, epochs=3 </td>\n",
- " <td>10</td>\n",
- " <td>iris_test_packed</td>\n",
- " <td>2</td>\n",
- " <td>Sophie L.</td>\n",
- " <td>Simple MLP for iris dataset</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>2019-12-18 18:09:27.128581</td>\n",
- " <td>2019-12-18 18:09:28.838569</td>\n",
- " <td>[0.982600927352905, 1.11963605880737, 1.24473285675049, 1.41093587875366, 1.70990204811096]</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>3</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>character varying</td>\n",
- " <td>1.0</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.983333349228</td>\n",
- " <td>0.198354303837</td>\n",
- " <td>[0.966666638851166, 0.983333349227905, 0.975000023841858, 0.983333349227905, 0.983333349227905]</td>\n",
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- " <td>0.966666638851</td>\n",
- " <td>0.255444854498</td>\n",
- " <td>[0.933333337306976, 0.966666638851166, 0.933333337306976, 0.966666638851166, 0.966666638851166]</td>\n",
- " <td>[0.333956837654114, 0.309911340475082, 0.291009396314621, 0.271284729242325, 0.25544485449791]</td>\n",
- " <td>[2, 4, 6, 8, 10]</td>\n",
- " </tr>\n",
- "</table>"
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- "[(u'iris_train_packed', u'iris_model', u'class_text', u'attributes', u'model_arch_library', 1, u\" loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] \", u' batch_size=5, epochs=3 ', 10, u'iris_test_packed', 2, u'Sophie L.', u'Simple MLP for iris dataset', u'madlib_keras', 0.7900390625, datetime.datetime(2019, 12, 18, 18, 9, 27, 128581), datetime.datetime(2019, 12, 18, 18, 9, 28, 838569), [0.982600927352905, 1.11963605880737, 1.24473285675049, 1.41093587875366, 1.70990204811096], u'1.17-dev', 3, [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], u'character varying', 1.0, [u'accuracy'], 0.983333349228, 0.198354303837, [0.966666638851166, 0.983333349227905, 0.975000023841858, 0.983333349227905, 0.983333349227905], [0.27795821428299, 0.251547634601593, 0.231610581278801, 0.213408783078194, 0.198354303836823], 0.966666638851, 0.255444854498, [0.933333337306976, 0.966666638851166, 0.933333337306976, 0.966666638851166, 0.966666638851166], [0.333956837654114, 0.309911340475082, 0.291009396314621, 0.271284729242325, 0.25544485449791], [2, 4, 6, 8, 10])]"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_model_summary;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "import sys\n",
- "import os\n",
- "from matplotlib import pyplot as plt\n",
- "\n",
- "# get accuracy and iteration number\n",
- "iters_proxy = %sql SELECT metrics_iters FROM iris_model_summary;\n",
- "train_accuracy_proxy = %sql SELECT training_metrics FROM iris_model_summary;\n",
- "test_accuracy_proxy = %sql SELECT validation_metrics FROM iris_model_summary;\n",
- "\n",
- "# get number of points\n",
- "num_points_proxy = %sql SELECT array_length(metrics_iters,1) FROM iris_model_summary;\n",
- "num_points = num_points_proxy[0]\n",
- "\n",
- "# reshape to np arrays\n",
- "iters = np.array(iters_proxy).reshape(num_points)\n",
- "train_accuracy = np.array(train_accuracy_proxy).reshape(num_points)\n",
- "test_accuracy = np.array(test_accuracy_proxy).reshape(num_points)\n",
- "\n",
- "#plot\n",
- "plt.title('Iris validation accuracy by iteration - warm start')\n",
- "plt.xlabel('Iteration number')\n",
- "plt.ylabel('Accuracy')\n",
- "plt.grid(True)\n",
- "plt.plot(iters, train_accuracy, 'g.-', label='Train')\n",
- "plt.plot(iters, test_accuracy, 'r.-', label='Test')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x12f560f10>"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# get loss\n",
- "train_loss_proxy = %sql SELECT training_loss FROM iris_model_summary;\n",
- "test_loss_proxy = %sql SELECT validation_loss FROM iris_model_summary;\n",
- "\n",
- "# reshape to np arrays\n",
- "train_loss = np.array(train_loss_proxy).reshape(num_points)\n",
- "test_loss = np.array(test_loss_proxy).reshape(num_points)\n",
- "\n",
- "#plot\n",
- "plt.title('Iris validation loss by iteration - warm start')\n",
- "plt.xlabel('Iteration number')\n",
- "plt.ylabel('Loss')\n",
- "plt.grid(True)\n",
- "plt.plot(iters, train_loss, 'g.-', label='Train')\n",
- "plt.plot(iters, test_loss, 'r.-', label='Test')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"transfer_learn\"></a>\n",
- "# Transfer learning\n",
- "\n",
- "<a id=\"load2\"></a>\n",
- "# 1. Define and load model architecture with some layers frozen\n",
- "Here we want to start with initial weights from a pre-trained model rather than training from scratch. We also want to use a model architecture with the earlier feature layer(s) frozen to save on training time. The example below is somewhat contrived but gives you the idea of the steps.\n",
- "\n",
- "First define a model architecture with the 1st hidden layer frozen:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 35,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "dense_4 (Dense) (None, 10) 50 \n",
- "_________________________________________________________________\n",
- "dense_5 (Dense) (None, 10) 110 \n",
- "_________________________________________________________________\n",
- "dense_6 (Dense) (None, 3) 33 \n",
- "=================================================================\n",
- "Total params: 193\n",
- "Trainable params: 143\n",
- "Non-trainable params: 50\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model_transfer = Sequential()\n",
- "model_transfer.add(Dense(10, activation='relu', input_shape=(4,), trainable=False))\n",
- "model_transfer.add(Dense(10, activation='relu'))\n",
- "model_transfer.add(Dense(3, activation='softmax'))\n",
- " \n",
- "model_transfer.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": false, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- "execution_count": 36,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model_transfer.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load transfer model into model architecture table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "2 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_id</th>\n",
- " <th>model_arch</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
- " <td>Sophie</td>\n",
- " <td>A simple model</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': False, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
- " <td>Maria</td>\n",
- " <td>A transfer model</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, u'Sophie', u'A simple model'),\n",
- " (2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': False, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, u'Maria', u'A transfer model')]"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT madlib.load_keras_model('model_arch_library', -- Output table, \n",
- "$$\n",
- "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": false, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
- "$$\n",
- "::json, -- JSON blob\n",
- " NULL, -- Weights\n",
- " 'Maria', -- Name\n",
- " 'A transfer model' -- Descr\n",
- ");\n",
- "\n",
- "SELECT model_id, model_arch, name, description FROM model_arch_library ORDER BY model_id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"train2\"></a>\n",
- "# 2. Train transfer model\n",
- "\n",
- "Fetch the weights from a previous MADlib run. (Normally these would be downloaded from a source that trained the same model architecture on a related dataset.)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "UPDATE model_arch_library \n",
- "SET model_weights = iris_model.model_weights \n",
- "FROM iris_model \n",
- "WHERE model_arch_library.model_id = 2;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now train the model using the transfer model and the pre-trained weights:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>madlib_keras_fit</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td></td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[('',)]"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_model, iris_model_summary;\n",
- "\n",
- "SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table\n",
- " 'iris_model', -- model output table\n",
- " 'model_arch_library', -- model arch table\n",
- " 2, -- model arch id\n",
- " $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params\n",
- " $$ batch_size=5, epochs=3 $$, -- fit_params\n",
- " 10, -- num_iterations\n",
- " FALSE, -- use GPUs\n",
- " 'iris_test_packed', -- validation dataset\n",
- " 2 -- metrics compute frequency\n",
- " );"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>model</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>model_arch_table</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " <th>num_iterations</th>\n",
- " <th>validation_table</th>\n",
- " <th>metrics_compute_frequency</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>start_training_time</th>\n",
- " <th>end_training_time</th>\n",
- " <th>metrics_elapsed_time</th>\n",
- " <th>madlib_version</th>\n",
- " <th>num_classes</th>\n",
- " <th>class_values</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>metrics_type</th>\n",
- " <th>training_metrics_final</th>\n",
- " <th>training_loss_final</th>\n",
- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " <th>metrics_iters</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train_packed</td>\n",
- " <td>iris_model</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>model_arch_library</td>\n",
- " <td>2</td>\n",
- " <td> loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] </td>\n",
- " <td> batch_size=5, epochs=3 </td>\n",
- " <td>10</td>\n",
- " <td>iris_test_packed</td>\n",
- " <td>2</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>2019-12-18 18:09:32.439417</td>\n",
- " <td>2019-12-18 18:09:34.068824</td>\n",
- " <td>[0.853152990341187, 0.990938901901245, 1.11821985244751, 1.24195981025696, 1.62932586669922]</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>3</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>character varying</td>\n",
- " <td>1.0</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.983333349228</td>\n",
- " <td>0.155750438571</td>\n",
- " <td>[0.983333349227905, 0.983333349227905, 0.975000023841858, 0.975000023841858, 0.983333349227905]</td>\n",
- " <td>[0.187174424529076, 0.17763115465641, 0.169175431132317, 0.161857321858406, 0.155750438570976]</td>\n",
- " <td>0.966666638851</td>\n",
- " <td>0.211615949869</td>\n",
- " <td>[0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166]</td>\n",
- " <td>[0.244408145546913, 0.234545931220055, 0.225818797945976, 0.218266576528549, 0.211615949869156]</td>\n",
- " <td>[2, 4, 6, 8, 10]</td>\n",
- " </tr>\n",
- "</table>"
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- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_model_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Note loss picks up from where the last training left off:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x12f85fdd0>"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "import sys\n",
- "import os\n",
- "from matplotlib import pyplot as plt\n",
- "\n",
- "# get accuracy and iteration number\n",
- "iters_proxy = %sql SELECT metrics_iters FROM iris_model_summary;\n",
- "train_accuracy_proxy = %sql SELECT training_metrics FROM iris_model_summary;\n",
- "test_accuracy_proxy = %sql SELECT validation_metrics FROM iris_model_summary;\n",
- "\n",
- "# get number of points\n",
- "num_points_proxy = %sql SELECT array_length(metrics_iters,1) FROM iris_model_summary;\n",
- "num_points = num_points_proxy[0]\n",
- "\n",
- "# reshape to np arrays\n",
- "iters = np.array(iters_proxy).reshape(num_points)\n",
- "train_accuracy = np.array(train_accuracy_proxy).reshape(num_points)\n",
- "test_accuracy = np.array(test_accuracy_proxy).reshape(num_points)\n",
- "\n",
- "#plot\n",
- "plt.title('Iris validation accuracy by iteration - transfer learn')\n",
- "plt.xlabel('Iteration number')\n",
- "plt.ylabel('Accuracy')\n",
- "plt.grid(True)\n",
- "plt.plot(iters, train_accuracy, 'g.-', label='Train')\n",
- "plt.plot(iters, test_accuracy, 'r.-', label='Test')\n",
- "plt.legend()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x12f8d5990>"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "# get loss\n",
- "train_loss_proxy = %sql SELECT training_loss FROM iris_model_summary;\n",
- "test_loss_proxy = %sql SELECT validation_loss FROM iris_model_summary;\n",
- "\n",
- "# reshape to np arrays\n",
- "train_loss = np.array(train_loss_proxy).reshape(num_points)\n",
- "test_loss = np.array(test_loss_proxy).reshape(num_points)\n",
- "\n",
- "#plot\n",
- "plt.title('Iris validation loss by iteration - transfer learn')\n",
- "plt.xlabel('Iteration number')\n",
- "plt.ylabel('Loss')\n",
- "plt.grid(True)\n",
- "plt.plot(iters, train_loss, 'g.-', label='Train')\n",
- "plt.plot(iters, test_loss, 'r.-', label='Test')\n",
- "plt.legend()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.10"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-cifar10-inference-v1.ipynb b/community-artifacts/Deep-learning/MADlib-Keras-cifar10-inference-v1.ipynb
deleted file mode 100644
index c5de290..0000000
--- a/community-artifacts/Deep-learning/MADlib-Keras-cifar10-inference-v1.ipynb
+++ /dev/null
@@ -1,601 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Inference for CIFAR-10 dataset using predict BYOM\n",
- "The predict BYOM function allows you to do inference using models that have not been trained with MADlib, but rather imported or created elsewhere. It was added in MADlib 1.17.\n",
- "\n",
- "In this workbook we train a model in Python using\n",
- "https://keras.io/examples/cifar10_cnn/\n",
- "and run inference on the validation set.\n",
- "\n",
- "## Table of contents\n",
- "\n",
- "<a href=\"#setup\">1. Setup</a>\n",
- "\n",
- "<a href=\"#train_model\">2. Train model in Python</a>\n",
- "\n",
- "<a href=\"#load_model\">3. Load model into table</a>\n",
- "\n",
- "<a href=\"#load_images\">4. Get validation data set and load into table</a>\n",
- "\n",
- "<a href=\"#inference\">5. Inference</a>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"setup\"></a>\n",
- "# 1. Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
- "source": [
- "%load_ext sql"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
- "# Greenplum Database 5.x on GCP - via tunnel\n",
- "%sql postgresql://gpadmin@localhost:8000/madlib\n",
- " \n",
- "# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>version</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql select madlib.version();\n",
- "#%sql select version();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"train_model\"></a>\n",
- "# 2. Train model in Python\n",
- "\n",
- "Train a model in Python using https://keras.io/examples/cifar10_cnn/"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from __future__ import print_function\n",
- "import keras\n",
- "from keras.datasets import cifar10\n",
- "from keras.preprocessing.image import ImageDataGenerator\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense, Dropout, Activation, Flatten\n",
- "from keras.layers import Conv2D, MaxPooling2D\n",
- "import os\n",
- "\n",
- "batch_size = 32\n",
- "num_classes = 10\n",
- "epochs = 25\n",
- "data_augmentation = True\n",
- "num_predictions = 20\n",
- "#save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
- "#model_name = 'keras_cifar10_trained_model.h5'\n",
- "\n",
- "# The data, split between train and test sets:\n",
- "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
- "print('x_train shape:', x_train.shape)\n",
- "print(x_train.shape[0], 'train samples')\n",
- "print(x_test.shape[0], 'test samples')\n",
- "\n",
- "# Convert class vectors to binary class matrices.\n",
- "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
- "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
- "\n",
- "model = Sequential()\n",
- "model.add(Conv2D(32, (3, 3), padding='same',\n",
- " input_shape=x_train.shape[1:]))\n",
- "model.add(Activation('relu'))\n",
- "model.add(Conv2D(32, (3, 3)))\n",
- "model.add(Activation('relu'))\n",
- "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "model.add(Dropout(0.25))\n",
- "\n",
- "model.add(Conv2D(64, (3, 3), padding='same'))\n",
- "model.add(Activation('relu'))\n",
- "model.add(Conv2D(64, (3, 3)))\n",
- "model.add(Activation('relu'))\n",
- "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "model.add(Dropout(0.25))\n",
- "\n",
- "model.add(Flatten())\n",
- "model.add(Dense(512))\n",
- "model.add(Activation('relu'))\n",
- "model.add(Dropout(0.5))\n",
- "model.add(Dense(num_classes))\n",
- "model.add(Activation('softmax'))\n",
- "\n",
- "# initiate RMSprop optimizer\n",
- "opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\n",
- "\n",
- "# Let's train the model using RMSprop\n",
- "model.compile(loss='categorical_crossentropy',\n",
- " optimizer=opt,\n",
- " metrics=['accuracy'])\n",
- "\n",
- "x_train = x_train.astype('float32')\n",
- "x_test = x_test.astype('float32')\n",
- "x_train /= 255\n",
- "x_test /= 255\n",
- "\n",
- "if not data_augmentation:\n",
- " print('Not using data augmentation.')\n",
- " model.fit(x_train, y_train,\n",
- " batch_size=batch_size,\n",
- " epochs=epochs,\n",
- " validation_data=(x_test, y_test),\n",
- " shuffle=True)\n",
- "else:\n",
- " print('Using real-time data augmentation.')\n",
- " # This will do preprocessing and realtime data augmentation:\n",
- " datagen = ImageDataGenerator(\n",
- " featurewise_center=False, # set input mean to 0 over the dataset\n",
- " samplewise_center=False, # set each sample mean to 0\n",
- " featurewise_std_normalization=False, # divide inputs by std of the dataset\n",
- " samplewise_std_normalization=False, # divide each input by its std\n",
- " zca_whitening=False, # apply ZCA whitening\n",
- " zca_epsilon=1e-06, # epsilon for ZCA whitening\n",
- " rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)\n",
- " # randomly shift images horizontally (fraction of total width)\n",
- " width_shift_range=0.1,\n",
- " # randomly shift images vertically (fraction of total height)\n",
- " height_shift_range=0.1,\n",
- " shear_range=0., # set range for random shear\n",
- " zoom_range=0., # set range for random zoom\n",
- " channel_shift_range=0., # set range for random channel shifts\n",
- " # set mode for filling points outside the input boundaries\n",
- " fill_mode='nearest',\n",
- " cval=0., # value used for fill_mode = \"constant\"\n",
- " horizontal_flip=True, # randomly flip images\n",
- " vertical_flip=False, # randomly flip images\n",
- " # set rescaling factor (applied before any other transformation)\n",
- " rescale=None,\n",
- " # set function that will be applied on each input\n",
- " preprocessing_function=None,\n",
- " # image data format, either \"channels_first\" or \"channels_last\"\n",
- " data_format=None,\n",
- " # fraction of images reserved for validation (strictly between 0 and 1)\n",
- " validation_split=0.0)\n",
- "\n",
- " # Compute quantities required for feature-wise normalization\n",
- " # (std, mean, and principal components if ZCA whitening is applied).\n",
- " datagen.fit(x_train)\n",
- "\n",
- " # Fit the model on the batches generated by datagen.flow().\n",
- " model.fit_generator(datagen.flow(x_train, y_train,\n",
- " batch_size=batch_size),\n",
- " epochs=epochs,\n",
- " validation_data=(x_test, y_test),\n",
- " workers=4)\n",
- "\n",
- "# Save model and weights\n",
- "#if not os.path.isdir(save_dir):\n",
- "# os.makedirs(save_dir)\n",
- "#model_path = os.path.join(save_dir, model_name)\n",
- "#model.save(model_path)\n",
- "#print('Saved trained model at %s ' % model_path)\n",
- "\n",
- "# Score trained model.\n",
- "scores = model.evaluate(x_test, y_test, verbose=1)\n",
- "print('Test loss:', scores[0])\n",
- "print('Test accuracy:', scores[1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "model.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load_model\"></a>\n",
- "# 3. Load model into table\n",
- "\n",
- "Load the model architecture and weights into the model architecture table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import psycopg2 as p2\n",
- "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
- "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
- "cur = conn.cursor()\n",
- "\n",
- "from keras.layers import *\n",
- "from keras import Sequential\n",
- "import numpy as np\n",
- "\n",
- "# get weights, flatten and serialize\n",
- "weights = model.get_weights()\n",
- "weights_flat = [w.flatten() for w in weights]\n",
- "weights1d = np.concatenate(weights_flat).ravel()\n",
- "weights_bytea = p2.Binary(weights1d.tostring())\n",
- "\n",
- "query = \"SELECT madlib.load_keras_model('model_arch_library_cifar10', %s,%s,%s,%s)\"\n",
- "cur.execute(query,[model.to_json(), weights_bytea, \"CIFAR10 model\", \"CNN model with weights trained on CIFAR10.\"])\n",
- "conn.commit()\n",
- "\n",
- "# check weights loaded OK\n",
- "%sql SELECT model_id, name, description FROM model_arch_library_cifar10;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load_images\"></a>\n",
- "# 4. Get validation data set and load into table\n",
- "\n",
- "First set up image loader using the script called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys\n",
- "import os\n",
- "madlib_site_dir = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data'\n",
- "sys.path.append(madlib_site_dir)\n",
- "\n",
- "# Import image loader module\n",
- "from madlib_image_loader import ImageLoader, DbCredentials\n",
- "\n",
- "# Specify database credentials, for connecting to db\n",
- "#db_creds = DbCredentials(user='fmcquillan',\n",
- "# host='localhost',\n",
- "# port='5432',\n",
- "# password='')\n",
- "\n",
- "# Specify database credentials, for connecting to db\n",
- "db_creds = DbCredentials(user='gpadmin', \n",
- " db_name='madlib',\n",
- " host='localhost',\n",
- " port='8000',\n",
- " password='')\n",
- "\n",
- "# Initialize ImageLoader (increase num_workers to run faster)\n",
- "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Next load CIFAR-10 data from Keras consisting of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from keras.datasets import cifar10\n",
- "\n",
- "# Load dataset into np array\n",
- "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
- "\n",
- "%sql DROP TABLE IF EXISTS cifar_10_test_data;\n",
- "\n",
- "# Save images to temporary directories and load into database\n",
- "#iloader.load_dataset_from_np(x_train, y_train, 'cifar_10_train_data', append=False)\n",
- "iloader.load_dataset_from_np(x_test, y_test, 'cifar_10_test_data', append=False)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"inference\"></a>\n",
- "# 5. Inference"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "10 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>id</th>\n",
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- " <td>1</td>\n",
- " <td>3</td>\n",
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- " (10, u'8')]"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS cifar10_predict_byom;\n",
- "\n",
- "SELECT madlib.madlib_keras_predict_byom('model_arch_library_cifar10', -- model arch table\n",
- " 1, -- model arch id\n",
- " 'cifar_10_test_data', -- test_table\n",
- " 'id', -- id column\n",
- " 'x', -- independent var\n",
- " 'cifar10_predict_byom', -- output table\n",
- " 'response', -- prediction type\n",
- " FALSE, -- use gpus\n",
- " NULL, -- class values\n",
- " 255.0 -- normalizing const\n",
- " );\n",
- "SELECT * FROM cifar10_predict_byom ORDER BY id LIMIT 10;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Number of missclassifications:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
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- " <td>2551</td>\n",
- " </tr>\n",
- "</table>"
- ],
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- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT COUNT(*) FROM cifar10_predict_byom JOIN cifar_10_test_data USING (id)\n",
- "WHERE cifar10_predict_byom.estimated_dependent_var != cifar_10_test_data.y;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Predict accuracy. From https://keras.io/examples/cifar10_cnn/ accuracy claim is 75% on validation set after 25 epochs. From run above test accuracy: 0.7449. MADlib predict BYOM accuracy matches:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>test_accuracy_percent</th>\n",
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- " </tr>\n",
- "</table>"
- ],
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- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT round(count(*)*100.0/10000.0, 2) as test_accuracy_percent from\n",
- " (select cifar_10_test_data.y as actual, cifar10_predict_byom.estimated_dependent_var as estimated\n",
- " from cifar10_predict_byom inner join cifar_10_test_data\n",
- " on cifar_10_test_data.id=cifar10_predict_byom.id) q\n",
- "WHERE q.actual=q.estimated;"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.10"
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- "nbformat": 4,
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diff --git a/community-artifacts/Deep-learning/MADlib-Keras-model-selection-MLP-v1.ipynb b/community-artifacts/Deep-learning/MADlib-Keras-model-selection-MLP-v1.ipynb
deleted file mode 100644
index cfe8c97..0000000
--- a/community-artifacts/Deep-learning/MADlib-Keras-model-selection-MLP-v1.ipynb
+++ /dev/null
@@ -1,5709 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Model Selection for Multilayer Perceptron Using Keras and MADlib\n",
- "\n",
- "E2E classification example using MADlib calling a Keras MLP for different hyperparameters and model architectures.\n",
- "\n",
- "Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this workbook we use the well known iris data set from https://archive.ics.uci.edu/ml/datasets/iris to help get you started. It is similar to the example in user docs http://madlib.apache.org/docs/latest/index.html\n",
- "\n",
- "For more realistic examples please refer to the deep learning notebooks at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts\n",
- "\n",
- "## Table of contents\n",
- "\n",
- "<a href=\"#class\">Classification</a>\n",
- "\n",
- "* <a href=\"#create_input_data\">1. Create input data</a>\n",
- "\n",
- "* <a href=\"#pp\">2. Call preprocessor for deep learning</a>\n",
- "\n",
- "* <a href=\"#load\">3. Define and load model architecture</a>\n",
- "\n",
- "* <a href=\"#def_mst\">4. Define and load model selection tuples</a>\n",
- "\n",
- "* <a href=\"#train\">5. Train</a>\n",
- "\n",
- "* <a href=\"#eval\">6. Evaluate</a>\n",
- "\n",
- "* <a href=\"#pred\">7. Predict</a>\n",
- "\n",
- "<a href=\"#class2\">Classification with Other Parameters</a>\n",
- "\n",
- "* <a href=\"#val_dataset\">1. Validation dataset</a>\n",
- "\n",
- "* <a href=\"#pred_prob\">2. Predict probabilities</a>\n",
- "\n",
- "* <a href=\"#warm_start\">3. Warm start</a>"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
- "source": [
- "%load_ext sql"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
- "# Greenplum Database 5.x on GCP - via tunnel\n",
- "%sql postgresql://gpadmin@localhost:8000/madlib\n",
- " \n",
- "# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>version</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql select madlib.version();\n",
- "#%sql select version();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"class\"></a>\n",
- "# Classification\n",
- "\n",
- "<a id=\"create_input_data\"></a>\n",
- "# 1. Create input data\n",
- "\n",
- "Load iris data set."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "Done.\n",
- "150 rows affected.\n",
- "150 rows affected.\n"
- ]
- },
- {
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- " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>19</td>\n",
- " <td>[Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>20</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>21</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>22</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>23</td>\n",
- " <td>[Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>24</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>25</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>26</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>27</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>28</td>\n",
- " <td>[Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>29</td>\n",
- " <td>[Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>30</td>\n",
- " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>31</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>32</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>33</td>\n",
- " <td>[Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>34</td>\n",
- " <td>[Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>35</td>\n",
- " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>36</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>37</td>\n",
- " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>38</td>\n",
- " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>39</td>\n",
- " <td>[Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>40</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>41</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>42</td>\n",
- " <td>[Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>43</td>\n",
- " <td>[Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>44</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>45</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>46</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>47</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>48</td>\n",
- " <td>[Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>49</td>\n",
- " <td>[Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>50</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>51</td>\n",
- " <td>[Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>52</td>\n",
- " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>53</td>\n",
- " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>54</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>55</td>\n",
- " <td>[Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>56</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>57</td>\n",
- " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>58</td>\n",
- " <td>[Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>59</td>\n",
- " <td>[Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>60</td>\n",
- " <td>[Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>61</td>\n",
- " <td>[Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>62</td>\n",
- " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>63</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>64</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>65</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>66</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>67</td>\n",
- " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>68</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>69</td>\n",
- " <td>[Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>70</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>71</td>\n",
- " <td>[Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>72</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>73</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>74</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>75</td>\n",
- " <td>[Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>76</td>\n",
- " <td>[Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>77</td>\n",
- " <td>[Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>78</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>79</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>80</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>81</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>82</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>83</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>84</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>85</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>86</td>\n",
- " <td>[Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>87</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>88</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>89</td>\n",
- " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>90</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>91</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>92</td>\n",
- " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>93</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>94</td>\n",
- " <td>[Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>95</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>96</td>\n",
- " <td>[Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>97</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>98</td>\n",
- " <td>[Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>99</td>\n",
- " <td>[Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>100</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')]</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>101</td>\n",
- " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>102</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>103</td>\n",
- " <td>[Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>104</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>105</td>\n",
- " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>106</td>\n",
- " <td>[Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>107</td>\n",
- " <td>[Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>108</td>\n",
- " <td>[Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>109</td>\n",
- " <td>[Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>110</td>\n",
- " <td>[Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>111</td>\n",
- " <td>[Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>112</td>\n",
- " <td>[Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>113</td>\n",
- " <td>[Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>114</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>115</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>116</td>\n",
- " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>117</td>\n",
- " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>118</td>\n",
- " <td>[Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>119</td>\n",
- " <td>[Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>121</td>\n",
- " <td>[Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>122</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>123</td>\n",
- " <td>[Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>124</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>125</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>126</td>\n",
- " <td>[Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>127</td>\n",
- " <td>[Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>128</td>\n",
- " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>129</td>\n",
- " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>130</td>\n",
- " <td>[Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>131</td>\n",
- " <td>[Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>132</td>\n",
- " <td>[Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>133</td>\n",
- " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>134</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>135</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>136</td>\n",
- " <td>[Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>137</td>\n",
- " <td>[Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>138</td>\n",
- " <td>[Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>139</td>\n",
- " <td>[Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>140</td>\n",
- " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>141</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>142</td>\n",
- " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>143</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>144</td>\n",
- " <td>[Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>145</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>146</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>147</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>148</td>\n",
- " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>149</td>\n",
- " <td>[Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>150</td>\n",
- " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')]</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(1, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (2, [Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (3, [Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (4, [Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (5, [Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (6, [Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (7, [Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (8, [Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (9, [Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (10, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (11, [Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (12, [Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (13, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (14, [Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (15, [Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (16, [Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (17, [Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (18, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (19, [Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (20, [Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (21, [Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (22, [Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (23, [Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (24, [Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')], u'Iris-setosa'),\n",
- " (25, [Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (26, [Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (27, [Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (28, [Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (29, [Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (30, [Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (31, [Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (32, [Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (33, [Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (34, [Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (35, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (36, [Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (37, [Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (38, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
- " (39, [Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (40, [Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (41, [Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (42, [Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (43, [Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (44, [Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')], u'Iris-setosa'),\n",
- " (45, [Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')], u'Iris-setosa'),\n",
- " (46, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
- " (47, [Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (48, [Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (49, [Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (50, [Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
- " (51, [Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (52, [Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (53, [Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (54, [Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (55, [Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (56, [Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (57, [Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')], u'Iris-versicolor'),\n",
- " (58, [Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (59, [Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (60, [Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (61, [Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (62, [Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (63, [Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (64, [Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (65, [Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (66, [Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (67, [Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (68, [Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (69, [Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (70, [Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')], u'Iris-versicolor'),\n",
- " (71, [Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')], u'Iris-versicolor'),\n",
- " (72, [Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (73, [Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (74, [Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (75, [Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (76, [Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (77, [Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (78, [Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')], u'Iris-versicolor'),\n",
- " (79, [Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (80, [Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (81, [Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')], u'Iris-versicolor'),\n",
- " (82, [Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (83, [Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (84, [Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')], u'Iris-versicolor'),\n",
- " (85, [Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (86, [Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')], u'Iris-versicolor'),\n",
- " (87, [Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')], u'Iris-versicolor'),\n",
- " (88, [Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (89, [Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (90, [Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (91, [Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (92, [Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')], u'Iris-versicolor'),\n",
- " (93, [Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (94, [Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
- " (95, [Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (96, [Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')], u'Iris-versicolor'),\n",
- " (97, [Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (98, [Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (99, [Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')], u'Iris-versicolor'),\n",
- " (100, [Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
- " (101, [Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')], u'Iris-virginica'),\n",
- " (102, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (103, [Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (104, [Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (105, [Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')], u'Iris-virginica'),\n",
- " (106, [Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (107, [Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')], u'Iris-virginica'),\n",
- " (108, [Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (109, [Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (110, [Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')], u'Iris-virginica'),\n",
- " (111, [Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (112, [Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (113, [Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (114, [Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (115, [Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')], u'Iris-virginica'),\n",
- " (116, [Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (117, [Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (118, [Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')], u'Iris-virginica'),\n",
- " (119, [Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (120, [Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')], u'Iris-virginica'),\n",
- " (121, [Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (122, [Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (123, [Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (124, [Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (125, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (126, [Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (127, [Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (128, [Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (129, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (130, [Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')], u'Iris-virginica'),\n",
- " (131, [Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (132, [Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (133, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')], u'Iris-virginica'),\n",
- " (134, [Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')], u'Iris-virginica'),\n",
- " (135, [Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')], u'Iris-virginica'),\n",
- " (136, [Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (137, [Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
- " (138, [Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (139, [Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
- " (140, [Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')], u'Iris-virginica'),\n",
- " (141, [Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
- " (142, [Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (143, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (144, [Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (145, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')], u'Iris-virginica'),\n",
- " (146, [Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (147, [Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')], u'Iris-virginica'),\n",
- " (148, [Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')], u'Iris-virginica'),\n",
- " (149, [Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')], u'Iris-virginica'),\n",
- " (150, [Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')], u'Iris-virginica')]"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql \n",
- "DROP TABLE IF EXISTS iris_data;\n",
- "\n",
- "CREATE TABLE iris_data(\n",
- " id serial,\n",
- " attributes numeric[],\n",
- " class_text varchar\n",
- ");\n",
- "\n",
- "INSERT INTO iris_data(id, attributes, class_text) VALUES\n",
- "(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),\n",
- "(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),\n",
- "(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),\n",
- "(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),\n",
- "(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),\n",
- "(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),\n",
- "(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),\n",
- "(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),\n",
- "(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),\n",
- "(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
- "(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),\n",
- "(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),\n",
- "(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),\n",
- "(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),\n",
- "(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),\n",
- "(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),\n",
- "(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),\n",
- "(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),\n",
- "(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),\n",
- "(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),\n",
- "(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),\n",
- "(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),\n",
- "(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),\n",
- "(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),\n",
- "(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),\n",
- "(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),\n",
- "(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),\n",
- "(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),\n",
- "(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),\n",
- "(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),\n",
- "(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),\n",
- "(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),\n",
- "(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),\n",
- "(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),\n",
- "(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
- "(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),\n",
- "(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),\n",
- "(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
- "(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),\n",
- "(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),\n",
- "(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),\n",
- "(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),\n",
- "(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),\n",
- "(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),\n",
- "(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),\n",
- "(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),\n",
- "(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),\n",
- "(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),\n",
- "(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),\n",
- "(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),\n",
- "(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),\n",
- "(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),\n",
- "(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),\n",
- "(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),\n",
- "(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),\n",
- "(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),\n",
- "(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),\n",
- "(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),\n",
- "(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),\n",
- "(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),\n",
- "(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),\n",
- "(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),\n",
- "(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),\n",
- "(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),\n",
- "(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),\n",
- "(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),\n",
- "(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),\n",
- "(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),\n",
- "(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),\n",
- "(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),\n",
- "(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),\n",
- "(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),\n",
- "(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),\n",
- "(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),\n",
- "(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),\n",
- "(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),\n",
- "(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),\n",
- "(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),\n",
- "(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),\n",
- "(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),\n",
- "(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),\n",
- "(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),\n",
- "(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),\n",
- "(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),\n",
- "(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),\n",
- "(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),\n",
- "(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),\n",
- "(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),\n",
- "(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),\n",
- "(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),\n",
- "(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),\n",
- "(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),\n",
- "(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),\n",
- "(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),\n",
- "(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),\n",
- "(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),\n",
- "(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),\n",
- "(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),\n",
- "(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),\n",
- "(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),\n",
- "(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),\n",
- "(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
- "(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),\n",
- "(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),\n",
- "(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),\n",
- "(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),\n",
- "(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),\n",
- "(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),\n",
- "(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),\n",
- "(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),\n",
- "(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),\n",
- "(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),\n",
- "(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),\n",
- "(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),\n",
- "(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),\n",
- "(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),\n",
- "(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),\n",
- "(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),\n",
- "(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),\n",
- "(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),\n",
- "(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),\n",
- "(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),\n",
- "(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),\n",
- "(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),\n",
- "(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),\n",
- "(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),\n",
- "(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),\n",
- "(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),\n",
- "(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),\n",
- "(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),\n",
- "(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),\n",
- "(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),\n",
- "(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),\n",
- "(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),\n",
- "(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),\n",
- "(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),\n",
- "(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),\n",
- "(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),\n",
- "(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),\n",
- "(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),\n",
- "(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),\n",
- "(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),\n",
- "(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
- "(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),\n",
- "(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),\n",
- "(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),\n",
- "(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),\n",
- "(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),\n",
- "(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),\n",
- "(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');\n",
- "\n",
- "SELECT * FROM iris_data ORDER BY id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create a test/validation dataset from the training data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(120L,)]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_train, iris_test;\n",
- "\n",
- "-- Set seed so results are reproducible\n",
- "SELECT setseed(0);\n",
- "\n",
- "SELECT madlib.train_test_split('iris_data', -- Source table\n",
- " 'iris', -- Output table root name\n",
- " 0.8, -- Train proportion\n",
- " NULL, -- Test proportion (0.2)\n",
- " NULL, -- Strata definition\n",
- " NULL, -- Output all columns\n",
- " NULL, -- Sample without replacement\n",
- " TRUE -- Separate output tables\n",
- " );\n",
- "\n",
- "SELECT COUNT(*) FROM iris_train;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"pp\"></a>\n",
- "# 2. Call preprocessor for deep learning\n",
- "Training dataset (uses training preprocessor):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "2 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
- " <th>buffer_id</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[60, 4]</td>\n",
- " <td>[60, 3]</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[60, 4]</td>\n",
- " <td>[60, 3]</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[([60, 4], [60, 3], 0), ([60, 4], [60, 3], 1)]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;\n",
- "\n",
- "SELECT madlib.training_preprocessor_dl('iris_train', -- Source table\n",
- " 'iris_train_packed', -- Output table\n",
- " 'class_text', -- Dependent variable\n",
- " 'attributes' -- Independent variable\n",
- " ); \n",
- "\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM iris_train_packed ORDER BY buffer_id;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>output_table</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
- " <th>buffer_size</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>num_classes</th>\n",
- " <th>distribution_rules</th>\n",
- " <th>__internal_gpu_config__</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train</td>\n",
- " <td>iris_train_packed</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>character varying</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>60</td>\n",
- " <td>1.0</td>\n",
- " <td>3</td>\n",
- " <td>all_segments</td>\n",
- " <td>all_segments</td>\n",
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- "</table>"
- ],
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- "[(u'iris_train', u'iris_train_packed', u'class_text', u'attributes', u'character varying', [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 60, 1.0, 3, 'all_segments', 'all_segments')]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_train_packed_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Validation dataset (uses validation preprocessor):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "2 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
- " <th>buffer_id</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[15, 4]</td>\n",
- " <td>[15, 3]</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[15, 4]</td>\n",
- " <td>[15, 3]</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[([15, 4], [15, 3], 0), ([15, 4], [15, 3], 1)]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;\n",
- "\n",
- "SELECT madlib.validation_preprocessor_dl('iris_test', -- Source table\n",
- " 'iris_test_packed', -- Output table\n",
- " 'class_text', -- Dependent variable\n",
- " 'attributes', -- Independent variable\n",
- " 'iris_train_packed' -- From training preprocessor step\n",
- " ); \n",
- "\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM iris_test_packed ORDER BY buffer_id;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>output_table</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
- " <th>buffer_size</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>num_classes</th>\n",
- " <th>distribution_rules</th>\n",
- " <th>__internal_gpu_config__</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_test</td>\n",
- " <td>iris_test_packed</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>character varying</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>15</td>\n",
- " <td>1.0</td>\n",
- " <td>3</td>\n",
- " <td>all_segments</td>\n",
- " <td>all_segments</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'iris_test', u'iris_test_packed', u'class_text', u'attributes', u'character varying', [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 15, 1.0, 3, 'all_segments', 'all_segments')]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_test_packed_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load\"></a>\n",
- "# 3. Define and load model architecture\n",
- "Import Keras libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Couldn't import dot_parser, loading of dot files will not be possible.\n"
- ]
- }
- ],
- "source": [
- "import keras\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Define model architecture with 1 hidden layer:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "dense_1 (Dense) (None, 10) 50 \n",
- "_________________________________________________________________\n",
- "dense_2 (Dense) (None, 10) 110 \n",
- "_________________________________________________________________\n",
- "dense_3 (Dense) (None, 3) 33 \n",
- "=================================================================\n",
- "Total params: 193\n",
- "Trainable params: 193\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model1 = Sequential()\n",
- "model1.add(Dense(10, activation='relu', input_shape=(4,)))\n",
- "model1.add(Dense(10, activation='relu'))\n",
- "model1.add(Dense(3, activation='softmax'))\n",
- " \n",
- "model1.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model1.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Define model architecture with 2 hidden layers:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "dense_4 (Dense) (None, 10) 50 \n",
- "_________________________________________________________________\n",
- "dense_5 (Dense) (None, 10) 110 \n",
- "_________________________________________________________________\n",
- "dense_6 (Dense) (None, 10) 110 \n",
- "_________________________________________________________________\n",
- "dense_7 (Dense) (None, 3) 33 \n",
- "=================================================================\n",
- "Total params: 303\n",
- "Trainable params: 303\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model2 = Sequential()\n",
- "model2.add(Dense(10, activation='relu', input_shape=(4,)))\n",
- "model2.add(Dense(10, activation='relu'))\n",
- "model2.add(Dense(10, activation='relu'))\n",
- "model2.add(Dense(3, activation='softmax'))\n",
- " \n",
- "model2.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model2.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load into model architecture table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "2 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_id</th>\n",
- " <th>model_arch</th>\n",
- " <th>model_weights</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>__internal_madlib_id__</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
- " <td>None</td>\n",
- " <td>Sophie</td>\n",
- " <td>MLP with 1 hidden layer</td>\n",
- " <td>__madlib_temp_96702431_1576708421_6956281__</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
- " <td>None</td>\n",
- " <td>Maria</td>\n",
- " <td>MLP with 2 hidden layers</td>\n",
- " <td>__madlib_temp_85244704_1576708422_1853942__</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'MLP with 1 hidden layer', u'__madlib_temp_96702431_1576708421_6956281__'),\n",
- " (2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Maria', u'MLP with 2 hidden layers', u'__madlib_temp_85244704_1576708422_1853942__')]"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS model_arch_library;\n",
- "\n",
- "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
- " \n",
- "$$\n",
- "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
- "$$\n",
- "::json, -- JSON blob\n",
- " NULL, -- Weights\n",
- " 'Sophie', -- Name\n",
- " 'MLP with 1 hidden layer' -- Descr\n",
- ");\n",
- "\n",
- "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
- " \n",
- "$$\n",
- "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
- "$$\n",
- "::json, -- JSON blob\n",
- " NULL, -- Weights\n",
- " 'Maria', -- Name\n",
- " 'MLP with 2 hidden layers' -- Descr\n",
- ");\n",
- "\n",
- "SELECT * FROM model_arch_library ORDER BY model_id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"def_mst\"></a>\n",
- "# 4. Define and load model selection tuples"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Select the model(s) from the model architecture table that you want to run, along with the compile and fit parameters. Permutations will be created for the set of model selection parameters will be loaded:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "12 rows affected.\n"
- ]
- },
- {
- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>mst_key</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>12</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
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- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
- "\n",
- "SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table\n",
- " 'mst_table', -- model selection table output\n",
- " ARRAY[1,2], -- model ids from model architecture table\n",
- " ARRAY[ -- compile params\n",
- " $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,\n",
- " $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,\n",
- " $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$\n",
- " ],\n",
- " ARRAY[ -- fit params\n",
- " $$batch_size=4,epochs=1$$,\n",
- " $$batch_size=8,epochs=1$$\n",
- " ]\n",
- " );\n",
- " \n",
- "SELECT * FROM mst_table ORDER BY mst_key;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This is the name of the model architecture table that corresponds to the model selection table:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_arch_table</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>model_arch_library</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'model_arch_library',)]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM mst_table_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"train\"></a>\n",
- "# 5. Train\n",
- "Train multiple models:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>madlib_keras_fit_multiple_model</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td></td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[('',)]"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;\n",
- "\n",
- "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
- " 'iris_multi_model', -- model_output_table\n",
- " 'mst_table', -- model_selection_table\n",
- " 10, -- num_iterations\n",
- " FALSE -- use gpus\n",
- " );"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View the model summary:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
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- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>validation_table</th>\n",
- " <th>model</th>\n",
- " <th>model_info</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>model_arch_table</th>\n",
- " <th>num_iterations</th>\n",
- " <th>metrics_compute_frequency</th>\n",
- " <th>warm_start</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>start_training_time</th>\n",
- " <th>end_training_time</th>\n",
- " <th>madlib_version</th>\n",
- " <th>num_classes</th>\n",
- " <th>class_values</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>metrics_iters</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train_packed</td>\n",
- " <td>None</td>\n",
- " <td>iris_multi_model</td>\n",
- " <td>iris_multi_model_info</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>model_arch_library</td>\n",
- " <td>10</td>\n",
- " <td>10</td>\n",
- " <td>False</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>2019-12-18 22:33:49.706384</td>\n",
- " <td>2019-12-18 22:35:34.547961</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>3</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>character varying</td>\n",
- " <td>1.0</td>\n",
- " <td>[10]</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'iris_train_packed', None, u'iris_multi_model', u'iris_multi_model_info', u'class_text', u'attributes', u'model_arch_library', 10, 10, False, None, None, datetime.datetime(2019, 12, 18, 22, 33, 49, 706384), datetime.datetime(2019, 12, 18, 22, 35, 34, 547961), u'1.17-dev', 3, [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], u'character varying', 1.0, [10])]"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_multi_model_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View results for each model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "12 rows affected.\n"
- ]
- },
- {
- "data": {
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- " <th>fit_params</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>metrics_elapsed_time</th>\n",
- " <th>metrics_type</th>\n",
- " <th>training_metrics_final</th>\n",
- " <th>training_loss_final</th>\n",
- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.148514986038208]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.975000023842</td>\n",
- " <td>0.12241948396</td>\n",
- " <td>[0.975000023841858]</td>\n",
- " <td>[0.122419483959675]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.172315120697021]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.975000023842</td>\n",
- " <td>0.123081341386</td>\n",
- " <td>[0.975000023841858]</td>\n",
- " <td>[0.123081341385841]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.274233102798462]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.925000011921</td>\n",
- " <td>0.171397775412</td>\n",
- " <td>[0.925000011920929]</td>\n",
- " <td>[0.171397775411606]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.155992984771729]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.925000011921</td>\n",
- " <td>0.51177251339</td>\n",
- " <td>[0.925000011920929]</td>\n",
- " <td>[0.511772513389587]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.220170021057129]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.908333361149</td>\n",
- " <td>0.214677110314</td>\n",
- " <td>[0.908333361148834]</td>\n",
- " <td>[0.214677110314369]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>12</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.191344022750854]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.833333313465</td>\n",
- " <td>0.524632036686</td>\n",
- " <td>[0.833333313465118]</td>\n",
- " <td>[0.524632036685944]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.181636810302734]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.758333325386</td>\n",
- " <td>0.393412530422</td>\n",
- " <td>[0.758333325386047]</td>\n",
- " <td>[0.393412530422211]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.181061029434204]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.658333361149</td>\n",
- " <td>0.474381148815</td>\n",
- " <td>[0.658333361148834]</td>\n",
- " <td>[0.474381148815155]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.20294713973999]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.658333361149</td>\n",
- " <td>0.475430130959</td>\n",
- " <td>[0.658333361148834]</td>\n",
- " <td>[0.475430130958557]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.207202911376953]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.574999988079</td>\n",
- " <td>0.885546028614</td>\n",
- " <td>[0.574999988079071]</td>\n",
- " <td>[0.885546028614044]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.374184846878052]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.433333337307</td>\n",
- " <td>0.82793289423</td>\n",
- " <td>[0.433333337306976]</td>\n",
- " <td>[0.827932894229889]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.216787099838257]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.316666662693</td>\n",
- " <td>1.10255157948</td>\n",
- " <td>[0.316666662693024]</td>\n",
- " <td>[1.1025515794754]</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " <td>None</td>\n",
- " </tr>\n",
- "</table>"
- ],
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- "[(4, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.148514986038208], [u'accuracy'], 0.975000023842, 0.12241948396, [0.975000023841858], [0.122419483959675], None, None, None, None),\n",
- " (10, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.172315120697021], [u'accuracy'], 0.975000023842, 0.123081341386, [0.975000023841858], [0.123081341385841], None, None, None, None),\n",
- " (9, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.274233102798462], [u'accuracy'], 0.925000011921, 0.171397775412, [0.925000011920929], [0.171397775411606], None, None, None, None),\n",
- " (5, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.155992984771729], [u'accuracy'], 0.925000011921, 0.51177251339, [0.925000011920929], [0.511772513389587], None, None, None, None),\n",
- " (3, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.220170021057129], [u'accuracy'], 0.908333361149, 0.214677110314, [0.908333361148834], [0.214677110314369], None, None, None, None),\n",
- " (12, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.191344022750854], [u'accuracy'], 0.833333313465, 0.524632036686, [0.833333313465118], [0.524632036685944], None, None, None, None),\n",
- " (8, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.181636810302734], [u'accuracy'], 0.758333325386, 0.393412530422, [0.758333325386047], [0.393412530422211], None, None, None, None),\n",
- " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.181061029434204], [u'accuracy'], 0.658333361149, 0.474381148815, [0.658333361148834], [0.474381148815155], None, None, None, None),\n",
- " (2, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.20294713973999], [u'accuracy'], 0.658333361149, 0.475430130959, [0.658333361148834], [0.475430130958557], None, None, None, None),\n",
- " (6, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.207202911376953], [u'accuracy'], 0.574999988079, 0.885546028614, [0.574999988079071], [0.885546028614044], None, None, None, None),\n",
- " (11, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.374184846878052], [u'accuracy'], 0.433333337307, 0.82793289423, [0.433333337306976], [0.827932894229889], None, None, None, None),\n",
- " (1, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.216787099838257], [u'accuracy'], 0.316666662693, 1.10255157948, [0.316666662693024], [1.1025515794754], None, None, None, None)]"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"eval\"></a>\n",
- "# 6. Evaluate\n",
- "\n",
- "Now run evaluate using model we built above:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>loss</th>\n",
- " <th>metric</th>\n",
- " <th>metrics_type</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0.15500420332</td>\n",
- " <td>0.966666638851</td>\n",
- " <td>[u'accuracy']</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(0.15500420331955, 0.966666638851166, [u'accuracy'])]"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_validate;\n",
- "SELECT madlib.madlib_keras_evaluate('iris_multi_model', -- model\n",
- " 'iris_test_packed', -- test table\n",
- " 'iris_validate', -- output table\n",
- " NULL, -- use gpus\n",
- " 3 -- mst_key to use\n",
- " );\n",
- "\n",
- "SELECT * FROM iris_validate;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"pred\"></a>\n",
- "# 7. Predict\n",
- "\n",
- "Now predict using model we built. We will use the validation data set for prediction as well, which is not usual but serves to show the syntax. The prediction is in the estimated_class_text column:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "30 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>id</th>\n",
- " <th>estimated_class_text</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>19</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>25</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>26</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>28</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>38</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>44</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>45</td>\n",
- " <td>Iris-setosa</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>51</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>53</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>57</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>59</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>62</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>69</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>75</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>77</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>97</td>\n",
- " <td>Iris-versicolor</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>102</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>107</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>114</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>118</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>122</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>132</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>146</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>147</td>\n",
- " <td>Iris-virginica</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(3, u'Iris-setosa'),\n",
- " (5, u'Iris-setosa'),\n",
- " (7, u'Iris-setosa'),\n",
- " (8, u'Iris-setosa'),\n",
- " (10, u'Iris-setosa'),\n",
- " (19, u'Iris-setosa'),\n",
- " (25, u'Iris-setosa'),\n",
- " (26, u'Iris-setosa'),\n",
- " (28, u'Iris-setosa'),\n",
- " (38, u'Iris-setosa'),\n",
- " (44, u'Iris-setosa'),\n",
- " (45, u'Iris-setosa'),\n",
- " (51, u'Iris-versicolor'),\n",
- " (53, u'Iris-versicolor'),\n",
- " (57, u'Iris-versicolor'),\n",
- " (59, u'Iris-versicolor'),\n",
- " (62, u'Iris-versicolor'),\n",
- " (69, u'Iris-virginica'),\n",
- " (75, u'Iris-versicolor'),\n",
- " (77, u'Iris-versicolor'),\n",
- " (97, u'Iris-versicolor'),\n",
- " (102, u'Iris-virginica'),\n",
- " (107, u'Iris-virginica'),\n",
- " (114, u'Iris-virginica'),\n",
- " (118, u'Iris-virginica'),\n",
- " (120, u'Iris-virginica'),\n",
- " (122, u'Iris-virginica'),\n",
- " (132, u'Iris-virginica'),\n",
- " (146, u'Iris-virginica'),\n",
- " (147, u'Iris-virginica')]"
- ]
- },
- "execution_count": 22,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_predict;\n",
- "\n",
- "SELECT madlib.madlib_keras_predict('iris_multi_model', -- model\n",
- " 'iris_test', -- test_table\n",
- " 'id', -- id column\n",
- " 'attributes', -- independent var\n",
- " 'iris_predict', -- output table\n",
- " 'response', -- prediction type\n",
- " FALSE, -- use gpus\n",
- " 3 -- mst_key to use\n",
- " );\n",
- "\n",
- "SELECT * FROM iris_predict ORDER BY id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Count missclassifications"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
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- "data": {
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- "<table>\n",
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- "</table>"
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- "[(1L,)]"
- ]
- },
- "execution_count": 23,
- "metadata": {},
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- ],
- "source": [
- "%%sql\n",
- "SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id) \n",
- "WHERE iris_predict.estimated_class_text != iris_test.class_text;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Percent missclassifications"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
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- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>test_accuracy_percent</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>96.67</td>\n",
- " </tr>\n",
- "</table>"
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- "[(Decimal('96.67'),)]"
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- "execution_count": 24,
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- "source": [
- "%%sql\n",
- "SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from\n",
- " (select iris_test.class_text as actual, iris_predict.estimated_class_text as estimated\n",
- " from iris_predict inner join iris_test\n",
- " on iris_test.id=iris_predict.id) q\n",
- "WHERE q.actual=q.estimated;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"class2\"></a>\n",
- "# Classification with Other Parameters\n",
- "\n",
- "<a id=\"val_dataset\"></a>\n",
- "# 1. Validation dataset\n",
- "\n",
- "Now use a validation dataset and compute metrics every 2nd iteration using the 'metrics_compute_frequency' parameter. This can help reduce run time if you do not need metrics computed at every iteration."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>madlib_keras_fit_multiple_model</th>\n",
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- " </tr>\n",
- "</table>"
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- "[('',)]"
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- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
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- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;\n",
- "\n",
- "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
- " 'iris_multi_model', -- model_output_table\n",
- " 'mst_table', -- model_selection_table\n",
- " 10, -- num_iterations\n",
- " FALSE, -- use gpus\n",
- " 'iris_test_packed', -- validation dataset\n",
- " 3, -- metrics compute frequency\n",
- " FALSE, -- warm start\n",
- " 'Sophie L.', -- name\n",
- " 'Model selection for iris dataset' -- description\n",
- " );"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View the model summary:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
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- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>validation_table</th>\n",
- " <th>model</th>\n",
- " <th>model_info</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>model_arch_table</th>\n",
- " <th>num_iterations</th>\n",
- " <th>metrics_compute_frequency</th>\n",
- " <th>warm_start</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>start_training_time</th>\n",
- " <th>end_training_time</th>\n",
- " <th>madlib_version</th>\n",
- " <th>num_classes</th>\n",
- " <th>class_values</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>metrics_iters</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train_packed</td>\n",
- " <td>iris_test_packed</td>\n",
- " <td>iris_multi_model</td>\n",
- " <td>iris_multi_model_info</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>model_arch_library</td>\n",
- " <td>10</td>\n",
- " <td>3</td>\n",
- " <td>False</td>\n",
- " <td>Sophie L.</td>\n",
- " <td>Model selection for iris dataset</td>\n",
- " <td>2019-12-18 22:35:49.962345</td>\n",
- " <td>2019-12-18 22:37:51.230499</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>3</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>character varying</td>\n",
- " <td>1.0</td>\n",
- " <td>[3, 6, 9, 10]</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'iris_train_packed', u'iris_test_packed', u'iris_multi_model', u'iris_multi_model_info', u'class_text', u'attributes', u'model_arch_library', 10, 3, False, u'Sophie L.', u'Model selection for iris dataset', datetime.datetime(2019, 12, 18, 22, 35, 49, 962345), datetime.datetime(2019, 12, 18, 22, 37, 51, 230499), u'1.17-dev', 3, [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], u'character varying', 1.0, [3, 6, 9, 10])]"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_multi_model_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View performance of each model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "12 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>mst_key</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>metrics_elapsed_time</th>\n",
- " <th>metrics_type</th>\n",
- " <th>training_metrics_final</th>\n",
- " <th>training_loss_final</th>\n",
- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.400555849075317, 0.175060987472534, 0.161082029342651, 0.159379005432129]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.958333313465</td>\n",
- " <td>0.370426625013</td>\n",
- " <td>[0.841666638851166, 0.875, 0.958333313465118, 0.958333313465118]</td>\n",
- " <td>[0.597030103206635, 0.467845916748047, 0.394165992736816, 0.370426625013351]</td>\n",
- " <td>1.0</td>\n",
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- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.157984018325806, 0.146160840988159, 0.446839094161987, 0.217149972915649]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.916666686535</td>\n",
- " <td>0.176682218909</td>\n",
- " <td>[0.958333313465118, 0.891666650772095, 0.841666638851166, 0.916666686534882]</td>\n",
- " <td>[0.340974450111389, 0.224177747964859, 0.315857976675034, 0.176682218909264]</td>\n",
- " <td>0.966666638851</td>\n",
- " <td>0.146555349231</td>\n",
- " <td>[0.966666638851166, 0.933333337306976, 0.866666674613953, 0.966666638851166]</td>\n",
- " <td>[0.306026995182037, 0.204480707645416, 0.291850447654724, 0.146555349230766]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.158334016799927, 0.492121934890747, 0.168816804885864, 0.160614013671875]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.949999988079</td>\n",
- " <td>0.137093007565</td>\n",
- " <td>[0.75, 0.808333337306976, 0.941666662693024, 0.949999988079071]</td>\n",
- " <td>[0.861838400363922, 0.306531131267548, 0.267581582069397, 0.137093007564545]</td>\n",
- " <td>0.966666638851</td>\n",
- " <td>0.0812632590532</td>\n",
- " <td>[0.533333361148834, 0.733333349227905, 1.0, 0.966666638851166]</td>\n",
- " <td>[1.17265951633453, 0.347328811883926, 0.0795030668377876, 0.0812632590532303]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.206979990005493, 0.175852060317993, 0.18351411819458, 0.173283100128174]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.841666638851</td>\n",
- " <td>0.319059103727</td>\n",
- " <td>[0.833333313465118, 0.916666686534882, 0.958333313465118, 0.841666638851166]</td>\n",
- " <td>[0.375581055879593, 0.235803470015526, 0.119093284010887, 0.319059103727341]</td>\n",
- " <td>0.866666674614</td>\n",
- " <td>0.294114112854</td>\n",
- " <td>[0.866666674613953, 0.966666638851166, 0.933333337306976, 0.866666674613953]</td>\n",
- " <td>[0.332203418016434, 0.206457450985909, 0.09817935526371, 0.294114112854004]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.154335021972656, 0.14276385307312, 0.160094022750854, 0.147177934646606]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.833333313465</td>\n",
- " <td>0.315035998821</td>\n",
- " <td>[0.850000023841858, 0.966666638851166, 0.966666638851166, 0.833333313465118]</td>\n",
- " <td>[0.39260533452034, 0.207864001393318, 0.14202418923378, 0.315035998821259]</td>\n",
- " <td>0.833333313465</td>\n",
- " <td>0.287047833204</td>\n",
- " <td>[0.833333313465118, 0.966666638851166, 0.933333337306976, 0.833333313465118]</td>\n",
- " <td>[0.350265830755234, 0.179627984762192, 0.119969591498375, 0.287047833204269]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.183771848678589, 0.442173957824707, 0.196517944335938, 0.183962106704712]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.683333337307</td>\n",
- " <td>0.773626208305</td>\n",
- " <td>[0.983333349227905, 0.783333361148834, 0.841666638851166, 0.683333337306976]</td>\n",
- " <td>[0.323956668376923, 0.355609774589539, 0.289077579975128, 0.773626208305359]</td>\n",
- " <td>0.733333349228</td>\n",
- " <td>0.598832905293</td>\n",
- " <td>[0.966666638851166, 0.733333349227905, 0.866666674613953, 0.733333349227905]</td>\n",
- " <td>[0.292185336351395, 0.310099214315414, 0.278687566518784, 0.598832905292511]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.215842962265015, 0.183883190155029, 0.181258201599121, 0.233398914337158]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.658333361149</td>\n",
- " <td>0.501300632954</td>\n",
- " <td>[0.341666668653488, 0.658333361148834, 0.658333361148834, 0.658333361148834]</td>\n",
- " <td>[0.947986364364624, 0.807084918022156, 0.549242556095123, 0.501300632953644]</td>\n",
- " <td>0.699999988079</td>\n",
- " <td>0.459856539965</td>\n",
- " <td>[0.300000011920929, 0.699999988079071, 0.699999988079071, 0.699999988079071]</td>\n",
- " <td>[0.971994161605835, 0.821518063545227, 0.513974606990814, 0.459856539964676]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.181059837341309, 0.156504154205322, 0.154800891876221, 0.165037870407104]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.675000011921</td>\n",
- " <td>0.500130057335</td>\n",
- " <td>[0.658333361148834, 0.908333361148834, 0.908333361148834, 0.675000011920929]</td>\n",
- " <td>[0.822371363639832, 0.354260504245758, 0.206746637821198, 0.5001300573349]</td>\n",
- " <td>0.699999988079</td>\n",
- " <td>0.511800050735</td>\n",
- " <td>[0.699999988079071, 0.933333337306976, 0.966666638851166, 0.699999988079071]</td>\n",
- " <td>[0.784473180770874, 0.314396589994431, 0.171932756900787, 0.511800050735474]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.16503119468689, 0.165420055389404, 0.163087844848633, 0.157285213470459]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.600000023842</td>\n",
- " <td>0.536593079567</td>\n",
- " <td>[0.625, 0.491666674613953, 0.508333325386047, 0.600000023841858]</td>\n",
- " <td>[0.877406716346741, 0.665770947933197, 0.563206613063812, 0.536593079566956]</td>\n",
- " <td>0.600000023842</td>\n",
- " <td>0.50565046072</td>\n",
- " <td>[0.566666662693024, 0.533333361148834, 0.600000023841858, 0.600000023841858]</td>\n",
- " <td>[0.898801684379578, 0.642534494400024, 0.529698371887207, 0.505650460720062]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>12</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.180193901062012, 0.230684041976929, 0.202606916427612, 0.182677030563354]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.5</td>\n",
- " <td>1.01774513721</td>\n",
- " <td>[0.341666668653488, 0.491666674613953, 0.524999976158142, 0.5]</td>\n",
- " <td>[1.10608339309692, 1.06158423423767, 1.02908384799957, 1.01774513721466]</td>\n",
- " <td>0.5</td>\n",
- " <td>1.01636135578</td>\n",
- " <td>[0.300000011920929, 0.466666668653488, 0.466666668653488, 0.5]</td>\n",
- " <td>[1.10331404209137, 1.05365967750549, 1.02413082122803, 1.01636135578156]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.181950092315674, 0.197594881057739, 0.187069177627563, 0.183701992034912]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.316666662693</td>\n",
- " <td>1.10080897808</td>\n",
- " <td>[0.316666662693024, 0.341666668653488, 0.341666668653488, 0.316666662693024]</td>\n",
- " <td>[1.1043815612793, 1.11140048503876, 1.09834468364716, 1.10080897808075]</td>\n",
- " <td>0.40000000596</td>\n",
- " <td>1.09380173683</td>\n",
- " <td>[0.400000005960464, 0.300000011920929, 0.300000011920929, 0.400000005960464]</td>\n",
- " <td>[1.09075009822845, 1.09998726844788, 1.10155093669891, 1.09380173683167]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.182392835617065, 0.206873893737793, 0.192094087600708, 0.185320854187012]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.341666668653</td>\n",
- " <td>1.10410153866</td>\n",
- " <td>[0.341666668653488, 0.316666662693024, 0.341666668653488, 0.341666668653488]</td>\n",
- " <td>[1.10291886329651, 1.10132431983948, 1.10635650157928, 1.10410153865814]</td>\n",
- " <td>0.300000011921</td>\n",
- " <td>1.10918176174</td>\n",
- " <td>[0.300000011920929, 0.400000005960464, 0.300000011920929, 0.300000011920929]</td>\n",
- " <td>[1.10382485389709, 1.09316170215607, 1.1332186460495, 1.10918176174164]</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
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- " (12, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.180193901062012, 0.230684041976929, 0.202606916427612, 0.182677030563354], [u'accuracy'], 0.5, 1.01774513721, [0.341666668653488, 0.491666674613953, 0.524999976158142, 0.5], [1.10608339309692, 1.06158423423767, 1.02908384799957, 1.01774513721466], 0.5, 1.01636135578, [0.300000011920929, 0.466666668653488, 0.466666668653488, 0.5], [1.10331404209137, 1.05365967750549, 1.02413082122803, 1.01636135578156]),\n",
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- " (8, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.182392835617065, 0.206873893737793, 0.192094087600708, 0.185320854187012], [u'accuracy'], 0.341666668653, 1.10410153866, [0.341666668653488, 0.316666662693024, 0.341666668653488, 0.341666668653488], [1.10291886329651, 1.10132431983948, 1.10635650157928, 1.10410153865814], 0.300000011921, 1.10918176174, [0.300000011920929, 0.400000005960464, 0.300000011920929, 0.300000011920929], [1.10382485389709, 1.09316170215607, 1.1332186460495, 1.10918176174164])]"
- ]
- },
- "execution_count": 27,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_multi_model_info ORDER BY validation_metrics_final DESC;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot validation results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib notebook\n",
- "import matplotlib.pyplot as plt\n",
- "from matplotlib.ticker import MaxNLocator\n",
- "from collections import defaultdict\n",
- "import pandas as pd\n",
- "import seaborn as sns\n",
- "sns.set_palette(sns.color_palette(\"hls\", 20))\n",
- "plt.rcParams.update({'font.size': 12})\n",
- "pd.set_option('display.max_colwidth', -1)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "7 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
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- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
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- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
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- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
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- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
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- " $(parent_element).append(this.root);\n",
- "\n",
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- "\n",
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- "\n",
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- "\n",
- " this.ondownload = ondownload;\n",
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- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
- " 'ui-helper-clearfix\"/>');\n",
- " var titletext = $(\n",
- " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
- " 'text-align: center; padding: 3px;\"/>');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('<div/>');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('<canvas/>');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
- "\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband = $('<canvas/>');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('<span/>');\n",
- "\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- "<IPython.core.display.Javascript object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "text/html": [
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\" width=\"1000\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x12e9ae7d0>"
- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_results = %sql SELECT * FROM iris_multi_model_info ORDER BY validation_loss ASC LIMIT 7;\n",
- "df_results = df_results.DataFrame()\n",
- "\n",
- "df_summary = %sql SELECT * FROM iris_multi_model_summary;\n",
- "df_summary = df_summary.DataFrame()\n",
- "\n",
- "#set up plots\n",
- "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
- "fig.legend(ncol=4)\n",
- "fig.tight_layout()\n",
- "\n",
- "ax_metric = axs[0]\n",
- "ax_loss = axs[1]\n",
- "\n",
- "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
- "ax_metric.set_xlabel('Iteration')\n",
- "ax_metric.set_ylabel('Metric')\n",
- "ax_metric.set_title('Validation metric curve')\n",
- "\n",
- "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
- "ax_loss.set_xlabel('Iteration')\n",
- "ax_loss.set_ylabel('Loss')\n",
- "ax_loss.set_title('Validation loss curve')\n",
- "\n",
- "iters = df_summary['metrics_iters'][0]\n",
- "\n",
- "for mst_key in df_results['mst_key']:\n",
- " df_output_info = %sql SELECT validation_metrics,validation_loss FROM iris_multi_model_info WHERE mst_key = $mst_key\n",
- " df_output_info = df_output_info.DataFrame()\n",
- " validation_metrics = df_output_info['validation_metrics'][0]\n",
- " validation_loss = df_output_info['validation_loss'][0]\n",
- " \n",
- " ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
- " ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
- "\n",
- "plt.legend()\n",
- "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"pred_prob\"></a>\n",
- "# 2. Predict probabilities\n",
- "\n",
- "Predict with probabilities for each class:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "30 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>id</th>\n",
- " <th>prob_Iris-setosa</th>\n",
- " <th>prob_Iris-versicolor</th>\n",
- " <th>prob_Iris-virginica</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>0.9999416</td>\n",
- " <td>5.8360623e-05</td>\n",
- " <td>3.9093355e-12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>0.99998116</td>\n",
- " <td>1.8880675e-05</td>\n",
- " <td>2.5342377e-13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>0.99994814</td>\n",
- " <td>5.1881765e-05</td>\n",
- " <td>2.5964983e-12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>0.99996114</td>\n",
- " <td>3.8810744e-05</td>\n",
- " <td>1.176443e-12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>0.99992573</td>\n",
- " <td>7.4317446e-05</td>\n",
- " <td>5.4237942e-12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>19</td>\n",
- " <td>0.9999845</td>\n",
- " <td>1.5514812e-05</td>\n",
- " <td>1.034207e-13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>25</td>\n",
- " <td>0.99992156</td>\n",
- " <td>7.845682e-05</td>\n",
- " <td>3.7364413e-12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>26</td>\n",
- " <td>0.9998591</td>\n",
- " <td>0.00014085071</td>\n",
- " <td>2.0146884e-11</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>28</td>\n",
- " <td>0.9999734</td>\n",
- " <td>2.6542659e-05</td>\n",
- " <td>4.8342347e-13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>38</td>\n",
- " <td>0.99992573</td>\n",
- " <td>7.4317446e-05</td>\n",
- " <td>5.4237942e-12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>44</td>\n",
- " <td>0.99990726</td>\n",
- " <td>9.278052e-05</td>\n",
- " <td>6.9040372e-12</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>45</td>\n",
- " <td>0.999964</td>\n",
- " <td>3.6013742e-05</td>\n",
- " <td>5.7615945e-13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>51</td>\n",
- " <td>0.00025041687</td>\n",
- " <td>0.99780566</td>\n",
- " <td>0.0019439155</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>53</td>\n",
- " <td>1.843269e-05</td>\n",
- " <td>0.9889865</td>\n",
- " <td>0.010995116</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>57</td>\n",
- " <td>2.4158675e-05</td>\n",
- " <td>0.99005336</td>\n",
- " <td>0.00992243</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>59</td>\n",
- " <td>0.00011159414</td>\n",
- " <td>0.9942708</td>\n",
- " <td>0.0056176083</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>62</td>\n",
- " <td>0.00014697485</td>\n",
- " <td>0.99189115</td>\n",
- " <td>0.007961868</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>69</td>\n",
- " <td>8.6406266e-07</td>\n",
- " <td>0.6961896</td>\n",
- " <td>0.30380967</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>75</td>\n",
- " <td>0.0005239165</td>\n",
- " <td>0.9965855</td>\n",
- " <td>0.0028905326</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>77</td>\n",
- " <td>1.5155997e-05</td>\n",
- " <td>0.97978914</td>\n",
- " <td>0.020195633</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>97</td>\n",
- " <td>0.00023696794</td>\n",
- " <td>0.9938279</td>\n",
- " <td>0.005935215</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>102</td>\n",
- " <td>1.3247301e-09</td>\n",
- " <td>0.18419608</td>\n",
- " <td>0.8158039</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>107</td>\n",
- " <td>2.5100556e-08</td>\n",
- " <td>0.30281228</td>\n",
- " <td>0.69718766</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>114</td>\n",
- " <td>3.2222575e-10</td>\n",
- " <td>0.08682407</td>\n",
- " <td>0.913176</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>118</td>\n",
- " <td>5.33606e-11</td>\n",
- " <td>0.34179842</td>\n",
- " <td>0.6582016</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>120</td>\n",
- " <td>9.134116e-09</td>\n",
- " <td>0.27099058</td>\n",
- " <td>0.72900945</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>122</td>\n",
- " <td>2.9710499e-09</td>\n",
- " <td>0.21993305</td>\n",
- " <td>0.7800669</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>132</td>\n",
- " <td>5.2177818e-09</td>\n",
- " <td>0.8370931</td>\n",
- " <td>0.16290687</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>146</td>\n",
- " <td>1.4404147e-09</td>\n",
- " <td>0.2293714</td>\n",
- " <td>0.7706286</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>147</td>\n",
- " <td>3.8019614e-09</td>\n",
- " <td>0.2240861</td>\n",
- " <td>0.77591395</td>\n",
- " </tr>\n",
- "</table>"
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- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS iris_predict;\n",
- "\n",
- "SELECT madlib.madlib_keras_predict('iris_multi_model', -- model\n",
- " 'iris_test', -- test_table\n",
- " 'id', -- id column\n",
- " 'attributes', -- independent var\n",
- " 'iris_predict', -- output table\n",
- " 'prob', -- prediction type\n",
- " FALSE, -- use gpus\n",
- " 3 -- mst_key to use\n",
- " );\n",
- "\n",
- "SELECT * FROM iris_predict ORDER BY id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"warm_start\"></a>\n",
- "# 3. Warm start\n",
- "\n",
- "Next, use the warm_start parameter to continue learning, using the coefficients from the run above. Note that we don't drop the model table or model summary table:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>madlib_keras_fit_multiple_model</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td></td>\n",
- " </tr>\n",
- "</table>"
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- "text/plain": [
- "[('',)]"
- ]
- },
- "execution_count": 31,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
- " 'iris_multi_model', -- model_output_table\n",
- " 'mst_table', -- model_selection_table\n",
- " 3, -- num_iterations\n",
- " FALSE, -- use gpus\n",
- " 'iris_test_packed', -- validation dataset\n",
- " 1, -- metrics compute frequency\n",
- " TRUE, -- warm start\n",
- " 'Sophie L.', -- name\n",
- " 'Simple MLP for iris dataset' -- description\n",
- " );"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View summary:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>validation_table</th>\n",
- " <th>model</th>\n",
- " <th>model_info</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>model_arch_table</th>\n",
- " <th>num_iterations</th>\n",
- " <th>metrics_compute_frequency</th>\n",
- " <th>warm_start</th>\n",
- " <th>name</th>\n",
- " <th>description</th>\n",
- " <th>start_training_time</th>\n",
- " <th>end_training_time</th>\n",
- " <th>madlib_version</th>\n",
- " <th>num_classes</th>\n",
- " <th>class_values</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>metrics_iters</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>iris_train_packed</td>\n",
- " <td>iris_test_packed</td>\n",
- " <td>iris_multi_model</td>\n",
- " <td>iris_multi_model_info</td>\n",
- " <td>class_text</td>\n",
- " <td>attributes</td>\n",
- " <td>model_arch_library</td>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>True</td>\n",
- " <td>Sophie L.</td>\n",
- " <td>Simple MLP for iris dataset</td>\n",
- " <td>2019-12-18 22:37:57.948805</td>\n",
- " <td>2019-12-18 22:38:43.967187</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>3</td>\n",
- " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
- " <td>character varying</td>\n",
- " <td>1.0</td>\n",
- " <td>[1, 2, 3]</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'iris_train_packed', u'iris_test_packed', u'iris_multi_model', u'iris_multi_model_info', u'class_text', u'attributes', u'model_arch_library', 3, 1, True, u'Sophie L.', u'Simple MLP for iris dataset', datetime.datetime(2019, 12, 18, 22, 37, 57, 948805), datetime.datetime(2019, 12, 18, 22, 38, 43, 967187), u'1.17-dev', 3, [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], u'character varying', 1.0, [1, 2, 3])]"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_multi_model_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "View performance of each model:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "12 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>mst_key</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>metrics_elapsed_time</th>\n",
- " <th>metrics_type</th>\n",
- " <th>training_metrics_final</th>\n",
- " <th>training_loss_final</th>\n",
- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.17091703414917, 0.163390159606934, 0.155634164810181]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.958333313465</td>\n",
- " <td>0.31917694211</td>\n",
- " <td>[0.958333313465118, 0.958333313465118, 0.958333313465118]</td>\n",
- " <td>[0.348434448242188, 0.334388434886932, 0.319176942110062]</td>\n",
- " <td>1.0</td>\n",
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- " <td>[1.0, 1.0, 1.0]</td>\n",
- " <td>[0.306039541959763, 0.28966349363327, 0.272621482610703]</td>\n",
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- " <tr>\n",
- " <td>10</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
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- " <td>[0.172316074371338, 0.188217163085938, 0.503840208053589]</td>\n",
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- " <td>0.193531006575</td>\n",
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- " <td>[0.147025644779205, 0.144938006997108, 0.193531006574631]</td>\n",
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- " <td>0.153077676892</td>\n",
- " <td>[0.966666638851166, 0.966666638851166, 0.966666638851166]</td>\n",
- " <td>[0.132363379001617, 0.116448685526848, 0.153077676892281]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.147105932235718, 0.158121824264526, 0.174723863601685]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.966666638851</td>\n",
- " <td>0.100400544703</td>\n",
- " <td>[0.966666638851166, 0.908333361148834, 0.966666638851166]</td>\n",
- " <td>[0.112152323126793, 0.197978660464287, 0.100400544703007]</td>\n",
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- " <td>0.0844493880868</td>\n",
- " <td>[0.933333337306976, 0.966666638851166, 0.966666638851166]</td>\n",
- " <td>[0.0945712551474571, 0.170254677534103, 0.0844493880867958]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.224463939666748, 0.412797927856445, 0.193319797515869]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.958333313465</td>\n",
- " <td>0.139601364732</td>\n",
- " <td>[0.966666638851166, 0.966666638851166, 0.958333313465118]</td>\n",
- " <td>[0.122705578804016, 0.0809410735964775, 0.139601364731789]</td>\n",
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- " <td>[0.966666638851166, 0.966666638851166, 0.966666638851166]</td>\n",
- " <td>[0.115778811275959, 0.0698963403701782, 0.131209135055542]</td>\n",
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- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.160850048065186, 0.224483013153076, 0.163106918334961]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.966666638851</td>\n",
- " <td>0.0839553326368</td>\n",
- " <td>[0.966666638851166, 0.908333361148834, 0.966666638851166]</td>\n",
- " <td>[0.124577566981316, 0.196399554610252, 0.0839553326368332]</td>\n",
- " <td>0.966666638851</td>\n",
- " <td>0.074150800705</td>\n",
- " <td>[0.966666638851166, 0.866666674613953, 0.966666638851166]</td>\n",
- " <td>[0.137340381741524, 0.232466518878937, 0.0741508007049561]</td>\n",
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- " <tr>\n",
- " <td>3</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.14374303817749, 0.154287099838257, 0.17367696762085]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.966666638851</td>\n",
- " <td>0.0860244855285</td>\n",
- " <td>[0.966666638851166, 0.841666638851166, 0.966666638851166]</td>\n",
- " <td>[0.0824147835373878, 0.337884455919266, 0.0860244855284691]</td>\n",
- " <td>0.933333337307</td>\n",
- " <td>0.0704526007175</td>\n",
- " <td>[0.966666638851166, 0.866666674613953, 0.933333337306976]</td>\n",
- " <td>[0.0690516456961632, 0.295713990926743, 0.0704526007175446]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.155812978744507, 0.158360004425049, 0.159363031387329]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.833333313465</td>\n",
- " <td>0.344228476286</td>\n",
- " <td>[0.666666686534882, 0.675000011920929, 0.833333313465118]</td>\n",
- " <td>[1.01126325130463, 1.33927237987518, 0.344228476285934]</td>\n",
- " <td>0.800000011921</td>\n",
- " <td>0.305708706379</td>\n",
- " <td>[0.699999988079071, 0.699999988079071, 0.800000011920929]</td>\n",
- " <td>[1.02303433418274, 1.36952638626099, 0.305708706378937]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.187958955764771, 0.186024904251099, 0.501762866973877]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.725000023842</td>\n",
- " <td>0.423261642456</td>\n",
- " <td>[0.658333361148834, 0.658333361148834, 0.725000023841858]</td>\n",
- " <td>[0.46866175532341, 0.445532470941544, 0.423261642456055]</td>\n",
- " <td>0.699999988079</td>\n",
- " <td>0.378630697727</td>\n",
- " <td>[0.699999988079071, 0.699999988079071, 0.699999988079071]</td>\n",
- " <td>[0.422465175390244, 0.398104608058929, 0.378630697727203]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>1</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>0.7900390625</td>\n",
- " <td>[0.176413059234619, 0.169157981872559, 0.15624213218689]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.675000011921</td>\n",
- " <td>0.470171242952</td>\n",
- " <td>[0.641666650772095, 0.658333361148834, 0.675000011920929]</td>\n",
- " <td>[0.504463493824005, 0.486825525760651, 0.470171242952347]</td>\n",
- " <td>0.699999988079</td>\n",
- " <td>0.436036229134</td>\n",
- " <td>[0.699999988079071, 0.699999988079071, 0.699999988079071]</td>\n",
- " <td>[0.470719456672668, 0.452698260545731, 0.436036229133606]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>12</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.164397954940796, 0.486438035964966, 0.192479133605957]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.550000011921</td>\n",
- " <td>0.975017726421</td>\n",
- " <td>[0.508333325386047, 0.533333361148834, 0.550000011920929]</td>\n",
- " <td>[1.00239539146423, 0.986684203147888, 0.975017726421356]</td>\n",
- " <td>0.466666668653</td>\n",
- " <td>0.981434583664</td>\n",
- " <td>[0.5, 0.466666668653488, 0.466666668653488]</td>\n",
- " <td>[1.00223970413208, 0.989481270313263, 0.98143458366394]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=8,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.467766046524048, 0.198179006576538, 0.186810970306396]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.341666668653</td>\n",
- " <td>1.10613942146</td>\n",
- " <td>[0.316666662693024, 0.316666662693024, 0.341666668653488]</td>\n",
- " <td>[1.1275190114975, 1.10920584201813, 1.10613942146301]</td>\n",
- " <td>0.300000011921</td>\n",
- " <td>1.10817503929</td>\n",
- " <td>[0.400000005960464, 0.400000005960464, 0.300000011920929]</td>\n",
- " <td>[1.10070872306824, 1.09047472476959, 1.10817503929138]</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
- " <td>batch_size=4,epochs=1</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>1.2197265625</td>\n",
- " <td>[0.467660903930664, 0.195011138916016, 0.185934066772461]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.341666668653</td>\n",
- " <td>1.10524618626</td>\n",
- " <td>[0.316666662693024, 0.341666668653488, 0.341666668653488]</td>\n",
- " <td>[1.10246300697327, 1.09976887702942, 1.10524618625641]</td>\n",
- " <td>0.300000011921</td>\n",
- " <td>1.10809886456</td>\n",
- " <td>[0.400000005960464, 0.300000011920929, 0.300000011920929]</td>\n",
- " <td>[1.09229254722595, 1.09808218479156, 1.10809886455536]</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(6, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.17091703414917, 0.163390159606934, 0.155634164810181], [u'accuracy'], 0.958333313465, 0.31917694211, [0.958333313465118, 0.958333313465118, 0.958333313465118], [0.348434448242188, 0.334388434886932, 0.319176942110062], 1.0, 0.272621482611, [1.0, 1.0, 1.0], [0.306039541959763, 0.28966349363327, 0.272621482610703]),\n",
- " (10, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.172316074371338, 0.188217163085938, 0.503840208053589], [u'accuracy'], 0.899999976158, 0.193531006575, [0.958333313465118, 0.925000011920929, 0.899999976158142], [0.147025644779205, 0.144938006997108, 0.193531006574631], 0.966666638851, 0.153077676892, [0.966666638851166, 0.966666638851166, 0.966666638851166], [0.132363379001617, 0.116448685526848, 0.153077676892281]),\n",
- " (4, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.147105932235718, 0.158121824264526, 0.174723863601685], [u'accuracy'], 0.966666638851, 0.100400544703, [0.966666638851166, 0.908333361148834, 0.966666638851166], [0.112152323126793, 0.197978660464287, 0.100400544703007], 0.966666638851, 0.0844493880868, [0.933333337306976, 0.966666638851166, 0.966666638851166], [0.0945712551474571, 0.170254677534103, 0.0844493880867958]),\n",
- " (9, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.224463939666748, 0.412797927856445, 0.193319797515869], [u'accuracy'], 0.958333313465, 0.139601364732, [0.966666638851166, 0.966666638851166, 0.958333313465118], [0.122705578804016, 0.0809410735964775, 0.139601364731789], 0.966666638851, 0.131209135056, [0.966666638851166, 0.966666638851166, 0.966666638851166], [0.115778811275959, 0.0698963403701782, 0.131209135055542]),\n",
- " (1, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.160850048065186, 0.224483013153076, 0.163106918334961], [u'accuracy'], 0.966666638851, 0.0839553326368, [0.966666638851166, 0.908333361148834, 0.966666638851166], [0.124577566981316, 0.196399554610252, 0.0839553326368332], 0.966666638851, 0.074150800705, [0.966666638851166, 0.866666674613953, 0.966666638851166], [0.137340381741524, 0.232466518878937, 0.0741508007049561]),\n",
- " (3, 1, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.14374303817749, 0.154287099838257, 0.17367696762085], [u'accuracy'], 0.966666638851, 0.0860244855285, [0.966666638851166, 0.841666638851166, 0.966666638851166], [0.0824147835373878, 0.337884455919266, 0.0860244855284691], 0.933333337307, 0.0704526007175, [0.966666638851166, 0.866666674613953, 0.933333337306976], [0.0690516456961632, 0.295713990926743, 0.0704526007175446]),\n",
- " (2, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 0.7900390625, [0.155812978744507, 0.158360004425049, 0.159363031387329], [u'accuracy'], 0.833333313465, 0.344228476286, [0.666666686534882, 0.675000011920929, 0.833333313465118], [1.01126325130463, 1.33927237987518, 0.344228476285934], 0.800000011921, 0.305708706379, [0.699999988079071, 0.699999988079071, 0.800000011920929], [1.02303433418274, 1.36952638626099, 0.305708706378937]),\n",
- " (11, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.187958955764771, 0.186024904251099, 0.501762866973877], [u'accuracy'], 0.725000023842, 0.423261642456, [0.658333361148834, 0.658333361148834, 0.725000023841858], [0.46866175532341, 0.445532470941544, 0.423261642456055], 0.699999988079, 0.378630697727, [0.699999988079071, 0.699999988079071, 0.699999988079071], [0.422465175390244, 0.398104608058929, 0.378630697727203]),\n",
- " (5, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 0.7900390625, [0.176413059234619, 0.169157981872559, 0.15624213218689], [u'accuracy'], 0.675000011921, 0.470171242952, [0.641666650772095, 0.658333361148834, 0.675000011920929], [0.504463493824005, 0.486825525760651, 0.470171242952347], 0.699999988079, 0.436036229134, [0.699999988079071, 0.699999988079071, 0.699999988079071], [0.470719456672668, 0.452698260545731, 0.436036229133606]),\n",
- " (12, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.164397954940796, 0.486438035964966, 0.192479133605957], [u'accuracy'], 0.550000011921, 0.975017726421, [0.508333325386047, 0.533333361148834, 0.550000011920929], [1.00239539146423, 0.986684203147888, 0.975017726421356], 0.466666668653, 0.981434583664, [0.5, 0.466666668653488, 0.466666668653488], [1.00223970413208, 0.989481270313263, 0.98143458366394]),\n",
- " (8, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1', u'madlib_keras', 1.2197265625, [0.467766046524048, 0.198179006576538, 0.186810970306396], [u'accuracy'], 0.341666668653, 1.10613942146, [0.316666662693024, 0.316666662693024, 0.341666668653488], [1.1275190114975, 1.10920584201813, 1.10613942146301], 0.300000011921, 1.10817503929, [0.400000005960464, 0.400000005960464, 0.300000011920929], [1.10070872306824, 1.09047472476959, 1.10817503929138]),\n",
- " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1', u'madlib_keras', 1.2197265625, [0.467660903930664, 0.195011138916016, 0.185934066772461], [u'accuracy'], 0.341666668653, 1.10524618626, [0.316666662693024, 0.341666668653488, 0.341666668653488], [1.10246300697327, 1.09976887702942, 1.10524618625641], 0.300000011921, 1.10809886456, [0.400000005960464, 0.300000011920929, 0.300000011920929], [1.09229254722595, 1.09808218479156, 1.10809886455536])]"
- ]
- },
- "execution_count": 33,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM iris_multi_model_info ORDER BY validation_metrics_final DESC;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot validation results:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "7 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
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- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
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- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
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- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
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- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
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- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('<div/>');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
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- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
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- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
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- "\n",
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- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
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- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
- " 'ui-helper-clearfix\"/>');\n",
- " var titletext = $(\n",
- " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
- " 'text-align: center; padding: 3px;\"/>');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
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- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('<div/>');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('<canvas/>');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
- "\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband = $('<canvas/>');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
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- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('<span/>');\n",
- "\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
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\" width=\"1000\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x130da4150>"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_results = %sql SELECT * FROM iris_multi_model_info ORDER BY validation_loss ASC LIMIT 7;\n",
- "df_results = df_results.DataFrame()\n",
- "\n",
- "df_summary = %sql SELECT * FROM iris_multi_model_summary;\n",
- "df_summary = df_summary.DataFrame()\n",
- "\n",
- "#set up plots\n",
- "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
- "fig.legend(ncol=4)\n",
- "fig.tight_layout()\n",
- "\n",
- "ax_metric = axs[0]\n",
- "ax_loss = axs[1]\n",
- "\n",
- "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
- "ax_metric.set_xlabel('Iteration')\n",
- "ax_metric.set_ylabel('Metric')\n",
- "ax_metric.set_title('Validation metric curve')\n",
- "\n",
- "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
- "ax_loss.set_xlabel('Iteration')\n",
- "ax_loss.set_ylabel('Loss')\n",
- "ax_loss.set_title('Validation loss curve')\n",
- "\n",
- "iters = df_summary['metrics_iters'][0]\n",
- "\n",
- "for mst_key in df_results['mst_key']:\n",
- " df_output_info = %sql SELECT validation_metrics,validation_loss FROM iris_multi_model_info WHERE mst_key = $mst_key\n",
- " df_output_info = df_output_info.DataFrame()\n",
- " validation_metrics = df_output_info['validation_metrics'][0]\n",
- " validation_loss = df_output_info['validation_loss'][0]\n",
- " \n",
- " ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
- " ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
- "\n",
- "plt.legend()\n",
- "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/community-artifacts/Deep-learning/Model-preparation/Define-custom-functions-v1.ipynb b/community-artifacts/Deep-learning/Model-preparation/Define-custom-functions-v1.ipynb
new file mode 100755
index 0000000..f1d301b
--- /dev/null
+++ b/community-artifacts/Deep-learning/Model-preparation/Define-custom-functions-v1.ipynb
@@ -0,0 +1,531 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Define custom functions\n",
+ "\n",
+ "This function loads custom Python functions into a table for use by deep learning algorithms.\n",
+ "\n",
+ "Custom functions can be useful if, for example, you need loss functions or metrics that are not built into the standard libraries. The functions to be loaded must be in the form of serialized Python objects created using Dill, which extends Python's pickle module to the majority of the built-in Python types.\n",
+ "\n",
+ "Custom functions are also used to return top k categorical accuracy rate in the case that you want a different k value than the default from Keras. This module includes a helper function to create the custom function automatically for a specified k.\n",
+ "\n",
+ "This method was added in MADlib 1.18.0.\n",
+ "\n",
+ "## <em>Warning</em>\n",
+ "<em>For security reasons there are controls on custom functions in MADlib. You must be a superuser to create custom functions because they could theoretically allow execution of any untrusted Python code. Regular users with MADlib USAGE permission can use existing custom functions but cannot create new ones or update existing ones.</em>\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#load_psycopg2\">1. Load object using psycopg2</a>\n",
+ "\n",
+ "<a href=\"#load_plpython\">2. Load object using a PL/Python function</a>\n",
+ "\n",
+ "<a href=\"#delete_object\">3. Delete object</a>\n",
+ "\n",
+ "<a href=\"#top_k\">4. Top k accuracy function</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g14a91ce, cmake configuration time: Fri Mar 5 23:08:38 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g14a91ce, cmake configuration time: Fri Mar 5 23:08:38 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_psycopg2\"></a>\n",
+ "# 1. Load object using psycopg2\n",
+ "Psycopg is a PostgreSQL database adapter for the Python programming language. Note need to use the psycopg2.Binary() method to pass as bytes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import database connector psycopg2 and create connection cursor\n",
+ "import psycopg2 as p2\n",
+ "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
+ "cur = conn.cursor()\n",
+ "\n",
+ "# import Dill and define functions\n",
+ "import dill\n",
+ "\n",
+ "# custom loss\n",
+ "def squared_error(y_true, y_pred):\n",
+ " import keras.backend as K \n",
+ " return K.square(y_pred - y_true)\n",
+ "pb_squared_error=dill.dumps(squared_error)\n",
+ "\n",
+ "# custom metric\n",
+ "def rmse(y_true, y_pred):\n",
+ " import keras.backend as K \n",
+ " return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))\n",
+ "pb_rmse=dill.dumps(rmse)\n",
+ "\n",
+ "# call load function\n",
+ "cur.execute(\"DROP TABLE IF EXISTS madlib.custom_function_table\")\n",
+ "cur.execute(\"SELECT madlib.load_custom_function('custom_function_table', %s,'squared_error', 'squared error')\", [p2.Binary(pb_squared_error)])\n",
+ "cur.execute(\"SELECT madlib.load_custom_function('custom_function_table', %s,'rmse', 'root mean square error')\", [p2.Binary(pb_rmse)])\n",
+ "conn.commit()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>squared_error</td>\n",
+ " <td>squared error</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>rmse</td>\n",
+ " <td>root mean square error</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'squared_error', u'squared error'),\n",
+ " (2, u'rmse', u'root mean square error')]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT id, name, description FROM madlib.custom_function_table ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_plpython\"></a>\n",
+ "# 2. Load object using a PL/Python function"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "CREATE OR REPLACE FUNCTION custom_function_squared_error()\n",
+ "RETURNS BYTEA AS\n",
+ "$$\n",
+ "import dill\n",
+ "def squared_error(y_true, y_pred):\n",
+ " import tensorflow.keras.backend as K\n",
+ " return K.square(y_pred - y_true)\n",
+ "pb_squared_error=dill.dumps(squared_error)\n",
+ "return pb_squared_error\n",
+ "$$ language plpythonu;\n",
+ "CREATE OR REPLACE FUNCTION custom_function_rmse()\n",
+ "RETURNS BYTEA AS\n",
+ "$$\n",
+ "import dill\n",
+ "def rmse(y_true, y_pred):\n",
+ " import tensorflow.keras.backend as K\n",
+ " return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))\n",
+ "pb_rmse=dill.dumps(rmse)\n",
+ "return pb_rmse\n",
+ "$$ language plpythonu;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>load_custom_function</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS madlib.custom_function_table;\n",
+ "SELECT madlib.load_custom_function('custom_function_table', \n",
+ " custom_function_squared_error(), \n",
+ " 'squared_error', \n",
+ " 'squared error');\n",
+ "\n",
+ "SELECT madlib.load_custom_function('custom_function_table', \n",
+ " custom_function_rmse(), \n",
+ " 'rmse', \n",
+ " 'root mean square error');"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>squared_error</td>\n",
+ " <td>squared error</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>rmse</td>\n",
+ " <td>root mean square error</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'squared_error', u'squared error'),\n",
+ " (2, u'rmse', u'root mean square error')]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT id, name, description FROM madlib.custom_function_table ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"delete_object\"></a>\n",
+ "# 3. Delete object\n",
+ "Delete by id:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>rmse</td>\n",
+ " <td>root mean square error</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, u'rmse', u'root mean square error')]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT madlib.delete_custom_function( 'custom_function_table', 1);\n",
+ "SELECT id, name, description FROM madlib.custom_function_table ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Delete by name:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>delete_custom_function</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT madlib.delete_custom_function( 'custom_function_table', 'rmse');"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Since this was the last object in the table, if you delete it then the table will also be dropped."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"top_k\"></a>\n",
+ "# 4. Top k accuracy function\n",
+ "Load top 3 accuracy function followed by a top 10 accuracy function:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>top_3_accuracy</td>\n",
+ " <td>returns top_3_accuracy</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>top_10_accuracy</td>\n",
+ " <td>returns top_10_accuracy</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'top_3_accuracy', u'returns top_3_accuracy'),\n",
+ " (2, u'top_10_accuracy', u'returns top_10_accuracy')]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS madlib.custom_function_table;\n",
+ "\n",
+ "SELECT madlib.load_top_k_accuracy_function('custom_function_table',\n",
+ " 3);\n",
+ "\n",
+ "SELECT madlib.load_top_k_accuracy_function('custom_function_table',\n",
+ " 10);\n",
+ "\n",
+ "SELECT id, name, description FROM madlib.custom_function_table ORDER BY id;"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/Load-model-architecture-v2.ipynb b/community-artifacts/Deep-learning/Model-preparation/Define-model-architecture-v2.ipynb
similarity index 68%
rename from community-artifacts/Deep-learning/Load-model-architecture-v2.ipynb
rename to community-artifacts/Deep-learning/Model-preparation/Define-model-architecture-v2.ipynb
index 8aa3716..b823f09 100644
--- a/community-artifacts/Deep-learning/Load-model-architecture-v2.ipynb
+++ b/community-artifacts/Deep-learning/Model-preparation/Define-model-architecture-v2.ipynb
@@ -4,10 +4,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Load model architecture\n",
- "This utility function loads model architectures and weights into a table for use by deep learning algorithms in Keras. \n",
+ "# Define model architecture\n",
+ "This function loads model architectures and weights into a table for use by deep learning algorithms.\n",
"\n",
- "The model architecture loader was added in MADlib 1.16.\n",
+ "Model architecture is in JSON form and model weights are in the form of PostgreSQL binary data types (bytea). If the output table already exists, a new row is inserted into the table so it can act as a repository for multiple model architectures and weights.\n",
+ "\n",
+ "There is also a function to delete a model from the table.\n",
+ "\n",
+ "MADlib's deep learning methods are designed to use the TensorFlow package and its built in Keras functions. To ensure consistency, please use tensorflow.keras objects (models, layers, etc.) instead of importing Keras and using its objects.\n",
+ "\n",
+ "The model architecture loader was added in MADlib 1.16 and updated after that.\n",
"\n",
"## Table of contents\n",
"\n",
@@ -25,17 +31,15 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
- "name": "stderr",
+ "name": "stdout",
"output_type": "stream",
"text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
+ "The sql extension is already loaded. To reload it, use:\n",
+ " %reload_ext sql\n"
]
}
],
@@ -45,24 +49,10 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 35,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
"# Greenplum Database 5.x on GCP - via tunnel\n",
"%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
@@ -72,7 +62,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
@@ -90,15 +80,15 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g9d9f756, cmake configuration time: Thu Mar 4 23:11:53 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g9d9f756, cmake configuration time: Thu Mar 4 23:11:53 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
- "execution_count": 6,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -120,28 +110,13 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 37,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Couldn't import dot_parser, loading of dot files will not be possible.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "import keras\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense"
+ "from tensorflow import keras\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense"
]
},
{
@@ -153,21 +128,22 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Model: \"sequential_3\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
- "dense_1 (Dense) (None, 10) 50 \n",
+ "dense_9 (Dense) (None, 10) 50 \n",
"_________________________________________________________________\n",
- "dense_2 (Dense) (None, 10) 110 \n",
+ "dense_10 (Dense) (None, 10) 110 \n",
"_________________________________________________________________\n",
- "dense_3 (Dense) (None, 3) 33 \n",
+ "dense_11 (Dense) (None, 3) 33 \n",
"=================================================================\n",
"Total params: 193\n",
"Trainable params: 193\n",
@@ -182,21 +158,21 @@
"model.add(Dense(10, activation='relu'))\n",
"model.add(Dense(3, activation='softmax'))\n",
" \n",
- "model.summary()"
+ "model.summary();"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_9\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_10\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_11\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_3\"}, \"backend\": \"tensorflow\"}'"
]
},
- "execution_count": 9,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
@@ -225,7 +201,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
@@ -255,15 +231,15 @@
" <td>None</td>\n",
" <td>Sophie</td>\n",
" <td>A simple model</td>\n",
- " <td>__madlib_temp_19839392_1576692433_56744839__</td>\n",
+ " <td>__madlib_temp_27065614_1614901189_16021319__</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'A simple model', u'__madlib_temp_19839392_1576692433_56744839__')]"
+ "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1340 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'A simple model', u'__madlib_temp_27065614_1614901189_16021319__')]"
]
},
- "execution_count": 10,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -294,7 +270,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
@@ -323,7 +299,7 @@
" <td>None</td>\n",
" <td>Maria</td>\n",
" <td>Also a simple model</td>\n",
- " <td>__madlib_temp_36064316_1576692433_8110861__</td>\n",
+ " <td>__madlib_temp_87665369_1614901189_11144097__</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
@@ -331,16 +307,16 @@
" <td>None</td>\n",
" <td>Sophie</td>\n",
" <td>A simple model</td>\n",
- " <td>__madlib_temp_19839392_1576692433_56744839__</td>\n",
+ " <td>__madlib_temp_27065614_1614901189_16021319__</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Maria', u'Also a simple model', u'__madlib_temp_36064316_1576692433_8110861__'),\n",
- " (1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'A simple model', u'__madlib_temp_19839392_1576692433_56744839__')]"
+ "[(2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1340 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Maria', u'Also a simple model', u'__madlib_temp_87665369_1614901189_11144097__'),\n",
+ " (1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1340 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'A simple model', u'__madlib_temp_27065614_1614901189_16021319__')]"
]
},
- "execution_count": 11,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@@ -376,7 +352,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
@@ -384,7 +360,7 @@
"output_type": "stream",
"text": [
"1 rows affected.\n",
- "1 rows affected.\n"
+ "2 rows affected.\n"
]
},
{
@@ -392,18 +368,31 @@
"text/html": [
"<table>\n",
" <tr>\n",
- " <th>count</th>\n",
+ " <th>model_id</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>has_model_weights</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
+ " <td>Sophie</td>\n",
+ " <td>A simple model</td>\n",
+ " <td>False</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>Maria</td>\n",
+ " <td>Also a simple model</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(1L,)]"
+ "[(1, u'Sophie', u'A simple model', False),\n",
+ " (2, u'Maria', u'Also a simple model', True)]"
]
},
- "execution_count": 12,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
@@ -416,7 +405,7 @@
"WHERE model_arch_library.model_id = 2;\n",
"\n",
"-- Check weights loaded OK\n",
- "SELECT COUNT(*) FROM model_arch_library WHERE model_weights IS NOT NULL;"
+ "SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;"
]
},
{
@@ -430,7 +419,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
@@ -438,7 +427,6 @@
"output_type": "stream",
"text": [
"Done.\n",
- "1 rows affected.\n",
"1 rows affected.\n"
]
},
@@ -447,18 +435,18 @@
"text/html": [
"<table>\n",
" <tr>\n",
- " <th>count</th>\n",
+ " <th>load_weights</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>2</td>\n",
+ " <td></td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(2L,)]"
+ "[('',)]"
]
},
- "execution_count": 13,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
@@ -467,8 +455,8 @@
"%%sql\n",
"CREATE OR REPLACE FUNCTION load_weights() RETURNS VOID AS\n",
"$$\n",
- "from keras.layers import *\n",
- "from keras import Sequential\n",
+ "from tensorflow.keras.layers import *\n",
+ "from tensorflow.keras import Sequential\n",
"import numpy as np\n",
"import plpy\n",
"\n",
@@ -493,15 +481,12 @@
"$$ language plpythonu;\n",
"\n",
"-- Call load function\n",
- "SELECT load_weights();\n",
- "\n",
- "-- Check weights loaded OK\n",
- "SELECT COUNT(*) FROM model_arch_library WHERE model_weights IS NOT NULL;"
+ "SELECT load_weights();"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
@@ -518,33 +503,43 @@
" <tr>\n",
" <th>model_id</th>\n",
" <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>has_model_weights</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Sophie</td>\n",
+ " <td>A simple model</td>\n",
+ " <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Maria</td>\n",
+ " <td>Also a simple model</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Ella</td>\n",
+ " <td>Model x</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(1, u'Sophie'), (2, u'Maria'), (3, u'Ella')]"
+ "[(1, u'Sophie', u'A simple model', False),\n",
+ " (2, u'Maria', u'Also a simple model', True),\n",
+ " (3, u'Ella', u'Model x', True)]"
]
},
- "execution_count": 14,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
- "SELECT model_id, name from model_arch_library ORDER BY model_id;"
+ "SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;"
]
},
{
@@ -560,45 +555,16 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 45,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(2L,)]"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"import psycopg2 as p2\n",
- "#conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')\n",
- "conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
+ "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
"cur = conn.cursor()\n",
"\n",
- "from keras.layers import *\n",
- "from keras import Sequential\n",
+ "from tensorflow.keras.layers import *\n",
+ "from tensorflow.keras import Sequential\n",
"import numpy as np\n",
"\n",
"# create model\n",
@@ -615,22 +581,19 @@
"\n",
"query = \"SELECT madlib.load_keras_model('model_arch_library', %s,%s,%s,%s)\"\n",
"cur.execute(query,[model.to_json(), weights_bytea, \"Grace\", \"Model y\"])\n",
- "conn.commit()\n",
- "\n",
- "# check weights loaded OK\n",
- "%sql SELECT COUNT(*) FROM model_arch_library WHERE model_weights IS NOT NULL;"
+ "conn.commit()"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "3 rows affected.\n"
+ "4 rows affected.\n"
]
},
{
@@ -640,33 +603,50 @@
" <tr>\n",
" <th>model_id</th>\n",
" <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>has_model_weights</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>Sophie</td>\n",
+ " <td>A simple model</td>\n",
+ " <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Maria</td>\n",
+ " <td>Also a simple model</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Ella</td>\n",
+ " <td>Model x</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>Grace</td>\n",
+ " <td>Model y</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(1, u'Sophie'), (2, u'Maria'), (3, u'Ella')]"
+ "[(1, u'Sophie', u'A simple model', False),\n",
+ " (2, u'Maria', u'Also a simple model', True),\n",
+ " (3, u'Ella', u'Model x', True),\n",
+ " (4, u'Grace', u'Model y', True)]"
]
},
- "execution_count": 16,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
- "SELECT model_id, name from model_arch_library ORDER BY model_id;"
+ "SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;"
]
},
{
@@ -679,7 +659,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
@@ -687,7 +667,7 @@
"output_type": "stream",
"text": [
"1 rows affected.\n",
- "2 rows affected.\n"
+ "3 rows affected.\n"
]
},
{
@@ -697,22 +677,36 @@
" <tr>\n",
" <th>model_id</th>\n",
" <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>has_model_weights</th>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>Maria</td>\n",
+ " <td>Also a simple model</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>Ella</td>\n",
+ " <td>Model x</td>\n",
+ " <td>True</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>Grace</td>\n",
+ " <td>Model y</td>\n",
+ " <td>True</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(2, u'Maria'), (3, u'Ella')]"
+ "[(2, u'Maria', u'Also a simple model', True),\n",
+ " (3, u'Ella', u'Model x', True),\n",
+ " (4, u'Grace', u'Model y', True)]"
]
},
- "execution_count": 17,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -722,7 +716,7 @@
"SELECT madlib.delete_keras_model('model_arch_library', -- Output table\n",
" 1 -- Model id\n",
" );\n",
- "SELECT model_id, name from model_arch_library ORDER BY model_id;"
+ "SELECT model_id, name, description, (model_weights IS NOT NULL) AS has_model_weights FROM model_arch_library ORDER BY model_id;"
]
}
],
@@ -742,7 +736,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Deep-learning/Preprocessor-for-images-distribution-rules-v1.ipynb b/community-artifacts/Deep-learning/Model-preparation/Preprocessor-for-images-distribution-rules-v1.ipynb
similarity index 98%
rename from community-artifacts/Deep-learning/Preprocessor-for-images-distribution-rules-v1.ipynb
rename to community-artifacts/Deep-learning/Model-preparation/Preprocessor-for-images-distribution-rules-v1.ipynb
index b457303..0ae2b4c 100644
--- a/community-artifacts/Deep-learning/Preprocessor-for-images-distribution-rules-v1.ipynb
+++ b/community-artifacts/Deep-learning/Model-preparation/Preprocessor-for-images-distribution-rules-v1.ipynb
@@ -1198,7 +1198,7 @@
],
"source": [
"%%sql\n",
- "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ "SELECT __dist_key__, x_shape, y_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
]
},
{
@@ -1465,7 +1465,7 @@
],
"source": [
"%%sql\n",
- "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ "SELECT __dist_key__, x_shape, y_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
]
},
{
@@ -1657,7 +1657,7 @@
],
"source": [
"%%sql\n",
- "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ "SELECT __dist_key__, x_shape, y_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
]
},
{
@@ -1805,7 +1805,7 @@
],
"source": [
"%%sql\n",
- "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
+ "SELECT __dist_key__, x_shape, y_shape, buffer_id FROM image_data_train_packed ORDER BY __dist_key__;"
]
},
{
@@ -1938,7 +1938,7 @@
],
"source": [
"%%sql\n",
- "SELECT __dist_key__, independent_var_shape, dependent_var_shape, buffer_id FROM image_data_val_packed ORDER BY __dist_key__;"
+ "SELECT __dist_key__, x_shape, y_shape, buffer_id FROM image_data_val_packed ORDER BY __dist_key__;"
]
}
],
diff --git a/community-artifacts/Deep-learning/Preprocessor-for-images-v2.ipynb b/community-artifacts/Deep-learning/Model-preparation/Preprocessor-for-images-v2.ipynb
similarity index 61%
rename from community-artifacts/Deep-learning/Preprocessor-for-images-v2.ipynb
rename to community-artifacts/Deep-learning/Model-preparation/Preprocessor-for-images-v2.ipynb
index cb76d1e..5fd5a69 100644
--- a/community-artifacts/Deep-learning/Preprocessor-for-images-v2.ipynb
+++ b/community-artifacts/Deep-learning/Model-preparation/Preprocessor-for-images-v2.ipynb
@@ -5,11 +5,11 @@
"metadata": {},
"source": [
"# Preprocessor for image data\n",
- "This is a mini-batch preprocessor utility for image data:\n",
+ "This preprocessor prepares training data for deep learning.\n",
"* training_preprocessor_dl() for training datasets\n",
"* validation_preprocessor_dl() for validation datasets\n",
"\n",
- "Note that there is a separate mini-batch preprocessor utility for general use cases\n",
+ "Note that there is a separate mini-batch preprocessor utility for non deep learning use cases\n",
"http://madlib.apache.org/docs/latest/group__grp__minibatch__preprocessing.html\n",
"\n",
"The preprocessor for image data was added in MADlib 1.16.\n",
@@ -39,42 +39,17 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
"# Greenplum Database 5.x on GCP - via tunnel\n",
"%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
@@ -84,7 +59,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -102,15 +77,15 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-85-g4bac900, cmake configuration time: Wed Mar 3 20:37:11 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-85-g4bac900, cmake configuration time: Wed Mar 3 20:37:11 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
- "execution_count": 3,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -132,7 +107,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -153,271 +128,271 @@
" <th>species</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[152, 186, 35], [102, 145, 138]], [[40, 249, 108], [175, 207, 70]]]</td>\n",
+ " <td>[[[17, 201, 110], [175, 136, 179]], [[102, 57, 24], [110, 199, 64]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[205, 85, 56], [209, 11, 117]], [[86, 82, 41], [226, 192, 132]]]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[234, 110, 251], [147, 18, 158]], [[55, 79, 14], [140, 50, 143]]]</td>\n",
+ " <td>[[[209, 227, 160], [86, 88, 177]], [[31, 198, 96], [167, 122, 198]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[146, 52, 167], [210, 33, 116]], [[38, 89, 69], [50, 207, 155]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[247, 125, 68], [124, 196, 20]], [[95, 100, 107], [183, 21, 138]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[117, 49, 248], [59, 18, 137]], [[110, 186, 91], [143, 46, 129]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[115, 179, 183], [14, 54, 175]], [[138, 122, 42], [79, 142, 137]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[249, 65, 200], [131, 191, 61]], [[180, 182, 119], [199, 63, 230]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[154, 117, 174], [27, 94, 33]], [[206, 21, 46], [4, 196, 185]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[238, 8, 12], [120, 187, 4]], [[184, 130, 135], [119, 191, 59]]]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[179, 202, 20], [219, 198, 173]], [[149, 233, 18], [38, 115, 59]]]</td>\n",
+ " <td>[[[55, 2, 109], [28, 130, 7]], [[146, 48, 34], [240, 81, 240]]]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[223, 234, 239], [37, 253, 217]], [[147, 248, 108], [166, 150, 162]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[164, 46, 39], [51, 130, 218]], [[253, 150, 181], [195, 66, 75]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[85, 113, 32], [144, 145, 255]], [[122, 127, 36], [118, 88, 183]]]</td>\n",
+ " <td>[[[128, 244, 200], [57, 113, 182]], [[64, 125, 46], [251, 129, 230]]]</td>\n",
" <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[195, 93, 4], [102, 81, 168]], [[148, 120, 219], [21, 82, 217]]]</td>\n",
+ " <td>[[[8, 93, 61], [67, 139, 115]], [[69, 248, 144], [199, 255, 33]]]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[8, 156, 237], [82, 72, 66]], [[196, 104, 210], [84, 103, 75]]]</td>\n",
+ " <td>[[[33, 17, 73], [17, 21, 201]], [[5, 222, 1], [118, 148, 66]]]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[139, 194, 43], [66, 48, 239]], [[159, 52, 84], [240, 220, 232]]]</td>\n",
+ " <td>[[[194, 61, 116], [168, 187, 124]], [[6, 247, 192], [145, 106, 5]]]</td>\n",
" <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[183, 253, 187], [144, 168, 194]], [[44, 150, 21], [116, 216, 216]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[170, 44, 68], [245, 256, 207]], [[183, 43, 17], [231, 25, 176]]]</td>\n",
+ " <td>[[[250, 204, 135], [27, 196, 168]], [[44, 12, 185], [65, 213, 190]]]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[110, 160, 246], [85, 9, 173]], [[82, 195, 61], [251, 134, 105]]]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[154, 222, 104], [114, 186, 18]], [[159, 254, 7], [158, 205, 190]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[222, 165, 227], [142, 191, 80]], [[46, 182, 165], [55, 99, 248]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[161, 243, 128], [10, 131, 26]], [[232, 235, 141], [162, 253, 43]]]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[4, 202, 109], [194, 147, 75]], [[103, 117, 217], [39, 197, 8]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[107, 63, 64], [99, 57, 224]], [[86, 185, 234], [216, 212, 210]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[96, 116, 192], [140, 21, 196]], [[85, 130, 135], [232, 206, 238]]]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[167, 20, 35], [174, 241, 142]], [[237, 48, 241], [38, 16, 70]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[251, 31, 179], [205, 226, 19]], [[65, 162, 159], [86, 103, 244]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[237, 220, 166], [219, 58, 77]], [[239, 93, 251], [224, 235, 232]]]</td>\n",
+ " <td>[[[215, 52, 179], [25, 39, 117]], [[86, 155, 29], [16, 24, 35]]]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[219, 14, 33], [34, 237, 28]], [[64, 160, 232], [34, 180, 41]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[83, 127, 43], [71, 87, 24]], [[35, 253, 243], [93, 74, 227]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[69, 195, 165], [45, 212, 129]], [[59, 245, 162], [40, 16, 226]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[248, 5, 124], [34, 201, 206]], [[161, 244, 21], [248, 13, 57]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[0, 150, 63], [227, 80, 132]], [[166, 245, 176], [121, 118, 235]]]</td>\n",
+ " <td>[[[215, 180, 113], [220, 61, 107]], [[168, 196, 134], [108, 108, 178]]]</td>\n",
" <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[104, 42, 37], [143, 227, 111]], [[96, 135, 172], [12, 207, 100]]]</td>\n",
+ " <td>[[[38, 244, 77], [228, 19, 36]], [[24, 198, 60], [63, 59, 146]]]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[221, 150, 126], [143, 129, 93]], [[92, 235, 60], [174, 100, 100]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[216, 163, 35], [249, 33, 139]], [[35, 70, 26], [6, 181, 122]]]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[97, 134, 93], [198, 94, 57]], [[92, 219, 200], [221, 56, 35]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[116, 210, 44], [216, 129, 4]], [[123, 164, 253], [156, 47, 32]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[73, 39, 151], [196, 180, 248]], [[74, 16, 190], [168, 74, 26]]]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[18, 246, 187], [53, 190, 47]], [[7, 234, 8], [136, 238, 131]]]</td>\n",
+ " <td>[[[89, 162, 242], [124, 169, 202]], [[48, 26, 166], [109, 134, 78]]]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[235, 31, 91], [11, 1, 164]], [[49, 152, 103], [229, 144, 177]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[78, 89, 63], [104, 220, 81]], [[94, 151, 134], [28, 199, 141]]]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[206, 21, 244], [81, 65, 223]], [[112, 155, 234], [113, 63, 27]]]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[166, 1, 152], [88, 246, 230]], [[176, 54, 78], [140, 135, 172]]]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[13, 200, 234], [155, 207, 185]], [[176, 195, 10], [240, 162, 122]]]</td>\n",
+ " <td>[[[12, 185, 157], [191, 49, 195]], [[178, 126, 167], [197, 162, 191]]]</td>\n",
" <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[140, 235, 202], [167, 244, 113]], [[168, 140, 200], [158, 114, 121]]]</td>\n",
+ " <td>[[[222, 254, 199], [112, 217, 32]], [[18, 203, 156], [187, 148, 204]]]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[192, 5, 91], [108, 41, 104]], [[52, 19, 3], [3, 204, 178]]]</td>\n",
+ " <td>[[[58, 56, 91], [136, 105, 103]], [[65, 6, 38], [114, 201, 216]]]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[214, 162, 103], [80, 46, 243]], [[60, 248, 154], [47, 105, 65]]]</td>\n",
+ " <td>[[[111, 157, 147], [46, 41, 113]], [[44, 240, 226], [5, 15, 244]]]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[49, 223, 45], [170, 179, 237]], [[175, 14, 89], [216, 118, 141]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[121, 144, 183], [43, 86, 141]], [[205, 189, 221], [251, 176, 25]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[74, 72, 92], [139, 3, 141]], [[106, 48, 55], [29, 30, 230]]]</td>\n",
+ " <td>[[[171, 175, 100], [119, 132, 158]], [[175, 224, 37], [24, 71, 102]]]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[119, 190, 161], [4, 168, 25]], [[148, 95, 68], [234, 236, 17]]]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[201, 13, 87], [226, 256, 161]], [[42, 92, 44], [45, 233, 150]]]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[33, 179, 122], [7, 222, 241]], [[196, 127, 246], [108, 152, 138]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[220, 116, 183], [237, 27, 128]], [[250, 115, 98], [250, 19, 140]]]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[64, 184, 64], [214, 21, 96]], [[137, 143, 103], [103, 129, 43]]]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[[[118, 151, 126], [1, 99, 90]], [[117, 26, 71], [144, 154, 65]]]</td>\n",
+ " <td>[[[174, 243, 194], [14, 219, 228]], [[86, 254, 177], [214, 92, 119]]]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[252, 59, 22], [136, 146, 86]], [[64, 209, 43], [85, 49, 181]]]</td>\n",
+ " <td>[[[24, 120, 130], [256, 167, 172]], [[142, 93, 141], [165, 156, 239]]]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[81, 253, 127], [77, 53, 45]], [[64, 246, 59], [27, 219, 145]]]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[140, 103, 118], [4, 127, 142]], [[124, 1, 142], [35, 173, 28]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[58, 193, 28], [41, 201, 109]], [[38, 72, 186], [90, 116, 250]]]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[176, 21, 44], [65, 47, 184]], [[168, 165, 187], [39, 50, 55]]]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[192, 90, 212], [220, 218, 14]], [[157, 246, 55], [102, 99, 93]]]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[[[152, 28, 101], [195, 2, 220]], [[91, 128, 220], [189, 218, 81]]]</td>\n",
+ " <td>[[[29, 183, 34], [23, 8, 210]], [[44, 51, 19], [91, 235, 187]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[166, 226, 50], [222, 9, 242]], [[56, 222, 206], [18, 236, 108]]]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[35, 210, 106], [127, 127, 134]], [[55, 162, 157], [62, 115, 201]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[134, 36, 93], [65, 36, 4]], [[35, 86, 225], [44, 73, 25]]]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[23, 42, 246], [130, 49, 24]], [[84, 155, 152], [212, 34, 206]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[191, 13, 233], [136, 126, 111]], [[173, 220, 176], [209, 223, 211]]]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[192, 255, 112], [217, 8, 134]], [[3, 254, 9], [53, 22, 93]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[174, 48, 241], [124, 166, 176]], [[136, 142, 56], [7, 253, 229]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[173, 181, 193], [127, 220, 130]], [[126, 76, 91], [135, 210, 94]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[219, 147, 155], [56, 99, 72]], [[104, 84, 196], [14, 4, 77]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[60, 83, 153], [33, 54, 70]], [[214, 247, 197], [179, 121, 67]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[212, 202, 209], [50, 78, 172]], [[196, 233, 227], [39, 49, 76]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[246, 89, 127], [66, 245, 187]], [[150, 142, 220], [203, 212, 178]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[153, 101, 60], [220, 100, 15]], [[166, 52, 65], [245, 224, 5]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[195, 44, 15], [15, 167, 4]], [[104, 38, 71], [94, 225, 220]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[189, 168, 192], [112, 107, 89]], [[213, 166, 54], [56, 181, 220]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[246, 208, 77], [251, 174, 16]], [[39, 189, 31], [206, 193, 135]]]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[8, 229, 214], [228, 209, 147]], [[140, 146, 3], [247, 235, 215]]]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[33, 16, 82], [252, 124, 72]], [[205, 201, 68], [123, 217, 107]]]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[[[248, 57, 249], [127, 46, 1]], [[100, 3, 229], [54, 150, 113]]]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[([[[152, 186, 35], [102, 145, 138]], [[40, 249, 108], [175, 207, 70]]], u'cat'),\n",
- " ([[[234, 110, 251], [147, 18, 158]], [[55, 79, 14], [140, 50, 143]]], u'cat'),\n",
- " ([[[179, 202, 20], [219, 198, 173]], [[149, 233, 18], [38, 115, 59]]], u'cat'),\n",
- " ([[[223, 234, 239], [37, 253, 217]], [[147, 248, 108], [166, 150, 162]]], u'bird'),\n",
- " ([[[164, 46, 39], [51, 130, 218]], [[253, 150, 181], [195, 66, 75]]], u'bird'),\n",
- " ([[[85, 113, 32], [144, 145, 255]], [[122, 127, 36], [118, 88, 183]]], u'dog'),\n",
- " ([[[195, 93, 4], [102, 81, 168]], [[148, 120, 219], [21, 82, 217]]], u'bird'),\n",
- " ([[[8, 156, 237], [82, 72, 66]], [[196, 104, 210], [84, 103, 75]]], u'bird'),\n",
- " ([[[139, 194, 43], [66, 48, 239]], [[159, 52, 84], [240, 220, 232]]], u'dog'),\n",
- " ([[[183, 253, 187], [144, 168, 194]], [[44, 150, 21], [116, 216, 216]]], u'bird'),\n",
- " ([[[170, 44, 68], [245, 256, 207]], [[183, 43, 17], [231, 25, 176]]], u'cat'),\n",
- " ([[[110, 160, 246], [85, 9, 173]], [[82, 195, 61], [251, 134, 105]]], u'dog'),\n",
- " ([[[154, 222, 104], [114, 186, 18]], [[159, 254, 7], [158, 205, 190]]], u'bird'),\n",
- " ([[[222, 165, 227], [142, 191, 80]], [[46, 182, 165], [55, 99, 248]]], u'bird'),\n",
- " ([[[161, 243, 128], [10, 131, 26]], [[232, 235, 141], [162, 253, 43]]], u'dog'),\n",
- " ([[[4, 202, 109], [194, 147, 75]], [[103, 117, 217], [39, 197, 8]]], u'bird'),\n",
- " ([[[107, 63, 64], [99, 57, 224]], [[86, 185, 234], [216, 212, 210]]], u'bird'),\n",
- " ([[[96, 116, 192], [140, 21, 196]], [[85, 130, 135], [232, 206, 238]]], u'dog'),\n",
- " ([[[167, 20, 35], [174, 241, 142]], [[237, 48, 241], [38, 16, 70]]], u'bird'),\n",
- " ([[[251, 31, 179], [205, 226, 19]], [[65, 162, 159], [86, 103, 244]]], u'bird'),\n",
- " ([[[237, 220, 166], [219, 58, 77]], [[239, 93, 251], [224, 235, 232]]], u'cat'),\n",
- " ([[[219, 14, 33], [34, 237, 28]], [[64, 160, 232], [34, 180, 41]]], u'bird'),\n",
- " ([[[83, 127, 43], [71, 87, 24]], [[35, 253, 243], [93, 74, 227]]], u'bird'),\n",
- " ([[[69, 195, 165], [45, 212, 129]], [[59, 245, 162], [40, 16, 226]]], u'bird'),\n",
- " ([[[248, 5, 124], [34, 201, 206]], [[161, 244, 21], [248, 13, 57]]], u'bird'),\n",
- " ([[[0, 150, 63], [227, 80, 132]], [[166, 245, 176], [121, 118, 235]]], u'dog'),\n",
- " ([[[104, 42, 37], [143, 227, 111]], [[96, 135, 172], [12, 207, 100]]], u'bird'),\n",
- " ([[[221, 150, 126], [143, 129, 93]], [[92, 235, 60], [174, 100, 100]]], u'bird'),\n",
- " ([[[216, 163, 35], [249, 33, 139]], [[35, 70, 26], [6, 181, 122]]], u'dog'),\n",
- " ([[[97, 134, 93], [198, 94, 57]], [[92, 219, 200], [221, 56, 35]]], u'bird'),\n",
- " ([[[116, 210, 44], [216, 129, 4]], [[123, 164, 253], [156, 47, 32]]], u'bird'),\n",
- " ([[[73, 39, 151], [196, 180, 248]], [[74, 16, 190], [168, 74, 26]]], u'dog'),\n",
- " ([[[18, 246, 187], [53, 190, 47]], [[7, 234, 8], [136, 238, 131]]], u'cat'),\n",
- " ([[[235, 31, 91], [11, 1, 164]], [[49, 152, 103], [229, 144, 177]]], u'bird'),\n",
- " ([[[78, 89, 63], [104, 220, 81]], [[94, 151, 134], [28, 199, 141]]], u'cat'),\n",
- " ([[[206, 21, 244], [81, 65, 223]], [[112, 155, 234], [113, 63, 27]]], u'cat'),\n",
- " ([[[166, 1, 152], [88, 246, 230]], [[176, 54, 78], [140, 135, 172]]], u'cat'),\n",
- " ([[[13, 200, 234], [155, 207, 185]], [[176, 195, 10], [240, 162, 122]]], u'dog'),\n",
- " ([[[140, 235, 202], [167, 244, 113]], [[168, 140, 200], [158, 114, 121]]], u'bird'),\n",
- " ([[[192, 5, 91], [108, 41, 104]], [[52, 19, 3], [3, 204, 178]]], u'bird'),\n",
- " ([[[214, 162, 103], [80, 46, 243]], [[60, 248, 154], [47, 105, 65]]], u'bird'),\n",
- " ([[[49, 223, 45], [170, 179, 237]], [[175, 14, 89], [216, 118, 141]]], u'bird'),\n",
- " ([[[121, 144, 183], [43, 86, 141]], [[205, 189, 221], [251, 176, 25]]], u'bird'),\n",
- " ([[[74, 72, 92], [139, 3, 141]], [[106, 48, 55], [29, 30, 230]]], u'cat'),\n",
- " ([[[119, 190, 161], [4, 168, 25]], [[148, 95, 68], [234, 236, 17]]], u'dog'),\n",
- " ([[[201, 13, 87], [226, 256, 161]], [[42, 92, 44], [45, 233, 150]]], u'dog'),\n",
- " ([[[33, 179, 122], [7, 222, 241]], [[196, 127, 246], [108, 152, 138]]], u'bird'),\n",
- " ([[[220, 116, 183], [237, 27, 128]], [[250, 115, 98], [250, 19, 140]]], u'dog'),\n",
- " ([[[64, 184, 64], [214, 21, 96]], [[137, 143, 103], [103, 129, 43]]], u'bird'),\n",
- " ([[[118, 151, 126], [1, 99, 90]], [[117, 26, 71], [144, 154, 65]]], u'cat'),\n",
- " ([[[252, 59, 22], [136, 146, 86]], [[64, 209, 43], [85, 49, 181]]], u'bird'),\n",
- " ([[[152, 28, 101], [195, 2, 220]], [[91, 128, 220], [189, 218, 81]]], u'bird')]"
+ "[([[[17, 201, 110], [175, 136, 179]], [[102, 57, 24], [110, 199, 64]]], u'bird'),\n",
+ " ([[[205, 85, 56], [209, 11, 117]], [[86, 82, 41], [226, 192, 132]]], u'cat'),\n",
+ " ([[[209, 227, 160], [86, 88, 177]], [[31, 198, 96], [167, 122, 198]]], u'bird'),\n",
+ " ([[[146, 52, 167], [210, 33, 116]], [[38, 89, 69], [50, 207, 155]]], u'dog'),\n",
+ " ([[[247, 125, 68], [124, 196, 20]], [[95, 100, 107], [183, 21, 138]]], u'dog'),\n",
+ " ([[[117, 49, 248], [59, 18, 137]], [[110, 186, 91], [143, 46, 129]]], u'bird'),\n",
+ " ([[[115, 179, 183], [14, 54, 175]], [[138, 122, 42], [79, 142, 137]]], u'bird'),\n",
+ " ([[[249, 65, 200], [131, 191, 61]], [[180, 182, 119], [199, 63, 230]]], u'dog'),\n",
+ " ([[[154, 117, 174], [27, 94, 33]], [[206, 21, 46], [4, 196, 185]]], u'dog'),\n",
+ " ([[[238, 8, 12], [120, 187, 4]], [[184, 130, 135], [119, 191, 59]]], u'cat'),\n",
+ " ([[[55, 2, 109], [28, 130, 7]], [[146, 48, 34], [240, 81, 240]]], u'cat'),\n",
+ " ([[[128, 244, 200], [57, 113, 182]], [[64, 125, 46], [251, 129, 230]]], u'dog'),\n",
+ " ([[[8, 93, 61], [67, 139, 115]], [[69, 248, 144], [199, 255, 33]]], u'bird'),\n",
+ " ([[[33, 17, 73], [17, 21, 201]], [[5, 222, 1], [118, 148, 66]]], u'bird'),\n",
+ " ([[[194, 61, 116], [168, 187, 124]], [[6, 247, 192], [145, 106, 5]]], u'dog'),\n",
+ " ([[[250, 204, 135], [27, 196, 168]], [[44, 12, 185], [65, 213, 190]]], u'cat'),\n",
+ " ([[[215, 52, 179], [25, 39, 117]], [[86, 155, 29], [16, 24, 35]]], u'cat'),\n",
+ " ([[[215, 180, 113], [220, 61, 107]], [[168, 196, 134], [108, 108, 178]]], u'dog'),\n",
+ " ([[[38, 244, 77], [228, 19, 36]], [[24, 198, 60], [63, 59, 146]]], u'bird'),\n",
+ " ([[[89, 162, 242], [124, 169, 202]], [[48, 26, 166], [109, 134, 78]]], u'cat'),\n",
+ " ([[[12, 185, 157], [191, 49, 195]], [[178, 126, 167], [197, 162, 191]]], u'dog'),\n",
+ " ([[[222, 254, 199], [112, 217, 32]], [[18, 203, 156], [187, 148, 204]]], u'bird'),\n",
+ " ([[[58, 56, 91], [136, 105, 103]], [[65, 6, 38], [114, 201, 216]]], u'bird'),\n",
+ " ([[[111, 157, 147], [46, 41, 113]], [[44, 240, 226], [5, 15, 244]]], u'bird'),\n",
+ " ([[[171, 175, 100], [119, 132, 158]], [[175, 224, 37], [24, 71, 102]]], u'cat'),\n",
+ " ([[[174, 243, 194], [14, 219, 228]], [[86, 254, 177], [214, 92, 119]]], u'cat'),\n",
+ " ([[[24, 120, 130], [256, 167, 172]], [[142, 93, 141], [165, 156, 239]]], u'cat'),\n",
+ " ([[[81, 253, 127], [77, 53, 45]], [[64, 246, 59], [27, 219, 145]]], u'cat'),\n",
+ " ([[[140, 103, 118], [4, 127, 142]], [[124, 1, 142], [35, 173, 28]]], u'dog'),\n",
+ " ([[[58, 193, 28], [41, 201, 109]], [[38, 72, 186], [90, 116, 250]]], u'cat'),\n",
+ " ([[[176, 21, 44], [65, 47, 184]], [[168, 165, 187], [39, 50, 55]]], u'cat'),\n",
+ " ([[[192, 90, 212], [220, 218, 14]], [[157, 246, 55], [102, 99, 93]]], u'bird'),\n",
+ " ([[[29, 183, 34], [23, 8, 210]], [[44, 51, 19], [91, 235, 187]]], u'bird'),\n",
+ " ([[[166, 226, 50], [222, 9, 242]], [[56, 222, 206], [18, 236, 108]]], u'cat'),\n",
+ " ([[[35, 210, 106], [127, 127, 134]], [[55, 162, 157], [62, 115, 201]]], u'dog'),\n",
+ " ([[[134, 36, 93], [65, 36, 4]], [[35, 86, 225], [44, 73, 25]]], u'cat'),\n",
+ " ([[[23, 42, 246], [130, 49, 24]], [[84, 155, 152], [212, 34, 206]]], u'dog'),\n",
+ " ([[[191, 13, 233], [136, 126, 111]], [[173, 220, 176], [209, 223, 211]]], u'cat'),\n",
+ " ([[[192, 255, 112], [217, 8, 134]], [[3, 254, 9], [53, 22, 93]]], u'bird'),\n",
+ " ([[[174, 48, 241], [124, 166, 176]], [[136, 142, 56], [7, 253, 229]]], u'bird'),\n",
+ " ([[[173, 181, 193], [127, 220, 130]], [[126, 76, 91], [135, 210, 94]]], u'dog'),\n",
+ " ([[[219, 147, 155], [56, 99, 72]], [[104, 84, 196], [14, 4, 77]]], u'dog'),\n",
+ " ([[[60, 83, 153], [33, 54, 70]], [[214, 247, 197], [179, 121, 67]]], u'bird'),\n",
+ " ([[[212, 202, 209], [50, 78, 172]], [[196, 233, 227], [39, 49, 76]]], u'dog'),\n",
+ " ([[[246, 89, 127], [66, 245, 187]], [[150, 142, 220], [203, 212, 178]]], u'bird'),\n",
+ " ([[[153, 101, 60], [220, 100, 15]], [[166, 52, 65], [245, 224, 5]]], u'bird'),\n",
+ " ([[[195, 44, 15], [15, 167, 4]], [[104, 38, 71], [94, 225, 220]]], u'bird'),\n",
+ " ([[[189, 168, 192], [112, 107, 89]], [[213, 166, 54], [56, 181, 220]]], u'dog'),\n",
+ " ([[[246, 208, 77], [251, 174, 16]], [[39, 189, 31], [206, 193, 135]]], u'bird'),\n",
+ " ([[[8, 229, 214], [228, 209, 147]], [[140, 146, 3], [247, 235, 215]]], u'dog'),\n",
+ " ([[[33, 16, 82], [252, 124, 72]], [[205, 201, 68], [123, 217, 107]]], u'cat'),\n",
+ " ([[[248, 57, 249], [127, 46, 1]], [[100, 3, 229], [54, 150, 113]]], u'bird')]"
]
},
- "execution_count": 4,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -463,7 +438,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -480,8 +455,8 @@
"text/html": [
"<table>\n",
" <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
+ " <th>rgb_shape</th>\n",
+ " <th>species_shape</th>\n",
" <th>buffer_id</th>\n",
" </tr>\n",
" <tr>\n",
@@ -500,7 +475,7 @@
"[([26, 2, 2, 3], [26, 3], 0), ([26, 2, 2, 3], [26, 3], 1)]"
]
},
- "execution_count": 5,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -517,7 +492,7 @@
" 255 -- Normalizing constant\n",
" );\n",
"\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;"
+ "SELECT rgb_shape, species_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;"
]
},
{
@@ -531,7 +506,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -551,7 +526,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>species_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -561,23 +536,23 @@
" <tr>\n",
" <td>image_data</td>\n",
" <td>image_data_packed</td>\n",
- " <td>species</td>\n",
- " <td>rgb</td>\n",
- " <td>text</td>\n",
+ " <td>[u'species']</td>\n",
+ " <td>[u'rgb']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'bird', u'cat', u'dog']</td>\n",
" <td>26</td>\n",
" <td>255.0</td>\n",
- " <td>3</td>\n",
+ " <td>[3]</td>\n",
" <td>all_segments</td>\n",
" <td>all_segments</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'image_data', u'image_data_packed', u'species', u'rgb', u'text', [u'bird', u'cat', u'dog'], 26, 255.0, 3, 'all_segments', 'all_segments')]"
+ "[(u'image_data', u'image_data_packed', [u'species'], [u'rgb'], [u'text'], [u'bird', u'cat', u'dog'], 26, 255.0, [3], 'all_segments', 'all_segments')]"
]
},
- "execution_count": 6,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -599,7 +574,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -616,8 +591,8 @@
"text/html": [
"<table>\n",
" <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
+ " <th>rgb_shape</th>\n",
+ " <th>species_shape</th>\n",
" <th>buffer_id</th>\n",
" </tr>\n",
" <tr>\n",
@@ -636,7 +611,7 @@
"[([26, 2, 2, 3], [26, 3], 0), ([26, 2, 2, 3], [26, 3], 1)]"
]
},
- "execution_count": 7,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -644,6 +619,7 @@
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS val_image_data_packed, val_image_data_packed_summary;\n",
+ "\n",
"SELECT madlib.validation_preprocessor_dl(\n",
" 'image_data', -- Source table\n",
" 'val_image_data_packed', -- Output table\n",
@@ -652,7 +628,8 @@
" 'image_data_packed', -- From training preprocessor step\n",
" NULL -- Buffer size\n",
" ); \n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM val_image_data_packed ORDER BY buffer_id;"
+ "\n",
+ "SELECT rgb_shape, species_shape, buffer_id FROM val_image_data_packed ORDER BY buffer_id;"
]
},
{
@@ -664,7 +641,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -684,7 +661,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>species_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -694,23 +671,23 @@
" <tr>\n",
" <td>image_data</td>\n",
" <td>val_image_data_packed</td>\n",
- " <td>species</td>\n",
- " <td>rgb</td>\n",
- " <td>text</td>\n",
+ " <td>[u'species']</td>\n",
+ " <td>[u'rgb']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'bird', u'cat', u'dog']</td>\n",
" <td>26</td>\n",
" <td>255.0</td>\n",
- " <td>3</td>\n",
+ " <td>[3]</td>\n",
" <td>all_segments</td>\n",
" <td>all_segments</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'image_data', u'val_image_data_packed', u'species', u'rgb', u'text', [u'bird', u'cat', u'dog'], 26, 255.0, 3, 'all_segments', 'all_segments')]"
+ "[(u'image_data', u'val_image_data_packed', [u'species'], [u'rgb'], [u'text'], [u'bird', u'cat', u'dog'], 26, 255.0, [3], 'all_segments', 'all_segments')]"
]
},
- "execution_count": 8,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -731,7 +708,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -752,271 +729,271 @@
" <th>species</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>[19, 126, 250, 219, 119, 255, 86, 152, 200, 36, 57, 188]</td>\n",
+ " <td>[168, 228, 110, 3, 51, 104, 192, 23, 120, 249, 96, 99]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[20, 145, 109, 135, 149, 100, 39, 66, 124, 102, 77, 140]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[125, 32, 244, 23, 201, 156, 251, 55, 159, 47, 160, 95]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[49, 201, 114, 38, 201, 8, 101, 172, 88, 233, 82, 78]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[203, 196, 132, 57, 220, 151, 183, 214, 113, 46, 213, 200]</td>\n",
+ " <td>[24, 88, 166, 123, 193, 186, 12, 46, 65, 161, 145, 104]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[157, 236, 255, 90, 38, 48, 35, 152, 86, 236, 160, 187]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[248, 164, 234, 70, 61, 181, 10, 193, 238, 229, 88, 165]</td>\n",
+ " <td>[14, 206, 47, 154, 85, 172, 186, 73, 196, 131, 229, 191]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[201, 210, 145, 145, 152, 46, 125, 151, 135, 163, 199, 170]</td>\n",
+ " <td>[131, 238, 90, 227, 51, 114, 59, 217, 237, 252, 147, 248]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[29, 150, 219, 216, 46, 211, 124, 24, 25, 186, 205, 35]</td>\n",
+ " <td>[211, 153, 187, 59, 123, 200, 10, 171, 98, 95, 87, 28]</td>\n",
" <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[187, 8, 211, 95, 196, 156, 50, 84, 45, 202, 130, 170]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[9, 77, 40, 179, 136, 69, 74, 98, 29, 120, 53, 153]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[78, 83, 93, 113, 206, 23, 121, 160, 119, 61, 60, 168]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[105, 114, 19, 19, 211, 28, 96, 251, 208, 232, 64, 25]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[93, 145, 128, 246, 33, 206, 73, 126, 63, 22, 150, 184]</td>\n",
+ " <td>[26, 159, 140, 217, 89, 15, 199, 179, 242, 250, 37, 45]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[12, 245, 243, 181, 134, 92, 39, 153, 112, 250, 181, 208]</td>\n",
+ " <td>[18, 41, 102, 10, 82, 57, 163, 13, 116, 30, 213, 126]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[133, 184, 53, 158, 3, 145, 47, 130, 135, 81, 80, 208]</td>\n",
+ " <td>[56, 221, 31, 84, 132, 58, 243, 16, 19, 76, 31, 218]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[143, 230, 101, 71, 156, 113, 61, 143, 37, 195, 235, 76]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[91, 70, 17, 43, 59, 150, 227, 111, 53, 229, 0, 100]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[136, 181, 184, 87, 132, 71, 61, 232, 143, 218, 89, 203]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[126, 142, 84, 203, 234, 175, 17, 251, 217, 75, 145, 188]</td>\n",
+ " <td>[17, 212, 36, 62, 167, 54, 103, 13, 64, 185, 70, 227]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[198, 162, 187, 42, 9, 67, 223, 193, 154, 99, 9, 215]</td>\n",
- " <td>cat</td>\n",
+ " <td>[186, 1, 155, 56, 201, 211, 21, 233, 38, 153, 34, 25]</td>\n",
+ " <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[151, 177, 164, 98, 25, 35, 240, 109, 237, 218, 28, 254]</td>\n",
+ " <td>[53, 101, 200, 15, 101, 217, 227, 137, 23, 138, 191, 126]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[255, 54, 220, 226, 252, 150, 227, 151, 207, 172, 105, 227]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[144, 124, 183, 169, 37, 237, 14, 237, 252, 115, 198, 222]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[246, 73, 102, 178, 4, 45, 84, 191, 87, 93, 2, 54]</td>\n",
+ " <td>[222, 104, 188, 92, 254, 187, 146, 219, 157, 142, 113, 128]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[156, 153, 39, 115, 228, 190, 35, 136, 32, 61, 171, 16]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[152, 234, 198, 149, 191, 188, 222, 37, 110, 226, 82, 194]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[169, 31, 163, 222, 61, 62, 119, 100, 177, 91, 34, 213]</td>\n",
+ " <td>[64, 44, 142, 35, 193, 30, 159, 120, 199, 196, 101, 213]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[67, 17, 141, 83, 188, 37, 61, 130, 187, 252, 62, 153]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[172, 123, 115, 110, 28, 28, 140, 191, 250, 202, 253, 113]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[225, 113, 99, 228, 109, 158, 250, 245, 47, 79, 52, 1]</td>\n",
+ " <td>[96, 72, 120, 63, 69, 86, 167, 0, 177, 165, 187, 67]</td>\n",
" <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[137, 50, 48, 110, 202, 76, 211, 142, 78, 174, 232, 206]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[166, 168, 219, 125, 201, 188, 238, 44, 160, 92, 202, 153]</td>\n",
+ " <td>[88, 210, 241, 216, 246, 48, 4, 132, 83, 197, 162, 242]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[249, 233, 133, 249, 100, 14, 43, 147, 124, 246, 223, 78]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[45, 253, 108, 251, 135, 18, 163, 98, 143, 108, 30, 126]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[190, 217, 97, 87, 41, 90, 64, 174, 84, 164, 188, 127]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[56, 117, 22, 134, 249, 67, 130, 101, 62, 9, 119, 225]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[6, 78, 138, 132, 230, 72, 93, 71, 159, 134, 161, 223]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[245, 131, 240, 116, 186, 40, 233, 209, 174, 226, 20, 48]</td>\n",
- " <td>cat</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[82, 57, 189, 52, 165, 195, 129, 46, 71, 103, 118, 163]</td>\n",
+ " <td>[105, 182, 162, 62, 104, 2, 134, 223, 65, 203, 53, 231]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[21, 41, 79, 244, 93, 68, 120, 78, 184, 50, 117, 161]</td>\n",
+ " <td>[230, 140, 134, 42, 12, 223, 251, 252, 183, 241, 44, 188]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[127, 129, 24, 113, 190, 129, 40, 96, 191, 143, 98, 69]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[162, 16, 163, 137, 219, 137, 21, 97, 179, 33, 64, 174]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[35, 131, 23, 83, 201, 105, 140, 134, 157, 48, 73, 30]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[144, 133, 213, 51, 51, 234, 93, 130, 222, 186, 198, 86]</td>\n",
+ " <td>[247, 159, 74, 179, 21, 201, 51, 45, 58, 241, 175, 98]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[126, 136, 125, 31, 139, 160, 161, 162, 242, 106, 11, 126]</td>\n",
+ " <td>[110, 241, 179, 179, 96, 85, 195, 3, 222, 158, 140, 244]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[168, 174, 58, 198, 13, 202, 75, 226, 254, 126, 204, 90]</td>\n",
+ " <td>[63, 21, 63, 237, 50, 54, 140, 124, 233, 162, 69, 28]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[170, 20, 197, 1, 28, 67, 137, 153, 97, 20, 57, 3]</td>\n",
- " <td>bird</td>\n",
+ " <td>[94, 111, 234, 231, 203, 73, 118, 97, 57, 254, 209, 131]</td>\n",
+ " <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[43, 109, 193, 169, 94, 105, 88, 152, 46, 101, 98, 121]</td>\n",
+ " <td>[246, 73, 151, 78, 201, 43, 59, 1, 215, 155, 138, 63]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[46, 186, 18, 158, 254, 111, 13, 232, 86, 216, 49, 204]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[95, 247, 19, 186, 247, 189, 206, 188, 190, 234, 254, 70]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[96, 90, 188, 98, 16, 231, 207, 209, 145, 45, 58, 232]</td>\n",
- " <td>bird</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[104, 77, 39, 226, 148, 134, 217, 166, 64, 207, 99, 14]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[33, 248, 137, 103, 124, 233, 194, 56, 75, 210, 32, 27]</td>\n",
- " <td>dog</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>[176, 72, 221, 152, 12, 70, 229, 51, 39, 121, 185, 0]</td>\n",
+ " <td>[106, 202, 9, 238, 104, 256, 55, 255, 78, 0, 42, 137]</td>\n",
" <td>cat</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[249, 207, 131, 7, 90, 164, 255, 228, 11, 123, 205, 205]</td>\n",
+ " <td>[1, 35, 139, 64, 121, 185, 250, 139, 87, 248, 250, 100]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[25, 160, 211, 51, 67, 131, 123, 33, 28, 135, 102, 1]</td>\n",
+ " <td>[81, 59, 17, 29, 116, 124, 231, 125, 105, 79, 124, 160]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[202, 160, 119, 83, 161, 120, 118, 44, 183, 239, 230, 177]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[61, 169, 117, 160, 136, 197, 220, 153, 226, 79, 21, 201]</td>\n",
" <td>bird</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[142, 122, 115, 142, 154, 108, 93, 29, 115, 184, 193, 114]</td>\n",
+ " <td>[126, 23, 73, 30, 100, 19, 191, 219, 102, 96, 83, 220]</td>\n",
" <td>dog</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[204, 237, 105, 153, 161, 129, 57, 116, 181, 124, 247, 47]</td>\n",
+ " <td>[10, 203, 113, 187, 70, 174, 99, 186, 78, 235, 128, 42]</td>\n",
" <td>dog</td>\n",
" </tr>\n",
+ " <tr>\n",
+ " <td>[98, 122, 154, 42, 70, 24, 66, 143, 54, 166, 161, 245]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[7, 84, 211, 227, 224, 221, 174, 82, 152, 244, 255, 251]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[78, 230, 46, 120, 106, 144, 241, 4, 186, 55, 28, 252]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[82, 162, 103, 71, 35, 110, 156, 246, 81, 124, 211, 255]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[106, 243, 205, 101, 161, 26, 75, 207, 146, 181, 94, 132]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[24, 187, 213, 20, 129, 39, 182, 232, 110, 217, 86, 10]</td>\n",
+ " <td>bird</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[168, 134, 161, 167, 83, 12, 154, 32, 113, 58, 58, 188]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[205, 113, 103, 80, 42, 128, 11, 255, 148, 140, 39, 74]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[149, 34, 203, 159, 241, 114, 37, 146, 25, 120, 158, 179]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[15, 237, 210, 202, 246, 159, 59, 94, 239, 101, 221, 250]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[113, 134, 139, 187, 250, 32, 222, 197, 192, 206, 55, 229]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[81, 93, 255, 4, 244, 13, 241, 198, 215, 231, 101, 18]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[84, 120, 34, 78, 220, 147, 212, 103, 79, 206, 136, 44]</td>\n",
+ " <td>dog</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[71, 251, 203, 44, 91, 28, 136, 90, 31, 124, 103, 16]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[62, 248, 167, 81, 60, 251, 200, 95, 72, 164, 242, 28]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[65, 235, 147, 109, 126, 219, 103, 73, 6, 195, 101, 143]</td>\n",
+ " <td>cat</td>\n",
+ " </tr>\n",
"</table>"
],
"text/plain": [
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- " ([203, 196, 132, 57, 220, 151, 183, 214, 113, 46, 213, 200], u'bird'),\n",
- " ([157, 236, 255, 90, 38, 48, 35, 152, 86, 236, 160, 187], u'dog'),\n",
- " ([248, 164, 234, 70, 61, 181, 10, 193, 238, 229, 88, 165], u'bird'),\n",
- " ([201, 210, 145, 145, 152, 46, 125, 151, 135, 163, 199, 170], u'cat'),\n",
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- " ([105, 114, 19, 19, 211, 28, 96, 251, 208, 232, 64, 25], u'cat'),\n",
- " ([93, 145, 128, 246, 33, 206, 73, 126, 63, 22, 150, 184], u'bird'),\n",
- " ([12, 245, 243, 181, 134, 92, 39, 153, 112, 250, 181, 208], u'bird'),\n",
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- " ([225, 113, 99, 228, 109, 158, 250, 245, 47, 79, 52, 1], u'dog'),\n",
- " ([137, 50, 48, 110, 202, 76, 211, 142, 78, 174, 232, 206], u'dog'),\n",
- " ([166, 168, 219, 125, 201, 188, 238, 44, 160, 92, 202, 153], u'cat'),\n",
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- " ([45, 253, 108, 251, 135, 18, 163, 98, 143, 108, 30, 126], u'dog'),\n",
- " ([190, 217, 97, 87, 41, 90, 64, 174, 84, 164, 188, 127], u'cat'),\n",
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- " ([6, 78, 138, 132, 230, 72, 93, 71, 159, 134, 161, 223], u'cat'),\n",
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- " ([144, 133, 213, 51, 51, 234, 93, 130, 222, 186, 198, 86], u'cat'),\n",
- " ([126, 136, 125, 31, 139, 160, 161, 162, 242, 106, 11, 126], u'bird'),\n",
- " ([168, 174, 58, 198, 13, 202, 75, 226, 254, 126, 204, 90], u'bird'),\n",
- " ([170, 20, 197, 1, 28, 67, 137, 153, 97, 20, 57, 3], u'bird'),\n",
- " ([43, 109, 193, 169, 94, 105, 88, 152, 46, 101, 98, 121], u'cat'),\n",
- " ([95, 247, 19, 186, 247, 189, 206, 188, 190, 234, 254, 70], u'dog'),\n",
- " ([96, 90, 188, 98, 16, 231, 207, 209, 145, 45, 58, 232], u'bird'),\n",
- " ([104, 77, 39, 226, 148, 134, 217, 166, 64, 207, 99, 14], u'dog'),\n",
- " ([33, 248, 137, 103, 124, 233, 194, 56, 75, 210, 32, 27], u'dog'),\n",
- " ([176, 72, 221, 152, 12, 70, 229, 51, 39, 121, 185, 0], u'cat'),\n",
- " ([249, 207, 131, 7, 90, 164, 255, 228, 11, 123, 205, 205], u'bird'),\n",
- " ([25, 160, 211, 51, 67, 131, 123, 33, 28, 135, 102, 1], u'bird'),\n",
- " ([142, 122, 115, 142, 154, 108, 93, 29, 115, 184, 193, 114], u'dog'),\n",
- " ([204, 237, 105, 153, 161, 129, 57, 116, 181, 124, 247, 47], u'dog')]"
+ "[([168, 228, 110, 3, 51, 104, 192, 23, 120, 249, 96, 99], u'dog'),\n",
+ " ([20, 145, 109, 135, 149, 100, 39, 66, 124, 102, 77, 140], u'dog'),\n",
+ " ([125, 32, 244, 23, 201, 156, 251, 55, 159, 47, 160, 95], u'cat'),\n",
+ " ([24, 88, 166, 123, 193, 186, 12, 46, 65, 161, 145, 104], u'bird'),\n",
+ " ([14, 206, 47, 154, 85, 172, 186, 73, 196, 131, 229, 191], u'bird'),\n",
+ " ([131, 238, 90, 227, 51, 114, 59, 217, 237, 252, 147, 248], u'cat'),\n",
+ " ([211, 153, 187, 59, 123, 200, 10, 171, 98, 95, 87, 28], u'dog'),\n",
+ " ([26, 159, 140, 217, 89, 15, 199, 179, 242, 250, 37, 45], u'bird'),\n",
+ " ([18, 41, 102, 10, 82, 57, 163, 13, 116, 30, 213, 126], u'bird'),\n",
+ " ([56, 221, 31, 84, 132, 58, 243, 16, 19, 76, 31, 218], u'bird'),\n",
+ " ([17, 212, 36, 62, 167, 54, 103, 13, 64, 185, 70, 227], u'bird'),\n",
+ " ([186, 1, 155, 56, 201, 211, 21, 233, 38, 153, 34, 25], u'dog'),\n",
+ " ([53, 101, 200, 15, 101, 217, 227, 137, 23, 138, 191, 126], u'dog'),\n",
+ " ([255, 54, 220, 226, 252, 150, 227, 151, 207, 172, 105, 227], u'dog'),\n",
+ " ([144, 124, 183, 169, 37, 237, 14, 237, 252, 115, 198, 222], u'bird'),\n",
+ " ([222, 104, 188, 92, 254, 187, 146, 219, 157, 142, 113, 128], u'cat'),\n",
+ " ([64, 44, 142, 35, 193, 30, 159, 120, 199, 196, 101, 213], u'bird'),\n",
+ " ([96, 72, 120, 63, 69, 86, 167, 0, 177, 165, 187, 67], u'dog'),\n",
+ " ([88, 210, 241, 216, 246, 48, 4, 132, 83, 197, 162, 242], u'cat'),\n",
+ " ([105, 182, 162, 62, 104, 2, 134, 223, 65, 203, 53, 231], u'bird'),\n",
+ " ([230, 140, 134, 42, 12, 223, 251, 252, 183, 241, 44, 188], u'dog'),\n",
+ " ([127, 129, 24, 113, 190, 129, 40, 96, 191, 143, 98, 69], u'dog'),\n",
+ " ([162, 16, 163, 137, 219, 137, 21, 97, 179, 33, 64, 174], u'cat'),\n",
+ " ([247, 159, 74, 179, 21, 201, 51, 45, 58, 241, 175, 98], u'cat'),\n",
+ " ([110, 241, 179, 179, 96, 85, 195, 3, 222, 158, 140, 244], u'bird'),\n",
+ " ([63, 21, 63, 237, 50, 54, 140, 124, 233, 162, 69, 28], u'bird'),\n",
+ " ([94, 111, 234, 231, 203, 73, 118, 97, 57, 254, 209, 131], u'dog'),\n",
+ " ([246, 73, 151, 78, 201, 43, 59, 1, 215, 155, 138, 63], u'dog'),\n",
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+ " ([81, 59, 17, 29, 116, 124, 231, 125, 105, 79, 124, 160], u'cat'),\n",
+ " ([202, 160, 119, 83, 161, 120, 118, 44, 183, 239, 230, 177], u'dog'),\n",
+ " ([61, 169, 117, 160, 136, 197, 220, 153, 226, 79, 21, 201], u'bird'),\n",
+ " ([126, 23, 73, 30, 100, 19, 191, 219, 102, 96, 83, 220], u'dog'),\n",
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+ " ([98, 122, 154, 42, 70, 24, 66, 143, 54, 166, 161, 245], u'dog'),\n",
+ " ([7, 84, 211, 227, 224, 221, 174, 82, 152, 244, 255, 251], u'bird'),\n",
+ " ([78, 230, 46, 120, 106, 144, 241, 4, 186, 55, 28, 252], u'bird'),\n",
+ " ([82, 162, 103, 71, 35, 110, 156, 246, 81, 124, 211, 255], u'bird'),\n",
+ " ([106, 243, 205, 101, 161, 26, 75, 207, 146, 181, 94, 132], u'bird'),\n",
+ " ([24, 187, 213, 20, 129, 39, 182, 232, 110, 217, 86, 10], u'bird'),\n",
+ " ([168, 134, 161, 167, 83, 12, 154, 32, 113, 58, 58, 188], u'cat'),\n",
+ " ([205, 113, 103, 80, 42, 128, 11, 255, 148, 140, 39, 74], u'dog'),\n",
+ " ([149, 34, 203, 159, 241, 114, 37, 146, 25, 120, 158, 179], u'dog'),\n",
+ " ([15, 237, 210, 202, 246, 159, 59, 94, 239, 101, 221, 250], u'dog'),\n",
+ " ([113, 134, 139, 187, 250, 32, 222, 197, 192, 206, 55, 229], u'dog'),\n",
+ " ([81, 93, 255, 4, 244, 13, 241, 198, 215, 231, 101, 18], u'cat'),\n",
+ " ([84, 120, 34, 78, 220, 147, 212, 103, 79, 206, 136, 44], u'dog'),\n",
+ " ([71, 251, 203, 44, 91, 28, 136, 90, 31, 124, 103, 16], u'cat'),\n",
+ " ([62, 248, 167, 81, 60, 251, 200, 95, 72, 164, 242, 28], u'cat'),\n",
+ " ([65, 235, 147, 109, 126, 219, 103, 73, 6, 195, 101, 143], u'cat')]"
]
},
- "execution_count": 9,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -1058,7 +1035,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -1075,8 +1052,8 @@
"text/html": [
"<table>\n",
" <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
+ " <th>rgb_shape</th>\n",
+ " <th>species_shape</th>\n",
" <th>buffer_id</th>\n",
" </tr>\n",
" <tr>\n",
@@ -1095,7 +1072,7 @@
"[([26, 12], [26, 3], 0), ([26, 12], [26, 3], 1)]"
]
},
- "execution_count": 10,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -1112,7 +1089,7 @@
" 255 -- Normalizing constant\n",
" );\n",
"\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;"
+ "SELECT rgb_shape, species_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;"
]
},
{
@@ -1127,7 +1104,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -1144,8 +1121,8 @@
"text/html": [
"<table>\n",
" <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
+ " <th>rgb_shape</th>\n",
+ " <th>species_shape</th>\n",
" <th>buffer_id</th>\n",
" </tr>\n",
" <tr>\n",
@@ -1164,7 +1141,7 @@
"[([26, 12], [26, 3], 0), ([26, 12], [26, 3], 1)]"
]
},
- "execution_count": 11,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -1182,7 +1159,7 @@
" NULL -- Buffer size\n",
" );\n",
"\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM val_image_data_packed ORDER BY buffer_id;"
+ "SELECT rgb_shape, species_shape, buffer_id FROM val_image_data_packed ORDER BY buffer_id;"
]
},
{
@@ -1197,7 +1174,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -1214,13 +1191,13 @@
"text/html": [
"<table>\n",
" <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
+ " <th>rgb_shape</th>\n",
+ " <th>species_shape</th>\n",
" <th>buffer_id</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>[8, 12]</td>\n",
- " <td>[8, 3]</td>\n",
+ " <td>[9, 12]</td>\n",
+ " <td>[9, 3]</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
@@ -1244,22 +1221,22 @@
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>[8, 12]</td>\n",
- " <td>[8, 3]</td>\n",
+ " <td>[7, 12]</td>\n",
+ " <td>[7, 3]</td>\n",
" <td>5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[([8, 12], [8, 3], 0),\n",
+ "[([9, 12], [9, 3], 0),\n",
" ([9, 12], [9, 3], 1),\n",
" ([9, 12], [9, 3], 2),\n",
" ([9, 12], [9, 3], 3),\n",
" ([9, 12], [9, 3], 4),\n",
- " ([8, 12], [8, 3], 5)]"
+ " ([7, 12], [7, 3], 5)]"
]
},
- "execution_count": 12,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -1276,7 +1253,7 @@
" 255 -- Normalizing constant\n",
" );\n",
"\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;"
+ "SELECT rgb_shape, species_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;"
]
},
{
@@ -1288,7 +1265,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -1308,7 +1285,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>species_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -1318,23 +1295,23 @@
" <tr>\n",
" <td>image_data</td>\n",
" <td>image_data_packed</td>\n",
- " <td>species</td>\n",
- " <td>rgb</td>\n",
- " <td>text</td>\n",
+ " <td>[u'species']</td>\n",
+ " <td>[u'rgb']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'bird', u'cat', u'dog']</td>\n",
- " <td>10</td>\n",
+ " <td>9</td>\n",
" <td>255.0</td>\n",
- " <td>3</td>\n",
+ " <td>[3]</td>\n",
" <td>all_segments</td>\n",
" <td>all_segments</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'image_data', u'image_data_packed', u'species', u'rgb', u'text', [u'bird', u'cat', u'dog'], 10, 255.0, 3, 'all_segments', 'all_segments')]"
+ "[(u'image_data', u'image_data_packed', [u'species'], [u'rgb'], [u'text'], [u'bird', u'cat', u'dog'], 9, 255.0, [3], 'all_segments', 'all_segments')]"
]
},
- "execution_count": 13,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -1356,7 +1333,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -1373,8 +1350,8 @@
"text/html": [
"<table>\n",
" <tr>\n",
- " <th>independent_var_shape</th>\n",
- " <th>dependent_var_shape</th>\n",
+ " <th>rgb_shape</th>\n",
+ " <th>species_shape</th>\n",
" <th>buffer_id</th>\n",
" </tr>\n",
" <tr>\n",
@@ -1393,7 +1370,7 @@
"[([26, 12], [26, 5], 0), ([26, 12], [26, 5], 1)]"
]
},
- "execution_count": 14,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -1408,15 +1385,15 @@
" 'rgb', -- Independent variable\n",
" NULL, -- Buffer size\n",
" 255, -- Normalizing constant\n",
- " 5 -- Number of desired class values\n",
+ " ARRAY[5] -- Number of desired class values\n",
" );\n",
"\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;"
+ "SELECT rgb_shape, species_shape, buffer_id FROM image_data_packed ORDER BY buffer_id;"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -1436,7 +1413,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>species_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -1446,23 +1423,23 @@
" <tr>\n",
" <td>image_data</td>\n",
" <td>image_data_packed</td>\n",
- " <td>species</td>\n",
- " <td>rgb</td>\n",
- " <td>text</td>\n",
+ " <td>[u'species']</td>\n",
+ " <td>[u'rgb']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'bird', u'cat', u'dog', None, None]</td>\n",
" <td>26</td>\n",
" <td>255.0</td>\n",
- " <td>5</td>\n",
+ " <td>[5]</td>\n",
" <td>all_segments</td>\n",
" <td>all_segments</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'image_data', u'image_data_packed', u'species', u'rgb', u'text', [u'bird', u'cat', u'dog', None, None], 26, 255.0, 5, 'all_segments', 'all_segments')]"
+ "[(u'image_data', u'image_data_packed', [u'species'], [u'rgb'], [u'text'], [u'bird', u'cat', u'dog', None, None], 26, 255.0, [5], 'all_segments', 'all_segments')]"
]
},
- "execution_count": 15,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -1513,7 +1490,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -1538,7 +1515,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>species_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -1548,23 +1525,23 @@
" <tr>\n",
" <td>image_data</td>\n",
" <td>image_data_packed</td>\n",
- " <td>species</td>\n",
- " <td>rgb</td>\n",
- " <td>text</td>\n",
+ " <td>[u'species']</td>\n",
+ " <td>[u'rgb']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'bird', u'cat', u'dog']</td>\n",
" <td>26</td>\n",
" <td>255.0</td>\n",
- " <td>3</td>\n",
+ " <td>[3]</td>\n",
" <td>[2, 3]</td>\n",
" <td>[0, 1]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'image_data', u'image_data_packed', u'species', u'rgb', u'text', [u'bird', u'cat', u'dog'], 26, 255.0, 3, [2, 3], [0, 1])]"
+ "[(u'image_data', u'image_data_packed', [u'species'], [u'rgb'], [u'text'], [u'bird', u'cat', u'dog'], 26, 255.0, [3], [2, 3], [0, 1])]"
]
},
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -1610,7 +1587,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Deep-learning/Train-multiple-models/AutoML-MLP-v1.ipynb b/community-artifacts/Deep-learning/Train-multiple-models/AutoML-MLP-v1.ipynb
new file mode 100755
index 0000000..c679b3c
--- /dev/null
+++ b/community-artifacts/Deep-learning/Train-multiple-models/AutoML-MLP-v1.ipynb
@@ -0,0 +1,6937 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# AutoML for Multilayer Perceptron\n",
+ "\n",
+ "E2E classification example using autoML methods for optimizing hyperparameters and model architectures.\n",
+ "\n",
+ "Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this workbook we use the well known iris data set from https://archive.ics.uci.edu/ml/datasets/iris to help get you started. It is similar to the example in user docs http://madlib.apache.org/docs/latest/index.html\n",
+ "\n",
+ "For more realistic examples please refer to the deep learning notebooks at\n",
+ "https://github.com/apache/madlib-site/tree/asf-site/community-artifacts\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#create_input_data\">1. Create input data</a>\n",
+ "\n",
+ "<a href=\"#pp\">2. Call preprocessor for deep learning</a>\n",
+ "\n",
+ "<a href=\"#load\">3. Define and load model architecture</a>\n",
+ "\n",
+ "<a href=\"#hyperband\">4. Hyperband</a>\n",
+ "\n",
+ "<a href=\"#hyperopt\">5. Hyperopt</a>\n",
+ "\n",
+ "<a href=\"#pred\">6. Predict</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g14a91ce, cmake configuration time: Fri Mar 5 23:08:38 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g14a91ce, cmake configuration time: Fri Mar 5 23:08:38 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"class\"></a>\n",
+ "# Classification\n",
+ "\n",
+ "<a id=\"create_input_data\"></a>\n",
+ "# 1. Create input data\n",
+ "\n",
+ "Load iris data set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "Done.\n",
+ "150 rows affected.\n",
+ "150 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>attributes</th>\n",
+ " <th>class_text</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>[Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>[Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>[Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>[Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>22</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>23</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>24</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>25</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>26</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>30</td>\n",
+ " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>31</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>[Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>36</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>37</td>\n",
+ " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>38</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>39</td>\n",
+ " <td>[Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>40</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>41</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>42</td>\n",
+ " <td>[Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>43</td>\n",
+ " <td>[Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>44</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>47</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>48</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>49</td>\n",
+ " <td>[Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>51</td>\n",
+ " <td>[Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>52</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>53</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>54</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>57</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>58</td>\n",
+ " <td>[Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>59</td>\n",
+ " <td>[Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>60</td>\n",
+ " <td>[Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>61</td>\n",
+ " <td>[Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>62</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>64</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>65</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>67</td>\n",
+ " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>68</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>69</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>70</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>71</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>72</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>74</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>75</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>76</td>\n",
+ " <td>[Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>77</td>\n",
+ " <td>[Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>79</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>80</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>81</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>83</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>84</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>85</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>86</td>\n",
+ " <td>[Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>87</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>88</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>89</td>\n",
+ " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>90</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>91</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>92</td>\n",
+ " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>93</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>[Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>95</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>96</td>\n",
+ " <td>[Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>97</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>98</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>[Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>101</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>[Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>104</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>105</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
+ " <td>[Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>107</td>\n",
+ " <td>[Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>[Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>109</td>\n",
+ " <td>[Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>110</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>111</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>112</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
+ " <td>[Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>115</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>116</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>118</td>\n",
+ " <td>[Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>119</td>\n",
+ " <td>[Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>122</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>[Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>124</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>125</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>127</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>128</td>\n",
+ " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>129</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>130</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>131</td>\n",
+ " <td>[Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>132</td>\n",
+ " <td>[Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>133</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>134</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>135</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>136</td>\n",
+ " <td>[Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>137</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>138</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>139</td>\n",
+ " <td>[Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>140</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>141</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>142</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>143</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>144</td>\n",
+ " <td>[Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>146</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>148</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>[Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>150</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (2, [Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (3, [Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (4, [Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (5, [Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (6, [Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (7, [Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (8, [Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (9, [Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (10, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (11, [Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (12, [Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (13, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (14, [Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (15, [Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (16, [Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (17, [Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (18, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (19, [Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (20, [Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (21, [Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (22, [Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (23, [Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (24, [Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')], u'Iris-setosa'),\n",
+ " (25, [Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (26, [Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (27, [Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (28, [Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (29, [Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (30, [Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (31, [Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (32, [Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (33, [Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (34, [Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (35, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (36, [Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (37, [Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (38, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (39, [Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (40, [Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (41, [Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (42, [Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (43, [Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (44, [Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')], u'Iris-setosa'),\n",
+ " (45, [Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (46, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (47, [Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (48, [Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (49, [Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (50, [Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (51, [Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (52, [Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (53, [Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (54, [Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (55, [Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (56, [Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (57, [Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (58, [Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (59, [Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (60, [Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (61, [Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (62, [Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (63, [Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (64, [Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (65, [Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (66, [Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (67, [Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (68, [Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (69, [Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (70, [Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (71, [Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')], u'Iris-versicolor'),\n",
+ " (72, [Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (73, [Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (74, [Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (75, [Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (76, [Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (77, [Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (78, [Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')], u'Iris-versicolor'),\n",
+ " (79, [Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (80, [Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (81, [Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (82, [Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (83, [Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (84, [Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (85, [Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (86, [Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (87, [Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (88, [Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (89, [Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (90, [Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (91, [Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (92, [Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (93, [Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (94, [Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (95, [Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (96, [Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (97, [Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (98, [Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (99, [Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (100, [Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (101, [Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (102, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (103, [Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (104, [Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (105, [Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (106, [Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (107, [Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')], u'Iris-virginica'),\n",
+ " (108, [Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (109, [Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (110, [Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (111, [Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (112, [Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (113, [Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (114, [Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (115, [Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (116, [Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (117, [Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (118, [Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (119, [Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (120, [Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')], u'Iris-virginica'),\n",
+ " (121, [Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (122, [Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (123, [Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (124, [Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (125, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (126, [Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (127, [Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (128, [Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (129, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (130, [Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')], u'Iris-virginica'),\n",
+ " (131, [Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (132, [Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (133, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (134, [Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')], u'Iris-virginica'),\n",
+ " (135, [Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')], u'Iris-virginica'),\n",
+ " (136, [Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (137, [Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (138, [Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (139, [Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (140, [Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (141, [Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (142, [Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (143, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (144, [Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (145, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (146, [Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (147, [Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (148, [Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (149, [Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (150, [Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')], u'Iris-virginica')]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql \n",
+ "DROP TABLE IF EXISTS iris_data;\n",
+ "\n",
+ "CREATE TABLE iris_data(\n",
+ " id serial,\n",
+ " attributes numeric[],\n",
+ " class_text varchar\n",
+ ");\n",
+ "\n",
+ "INSERT INTO iris_data(id, attributes, class_text) VALUES\n",
+ "(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),\n",
+ "(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),\n",
+ "(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),\n",
+ "(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),\n",
+ "(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),\n",
+ "(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),\n",
+ "(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),\n",
+ "(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),\n",
+ "(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),\n",
+ "(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),\n",
+ "(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),\n",
+ "(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),\n",
+ "(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),\n",
+ "(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),\n",
+ "(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),\n",
+ "(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),\n",
+ "(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),\n",
+ "(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),\n",
+ "(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),\n",
+ "(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),\n",
+ "(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),\n",
+ "(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),\n",
+ "(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),\n",
+ "(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),\n",
+ "(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),\n",
+ "(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),\n",
+ "(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),\n",
+ "(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),\n",
+ "(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),\n",
+ "(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),\n",
+ "(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),\n",
+ "(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),\n",
+ "(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),\n",
+ "(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),\n",
+ "(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),\n",
+ "(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),\n",
+ "(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),\n",
+ "(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),\n",
+ "(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),\n",
+ "(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),\n",
+ "(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),\n",
+ "(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),\n",
+ "(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),\n",
+ "(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),\n",
+ "(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),\n",
+ "(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),\n",
+ "(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),\n",
+ "(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),\n",
+ "(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),\n",
+ "(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),\n",
+ "(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),\n",
+ "(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),\n",
+ "(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),\n",
+ "(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),\n",
+ "(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),\n",
+ "(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),\n",
+ "(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),\n",
+ "(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),\n",
+ "(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),\n",
+ "(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),\n",
+ "(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),\n",
+ "(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),\n",
+ "(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),\n",
+ "(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),\n",
+ "(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),\n",
+ "(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),\n",
+ "(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),\n",
+ "(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),\n",
+ "(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),\n",
+ "(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),\n",
+ "(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),\n",
+ "(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),\n",
+ "(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),\n",
+ "(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),\n",
+ "(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),\n",
+ "(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),\n",
+ "(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),\n",
+ "(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),\n",
+ "(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),\n",
+ "(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),\n",
+ "(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),\n",
+ "(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),\n",
+ "(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),\n",
+ "(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),\n",
+ "(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),\n",
+ "(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),\n",
+ "(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),\n",
+ "(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),\n",
+ "(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),\n",
+ "(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),\n",
+ "(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),\n",
+ "(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),\n",
+ "(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),\n",
+ "(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),\n",
+ "(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),\n",
+ "(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),\n",
+ "(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),\n",
+ "(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),\n",
+ "(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
+ "(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),\n",
+ "(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),\n",
+ "(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),\n",
+ "(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),\n",
+ "(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),\n",
+ "(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),\n",
+ "(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),\n",
+ "(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),\n",
+ "(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),\n",
+ "(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),\n",
+ "(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),\n",
+ "(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),\n",
+ "(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),\n",
+ "(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),\n",
+ "(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),\n",
+ "(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),\n",
+ "(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),\n",
+ "(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),\n",
+ "(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),\n",
+ "(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),\n",
+ "(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),\n",
+ "(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),\n",
+ "(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),\n",
+ "(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),\n",
+ "(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),\n",
+ "(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),\n",
+ "(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),\n",
+ "(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),\n",
+ "(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),\n",
+ "(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),\n",
+ "(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),\n",
+ "(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),\n",
+ "(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),\n",
+ "(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),\n",
+ "(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),\n",
+ "(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),\n",
+ "(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),\n",
+ "(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),\n",
+ "(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),\n",
+ "(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),\n",
+ "(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
+ "(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),\n",
+ "(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),\n",
+ "(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),\n",
+ "(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),\n",
+ "(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),\n",
+ "(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),\n",
+ "(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');\n",
+ "\n",
+ "SELECT * FROM iris_data ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create a test/validation dataset from the training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(120L,)]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_train, iris_test;\n",
+ "\n",
+ "-- Set seed so results are reproducible\n",
+ "SELECT setseed(0);\n",
+ "\n",
+ "SELECT madlib.train_test_split('iris_data', -- Source table\n",
+ " 'iris', -- Output table root name\n",
+ " 0.8, -- Train proportion\n",
+ " NULL, -- Test proportion (0.2)\n",
+ " NULL, -- Strata definition\n",
+ " NULL, -- Output all columns\n",
+ " NULL, -- Sample without replacement\n",
+ " TRUE -- Separate output tables\n",
+ " );\n",
+ "\n",
+ "SELECT COUNT(*) FROM iris_train;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp\"></a>\n",
+ "# 2. Call preprocessor for deep learning\n",
+ "Training dataset (uses training preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>attributes_shape</th>\n",
+ " <th>class_text_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[60, 4]</td>\n",
+ " <td>[60, 3]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[60, 4]</td>\n",
+ " <td>[60, 3]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([60, 4], [60, 3], 0), ([60, 4], [60, 3], 1)]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('iris_train', -- Source table\n",
+ " 'iris_train_packed', -- Output table\n",
+ " 'class_text', -- Dependent variable\n",
+ " 'attributes' -- Independent variable\n",
+ " ); \n",
+ "\n",
+ "SELECT attributes_shape, class_text_shape, buffer_id FROM iris_train_packed ORDER BY buffer_id;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train</td>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>60</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[3]</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train', u'iris_train_packed', [u'class_text'], [u'attributes'], [u'character varying'], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 60, 1.0, [3], 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validation dataset (uses validation preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>attributes_shape</th>\n",
+ " <th>class_text_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[15, 4]</td>\n",
+ " <td>[15, 3]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[15, 4]</td>\n",
+ " <td>[15, 3]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([15, 4], [15, 3], 0), ([15, 4], [15, 3], 1)]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('iris_test', -- Source table\n",
+ " 'iris_test_packed', -- Output table\n",
+ " 'class_text', -- Dependent variable\n",
+ " 'attributes', -- Independent variable\n",
+ " 'iris_train_packed' -- From training preprocessor step\n",
+ " ); \n",
+ "\n",
+ "SELECT attributes_shape, class_text_shape, buffer_id FROM iris_test_packed ORDER BY buffer_id;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_test</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>15</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[3]</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_test', u'iris_test_packed', [u'class_text'], [u'attributes'], [u'character varying'], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 15, 1.0, [3], 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_test_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load\"></a>\n",
+ "# 3. Define and load model architecture\n",
+ "Import Keras libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow import keras\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture with 1 hidden layer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/tensorflow/python/ops/init_ops.py:1251: calling __init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 193\n",
+ "Trainable params: 193\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1 = Sequential()\n",
+ "model1.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model1.add(Dense(10, activation='relu'))\n",
+ "model1.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model1.summary();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model1.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture with 2 hidden layers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_1\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense_3 (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_4 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_5 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_6 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 303\n",
+ "Trainable params: 303\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model2 = Sequential()\n",
+ "model2.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model2.add(Dense(10, activation='relu'))\n",
+ "model2.add(Dense(10, activation='relu'))\n",
+ "model2.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model2.summary();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_1\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model2.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into model architecture table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>model_arch</th>\n",
+ " <th>model_weights</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>__internal_madlib_id__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>None</td>\n",
+ " <td>Sophie</td>\n",
+ " <td>MLP with 1 hidden layer</td>\n",
+ " <td>__madlib_temp_71395301_1614988659_10232289__</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>None</td>\n",
+ " <td>Maria</td>\n",
+ " <td>MLP with 2 hidden layers</td>\n",
+ " <td>__madlib_temp_60560187_1614988660_9612153__</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1340 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'MLP with 1 hidden layer', u'__madlib_temp_71395301_1614988659_10232289__'),\n",
+ " (2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1835 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Maria', u'MLP with 2 hidden layers', u'__madlib_temp_60560187_1614988660_9612153__')]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS model_arch_library;\n",
+ "\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Sophie', -- Name\n",
+ " 'MLP with 1 hidden layer' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Maria', -- Name\n",
+ " 'MLP with 2 hidden layers' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT * FROM model_arch_library ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"hyperband\"></a>\n",
+ "# 4. Hyperband"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Print schedule for run:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "6 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>s</th>\n",
+ " <th>i</th>\n",
+ " <th>n_i</th>\n",
+ " <th>r_i</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " <td>9</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
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+ " <td>1</td>\n",
+ " <td>0</td>\n",
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+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2, 0, 9, 1),\n",
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+ " (2, 2, 1, 9),\n",
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+ " (1, 1, 1, 9),\n",
+ " (0, 0, 3, 9)]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS hb_schedule;\n",
+ "SELECT madlib.hyperband_schedule ('hb_schedule', \n",
+ " 9,\n",
+ " 3,\n",
+ " 0);\n",
+ "SELECT * FROM hb_schedule ORDER BY s DESC, i;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_automl</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table, automl_mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_automl('iris_train_packed', -- source table\n",
+ " 'automl_output', -- model output table\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'automl_mst_table', -- model selection output table\n",
+ " ARRAY[1,2], -- model IDs\n",
+ " $${\n",
+ " 'loss': ['categorical_crossentropy'], \n",
+ " 'optimizer_params_list': [ \n",
+ " {'optimizer': ['Adam'],'lr': [0.001, 0.1, 'log']},\n",
+ " {'optimizer': ['RMSprop'],'lr': [0.001, 0.1, 'log']}\n",
+ " ],\n",
+ " 'metrics': ['accuracy']\n",
+ " } $$, -- compile param grid\n",
+ " $${'batch_size': [4, 8], 'epochs': [1]}$$, -- fit params grid\n",
+ " 'hyperband', -- autoML method\n",
+ " 'R=9, eta=3, skip_last=0', -- autoML params\n",
+ " NULL, -- random state\n",
+ " NULL, -- object table\n",
+ " FALSE, -- use GPUs\n",
+ " 'iris_test_packed', -- validation table\n",
+ " 1, -- metrics compute freq\n",
+ " NULL, -- name\n",
+ " NULL); -- descr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_selection_table</th>\n",
+ " <th>automl_method</th>\n",
+ " <th>automl_params</th>\n",
+ " <th>random_state</th>\n",
+ " <th>object_table</th>\n",
+ " <th>use_gpus</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>automl_output</td>\n",
+ " <td>automl_output_info</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>automl_mst_table</td>\n",
+ " <td>hyperband</td>\n",
+ " <td>R=9, eta=3, skip_last=0</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>2021-03-05 23:57:44</td>\n",
+ " <td>2021-03-05 23:59:24</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[1]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_test_packed', u'automl_output', u'automl_output_info', [u'class_text'], [u'attributes'], u'model_arch_library', u'automl_mst_table', u'hyperband', u'R=9, eta=3, skip_last=0', None, None, False, 1, None, None, datetime.datetime(2021, 3, 5, 23, 57, 44), datetime.datetime(2021, 3, 5, 23, 59, 24), u'1.18.0-dev', [1], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], [u'character varying'], 1.0)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM automl_output_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View results for each model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "15 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " <th>metrics_iters</th>\n",
+ " <th>s</th>\n",
+ " <th>i</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.04232194170481019)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[21.911346912384, 29.2674539089203, 36.8268938064575, 44.9022789001465, 51.1760609149933, 57.6593999862671, 64.184476852417, 70.5566418170929, 77.0253269672394, 83.4826798439026, 90.1138219833374, 96.4566838741302]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.975000023842</td>\n",
+ " <td>0.0775080993772</td>\n",
+ " <td>[0.791666686534882, 0.608333349227905, 0.966666638851166, 0.975000023841858, 0.966666638851166, 0.800000011920929, 0.975000023841858, 0.683333337306976, 0.733333349227905, 0.949999988079071, 0.949999988079071, 0.975000023841858]</td>\n",
+ " <td>[0.374035209417343, 0.732228577136993, 0.170820266008377, 0.112313792109489, 0.172022193670273, 0.384404003620148, 0.115418829023838, 0.450868725776672, 0.457187473773956, 0.140348106622696, 0.15950845181942, 0.0775080993771553]</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.0383280552924</td>\n",
+ " <td>[0.899999976158142, 0.566666662693024, 0.966666638851166, 1.0, 1.0, 0.800000011920929, 0.966666638851166, 0.833333313465118, 0.899999976158142, 1.0, 0.899999976158142, 1.0]</td>\n",
+ " <td>[0.273769021034241, 0.709117114543915, 0.154145583510399, 0.093109056353569, 0.130981177091599, 0.318724304437637, 0.102762393653393, 0.268609821796417, 0.253254026174545, 0.103913448750973, 0.194639429450035, 0.0383280552923679]</td>\n",
+ " <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.009905852828976726)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[44.6836378574371, 50.92365193367, 57.3649659156799, 63.7576060295105, 70.1174209117889, 76.788703918457, 83.2217078208923, 89.8764188289642, 96.2273638248444]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.975000023842</td>\n",
+ " <td>0.0799522325397</td>\n",
+ " <td>[0.949999988079071, 0.791666686534882, 0.800000011920929, 0.850000023841858, 0.958333313465118, 0.975000023841858, 0.899999976158142, 0.975000023841858, 0.975000023841858]</td>\n",
+ " <td>[0.447714686393738, 0.36309215426445, 0.324623554944992, 0.301780551671982, 0.142947062849998, 0.120139442384243, 0.255296260118484, 0.0816238224506378, 0.0799522325396538]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.0760450512171</td>\n",
+ " <td>[0.966666638851166, 0.899999976158142, 0.800000011920929, 0.899999976158142, 0.966666638851166, 0.966666638851166, 0.899999976158142, 1.0, 0.966666638851166]</td>\n",
+ " <td>[0.370463252067566, 0.25237438082695, 0.317549884319305, 0.187985330820084, 0.104904659092426, 0.112288065254688, 0.160248279571533, 0.0687147378921509, 0.0760450512170792]</td>\n",
+ " <td>[5, 6, 7, 8, 9, 10, 11, 12, 13]</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01678679876224294)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[11.5813798904419, 21.0226759910583, 28.4713099002838, 35.9315679073334, 44.4656569957733, 50.7044010162354, 57.1448848247528, 63.4595718383789, 69.8967549800873, 76.3639938831329, 82.7779839038849, 89.6248579025269, 95.9936518669128]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.958333313465</td>\n",
+ " <td>0.113370150328</td>\n",
+ " <td>[0.641666650772095, 0.908333361148834, 0.891666650772095, 0.891666650772095, 0.866666674613953, 0.941666662693024, 0.941666662693024, 0.933333337306976, 0.933333337306976, 0.858333349227905, 0.966666638851166, 0.958333313465118, 0.958333313465118]</td>\n",
+ " <td>[0.656313836574554, 0.41341444849968, 0.324400961399078, 0.304112106561661, 0.336616456508636, 0.160554125905037, 0.135852053761482, 0.159805878996849, 0.174078181385994, 0.316538035869598, 0.104411341249943, 0.105065681040287, 0.113370150327682]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.121701933444</td>\n",
+ " <td>[0.766666650772095, 0.933333337306976, 0.866666674613953, 0.866666674613953, 0.899999976158142, 0.966666638851166, 0.899999976158142, 0.966666638851166, 0.966666638851166, 0.899999976158142, 0.966666638851166, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.563848853111267, 0.330921590328217, 0.292684972286224, 0.273869335651398, 0.317834258079529, 0.144475534558296, 0.147552534937859, 0.153202146291733, 0.158350095152855, 0.22741986811161, 0.114596471190453, 0.117612592875957, 0.121701933443546]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]</td>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='RMSprop(lr=0.01930169481426345)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[44.2033138275146, 50.4514999389648, 56.8880548477173, 63.2074518203735, 69.5435798168182, 76.1080069541931, 82.519660949707, 89.1418299674988, 95.518424987793]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.941666662693</td>\n",
+ " <td>0.206491559744</td>\n",
+ " <td>[0.566666662693024, 0.916666686534882, 0.883333325386047, 0.958333313465118, 0.841666638851166, 0.875, 0.783333361148834, 0.766666650772095, 0.941666662693024]</td>\n",
+ " <td>[0.774700284004211, 0.651901543140411, 0.496851295232773, 0.405008375644684, 0.356276631355286, 0.340960919857025, 0.381286114454269, 0.388935476541519, 0.206491559743881]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.170937761664</td>\n",
+ " <td>[0.733333349227905, 0.933333337306976, 0.866666674613953, 0.933333337306976, 0.866666674613953, 0.866666674613953, 0.833333313465118, 0.833333313465118, 0.966666638851166]</td>\n",
+ " <td>[0.76544976234436, 0.657529413700104, 0.4853755235672, 0.377188384532928, 0.356318116188049, 0.332274377346039, 0.372768431901932, 0.397462010383606, 0.170937761664391]</td>\n",
+ " <td>[5, 6, 7, 8, 9, 10, 11, 12, 13]</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.011578246765795313)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[12.2524900436401, 22.163911819458, 29.4894979000092, 37.043762922287]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.791666686535</td>\n",
+ " <td>0.595036566257</td>\n",
+ " <td>[0.0333333350718021, 0.633333325386047, 0.816666662693024, 0.791666686534882]</td>\n",
+ " <td>[0.947992205619812, 0.782966256141663, 0.679944217205048, 0.595036566257477]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.56917822361</td>\n",
+ " <td>[0.100000001490116, 0.766666650772095, 0.933333337306976, 0.899999976158142]</td>\n",
+ " <td>[0.972650408744812, 0.776390075683594, 0.681758761405945, 0.569178223609924]</td>\n",
+ " <td>[1, 2, 3, 4]</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.0699102360375282)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[22.5178759098053, 29.7163498401642, 37.2609059810638]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.699999988079</td>\n",
+ " <td>0.389858365059</td>\n",
+ " <td>[0.641666650772095, 0.641666650772095, 0.699999988079071]</td>\n",
+ " <td>[0.79219126701355, 0.460052192211151, 0.389858365058899]</td>\n",
+ " <td>0.866666674614</td>\n",
+ " <td>0.250768661499</td>\n",
+ " <td>[0.766666650772095, 0.766666650772095, 0.866666674613953]</td>\n",
+ " <td>[0.765598654747009, 0.334016799926758, 0.250768661499023]</td>\n",
+ " <td>[2, 3, 4]</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.024714880320122704)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[12.0343589782715, 21.4979238510132, 29.0434989929199, 36.4899458885193]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.649999976158</td>\n",
+ " <td>0.673553228378</td>\n",
+ " <td>[0.691666662693024, 0.841666638851166, 0.983333349227905, 0.649999976158142]</td>\n",
+ " <td>[0.388701051473618, 0.424284487962723, 0.180928915739059, 0.673553228378296]</td>\n",
+ " <td>0.800000011921</td>\n",
+ " <td>0.384207844734</td>\n",
+ " <td>[0.833333313465118, 0.800000011920929, 0.966666638851166, 0.800000011920929]</td>\n",
+ " <td>[0.255310624837875, 0.357394397258759, 0.147510275244713, 0.384207844734192]</td>\n",
+ " <td>[1, 2, 3, 4]</td>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.05573574908119242)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[45.168872833252, 51.7013738155365, 58.1221590042114, 64.4642739295959, 70.8308379650116, 77.295382976532, 83.7621510028839, 90.3811860084534, 96.7183079719543]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.816666662693</td>\n",
+ " <td>0.457783430815</td>\n",
+ " <td>[0.358333319425583, 0.925000011920929, 0.675000011920929, 0.733333349227905, 0.949999988079071, 0.666666686534882, 0.741666674613953, 0.908333361148834, 0.816666662693024]</td>\n",
+ " <td>[1.03486049175262, 0.43449866771698, 0.842896223068237, 0.392013370990753, 0.195524752140045, 0.572380185127258, 0.43743160367012, 0.278554767370224, 0.457783430814743]</td>\n",
+ " <td>0.733333349228</td>\n",
+ " <td>0.670406579971</td>\n",
+ " <td>[0.233333334326744, 0.966666638851166, 0.866666674613953, 0.866666674613953, 0.966666638851166, 0.666666686534882, 0.899999976158142, 0.933333337306976, 0.733333349227905]</td>\n",
+ " <td>[1.05548679828644, 0.372740298509598, 0.427788466215134, 0.282503575086594, 0.135918349027634, 0.589654743671417, 0.253296822309494, 0.159830048680305, 0.670406579971313]</td>\n",
+ " <td>[5, 6, 7, 8, 9, 10, 11, 12, 13]</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.09641245863612281)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[12.7177708148956]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.658333361149</td>\n",
+ " <td>1.25986480713</td>\n",
+ " <td>[0.658333361148834]</td>\n",
+ " <td>[1.25986480712891]</td>\n",
+ " <td>0.633333325386</td>\n",
+ " <td>1.26717245579</td>\n",
+ " <td>[0.633333325386047]</td>\n",
+ " <td>[1.26717245578766]</td>\n",
+ " <td>[1]</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.003730347382813742)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[11.1050899028778]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.600000023842</td>\n",
+ " <td>1.23435640335</td>\n",
+ " <td>[0.600000023841858]</td>\n",
+ " <td>[1.23435640335083]</td>\n",
+ " <td>0.5</td>\n",
+ " <td>1.37250542641</td>\n",
+ " <td>[0.5]</td>\n",
+ " <td>[1.37250542640686]</td>\n",
+ " <td>[1]</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.0018352035707327032)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[11.3283720016479]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.466666668653</td>\n",
+ " <td>1.01645076275</td>\n",
+ " <td>[0.466666668653488]</td>\n",
+ " <td>[1.01645076274872]</td>\n",
+ " <td>0.433333337307</td>\n",
+ " <td>1.01912522316</td>\n",
+ " <td>[0.433333337306976]</td>\n",
+ " <td>[1.01912522315979]</td>\n",
+ " <td>[1]</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.03837714620063437)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[12.5016968250275]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.308333337307</td>\n",
+ " <td>1.0995465517</td>\n",
+ " <td>[0.308333337306976]</td>\n",
+ " <td>[1.09954655170441]</td>\n",
+ " <td>0.433333337307</td>\n",
+ " <td>1.0980553627</td>\n",
+ " <td>[0.433333337306976]</td>\n",
+ " <td>[1.09805536270142]</td>\n",
+ " <td>[1]</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.0017052377620857802)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[10.8097839355469]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.341666668653</td>\n",
+ " <td>1.28075575829</td>\n",
+ " <td>[0.341666668653488]</td>\n",
+ " <td>[1.28075575828552]</td>\n",
+ " <td>0.366666674614</td>\n",
+ " <td>1.43494951725</td>\n",
+ " <td>[0.366666674613953]</td>\n",
+ " <td>[1.43494951725006]</td>\n",
+ " <td>[1]</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.0015217424326594508)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[21.2741727828979, 28.7427089214325, 36.1846778392792]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.333333343267</td>\n",
+ " <td>1.07403242588</td>\n",
+ " <td>[0.474999994039536, 0.358333319425583, 0.333333343267441]</td>\n",
+ " <td>[1.08657968044281, 1.07721281051636, 1.07403242588043]</td>\n",
+ " <td>0.333333343267</td>\n",
+ " <td>1.09314000607</td>\n",
+ " <td>[0.433333337306976, 0.300000011920929, 0.333333343267441]</td>\n",
+ " <td>[1.11294913291931, 1.10347521305084, 1.09314000606537]</td>\n",
+ " <td>[2, 3, 4]</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.051964270528848694)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[11.8142108917236]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.333333343267</td>\n",
+ " <td>1.09948420525</td>\n",
+ " <td>[0.333333343267441]</td>\n",
+ " <td>[1.09948420524597]</td>\n",
+ " <td>0.333333343267</td>\n",
+ " <td>1.09620642662</td>\n",
+ " <td>[0.333333343267441]</td>\n",
+ " <td>[1.09620642662048]</td>\n",
+ " <td>[1]</td>\n",
+ " <td>2</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
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+ " (5, 2, u\"optimizer='Adam(lr=0.01678679876224294)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [11.5813798904419, 21.0226759910583, 28.4713099002838, 35.9315679073334, 44.4656569957733, 50.7044010162354, 57.1448848247528, 63.4595718383789, 69.8967549800873, 76.3639938831329, 82.7779839038849, 89.6248579025269, 95.9936518669128], [u'accuracy'], u'categorical_crossentropy', 0.958333313465118, 0.113370150327682, [0.641666650772095, 0.908333361148834, 0.891666650772095, 0.891666650772095, 0.866666674613953, 0.941666662693024, 0.941666662693024, 0.933333337306976, 0.933333337306976, 0.858333349227905, 0.966666638851166, 0.958333313465118, 0.958333313465118], [0.656313836574554, 0.41341444849968, 0.324400961399078, 0.304112106561661, 0.336616456508636, 0.160554125905037, 0.135852053761482, 0.159805878996849, 0.174078181385994, 0.316538035869598, 0.104411341249943, 0.105065681040287, 0.113370150327682], 0.966666638851166, 0.121701933443546, [0.766666650772095, 0.933333337306976, 0.866666674613953, 0.866666674613953, 0.899999976158142, 0.966666638851166, 0.899999976158142, 0.966666638851166, 0.966666638851166, 0.899999976158142, 0.966666638851166, 0.966666638851166, 0.966666638851166], [0.563848853111267, 0.330921590328217, 0.292684972286224, 0.273869335651398, 0.317834258079529, 0.144475534558296, 0.147552534937859, 0.153202146291733, 0.158350095152855, 0.22741986811161, 0.114596471190453, 0.117612592875957, 0.121701933443546], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], 2, 2),\n",
+ " (14, 2, u\"optimizer='RMSprop(lr=0.01930169481426345)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [44.2033138275146, 50.4514999389648, 56.8880548477173, 63.2074518203735, 69.5435798168182, 76.1080069541931, 82.519660949707, 89.1418299674988, 95.518424987793], [u'accuracy'], u'categorical_crossentropy', 0.941666662693024, 0.206491559743881, [0.566666662693024, 0.916666686534882, 0.883333325386047, 0.958333313465118, 0.841666638851166, 0.875, 0.783333361148834, 0.766666650772095, 0.941666662693024], [0.774700284004211, 0.651901543140411, 0.496851295232773, 0.405008375644684, 0.356276631355286, 0.340960919857025, 0.381286114454269, 0.388935476541519, 0.206491559743881], 0.966666638851166, 0.170937761664391, [0.733333349227905, 0.933333337306976, 0.866666674613953, 0.933333337306976, 0.866666674613953, 0.866666674613953, 0.833333313465118, 0.833333313465118, 0.966666638851166], [0.76544976234436, 0.657529413700104, 0.4853755235672, 0.377188384532928, 0.356318116188049, 0.332274377346039, 0.372768431901932, 0.397462010383606, 0.170937761664391], [5, 6, 7, 8, 9, 10, 11, 12, 13], 0, 0),\n",
+ " (6, 1, u\"optimizer='Adam(lr=0.011578246765795313)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [12.2524900436401, 22.163911819458, 29.4894979000092, 37.043762922287], [u'accuracy'], u'categorical_crossentropy', 0.791666686534882, 0.595036566257477, [0.0333333350718021, 0.633333325386047, 0.816666662693024, 0.791666686534882], [0.947992205619812, 0.782966256141663, 0.679944217205048, 0.595036566257477], 0.899999976158142, 0.569178223609924, [0.100000001490116, 0.766666650772095, 0.933333337306976, 0.899999976158142], [0.972650408744812, 0.776390075683594, 0.681758761405945, 0.569178223609924], [1, 2, 3, 4], 2, 1),\n",
+ " (12, 1, u\"optimizer='Adam(lr=0.0699102360375282)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [22.5178759098053, 29.7163498401642, 37.2609059810638], [u'accuracy'], u'categorical_crossentropy', 0.699999988079071, 0.389858365058899, [0.641666650772095, 0.641666650772095, 0.699999988079071], [0.79219126701355, 0.460052192211151, 0.389858365058899], 0.866666674613953, 0.250768661499023, [0.766666650772095, 0.766666650772095, 0.866666674613953], [0.765598654747009, 0.334016799926758, 0.250768661499023], [2, 3, 4], 1, 0),\n",
+ " (1, 1, u\"optimizer='RMSprop(lr=0.024714880320122704)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [12.0343589782715, 21.4979238510132, 29.0434989929199, 36.4899458885193], [u'accuracy'], u'categorical_crossentropy', 0.649999976158142, 0.673553228378296, [0.691666662693024, 0.841666638851166, 0.983333349227905, 0.649999976158142], [0.388701051473618, 0.424284487962723, 0.180928915739059, 0.673553228378296], 0.800000011920929, 0.384207844734192, [0.833333313465118, 0.800000011920929, 0.966666638851166, 0.800000011920929], [0.255310624837875, 0.357394397258759, 0.147510275244713, 0.384207844734192], [1, 2, 3, 4], 2, 1),\n",
+ " (15, 2, u\"optimizer='Adam(lr=0.05573574908119242)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [45.168872833252, 51.7013738155365, 58.1221590042114, 64.4642739295959, 70.8308379650116, 77.295382976532, 83.7621510028839, 90.3811860084534, 96.7183079719543], [u'accuracy'], u'categorical_crossentropy', 0.816666662693024, 0.457783430814743, [0.358333319425583, 0.925000011920929, 0.675000011920929, 0.733333349227905, 0.949999988079071, 0.666666686534882, 0.741666674613953, 0.908333361148834, 0.816666662693024], [1.03486049175262, 0.43449866771698, 0.842896223068237, 0.392013370990753, 0.195524752140045, 0.572380185127258, 0.43743160367012, 0.278554767370224, 0.457783430814743], 0.733333349227905, 0.670406579971313, [0.233333334326744, 0.966666638851166, 0.866666674613953, 0.866666674613953, 0.966666638851166, 0.666666686534882, 0.899999976158142, 0.933333337306976, 0.733333349227905], [1.05548679828644, 0.372740298509598, 0.427788466215134, 0.282503575086594, 0.135918349027634, 0.589654743671417, 0.253296822309494, 0.159830048680305, 0.670406579971313], [5, 6, 7, 8, 9, 10, 11, 12, 13], 0, 0),\n",
+ " (8, 1, u\"optimizer='RMSprop(lr=0.09641245863612281)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [12.7177708148956], [u'accuracy'], u'categorical_crossentropy', 0.658333361148834, 1.25986480712891, [0.658333361148834], [1.25986480712891], 0.633333325386047, 1.26717245578766, [0.633333325386047], [1.26717245578766], [1], 2, 0),\n",
+ " (2, 1, u\"optimizer='RMSprop(lr=0.003730347382813742)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [11.1050899028778], [u'accuracy'], u'categorical_crossentropy', 0.600000023841858, 1.23435640335083, [0.600000023841858], [1.23435640335083], 0.5, 1.37250542640686, [0.5], [1.37250542640686], [1], 2, 0),\n",
+ " (7, 1, u\"optimizer='Adam(lr=0.0018352035707327032)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [11.3283720016479], [u'accuracy'], u'categorical_crossentropy', 0.466666668653488, 1.01645076274872, [0.466666668653488], [1.01645076274872], 0.433333337306976, 1.01912522315979, [0.433333337306976], [1.01912522315979], [1], 2, 0),\n",
+ " (4, 2, u\"optimizer='Adam(lr=0.03837714620063437)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [12.5016968250275], [u'accuracy'], u'categorical_crossentropy', 0.308333337306976, 1.09954655170441, [0.308333337306976], [1.09954655170441], 0.433333337306976, 1.09805536270142, [0.433333337306976], [1.09805536270142], [1], 2, 0),\n",
+ " (9, 2, u\"optimizer='Adam(lr=0.0017052377620857802)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [10.8097839355469], [u'accuracy'], u'categorical_crossentropy', 0.341666668653488, 1.28075575828552, [0.341666668653488], [1.28075575828552], 0.366666674613953, 1.43494951725006, [0.366666674613953], [1.43494951725006], [1], 2, 0),\n",
+ " (11, 2, u\"optimizer='Adam(lr=0.0015217424326594508)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [21.2741727828979, 28.7427089214325, 36.1846778392792], [u'accuracy'], u'categorical_crossentropy', 0.333333343267441, 1.07403242588043, [0.474999994039536, 0.358333319425583, 0.333333343267441], [1.08657968044281, 1.07721281051636, 1.07403242588043], 0.333333343267441, 1.09314000606537, [0.433333337306976, 0.300000011920929, 0.333333343267441], [1.11294913291931, 1.10347521305084, 1.09314000606537], [2, 3, 4], 1, 0),\n",
+ " (3, 1, u\"optimizer='RMSprop(lr=0.051964270528848694)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [11.8142108917236], [u'accuracy'], u'categorical_crossentropy', 0.333333343267441, 1.09948420524597, [0.333333343267441], [1.09948420524597], 0.333333343267441, 1.09620642662048, [0.333333343267441], [1.09620642662048], [1], 2, 0)]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM automl_output_info ORDER BY validation_metrics_final DESC, validation_loss_final;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import MaxNLocator\n",
+ "from collections import defaultdict\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "sns.set_palette(sns.color_palette(\"hls\", 20))\n",
+ "plt.rcParams.update({'font.size': 12})\n",
+ "pd.set_option('display.max_colwidth', -1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "15 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "<img 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width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM automl_output_info;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM automl_output_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "ax_metric.set_ylabel('Metric')\n",
+ "ax_metric.set_title('Validation metric curve')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Validation loss curve')\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss,metrics_iters FROM automl_output_info WHERE mst_key = $mst_key;\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " iters = df_output_info['metrics_iters'][0]\n",
+ " \n",
+ " #ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
+ " ax_metric.plot(iters, validation_metrics, marker='o')\n",
+ " #ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, validation_loss, marker='o')\n",
+ "\n",
+ "plt.legend();\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"hyperopt\"></a>\n",
+ "# 5. Hyperopt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_automl</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table, automl_mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_automl('iris_train_packed', -- source table\n",
+ " 'automl_output', -- model output table\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'automl_mst_table', -- model selection output table\n",
+ " ARRAY[1,2], -- model IDs\n",
+ " $${\n",
+ " 'loss': ['categorical_crossentropy'], \n",
+ " 'optimizer_params_list': [ \n",
+ " {'optimizer': ['Adam'],'lr': [0.001, 0.1, 'log']},\n",
+ " {'optimizer': ['RMSprop'],'lr': [0.001, 0.1, 'log']}\n",
+ " ],\n",
+ " 'metrics': ['accuracy']\n",
+ " } $$, -- compile param grid\n",
+ " $${'batch_size': [4, 8], 'epochs': [1]}$$, -- fit params grid\n",
+ " 'hyperopt', -- autoML method\n",
+ " 'num_configs=20, num_iterations=10, algorithm=tpe', -- autoML params\n",
+ " NULL, -- random state\n",
+ " NULL, -- object table\n",
+ " FALSE, -- use GPUs\n",
+ " 'iris_test_packed', -- validation table\n",
+ " 1, -- metrics compute freq\n",
+ " NULL, -- name\n",
+ " NULL); -- descr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_selection_table</th>\n",
+ " <th>automl_method</th>\n",
+ " <th>automl_params</th>\n",
+ " <th>random_state</th>\n",
+ " <th>object_table</th>\n",
+ " <th>use_gpus</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>automl_output</td>\n",
+ " <td>automl_output_info</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>automl_mst_table</td>\n",
+ " <td>hyperopt</td>\n",
+ " <td>num_configs=20, num_iterations=10, algorithm=tpe</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>False</td>\n",
+ " <td>1</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>2021-03-05 23:59:31</td>\n",
+ " <td>2021-03-06 00:03:57</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[1]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_test_packed', u'automl_output', u'automl_output_info', [u'class_text'], [u'attributes'], u'model_arch_library', u'automl_mst_table', u'hyperopt', u'num_configs=20, num_iterations=10, algorithm=tpe', None, None, False, 1, None, None, datetime.datetime(2021, 3, 5, 23, 59, 31), datetime.datetime(2021, 3, 6, 0, 3, 57), u'1.18.0-dev', [1], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], [u'character varying'], 1.0)]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM automl_output_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the results for each model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='RMSprop(lr=0.0084793872639979)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[56.9403030872345, 59.805566072464, 62.2339789867401, 64.8922078609467, 67.5616340637207, 70.2253429889679, 72.8736228942871, 75.5874469280243, 78.2902030944824, 80.9871909618378]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.975000023842</td>\n",
+ " <td>0.0910520926118</td>\n",
+ " <td>[0.649999976158142, 0.891666650772095, 0.883333325386047, 0.949999988079071, 0.975000023841858, 0.883333325386047, 0.850000023841858, 0.949999988079071, 0.975000023841858, 0.975000023841858]</td>\n",
+ " <td>[0.559232711791992, 0.335382640361786, 0.259929001331329, 0.158979862928391, 0.114544428884983, 0.269487291574478, 0.293675005435944, 0.0902178362011909, 0.0766977593302727, 0.0910520926117897]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.0768957436085</td>\n",
+ " <td>[0.833333313465118, 0.933333337306976, 0.899999976158142, 0.966666638851166, 0.966666638851166, 0.899999976158142, 0.899999976158142, 0.933333337306976, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.485892802476883, 0.249617904424667, 0.258282363414764, 0.11016520857811, 0.0912857726216316, 0.280073672533035, 0.178015038371086, 0.087411992251873, 0.062506839632988, 0.0768957436084747]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.03366551083145706)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[161.690346240997, 164.041937112808, 166.454420089722, 168.906048059464, 171.067217111588, 173.555004119873, 175.944698095322, 178.445127248764, 180.502294063568, 183.037788152695]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.949999988079</td>\n",
+ " <td>0.144752591848</td>\n",
+ " <td>[0.316666662693024, 0.666666686534882, 0.716666638851166, 0.641666650772095, 0.675000011920929, 0.975000023841858, 0.791666686534882, 0.975000023841858, 0.966666638851166, 0.949999988079071]</td>\n",
+ " <td>[1.57745933532715, 0.405172228813171, 0.471270889043808, 1.00022745132446, 0.840015530586243, 0.128021001815796, 0.473532497882843, 0.091586634516716, 0.112696528434753, 0.144752591848373]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.100186347961</td>\n",
+ " <td>[0.433333337306976, 0.666666686534882, 0.899999976158142, 0.766666650772095, 0.866666674613953, 0.966666638851166, 0.899999976158142, 0.933333337306976, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[1.36917245388031, 0.372486144304276, 0.266377687454224, 0.571163833141327, 0.457086622714996, 0.103041857481003, 0.276452839374542, 0.0908508822321892, 0.0997116342186928, 0.100186347961426]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.009794369846837002)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[186.611872196198, 188.95641207695, 191.037184238434, 193.397297143936, 195.740861177444, 197.805513143539, 200.180992126465, 202.689172029495, 205.040098190308, 207.208242177963]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.949999988079</td>\n",
+ " <td>0.116746708751</td>\n",
+ " <td>[0.75, 0.975000023841858, 0.850000023841858, 0.941666662693024, 0.933333337306976, 0.949999988079071, 0.949999988079071, 0.983333349227905, 0.958333313465118, 0.949999988079071]</td>\n",
+ " <td>[0.5159512758255, 0.353324204683304, 0.333910763263702, 0.245715036988258, 0.188893154263496, 0.161517903208733, 0.137443989515305, 0.122971840202808, 0.14612153172493, 0.116746708750725]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.106192082167</td>\n",
+ " <td>[0.899999976158142, 0.966666638851166, 0.899999976158142, 0.966666638851166, 0.933333337306976, 0.933333337306976, 0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.423073083162308, 0.298538327217102, 0.234973803162575, 0.176778241991997, 0.170526877045631, 0.145023569464684, 0.119270212948322, 0.103897586464882, 0.104170136153698, 0.106192082166672]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.007581048101981366)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[57.225380897522, 60.0725290775299, 62.731920003891, 65.1544499397278, 67.8143260478973, 70.4762139320374, 73.1227269172668, 75.8475530147552, 78.555095911026, 81.2564718723297]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.958333313465</td>\n",
+ " <td>0.133664950728</td>\n",
+ " <td>[0.649999976158142, 0.883333325386047, 0.941666662693024, 0.899999976158142, 0.958333313465118, 0.925000011920929, 0.941666662693024, 0.933333337306976, 0.983333349227905, 0.958333313465118]</td>\n",
+ " <td>[1.03808128833771, 0.883756637573242, 0.686505734920502, 0.517532765865326, 0.401096671819687, 0.259793311357498, 0.177235946059227, 0.168946355581284, 0.128713861107826, 0.133664950728416]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.122641228139</td>\n",
+ " <td>[0.633333325386047, 0.899999976158142, 0.933333337306976, 0.933333337306976, 0.966666638851166, 0.933333337306976, 0.933333337306976, 0.933333337306976, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[1.02865540981293, 0.850838720798492, 0.612184524536133, 0.452387690544128, 0.33221709728241, 0.23906472325325, 0.165990635752678, 0.164969280362129, 0.12097629904747, 0.1226412281394]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.012596538573477555)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[137.034833192825, 139.500956058502, 141.807043075562, 143.897747039795, 146.264532089233, 148.888093233109, 150.934833049774, 153.475459098816, 155.848874092102, 158.239592075348]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.916666686535</td>\n",
+ " <td>0.184266731143</td>\n",
+ " <td>[0.566666662693024, 0.933333337306976, 0.649999976158142, 0.666666686534882, 0.808333337306976, 0.891666650772095, 0.891666650772095, 0.975000023841858, 0.933333337306976, 0.916666686534882]</td>\n",
+ " <td>[0.829304933547974, 0.631127297878265, 0.597909092903137, 0.552545011043549, 0.428654760122299, 0.233174994587898, 0.236562281847, 0.119615346193314, 0.191903278231621, 0.184266731142998]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.124655880034</td>\n",
+ " <td>[0.699999988079071, 0.899999976158142, 0.800000011920929, 0.833333313465118, 0.899999976158142, 0.933333337306976, 0.866666674613953, 1.0, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.74066150188446, 0.532437741756439, 0.480882078409195, 0.435436576604843, 0.310187846422195, 0.234515085816383, 0.247250944375992, 0.107902131974697, 0.136675015091896, 0.12465588003397]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='RMSprop(lr=0.005441362966347114)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[109.630754947662, 112.341638088226, 114.763943910599, 117.389002084732, 119.991095066071, 122.593973875046, 125.238317966461, 127.8488509655, 130.375853061676, 133.020073890686]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.916666686535</td>\n",
+ " <td>0.216380029917</td>\n",
+ " <td>[0.641666650772095, 0.733333349227905, 0.649999976158142, 0.858333349227905, 0.958333313465118, 0.699999988079071, 0.916666686534882, 0.858333349227905, 0.966666638851166, 0.916666686534882]</td>\n",
+ " <td>[0.73615950345993, 0.489604264497757, 0.45263683795929, 0.37542662024498, 0.334106951951981, 0.419453173875809, 0.284875482320786, 0.27151495218277, 0.185230866074562, 0.216380029916763]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.127150848508</td>\n",
+ " <td>[0.766666650772095, 0.699999988079071, 0.800000011920929, 0.933333337306976, 0.966666638851166, 0.666666686534882, 0.899999976158142, 0.933333337306976, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.631976008415222, 0.448555260896683, 0.323729306459427, 0.28750941157341, 0.281407296657562, 0.421543717384338, 0.259464651346207, 0.158164814114571, 0.135125860571861, 0.127150848507881]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.04966544234738768)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[2.26167011260986, 4.64152717590332, 7.38891315460205, 10.1575191020966, 12.6948411464691, 15.5193021297455, 17.8266370296478, 20.364767074585, 23.1241211891174, 25.6702241897583]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.949999988079</td>\n",
+ " <td>0.183234125376</td>\n",
+ " <td>[0.641666650772095, 0.666666686534882, 0.966666638851166, 0.958333313465118, 0.716666638851166, 0.983333349227905, 0.966666638851166, 0.983333349227905, 0.975000023841858, 0.949999988079071]</td>\n",
+ " <td>[0.829066038131714, 0.490932732820511, 0.341925740242004, 0.215810611844063, 0.400910943746567, 0.107548490166664, 0.0985226780176163, 0.0732712596654892, 0.070111908018589, 0.183234125375748]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.137178555131</td>\n",
+ " <td>[0.866666674613953, 0.666666686534882, 0.966666638851166, 0.966666638851166, 0.699999988079071, 0.966666638851166, 0.966666638851166, 0.933333337306976, 1.0, 0.966666638851166]</td>\n",
+ " <td>[0.843752980232239, 0.435137718915939, 0.26888644695282, 0.157842606306076, 0.406648069620132, 0.0976309478282928, 0.0788726136088371, 0.0751720294356346, 0.0686705932021141, 0.137178555130959]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.0023223781742022285)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[29.5305609703064, 31.8714768886566, 34.5841488838196, 37.194139957428, 40.0518889427185, 42.6473689079285, 44.9830038547516, 47.734827041626, 50.3327059745789, 52.9546790122986]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.247659549117</td>\n",
+ " <td>[0.641666650772095, 0.691666662693024, 0.641666650772095, 0.966666638851166, 0.941666662693024, 0.958333313465118, 0.933333337306976, 0.925000011920929, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.949797213077545, 0.797480285167694, 0.672827661037445, 0.523928940296173, 0.444453626871109, 0.386174380779266, 0.347499698400497, 0.321201831102371, 0.278330504894257, 0.247659549117088]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.198830261827</td>\n",
+ " <td>[0.766666650772095, 0.833333313465118, 0.766666650772095, 0.966666638851166, 0.966666638851166, 0.933333337306976, 0.966666638851166, 0.899999976158142, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.931245148181915, 0.763193428516388, 0.600658059120178, 0.456866592168808, 0.373299777507782, 0.326453566551208, 0.275230079889297, 0.284671515226364, 0.221188083291054, 0.198830261826515]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.0014467801648012073)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[84.6684620380402, 86.9988820552826, 89.4947271347046, 91.8522582054138, 93.9668672084808, 96.3584721088409, 98.7448561191559, 101.170181035995, 103.282526016235, 105.678196191788]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.395356565714</td>\n",
+ " <td>[0.625, 0.641666650772095, 0.741666674613953, 0.883333325386047, 0.833333313465118, 0.941666662693024, 0.949999988079071, 0.983333349227905, 0.875, 0.966666638851166]</td>\n",
+ " <td>[0.941141128540039, 0.820547699928284, 0.723011374473572, 0.646571576595306, 0.57060444355011, 0.514499425888062, 0.46852970123291, 0.439025938510895, 0.416335105895996, 0.395356565713882]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.326709568501</td>\n",
+ " <td>[0.766666650772095, 0.766666650772095, 0.899999976158142, 0.933333337306976, 0.933333337306976, 0.966666638851166, 0.966666638851166, 0.966666638851166, 0.933333337306976, 0.966666638851166]</td>\n",
+ " <td>[0.941896796226501, 0.816571235656738, 0.707062959671021, 0.61734527349472, 0.521940350532532, 0.45942959189415, 0.405136495828629, 0.374987095594406, 0.33730074763298, 0.326709568500519]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.0011757973913283008)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[237.369596242905, 240.072783231735, 242.490661382675, 245.210073232651, 247.916568279266, 250.679228305817, 253.465747356415, 256.328309297562, 259.131007194519, 262.085569381714]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.916666686535</td>\n",
+ " <td>0.567601382732</td>\n",
+ " <td>[0.333333343267441, 0.441666662693024, 0.433333337306976, 0.349999994039536, 0.349999994039536, 0.675000011920929, 0.683333337306976, 0.800000011920929, 0.774999976158142, 0.916666686534882]</td>\n",
+ " <td>[1.14459049701691, 1.08465170860291, 1.02045607566833, 0.952999651432037, 0.883513271808624, 0.809052526950836, 0.748401284217834, 0.682490110397339, 0.620046377182007, 0.567601382732391]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.512901842594</td>\n",
+ " <td>[0.333333343267441, 0.400000005960464, 0.400000005960464, 0.366666674613953, 0.366666674613953, 0.800000011920929, 0.800000011920929, 0.866666674613953, 0.866666674613953, 0.966666638851166]</td>\n",
+ " <td>[1.21284127235413, 1.13662087917328, 1.04775261878967, 0.957895994186401, 0.871163666248322, 0.780500650405884, 0.705705106258392, 0.636253237724304, 0.558390736579895, 0.512901842594147]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.003695186053629043)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[211.166339159012, 213.628123044968, 216.070516109467, 218.28012919426, 220.77138209343, 223.166202068329, 225.899930000305, 228.167545080185, 230.690491199493, 233.073115110397]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.114436306059</td>\n",
+ " <td>[0.641666650772095, 0.875, 0.824999988079071, 0.824999988079071, 0.958333313465118, 0.866666674613953, 0.966666638851166, 0.941666662693024, 0.958333313465118, 0.966666638851166]</td>\n",
+ " <td>[0.718437075614929, 0.535359025001526, 0.403026401996613, 0.348048120737076, 0.244051590561867, 0.264052510261536, 0.1720110476017, 0.164029255509377, 0.154526039958, 0.114436306059361]</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.107093170285</td>\n",
+ " <td>[0.766666650772095, 0.933333337306976, 0.899999976158142, 0.833333313465118, 0.933333337306976, 0.933333337306976, 0.966666638851166, 0.933333337306976, 0.966666638851166, 0.933333337306976]</td>\n",
+ " <td>[0.647899329662323, 0.474290877580643, 0.308415770530701, 0.318869024515152, 0.206334576010704, 0.250639617443085, 0.129751890897751, 0.156522572040558, 0.112601205706596, 0.107093170285225]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.03271173767396424)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[136.810528039932, 139.271977186203, 141.348733186722, 143.678931236267, 146.042705059052, 148.659409046173, 150.713799238205, 153.240673065186, 155.618861198425, 158.003229141235]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.200596183538</td>\n",
+ " <td>[0.341666668653488, 0.925000011920929, 0.958333313465118, 0.675000011920929, 0.966666638851166, 0.766666650772095, 0.975000023841858, 0.75, 0.975000023841858, 0.899999976158142]</td>\n",
+ " <td>[1.04381895065308, 0.384325951337814, 0.263480663299561, 0.593676149845123, 0.141404688358307, 0.362050473690033, 0.0923048332333565, 0.351189643144608, 0.0946881100535393, 0.200596183538437]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.257636517286</td>\n",
+ " <td>[0.533333361148834, 0.866666674613953, 0.966666638851166, 0.800000011920929, 0.966666638851166, 0.833333313465118, 0.966666638851166, 0.866666674613953, 0.966666638851166, 0.899999976158142]</td>\n",
+ " <td>[0.91362202167511, 0.340268641710281, 0.21289549767971, 0.362329840660095, 0.136535987257957, 0.327440768480301, 0.111000411212444, 0.227803841233253, 0.111130490899086, 0.257636517286301]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.0027814197503322115)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[161.464279174805, 163.823823213577, 166.230634212494, 168.657960176468, 170.846117019653, 173.335704088211, 175.715650081635, 178.106207132339, 180.278338193893, 182.815184116364]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.351116120815</td>\n",
+ " <td>[0.600000023841858, 0.558333337306976, 0.541666686534882, 0.600000023841858, 0.899999976158142, 0.916666686534882, 0.850000023841858, 0.925000011920929, 0.933333337306976, 0.933333337306976]</td>\n",
+ " <td>[0.9764444231987, 0.860457479953766, 0.76110851764679, 0.688599288463593, 0.623845517635345, 0.558117687702179, 0.516568541526794, 0.438872784376144, 0.389935582876205, 0.351116120815277]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.326276868582</td>\n",
+ " <td>[0.433333337306976, 0.433333337306976, 0.5, 0.433333337306976, 0.899999976158142, 0.866666674613953, 0.933333337306976, 0.866666674613953, 0.899999976158142, 0.899999976158142]</td>\n",
+ " <td>[1.03803777694702, 0.913994610309601, 0.794906616210938, 0.723303020000458, 0.652373254299164, 0.566653609275818, 0.481746405363083, 0.454111516475677, 0.379933565855026, 0.326276868581772]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.035340389425615855)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[2.53016018867493, 5.03768014907837, 7.64597201347351, 10.4127900600433, 13.0584251880646, 15.7928349971771, 18.0860531330109, 20.625785112381, 23.3826050758362, 25.9297461509705]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.875</td>\n",
+ " <td>0.28657540679</td>\n",
+ " <td>[0.850000023841858, 0.491666674613953, 0.941666662693024, 0.783333361148834, 0.925000011920929, 0.850000023841858, 0.966666638851166, 0.958333313465118, 0.908333361148834, 0.875]</td>\n",
+ " <td>[0.313798636198044, 0.746647894382477, 0.232485517859459, 0.384049296379089, 0.201492115855217, 0.276773244142532, 0.144450753927231, 0.116710871458054, 0.210491970181465, 0.28657540678978]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.334158778191</td>\n",
+ " <td>[0.833333313465118, 0.533333361148834, 0.833333313465118, 0.899999976158142, 0.899999976158142, 0.833333313465118, 0.966666638851166, 1.0, 0.899999976158142, 0.899999976158142]</td>\n",
+ " <td>[0.281513780355453, 0.781840145587921, 0.252955704927444, 0.219736546278, 0.268592208623886, 0.332309901714325, 0.131899908185005, 0.0595534667372704, 0.255705177783966, 0.334158778190613]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.08208550087461897)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[84.8872451782227, 87.2208690643311, 89.7248260974884, 92.1014380455017, 94.1999170780182, 96.5836410522461, 98.9723200798035, 101.409075021744, 103.511981010437, 105.902093172073]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.766666650772</td>\n",
+ " <td>0.591654956341</td>\n",
+ " <td>[0.333333343267441, 0.641666650772095, 0.566666662693024, 0.591666638851166, 0.758333325386047, 0.666666686534882, 0.966666638851166, 0.916666686534882, 0.949999988079071, 0.766666650772095]</td>\n",
+ " <td>[1.02616000175476, 0.486202239990234, 0.636112213134766, 0.692184090614319, 0.505898773670197, 0.467963546514511, 0.268672525882721, 0.176122322678566, 0.122547559440136, 0.59165495634079]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.33915963769</td>\n",
+ " <td>[0.333333343267441, 0.766666650772095, 0.733333349227905, 0.766666650772095, 0.733333349227905, 0.666666686534882, 0.966666638851166, 0.899999976158142, 0.899999976158142, 0.899999976158142]</td>\n",
+ " <td>[1.12080752849579, 0.381140530109406, 0.5174320936203, 0.473030716180801, 0.607473373413086, 0.409501492977142, 0.212725415825844, 0.296080023050308, 0.18353745341301, 0.33915963768959]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.0025753473010720596)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[186.835414171219, 189.195631027222, 191.254861116409, 193.619318246841, 195.966614246368, 198.311106204987, 200.407158136368, 202.908673048019, 205.313441038132, 207.438939094543]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.891666650772</td>\n",
+ " <td>0.950204193592</td>\n",
+ " <td>[0.358333319425583, 0.358333319425583, 0.358333319425583, 0.449999988079071, 0.400000005960464, 0.516666650772095, 0.583333313465118, 0.608333349227905, 0.899999976158142, 0.891666650772095]</td>\n",
+ " <td>[1.09640550613403, 1.08471190929413, 1.0751405954361, 1.06720495223999, 1.06165635585785, 1.0469172000885, 1.0301650762558, 1.00463593006134, 0.979537725448608, 0.950204193592072]</td>\n",
+ " <td>0.866666674614</td>\n",
+ " <td>0.931918859482</td>\n",
+ " <td>[0.233333334326744, 0.233333334326744, 0.233333334326744, 0.366666674613953, 0.266666680574417, 0.400000005960464, 0.400000005960464, 0.433333337306976, 0.833333313465118, 0.866666674613953]</td>\n",
+ " <td>[1.10118973255157, 1.08938491344452, 1.07863283157349, 1.06669425964355, 1.06701147556305, 1.04324889183044, 1.02750730514526, 0.996739327907562, 0.966157376766205, 0.931918859481812]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='RMSprop(lr=0.056702169442788934)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[237.640507221222, 240.350925207138, 243.027331352234, 245.468372344971, 248.176068305969, 250.940598249435, 253.720994234085, 256.680767297745, 259.394066333771, 262.358667373657]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.649999976158</td>\n",
+ " <td>0.545458972454</td>\n",
+ " <td>[0.658333361148834, 0.649999976158142, 0.641666650772095, 0.641666650772095, 0.641666650772095, 0.666666686534882, 0.666666686534882, 0.666666686534882, 0.666666686534882, 0.649999976158142]</td>\n",
+ " <td>[0.616556286811829, 0.493021905422211, 0.488242834806442, 0.486079841852188, 0.479775160551071, 0.482189744710922, 0.496939599514008, 0.479279518127441, 0.543927192687988, 0.545458972454071]</td>\n",
+ " <td>0.800000011921</td>\n",
+ " <td>0.362693428993</td>\n",
+ " <td>[0.633333325386047, 0.800000011920929, 0.766666650772095, 0.766666650772095, 0.766666650772095, 0.666666686534882, 0.666666686534882, 0.666666686534882, 0.666666686534882, 0.800000011920929]</td>\n",
+ " <td>[0.53449285030365, 0.394388288259506, 0.390281409025192, 0.385460764169693, 0.392662823200226, 0.410547375679016, 0.439140349626541, 0.395850986242294, 0.503270268440247, 0.362693428993225]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.06312207575548352)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[29.8041570186615, 32.351331949234, 34.8423700332642, 37.4489560127258, 40.3089909553528, 42.915864944458, 45.6521019935608, 47.9889349937439, 50.5978739261627, 53.2138829231262]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.641666650772</td>\n",
+ " <td>0.471347987652</td>\n",
+ " <td>[0.666666686534882, 0.641666650772095, 0.641666650772095, 0.366666674613953, 0.666666686534882, 0.966666638851166, 0.483333319425583, 0.641666650772095, 0.941666662693024, 0.641666650772095]</td>\n",
+ " <td>[0.520724713802338, 0.578253924846649, 0.518827021121979, 1.67398142814636, 0.512235522270203, 0.131752595305443, 1.25291848182678, 0.453146934509277, 0.185879185795784, 0.471347987651825]</td>\n",
+ " <td>0.766666650772</td>\n",
+ " <td>0.337222576141</td>\n",
+ " <td>[0.666666686534882, 0.766666650772095, 0.766666650772095, 0.333333343267441, 0.666666686534882, 0.933333337306976, 0.566666662693024, 0.766666650772095, 0.899999976158142, 0.766666650772095]</td>\n",
+ " <td>[0.462848216295242, 0.38900101184845, 0.356701970100403, 1.92939758300781, 0.458910882472992, 0.14183434844017, 0.83171159029007, 0.332331091165543, 0.311882764101028, 0.337222576141357]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.0658839037116738)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[211.389490127563, 213.85792016983, 216.39094209671, 218.503707170486, 220.992606163025, 223.471295118332, 226.145341157913, 228.394971132278, 230.912840127945, 233.303599119186]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.641666650772</td>\n",
+ " <td>0.482037603855</td>\n",
+ " <td>[0.800000011920929, 0.666666686534882, 0.666666686534882, 0.808333337306976, 0.683333337306976, 0.666666686534882, 0.875, 0.666666686534882, 0.666666686534882, 0.641666650772095]</td>\n",
+ " <td>[0.508525788784027, 0.431755125522614, 0.435203284025192, 0.344938695430756, 0.478766769170761, 0.330143094062805, 0.273075610399246, 0.479535788297653, 0.48390719294548, 0.482037603855133]</td>\n",
+ " <td>0.766666650772</td>\n",
+ " <td>0.39202862978</td>\n",
+ " <td>[0.833333313465118, 0.666666686534882, 0.666666686534882, 0.899999976158142, 0.666666686534882, 0.666666686534882, 0.933333337306976, 0.666666686534882, 0.666666686534882, 0.766666650772095]</td>\n",
+ " <td>[0.443316102027893, 0.401963800191879, 0.399077832698822, 0.240811541676521, 0.410095393657684, 0.290450870990753, 0.254536032676697, 0.396871030330658, 0.413692444562912, 0.392028629779816]</td>\n",
+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='RMSprop(lr=0.0020197253642543623)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[109.946217060089, 112.64745092392, 115.244435071945, 117.609509944916, 120.211313962936, 122.821636915207, 125.485604047775, 128.088088035583, 130.593991041183, 133.237344026566]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.725000023842</td>\n",
+ " <td>0.553534567356</td>\n",
+ " <td>[0.358333319425583, 0.358333319425583, 0.508333325386047, 0.916666686534882, 0.658333361148834, 0.633333325386047, 0.633333325386047, 0.641666650772095, 0.649999976158142, 0.725000023841858]</td>\n",
+ " <td>[1.44853103160858, 1.05627000331879, 0.960374653339386, 0.903020858764648, 0.811912178993225, 0.744573473930359, 0.693612813949585, 0.683376550674438, 0.593345999717712, 0.55353456735611]</td>\n",
+ " <td>0.766666650772</td>\n",
+ " <td>0.458936661482</td>\n",
+ " <td>[0.233333334326744, 0.233333334326744, 0.433333337306976, 0.933333337306976, 0.733333349227905, 0.733333349227905, 0.733333349227905, 0.766666650772095, 0.766666650772095, 0.766666650772095]</td>\n",
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+ " <td>[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
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+ " (2, 2, u\"optimizer='Adam(lr=0.035340389425615855)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [2.53016018867493, 5.03768014907837, 7.64597201347351, 10.4127900600433, 13.0584251880646, 15.7928349971771, 18.0860531330109, 20.625785112381, 23.3826050758362, 25.9297461509705], [u'accuracy'], u'categorical_crossentropy', 0.875, 0.28657540678978, [0.850000023841858, 0.491666674613953, 0.941666662693024, 0.783333361148834, 0.925000011920929, 0.850000023841858, 0.966666638851166, 0.958333313465118, 0.908333361148834, 0.875], [0.313798636198044, 0.746647894382477, 0.232485517859459, 0.384049296379089, 0.201492115855217, 0.276773244142532, 0.144450753927231, 0.116710871458054, 0.210491970181465, 0.28657540678978], 0.899999976158142, 0.334158778190613, [0.833333313465118, 0.533333361148834, 0.833333313465118, 0.899999976158142, 0.899999976158142, 0.833333313465118, 0.966666638851166, 1.0, 0.899999976158142, 0.899999976158142], [0.281513780355453, 0.781840145587921, 0.252955704927444, 0.219736546278, 0.268592208623886, 0.332309901714325, 0.131899908185005, 0.0595534667372704, 0.255705177783966, 0.334158778190613], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),\n",
+ " (8, 1, u\"optimizer='Adam(lr=0.08208550087461897)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [84.8872451782227, 87.2208690643311, 89.7248260974884, 92.1014380455017, 94.1999170780182, 96.5836410522461, 98.9723200798035, 101.409075021744, 103.511981010437, 105.902093172073], [u'accuracy'], u'categorical_crossentropy', 0.766666650772095, 0.59165495634079, [0.333333343267441, 0.641666650772095, 0.566666662693024, 0.591666638851166, 0.758333325386047, 0.666666686534882, 0.966666638851166, 0.916666686534882, 0.949999988079071, 0.766666650772095], [1.02616000175476, 0.486202239990234, 0.636112213134766, 0.692184090614319, 0.505898773670197, 0.467963546514511, 0.268672525882721, 0.176122322678566, 0.122547559440136, 0.59165495634079], 0.899999976158142, 0.33915963768959, [0.333333343267441, 0.766666650772095, 0.733333349227905, 0.766666650772095, 0.733333349227905, 0.666666686534882, 0.966666638851166, 0.899999976158142, 0.899999976158142, 0.899999976158142], [1.12080752849579, 0.381140530109406, 0.5174320936203, 0.473030716180801, 0.607473373413086, 0.409501492977142, 0.212725415825844, 0.296080023050308, 0.18353745341301, 0.33915963768959], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),\n",
+ " (16, 1, u\"optimizer='Adam(lr=0.0025753473010720596)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [186.835414171219, 189.195631027222, 191.254861116409, 193.619318246841, 195.966614246368, 198.311106204987, 200.407158136368, 202.908673048019, 205.313441038132, 207.438939094543], [u'accuracy'], u'categorical_crossentropy', 0.891666650772095, 0.950204193592072, [0.358333319425583, 0.358333319425583, 0.358333319425583, 0.449999988079071, 0.400000005960464, 0.516666650772095, 0.583333313465118, 0.608333349227905, 0.899999976158142, 0.891666650772095], [1.09640550613403, 1.08471190929413, 1.0751405954361, 1.06720495223999, 1.06165635585785, 1.0469172000885, 1.0301650762558, 1.00463593006134, 0.979537725448608, 0.950204193592072], 0.866666674613953, 0.931918859481812, [0.233333334326744, 0.233333334326744, 0.233333334326744, 0.366666674613953, 0.266666680574417, 0.400000005960464, 0.400000005960464, 0.433333337306976, 0.833333313465118, 0.866666674613953], [1.10118973255157, 1.08938491344452, 1.07863283157349, 1.06669425964355, 1.06701147556305, 1.04324889183044, 1.02750730514526, 0.996739327907562, 0.966157376766205, 0.931918859481812], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),\n",
+ " (20, 2, u\"optimizer='RMSprop(lr=0.056702169442788934)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [237.640507221222, 240.350925207138, 243.027331352234, 245.468372344971, 248.176068305969, 250.940598249435, 253.720994234085, 256.680767297745, 259.394066333771, 262.358667373657], [u'accuracy'], u'categorical_crossentropy', 0.649999976158142, 0.545458972454071, [0.658333361148834, 0.649999976158142, 0.641666650772095, 0.641666650772095, 0.641666650772095, 0.666666686534882, 0.666666686534882, 0.666666686534882, 0.666666686534882, 0.649999976158142], [0.616556286811829, 0.493021905422211, 0.488242834806442, 0.486079841852188, 0.479775160551071, 0.482189744710922, 0.496939599514008, 0.479279518127441, 0.543927192687988, 0.545458972454071], 0.800000011920929, 0.362693428993225, [0.633333325386047, 0.800000011920929, 0.766666650772095, 0.766666650772095, 0.766666650772095, 0.666666686534882, 0.666666686534882, 0.666666686534882, 0.666666686534882, 0.800000011920929], [0.53449285030365, 0.394388288259506, 0.390281409025192, 0.385460764169693, 0.392662823200226, 0.410547375679016, 0.439140349626541, 0.395850986242294, 0.503270268440247, 0.362693428993225], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),\n",
+ " (4, 2, u\"optimizer='Adam(lr=0.06312207575548352)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [29.8041570186615, 32.351331949234, 34.8423700332642, 37.4489560127258, 40.3089909553528, 42.915864944458, 45.6521019935608, 47.9889349937439, 50.5978739261627, 53.2138829231262], [u'accuracy'], u'categorical_crossentropy', 0.641666650772095, 0.471347987651825, [0.666666686534882, 0.641666650772095, 0.641666650772095, 0.366666674613953, 0.666666686534882, 0.966666638851166, 0.483333319425583, 0.641666650772095, 0.941666662693024, 0.641666650772095], [0.520724713802338, 0.578253924846649, 0.518827021121979, 1.67398142814636, 0.512235522270203, 0.131752595305443, 1.25291848182678, 0.453146934509277, 0.185879185795784, 0.471347987651825], 0.766666650772095, 0.337222576141357, [0.666666686534882, 0.766666650772095, 0.766666650772095, 0.333333343267441, 0.666666686534882, 0.933333337306976, 0.566666662693024, 0.766666650772095, 0.899999976158142, 0.766666650772095], [0.462848216295242, 0.38900101184845, 0.356701970100403, 1.92939758300781, 0.458910882472992, 0.14183434844017, 0.83171159029007, 0.332331091165543, 0.311882764101028, 0.337222576141357], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),\n",
+ " (18, 1, u\"optimizer='RMSprop(lr=0.0658839037116738)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [211.389490127563, 213.85792016983, 216.39094209671, 218.503707170486, 220.992606163025, 223.471295118332, 226.145341157913, 228.394971132278, 230.912840127945, 233.303599119186], [u'accuracy'], u'categorical_crossentropy', 0.641666650772095, 0.482037603855133, [0.800000011920929, 0.666666686534882, 0.666666686534882, 0.808333337306976, 0.683333337306976, 0.666666686534882, 0.875, 0.666666686534882, 0.666666686534882, 0.641666650772095], [0.508525788784027, 0.431755125522614, 0.435203284025192, 0.344938695430756, 0.478766769170761, 0.330143094062805, 0.273075610399246, 0.479535788297653, 0.48390719294548, 0.482037603855133], 0.766666650772095, 0.392028629779816, [0.833333313465118, 0.666666686534882, 0.666666686534882, 0.899999976158142, 0.666666686534882, 0.666666686534882, 0.933333337306976, 0.666666686534882, 0.666666686534882, 0.766666650772095], [0.443316102027893, 0.401963800191879, 0.399077832698822, 0.240811541676521, 0.410095393657684, 0.290450870990753, 0.254536032676697, 0.396871030330658, 0.413692444562912, 0.392028629779816], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]),\n",
+ " (10, 1, u\"optimizer='RMSprop(lr=0.0020197253642543623)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [109.946217060089, 112.64745092392, 115.244435071945, 117.609509944916, 120.211313962936, 122.821636915207, 125.485604047775, 128.088088035583, 130.593991041183, 133.237344026566], [u'accuracy'], u'categorical_crossentropy', 0.725000023841858, 0.55353456735611, [0.358333319425583, 0.358333319425583, 0.508333325386047, 0.916666686534882, 0.658333361148834, 0.633333325386047, 0.633333325386047, 0.641666650772095, 0.649999976158142, 0.725000023841858], [1.44853103160858, 1.05627000331879, 0.960374653339386, 0.903020858764648, 0.811912178993225, 0.744573473930359, 0.693612813949585, 0.683376550674438, 0.593345999717712, 0.55353456735611], 0.766666650772095, 0.458936661481857, [0.233333334326744, 0.233333334326744, 0.433333337306976, 0.933333337306976, 0.733333349227905, 0.733333349227905, 0.733333349227905, 0.766666650772095, 0.766666650772095, 0.766666650772095], [1.5010712146759, 1.05707538127899, 0.939342617988586, 0.863560140132904, 0.760088205337524, 0.681271374225616, 0.617161631584167, 0.568128407001495, 0.501981496810913, 0.458936661481857], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM automl_output_info ORDER BY validation_metrics_final DESC, validation_loss_final;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "20 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
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c8TjRbCIbjJ++ZaqydsP9qvPP/+8WK/XfzJmzJhmi8XSkdVaG43b7TZt3749IxAInHHKKadEJ8LTfleEsB9Meh8RURKmRNIWyyYbK0GJbLpiVZHDuRyoZUOVODkhkLIByUbbVQuhDF/cOmRzlM1LiKX0K370scpORDZtcW2VzVuIowRSi0+/3KC1JWiR7GRi8RRFIhuhuJZ2VnZCXAEl8YhY3iR+UG4XJXGKEKDONvBEWwhFQQhZElImY5P5k3FKjIP8m/xvIW3SRBEKORTrlySPkXTZQprlne4QQrHCClaiiMX9Uix5kulMyk6Ia424sHTU5HZTrH6SGU2UnJSdkKyuEkQvcx2pbSnKV2JIhLyJdVpiVeX/C6GWi4SuEkJJLS5rSNatYCLW1egsuyKzEFY5nMhNsMgmB0T5jqSmF/lkrakso31kA1NiKAR6GAGlE0OARspOJEonxrpIFh0vdWLF3VEsXxKqIrkEZB+PLjshXicyb5KARS6X5RAtZxmpNygkTrw95CJP9IzEDIq3j5wLJOZcLgiliT6JZDKNtaTkXC1nAwlVkAt08ZCRM4voJNG3ktsgckYQ10zRLZLZWnScnHXknCIeTqJPumIhlD6lfq4k/xO9FdF9bWUU66UklpFnRG+KB4wk3ZN4S7F+SiIdlWW0hzeL3uhOEcLeQFX1GQ8CsmHIjYIQK8lQJWQrVpNyCkKupNaOWNBkQ5RNSgKVpfZfdwihfEf6FSuXuAdGCtOLC2vbwvTyrBA0sXDJwVziGWVjl1s32djbEkIhJhG3DSEq8RamF+UgG6iY6cWiJnFm0Qli+pLLqGAi8ZsSXyAkSm5Rpcl8RmIt5YY00kQhSHyDPCeEum1h+nhjCCP9CYET/EURiTVSiL0oO1Fcba2GsdaUpNMWRS63nUJuRW6RVyyfkRtOIeaiVIWIyRqVW1FZd6L85WDQVUIocoiyljkWS6DEm8RqchEgcohlUgi2WKUlQ51gJnU6O8siG8/fnnpGIaAQ6HsIKJ14eE4kCUmidGJ7Lv9C7GIVphfSE2ni6iueTJIUrCh8RpFLYbnQk3q3os9F54k+F/ImRFAuBCO1eOUMEl1Lub1VKVm9Rd9K2QlJLCZeKuJRJaQ1+pJQ9ItceMrFuXxbMn+Kt5J4nESXg4rXQighNqJ/5PvRmeHbyikeV/K7YCYEWPSoEEO5OJZL97aWw77316ckQhFCtQgUAgoBhYBCQCGgEFAIKAQUAgoBhcBJioAihCfpxKthKwQUAgoBhYBCQCGgEFAIKAQUAgoBRQjVGlAIKAQUAgoBhYBCQCGgEFAIKAQUAicpAirL6Ek68WrYCgGFgEJAIaAQUAgoBBQCCgGFgEJA1SFUa0AhoBBQCCgEFAIKAYWAQkAhoBBQCJzECKxZs+b/zGbzj0tKSlzJyclOnU53VCmvYDCoczqdyeXl5Ukej+cX06dPfzIWZKrsxEm8kNTQFQIKAYWAQkAhoBBQCCgEFAIKgf6HgJC9tWvX3mQ0GiWzvmRYj8XrhCS6fD7fY9OmTftzLNIoI1eEsP/Nv5JYIaAQUAgoBHoWgSsAKacixZozgB3A74AXOvmMpKj/ZfhdSU8v9Tol3bwUyo5uZ0aVt5EU9o+E08337ChUbwoBhYBCQCFw0iEg7qODBg3KNZvNhraD93g8/rKysrorr7xSyp+0204UQniUifSkWw1qwAoBhYBC4ORCoCf116eA1CN7HagL18y8G7gNeLwDWKXWpBSgvhOoBe4HsgGp8+UKvye1V6Ve6pvheqxSLFtqkUm9VKmlGm9Tei5epNRzCgGFgELgxECgJ/Vch4gk7EO9PC/BYFDpyl7GWHWvEFAIKAT6BAI6naa6elJ/SXFmIYLR7V/ATGBoO4MuCVsCpfi0FLaWNiBMLG+OIntCGqUA9LhwIWx5Too/XwwMAuJVXkrP9YnVp4RQCCgEFAK9j0Av6DlFCHt/2tQXFAIKAYWAQiBRCCRIUX4vbMkTt9BYTYjgU0Am0Br1wBKgEbgs/G9lgJDLH0Y9MxtYDkwCNsWJmyKEcQKlHlMIKAQUAv0dgQTpuUMw9eQN6/HEXinK44m++rZCQCGgEEggAglSlP8BRobdP2ON7jfAlcCQNj/+AZgLjAckrlDI4jeAZ6KeywNqwu//O07olJ6LEyj1mEJAIaAQ6O8IJEjPKULY3xeKkl8hoBBQCJysCCRAUS4A3gfEChhN5KIh/wswA5jSZh4kPlDeKw67kJYDl4bjEyOPGgEvcFPYyhjPVCpCGA9K6hmFgEJAIXACIJAAPXcESspCeAIsGjUEhYBCQCFwMiHQy4pSLH6rgE/CRK49aHubEEqCmvuiP65i5U+mVa7GqhBQCJzMCPSynjsKWkUIT+bVpsauEFAIKAT6IQK9qCglQ+jHQEvY7dPRATziMirlKtomnYnlMnod8I+ovpTLaD9cd0pkhYBCQCGQKAR6Uc/FHIIihImaWfUdhYBCQCGgEOgRBHpJUaYAHwAF4eyiEuPXUYsklZG6hfaoBxcDTW2SyjwP3BP1zFnAhyqpTI8sB9WJQkAhoBDoUwiIN4fP5yOWV4foL6PRSFiPtSt3L+m59r/XpxDsvjAqtqL72Kk3FQIKAYVAv0KgFxSlxPT9F5AagWcAu+IAJFJ2Qqx/z4Wfl7hBKUrftuzEnHCSmUhh4CeALwCDVdmJOJBWjygEFAIKgX6AgBDAuro66uvrY5LByBBEh+Xk5JCbm9suMewFPdchgspC2A8WmBJRIaAQUAgoBA4j0AuKUspH3AjcDqxug/U6wA2I5U+aJJyJNKkxKOUlogvT57RTmP4NIBJ3+Avg210tTK9iCNVfgUJAIaAQ6LsI1NbWamSwsLCQlBRxOondHA4HVVVVGinMy5MIgqNbL+g5RQj77tJRkikEFAIKAYVAVxHoBUUpVj2x1sVqEiMovy8L/yglJSJNahQ+CFwDiPaX2oK3hIvTR/c1C3g47CJaFf7fv+/iuJUnTBcBU48rBBQCCoFEISAXdtu3b6eoqIjMTClP23FramrSSOHo0aNjWgl7Qc8pQtjZpKjfFQIKAYWAQqD/IJBoRdlHkFGEsI9MhBJDIaAQUAi0RcDr9bJ7926GDx+O2WzuFCCPx8OePXsYMWIEJpPpqOcTred60mV0BPC9cDC+FORdEc7S1hkoEpD/KPBFQA+8CdwG1Hf2YtTvSlF2ASz1qEJAIaAQ6M8IJFpR9hGslJ7rIxOhxFAIKAQUAm0RiBC8rhLC9p5PtJ7rSUJ4CSCB8iuBCUB1nITwPWAU8F0gAPw6/K5kYYu3KUUZL1LqOYWAQkAh0M8RSLSi7CNwKT3XRyZCiaEQUAgoBBQhbH8NiHVPCJ20V4DcOAjhzHDxX8nAJim4pUmWNykKfHY4BXg8q04pynhQUs8oBBQCCoETAAFFCE+ASVRDUAgoBBQCJxACykIYezLjJYQPAN8CCtt0sxd4Dbg7zrWiCGGcQKnHFAIKAYVAf0dAEcL+PoNKfoWAQkAhcGIhoAjhsRHCl4H8GJbEt8LdXhjnclGEME6g1GMKAYWAQqC/I6AIYX+fQSW/QkAhoBA4sRBQhPDYCOH7gD2cUCa6JynyOyxcIDieFaMIYTwoqWcUAgqBY0agxuvk3f0VbP2Tl5w8MxefW8ioKSno9T0Zkt09MYNeL3UvPotjy2bSTpuJdfY8TDnivX9iNUUIT6z5VKPpGgJlLjdPlFfz7QEFDE2WyieqKQQUAscbAZVl9PgQwvuB+6I/rQr2Hu8/BfV9hcCJi4AvGGCNvZbFzRVscjRQ8Fw+qTsOF53NyDEybW460+dZycw9On10IpDxNdRT+cQjuPfuPvw5nY6UiVOwzl1A6uSp6AyGRIjS699QhLDXIVYf6MMI/Lu6nldrG7kkN5OvFJ54Fz59GHolmkKgXQRUHcJjI4TiMpoHzGvTjXIZVX90CgGFwHFHoNLjYImtguUtB7H5vZo8BVszSP1XFin5OvbPqyZ9TTrJe5K133Q6NGvhjAVWRk9NxWBIjNXQuWMbVX94FL+tmaQRo8i96hrs69diW7EMf3OTJpshMwvrWXNDVsM88dTvv00Rwv47d0ryY0fgD+XVrGhq4XRrGncMapuC4dj7Vz0oBBQC3UOgtraW+vp6CgsLSUk5fGnctjeHw6EVpc/JySEvT2jQ0S3Req63TitdSSpzI1DUBoo9wOsqqUz3FqR6SyGgEOg+At5ggNWtNRoR3OJs1DrSo2Nqag5nGYpZ8hM7rc1+rr+3mF0l9TxTt4PM5iTO3TGC7SuctDT6tXfSswxMm2vVrIbZ+b1jNZQbyeYP3qXuxefA78c6/2zyvnotOqNRkyHo82HfsA7b8iU4Nq2HYFBjrcnjJ5IxZz6pU6cferb7iCX+zUQrysSPMOYXVWhEH5mI4y3GfXvL2eFwMSTJwq9GDDze4qjvKwQUAmEERCfX1dVppLAjz0XRYUIGc3NzCeuzozBMtJ473oQwUnZCag5+FEZjOvCZKjuh/r4UAgqBRCJQ4bFrLqErWippCYSsgbnGJBZYBzDHWky20cJrf6nhs8U2TpmTzuXfLtCeebp2O4uayxloTuW+oumUbXBrz+xc74jwL0ZMTGb6/AzGTk/FaOyZbTfgdlP7zF9o+fQjdEYTedder1kA22ve+jpsHy6lZcVSfA0N2mMGawbps2ZjnT0fc2Hbe7lEot+1byVaUXZNul57WhHCXoO2f3V88/Z9NPj8JOt1PD12WLsHyv41KiWtQuDEQUDIoM/ni0kKRX8ZjcZO/24Tred65mQSmkOxjV4Qnk4pF2GNivN7G3AAEtyyHLg+atqlMP3INoXpawBVmP7E+dtQI1EI9EkEPAE/K1urWWw7yA5X2LUSHdNS81iQMYCJydnoxQ8U2L/dyVP3V5CSrueuhweTkh6Kx/MHA/z64Ho2OhuYmpLD94qmaO801XlZs9TG2qUtNDf4tGdTMwxMm53O9PlWcovM3cbEW1tD5eMP4ynbjzE7h8Lv3EXS0OFx9RcMBHBsXI9t+WLNekggVD42eex4rHPmkzbtVHSm3rFoxiVgHA8lWlHGIVIiHlGEMBEo9/FveAIBvr5VKnOF2p/HDCEj7BHQx0VX4ikEFAJdQCDReq4nCeEQYF87Yx0q56nwf5YB10U9lwk8AlyqeWbBm8BtQF0XcFOKsgtgqUcVAic7AmXuVhbbyvmopQp7IETWCk3JzBNrYHoRmcYjM/f5vEEe/2EZtRVerrilgKlnpR8Bod3v5Sfln3HQ6+D8jIFcmzf60O+BQFCzForVcMc6e4R/MWx8MjPmWxl/ahpGU/xbsWPzBqr+9DgBe6tG4gq/fTsGq9y/db35GhuwrViukUNffWjL1aelk37mWWTMWYC5eEDXO03AG4lWlAkYUjyfUHouHpRO8GcOuj3ctavs0CgfGDaAUSmhGGbVFAIKgRMHgUTrufhPIX0bY6Uo+/b8KOkUAscdAVfAz6etVZpb6G63TZPHiI5T0/KZbx3AuOSsQ9bAtsIuebWBD/7doLl+fuNHxTFdPaq8Dn5y4DPN3fSGvDEszCg5asy2Bh9rl9s0cthUFyKiYnEUgjljQQb5A9q3GooLStNbb1D/6otaLGDmeReSc8VXeyRzqFgNnVs30bxsMfZ1a7V4RGlJo0ZjnbOAtBmnozd336LZ05OfaEXZ0/J3sz+l57oJ3In02voWO78qrTw0pJsH5DM7q3sXQicSLmosCoETDYFE6zlFCE+0FaTGoxBQCByBwD6XjcW2Cj5uqcIZDBGdYlOKFht4lrUIq6FjolN70MPvv1+mkcDbfzuInML23Sm3ORv5ecXn2jfuKZ7KhJTsmLMhVsM9m5ysXtzMtrV2AiGxGDw6SctQOvH0NExmcZgItYDTSfXfnsS+ZhU6s5n8b95E+uln9spM+5qbaPlIrIZL8NZUa9/Qp6SSfsZZmkupZeCgXvluVzpNtKLsimy9+KwihL0Ibn/pelF9M09X1pJrMlLn9XFZXs/g27cAACAASURBVBZXFOT0F/GVnAoBhUCcCCRazylCGOfEqMcUAgqB/oOAI+Djk5YqLVPoXneLJrhJp+f0sDVwTFJmpwHd8o5Y5f76QAX7trk49ys5zLkkq1MQltkO8mTNVlL1Rn5WMoNic2qH77Q0+fh8eQufLbHRUB1KZpOUqmfKrHROXWAl21xP1e8fxnOwHGNePkW33Y1l4OBO5TjWBzSr4fatGjFsXbsafCGLpmX4SC1DqRS+11uSjvUz3Xo/0YqyW0L2/EuKEPY8pv2ux+eq6nizron5WVaWNNqYlZHGrQNV6Yl+N5FKYIVAJwgkWs8pQqiWpEJAIXBCICDkTVxBhQR+0lKNO2wNlOyf4hJ6VnoRaYauJUuRpDD/+XMNhYPM3PLLgRjizBD6fN0u/tdUSqEphZ+XzIjru2I13LfVqRHDLatb8Yf4F3mGCkbrVjF+EpTcfDOGtLSEz5e/xYbt4xXYli3GW3VQ+74uKZn0mWdqLqVJQyRMPHEt0YoycSPr8EuKEPaRiTieYjxcVslqm507Bhby6IEqRiRb+PlwVXrieM6J+rZCoDcQSLSeU4SwN2ZR9akQUAgkDAFJ6LIibA0s87Rq37Xo9MxMK2BBRgkjLNa4rIFtBW5t9vHIXWW4HAFueqCEQSPjt4YFgkEeqdrIZ/Zaxidnae6jRt1hF9DOwGlt8vLxkx+zfoOF5mCoaK0lWcfkMyXW0MqAofHL0tm3uvK7kG7Xzu0aMWz9bBVBX8iiaRkyTHMnTT/9DPTJ7Rfj7cq3Ono20Yqyp+Q+xn4UITxGAE+E13+4+wD7XW7+NHoIt+0sJUmv4y9jh50IQ1NjUAgoBKIQSLSeU4RQLT+FgEKg3yEgxGSnq1mLDZSyEZ5gqHTCEHMa8zNKmJVWSIohVJy9u+3lJ6pY/1Erp5+TwRe+GSJlXWmugI/7y9ew39OqWShvzBsTFzH12+1UP/UEDikJYUnGc8FtbD5QzOZVrUi2U2kDhlm00hWTz0gnKSV+otkV+Tt71t/aSsunKzSXUk/5Ae1xncVC+mlnYp07H8vQ4XGNt7PvxPo90YqyOzL2wjuKEPYCqP2ty+u37sUXDPLMuGF8d3cZFW4vfxs7lFRDqAyOagoBhcCJgUCi9ZwihCfGulGjUAicFAi0+D182FLJkuaDVHjt2piTdAbOTC/USNcwS3qPkJCdG+w882Al1mwDd/xucLdJV73PxY8PrKbJ7+Ga3JFcmNlx7J+7/ABVjz+Et7oKU1ExRd+5+1DpB0ern/UrQrGG1Qc82tjNFh2TzkjXyleUjLD0yNi7upA0q+GeXaFYw1WfEPSEZRs4OGQ1nDkLQ2rHcZRd/WaiFWVX5eul5xUh7CVg+0u3rX4/N2zbR4nFzO9GDuK3pQdZ2+Lgl8NLGJZ8fLwG+gt2Sk6FQH9DINF6ThHC/rZClLwKgZMMASEcW52NmjVwdWsNPkJWsuEWq5Yp9Iz0ApL0x2YNjIbU4w7w2PfKaKzxcfVdhVqdwGNpe1zN3F+xFl8wwHeLJmtF72O1ltWfUvO3Jwm63aROm0HBDd+O6X4peBzY7eazxc1s/LQVrzuEh8Q5ijupJKNJTj0+1gK/w0Hryo9oXrYET5mUnkXLiiplK6xzF2gJafT6Y7doJlpRHsv89+C7ihD2IJj9sat9Thf37CnnlPQUvj+4mH9W1vJ2fTO3DSzgjIwja6P2x/EpmRUCCoHDCCRazylC2IdXX5WnlneblzM7/VSGJR3/VO/RUK1f9zTNB3cwurS4TyFYl2zj/RHlDPTqmWTTYw6dlY97azQkszhlJHZdCleWzGDs6DHHXSYRIBgM0HhwHeuf3Uf2wF1kDijvE3KJEFVGHc94T6WpYRSMs4MpiB4f2bpKcnQHSdGF4gV7ujnfmYHnw0kYx+0n9ZrFHXZvMFgxmnLR6TompPW+ZHa6ctETYEJyDamGUOyd1oJB3AfK8FZWCH3CXDIwZBXUdb49B5wGXGvzcX5aiK8iDaFaVn0Ac1IAnU4Wfx/5A5DSFcEAM1w72Jtl5at/OPuYpi3RivKYhO25lxUh7Dks+2VPK5tbtUQy5+VkcF1RHu/VN/H3yjq+nJ/NpfmxS9z0y4EqoRUCCoGIx0/nB4EewiphH+ohedvr5oRTlJ/bN/NE9T9xBJyYdEa+mXcl86wzexnG+LrfZfuEd2p+rz182rOQUxrfe7391J5JJl4634rDFLJAWPwBpjS5md7gpMgVLvTW20JE9e8P6tiSPJI1TGefYzRBDlttSvw1zC00MG/aBFJ72J0uniH6vI3YmpfRVL+cTf84j+a9I0AXYPCCRRSf8VE8XCSez3T5GUms+Yk+l5X6QmzkwjuzocUMVjfM2gjp24EmqczX5b7jecFcmcuAP32FoMnLgduew5/Rk6RzFDAWcAAfAu54ROr8mQBkb5vB4BVTKahIQh/si9t6kJHsII9aGnXpTH/+MnT67supCGHny0I9ceIh8L/aRp6vrufrhblckJvJhhY7D5ZWMjcznf8rKTjxBqxGpBA4iRFItJ7rvkbuW5N0whDCQDDAa43v8UrDOwQJMiN1EkIO/QRYaJ3FdXmXYezEGtGbU1Pt2sMrB+7HrwtZOIoYwkV8Ex3HbykJZm/YX+O/qeUEdTqmH3TiderZMiwZny6cbCSYxazgYGYES0iia6UHuopnlcfF4iY/nzcW0+rN0F43G+zkZn+O2dRCTf00HO6Q8jYHPEwz1XH2uGLGjBjWI+507ckr1kCHfQO2xiXYW9dq1sG9b11M9drTSLLU4/FnEvAZKB5vZ+oXGzBaEmddKnXX8UpwL7uTLAT04ULxLYPhbbGkihw6MHnhtE0YC6sY2eLlFGshQ/JKjolYRGMVCMCiX+fSsN/MtKuaGD1PiFv7LRjw4LBvxmHfCMFQ3JzRmE+qdRopKVMwGI/Mtimunq801LDR2cpAs4WrfTpsr7yIv7kJY34huVd8BWN2/Lf8peVVrHnXh3VDAWktoUsQrylI9YgmnAMqaDUZ8BHCUh+ETHTkBSE9AX+rAdwEsOPXyG+QIXtg2G4driQde+Zmc8l1X+jqn9URzydaUR6TsD338gmj53oOkpOrp78drOH9BhvfHVTEdGsqVW4vd+wqZUxKEvcPKzm5wFCjVQic4AgkWs8dv1N8z07kCaEoHX4nf6h5lrX2TSTpLNxc8DVOTZvCNuduHq16mmZ/C6OShnJH4fVkG0NEI5GtxVfPywfuxe5vZMwHUDk7g2ZzM5cW38vAlAmJFOXQtxweG4/vepB1llZMgSCXbG5h2u5k/FubSL/jNtYO8bDE9jHlnirtHcH1jPRpLLCewTDLoB5LwuH1+/iwbDOL97awr6kAcW6UNjijijMHGzjAUwR1XlLxYQuCpWYgzVUz2BycgFcXOrQX+euZmxdk/vQJpKcfW9xa9GT4vPXYmpZha1qKz1cXIgiGdBrWf5mt/xmCSdfK5Ix/UPC9X7D8sRpaa3xklJiZd3ch1qIwOeuF2fX6PLyy/zWWesuwGZMPuUgm+ZwU6bLZt288rM1h1lQfpa6PObBtTkj2MbsITNqrccSCRg+z/YUsHHcOGdacY5Lyk3ebePOZOgaOsGhlJvRxWrACficttk+wNS3B7dqjyaDTmUhNP5WMzAUkpYw9tM48AT8/O/g5u1zNTNq6lyv+s0Qr1ZD/jW/FVeTd7XHz8rufUvNpNjmlyegDoS28Kc+LbfIOkk57kymZk1hQcitrGv/LBw1vUU0BDRQToqxQZMpkvnUcc6xjsBqSjwmz6Jf9Phu25uXaOvN6QrUK9fpkLHtm4fynHl2SkZIHLsQyKH7S255wiVaUPQbSsXV0Qui5Y4Pg5H77wf0H2dDq4DcjBjIoyaJlG/36lj1kGA38aUxia4Ge3DOhRq8Q6H0EEq3nFCHs/TmN6wvlnkoeqvwrld4aikz53F10AyXmokPv1vsaeaTyaXa795NpsHJn4fWMTk5c7SFvwMUrFfdT697P4N3pjHuxBe8DX+MDz3MUJ43msgH39xi5igsw4EDTVh46+CRV5iBZXrh2RxK52w6QOfFiml75H9bZ88j/5k2EShTsY4ntEz5t/RxPMGTdHGIuYX7GGcxKm05KNw/GB5qreHPXLtYcTMPuDQX1JxvtzChu5oKRIxiSWcxHdc/zedP/mJxxLl7bR+wM2hHXyBmlRtKajWw1z2OlYzSVhlztfVPAy1RjDWePKWT86O4l4QgG/Tha19HctET770gsWXLqRKyZ82nePZ6lv61Bpw8y3vBnimcNpeBbt+Bu9bPi8WoObnBgStFz1i0FlEzr2QyRO+q28Hz1++wyQVAfttYGfQzy+Lky+0yG547ljtLluD8aDBUp3Ht1MeacDTy/dhG7V12G35dEYXoFhqkbqCgKueEafQEm1+pYmDeVyaNmdtnS2lTn5dHvlmllHaQAfdFgS7zL8Ijn3K79GjFsaV5BIOAMzae5SMPcmjEHPSnsfeU5fjMyg+aMdC5u8PHVGed2+rezYfd23n+znrTNeaS0hi4bPOYgDaMaGZq/lMDodVQPgBLLWC4p+TGGsBfBfvs63qt+QnM9D5qm0KAfwm53rfa+AT0z0oaywDqe8ckl6OOIWWwLiliZnY4t2BoX09ryGRByzU5KHok1cwHG2pFUPrCYoM9P0XcXkjqtZwpoJ1pRdmsx9PxLihD2PKb9qsc7d5ZS6fHyzNhhJBlC+8DtO0upln8bN4ykHkjY1K8AUcIqBE5gBBKt5xQh7AOLaVXrev5U/RyuoJtpqRO5Jf+amATFG/Ty99pXNGIjh7lr8y7jbOtZnR4mj3WIcuh7q+oR9to/Y4BpFBMf2Ik5M5eBv32MF8p/QIOnnC8W/5hBKROP9VNxv7+y/A2etC/CZdAxym3mVuvV2J77DaasQgZ87QH23/5tDCmpDHnsSXRRStLud/Bx6xo+aP6YsrAVw6IzMzPtFOZbz2Bk0pBO8fT4vSzdt5ml+1vZ3yyun6KYAwzLrGbukDTmDpmA2RAiOg5fM8+U3kaQANcN/j176v7JrtZPkfQh6fosztptBHstxrQSbIVXsWyPnTXePDxht8kCXwOzc30snD6BjAxrp/h4PTWahUbiA/2+xtDB35CBNXMu1sx5mMyFNB5w8+7/K8frDDJx7DrS971K4XfuIm3aqdrzgUCQDS83sOn10PuTL89m0peyjsk10+1z8fK+/7DcV0FrlDUw2efgDF0+Xx16GanmkFX0gbLX2OpMR/f6QMw6I3+9axgmo45VDa+ydP9Sdq64GkdrHrn6Ji6f7WRLYAurs924LaHELrlNHmZ78zl77NlkZeZ3iplcGDz3uyq2rbUz+wuZnPfVEDE/lhYIuGi1rcTWtBiXc1e4KwP68hQCH7VQoyvhz1efj1sX5PaCicxMPzr+x+Vx8cJbn9KwMpecssOxgY0FHozTqjk7dw/6DR+wfRLsHQ2ZpkKuLPk5SYYjrctN3ireqnyYek8Z6cZcTsn5FutdzXzYsgN7IBTHmG+0HrIaZhk7vwDw+ZpoaVpOc9NifN4arQ+9PpX0jLM0ImhJGoi3rpXye9/E3+Qk95pTybxw/LFAesS7iVaUPSb4sXWkCOGx4dev3w6INXDrHlL0Bp4ae9ga2NZq2K8HqYRXCCgEDiGQaD2nCOFxXHwS+/ZS/Zv8t+l9LQbv8uwLuDTrHPS6jtOyL27+WCOGPnzMST+N6/OuxByJu+qF8Xxc9wJrm/6rHTgvqLyIpif/inXuQvKvu4FdrSt5p+pRipJGcfmAn3ZKpo5VPH/Axwu7HuMt/T4tXvBsXzHXjfoe9e89jW39EnIXfp3MGRdQ8dtf4NyyiQH33EfyaEnkcWQTErDHXaqR649b1uIOx4CJVVbcSc9Kn0Ga4ciD8b7Gg7y1azdrKzNw+kK/pZpaOLW4lQtGjmJgxtGH+hV1z7Ku6S2mZJzP7LxrqbAtY0vNkzQY86nx1TA1/VyG7a7EWbMRvSmFgmm3gHUMS9dsYll1kHJDqESBMeBjsqGGs0fnMWns6CMsYMGgD3vL5xr50OLZwjF3KamTtMN5avoph7JgOpt9vHNvOa21PiZdmkH60u9DwM/Qx/+C3nKkVaxsdSsf/bEanytIybQUZt1SgDmla+UMNtds4IXaJew16wjqQiRZF/Qx2OPnqtw5TCmYdsTEfNq0icfqqjDUGPAvHcQpI1L4/pdDmWxlzt6rfpytjWsp+/hKqqpHY8bDN091ceqskXy45QOW+vZTmhtyczX4A0yshQXZkzll9JkY2incvHl1K/96uIqsfCO3/3YQZsuxl0WIHpTbdYCG0lexO1ZBUigu06jPpdR6Lk/akzHq9Nw/YBrDk0Ju4Gt2bGbZWzbSN+eS7DhsDWwc28gpZ+uYM2YkVa89gqt8BwdHJbF+sguLPpUrS35Gljl21l+x8C+ueYqdrZ9g0JmYn3cjw9POYLV9D4ubt7LNFXbxRMe01KEaOZycMvCIvSgUg7pRW2ey3g5bA8eQkSXr7DT04X0o4PJSfv/bePY3YJ0/irwbz+jRvSHRivJY960eel8Rwh4Csj92U+/1ccuO/QxPtvCL4Yct7U8frGVRQzN3DSrkVGvPhRr0R4yUzAqBEwmBROs5RQiP0+pp8dt5vOoZNjq3k6JP5jsF1zI1Nf4b9F2u/TxS+Tca/E0MswzkzsIbyDMde2xOWzi22Zbzfs2fDh04Pc+8RssnKw5ZlOSQ+K8DP9SsD5cU3cPg1Mm9hmiLu57Hdv+azRYn5kCQ65NmM2fQlfidrez/wy3ad4fc+kcMSak0L1lE7T+fJuOc88n76rUdyuQMuDRSKORwr7tMe1Yyu56WNpWzUk6lvLKV5aUuymyFIUJDgOFZ1SwYamXWoPGYDLFLDth9Tfyj9DbtnWsH/55UYyZuXyPL938bozGPHf5WfEE3Xy75OYY9q2jc+V/t2ewxl5M15kvodHp27t7Loi0H+MyTg1sfKjyc52tkdo6HOZML0AdXa5Yav785RIKM2YetgaYj6935vUEW/byC2h0uhsxM45S5lVQ98mtST5lB0W13x8SoqcLDsocqsR30kl5o0uIKMwd27E7p9Dp5ce8rrAhU4YhYA4NBUn1OZumLuGrYZSSbjo5dcwc83LrvLVqC6YzYnsXuDRl849xczp2eeUg2X8DDqxUPUOXag33jJWzafooWr7mw+CDXXXMGRqORPWVbWFT2EaszXTiTQnOT3exhljuXc8YsJDf7sCu2y+Hn0bvLsDX6+cY9xYycfGQimGNdzEJibUs/oPb5Z0DnJ+niMTApiMu1Q+v6Y/1E3jGcRrpfx9SNBlyrc8k5YDmUpKmhyI1leg1f+cIpZKZn4CzfqZFBf2sjLaOK+HhyyDp3SfGPGJjS8f4hsqxvfltzYRaL9aSMczgr9+uae+lBTyNLbFtZbttOS8Cl9ZlrTGOedRyzkoswta46KgbVmjFbc4U1WwYcAVMwEKTqkSXYPysjeXwhxfeci87YsyQ70YryWNdBD72vCGEPAdkfu9lmd/LTfRXMzEjj9oEhXSTtrbomnq2q4+rCHC7OzeqPQ1MyKwQUAjEQSLSeU4TwOCzD/e5yHqr8C7W+Bgaai7i78EYKzbGLVXckXpPPxmNVf2ebazfp+jRuL7yOCSmje2xEFc5tvFbxcy3b6ReL76EkaTz77/g2fnurZlEypIQOz7tbV/F21SMUWEZoVorwIu4xOaSjfQ0beKjqr9SZIdcDdxV+k2HZU7VvNK1+i7rFz2KdupD8827Q/s3X2MD+O2/GmJvH4N/+Pm6Z9rkPsKT5E5bX7sRTPwoaxoE/RF5SzTZOH2DnwpFjKE7vfL4+rP0H65vfYWrmhZyVe80hPD4p+z6tnjKysy7jk8ZXybcM01z9HAfXUP35Hwn6XKQUTqNg+i0YTCGMHQ4Hy9duZlmlj1JDyA1SckiOs25leuZaxhdkkJW1gJS0Keh0R1vxhAx8/Mca9q5oIXe4hXPuG0DDv/6Gbdli8m/4NtZZoYQtsZrHEeDjP1ZzYI0do0XHmd8uYPDpR99Er6tay0v1yyk1GwiGY9h0QS/DPEG+kr+ACXmTOlwTT1S8xUdOC/n6ZpKXzKC02sOjNw+mMOvIrLBCtF8uv5cWXx05lZfy/ofjcJHMyOQq7r52Apk5IQLpcjlYIVZDz1725oX60PsDjK8NsiBzAjPGzubNfzSyalEzU2alceWthw9ZPbF4Ax4Ptc8+TcuKZeK3S97V12Gdt1Bbix53hRZruHrXXtasvIjCLVaSnKHt2J0UpGlsA6eeY2LO1FO0f9OI5frF1C76u2bR1U07jQ9GbMYdsGvWvgkZC+IW+YBjC+9WP4bTb9Pif88vvFO7rJDmDfpZ07qXxbYtbHaKY7NYdIOMCjYwI1DFpKQisrIXkJY2A10k/rPNl+v+tYamNzZhKrRS8vOLMKR1Lx6zowElWlHGDW7vPqgIYe/i26d7X95o408VNVySm8VXCg8n0Fprs/PbskoWZlm5YUDnLvJ9epBKOIWAQuAQAonWc4oQJnjxfdTyGU/VvKAlNjk9bSr/l381SfruH5h8QT/P173OO83LNKvCV3Mu4aLM+XEToPaG3+yt5qUD9+IKtDAv7wYmZizEtX8f5fffQ9LosZTcc9+hV8VK+MKBH1LnKeMLRT9gSGqIqPVUW172Cn9zLsNj0DHencQdw39IelJIIcq3y/58F97GKgZe/xss+YMOffbAA/fi3rubgT/9FZbBQzoVx+l18f7ezSwvdVPREnH/9EPGPsjejCG9glPTJ2kupeOSR3bo2mv3NWqxgzr0WuxgSlRW2J11z7O/6X+MzrmWla0fU+3ezezca5mSeT4eWzmVqx7C21qJKbWQwtPvxmIdiMd9UHPVszV/SKU9mTVN01nXNBVXIERWc3xNnJXlZuG0seTmHG0p3vRaA+teaiAl28gFvyghOUMfIvetLQz9/VMY0jp2NRKrj8QUrv93g+aROv4LmUy9Kgenz86/9r3CJ8FanJEyC8EgaT4nc40lXDH0UizGkGWzo7bTXsp9lVJjEO62juJ3f/FrRFAIYaxW5y7l3+X34Q26mB64gZdfS6fKl0Om3sYdF2cyZsKRCZf2l29n0f4VrMxw4EgOxxpuT8X63Jkkpei465EhpGV0XFy+szFE/+6tr6PqiUdw79uDITOLwlvvJHmE1CGEFkcrL7zxGfbP8siuCFkD5dKlbkCA+mmVXDXxb+RmTtQylArBD/r91C36O7YNSyU9LBlnf4V3c5bQ6D3IlIwLmJ339a6IFpLBW8dbVQ9T495LqiGLCwrvpCh5FF5vbSgGtWkZNT4Ha/WFfK4vpDXs7ptlSGWedaz2nzzT0TGttmW7qHnyI/SpZkp+dhHm4t7JhpxoRdllgHvnBUUIewfXftHrv6vrebW2kRuL81iQffjvqsLl4e7dZUxITebeoUda6/vFwJSQCgGFQEwEEq3nFCFM0ELsLeIWEX9Fy2f8JUw0JUHKTflf7TbRdPsdvFz+Exq9FYdi3+Q7DW++TsMrL5J9+VVkX/TFI5Db07paO2AWWIZr1q6esBJKvOA/dv6ORcYKzUpxYWAIXxl5Bwb94YO7fc96Kl/+FUmDxlJy9WGSKsI1vvVf6v/9AlmXXEbOpVe0O9Pba0t5Z3cp66uzcIetgVZzMzNLXFwwcixuk0tzJ13Rshp7OHNkgSlXS0IjMZyZxqMPxstrn2FD87ucknkxs3KvPuLbDY4trDn4M/JSpjEw50pePPAjLa7ra4N+R7opF7/XQc2aP2CvXgsZRkwDCvD6Q9YaSWCTmj5Niw3UG0az4vPNLK3wsM8QIrD6oJ8JumoWDMti+qRxWtxc6apWlj9SpVn3zvtpCdlDLDh37aDiF/eRPHY8A37wk7j/CirW2VnxRDUee4CWoTWsvGo93tRQXJwu4GWkF64uOJvRufG7PwcCAe7c/xrVgQzOTHYzuf4s/vi/Gs6ZlsE3z2vfErvXvpY3K3+HUWfigqx7eOn5WtbZijHi5ZqprZx7wYyjxuXxuPhoy2IWt+7G/cpszNXp1H5xE4NKyphvHc9p4+ZgNB5bnUrHti1U//Ex/C02kkaOpvCWOzBmZrFi/TpWvuchc1s2Fldo63UlB2keV8/0cy28bm2h2udhTnA7Z/s+0n436DOgTI9/SyMGQxb5X7yN9/WvUebcyJCUqVxU9L1OY47bm1xxv11W+zRbW5ahR89kQxGFropwlUIdoRjU+VjSprDOUc5i21Y2OsoiVSGZlDKIBdZxnJI6BKPOgHNbFRU/f0+LYS2+5xxSJsSOZ4x7sXXwYKIVZU/I3AN9KELYAyD21y7+WF7Nh00t/HhIMRPTDru2ewIBrt26lxyTkSdGd37x2V/Hr+RWCJxsCCRazylCmIAVdqRrZyq3FX6DiT3o2hkZQk+4ogaCft6o/A1ljg1HHTjLH/wprh3bKLn/QZKGHFnzSFzaXiy/RytLcXHR9xmaGnJ1625rdtbw8N7fsMPiJskf5FspCzlj4JEkVPo++PKvcexZR+EX7yBt7OlHfM5TeZCye+7CXDKIQT//zRG/tXocvL9nMx+W+ahsDbnZ6HU+RmfXsHBYDqeXjMGgP9L10hPwsLJ1vUYOt4frzUm212mpIauhzKkkBGr1NfCP0tu1Q/a1Qx4nxXAkYQwEfSzde732zXnD/sqn9a9oSXuGpkzjoqLv4nGXY2v6AFvjUoLh6nH6YAqZ+RdjzZiL0XR0nMi+0gO8v2kfKx2ZOAyhw0Km38YsjxveyyPgg3l3FzFweigZTt2Lz9H07pvkXn0dmWefF9c02ZzNPF/6KhsbXIx/aSbW6nQcmU52XPEppw7O5PIhX8Rk7Hrdwherl/J6i580XQt/GHohf36jgU+2tPK9K4uYNrLjjJefN77FR/XPalauKwY8wHv/3cVrO3MIYGB2OM05CAAAIABJREFU/kFuvHYmJvPRBG/Z6w0serGBpAGNlH39Y1pTQ89YW72cac/gnJHzKMrv2uFK/gaaF71N3UvPS6pWMhaci/GiL/DS2xtxrcknuzLkCSDWwPqBbtJPreWrF51GanJovg567Pyk/DPsAR/fSE9iXNM7uP17tHqLwsKSk8ex1Wxim3MDOeZBXFHyU8z67tcR9HqqaGpcwubmRWzWuQjqYGDQzJnW88jKWojJfLT7WY3XxjLbNpbattHot2tyZxiSOdc1nEkPlUOrh7wbziBjYc+5rsdanIlWlHH9gfT+Q4oQ9j7GffYL9+0tZ4fDxWOjBlPQZk+TZDMNXh//HDccU5z1U/vsQJVgCgGFgIZAovWcIoS9vPASlfwlMgxJVvP7qmfY5NxOqj6ZW7uYrGZZ7d/Z2PweOeaBXF7yUyz60GHV73Cw7zs3YkhNY8ijfzqilEPk23vtazSLjcTEfbnkF922Eu6q/4xHav5JgwkKPDruLr6JQZlHW5y8jdWUPnkHhrRMhtz8OLoYyV1Kf3Q33oMVDP7No5jyC9lSs5d3dh9gQ3UO3kDIlTHT0sgZA71cMHIcuSmHE5h0tDQqPFUaMfzQtpqWQOhgnGfMZp51JhZvKXtaljIt8xLOzP1KzG7WHfwttY61TCu+l4ykUTxXdjc2Xy2n6QvJcVeF3zGQZBqJu3Q/QZuL5LyJFM64DYMlVO8wVvO4Paz4fBNLy50ccBdy5geQ5ILGiTZmnGfgtCnj0esNlP3gDrw11Qx+6AlMOR2XWfik4mNebVpJhRxCwvGJJpefma9NIW1LMQaTjtNvzGP47M7LYrSVucZTz91lK/Fi4Tu5+cy0TuSmR/fh9AS0chNJ5o6TkQgJW1L7F7bYlpBnGcLlA+5n42d7+cMHPhykMNRSw93XjCG34LAbbX2Vh8e+d0AT5bZfDyQjL8gnW5ayxL6DnfkS/6jTLNKja/zMSxvNGePmYTJ17NYdcLup+ftTtK78GJ3JxO6Lv8TGLQPI3J6FxR3aZp0pAVom1DH3QivTR0+IOX+bHQ08eHAdBANct/xthtorsZw2DF92I3uCjWw1gDmo4/z0hRRnX6iVEelKCwa8tLau0eoGOh2bw6/qcKaMZJW/AkfArln5xYVUrNXtNX8wwHpHaShDaX0pNz5lIb9Wz/bZFnKumcH0tGGYYsSydkXWjp5NtKLsKbmPsR9FCI8RwP78+s3b99Ho8/Ps+OEY29QM/dm+CrbYnTw8chDFlq5fyvVnXJTsCoETFYFE6zlFCHtxJSW6PERkKN0tZ7GxeZHmQpZssGqZL62mwxaC1rWrqXr8YdLPOEsrYB6ryeH8pfIfa3FJYukaljq9y+i+v/85/ulZiVevY4o7ldtG3kOKOXYcUt2S52ha9SbZZ11B9qzLYn6r/pUXObj4bTZ+6RJW+4uptodcEA06H2NzxRqYz6nFo7pcyDzyMakN+VnrRhbbPmbLoXpzQXLwcU3B9ZyaNi2mS19Z8yK21z5NSdps8khib/NyVhncWIKwQJdPTtbZWiFzg9GKp7WKqlUP47GVYUzJpei0u7FkHmmhbTt4ryvA/368j9aKIFWDvXw+Q8gcZPhbmGmuY8IHr1CQl8PA+34ZE7cmRwPPlr7KGl0zbskUKi0YJNPrZKFlGJcM+QJGvZFtbzex9vl64S+MOS+D6V/LRW+Mf1v5yf5X2eXLYJzJxv8b/CV2V7i495lyJgxJ5t6r44uH8Qd9vH7wl1Q4tzIsdQYXFt5JVUUdD/1rD+XePKy6Fm47P40JU0dqyVme/sVB9mx2svDKbOZ/6ch4y4PV+1i0eykfp9loCVsN0+xeZrakc+6IOZQUjjgKLyHWlY8/RH1DHStGX0xwz2iyqkOHsoAuSMNAF1kz6/jKhTNJMnccTxnwuvnvpy/zUnERKW4XPwqkMWLiXKTA/P8qf6MZC0/zBckKeemSnDIBa9b8DpO8yHOhGNQl2JqXE/C3aO8ajTlabcr0zLmYTLlI3OvbVY9S6dqh7QHnF9xOSWeZS/0BSn/1Lr5N1ewdA3//qoOgXmpsJjHHOkYrX1Fs7vnMh4lWlF3eyHrnBUUIewfXPt+ruIV+fete8kxGHo/hFvpURQ1LGm18f3ARp6R3Xke0zw9YCagQUAgoC2E310CfUpTHq4B8W+yk4P0fq5/VauxNT53Ize0UvJf3Sh0beOPgr7UkKJcN+AlFyUe6fNU881dsyz6g4KZbSZ85q91p2mdfy/8qf6tZa64qeTBuK6HX7+ZvO3/DMlONZp35YnAUV4y4tV2iJgfn/U/cTMDjYsgtf8CYdqRlT+LSNok1cPt+tjQW4Q2ELDzZSQ2cOdDHBSMnkJXcdYtWR+uz0lPDM1V/ZqunCq9WrB6yjZnMS5/JPOvp5IbLggT8Tmob32VD00uYg1DiE9cAE5stmZT6a5loXci8/FC21EgL+FzUfP5nWis+1bI75k29Eeug2THFkQQwyx+tomy1nYKxScz5fj6rNm1hcamdXbp8gjo9umCAUe4yFo7K4fQpEzCZQi6Ty8uX8XrzGirN5kPWQH3Awzifka8VX8iQzCOTtcg7VVsc2vfcLQHyxyQx545CkjM7T9CypHENT9U3YcbJo4Nmk23O4NUVDfz7wwaunp/DxTPjJxIufysvld9Ls7fqkGXW5XLzp7+vYlVDsZaR9aoJTQzIGscrf6whv8TMrb8aiLEd8urzeVm1dRmLW7ayLc9AMOyGNbLGy7yUUcwaNx+zOQn7xvW8/f777LPPJnt3NmZPiAw70gK0Tqjj7IuymTxiTFzbmli8K//zEJ6aMt6dMZePhoyg2JTCnQUDeOvg/XgCTs4tuJVhSWNoaVoWLgNRr/WtN6QTKgOxALMlFLsXCHiwt6zWise7HNvCMuhJTZumkciU1MlaaZPoJuRaameKl4DsBRL/Kolr2osJrn36U5oXbcc8MJPi+89nE9Va+Yq19n0EtGhDGJtUzIKMcZyaOhxzVPxvXKC081AvEEJh+t8DZgLijrACmNuJjPcDRwYuH37hR8CD4f/7jFSeidGXFEgNZVKKr/UpPRefyOqpnkDgoNvDXbvKGJ+azE9iJI75b20jL1TXc21RLueHsyz3xHdVHwoBhcDxQ6AX9FyHg4n/Kv/4YRLPl/uMoqz3NfJI5dPsdu8n02DljsJvMiZ5eDxjOOoZv9/DpxXLGZ09lry0km71Ue6p5HeVf6HKW0uRKZ/vFt3IgDZuZg2eCi2Nvxw4zym4lTHpRxI+saqUfvc7+BrqGfrYn9Gnp7O7wU15i+comeTZzxpfw+atYVLGueQndWzJkg5a3Y28V/cedUa/Vl9wbvJkhmZO7HC8zgPbadm4DEvxCDKmLjz0rN/nZN2G7ew3maj3hssz6DyMMm1m3gg9kwqy0fdSjEWrv4X/NL6IDgMj0+ax0l3BVm+oTpz8oY0yFmINWvDJvwUD2PViQYJs02CSzAPw60CS8/jxMTR5KsltktUEg+BtOYCrca/Wpzl9AJas4Ucd1k1v5mF+P5dArgfnXfsh1X8IH0dLgAPbvNR5R+ILhm6STT4HmS0OmoZW4T2UbDRIqt/LeFM+k3OmdGpB9TX5qXu2GW+FD4NVT87XMrAMaj85i8fv4fm6LXiwMCPFxCnpIzVZXn+vkeo6L5dfkE1OlhFXYyPeBhtpwwZ1erng8DVpa88bcDPOOpfi5DHIxcDG1ftYvScVnU/PkF0B8OmZ8X+ZZA6Jz7WquaWerVXb2WV24EwKxZUmOQKM3G4lfesAMmtD4wzog9QPdpI3s4EvnzcTizn+7MGSHKn6jccJuOwkD5lA/he+w8PN+1jnqCNHV8Oo4P84LetSTs+58tBchgrFb8DWuAR761qRICRbyljM5hJabZ8QCLszG035mjWwvRjUtn9sUn90Se1f8Qe9jE47k/n538LUJhty03vbqPv7SgzWJK28hCn/sCtzo8+u1TQUcljjs2ndp+otzE4frVkNB1oOp83vzsbWC4ryEuAJYCUg/rzVcRBC2ZTbbswS6PwDQFItrw+PTQjhacA32oxVfg8VfYyv9Rk9F5+46qmeQmB9i51flVYyL8vKTTFKS6xqbuWRA1Wcl53BdcWdl0TqKblUPwoBhUDvIdALeq5DYRUh7MG53ObczaNVT9Psb2Fk0lDuLLye7KhyA135lBCrFzb/kTeSJXNkkHEeP/MzJnBa8ZwjsmzG06fD7+QP1f9krWMzSToLNxd8jVPTpmivSi0yKS9h89UwI+tSZuZ8+aguPQcrKPvR3fhHjGHPl+9gyT5bTDIYjyy9/oyE38mRbgjknF7J1KzPmG7dSJqhK+eu7km5SQ8HDDDCD6NCZ3Ma0LNWn8QafTp2nVjXOrecde/robeK1xUx5dVJeJO8fHLTKux5ofjGo1pAh2XnINJXDyZtbxJ6nw5fElSfAe5untX1fpjwOZTsB78etk6FA0cbFNsfnvDWXWGI5A5FBzcsbWJQnZffn5dFXfoxYOeAvE8hvQFseVB3pjDhriOd3gSD9kJxGZi8offt6QEck2o4/6JCxg092qW0o6/I33njp6/TsPxlLXNM5mkXkzP3KnR6Ay1+J9/dv4jmYCqjTfXcP+iKoyx6kb593kZszWGrYfgSQipVpqXP0DKFJqdOaPfd9uSrce3VMgdLvcdc8yAuLLqbDFMom619QwWVv34fnUFP8b3nkjw6UqblyN4CwSBbnKEMpZ+17sVPgGGWPH458DCx7fos9EqwvZhKw3+1vAJIAGVnFsJYor8FyKoX61+kCSEUktl1H/ojv6AIYXcWywnwzqL6Zp6urOXL+dlcmn90WaFSp5sf7DnAlLQUfjik97L7ngBQqiEoBPoNAooQdm+qjquilEPdu83Lea7uNe3As9A6i+vyLsMYLs7dnSFV7nyNHwdKcRqMpPj92I2hw3CG18uZ+gzOLppPkTV2jbaY5/9ggNca3+OVhne0LIeXZJ3N5Vnn8d+DD3LQtY0RqadxfuHtRx0aNYvfO8tYvKueLSWT8IVdIYdlWZhSkIIhhrVN3pEkH5Jtc2TaTHIssa2bOxs+Z2OgUstuWOy3cFrObIyGzq023uY6rSabwWrFMmoobtc+/H6bdprb/vlQqtYOw2j088v/t5W0tADe2mpaPvoQU2Eh6TPP6s50dPpOa8DOGy3vYMDAF60XYtEdHoczGOC3rfVUBfwU6iHbbNWKx3v8LbR4yjAbrKSbB2rfEOxqPaVawfEMYz5WU+zb3oDfg8d2AHEl1emNWq3C5AP5FDw5BgG05oYduEaHLDPRzVvRhHdlOrrd46AmFJsZNAZx5+hIqgbxIBx4QQp50zqvHRgLFJHft8aN512Hdrw2TrVgviAFXZRrZrW7gQ9bG7Tqewus/5+98wCPqtre/i/TkskkmfRMekIvAiJFEJRmQ1RE7NgFxY6A5SrYwC5FRFD02gCvil4LYKU36b1Depm0yfTevuecCYRAQhIIXP9+s54nD5rss8/ea++Ztddea72vhhh5ILJUVOxi42YLWZkKIjvmUVZ5lCc/7yT+bWUfLfv6eOgZcQnKGhTVhhal3JFDnnU7shCFSNgeJo3AlOvmyHwzPrmPwrYSpHIXF3eTkJzdOJGzx2rDsq0Q9oUhrwikGfslPkyt9MT0MHHrtUINXvO9S5/TTvmSOVgPbyFEHkriNQ8R2emS4/tgWcVcdpi3s48bcaLg3vj2XB0d2CcNiRA1tFv3inyCqsieyM7wQupY/zavid/K3qPYvo9QiYqrkh4nRZ9F8eQl+Oxukh67jMj+TcuAMHpsrDEfIkEeSZ+I5jnOJ8/3HBvKM3UIhasULTAVePWEMQcdwka/QYMNTqeBBWVVLKky8FhaEv2jTwUVs3t93HcgF41Czsx2TT8XBLUe1EBQA39fDZxjO3fKxIMRwrPcC06fi48rv0YgnJeHyLg/4RYRafJsxFK6hW9yv+VPTRb9QlMZmzKU9SUrWGE+xOHQwMFTqLPrIEQNIzvSJ3UAcmnT0tO2W/cyu/xLbD47adIIkr35pIZmMzL1pTopYSanl9UFJjEaqLUEQiFhEj+XZqkZnK0mO/r07yuw7uIn7RsiPP4d6W/WcTRdHjsfHX6L9QqdGP28OeQChrd6sNG0RPEg7vdT+udr2NkLaVIICaRDKkKzKZW6+PGrS8nfH0BwvOuOOIYNjRGJvfOeeBABCbLV+/OQ1MD8n80anfzs8op5ohPcO2YkfeJqOQ8FNMa3tUvZZSukW3gGzyQPQ1pTt+XxOViZOxqpJJRB2R8f15HBpWVh0TPiK0alv020IrneoQpOYeXOf2MuXI3DFs/eFY/jskq5+P4E2l9ZC8Qj6KzoiIPNy03sXqPH4w9cLmgyFPQcEsXniTpMMj+R2yHpD/B5oefgKK6/LwGZ/My+IioO2UXuQ7vBS3zrUAaMT0YVJ8Pr8/J43o9U+9UMVnl4MLmW8uLDJeWs2mWm/cC/qExaSo/NFzHg54ADr0838NlDXxAhjWK0ZhztlAFHsSFZU/kFO42/EiNPZUTiq3z0ryp0ZW5ueSye9ft3s7YyBQleRrbTMWJkn1P2nlfYMzs3cWB5JYa9bfC7AvtdFl9JSi8tF17dm+ikM0vjFvpx6UrRfv8ubl0p8ugkNDdNJDSh1tnbqv+JDbr/ECGLpUf8v3i3/Agev4/nkrvTTXWGIdwz3PACFY0wlu2GJcitCgZ/PARplY+YEd2Iu/Xs6GXOcEjnutj+TB3CB4GPhOzwmlj3sekJDqEQEvUAwkbaArwglOw2c/7/04vPZo412LwFNTC9UMtmk5UprdJoG17/Zd1DB/OweL0i9YT0JBTSFhxKsKugBoIaOE8aCDqEZ6bo/4mhrHDrmK79hHxXMXGyGMZrHqB12NndzjkNeRxd9yrvtumGUyZnesYoNIpawJQSYw5/aFezARPmGgLtSI+bS4jiyuRBpKobr9kTwE9eK5lOldeKEj/PpDxCx/BOiOldlXaW55nYUmLBW4NkmG4oonfJLq599lGUNQ5pY8skOCGLSl6kzHGEoZpxtI0IcARWWPJ5t2AmhaFeIjx+Ho8eQTfNkMa6w+sxYTKuwVj9Jx6PUN4jpI2FEanuLwJp6D0l7CmfzQ9zJqCvDICRJCXKmfFOhlgzWP7xHMzr15D08BNEXhyIwrSUGN0VzC94CpkklPsy3ydUWovy9nnlWn4z7iZNEcOrqSMJP8lx31ryGtX2PfROm0J0WKCOTpAt1T/wV/U3pCk7MyJlUoP1c4KedQeWsXKmCrspkYyLihgwoT8hUjk2i5eda82iI1hRHKj3lOGktfIwg/51Feltley32pmSX3r8vfe54tg+z4DZ4CWtdSijxiejjjuzVE2b3iM6hZWHHYSppVz2pIZfY9fyu0VCdIiJ2dnDkdVwPQrzGDsrB6PVR/TwN0iJiOWu+Xfi3esgRB6C3+1n16tHWC75ReR3vDH+TgaphzaoFwFpd4n2HfJtOwhZeQfa5Z1o3z2cu59JFp/5belmFuyMwoOMHupSHru/F8pwJWZ9JTt+XUfJ5njcZTWOuMyNutNR2g2Mot3F/ZFK6/JTNncfWQ5vpXzxB/hddsJbdyfp+seQhtXuGaGOVEjVlIWEclPayySGZrPOrGV2+T6UEilT0nqRpjhe7Nnc159x+8PV6zC+vYWY/GhM3Zx0evouwmT/G0TDc2woz9QhXCEkcQA9TlLyk8IdALBfYKcBJtS0EYq1NzdjQf4ndq4Z4ws2PUcaeO5oEfkOJx92yCK6Jlvo5Fcd4ymc1S6TxHq4V8/R0ILdBjUQ1MA50sA5tnOnjPrMrv/P0eTPotvzbih32w4yq+wzLD4bnZRteTLpPtSyhvnhmjI3j72aolWTWBapYrkmi4GRHRibVL+z5PG6+atkJSvMBzioCHCnCdLO6WZwZHv6pQ5uMGqYY9nCz2XTyUFNFXLknih6um4jpzSSCqtwiQ0quYRLMyLp69Einf06qp69SX5sfFOmcbxNoW23SAcQq0gTI127y1fyvuEHLLIQMpxSJmaOIzGiYfJvMf3Ntk+Ey7eYhHNTDThKNShDupLcZzwSSZiYerm+YDwOl435b7yMOkqGRiPnwEEHz05Mpns3FZZtWyh7fxoRvfuieUQ4n7WcLCv/kP3mVVwcezMXx9bSX/xh3MunlatFCP7X0m8mUX4qqmm+fgmHdQtoFTOSNnUiix7+U/Qc1a5irkh8hI5R9SOK+rx+VrytpXSXjZiUPNpf8hl6Vx/yK29g31Y3HnfAq09tFcoF6VoSNn5A/JDLSLz7AfH3n2sr+U1npJUylFy7k+vjo7lWEc1XAkrpYQcqtZTbn9TQqtOZEaB7PX62flnFoT+MYjrq/qsryOtr4WlNBj0ia9E3f83fxBcL45DFFTJg+E5GRYyhdOw+JOFSIvvHY/y1jPj7sjnQ9yD/qfw3Hr+bXhH9GJX4IIqTwE6OrazTZ2PB1pnkzByJQFE5florYhJqUzsP7DnKzMUmjL4oWnmq6S6pwnEkC787kO4rTywnpXcF3Yf2JSqueXx/9e0uv89H9dpF6Df8IP45pt9IYi8dWSd6XuHM47vil/H4XQzTjKd1RK/jXX2ry+G/+jwSZUqmpvciqgnp1S21ywWHvWLuOsxrjmJOs7H+/vWoVRqxrjBW0TR6kJYai9DPOTaUZ+IQCrcHxTWAMu82MleB3HUfsAsQQGgaklNQTIV1CMr/fxp4YH8uHr+fzzu1avASbE5xOWsMZp7PSqFrRIA/OChBDQQ18H9XA+fYzp2imKBD2My9Ihjknw3L+Fq3WKzFGxY9iDvihiM9SxJmn8dJydpXMJoKmNbpElwhEqZn3kGSvH4OvhOHrTUV8Kd2Bet9Row1tUwqj5u+RHClZgAZ0bWRp2MHTrfPRVvZ06wsklJUKaSgBKIe7eNCGZKtpk9aBAqphMqvvsD4x68k3Pcg6gGDm6UtQVfflbxMqeMQKrqwwq/FFxJCP1ccD7V7FsUxfruTevV4hAjVatERdLsD0UCJREVkVD/Mv23EV2Ymc+wM5DGBQ/re8g8pNa9CZr2Oee9eTJfOSoYMVjPz/TK6dwvn2YkpYrpo3uNjBHx+MW1UIA9vCTG4y5hfMB6FRMm9YnQwYIh324p4s3QxEkKYnHoD7ZX1p32anUX8VfQ06tA2XJwulB7VSqn9EN+VvESYJJK7MqeJ3HAny+bPKzn4m5HIZDnRfaRsWVmOwRDYM4owPxf2V9NrSBSp2WGUzngb267tpEx8nvALuorpt48fLkDn9oiHiNfyS2kfHsYrrdLwePws/aKSTX+akEhg6J3xXDJU3SjSZ0M6PbrKxLpPypB4JNi6Gxg97iJkoRK8fi8/6r5iyUYD9l1D6d6rgmeu6ItlfRXls48SOTCBqIGJlLy8D+UFalIndaLAkcO8sunoPTrSFJk8mDye+BqwkxPf7/P5mftiPiVHvUQO/YVrhrenW/RVx5uYdGX89eNG8je0RWkNpIT6ZW5iuhyhw6BYWvfoe9bRwGMv89otlP88G1vuTiShSpKuexRV27oYI0LNrQDwZPVWc0nc7fSMEYAva0WI4M8q38NGSwUdwqJ5IfUi5CfRRrTEnq6vD/1Pu9H9ZxvS2HCSXh3CMte/xeirPETJlUmP1HFcz9UYTuz3HBvKM3EIhVumGYCQIlLUBB18AFwHZDSh7bEm5/3isxljCzY9RxoQ0kBHH8gjLVTBu20b3i7fV1SzqKKaB1ISuCK28XPDORpusNugBoIaaCENnGM7d8oogw5hMxbO7nPwYflCNll3ogiR81DiHfSLPFvgOKEuzkf5lllYSjayJvMi/ohSMSiqIw8lNs8B8/o8bCpdzQrjPvYrJKLzJUhrp5tBqjZcpOnB14UzyKtshUF/GWZnIBKilPvxRO/FHbuDjtFxIlXGsWhnwb/G49aWkjltNvI4AXiveXLUvIX3yudRSRhSn59Rsou4ptX9p3QSgNDfLTqBVrMAoR+IBoYpOwSItyP7YDu8nbIfZoppdim3CMjuUG3bx9bSKSikaiSlU5nzoZGhV6kZdVs8T4zPR2/wMuOdTDRJcrSzpmHdvoXkp55F1U1AhT97+bN8LgfMq+kTewu9Y28UOyxx6Zlc/B02n4tHEi/nsqi6nI4nvlVwytbkP4rTq2dg9jwU0rpR5pUVn7DHtIwOkZeJB+8T5eAfBjZ/WiUCl+b5wVkTQNWkmGiTuozsjP0kd78Fdeuh+AWH+LExoiOcLTjEMhm5dgfP5xTTLjyMl7NTGXMgD5ffz2cdWyGrAQvatsrET/+uFCONF/aP4IYxiShC63LXNUWLS3V/8fNeNz2+SiDMKCcmS0GvJyP52jebw/Z9WFeNxlmexdR702iTGoZ2+iGsm6tJfro94d1jyB+7Fa/FQ/a8nkgj5Ji9Jj4te49D9n2ES1Tcn/QEnVTd6gxl4x9Gfv60koQsP7L7poDUw7VJz+DYbeDQKjOmA23AE7gYcKktHFJFUBbtY2jbCm679ZIm1bQ2Ze7OikLKvp+G21COPC6V5JETUMTVRQIUqDK+L3mFCmcuHSMHcHni2Hqdb6fPyysl28h1mrgsMpmHEzudsZPelLELbSxbCiibvoIQhYy0l68hNDtO/M7aVP09m/Xfi90ICMVChFxynhzUc2woz8Qh/AsQiq3rD+WfqmyB4kJwCJtTYxB0CJu6af9B7fLsDv6VU8xFkeE8k9kwguh6g5n3i8sZFhfNXcnNt9X/IJUFpxLUwD9CA+fYzp2io6BD2MRtU+qqYHrZxxS7ykiUxTMheTSZoS2TKqXb/y36Q//FHanhnexOOP1eZmSOqjfFsInDpdJSzB+lK1jr1WGQhYIpnhBdCn6zgKoYONB3TlAyJDuKXikR6H3VTC/7hHxnsUimLtRDZlpUIv+gIiWNjNcby4I6dWSlxsO8WzKbUoWlI0rGAAAgAElEQVQfBV5GhfXiqrS6VFwed7VIsG0yrsTjrhI7qSXZHoziBB2XLHwVe+F+km95FlXr7nh9Lv4qegabu4yuSU+w+vf2/PCTnjH3JzBkkJrvf6xm0ffVDBsazV13xGNav4aKj+cQNWAwifcJ+A9nJwZXGfMLxxMqCeeerFniv2avg0nFiyh3mxgecxG3xzUOMLSv4iNKTCvFOWgi69Y3Or1W5hdOwOY1cEPKC2SEd8Fs8LD+Gx3lK80i9bcQjvCrJHS/NJJeg6PQZIRiKlglAs74fW4i0vuj8najfO4HRPTtj+ahx8SJf1Ou44dKPaM0cVwXH8Ob+aXstNhOAS4oznGwcHoZRp2H5CyFWFcYm9j0CKvRY+aJ/JU4CeceWRTS+amU7bPjVTo4fMvXqNrBwUX3EqaQMG9cNnh85I3ZKo4x++NeSBQSKublYFpRQeIjbYi6LIC8KkQXf9L9h2WGJYQQwnWxt3JVzHDRQTJVe5gxoRCXw8cjr6dRHrKcLatzUGzpibQqgCwaEuog+oIcOg5OpE2Pvqz4YxufbVHiRkGXSC3j7rsIVeTZ1ciZ92+g4peP8LudqNr3JmnYw2KE8EQRnKtfy97jqHUTKWEdGJE6CelpEIr1HicvFG+m2uPk9rg2DI9pOO367HY4OPN0FL/8C36XB834wUT0quu/5Fq38kf5ByKHaWb4hVyV9JiI6nqu5RwbyuY6hMIC5AHCjc3cJsxd2ABCPeEOIHCL1DQJOoRN09M/qtVxjsE4NfcmN8wxmGNz8EJuMT0jVUzMrD8j5R+lmOBkghr4h2vgHNu5U7QXdAibsKG2WfeIyJxChLBbeEceT7qHiBOAQ5rQRYNNzEXrKN86G4lMyeYeo/jRdkgkbn4wcdDZdCs+W2l1syLPyJ+5VVjcNcAgMifElJAWmccV0RoGpF1BmDyQ5ujyufik8hvWmDcjQ8bt1RfQfs6fRF81jPjb72rWeLaU/spc01JsshDS3RKS5BUkyZMZlfGuSDdgs+zEaFiBzbJd5F8TRBneWQSIEbjTQiR1HQ5nZRFFnzwtIjJmjJ0h1l0d1X1Lrv6/xId3p3vyM0yfVcaWrVZemZxK+3ZKDAYPj47LJyxUwpxZWcjcNhFtVBoRSdbMuYQIuZBnIX+Uz+GgeQ19Y2+jV+wNePxeXiv5mQOOUnqrWjFOczWSJqC9lVk2srtsJimRA7gg6eFTRnTEspFfSt9Dnt+TqL23kbPVToYvkOTr0sjpcWMMF/SJQK6oOx+HPoeyTdPx2HVI3Cp8q6xo7nuKiF4CRzZMPFJIsdPFzLYZaEIV/FBRzTcV1dypiePa+AAwzzGxmLx8/V4ZufvsKFUSbntCQ9tuTatTeb3wR3a7IsiWGXkjayTr9StZv7AIzbpL8If4SRyi4osyG5d0juCJERqs26rRvnMI1cWxJD8ViK5ad+jRvnUQVa9YkifUjbhuNW9gQcVHuPxOuql6cU/SI3w/08C+zVa6dDMS5inDfLA1eAOfAW9aEa176ekxdBCqqLrzPHownxk/VKHzRZMg1TPx5mQyWzcfTdTv86Jb+RWGzUuFgjfiBtxGdJ/r643m/aX7hi36H4iSJXJr+tR6U4NP3hR5ThMvF2/F5fcxXtOVXhGN02c0d6t7qm0UTVqMt9pG3O09iBnetd4u9K5SlminoXeXiDyFwzQTiA9tTiZkc0d2TmoIhc18Tc1IBNAXIT/7pZr//wWwAUdrkEEDBbi18hwwBRBO4YFbrVoRcveWAAtqnhdCN0/VENcLbJiBm4+mSdAhbJqe/lGtFlfqWViu425NPNfE1wLMnTxJi8fL6IN5pIcqeOc0qaX/KOUEJxPUwD9YA0GH8MwW95wYSgGp8PvqX/le/5s4qhExV3Fz7DUtlhZl1x2mdN0U/D4PUX0n8qxtOy6fh5mZo0ioB4CkKarx+Pxs11pFpNDd5bYaV8tPdOQRUuOPkhmuZpO/Gp08kC4a5vXQ2xfGlYn9aBN3gVhX9rtxDfOr/ityKvbaZmd050dRX9C0FEufz8eio7P5MeSwCHQz0J3IA+2e4eeytyix7+cSZS/ibDl4PdXi+6VSNZHRA4iKHoSiAXoFoV3Fb59g2rGMuCF3EdN7GBZXMX8VPktIiIx+Ge+glCfy1NMFaMvc/PvDbFSqQE3k+3PKWP+XhQcfSGDwQDUlb0/Fvn8vqc+/jLJdLahJU3R7YhvhELygcAKhkgjuzZqFPCSMjypWssp8gOzQBF5KHUHYSU5tQ+9wey2szBsjpr0OyJpbx2kwVnvYttLE2uVFOKtVYmw3OwTkfmg1JIr+Y07vCHidJrSbZuLQ7RdxDjX9JhKR1hOt08VTRwrrHB72CdHB/FJ6R6kYn3HqDbPX6+f3r3SsW2oQfByuuDWWAcNjTpuyuNm0n+kVJchw82Zad9aalrLWtExECh2a/zCG+Sl4nX60UdB7TDyDekVT/uFRzKsqSXqsDZH9AzfifreP3DFbRG7D7I97Igmti/RZ4ixkXtk0Kt3lJO28FNV3VxEj8SL3BdqFhNmJ6ZKDs18uJfGbxUjcDakvIAs5NdJp0puY8fkuDtiSCcXJmH5u+g+8sMlbxGszUfbje9gL9iEJi0Az/HHCW9VNZz3W2UHzWjHCJtSg3pw2hThF053PLRYha2E3ihAJL6f1JDv01BrTJg/6pIY+p4eSV37FmVtF5GVtSHy4/2nXWYgQCunTOdbNIjrq5YkP0e6kaPeZjqW+586BoTwW5avvdQJ0c37Nzyrg3pMa7QTKgFoOldoGQoH2V0JWLSB8WB2AkF4qAMZsbKZOzomda+YYgs3Pswb+XVrBn9UmJmZo6Bl1+uj7AwdycfsC4DNNuYw8z1MJvi6ogaAGmqGBc2DnTvv2lowQCsRg7wNCjpwB+AR4pRYassFxdK4pxhcguIVb2EXA00LpSjP01uKG0uq1iVHBHbZ9KEPCeCTpTnpF1H+oa8Y4jzd12yopXjUJr9NIfNd7+T06hh/0W7k8qjOjEwc2u8tyi5sV+SaRO9DgCBSTxYRJ6ZJixBL6IVFhzuMHToELbmf5Bpbpd7JbBt6aSFmG08VAZSYD066gwFXKtJxZWFQS2igyGZ8yWkwlPZ3YXEZmHXmDnaFW5D4/9yj6MiTzVqzm7eRU/8xqz1HC/XCZB1Sqrqijh6CK7CE6dacTr8NG/uyHhWJLsh6bgyQsnC0lr2BwHKJd3F1kxQzD5fJxz+hcYqKlzJlVS71x6LCdl6aUiGTnb0xJx7j8d6oWfH5GUc8Tx/h72WwOWdYdB/9YrN/BQt0GYqQqXku/iVhZ89LmNhe/iMFxmL7pb6GSZ3B4h40tK0wc3G4Vpi1KaHYeqRYVispEMnqrGDBOQ0hNrd/p9GfdswPtb2+ByAUeQlyn21gTeyn/Ka/mxoQYbkkK8No5fD7u359LpEzKh+2zGnQAdm0w89+PKnA7/XTureKmh5MIVZ4abRU4Oh/PW4rJH8nl4S70vr/IcxwhUqoWuQTbKjuiL3Sy6MUiwhwQkSxnyHgNuld347N5A/WCqtq9UTbzMJaNOjQT2hPRK7bOlL0eN9tX/8m2NT6Uh9qLKaSiztILyehj4cIrB6CMVNPUWj3hM7JgwQZ+LQo4xkPTtdx55yVIa2gyGtK3Q5tD2X+n4zHpUCRlkXzjeOTR9TvtWvshvi+Zgh8f1yc/S+ZJNZBN+RL4SZ/Pf3RHiZWGMjW9N7FCevhZit/np+y9VVg35RPWIYnUF64iRN441YZwmbTN8DN/6b4WAbe6Rw+jX9wdSM4ScKu+6ZxvQ3mWKm2px1vczrXUwIL9nDsNvJFfyi6LjbfbpJMRdvrP9ws5ReTYncxpn0Ws/Mzogs7dTII9BzUQ1EBzNHC+7VxLOYRC3pUAoy3URbwl4JgA02ocvUmnUYCQTnO45udtQDiZCv9uaASO++QuW9RQFjlLmVb2CWXuSlLkiUxIHkOq4uxh548N2ue2UbzmJVymItStrkR5wR08nv8lbr+XmZl3Ei9vGn2FEA3cUmoRyeP3VNjF7oUFvVATLiKFatQl/FQ2BZ/fw/XJz9V74DTYKvizZBlr3OVUKgJRw1Cvlx52H22W72P1pZEUxLpRSyNFsJmOStGrOEUKDfuYVvoR5Qo/sW54InY4CSGVmAyr8XqF+wHYLJNRFeJhcOydXBB7bZM/F4Ytv1K17Auiug0m8ZoHKTIu40DlJ0SGZnNx2lTxwFlQ4OTZSUV07aLk+WdqazuFQ+q/JheTX+AUU0lbx9vIH/8osoREMt9+74wAOapdJSwsnFgTHXyf3XYt07S/IA+R8XLqCFqFNT99L6f6e/bl/oHrwL3k/JWMEBkURKB96HFZJD0GRbLxu73oN6gJSdVx69QeKJRNM/gVX3yCaeUyou68HLNtDX6vi09ajSdfGssbrdPIVtYSHT+fUyTST8xsm4nmNJyT2gInC6dpqa7wkJAq584JySSkBPbPMZlb+iurbXKiQ/SoQ5Zj9hnICm3DmOSniJEFnNAyvZuJ7xfQtzyESJ2fWIWHDi49yq5qUp+vSz5v3lBF+awjRF6WQNIjgX1YVZzDjl93UbEtHa8hkP4pXIdUx1VSOuorBrbvz7CTwE5ORPMUHJYeMdc3uBfXrtzJJxvkOAmlY7iWp+7tRlRM/ZE40+5VVP72b/xeNxGd+5M4dAwSef0HOJO7gm+KJ2H3mhgQf18d9NMmfzCEyKnfz4cV+1lt1tIqNIqXUnsQ2ojT2lj/um+2o/9hF7LESNKnXos0qn4i7Ib6KbDt4reyWTh9VpFH82rNk4TXg5Db2DhO9/fzbSjPZqwt+GyL2rkWHFewq3OogacOF6B1ufm8YyvCpKcvc5hVVMYGo4UXs1PppDozqqBzOJVg10ENBDXQDA2cbzvXUg7hv4BnahDTTDXzFf5fSIsRPKljvztZFcJzwo9QcBLwGgLIaz/XpNg0tb6ixQzlX+btfFixEKffRS9VVx5OupNwSct9sQoAEtq/3sFWvgNlYldS+j7LN/ot/KjfxpXqC7g/YUCj26XU7BKdwDWFZkw10JJxShmDsqIYmBVJfLgck7uy5sBpZED8vXSLri+bqfZVQqrnnopN/Fm9jZ0yP56aqGGK3U64UsZR8sSatbvib+Qq9WV1HKkNRT8yz7YMhzSEdi4JdyqjkTgO1XQeQnjEhWJtoFEazvelr6KWa7grY1qTIgeCvgrnTcBdrSX9/jchTs36wgl4fHb6pL1GVFgr8T3rNpiZPbf8OIDMiUpcudrER59UcEmfCJ54VEPRKy/gzMshfcpbhKY3B+Qv0Ktw2D1s2UC/uFHEhffhpeL/4vR7GK+5mt4Rwl1I00Xg6Tu0w8qGP8vI3eMDf8Dgt+2qpOdgNR17qpDJQti3WM+2hTqIsmIcO5sBrW+mq/rKRl8kcN8JDrDXaCB71kd4fHoObZnL65rRxHjNzMxOJDSq1oH+QlvJrzojj6QmclkDjs+xl9otXr6ZXc7hnTYxQnjLo0nieAXJsRUzuXQvPkKI5kck6OkXNZhbEu5DfkKa5h9bjXz6eyXXXaymk06C+YciUrDj6J5I56db1YmA+mwech/ciiRMgvEmPbkbfFiPtoKatFBZspYCrQaPMoTLpxpY5PgAh99O5/ALuS/pMcJPADupcOSJlCj18f2drNT8o8VM+05LpTeGOImBp0bE06ZDLZCL3+uhatmXGLf/gUC2GD/kTtQ9hzZ42SDwI35X/BI6V5G4hgMTTkXdbXRhT2jg8fuYWrKdgw4DfSISeSKpyxmni5nX5VA+ew0SpZy0KdeiSDt9VkBD4zS6K1iqnUaVq4AIWZzIqZgU1rzPxul0cL4NZXPW4xy2bTE7dw7HGOy6BTUgUM3cvT+HcImUeR1rM18aesUxoLCHUhMZ1Mj3dwsOM9hVUANBDZwDDZxvO9dSDuEaoBS47QSdCE5eASBcvy9uQFffAEKR0In8CsJ1tJA6KqSbCj9NkbM2lAJiocAtuNiwXEw3uyV2GMNjrmixesFjk6jc/SXGnF+QR6SQNmAKVqmEJ/Lni4Ak72XdRVwDqYYur4/NJUJtoJEDVUIZCgjZgj2SVQzOjqJbUvjxQ6BQz7Oo+EXxwNlFfSWDmnngNNp1LP5zBlsyoyhXBQ74Up8XX4gZf4iB/qouPJh0G1IkfHVkJkulwjLDAK+DIT6zWOcmk8WJdYGR0QORy2shsH8oeY0i+x4RVr9TVOOpsba83ZR+/Tphae1Ju+sVdpXNpNyykUz1NbRPuPv43vh6kY4ff9bz0OhEBg2oG8FxOn088mQ+DoeP2TOz8K9bQvV3XxN7w03iT3NE5ypmYeHTIvDHDalv8HLpYnQeC7fGXsyI2KZTkFRXuNm6woRA62A2BFJ8FZFmki/azo0jbiFBUxslLtpqZeU0LVJ5CL2fD+E32STkklDuzJhGhKxu6uTJc7EfPUzJ1BdRduhE6nMvin/+vaKCzypM9NVv4DrDSpJ6PooqOTD2jUYLM4vKuDwmitGpjUc6Ba6/5YuqWfmDXnx+0I0xDLoxmgmFP1DuiyaM7USxW3QE+6uHnKLqd74tZdsRG5PvTKFThpKcB7eB2c0WYknsHsmljyWhqKkHLc87TPk7JYRXh7EXNSYUSMItxHXPo/PlbfjpczXaAhcjxiTQa4iaMlcJH2mnUe4uJUGexIOaCaSeAHZy1LKZX8qmizVvN6W9TGJowwcuq9nKe59tY7c5BTku7uvtYPAVF+Gx6EUqFEfxIaThajQjnkSZUTeyeeKkhbrkJdp3RA6/DGVXrk95tkkXI43tUZPXxaSiLVR47NwYk80tcc13vuyHKyid8ht+j4/k565A1e3sUJSF9NwVFR+LqdXSEDmDEh5o0me+sbkKfz/fhrIpYzoPbc7azp2HMQZf0YIaEDhiHz2UT2tlKK+1Tm+059V6E3NLKhgeH8PtmkAWRlCCGghq4P+mBs63nWsph7ACmFMTETxR89aa373TwHL8JGQonlSML+TBCR6PAP19ooN5uhU9K0Np8lqYVfYZe+2HUUmUPJZ0D91VQmljy4oxbxmVOz9BoogkfcAU5BEa/lP1Fz8ZtnOVugv3JZxKYZVX6WBNqZm1hWYsLp84oIRwmegEDsyMIuaktMETD5zpyi4MT3mu2QdOj9FA/pNjkSQkYJt4F3/qtrJd5sF9PBXNQbTPSaKzmsNKJwq/j5FeM539HlQRPUTewHBVNxEJ9GTR2g+zqORFEVHxrszpp4XXF57VfvcO1iPbSBr+BI7MUHZo3yFMFs8lGe8ik9Smsr07Q8vW7VamvJRGcloIdomvToTmh28NLP/NwrAborj8Yhslb08R6TRSJggB6qbLqsp/k2fbTnf1CP60Oyhw6eitas3d8QJg4Ok/Tl6Pj+IdTnattHF0T02Kbwi0uzBcJI93JX9GhX09FyZPJFEVcNCqC5z89mIxHqdfrBnM7BPB2qr57DAspbWqN8OSx5928FXfLsTwy2Li77ib6CsDIIqv55ew22JnXMgh4g/PF38X02EkUZmDqPb4eLLQRJpcwhvpTQcpObDdxQ+f2HA6QNXRzL4b9aCsJiNkOQ/EPUCW4lRqBLfHz8MfGkWtzX1YjbfYSdkrRUjTQ9nlicak9RGRGELqgFyKdviw5WST5HfSGguVUS5k11vodoVATaJkzWI9vy3Ukdk+jDEvpSKpqa10+Ox8WT6XndbNKEJCuTPxIXqeAHayVf8TG3T/ER3rW9NeQyWrizp6onKFKPrX32xgcW4ifiQMTiygl+13vFYjoYmZYoqoNPL0EbVNuu/Yb16FWpbEtclPo5A2Lx3zdItd5rLzrnYXDp+H66JTaa9sGgqs0KdEZydi+mYkZhe2mzrguqzxw2dTPzX51m3sM68S6wqzwi7k4oSr6BhzdrD459tQNnWu57jdWdm5czy2YPfnQAMHrHZeySuhrzqCJ9MbL1s5ZLXzUl4JfaIiGJfRePtzMORgl0ENBDXQQho433aupRxCgZBXAIKZeZIeioEvgecb0I9QZ3hHTcqo0IcgAia+gL72J9B4TlzgmTM2lCavmeeL3qXKU02GIoXxyaPRyBvm+jnTdbZV7qV0/Rvi46n9J6GM74jJaxdrB334eS/zLmJldTnPlq3Rs3yODkMnMF0APVNUYm3gBYnKBlPCjjkLMfIUbkmbQugZ0GOYN6ylfN4HRA2+gsS7AwjrZqeBlcV/sMJZQplCQe/PeqCqCmfXI99wt7KadrFXEqUegEx++oiV0NePpW9QaNvFkMQH6Rx1YnC4rnbdhgoKPnxSjLykPfwuf5U8h8NTJVJMJKguqtN43MQCysrdjHrYQ7vZ1fxyjYtNfQJ1eKLoZTArFSK8MK44wNlwHkWmk6H5KhFFeaDOLjzGT5/BsfQcFEV0fADpstS0hr0Vc0hXX0nHhPuxGzwsfaEYm85D91tj6TIioFu3z8GCwomYPVUM00ykdUT9kUmhvqzwuadwl5eR+e77yOMTsHq9PHggj3CphA87ZGPXbqV82xz8noCDKsi7WRMwytQ8n/s6Sl8gGt0UMZji+fWvO7Eb4nHHugm5eRGjLPtRnbAMJ/aTY23FZwX30yHiAHdmLIQ9fWD/xdB1Hd42eziyeSTVxbUXM5IIM5rOBWRtTkIWoyDzg4tEp1+Itr43sRCf18/jb2eQmFq3llFEztX/xOLqb0SnZEj0MG6IuwNpiFSswfuzYq5IH5IU2pobU18Uo6+nk03r9/LhKj92Wi6VvCn6PVdtQr0exuTsQuO0sTE2mSWp9dcJt9T7JcnVLLyvZ70XRk19x/k2lE0d1zlud8Z27hyPK9j9OdLAGr2JOc2I+BncHsYeyic7LJQ32rTcpc45ml6w26AGgho4jQbOt537XzuEAu7/3hpEUqHeUMhxEBxIAc5zWQMw3oL6hLbHOKJEdQoHuzMR4bm5FQvElM0HE28nrJHD4Jm8w2UupXj1ZHxuK4kXjSUqM5AqubBqA4sNOxiq7so9CZee0vXLT+fhKgqkEw4bE0+/IaePPuw1LmdF5ceESSK5JX0K0fIzuyEs+2g2lr/WoXliIhEX1XU2hCjJ+m1fkzettziu4u4ldB4TwfUxgcN5U6TMcYRviycTJUvgrswZDUYJqwQet40/E9N/JFUd7BQYfyEpog/dNOPqvOYYwqhc7eO61DK675RjioefnpWJHHDHpPhzFdZDclLusKJMLsVTrUOekIg0pnEnVuhD7yrB7jMTIonF5PMgQ0pWaALSeiKhJw7Qt1+G96twcEiwZzkwXGrE33Y9mYpSMY2yT+RlRMrUOD0GVuePRSlLpE/yDP58tZSqHCetLo2k3yOJdfSbb93Bz9q3xMiWkDoq0BacLM6SIopeeJrQzGzSXwlcRqwzmJldXM7A6EjGpiWJv3OZS9Dt+xqvK1Dqu1DVnx2h2Yw2L6eDW8gEb1wER2uTSs+S0M6o/9sX1X6h9tHN4MF/0aZNIKX4ZFmS151VxRdwY+tNXJJyGOfX/fBXR6K4Yw2SaBvuEA87c5JwF2XSuoecrkMGIQ9VUvzSXhyHzKS91oXQVio+f1PLkV02Bo+M4fKbG06T2m/dxafls7D5rLRXduZ+zZNESqPw+N38WDKVUsch2kT0YWjSE406KyX5Wr5cfBSrLBapSsDGOr24vFaEdGNB4kLTUUiaHr07bc9+P1afFZPXgM1rwedX4vMH1pUQL5KQWke/vn5C/H7u2JNLe52JozGRLOjaGl8TkGsbm29Dfxf2SUSCiY9vbLxW+nTvON+G8kzn28LPBR3CFlbo3727ReU6vq/UMyYlgSGxjX/PCOeZew/kIiGETztmN9km/931EBxfUAP/P2rgfNu5pp3gG18JIWX0g3pq/hpLGRV6FhAVZtQQAQs5kfNqAGUER/FkvqcGzxln6hAKHQrOoFAP11SHpnF11LbwuiwivYTbWkZ02+uIv2CU+Eejx8YTBfPF6OCszLuIOSk6WFroZPYzRXhDQeYGAeNl9ORUMtvXH5Uosu3jp9LXxb5HpE4iVdmxOcM83lYAIcl78iF8NhutZn+CRFn3fW5XBWs/Xkrx2loHdtN9W2jbLYaxiYMJk9SNzjQ0iJ9K36TAtpPBCWO4oJ7aMp/HRf7sR/E5bcSOeZqt1W8hkyjplzGN0JPS+jYd0TPjVR3yVlaez7MidwW2ddqUCwhrW1uLt3O3lTff0dKxfRjP3uGg+NVJhLXrQNrzwv3C6aXKWcBXRc9iCslirz8dZYicV9NGkh7asAMi1NcJtXUrvqsWqSMcl61GfrUerf9CXH6QU4iKVcjx0i2ilwi6oq9ciNlVgHzpGxRv9JLQLowrJ6eK9YMnyzFwm27qqxmQcOpHpfrnH6j+7zfE3ngLsdffKD4+o1DLJpOVpzOS6RFVNyJ9rP/fdQY+01YxIiGGW2soKU6nHacQsaz4iI2WPMxci8xv547dPVj/nU2c96XXRnPl7XFIpXXn8MzHhRRWuHjvkUxinB4Kx+1EkaYk490A51+h8Q8OVn56PGJ6bAz6xaXoFhYQc0MqxekxfPN+OfEpch5/Mx254vQofFXucuZpp1PsKiBWFs+DmvFkhLXC5jXxbdEkTJ4KesfcSJ+4WxrbEk3+u8BZKVyACKibTa2dbazzKncFG0wr+cu0CqM3UL8ZIUmgyjEWmy+UWJmUao+XB1ISuOI0B8nKLzdj/GUf8hQ1aVOGIVWdPW3F6cbu9XuQID3r79rzbSgbW4/z9PegQ3ieFP13ec2c4nLWGMy8kJVCl4imXSI9e7SQAoeLeR2yiZKd51SYv4viguMIauAfoIHzbedayiEUQGVKgNtPWAMhX6GwEVCZY82FQpq2Av+4gCQP6IDngA+buKZ/S0MpEM4LaaL2qn0iaIfm4vHHIw/zq9az1LCTa9TduDtBoGCsK5/OLuXoOhth/eRc2T6anz+tRKjaLZIAACAASURBVBUl5ZGpacQk1iXTbskDpyMvh+JXXqgDQnJsZILTXVrwJuvevBynIYaL7ohj+1c6HPF2Vj26jhSVmgmaa0hWNI5MWO7I4ZviF4iUxXN35sxTooSm3aupWDoXVceLye9RjtmZT8eE0aSrL6+jqI2Wcmb/kY/nhzgubWfhqn12JEopPrsX9VUaEu6rBQoRHLTxzxSKqaVvTU3DN+MpEX0z670PkUWd/vZ1qXY6O60H2E93gRudZ5KH0V3VMEKpw+Zl0QcVHNhmRREWQq/7LCxNfYNLogYxKPpOpml3Ueq2ESHxoPL/gdsfiMRFScJptexiwlYMISJBxtCpaSjV9dNL2DwG5hdOQECtFNKDNWF1U/2KXn4eZ34u6VPfITQtHZfPx5iDeeJ7Pu6QjaIGSfbkvZdvd/JcThGdVUomZ58eWKTCVSYSwpe4SjExAi/R3BQl56bEARzeZeWbWeXYrT5aX6Dktic04h4WpNrs4ZFZ+STHypnxcCb6xSXoFhYSMyKVuFsFPCrYVDwZo+MIvVNfJVrZ7vgw3WUOCsbtQJYcxkJrNFaTl9EvptKqU9NSOF0+Jwsr5rHFsl4kpr894QH6Rg1E5ywS61sFUKarkh6jfeSpn8smfh8db+bwWvi2eBIGdxk9oq+nX7yQIX9m4vF72G3dynrjCg7a94jprwIAVqfwbuJlwk5TFr9Vm8T6oeEJ0TyfU0yYRMK0thn1cpIZlx2i8pMNSCJDSZ9yLXJN02tGz2wGLffU+TaULTfys+rpb2nnzmpGwYdPq4GXcos5ZHPwXrtMkhR17X5DD04v1LLZZOXVVqm0C2/ad2JwGYIaCGrg76eB823nWsohFJA5hBpC4YRsrlHrRODVRmgn6luBe2oI7gUUiuomLtHfzlAKzlPlzo8x5a9Aoc4i7bKXkcgCABIGj5UnChaI/y1EB6NldW/+HDYfU8bm4vXA4EnxXNkpmp8/q2Tj70YS0xSMfTWNsPBAJCRw4JyMwa2lR/R19IsPRCDPVI5FleJuvp2YYcPrdGM2buDIxp/Y+9kYEtqHcvXLaax4W0vJDhumK6pYN2AbSomCx5KuoIfqVBCRk8f0c+nb5Nu2MyhhNF1OcPQE3RV//gLOslz8t11BnvdPosPa0yv1peMOtQDH/bXuKD8bCmBZNKyP5oVkO8pCC4mPtKHioxwk4VKy5/YgRFYbNVr6q4H5X1Vx+eAohkuXYFzxB4n3PUjUgIZrGSud+Xxe9BJ76I4LOffE92dotJDVXL+UF7tEjr4qrZv4ZDmjJiSzPXypWMN2c/w9DIoeis3nYW75PrZYKwkNkXBFlJRK5yqqN8to++2teEMduJ/cQL/2vegY3q1BtNu9xhWsqJxHvCKTW9NfO+5Yu3VVFEx4DHmShow3Z4gRmW0mK+8UaukdpWJ8RsOgHoJuHziQi88P/+7UClkDqcB7rTv4rHw2dp8VacilVPrbkywxMC3rRiQ1zmZ1uZuF07Ui+md0vIxR4zWktgpj1S4THy6p4Oqeau69KoHiF/fiOGwm7fUuhLWKwOYuY13BOJTyJPpnzDwlolT4zC5chTa+I4Z2g2K58aHGEVFPXC1hj600/sp/qxbgw8dl6iu5Kf5uim17Wax9C0mIjBtTJpN8giPa3M+UEA37qfQNiu37aKXqxTDNU42motb3jgqXlvWmFWw0r8HsNYpNoqWx4uWC4MjGyRPIsTuYlFOMssYBjJHLmK+tYqnOwMVRKp46ab1te0spfeMPEQgpddJVKDueWXp5c3XSUu3Pt6FsqXGfZT9/Ozt3lvMJPt6IBh45mIfe42V+59YNfg+f3MXCsioWVxl4NC2JS6ObxmkcXIigBoIa+Ptp4HzbuZZyCAVoPoGUXkjzFIjpBXK46TUgMycS0x8FVgMBpBIQrqRfAIQIowA9MQiYAIwBPm/G8vztDKXh6FKq9sxHGhpN+qDXkClrUwu/rFzHL8ZdDIu+kLtEdMq6suE3A0s+r8KcBa+9kk1UqBSv18+XbwdqpQRUyrueTgaJ94QDZ0+R66s+ZM9m6JHi11/CcfgQ6a+8SWhmrVPn9VoozJnA4R8HUr69F31GJ9DucjWWSjc/TyxE4NOTPqfnZ+Vm8XUjY3oxMrbXafnQKhy5fF38vMhTJkQJhWiNII6SIxR/ORnSU8jvq8Pv99A34y0iFGni381eF7PK9rLHXk24REbK91lod7n4l0+HNFJO1pweaKcdwrZdT/LTHVD1qEWOtFi9PPpEvtjPtEdsGN5/jfBu3Ul56tkG1fRD6TQW2zzYiODyqM48kDCgwZS3PRstfD+3HJfTT8ceKm5+NJGwcCmflM1ku2Uj41Im0y48AJQiOF4/6fP5tjoHoQL2WkMmzAzB6/WRc9d8qtodEdsJqY3HDv/HCN2PDVbgafy+5FVKHQdFXsQeMQKNJxj+/JWqhV8QPfQ64m8NXBJ8WFzOKoOZx9KS6N/IQeG1vBL2WO283jqNVicQ1wfG7eM3/Q8srf5OjFJ1V13FCmsKIfiZknIBrcMD63RMXE4fP8yrYNd6CzJ5CDeMTmBttY2/9lt49tZkusTLyX94G7JYBZmzA7WoOdXfiT+tYkbSJu7mU9bm6JxcWFPOrrAIhs7uRHjEmaVGHbbt45Py97B4TbQOa89ozTjyLZtYXfU5SqmaW9OmEnUGIFOCwynU8+4zrSBekSXSWihOQMVt7DPp9rvZZdnCOtNyDtv3ic2FaGAX1UViNLBT+IUiKI4gXr+fF3KKyHe4GJ2SwOU1KaIOr4+JRwupcnvqpAi7So0UT16Cz+oicWx/ogYKyRn/t+R8G8q/iXb+dnbub6KXf+QwhIyOe/bnEi+X8X77xi9YjylhebWRj0srGZkQw81NSPn/RyovOKmgBv4BGjjfdq6lHEJB9QL51mygbw3J/Cc14C8BVJSACCfxVSfUBgpFTD8AAnKJkNsgOJSvCUCUzVzLv5WhtJZtF8nnQyQyUi97mbCYWk4wvRgdnC8WfQvRQfVJ0UHhIPn2uAKM5R7kNyl45aZA+pwgQhri3MnFVJa4ueRqNeHX/Mhe0/IzOnDWp1+v1Ure4wJ0fhRZM+fWcXoqtPMwVK1m6/RJ+Nxybv4wm9CaQ/j+pQa2zq8S691iJzqZW7kch9/NReFZPJp0OSppw3VJi7XvkGfdJpJzHyNaL1/8Aaa9a6kekYZRVkyrmBtpU1PTlec0MV27m0qPg3SFigmabrz+XAWty0wM81lRD9WQcE825g1VlM86QkTfODRP1qYbCvOe9+8KVqwycdftMXRc8gw+l5NW7398Sr2k0LbMnssrJQvQE0dnZSr/SrkOWc1B/EQdCumof3ytY83PBhHHZshNsQwcEXOc/uCVgvEiH9472Z+gOoEgXehjh7WKjw8doPucREItUuJv2EdIz/+gjBnBbsdRMT1QEMEh6Bzenf7qweK/xxyCalcJXxUKfHYSRmW8i1qeSMlbU7Af2EfqC6+gbNtedBrGHszD5vWJBMcq6ekdqGNgBvckxzM0rjYF2O618UXFB+y2biNUoHFIGMunlaXo/GoGhrsZmzK03o+usK83/Grk1wVV+HxgSwSdBj6e2ArnmkoqP8lFfbWGhHuzRYCo9YVPiVHCfhkzUCnqRjM9bj+fj89hYGUl3iQl7d8L1Byeqeg9Oj7WTiffmYNaGiM6hYWW9ewx/kGcIp2b016tF7TndO/brl/KOt18VNIYbkmfSqSsaZxgAneiGA00rcHqCyRbCBcCghMoRAOj6+GdXFKlZ0GZjvbhYbyUnVrnEmaH2cpbBVri5DKmtclAbndTPGkJ7jIT0dd3If6OpnNnnql+z8Vz59tQnos5nEGffys7dwbjDz7SDA2UOl2MP1LYpNT9E7vda7ExNV8ALIvgsSZQVTRjSMGmQQ0ENXAeNXC+7VxLOoTnUU2nvOpvYyidxkKK17yI3+MgqdeTRKYJ/nGtfF65lt+Mu7kuujuj4i85ZSJH99j49LVSHHFw+bh4rmlbtx5PSMGbM6kIm9lH1PU/Ed/3iJgq2NQD5+kWybJlE2UfzCCy32UkjXnkeFO77QAlBa+gP9SDA1+PIKOXioETag/pAtz/L5OKqc5zipFD1WVepml/pdRtQCMX6gqHNgi+UuHM4+uif6GSxnJP5kyw28n/4FGsGRLKe7oIlyfTN/0tpBIFa81a5lUcwO330SciibGJHcEl4d4xuTyOgSS3W0SeDGsdgc/pJW/sNvD4yP6oJ5Lw2jq8ggInz04qQqOR82zrxVg3rkPzyDgievc5RT2v5M/kgEdOrFTO2xl3E1EPb5zN7OXrWWUit6CQynvr40m0714L2OLyuXgq9x7UshhezxLoOuuK2+5j8YuFWIo8FPQ2UTpcx6X+H+kR3Yf28XdR6S47DiBiqkkZFByXS6IGipHDOHkiG3WL2Kz/nozwblwb+ajIIyk69jPmECKRcIzPqotKyQuN1AUKo9ttsfF6fmkdPiutq1gkeq9wa0mUa3goeSJ/Vh/kFwtEhZj5IPta5JLT17nk7rOxYEYZDosPSayEZ17LwPLhYWy7jKRM7kR4ZzUGxxE2F09GHdqGi9OnnqKvZYt0rPi+mjvkesLcXjEiLEQXz0bcPhffVn0uOmNSpIyMv4tqy1aKHXvJCu8ucgYKDndTJNe6jSXad8X03ZtSXyYp7PQk8cL+2GHZKL77qOOg+AoBeKWrqgf9o4bQIbxLg++ucLl5+kghHvy81TqDtLBT9TCzsIyNJgvXREcy5LPt2PeVoeqVgeapwYScQ0TRpujqTNucb0N5puNs4ef+NnauhecV7K4eDew0W3mzQFsHEbopiqpyuXnscAFtlaFMaQKZfVP6DLYJaiCogfOvgfNt54IOYQuuscdhEBFFPfYqYjveTGyHkXV6r/ZYeLJggRgdfD/rbqKkpxZ8L5imZf8WK+UXwxsPZpKoOvWAvWHHLpa8Gwp+CSMmSOjVo2V4wyo+m4dp9QqSxj5BZJ+As+r3uSnMexa3q5T8nydRuiOMgeM1ZPSOqDM3XZ6DX54vRqaUMHxaBqh9zClfxlZrHqEhMh5OGkKfiPrHuUQ7jVzrFgbE30v6fjuVG76h5DoFHqmLnimTiVJ2ZEHVEX4zFokk5nfEteXa6IxAamGugxkv5jPOo0eeEkbGtAuPRzbL5x7FvLqSxLGtiRpYt8bs5anFHDzkYNwNZuIXv0VEn0vQjH2izpx+1K3ma/1e5Hh4I2MUaYr4U3ZLaZ5TrJHTV3pISldw5wQNcZq6h/JCRy5vFj8vRvUeTambmipEFldN01K8zUZi5zAO369ng70cKR4GSw/wQPZTx98p1KTtsW4XHYf9tl3HQUU6hnelT+QA9ukWYfRouUw/hIgPlhM18HIS7x0tPv+FtpJfdUbuT07gyrjG4cvtXh/3H8glRiZjTocsdlg2iQTvTr+DLuE9uDfpUfRuK88Ub0Eg4BifmEbvKCFJoHFZuLSSbd8ZCbNDTHQII8yVgXrPD3sSIg3hQOVnFBl/p0P8fWREX1Wnw4oSF+8/U4hEGsLoS8C5qoKE+7NRX9kyNXDrjMv4pvIzvHjpHdGPEEeuqNML1ddwWcLdjU5OQKNdVPwSbr+DoZpxtI049ZLhWCclzkJxLTeZ14p1mIIkyJPEaODFkQNQy04PziREUoXo306LjRsTYrilgfQwgZts/OECrlhymF47ylBkxZL28jVIwpoGUtHopP8HDc63ofwfTLG+VwYdwr/JQpyPYfyhM/KptpJbE2MZkdg0aiRhXEIpgpBqGiYJ4eOOQvVOUIIaCGrg/6IGzredCzqELbRLfF4Xpeum4Kg+QkRaP5J6PnZKndlnlWv43biH4dEXcXt83cihMAxDlZt3Hi/ArQDJHXLevOpUFMsqZyGLil/EtK0jxv+OJEwl4eEpaSSknF2ERDhcCiAkHn012e/PQxoRKEavrvyO6qrvkIX0YN3UEchCJWK6aH00CELaqJA+mtknggHjNKJh+lG/jUXVm8QaueujL+K2uItPiXZUOgv4T9GzYnrdwN9CqGhdjbkVpEQOJC3uPt4r28MBh4FIiZwnNF3oEl5rHFevNXFkbh4DfTZib00ndkRt/Zptr5HSqftRdo4idXItwbkwrw0bzcz6oJyLuoUx8tC/CJHJyH7/Y/FfQfbZSphaKmQz+xmlzuTahOtP2Sk71prF2jghhbFLnwhuHJtIaNipUSSBHkCgZbgyejg3xJ8IxAtbF1Sxf4mBqGQ5Q6ekoVBJ+MVYyIKqQ/iRMCQynvsSuyI7KTqlc1eKlANC3wZvAHtJJVER6rOS6JRy+fteMh/9F6ou3cQUzMcPF4i1ZHPaZ9WLOFnfx+C5o4ViXdr1ibtYZ1okpqwOi72Jq2NGiGv4XP735HvUdFVYeD7jhiZ/kiZ/XsTRIidDo1S4/tIxEDOOtmo6v9oRP15W5z2Mx2djQPZcFNJa5EvBef7k1RLyDzq4elQcvdpIKXllH8oualJfaJoz2pRB5jmOiNQUAp1DqiIdtVuHz29rkCLlWJ8C6us3xZMwe6roE3szvWPrXggJ7QSKDqGWdJ1xOXnOQI2oEJG8MKK3GA1sq+zU5EjkBqOZWUXlJCvkvNVG4DZsOIK5edFWYr/fgy1CQfs3hxMaX/dCpyl6+Tu1Od+G8m8y96BD+DdZiPMxjAVlVSypMjSp5vvk8Uw4UkCJ0y1yEYY3Uh5wPuYSfEdQA0ENNF8D59vOBR3C5q/RKU8IB+7yrbOxFK8nNKYtqZdORiKt66DphOhg/nyx/kyIDkbWk3r4+390rP5Jj74jXH5LLCM71r0VtHmMIlXDsQOn/vcBrP7JQJxGLjqF4ZFnBqwhTMhVUkzhCxMJbdWa9BeFMk5wOUvE6GAIElwFr7HlMydth0TRd0z9iI5uh08EmLFWeRj8dDJpPQJpkzusBcwu/xOrz0kXZRpPaK46Zf4CrUOOdTNtD4O0HcilUSQlvsz7FTnovU5ahUbylKYrCfK6UdWFX1XS7udcYvCROas78sQAkqsgfp+f/Me249W7yJp9EbK42lpGj8fP40/lYzB6eeGC3wnbv5bk8c+h6nohWpeBF4q+web30FlSyvPZr/w/9s4DOqpq6+O/TE2Z9N4TehEEpEhHwAKK2H0+xYooqIgggqIiAipSRQRExYrlWVARRVQERDqC9JY6KZNM2vQ+8617AwRIm4SQh9+bs1aWS+bcc/bZ58zss+/e+/8/hxZDAND58ZMStq3TifWC1/07kn43hNUKNPOl9kMR0fKB2CfoEVwFInRig55tK4pRqCQMn5lESHzVmfmp8DO+MKmwEkBb/zCeiutEmKx6LabL4+KweR9bdBs4aP5LjBoKLcIII9Meo0voleRZ3TyboW5wCtHb+QX8Xm4mSP4fwuQnuT/2cRHURGjrSnfwQbkBJWYWp11FqMw7NDuD2cWYRVmEq2QseTyFI9OOoMzS8yshRA+Kos+/8jhQMpeowG50S3jmnO/art90rH5HS3yqgnGzk0VuzqxHduM2u8S0YKmqZnqOxnzFdc4K3tUsJMN6jABJIDFuK0H4MTLhWZIDL6s2pNNt55v8mWhsJ2ir6ss1see+EFLbskQnUKC6sLoryeJj5QmV0cCQAQSf5fh6I6/R5WLS8Vx0LhcvpCXQsQ6OMtMeNYXzfsUplfDOvZ0Z1C2NEVFVQEvezHep9WluQ3mJrN/nEF4iG9EcYpymj5jZIonWgVV2zZu5X88p4C+DmVdbJpF+HiiYN8/7+vg04NPAf18DzW3nfA5hE+x52dGvKTvyJbKAKJIGzULmXz3Va6V2E+t1B7kp/Ar+FVk9jcxhdzPnsWzMRjc5w+HVG5NJCT3LgREunAUz0VirLpwC6fenCzViiml6hwAeeC4BmaxxW1q+bi2ln39M+Mhbibz5dgT0yvycl7FajhIZ8292vNmN4qNWrp2eSGz72rmN8vaa2DCnkMBImZg6Kj8VMdM4dCwo/IlceylRsmAmxQ8jXRl9RvtC5PNT9TPI3NBCAqbgR1ltcIm1UQOD43kouh0KSXWH993pmQw6VgRpQbR6rXO13SxZlUPFmgIi704hfMS5nHpffVPGV6vLuKazgQGH5ogploGj7uGFvK8pdFSQQB4PRl/NZaFVlBSGCiefvaEh+4iVwGCJyK3XqlPdhMGL8meKSJHPJ88lQSnQc4LmkJlfXqnkHrz6uQTiOp47hta0lz8Kl7BZMhKNO5BwqUJ0iNsE1J5GKICj/Hb0A/5Q7MJxKlgUJAkm2O/fHDYlc1dsJCOjvXME8mw5zM1dQ5HtOiLkB3kpvRcxp8BdDE4TT2T/hpVARoUHcX1k9Wh3bV+rrYcNLF5dxOAuIYy+OoqsMbvwuOCboGgqKtxEpFTQ5o536Nn2PuKCq2psBb0vnJSLzexm7KwkklpWXpCKlp/EsFFL7OOtCO5XdZ6a4GuNwPv3dcnHbNL9LEZHo/EQ5xfIncmzCFcknJlCeCH0c9GbHDduJc6/tUhXIZMoRMdvt+FPtug3kGvLFPsLSLrdVL3oGzKEVv7tGk3QviK/mA3l+nrri2w5ZeRNX4vH6kQxrh/PRvohw4+5rVOI8ZLXrCl02dRjNLehbGr5GzmezyFspOL+iY9NPSkgB9tY3i6NsFOZK96u43SJwJPJsfQO9e5lnbdj+/r5NODTQPNooLntXOO8h+bRRUNm+a8ZSkPeNop2vYGfzJ+kATNQhtaQ5ukwiLWDilPRwZqASf7arOerpcUYk8BvqJxF11bWyAlNuHCuL1rCMeOfxClbc0ti5YVTaHarmxUv5VOQbaP7VSHcPCa6UZfM/HmvYDm4n8TnXyagVRt05RvQalagUKYQrprB6vH5IlH6zW+k1gtEsWmRhpztRtoPC6XHfVWXdKvbwdvFG9hmPIncT8rD0VcxIKRt5TpK8vkyZxLaUAlav6FkeFKQ4sd90W24OiSp1jV98+DfdDabCb8/jcjrqnPr2XJMqKfsR5ESSMrr53IHllc4eXxCNgH+fkyyzUAeFsjnT1/HQUs+4ZTSU1rCvWkLzkQH1SesrFpYiL7MRUK6UuTUC4+uuw5L2LtnssZgc1tY2PIDcSy9xi6C8NiNbnqPiaH14OqE4C63jd+zRuPGnwzVk2wyaER93B/dlqEhibXqo+jtJRzRb2HrzWD286fC46DcNhaXJ45Owd8xNKInXYN6oTh1fmr6ku00bBGJ263uQHS2p0n1F1ISq871a+pv2WdTkSKt4PX02xryPWXZmiI27Tcw4ZY4OlkcFL5+lKCeEagebMmqhQXkHrMjDzIx6ql0Wl1WpRfBCT+wzUjv60IZcX/VmTLtKadwbuUY8RMrz1JTNwHx8zPtOwhUEMLVqp0shruSX8H/FFrsjrKv2VH2JcGyKO5InInWVSrWBgrOoM1jE8VJUCSL0cCewf2rocw2VN7TAEEhUqlIOh8sqzkzwFlhIe/5NThLTETc3pWIW7twGj32clUgU1PjG/Vb0VB5L0b/5jaUF2MNjRjzv2bnGiGr75EL1MBDhzNxejx80KFFg7+nP5dW8H5hSYPrDy9QZN/jPg34NNCEGmhuO+dzCC9g86zlGeRvfgmP20n8lU8TFH9FjaO9W7yRX/WHuCW8O3dE9qqxz9JpavIybBQMhCF9wxjVuQrAZGfZN2wv+4944RR40QLPA5vQlTpF5FFDuYth90TS/wbvokCnBXHbbGQ9Nho/hUKsH3R5DCLnoNttJiltJsfXRbDvizI63RxO1zvrh8+3VDj5dmIuToubYbOSiDoVzTnt3K6t2Meq0m1ieuN1oZ24J6ovhb8vZWviXn6RDMXoF0OoVM7EuMtpW0dEzGJwcuLh3Sjx0OrdHrWmDJ4mMU+e0xllahX6pyDPG0s0bNth5I7E7WR1PcquK+IJ83PR1rOda2IepmOIQI0JQrri9+9rcTmh24BgRo6ORq6oH3WywlnGc9njSFam8Wzya9iMLn56MQ99gYMO14fRfVR1oJrT+7In/xVKLfvpkfASuxwqPtQew4WHQcEJPBjdtlrE1ON0kjX+EZFG4+D09uTaDpASdAufl3ZDISlFpVgoDh0gCaJXcH+xZu10xFL4dwG05puSVWJ6qxARGxnxb1YXX0aF08XK9i0IkErYoz/K3OJcEfRmTlI3kvy9B3MRnONxi7PRmVy8MzEd04fZ6H8vJvaxVgT3jya3bCNrPlKTv723mA563d1R9B0eyvF9Zj6cU0hohIwJ81NQBlTp3W13k/XwLnFd6e90R6JofNp0XT8FudYsVmjmU+YsQYjbX6FszV1J08kw7mJd0RtIUJIeNoy/zXvJs+eIQ8n9FHRX9REdwXT/1g2+1NUkj8PtYUpGLgU2R521RW67k/yZ67Cd0KLq24LYxweI8wvcZlNOqim0OxifFEuffyhxdXMbygswE035qM8hbEptXsJjCSnho49kkaRUMK91FfWUtyI3FqHU2/F9/Xwa8Gng4mugue2czyE8tacehwM/ufeoew5ziYgo6rJVEHnZKMJbX1/j6dA69EzIWYVSIhN5B2uKDqpPWln2fB5+EX5kDPYwY1AibaMq0zJPGLfzk2YRcj9/kQ8tSlmzccjLsPLOjHwR4OSep+NFUnRvm2n/XgoXzEHV40riHpuAJn8xRv1WQsOvIyr2Pr6flIuuwMGN81MIS/QOvOb4rzq2v6slPE3B9UK9l/Tco3bQnMcbmp8xuK20VcSQrt3JxtABYs1cMEXcEx7KwMgb6lzCye818GkWBeEBDFhWOxdd+Zp8SlflEjYigai7z43gHj1m4aVZ+YRFm6gYu4Ugl4d2sl1Ey0IZlboAj1PKmve17NqgR8hYvf7eKK68JtTry/0h0z7eKnyNK4MHcE/kWH6bU0DhAQtJVwSK1B2SOmD/cyrWcqzk4zM8jMcsFSzS7KfcZaelMkRMIY2SV9WWB40FygAAIABJREFUmA8doGDubAIv70rQ4w+wKvdpch29yHZey8ioMLqGFIqRq33GnSKKpuhE+bc+A2bycdEyTliPIKSZPhQ3XqQ7OE1ZMC0tgXaBcp7IWkOFJ4RrVW4eiLvG2yMm9sspsjHlXTVtk/x5aVQi2Y/uxmWqqv/bnT+TMssh/LNe5NePlOJZ7txHRe5xKxUlTkYJ57p79XNduPAYph1lxE9uS9AV3qPxNUh4EMnr39W8Iab/Ci5pN2UrSm1ZlOHChAwnTnHIJEUafUMH01PVjwBp3enEDZXhq+IyhL/OqgCeTU2o8RyKNc1vbsa4NRP/1tEkvHAdEkVVfeUho5mZ2QWEnoowqmqJMDZUNm/6C7IJKcKSRqa2n56juQ2lN2trhj4+h7AZlHwpTJFlsfJsRh7dggN5JrUqPd1b2TQ2OxNO5NJe4CZtUQW05u3zvn4+Dfg08N/XQHPbuf95h9BtMaNZvgSXroKk6bO9uui7nVbyNr+EXZdNSOpgors+XOtzK4p/Z4P+MLeG9+D2yJ41nrAvlxaxd7OBUgGvo4OUZdenieTSAuH4Z+qpYuRmRPzTpAfVHIE8PeiB7UY+W6RBofTjkZeTiE+tnRD+bEG0qz5A98s6Yh58BGm3cArVryGTRZDSYj7lOX6sfS6PyBZKrn+lsv7NmyYAuvw8I5/iY1auuCeSjjVELUscBpGvMMvuAASgGj/6B4XiMC0iSBrE/amLkUtqL6bf/9xBAjMNnOwVz3VPpdUqlrPUJoLLSMMVIrjM2dxrwgV1/LMZaPNBev82+gRsxRVTztCYR0l09OPThYWoT9oIDpNy14Q40trVXj9ZkwDry7/n29JPuSViFKpvenD8Vz3hKQqueznpTH1lbYIb7XlszX2aEGVLrkyuBPopd9pEp/CYVUeIVM6TsZ3oeAp1VfvRSnQb1hPzwBhCBg7mr/I1LCsIxOBJZVaLRFoFVspucOnZod/MFv1vIqfg2S1Zmc6YuIlEyivTMn8sqeAjTQm3x0RQ6trJBpOMSD8di9NvQlpDTWddZ+P7beV8uqGUOwZGMCxSdg5CqNVZyubsx1FKwxmQtoSCbDur5mtER1BoHXsGcffE6inB4nq2aClacpLgQdHEPto0FCy1rUMA8flK+z6Hju1g8Np2BBorX5AIwEv+kgARgKaSi7Hpf1qF9DGt+F1BrP+TnkopP19Wj9OFs9iILCqIpFkjkIVVP7PL84rYWGFgcHgIYxJrBony5nvubR+n2UXpFiPa33SEdA4k5e7aI+PejNnchtIbmZqhj88hbAYlXwpT7NAZWajWcF1EKPcnNLw2WvituPdQBqEyKcvapV8KS/LJ4NOATwMN1EBz27mmv7U0cMFN1L3RhlJwCNQvTsWuzjlTP1eXTALYimbHQkyFuwiI6khC32fxk9SMbljs0PPUqejgm6n3EiSt7qAZ9S5efyxbYKLmxHAPg1uH8HC3ygvattIv2FW+mh7hN9M78k6vVLXhmzJ+/U8ZoZEyxs1OIjisfuTFnKlP4dAUkjJ/IQXlr+B0aIlLehpVcHd2fajlyE86etwbRfvhdfOinS9gRb6dHwTOOJkfN85NQRVzbgTW5naxVLOXHeYKgT0JP4p5KLorLssGsV6yb+TdXBE+osZ1u4xOMsbsxuYG3bj29BtQN7de/qzDWA7qSHi+A4GXVfVV20p57rttONa0IzEpk9SHVhLiF0V/22t8/oYWk85FSmt//v1UHCER9evyfGHf1yxhl3ELdxx6kdzPFPiHShk+OwlVVP3RaOFs/pHzOFZnGYPS3z5DweD0uPmo5DjrdXkip+XdUa0YFpwk0oYILzbS33gbaUgI5Q47Y4/loEDH5CQdncKGnCOeML5AhC6gX+4z7RTTG++MfvCc+sIMi5VpGXm0DpCQ4RGoMKQ8F9uCTsENd7xmfpLPoRwLsx9IIuRXDbofC89wCGaVr+FE6SrSwkbQJupuUU6T3sVXy4rQ5tsZ81JSrfp3mZ1kPbz7HC5Dr74sjexk2JJB4dubkVT6Zpdkk0YEkjD1apQpNUdMjU4XE0/kone5mJ6eSPughr3o8GbRwvkyHrei3aCnbJsRt70SATekUwDtpp0L8OTNeGf3aW5D2VD5LlL/Rtu5iySPb9iLpIE12nJWFZVyb1wUw6MaZndPi/Tk8RyK7A4+7NACZR2UNBdpCb5hfRrwaeACNdDcdu5/3iEU9ku3aQPa91egurIvcY8+UecWlhz8lIoT3yMPihMRRaWK2vm8BACV3/VHuD2iJ7dG9KhxXIFmQqCb8O8i43BrJ1P6xtM1rjIt7qu86RRYj3F38lwiT6FT1ne+hEvYl28VsW+LkeRWSka/mFhnrZtDW0zO5PEokpIJfOJyKsp+ICi4B/FJk3C7PHw1LhubwcVtS9MI8MK5PF++fV+Wsv/rchIuD2TI1CoQi2KHhQWFf5NtNxKEkQ7Gw/ylCseFm75BqbhNn4q1bvenvYmihiih7tcitO9mskviT+9XO5BWTzRUv7GY4uUZBA+MJnZspTOjc5qZlvcVJWYz8kWDcdo8dJvwOslbbuDg9k64XdBzaAg33B/daPTWWbmTMR/0p93H94mO8bUvJhLd2nsI8UPFK8jXb6BT7BPEn0VZIci/SV/Au9qjODxuriSIa15bQkjLNiQ9O11c369lOt4t0BIv3UZb5e+MSllAUC1k58K5OQ1idPYeCm+aBXADu8eJR6mmR4CZSUnVORnrO5dWu5uH5mcS6C9h+ZNpqJ/ch1NrI21pN2QRSrbmPoPRnkvv5NcJPi8tujbZzp4z/5XDWPbrSHihA4Ed6345UJ+stX3ucbopWbUL3U+HEfhGIv7VjbBhTcd/WJdcG8sNIkl1slLOjBZJyGqJDp4ew08mwa+eS+CWCgNL8opIEHkMU5DXkb7cEJ05jS5KNhtER9CSZxcflSj9iOwbTPSQEIJaKL3KxKhrzuY2lA1Z/0Xs63MIL6JyL6Wh3yso5pcyPU+nxNE9pHGcoa9k57PfaGFuq2SS/b3LFrqUdOCTxaeB/3UNNLed8zmEQmzKZiP7qXG4bVbSFryFLLTmN3L6nI0U/7UciTyIpIEzUQTXntsv0CxMzFlFgETBm6mjCKwhOigQbc8bnyOmxWmGgyfUjxU3tEAu9cPhtvF2phCtCeTh9LfxO4+YvK4vikBh8d6sArH2SqjBuvOJ2FovYLoNv6D96D1UN/fH2OZP/CRKUlvMRyaPIH+fid9eKxSduaHPNryOQZDRZXezZooafaGD/uNjSe8TzN/mUhZrDmByO4mjgKHmDfSV3Etxy/Ys1KyjwmUmSuIh1b2TqyJvpXv4yGrLzXvpINajBt6VhfLyynYo6gF4cQtRpEd2g0xC+vIrcMo9zCr4juNWDX1UrZGuS2HLrzLSIwqgOAGZ3I8bH4wWkVsb2wRUyue2PkOHFaOR2pRn1t+Q8YqMO/lbs4D44AF0ih1X7dFMq54Fmv2UOK3EFZXymFVF6yGV9ayvZRewz2jmxvCDlFg+p7WqN8PinmzI9GLf8ccPUWxX4q84ydIWAwmUNTyatOe4iblfFtK3o4oxXYJRT92PsqWK5NmdMNhy2KaegkqRQp+U1xssn/CA7hcN2veyCB0WR/R9TZ8i5dRZ0CzaiPWIBolKSdz4gQR2vrAol7cLrXA6Rc5Bs9uNwEnWqoGcZLU6uB4Pr+YUiJfG22IixL/GNsFpNxwRooE6ynaY8Dgqo4GC8xc9OER0BqVngQE1dp7TzzW3obxQeZvoeZ9D2ESKvNSHeTW7gL+NZl5vlUxKI525lQVa1pfpmJQSR49GOpWXup588vk08P9ZA81t53wO4anTVPLZx1T8vJaIW+4g4sZbqp0xS8kR8rfMEkggSOjzLIExneo8h8uKfmOT4Sh3RvTi5ojuNfY9vNvIJ/M0xLRRsP1yO32SVIzvVYnamGvez7cFr9AqqBfD459q8Jk36pwsnZYnOptDbosQ/2pqhW/Mw7RvN/LJ8Tj8ComKfYCwiGvFrn8s0ZC1xUi/x2Np0a/xXEaawxbWv5yPf4gU+Yse/mPNFOnTL+cEXdlM4u4ALrvtbfxkcsqdJtEpFBw1OXYu88vhqfRXUUiqnBBHsZWc8XspR8KqpFgWzau9fvDsNWveOI5xWymx41vxYct9bDEcp5UylhcTb+LDze9wdPnVIoJkEBXcPTmRtCtqrlnzdjMytdn8+oIW/4pwOt8aTpfb60doPX9sh8vMxqzRyKXBDExbVuOLAb3Lzpwt35CRGEWQn5Qn4jrRxj+CMUcz8ZdIeLNNHJ+pJ2N0lnJj/BTSgrp6uwTK7DoezziC2xVFv1ALjyfXfe5rG3jlOi3r9+gYd2MMHY/qKP86j8i7UggfmcjxklVkV6yhTeTdpNWSIlyfwM5yO9nj9iCLVJD6ZrcLjkCdPZ/1pBbNgg04y8wo0yKImzgYeUzjvw/1reX8zxerNWzVGRtdT1TXfEJK2eQTubjxiFHCRKV3oFGnx3ToXZRs0qP9XY+1oDKHVnD8IvupiB4cSlD6xYlMNLehbOieXaT+PofwIin2Uhv2qeM5IhLwB+1b4C+tH826JvnXllTwsaaEu+MiGRHVMOTxS00fPnl8Gvhf1EBz2zmfQ3jqlNmLNOROmYA0PIK0uYvxO4sI1mHUoN70Am67geguowlNH1rn2dTYK5iY+ymBEgWL0+4V/1tTWzk7n5MHLESODGCXwsL4nrH0Sa68aG4t/Zzd5d8yKPpBOoc2DM3x9FwatY23X8zDZvHwr/GxdO5z7iVWoCnIfHw0nssdMMiFMqA1SakzRKfDYXXz5SNZ4lC3v51eLwBKfV/Wzcs1ZG80knuFgeO3lHNjQDkh5i8JzIP2fjcTObCqRtLpcfFRyRbW6w6KDvhVgaGMib/nzCW/bHUeZV+o2SgJRNczhkkTvHPcTH+Vi7x3+sv8eP1fe4iUqZiddBuH/8ph7TInHksgFj/oK/+aofd0Iuya4fUtq9bPXQ4P37x0GEuGElm3Eu6a3KvRTsrOvOlUWI9xZfJrhCirO7/2gnyypj3NbzcNYXPHNBHO5MqAdLaVexgQFsy4pFgyTXv4oXCuSF1yd8q8GtNwa1rMjJzVHLHGgCOWfqEqHk/2nmbi9HhC9OjJpTkUVzjFdFH9y4ew55pJWdAFebxSBJOxucoZkPYW/rLGR6nyXjiA9YSRpFc74Z/euDSr83Wg33Cc4pXbwOkmuH9Loh/ucw5iZ6MPiJcPnoaPj5BJmdc6lcBGXg7rmu57bTmfFpXSLtCfF9MTRUCrupoAGKU/ZEH7m57yXUYRNVRoqtb+YkpoxJUqpP6Nu8R6qZbT36X/L/bL22X7HEJvNfUP7ucWAGEOZxAgkfBO+xaNXsluvYl5uYUMDQ9hdDMARzVaUN+DPg34NFCjBnwOYeMORpMYyoIFr2Hev0+kXhAoGITmspvI2/wiDkM+oS2HEd35vnolXFr0K5sNx/hXxJXcFFEzMmhxvp1Fk3IJi5JxbKgLm8cjposGyisvUv/JewGN9YR4eY9UNB42+theEx+9XohU5sfD0xNJblVVv2Y5epj8t16GMRKQ+ZGc/ipK/0pai4zNev5cWkyL/sH0eyy23jXX1aHAbmJRxgFazwtHaZLSdrILfegMcLhJ/tmPVg8uQRZSPXr2Y9l2Pi7bhQcJvVUteDRmKAo/GbmT/sZRYOENWTj9RkZzp5eRN6EG7MSjO/GY3SyaepgpbUaSvU7K+i9KwCMhvquVPw/4k+6XyWMdtp2pxWvo4gUHSNBd5h8GjElquk6V0jtqQEOHOdM/s2w1J8u+oHXkXaTXkD5b9sO3lH31ORE33cbRIf14u/gwNlskuIN4IjmGvqGVaa9rCxeQYdpJ17Dr6R81ql55tlT8zZISLTKPA6etNdFyGW+29S4ae/bghWV2nlqWS1qskpkjosl5ci/yxABS53eh1HyAPQWziQi4jO6Jz9crU10dyr/Pp/TTXMJvTiTyzoZzd509tsfhQvvhDvS/HgOpH1GjehJ6bftGO/WNWZjV7Rajd1qH84JqieqbW6gTnZahJsdqZ0xCNIMjaq7BtFc4KdlkEJFCbcWV6K/SIAlR/StrAwOTL040sCb5m9tQ1qfDZvq8SexcM8nqm6aRGihzOBl3LJuWAUpmt/Qe2fv86fKsdp4+mUunoACmpTdPensjl+x7zKcBnwZq0EBz27n/L29Ym8RQmv7eS+HCOQS060Di1BfxuF0UbJuDpXg/gbFdie89ud5avgJ7BZNyP0UlUbI4bZRYQ1hTW/OBlm3rdFw+IpjV/ga6xgUypW9lnZ7dbWVF5kMopSpGpy2/4Evonz9WsPajElShUhF5NOwUwqX2y0/RKb+H1hAeOZLImLvOiPrLK/kU7reItYNCDWFj226TlreKDmJxu7jySCwRqwKQR1cQ8dhCova7SJD1JP6WibUO/1nBMn4ym7HjT5oiiietgzC9mIkxVMlr5hCeGBdL397epe9lWIvZsXwHPbdHYbg9mMM5kRzaaQK5ncTbNvPIDQ8zcbKaYq2TJ6RvcuWbM5EGN7yG8MDqMvZ+UYY7zMTeR95kcocXSFKey3/YEH3qrZlsz3uOcP/29EiqBIw5u6lnTMOWlUHyzDkok1PJshh4LqNQTMuNU5UwOeFyEhVBGJ1lfJI7CYfbyp1JrxDjX3utnc1t57GstRg9wdwULGW3viV5NjtL26YRIW8Y2uq6XRV8sL6EkX3Cuc5ko/STHDFVVEgZPVi0jALDJjoKNB8hgxqilmp97YUWcp/ahyI5gJS5tfNS1jeJs8xE4cLfRVJ3aWgAcRMGEdC+4ZHR+uap7/NPNCX8UFJBj5AgJqV4FwWvb8zaPj9ptvJCZh6BEgnz26QQdipDQogG6vabxWhgxV+mM9HA4HZCNDCUiF5BSOqp322sTHU919yG8mKsoRFjNomda8S8vkeaUQNHTBZmZOXTO1TFk43IyDgtqt3t5r7DmUQ18kVeMy7ZN5VPAz4N1KCB5rZzPofwrE3wuN3kTJmAU1tM8qy56Et/QZe5HkVIMkkDZiCR1+8YLSn6RaxNuyuyNyPDBWLB6s1mcfPauCxcTkgZq+LXQgNjukUzOL3yzXyO6W++K3yV1qorGRY34YK/KELE6tt3tez6TU98qoIxM5JQ+kvIXvEEzv5aZJIoUlovQHLKeTWXO/l6XLZIkXDr0rQ6ydNrE05Ie/mqLJNvyivTTm8OT+O28Bb8OOsA5YeDCOu7ifbGX0j89wsEpnasdY06RxErc57hGO2pIJgbf0ql55+R7E0M4+tiOXNmJ5OaUn9kosxpZJr6K4KyPTz6djvKFXJW28NQRBgIvutDRnS5kzbBvVmztpxVn5fS028Hox9OJKR/w5yUnB1GNi3UIFP6ceyRDyiNyWRhyw+Q+9VPM1GbEgSqk43Zj+J0mbiqxTvIJFXn0FlWSvbEx5BFx5D6+hviy4O9BhNzcgoJlTvQSfMJ8JMyLrYjPVQx7NetZ6N2JTHKFtyRNBOJn7TGaRfn/8BWiz+xEh0L027mvcISfivXMyE5jitDG5aOOeeLAvaeNDN9VCLB72VgPWYgaXYn5OlyNmY9igcXg9KXn7Ouxh763Kf3Yc+zkLKoC4q4hoPfWI5oRPAYl86CsnU08U9dhSyiEvW3OVu2xcZzGWqUEj/mt05tsBPeGFk/KNSyrlRHn1AVjwZFiXWBwp/9FBekLFhC1IAQESQmILFhtYaNkaeuZ5rbUDa1/I0cz+cQNlJx/6THNpfrWZpfzMiocO6Ka3jd+dlrHXc0m3Knk487tETWRCjC/yRd+mT1aeCfrIHmtnM+h/C801L+0w+UfvEJ/te2w+p/FKkiRKSXkAfVT96cby/n6dzPxOjgm2mj8K8lOrh9vY7vV2rpOiCY39MslFucIhl9mH9l5OXPkk/ZU/E9V0U/RKfQq5vkPLucHt5/tYDMQxbaXxHE7Q94yMucCMGQkDKNwKAqsJDDa8vZ/XEpHa4Po/uohhNIG10OMSq411x6jjNidZSwad8sihaPwc8poXuvr2j35LR6I6C/Fr/NIf3vOOTXMmRmSwLNMt4Mi6DMJOPDd1sil9d9jK1uBy/lf0O2rYSeWd0YtFJKiMfFltYhVNw+j+iwKP6dPEeM/hqNLsY+kYmf0870LutpOcl7QJ/STCvrXspHqB+8ckIIiyLHk6BI5vmUuRe8hwc0Syg0bqFL3CRiVFUUJhW//kzJJ+8Tdt31RP2rMg10RX4xG8r1jE2MocitPc8pT+ebghkUWo+LaaNC+uj57agpixmFx/HDw0vx7WkTlMqmcj3L8osZFhnKffHeEyXbnW4eXpCFcBdZ/lAS6sf+QhquIO2tbmiM2zhQtJg4VR86x42/YB0JA5R+kUv56nwi704hfIT3aVLCSxPdz0co+XgnuDyEDG1L9H298JPX7DA3ibC1DCK8THk+M49Mi40H4qO4NrJxPGQNldFsd7HopxzSd7tpeVIs3xVbSMcAMSU0vIcKST3ftYbO2dj+zW0oGytnEz/ncwibWKGX4nBfFpXytbachxOiGVJL+ra3cr+clc9hk4UFrVNIaCBglLdz+Pr5NODTwMXRQHPbOZ9DeN4+uoxGsl55FK5wglRGYv8XCIhs69VuL9asZ6vxBHdH9mZELdFB4eL5hpCWmGfnxikxLMospm2kPzMGVdUJfqF+niLbSe5JmU+EwvtLbX1CWowulr2QR0mhg649j9B12CrkJfGk9l94zqM/TFVTlm3j+leTiWwgSmCuzch8zd8UOSwkyAOZFF+Zriise1/hPLTmPbDuBjRbriQyycLw1y/Dr543l3pHMR/lPEXsyZZ0/WAYJ1sa+SA3Hf9oO8vmtao1LVdYlHC5XqRZx05DJq3/6ILrl1i6YKIbZgqGnGDfVT+KUVghGnu6vb1Cw+9/GLlB9hP/XvYwEv/6eQPNZU7WTlNjKXdxxd2RSIfksbhgNj1UfXkgrm5uy/r2Tfi8wPAHB4veIilkKB1iRp95JP/1WVgOHyTxuZcIaNNOXO/Yo9kYXC5WtEtHJZOy21jMW0WHsHhcXB4YyT3h4XyXPw2pn0ysUQ2RVzl4brebJ7NXo3WH0i/AxuOJlQ6jxmZnwoncBte1HMgyM/vTAnq0CWJ0hATtO5mEXhNL9IMt+KtgDiXmvXSNf4booJqj6d7o5uw+1kwjec8dwL9NMEkvX+bV4267E+27WzFszhBpSWIe7E3I4DZePXsxOv1YUsFHmhJaByhFzsH6QF4uVAab1lEZDdyox1FWiRBjCYKUwaHEDwnFP+6/Gw2saX3NbSgvVMdN9LzPIWwiRV7KwyzNK2JzhYFpaQl0UtWflVTXWk6/HJySGk/X4ObPdLiU9eyTzaeBS10DzW3nfA7heSfCrs8j99cp4OdCpexP3PDHvDozefYyJud+Rog0gDdShehgzSmCGYfMvDezQCSND/9XIN8eK2dUp0iub1MJC213W3g78yECpCE8JNIMNO0WlRTaWfp8DlaTH/2u+4Z+l/cmpGv/M2usUNv4frKa0CQFN85NbtD82wxFLC8+hM3jpkdQNGNjOxIoqYx6Fhl38LdmIQHSaGK+1HHwyCOYLLH0HhNN68H1E4n/VrwC2XtyEv9uR9nIaBb8CLQvIvHuHCbFDyNBUTOs9mel21hTsJ/kr7shPRqOMsCP2+4KQ7nyOKbwCg5N/Z27Ul47pzY0K9vGsy+oiULLq09ICO7Zq84zICCy/jwjn7IsG60GBdP7kRg2VPzI16Ufc1PkXVxTAxCMV4fqrE42p45N2Y/gL4umf+picV/ElxfjxyBVBZO2aJlIRH7UZOGlrHwuCwrg+bOABARgn/mF+8l3mIiRBTA0oJBsw1ekBXZlRPwzZ/b506INfG9wE+xnYEn69ShPRbkFh/7Ro9kYXS5WdmiBsh7S89Oif/JbCT9sr2D0sGg6rC/AvK+ChGkdkHXwY1PWo8ikKgamLUXi17C6xNr0J8iZ88RfOEvtpC27AllY3c6MQ2sUKSVsWaVIIwKJnzgY/1beR0Abuo/19S+xO5h0Mhen28OrF8BBVt88bqdHrAkUagOFGkExGugHoZ0D2d7Zxc9JNobHhnFPXMMzBOqbuyk+b25D2RQyN8EYPoewCZR4qQ/xUmYeR81W3miTSqyi8aUGwjq/05bzWVEp98VHMayZMg0udf365PNp4J+igea2c03rbfz3tNwkhtJl06Pe9DxOUzEcA3lFPCmvzBcv2vW1NzQ/s814klGRfbk+vHZAi1ULCkUgk9vHxfCJtZx8g4M3rk0lVlX5w59t2sv3hXNoo+rDdU2URne27B6Pk22/Lmbt+9eLl8AHp8TRskuVQ/bXZ6Uc/K6crndF0mmkd9xFLo+bz0oz+KEiR6Q8uCOiJSPD085ENgQuva25k0RqgTYVV+H85Xc8Kbex7YcuKAIljJyfQkBY3Q5BhaEIzbhj4qU1599teH+VkdghWor6/SVGCB+LHUr3oHNBUjbrj/LOgR3Ef9oVWWkgUQly7pkUT3SCnL+n/ogqJwKeldDq8uoO37RnjpBRKGfsZbsYOKUKbOf8cyCAbmxapCF3p4nY9v4MnZYoIrp+VLSU7YbNPBY/hY4N4P2r65xtVz+H3pZJ35QFBCkS0P+5meJ3lhIycDAxD4wRH/24sIS1pRU1phpa3E6WFR1ip0mL0k9CO8luglx/nYmQamwlPK3egRMFT0bH0zv03Ajb/JxCdhlMIjVBhyDv6vMmr8hFrbXzxoNJGCbvQ6KUkP52d9TG9Rwt+YCU0OtoF31/fV+vBn2u/SAL3ToN0aNbEDq0doRc84ECNIs34jbY8G8fS9yTVyEL825dDRLIy86CMyvAxO8xmJukfqimaa0aIRqoo2SjAYeuMhooD5cSPSiE6KtCUMbIEVAOJ53Iweb2iCiH6QH11+h6ucS2pvjTAAAgAElEQVQm69bchrLJBL+wgZrEzl2YCL6nL7YGxh3Notzp4uOOLZFd4Avh7Toji9QarosM5f4GpPpf7DX6xvdpwKeB+jXQ3HbO5xCe2hOPy0H+n7Oxlh4lKKEXzl/LsZ04TsLTzxF4Wec6d05tK+UZ9edidHBx6iiUtUQHK0oczBufQ4BKyqjXEnjmdzUpoQpeH1oFkb+lZBV/VaxhcPRoLgutm++w/uNUvUdZyWrKtF9wbP0V/LntZgJUEsbNSiIyToHg3HwzPgdTiZNblqSiOoVGWtc8Ain6Ys1BDlrKCJLIeCL2MroEnRtVOFz8Hnn6X4hX9SP4m6M4dSWkjJnPvjUKjq7TkdZHxYDxdaM46jdrKV56krwuR9ge2ZZ9m2N5YlwMeW2OsLp8tyjireHduTWip+iIHrUUMH/ddiJXd0Bil9GhRxC3jY3FP1AiOt37vv2Ry9YMJmRoLDGjq3M9bdlUxpJ3y2gvPcaL7157Di/l2frY+3kpB74tJzhWzrBZSfgHV9acvaqeitqWzStpSwm7AG69s+c6Ufo5WeXf0jbqXlLDhlP45nxMe3YRP3EKQZ27imm5Tx7Podjh5K22aUTWgAYq9Pm+IofPS0+KQaF4DtBecpz7UucxQ72OTGcoHRUGXki5udq2rykpZ5WmlDtjIrg5pn6+wFK9k8fezCYhUs6MzkEULT5B8IBoYse1Yrt6GnpbBr2SZhHq36oxR7nWZ8yHdBTMPEzg5aEkPNuhWj9BBxU/HKT00z3g8RA6rANRd/fAT1b/i58mFfS8wXbojCxUa8SowNxWySi8eBHljTxuh4fy3UYxGqg/aKl8RIgGdgkkZkgoYV0D8ZOeawrWl+pYWailRYCSWc2QturNOs7u09yGsqHyXaT+PofwIin2Uhn2NDKo8Nu9pBEUP+evQwCnmpqhpqsqkClplSjmvubTgE8D/wwNNLed8zmEAnaCx0PxX8sw5G5GGdaCxP7TMe3eTdHyNwnqegXxT06u8/QINWrbjRncG9WP4WGX19p3/RelbFxdzqCbwrF0lfDZwVJubR/O7R2qkMS+UE+jyJbBvSkLCVM0LdS83V6IOvMZPC4XvOtmf/hT7D4cKUbMHp2ZhC7HzvqX88VI17XT6+c+zLTqWaDZT4nTSopCxaT4zsSeh8RaYTnGzvyXkEtUdHWNouSrpQSkdSLxrmk4LG6+m5SLUH83ZGo8iV1qr3HIf+Uwlv06dt33PZu2DaYiK525rySTnKxklzETgfvR4nHQNTCV28J68ub7hwj8Ixn8PFx9RyQDR4aLaKnCXn+RN43y8gKunvMIkgAZ6cuvqOYMOJ0exj58CKNTwZxHbaT0rQLdOb3Bp7ka5YEShs9MIvQU8qLL4+KpjPtQSgJ4PX1Fg9Ju6zpo5ZYj7MqfQVRgF7pEPkXWEw+DREqLN1fgJ5eTY7Ux5aTaqzq/v82lLNYcwOR2EkIBbeV6djnaocTCG2kDCZNVp9s4brbwYmY+XYMDmZJa/+Viwz49K9YWM6xHKNceKce4rZS4iW3w62Ljz9yJBMrj6JuysMn0c1p3HpeHrEd34za7SH+nO9LAquiz2+qgePkWjNuz8VNIiRnTl+B+Lf/r1sHscjHpRK4YGWiK2iFhQZYCO9oNeko26XEa3OIaFZEyMRIYNSgYZR0vfIRa1OmZeZyw2Lg3LorhUc0DbOPtRjS3ofRWrovcz+cQXmQF/7eHL7DZmXgil45BAbzQBNyBFpebB45kEq+Qs7BN46mP/tt68c3v08D/ogaa2879zzuEdoeTLZ9vwlmgo3XnX0keNAtZQAQep1OE83cZ9CKcvzy6ZpTRHFsJU9RfEC4NFGsHFadq5s4/vE6HhzmPZ2PWu5j8ZirzDmg4WWZjzpBkUsMqU7JsLjMrsh4iUBrGg2lLcZkq0O/bgKpDHxQRF+YcCo5QQe4sLOZDyA5G4lxbSuKM1/jyCwVH/zLTqlMAbSNlZGw0eFXXt91YJAKVODxu+qhiGRPTAX/JuYiMbiE9VT0Vkz2PjjFjYe0WLNkHiLv1aVRtuotrVu828fu8QlTRMkbMTUHuXz1K4yy3kz1uD9JQOdkv7WfljK64zEF89F5rZLLKIywgvM4v+InSbBfh69sQmBmJJMDNqPGJtO1a5WhmmvbwQ+FcopXpDPz8Psx7yol/ui1B3atHvD5esIe1e0MZmpbL6JmDz9nSoqMWfpmVj8cNQwSuxk5Vxf+F9jxm5j5Nm4COTEh8ocl+xwR9bsx6GLfHRY+KR9G+uRhVz97EjXtSnOOr4jLx71+xEdwUXX8Er9hhYW7hXtR28xkZ7woLYGRU3xpldrg9PHgkE4XEj3fapdcLdrLoGw3bjxiZelsswa8dEdE7BQct0/gNmeVf0zLidlpG3Npk+jl7oKJlJzFs0hL7RGuC+1ZGrO2FOjTzN2DPq0AWoxLrBZVpFwbr3lTCv1dQzC9levqHBfNYUu1prt7MZzxpRb2qBMMRa2V3CYRfESTSRYReHlgviNPpOXKtNp49qRbh6ue3SiHqAuuZvJHd2z4XwVAKYWrhzV9vQODB+QOoj3MmDajk1Tm3fQH867x/GwnMQmR9JROYAQj9GtJ8DmFDtPUP7LvPYOK1nEIGhQXz6AX+Dpxe/iNHsjC6XSL1xMUGqPoHqtwnsk8Dl6wGLoKdq3Ot//MOoU1XxtGJRwgw+xE2LYKYy9qdUVjp6i8p/+5rwobfSNQd/65RkQsKf2KnKZP7ovoxrI7o4L4tBv6zpEhMXRw+LppxP2YTHShj8XWpZyIkWaY9rCmcS1tVPwY4r0azehEuYzkSZQCxIx4jqHWlE9WYpq/YRHHhMuTyBByvFiANDidt4VLsVg9vT8+jKNdOWxmiwbhjeRqKoNrh9oXowSNZmzG6HdwT1ZrhoSk1Rnkyy1ZzsuwLIgI60kn5IOp3JiELiSJ17OJz6jI3LigUa/A63BBG93uqg1hUrC2g5OMcwq6PxzkiiAlP6AiM0vLWvI4i+I7V7GLfFiM7fqugKMdReQeOt/LklNZEx1XVPwlO8ed5z6G1ZXFD/GRiDqShWXQc1ZWRxE2ojiqpzS3nyWnF+PvZWbaiPcpTtCCGYgc/TlNjM7jp9WA0ba85FxRnt2ErK4sWc1XoMG6Pvq8x21XrM/sKF1Bs2kmLfZfBDweJHTue4F59xP5TTuaSY7Uzv3UKiV5CjNvcLuYVbOKA1U2yVMec1JuR1JGqKESNjpmtzGuVQpJ/7YAtLreHMQuzsDs9LBkSjnbeMYJ6hBM3sS1bcp7E4iymX+obBMovzPmpTVGm3WUUzjt2Zm9Ne9QUvbUZt9lOQOcE4sYPQqq6NGrjjpktTM/MJ1gqETkHQ2QXRnVxZGY+hkMWlDGno4EhKMIbB9rzmaaU70rK6RYcyOSU+CaP5jb2y3ERDKXgsC0BtgNC8WxRAxzCpwW2oLPWUgIIxB2nWz9gI7AUWA0MByYB1wHrG6ADn0PYAGX9E7ueTtX2Ni3fmzW+mJnHcbOVxW1SibmEXup4I7uvj08D/8sauAh2zucQ1negfvhkO+1+8GDrHkTHp6vqBZ3lZWRPehxJYBBpC95Cojj3Apxt0zJV/R/CpUG8kXpPrdFBYf5lL6hRn7Dx0PMJZAQ4WLlPy/BWodx7eRWi4R8lH7O3Yi19LP0I+3EbuF34J7XFmndMXEJ431uJ6H/rOYiY9a1N+Nzl1JOTORG3y0iE6Q7KlvyH4P6DiH3oUfHxcq2D96eoiTK7CUpVcOucqprGmsbPsOqYlreLjgHhvJB4RY0imOyFbFM/I37WO/l1zJt+Rrd7HZGD7iK8t3D3qmpCyqiQOuq0uRk+uzrVhXrqfmzZJpJf7UymXcKMWflEtj/IsKESrHt6cWCbEYe9kjQtLkVB+gAJQ4bGEuh/LkJbhnE3azXzRFL2O5Nm43F4yH5kNx6nm7Tl3ZEGVb80vzLuN/YbUnn4Jg9Dbm2N3ezipxfz0eXZaXddKD3vr45I+W3JZ6yv+I57Yh6hT8hV3myR133ydL9yWPsuoXvkRP7iEdNFJQGBFNsdjD+eQ4JCzoIGpgYJjvJfhiN0DGqBv7Ruio1VmhLWlFTUy5F1LM/C9A/zubxFIA8ZLOg3FBMzrhXuHmXszJ9OmH8beia97PW6G9rRbXeR9fBuPHgIH66gfPXfld+hkZ2JuLOrV0BRDZ2zMf0FNFGhxifPZhd5IweGV0/Vbci4Qh3wngeFABRc8V6LarWBDRlL6CvUNE0+qabI7mBCchxXhqoaOsRF6X8RDKWQmlCZVwtfAcKbKW8jhCOAH+pY6M8Cdg9wdpqBgJMsbLbgLHrbfA6ht5r6h/b7RFPCDyUVPJ4US7+w4CZZRVPSWDSJQL5BfBrwacArDVwEO1fnvP/zEUJBO1tLCgiYmoXKJCFlTmeUKVUphpq3FmHctZ2Y0WMJ6TfwHGXOL/yRXaYsHogawLVh1WvMTnfOz7Ty1nN5RCfKmTAvhVe2FHCg2ML0gYm0j6pCNfwsdypaezYDf4Qgi5Toax4gpMsQTCd2U7RmKR67hcCWXYm98XGk/t5zChUVvIVB9wchYVfDT070m38/J7IkyLn2pTxKj1rJF8BZpsbTpo56vq/LMvmyLJO7IluJaKLnN8HB2FMwizLLIVpF3EmaahjZS8aKabhpj7+FNLD6pffoeh07V2qJbKEUwVmEej+h2dRm1JP/RpEUQPLcy/npxwq+XVVKZIAJP1OlDhRKPzr3CabHkBCSWiprjGIIMn2mfpYSezY3xk8h7RTyZ/HbGeh/LyZmTAtCBlePVu386HcW/CKk9Rp5ddHlbHi9kIK/zSRcHsjgZ+KRnAfGIcjzVsEcDpn3MiVpNqn+TVufZnFo+SPnCeRaaLutCwkTp4o6WFtSwceaEkZGhXHXRaQK2K03iUiYA8OCGVtHStN/NpXyzZZyRg2OpMMHGbgMDtJXdOeY+SMRYKh99IMkh17j1Y9iYzsVvH4I81+HASN+/jJix/VH1bP6eW3s+E3x3OriMr4oLhNrhp5PS7jgCJxZbePgZDXB7f1p70UdsDdrOGA0Mzu7gHCZlHmtUwiSXlgE05s56+tzkQ1lUzqEQhjaAIwHlp+1rnuB9wEht1tX33pPfe5zCL1U1D+124LcQnbqTbzcIpE2gU2DePx1cRlfFpfxUEI0V18g0f0/Va8+uX0a+Cdq4CLbuWoq8TmEgrV22Vm5aivX/6gkqGcE8ROriOgtx46Q/+oMlOktSJ7+yhkFZtm0PKv+DxEylRgdlPvVfkn6enkRezYaGPFAFJ2uCuaRH7IIUkhZfn0VNYOxPJeVJc/gb4Ghm8KIv3kiAUlVaYz20nwKv56Po7QAeVgscbc9jTI6ud4zbjYdoCB3NlJZOMnp81BPnoyropz0N1eI/HVCsxpcfPVoFn5yPw5ZPSJX36MvJxGbXHNK3Yt5uzhu1TEnuRepyupvMfP1mzhUvIwgRRK9k1/DsPd3tD+/R/BlA4gdMa5GmYXIxk/T8yg5YaPHvVG0H14JYlH6WS7l3+XjNziePXZ//t5qFOv2hCZLyKfdABu3XDNURA+tq2UYd7JWs4BYZSvuSJp55uJ9GpEyoEMIiS8KpUPnNntxEU9POkkxsTzYPQjNNpPI0Tjs5UQUgTXv+XPZ49A5y1nY4kMUp3j86t2oBnTYvP8hrIEmuhTeRUz/ymjrae4qARGyVWDdUb4GTFWtq97pYszRLOIUchbVEYmctlJNRqGN14dGYF94jICOIcQ/31bkHnS6LQxMX45C2jRvwGtaj01dTsGsX3DpTEgCAkiaeR2KpEsLGKXQZueZk2pR/NdbJRPvZZpvXfsnkMtnLS8mbkQYKXc3HYfg6SjD0IgQRifUXE99Ieeqoc9eZEPZUIdQSBEVHLti4DNgmoDpc2pNAsztIUBIFRDSRk+3HsBOoCewy8v1N9ohFECytm43YLV6uGZo/byvXsrj69bEGhBqdrOsNpa3SyNM1rg07/NF2lJhYEleETdEXbq8ok2sRt9wPg38v9DARbZz1XTkcwhPqWR65g5GvuokxCAhWYgSplZGn4TIkvqFZ7DnqUl6cRb+LSoh8ucWrmWPKZsHowdyzXl8bWdr2Wxw8dq4bAEMkqlL09lVYuKtXUVclRbCI1dUXqzMOYfYt3Ueu3tYSNGGcEOn15Gpql9e3TYzRT8sw3R8F35yJTHXP0pwewEDoebmdttRZ07G4SgiLvEp5PoE1M9PRtmyNckvzDzz0LH1Onas1NLm6hCMIVI2fF1OeIyMsbOSUYWc6/QYXQ4eztpEqFTBsrT+1SIadpeeP3Mm4XAb6Jk4g1D/NqjfnYy9JI+k+2bjn1B7xKw818YPz6pFHr8b56fgkfmRP2kvMpOTL4jAhFQEyNADdz+u4q/Yyfgh4X6hFk1W+2XfI/AkqqdSYs/lxvippAVV8UQKjuhpIvPUJd2QR1V3gv/z1AfsLurHZW5QBkvEtNbgmJoJg00uI5OzRhMrT2B66oIm/1ESzuOur++noouNtqq7SY0bgc7pFEnjhQiOAFV+sYEDnjqeQ6Hdwdvt0git4dKiN7t4ZGEWkSEypoeBbm0hUfenYe+fz77CeUQHdadrvFB2dXGagCBatOwPPDYnoEISnEz62728BlO5OFKdO6qwj7OyCzhksnhN4+GNXNnvFlP8q55WE+KIuLLp0juFFwECN6HB5WZGeiJtveSh9EbmxvS5yIbSW4dQQPoSnD+hDlD4WRJSTKec+v/TefECQtMWoCuw76y1CobkBHBtA+oIG+0QlpU5eWJiNv5KCUsWpREQ8N+lWGnMnv8vPPPQ4UycHg8fdGhxwdkCp/V10mzl+cw8egQHMSn1wsDp/hf2wLdGnwYuFQ1cZDvncwhr2+gvSzPI/1HNDWuVBPWIIH5SVZRQ9/uvaD98l+A+/Ykd8xgZ1mKm5X1JpEzFonqig5vXlLNuVSlXXhPKjQ9Gs2BbITsLTDzTJ56ucYFU7PqR0g2rONzZTXYbGBL1MB3DhtR6HgXnpnzbd5Rt+o/grhLW6waxLs/vPIRPYYDS4s8oL/2OQNUVxCc9TcW6Hyj9YhURN90m/p1uP72Yh/a4letmJBLV2p8vFhdxYLuR1Lb+PPR8IjJ51XsDAV10keYAA4PjGRtbPaJ2oOgtCg1/kBRyNR1iHsKSe5j8VS+jjG9J8v2z6/2e7fm0hEPfV+AXLkVXYeE6j45C5BxsHUuPwaG882UJRrObD99tydbyj9mn+5GuYdfTP2pUrWOfMG7nJ80i4pStuT3p5WqGtuSzHCq+KyDyrhTCRyZWG+fA0l/Ys7ky1XDg5ATSr6g9Xfe45TCL8l+mm+pKRsdNqHe9De1gzcrg+MfT0PwbYoJ60iV+IhvK9KwoKOaaiFAeTKhe09jQOerrvzyviI0VBp5OiaN7SHWn489DBt78toghXYK5+ud8nMU2BGf7sGMpRaYddI6bQJzqyvqmafDnHreb0s//ouL7AyLPXsTtXTEflmM9qCdxekcC2l9YfV6DBarjgU3lepblF5OkVPBay2QRybMp2sHn1JgzbVy+JLVOWonGzHWxZG6MLBfZUHrrENYk+thT4DHCWyehcPVCHMKXgOlnTyK8SGhsW7aiiE1/GBj170iuHxbe2GF8z10kDRhdLkYfyRJ/E4TU7KZqRqeL0UezSFYqmNuE4zaVfL5xfBrwaaBmDVxkO1dt0qa5hfz3d7PRb05Pi37cUsHL2bt5ZlEQgTpIfq0zyrTKi7/baiX7qbG4HQ4RXGa+8Q/2mnMYHT2QoXVEB91uD/OfzKFc62TC/BTC4mQ8vCYLPz9Yfk0CFT+/g/HIVvxkcraNDKVMUsJ9qYsJldefkmXK2EvR90twW00EpF1G3Mjx59Tm2ay5qLOexU8iJ6XFPOTyKPLnzsZy6ABJz8/Ev5WAfg6GIgern8xBFSPj5jcqEU8ddjfvzMgnL8NG1wHB3DY25owT9XbxYX7XFzA+9jL6BJ9LJl9qPsCegtkopeH0SZmPXBpI4eqFmI7uIOaGcYR0GlDrSTFUOPlrk4Fdv+kIK3YiwPckSAykua0ob00h+fZE9AYXY8ZlkZykYO6rKZidFXyQI5TmIOotqIYooeBAf6qeQqldzciEZ0kNrM4TeX6d4qkvoTiuLt/Oj9NycVhhnxQG3BnBTTfWTunwe8U6viz5gBERdzIsojq5+4V+VUq/+pzSdd+S84wUqUzBoPR3mJtbzF6Ducn46+qT8bQDOiIqjLtrqFdc+n0Rmw8YmNw3lNDlJ1G2CCJuZks2ZT+KxE/OwLTlSJs4ldZlsKJZvAnLgQIkgQpinxhAUNdkdOs1aFdmETo8nuh7L436QSHaNvFEDkYh2tYikbZNVCvktrvZ80AmMpWULsvTmizCcPo8XKyoZn3nrabPL7KhvBCHUHgjI6SOPgSsBE6njArRw01nraVZU0aFedV5NiY/qyYiQsbi+alnKHsao3/fM02vgSyLlWcz8kRE32e84HltiARC5NHh8fBhE0YeGzK/r69PAz4NNFwDF9nO+RzC2rbE5XEzJmsznbf6cf0PCoK6hxP/dBUFhXbVh+h++Qn9PbfwepqWKFkwi1LvRlZH7eDRPSY+mltIi44BjH4hkd0FJuZtK6RXjIxbjy3Frs1FFhpF2M1j+cg0i2BZJA+kCcjn3jVHuYbCrxdUjhMSRdytk/CPS0dwgvKyX8RmPUlU7L2ERQzHbbOS+dhoJEol6W++cwZlcf/XZez7sozOt4bT5fYqTjZ9uZNl0/LQlTm55l8RDLopQkyffSx7C+UuGyvSBxAsrUJddbntbFVPxuIo4vK4p4hV9cKpLyV76RNI/INEMBmJ7FyUVsFhPnnAzK7f9BzZYxJAVcXWIlmOUm2nB6UIGYnpb1cigB4+YuHlV/Lp3UvFk49XOqNbSj7hr4of6BI6jAE1UDwcN2xjXdEbxPu35bbEl2q9JOdO/Rt7tvmcFwFCbeVPz+eJTnOk6gAf2zoRGe73f+ydB3iT1ffHP03TJE3bdO9dZhmyERSR4Rb1p/7cezBE9lRkCoLsIUuG4sDtTxAFJ0tAlCF705GmbTrTjKbZ+T9v2gqlha60+tfe5+mjtPe949z7vueee875flm2OAnPKsBkhPFszF3DXt12hkSO5zqfqhFYa7a6VddSThqHJUtF0bRkCu2naR85nfFpYqQiEW+3TkQs3DY0cMk0WRh7QUlLuYzXk2Iq9CZQkry0NA290c7iZhL0X2US9Egsxv7nOJW7hmhFP9qGDXLrCM2pBWQv2o4tz4AkNoCIsf2RRJR6A22FZtKGHkYcKiV+WSe3G0l1mcgKVQ6/FOm5NUjBC27MxzOcN3FqioqALnJajo+qy9CqfaYh8h6r7bSKCg2sKOtjEAqJm3nA82WgMeWgMsOBty+bihDSsKGxQWXmLczi8BEjQweH0bvX38djXpc98E975jetgcUZau4I8udZN0d6TLqYQUqJmZWtEgjyck9u4j9N/k3zaZLA300CDaznKk234U+PjSPhensIhWEKoZAHinKYttQfkcZGzOz2yJJKQ+Is6iyUr4zhg6c6cjbej0GhfennL1z+Xr28OyeL80eNPDEmgrbdfVl1MIdd6Xoeyd9M+8IDeCe0J+K+4aQ5zvKteiHJfjdza7gQcVTz4rCYyN22BsOpUk9j6B0DcUSXkJ/zLlJZEjEJs1w0FcVHDpO9ZF4FInPBwNs0WolebeU/i+JQRFU02LLSzKyZpsJidvL4mAj8O8D4jP00lyqYFStgIVwq5ws+JlWzmVB5FzpGjnMdvAt2f4Zm7/9cNBNCWGt5EYzMQzt1HNyuoyhfyPMCuZ+Izr0VdO2nICxawoEZFwk8nYs5zIe2y0qpQH74Scs77+Xx0INBPPifUi+d0aZ1eQmdOHjW5SW8FArlcHkHJ1BoUXF/1GvEyq+OBKv5NouCMq7DkKcSsNuc/PRGJjmnTcR196Ft5M8s3RrMBVowdmQE3bpWnZ81L+M10swXmRn/FsFe7g3fLN+Dktg4nGP7cDb/fQyy5/msKMkthOY13XWC0ScAy5gcDt5JTkJyGW9hmtrMK+szaB0r4/lTBS4jO25hB46yAE3JKbpGTyXI+9rvTU3HIdTT/3KR3DV7cVrt+PZIIGxIL0RX0I1kvHYc80VDBWO/Nn24s+4xg5HZZYidAl+k3I2IneptRSjfyyf6oSCiH7y6F7u+83E3MmpdxtPAirI+BqHA5bMKEEIRjpXNTaCdEJKxb7lsrgJNhZD43Ki0E2fOljB9ViYx0RLmzY79E825LmvQ9Ix7JbAlX8NGdQFPR4RwV4h7AbCWZajZpzUwLTGa5L84/9e9UmtqrUkC/1wJNLCeqyS4JoPwMpHs1GWxOvcUzx4PpfmnRuSdA4macMlLuG/9XJbd7EuIQ8qSFs9d0zuYn2Vh0Rgl/sFixi2Ldxksgzefx+SAVy/OIaL7bQTf/Kgr929X3gaOar/j1rCXSFZUpLaoyVYXDDvtga3kb98IUgfcJoCvOIlNnI1UVhoml/fBu2h//p6wF4aguKmUXiv/gomtk1WENJO6gFKqKqcOGti4UI1Y4kHyGE82+VzgwcBEHgq+BA6jNyvZn/GqKxzwxrgFyLxCcNqspK0Yhr1ER/xLb+HpF8y5P4wc2K7jzOFiylNhBO9pt34K2nTzwUtyCehANec0pqNFnEbB9TOSCGvlzTsb8vjhZy1jRkTQvdslg2xv/kYOFW2hg/8d3Bz67J/TOKffx3c5y4iStebB6GnX9A7ZCi2kvXwIzwAvV77b/rV5XNipJyhRyu3TorFlnOenWR+z0fEk7dp6M/mVyrmGggE6OuVZPD08WZj4jtu9UZpvN1Pw+ccE3vcgsrtvYK9yLHvtz3HW2owxsRF0b0SOuHnpWRzWGyuFPG7ep+HjHQU82cmX1u+n4hUpI3RuDA/tcqUAACAASURBVHuUw5GJg7kp/q1a82hWtS8F7sj8D39H+91phBjs4Me7EDCgXZUyF1BqBbTawAdjCH6oemTemrxzdalzOaff6NgIrnfzel1crqZgj4GWr0YS0KHmtDS1nYu7uRNr279QvwEUpbyMMF5oXiCNF9xn5fl7AmegsYxsXgj7FMJBhSLk+AlQuQIpvQAqI8TEjweE+g9eNq9yYnoh/GNTWT8CqlKjE9MLumLq65mcv2BiwthIOl+DXqgu69L0TN0lsD4rlx8LdVfNza57y/BpTgFf5WkYHB1G33pyndZnHE3PNkmgSQI1l0AD6Llrdu5Og1C49n9L4CEHioB1wAyBF72a6XcFBD4H4b9COVyG3PZbzcUmRDPWPdm+vJ9Cm4mhaXuI8pAzYrEUW76FmDfaI2tWanzMPPMhJ8VaHj5UzAOPlJKuX618+14ee7dpue2RIHrfJWPvN5+xgl60MF5gQlf/CuigHyknuBAwn41/C0U9vEoCWmlW6hwIt+GpDiS225w/0UrTJ47CmqMmYckqxAGlXrTfN+Rx5jst3Z4NIfmOq99IlgPjeCgcpA3JZHqbzrSQlUKXC+Gpv6umoTWfp1XI08QH3OX6vf7kntIcx5jeKGWPcGiH3hV+KhQff0+69PZzeQNDIit6JYW/C5x1qUMOgVjEr+ZAFDFSBrwZy6w3Mzl91sSieXFEXfZciV3HhrThOHDwTPxSfMVBCMbZRuU4NNYs7o+aQqy8MgDOlWuX+cYpSo5rsfSL5eB2E96Bntz9RizyIDECYEnKyKHML3qBIgJZ+GYc0dEVx55jyWKGcgzNZK0YGyNsffeWjNcnY065QOyMN5HExbMzbSTvFA8CDxlrk5OQXeapc2/PlVvblFfIJzmFPBEezD2hl7yyMz5QcVpp4o0kCc7NmQTcG4X+ziMIHuTEgPtoEXLJU1zXMdqKSlAv3YHpdA4iPykRI/ogb3/1EElLVgnKMUeQxMmJm1c5h7Su46jtcx+rC9icr6GLn5xxcZFuvzA4NjodU7aVzusSXXmEDVnOGUuYlpKJj6eIRS3iUYgbtr8r59IAilK4OUu9iswSgbSyH4E6ovzW6VFAMOyEhGyBNE4JfAQI6FnmK9r6DzCrrK7Qj2BMflLLNXKLnjtw0MDCpWqSW8mYNrliyHctx9NU3Y0SmJOWxVGDkbnNY4mXVU35VNfudmp0rM7M5T+hgTwafik1pK7tNT3XJIEmCTS8BBpAz11z0O4yCIUTocC1JLBAzwUE99FCYDEw+RojEK7rj5cZgUJ9oQg3rJ0BIb4vvYYid4uiFPqaoNyP0mJg/sVWmN9VIe8UQNTEZM6WZDMt838EaS2MXPE7ibMWIImq7CUS2jCbHMwdmuYCZxk9S4b+h0VsEnXk18AbeLa5mDs6XAK3EIyZtamDUIjDeDZhWQ2nW3U1g/4gatUCMInhRxue3oFE3D8asURB+oRRCKGGcTPnuR522Jx8MTQNs8HOQ6sTkV1BL3F5D4Kx/fnqHI7sMmCNtjDzjVbIZKUHQKX2B87kvYNCmsT1MaXhqUK45d5F6zhxPgqVoSU4S7dZi+u86drPn+SuPtcENND+qCZvfSqKW8I5opaiPlFCx4eDeOvHIkpMpQijV+bw7c3/mENFm7nO/zb6hD7PGf0efshZTrQsmQdjKgD1XVXGut155K4UOAelpEr8uWN6NMFJlzj9cjes5dvtZr533sHtt/rz3NMVQ0IPG/azTr2E3v638WiokELkvmLTFJI2eijikFDi5y9zGRNfZ/yPj7TX0c7bxORm7dzXWQ1aOl1cwozUTLr6+TCuDMrcaHYwcFEKPjJPpmqLMZ3REz2zLYelr1NsUXFD3AJ8JfU7gJrO55K9aAd2jRFpUjARo/vhFVo9vUL62CNYM0uIX9oJr/CG42m8muiUJjMCx5iXyIMFzeMIkVRNW1ID0VdZxWawc/jFVKQRXnRYEl/XZmr1XLlH46YAP16OCa/Vs/Wt3NiKsr7jddPzbtFzQu72uFeUZGVbmTkthhbNG/99cJM8/lHNlNP5vJuchLene2lBzhSXMD01kx4KX0bFVQSD+0cJsWkyTRL4B0mgsfWcuwzCVwVbChBOIkLojFCEfwu3oMLXp/x3Vy6VkG+x4orEesG4FIh+h5XlYtRked2iKIWONuafZ0tROs8HtqLltDxs+WZiZrZjoXw7x0tUPK0OouW6zfj3v53Qp56rcmy//aRl87o82nUwc71kAQ5LCYtavEqRhw+r7k4gQHYpqfuC4Te2qhfTxq8Pt4QL4qhbcdiNpKeMw24rJCJqHMV7DqI/thOBANE37noM3+8j4K57CHn4CVcHqsPFbJ+XTXQnOf0nVg9AcUibx4dzcvBOk9G2uw+PjYrA4tC4OAcdTjPXx87GVhTtCgk9tEODoWzF/QI86dJXQde+CoKuwt135YxV005gOqsnekY7rP5StozPcAHa7ACC44Tcl8qQ3CV2vctLaHfaeDp+EZuy5lBkzeaB6KnE1DBnreCskfxpAlK8B9JhbUi4AnSh+NgRLixcxnznRLykYlYuS6zA57Wl4DO2af7H46Ev0sv/8nShuq3p5U9pt/9A3vvv4H/7XYQ+9rTrT2+lnWSvQcoAv+M8Ge9+RNNrjVoIf3zudAo+Ik8XH6Hw4Tp41sCCL9T0bS6n/6Z0V/ht0KJgfst8FT9pAj1j36yXILQ/nSVvw36wOfC7uTmhL/REJKkZQIIQMiqEjgY/GU/ggOr3e70GesXDQs7l1BQVF0rMDZIfJHSnPWbk7Owsgm/0pdnwxjnwGe0CN6ESjc3OpIQorvMVoi4bpzS2omycWVXbi9v03PadOtasz6VbVx/GjmzipqtW8g1cQfhGPH3qIt4ikSvaw92lyGpjyNk0EmVS5jT/68Lm3T2vpvaaJPBPlkBj6zl3GYS7gSxACKEpL8KpXfDw3QtsucqiCchrC4QoQgEQsKyOcHVuAEaX8TnVZL3dpihPGAuZlXWYLj4hDDodSd6aFBztZUx9ZB8RXv7MC7qHjDHDwNOTxMUrEXlXPAQJhsuyCRnkZFgY0Hw1YT4q9Nc/yVxNa1oGy3i9T0UPyc68dzmm/Z7bwl+mtd9NNZlrlXXy1BvQar7DV3EDEdEjXAaU7o+fyPtxAy74ziKIfOpVfNqVhsztXqYmbZ+Bm0aEk3iDkAZz7bIh7yw/ZGWSvC6ekjwnfe4PJKz3e6i1h/BQPkHmgQ5cOF7iasTDw0mM71m69ven80O9rorIWVWP1hwT6SP/QBwmdXlzhBfi+FeF/PFpIfkeIO7lw4iXqz7A/FrwKQc0XxHgFekyBmO82/JA9JTqpub6e0mRjW9fUxFdUEgoZsKHNcevV0UPoNNqJWX4IL403c1he0eefyaU224pDZ0VyursBRwrPsj4mJkkykppPdxVyilDol+dhnerZFxonmdT0dmsPC1fwR2JS9ySm1eb8U6+mOEycha3iCNSKmHdtlx+OqxjfJwX/t9mobg1HM19e0kv+paWwU+REHh3bZr/s64AGJP37n5028+Bpwehz1yP4tbWtQq5NF00oHrtOLJWfsTMaFxv6vcFRbybnU+St5RZSTGIGgAJNuurQlSfFhL3dAgRd7kXkOJai1aOjBjmJXZxnAlot41RGltRNsacatCH2/Sc1epk+Og0tDo7C+dWDMGvwTiaqrhZAoVWG0PPptHMW8obzdxvsAnngWdPp+CJB+uTE2v17XTzVJuaa5JAkwRqKIHG1nPuMggF3qWVZR7By6daXPa7+VeZv3BVL4Savl+WdyFUmwoIrOlCyKgA312T4j5F6XTwYoqQJuLBuribyBx7zEWs/fags9zTtSe9Fa3IWb8a/S87CXnyOQJuub3C+C4eKWT9m4WEeKu4r917RNw7jG8sCXx1RsMT7YO5p2VFQuAPleNcKJjPJaxw0U5cXoSPuPlCBpKYcETeV88pMJVcQJU2BZFITlyzhYgv4+MrST9N5vszQAzSiCQiHhyDUxzE54NTEYlxhYuKpdUf4kal70NtNTJT0oOPp+diMjoIu+4oRRdbYCkuNYr9g8R0vklGWMpMfGXFJA5biUhaO69B4ZcqCj/PIPCBaIIfLvUECiGonwxPw66xI7veh4dHV20QmuwGNqQPx+IoNUwFmoko70ugQFfbSDaLgx9mZJJ/0Uzrtp4EnVQj7xhA1CvJlR5Rr1rG2f1prHIMJTrKiwVvxv2pXKekjaDAlsuipA3IRO4Lw7IXG0gdMRhPuQ8JS1e7KEPOluVwxYrV3CpeTo+YOShkQqpT3UrJuVwcelOtHhb4CA/qi7kz2J92PnLWfJuL1mjnOQ8btrRiAv8bw3n5+9gcRtqFDcHLs/rQzisHILwDmk3HMV/IwzPAm4jRffFuVfvwRKGdtJcPY9dYSFjVBXFA5dzVWk2+hpWFg97Y8+mYHU5mN4sl4RrvcQ2brLLaufnZFB0qJnlGNH6thHS2ximCXBco1RzSF3NfSACPVcFLeflItD//jk/XNojrCajT2IqycaRZbS9u03NCT5u/0fDxpwX066Ng0AvVc99WO7qmCnWWQHkIfk9/X0bGNoyHf8IFJUqThTWtExs957fOgml6sEkC/2IJNLaec5dBaC3L/Vtyxdqpyoy9SddY046AAMFdnpCXDdwJCLF7NS1uVZTzso5w2JjPlKjOeO7JQ/xOIcoWRm5+vS+eHiJMaSmopk/CKyqauDcW/GkQmHPS+WDWKVLyWtAneQd9ht2NJDiKcT8qUeksLLk9jgjfSwdRgTJhXdpg/L0ieCb+StGB9sf95K3fhIdUgt+NHVD07440KabC7Z7TaSMjdRIWs5KwyEEoAvpVkJnx9EmyFsxE1NIHh6PYRV5fEjuag5950exmP258qfrDtWAICgZhnMSXeXE9OHk4i48WGHA6RHiInLTu7Ev3/gpadJCjPfANBds34t/5NkJvr10enXC4FMA/rNkm4hZ1RBJ16WC7fnEW4t+MiGUePLgs4ao5j/sLPuN3zf+I9W7P/dGvVbt/hD5/WZZD2q8GQlvKuHVSFBkjD7uAbRJWVjYc9L//Ss7KpayVjyNdH8CUV6No20ZOicPI2JTnCfUKZ0b80mr7rU0F/b495KxZjqJ3X8KeH+x69EN1Pt/kFzFAkUWYZSXNgx4hKahuYaMlZ3PInCYAI/59i6xlGBGj+iIOqt0Fw+UzynsnBe0POYQOTMK/f/X73h3SWKjM5oCumHtCAniiGmOprv0Je/jIS2lYdXa6bkhCdBlab13brM1z+RYr4y4osQpG7zUAMYzHL5D1xjrXBVfs/FH18lI0tqKsjTwasK5b9VxxsZ1ho9Kw2pwsX5xAQEDNwq8bcH7/2qZ3a3SszMyt0aVKXYW0SJnN77piZibF0ELuvgvLuo6n6bkmCTRJ4NoSaGw991cbhIKrRwg3FcBoBO4mobwMdAJuKENtq0piQm5iBaQQd6CMlnf0fVEG7+af5d6AeC4Un+D2uQEEF8qIntEW71alZL4ZM6dgvnieqAmTkbdph/7kXlI3fcSnx0cgldiZuCIBqZ+cbL2F0T8oiVVImH9rxdy384b9bFMvoa2iH/2vIOt2WKykj5yPXaPDQ+KF0yLY3CCJj0TRrzt+vTri6eONpuBrCnI/QiZPJjpuaqVDVv5nGynauoWQJ57B6lSjPfQ9Jy8+h9bQjFteiySqffXw9D9oM3gn7yz3BMTzSFCMC1U0/bgMif5Gbr2tP4qg0oOEgMSZ/vYobEW5xA1cgCSkdgAi5WF90ma+xL5RkTNw2kwV4tMm4h2Q1MuPXsOqPtBbHWaOFG2llV+vGiG2Hvm8gGNfavANFXPnrBi8/cXkvZeKdpuakGcSCLizojfSUVJCyvCBHPXozOeme+ne1YcxIyO5WHKWhZnT6ODTjcGRAmq9+0r28kUUH/ydyFET8OnY2RUOPOq8khyLlTkJIs6rJxEga033GOG1qH3RfH2cgo8O4t0mAklsRQ/2tVozOxzs0Ojw9fQk1izhdEYJbeQi/DOK8Yr0pqR5BsXWTIK92+MjqRqAqSajFYf6EnBHMh71RLI0HteS9capP4GiatJ3feoc0BlYqFQTWhZO2VAosJYCG0deTkOeIKXdm+4PN6uJDLbmF/G+Op/m3lJeryIsVvieZYxfgjWngIhRT+Db4+qcoDXpr7EVZU3G1Ah13GoQCuPd+HE+W7YWcd+AAB57JKQRptDURVUS+CK3EOFnYFQo/YMupSG4U1ob1flsyS9yAUAJQFBNpUkCTRL4e0ugsfWcuwxCIWRUAIe5Emu/upDRRcADZVDcpRYPCC6080JECzCihsvlVkVZ7hELF0vJsR2j77FY+n8Winc7f6Inl5Jql3tt5J274tUmzMUDeFjdnyM5fel9bwB3PF6qXL8+q+GjEwU80DqQh9tWDAndkbue47ofuT18mMuAubwUbdtL/ntb8OnejrDBD2LYewQh3MqSLjhQcRmJ8u4tKG65C2LNxCXNRSKtfOhWTpmAJUNJ/LyleIWFk7P3F75/KwKJWM/ND+4hfMBgRF7Xhrien32EQ8X5vBbZEav2HfKNh12H/E5RExF5XLpVLr5wmOzP5+Ed35box2uWu3f5nPM2pKL9Tk3IswkE3HHJEBMMoBdfSsVhdnCX1JOSIju3TIoi6rq6e4uEflP36V3eQS9vD+58PYaA2FI5/GmYJvkQO/u6Slswa9FcdEePsVA2HUOJB28tjueUeAef5L3DXYEPMiD4oRpu2+qrOSwWUocNBJEHicvWIJJIyDCZGX8hgySZkG8Sza60l7Da9fRJXIeXZ+1lol66E8OvqURNvh15u9oBrow4m0au1Ua7496cSCnhdU8roiNFhI5K5GDoRAQymD6JbyN2Ywht9VKruobAXZg6+KBrHyWt6YpI3nAeEaPdwbjz6RTa7LwSH0lHv+ovXuo6r8LfDVxYpCb0FgWJL/41oX9CTuvkFBUpJWaeiwzh9uCKeYwFn3yPZtMO5J1bEzn+mXp5B13fv9I8THfpr7qKvrGfc6ueEwZfWGhj+Jg0pFIRy5ckIPeuPn2gsSf9b+hvpSqH3UV6XkuIon0DgTP9VKhlXVYe/w0Lcv00lSYJNEng7y2BxtZz7lKogpcvE7icZEy4qhZ4ma4FKiPEqQlnxivRJq72+6utntsV5aj0vaitQi7aBYaH9CVmuhGr2kT0tLZ4JysQAEZSx72EQ2EAOTglCj47PR5jsQfjlsYTWIaoOWWHivOFJub0jyUxoKLh9UH6WDTWTJ5PWOnizisvLu/giHnYtQZi541EWpZT4MopTFGh+/kA+r1HcJotrkc8I2QE3nYLfr0743mZMimnKvAKjyB+bmlI6sktGg5tLCAm+iBxIZuQhMUR+cBYvAKr9rjZnA5eSBG4mGGCQkWm9lt8vKLoHjMTL8+Kh9ysT+dgTDlKxANj8G3VvVZvmnBYTxt6CLvBRsKqroj9L8Hya4psvDQ8jYR4CS/dE8SuxWp8w8TcOz+uRvmPVQ0k77yJ71/PxGlz0m9CJNGdLs3FFboq0BRkmYhb2BFJdMWcLO2u7eS9u4bdcYP4ITWO++8NxHHzV/yi+4mBEWPo5Fu7uV9LUMV/HCJ76Xx8u/Ug4uVRrqpf5hbyeW4hj4QFcX9YEMdzVpCt/4UOEWMIr0Pf6aO+xKrWkbjucTx9a8d/tTwjhz2FesTfgdTDg0mnC1wooN6LbZwoWk6kby/aRwiAwX+PkrPyAvrdeYSPaIHfDQ3nEdmQlcd3hVpu8PdlRAPlBJVLNOOjfLK/LiJxcBihfUsjGP6KklZiZtLFDKQiDxa2iCfIq9TgNivVZLy6DA+xmLiFY/AKqT/oTWMryr9CnlX06XY9J/Sxem0OO3frefKxYAbcVfMIgb+JTP4Rw5ieouKM0cTSlvGEu5mSplxAJwxGZqVl0cvfj2GxjRMy/49YnKZJNEngL5JAY+s5dxmEAu2EwB8o0E7oy2QnEPa+Xg3thBAmKjCZC5CMpdYNCCdSwUMoIJMK4aM1KW5XlPOzDnLIWESAp46VCf+heE8BOSsu4N1WQfSUtpiyLpK5cRZOWwmeUn807afyxfoSkrv48NT4Uu+WpsTGS1vTCJGLeeuO+Aq34sW2ItanDXEhYj4dL9A1XipFW/eQ//43+Pa4johRj1c5f23OTvJ+/BCPP4JwZpbe6np4ifG5vh3+/bojS05Ev2cXuetXV6DI2DJBiUZp4a4ZQZgPr6Qk7QQimQ/h9w7Hp5mQzlmxnDQWMjPrMMkS6GlZh5fI18U3KJdUTHy3FKpRvj0KsV8Q8UPfwkNUO6Lq4j80ZM89U2U43/ETRt6Ym0WvG3x5eUg4OxaoUR0qpt19AXR+rPaHekO+la2vqTBp7XR7OoTkKlAZC79SUfhpBoH3RxP8SMVQX5tOS9rIIej9YplXNAg/X0+aT3iPNNsZZsQvIdTLfaAA5QBG4UOG49fjRtfiCHx2qSYz85vHEiuTkq3fw/Gc5cQo+tMmbGBN3pc/6ziMFlKe34g4zJeEZbX3bP5YqGX9kTz4Fe72F9FzTw4+XQLJfmIT+cYjdI58hRCfyvuqVoN0Y2XD7wWoF53Dt2cwESNburHlS02dN5pcNBNykYiFLeMIEDecJ1Lo9fTMTPQnS2g3LxZ5XO0MencLoDwsrZvCh7Fxka4wctW01ZjPKwl5egABd1WMhKhr/42tKOs6Tjc/53Y9J4wvM9PC2FeUBAV6smxRwjU5Yt08n6bmyiQw9Eyqi77lg7bNEDcACrHQjZDrO+xcOi28pcxsACTTpsVskkCTBNwrgcbWc+4yCIVrRSEP8EQZMb1ApCOEgwpuqcuJ6S8AgrvphTKxdQH2Az+UoZQK4xGMQIHErWstgGXcqigFD9E45RYyrXJaSuW8HnsDTocT5bhSr1HA4xa0Z9bhtFtdDIuiYj9+DJhK+jkzz02KokVZKOOPKVrW/5HHnc39eaZDRQqDc/p9fJezjHaKW+gX9uKlA7rZUuod1BUTN3+UC4DhymK36VGmjMVu1xEdPw1RjgLt9gMY9vyBo8Tsqu4VFYqH1IQl6ySRo8e5cs806Wa2TMwgIE7CvfPicDrsFOz8mKLfBEwfD4JufpjAnv+pYLiW8zL25FfacIYu0a8RVAWvX95P77vCZoN6P0LQjbUHN1EvO4dhX0GVnput3xfx/of5PPpQEP+5N4jifCubxymxW50MmB1LYHzND8HWEgffTVO5jOKWtyi4/oXQKsPXrLkm0kf8gThUSvyyUvqLy4tq9jRM587yVctZHDoNAfdvxrvDMRYmvYvIwz1hV067ndSRQ3CUGEl8ay2ecjl5FivDz6UTIfFy0T0I47LYdexMHYxMHMxN8W/VKhyv5LSazBnb8OkeT+SYioBENfm0CYTrEzZlwEWY6GnD74iGoEGRHIqbhJenH70TViLyqN3lQE36rWsdh9lO6sCDLvoKIWzUw8s9a1U+HpvT6fKSCWh+g6LC6BfUsB474bt06IUUcECXd5PwELnrk143CZscDsafV5InoKvGRdBy/3Hy3tnsAsOKmTXUhZDrjtLYitIdY3ZDG27Vc5ePZ/7ibA4dLmbIwDD69G7YPesGOfyjmhDAmAQOwmAvMctbJTTY3Mq5DuUiT9Yk1x2RusEG2NRwkwSaJFBBAo2t59x5ehCS65YDPXGx3rGujHLCftkM0wCB0+HZy37Xvwwgppwc7HjZv4V6NS1uVZTHjBnMzhIclC3wFXmxJvFmF3eYbrea3JWp4KOCFpsIueUpjPtOknEwg82WYYREejFqYRyiskPZ7F8yOZZbwtTe0bQJrRh2uD13HSd0P3FH+Aha+gn4OaVF881uCj7ciu8NHYgYcXkE7iVR5GStRq/d6UIUFZBFy4vDZMGw/xi6n3/HdF6I1hWK05WH6H9rT04f9efkN1o6Px5Mu3svhQbpT+0jd+vbOK1mfFp2I3zAS3/SRYxP/4UMq5n/8ik9w54gWtGn0po4LCbSlg/FYbOQ8PIKxD61S4p3lNhduV2IIPHtroikFQ0IgUBZIFIePzqSLp1LQztPbyviwHv5hDSXcsfrMX/K/FobxuFwsnNhNqpDRiLaeXPLK1GIxFd/BVTTTmA6qyd6elu8W1c8JGm++5aCTz4g9/onWbavNZ4xSjoO3cH42Jk13bPV1nMhxM6dibxdB6LGCU54KAfvuBK1cn/Ga+jMF7khbiG+tQBwKdp6kvz3fyfokc4E3V/KUVmbIhwynnjrIuiczFIW4dTbEM8v4pzpPeL876R16DO1aa5R6mYvPEPxAQ2RE1vj08m9IXKb8zR8nFNAslzG1MToWhnndZl8SaaF42OV+CXLSJ5WOxCnuvRXk2eO6It5Mz2bOKOJwas+x2m2EvvGy0gT6w4sdGW/ja0oazLvRqjjVj13+XjPnith2sxMYqIlzJsdW6PvaSPM91/RRZbZwpjzStr6eDPFje9IVcIbcz6dLLOVd5ITkXv+fS7q/hUL3TTJJgnUUgKNrefcaRDWcqpure42RSl4B6eqvuS8OYdYSQcyLCZmxnQj0Won68vFWHZcD+ZgQgb5ENDvOoRD+2ezjnPO3pUBz4Rww52l+THFFjuDvklF7iVi9d2JeF5xc/9B+hg01ixeSFiNTxlvoGDQpY+Yi11vJG7BaCTRlQEijMUnyVLOxNPT38U56HkVbjf93t/IWfkeoIAyk9wsUqB2tKLb7H4okioehM15Gai/WIC1KAev4CgiHxxLka83IzIOo0DLuAATLUOeqHLRtEd+Jm/bWnzb9nLxLta26HblkrvqIn69Qwkf2rzS41NfV3HuvImlC+MJL8vNFIy7bVNUFFw00/25EFrfXn1e0sEP8jn1bRGKSC/unBmD1PfaClH7Uw5561JQ3BJO2IuC0/tSseblkj5+BOKIKObbB1Og9qT3qIMM7fJobad/1XAAVQAAIABJREFU1fp5G99D++M2Qp95Ef++gtMcZqSoOG00VYIOv1DwKSmar2gV8hTxATUngM9ZuRv97otETrwVn061NyjytVaGLU8nFiuDjxfh3UZBxnMb0ZlT6BEzG4WsotzcJpx6NKTbnUfuygso+oURNqhZPVqq+KjabEXg+nLgZG7zOKKlDc91mLdLR+qqXCIGBBD3ZO3Dp902+SsaWpahJn79JtqeSSNgQG9CnhQyA9xXGltRum/k9WrJbXquqlGUf2fHj4mky2U51fUacdPD1Uqg/AKlT4AfQ6qICKq2gVpUmJeexWG9kTnNYkj0bqKeqIXomqo2SaDRJdDYeq7JILxiiY8Up/Nm9jfESYK50bcHHxde4D5PBT22rMderEXMTdiOdkbWys/lOSox2Hlz0HmBc4Ex06QEJJcaNHuUepYfyKFPvB9DulYM+zTYCnknbSiBXlE8FS9E1pYWzZZdFGzchu+NHYkYXtmwcDgsZKROxGrJJjx6BH6KS57FK3dq4aYvEH6CHnwUr4B48rb8ikNZ5jX0FOHTOdnFayi/rsWfYVx2UzE5Xy/HePEPkMrYfU9HfvBoR2dxHuPjH8GjilBIwYDOeGcillwlMU/PRBYtpIPWrmS+cYqS41qiXmuDvH1F76LQ/gtDUl3k9O+uTapwc12YZubbSRmIpR7ctzAeeRn9RVW9n9+u49c1uUh8RNw1KwZFZPWHdQHgJnXIQURSEYmrK4cXKqdMxJKRzqY7n+PgN81odb2OGcM6127yV6ktzDt93HBshQUkLF6FOCAAnc3O4DOpBIg9WdEqweW1/nPvlJzlQOY0guUd6BJV6k2sSVFO2IRFqSFh9SOIA2qPUPrzH1rWbs3j9hIDN10oQfFkIEdaT3cBDwneyitDbWsypoauYzdYSR10EE8/LxdJvTvCLIX1mp2WxfHikkZF8Ut7J4/cH7Q0HxVBUA/fhhZdjdvP+e04+sUb0fj7EvDmCFoEujcMsbEVZY0n3rAVG9QgPHjIwIIlalq3kjF9cu0vhxp26v/c1n8o0PJOdh4PhwXxQAOjf76Xnce2Ai0jY8Pp6d9EPfHP3VVNM/snSKCx9VyTQXjZrhEOdZNVX3DRnMuYiDsIF4cxUfUbsQW5DN7+NYqO/Qnp/wyqSaewqEqIei2Zw0onWz8ooLXnb9zeV0f4iy+5Wly8P5vfMosZ3zOSLlEV0TjP6vfwfc5y2itupW9YaTqlw2Qmbfg8HAajC4lPElUx51CoU5D3GZr8/yH36Uhk7MRrHrbLeRJjZ81HGhPL3tU5ZO3MoFOHDEQpx3Hoja5+xSEBKPp2Q9G3K+Igf5xOBwW/fMH5kv/xdbO+pNKMMWGt6a6o+oBQknGazA9nII1IIubZN2ptANgKLaS9fAjPAC8SVlQ+nAuw6ENHppGUKGX265U51g5tzOfkliLiuvnQZ2xFzsDypVWfNPLj7CzXPwXi+Yi2NTd8sheepfhAIRFjW+HbrSJUd8FXn6PZ/CXfDkni17WPI3ZIWbUsCT+/+ofimNJSUU1/FVnzFsRMLg1D3anRsTozl1uCFLwYVdF77HDa2Zk6EIfTSt/E9XiKqjd4HRYbKc9+iKe/jMRVdfNsLvoim9/PGBil1BCis+OcmUmqxxc0D3qEpKDa55I21kc8c+ZJSk7qKnCL1qfvPUV6lqtyiJJ6MbdZHF6NlMt3clIGxSlmOiyLR1rmPa/PPNzxrJDHrBy7CFuhlvcevR1zu2bMbhbrVrCMxlaU7pCLG9poUINQiLoY96qSrCwrr0+NpmWLimkObhh/UxNVSKAciGlYTDi9Gpgf8LuCIjZk5/NoeBD/CW2inmjakE0S+DtLoLH1XJNBeNlu+KM4jbnZ3xIvCeaNiPvJ/2E9U+KjMci8WVjsSVRHId0RDPsLUC85h7SlL59qfCnMs3O/3xqCnNkkLF6BXe7LwC2prrpr70lE4lkRSOHn3DWc1G3nzohRtPDt4aqn2byTgo+/w++mToS//EilPWoxq1CmCEagmLik+XhJrs43ZjfoSR0+CM+AQBIWrXCBr3w+OBWHDR56OxEvLweGAyfRbT9AyQkB58dF7OXiCPPv1428hFTOaD7kY+cT2BxezDp2hLgBL+PpXdkDod60FMPpXwm7ewiK6yrnF1b3smm2ZFGwMZ2AAZGEPFk5of7ocSNz5mXRu5cfQwdXBtixmhwIyKmGXBt9xkYQ163iGHXZFrZOUWExOOg5KIwW/WrnqShHpfTpHkTkmFYVpmNWppMxdSLvjvBCtec2zAd68vgjwdw7oP55aQVffopmy1cEP/wEgXfd4+p3fnoWh/RGXo2PooNfZaP2aPZicop/qzGyp+lCHqrJ3yDvFEPUxFurW6pKf7fZnQxcnEqQwcpLZwpRR4qxj3gfsz2Pm+KX4e3113Di1WQiRd9lk78hjYC7Iwl5qn5ADnqbHSE3R293MC0xmmSfxjlIOywODj2XgqePJ53eTqj1ZUxN5FSXOnkbvkb73T58e17H2/f0doU4PxYezH2h9X8vysfT2IqyLnJogGca1CAUxrtjl4631+XSrYsPY0dVfcHWAPP6Vze5SJnN77piXk+KpqW8Yb8djRme+q9e1KbJN0nADRJobD3XZBCWLZrgHXxN9QUp5lxGKW4k+ttPMatT2dTzFg7GJDAyvD09/UoNEgHZL+OVY1iURr7DH2mygv8k/Yj25+8JfvhxUjr1Z/6+bLpH+zCmR2Wl+l76KLRWNS8mvI1c7O9CBk0bPheHUeC9G4MksmIukOC1y0yfgankLMFhTxIYPOCaW03/2z5yVi1D0bsvYc8PJu1XPbuX5pDQ05feI6+gi1Dno9txEP3Ogy7eQ6HYFDZyOsPHXW4mxFnMsz9vQhwQ5sorlIYJzCKlxWbQkLZiGCKJNwnDViLyqt4rdeXAlROPYkk3Ejv3OqTxlcm7v91WxAcf5V/T0Mo8WszPc7KRB3ly74J4JPJSA9xssLvyDHXZVtrcHUDXp2qfY+W0OlxhowKZuRA26ul7iUJA2DPnJ41gyfN5+BQ2J2P5s4SEiFm2ML7eoAzKyeOxqDKIm7sESXgEJruDgWdS8fLwYE3rRMRVeKBU2u2cyltTYzAX7U9nyFv3K4H3dyD4kdqHup7JKGH6+5k8arPQ7rSWn26WE9p/FbHyCLrHTHfD57DhmrDmm0kfdhhxmJT4pZVRZGvT82pVDjuL9PQLVDCoirzf2rRVm7qG8yZOTVHh30lOq4lRtXm0weqaLmSgmrISkVxK3MKx5HpLXXmVIjyY1zyOCOklftH6DKKxFWV9xurGZxvcILRanYwYk0aR1s6CN+OIjqr9N92N8/1XNFVOI7S6dUKDU9SozRZGnVe6QK+mJTWFBf8rNljTJP/fSqCx9VyTQVi2VQ4VpzFf8A7iy6Bv9uAs0SOLTSbtjqdYXpRCH79IhoS3/XNjGX4rQL34HLmI8RvZmlbxOpSTxiIODuG7x6awM13PsG7h9IqrGKevtxXwbtrLBElieDJugau9wq92UPjp9y5i+fChD1favFrNT+Sp1yGVJhCTKIRlXjsksZy7LmLoKHy792D7vCxUh430Gx9JTJfKRpfQodNmJ/+33ai/+xrZBQkeTg8cHmBsHUNwoA6z4RgeEilhdw3Gr01p7mLhL19QuOcLAnrcQ0jfqgFnrvUmmpXFZEw4hiROTty8qhEuhdtq4dZ6wthIOneseuxCH7+8pSZ1r4FWt/tz/XOhOGxOfnozC/WJEmK6yF3hpOXor7X9OuSuvYju51xCBybh37+il/LI12+xps1eWhtj0W0dxYmTJRXQUGvbl1DfkqNGOVGgHIklbtZ8VxO/aQ0szlBzo78vw69CdG6y5rM7fZgrf+/Gy3JTrzaG3LV70f18jogx/fDtfsnQr+mYP91ZwFd7NUzO0SPLNbFscCDJUV9xT0Q3YvxLQXD+ziVj0jHMKcXEzrsOadzV99a15nDSYGRmWhb+Yk8WtojDtxGR+3K+KyJ9Qz7RDwUR/eBfH/4lfEMyXluOJT2b0EEPuPhQhfJlbiGf5xbS3sebSQlRbvFkNrai/Jvs4wY3CIV5fv2Nho8+LaDvzQoGv/j39fL/Tdak3sN44VQKAl3NhjZJbnk3rjUgoZ+nT1505aGvbN1EPVHvxWtqoEkCDSiBxtZzTQahYAwJ3GEZn5NqyePJvadonVWAf7c7XUZOMU4Gpu4iwFPKyoRef36w87PMpIw5ShB2wse3wq9LEJnz38Bw6iTz751OiVPEmgGJ+EgqGm9n9L/wQ84KrvO/jT6hz7u8gi7vYImZ+EVj8YoIrrC9bFaNi3PQ4SghJuENZN7XRm0U5pI2eih2bRGJy9ditXvz+UupLq/ZQ6sSr0qzYLZp+U31GiZbPknOezm600Li76ko9KW8hh5yL5y++RCkJ+Cm2118g+mrRriAduJfWopXQO0PDvkb0ynakkXw43EE3ls1JP3k6RlcuGhm2aJ4wkKv7l0o0drYPEaJxejgztdjuLhLx7mfdATGSVy0FF6yuvOflZzWkTnjJLLWfsRML2dHKV2mHec28rloCzeeCaVlxBwWLlVzXXtvJk2oO8S+ZusWCj7bSOA99xP8YGn48PIMNXu0BkbFRtDD/+rgIXuV4yi2qGoUspkxaQvmlHzi33oIr9DaA5JMWp9BodLIuLOF2EK9mD7In9big7zW8h68roJ+24Dfzlo3XfiVisJPMwh6KIagByvnp1bXoMXhYMKFDNQWKyNiw7mhkUEaLq7IoeAXPS1fiSTgGpcl1c3DXX/XfL2Lgo+2IWudQPTUQX+CVQk8axMvKl1w9+7Kk2psRekuGdWznTobhEK0xIH3813f/xsGXftbbTTaeXlUOlarw0VUHxR4KSqinuNvevwKCRTb7bxwOpUYqYQFLeIaRT4jzqaRa7XxXpskpG7iBW2UgTd10iSBf5kEGlvPNRmEgncpfz8rig4RXahnyC8nCb/9BXzLvGDC/puWeYTzZh1vxnQhTlLqSdj2oQbV1nz6o0OaJCdqZmuMRw6z/+PNrO/5Iu3DZLx6Y2XF+3PeOk7rd3NH2HCa+3ZD89VONJ9vx/fmToQNrgzCkZ25Cp3+AP5BtxES9mS1r4NZpUQ1YzKyZs2JfmUq53/QcmhDAS1uVdDluapDJu0OK4ezZ6M1XSTCtwcJwQMZmr6HQMTM0QRj2HEQ05GzguXs6t/ha8IjzhOnKBuf5h2JfGB0teO6soIQdps++ggOjYW4JR0RB1UmlxeM28Evp+JwwpoVidV6+C7u1LP/nVIkUUuxA5nCkztmROMTXL8wNWGsyrFHsReYiVvYAXHoJbjuL/Le5xfdT9z5NfR/cSXjXy+gsMDG3NmxREZUDrfyEHtWS86tmjUV04VzxEyfgywhEZvDyaAzqVidTta2TkR2RU7q5bI9m/8B6UXfkhz6IrHX8NI5bQ5SnvsQD6mYxLWP1fpmWltsY/CSNG4pNtMnRYfjVhFTrw8m1FPLW8ldar0f/ooHLJlG17pKEuTEvVl7DsZPs/P5KqeI63y8XSTsjY2oenpiBuZsK+1WxCN2A5BRfdbAlldI9qtLcTocRM4cjtcVobNnjSbeSMsisdDB+B6JBMnr9042tqKsj2zc+GydDUKH3cnHz6Ug8vTg0fWJ1SLrbvwkny3fFnHvgAAef6T2ofZunPM/uqnUEjOvXsygs5+cCfGNE/Y9Oy2TY4YS5jePJVZWWe/+owXeNLkmCfw/kkBj67l/vUFo1ucyRv0uBR6+PGo7QTunptJ22S7qxHbPLtxu/42bHMexWb34ZNEEbGYxT4dcRJQXDI9thpZpbFXfyb7CG7kn4muuD/q9Uls7xWD0gFusIDF5wNIYsIhgWCYE2irU/0VzI9/vuQMCPECI6BPSEWvp6Or5LgRmwt7nQFt3pxUKXTFdjpx1/QToiv8fvVJ/r6F6RYYQ9dqLeIVUzZtoKyoibfRLiIOCiV/wlsvIOGYwuigNanJoKDAe41DWbMJ8utExcuxVJ29OLyRj4ma820USPfmOWgtpzwk9yzfnMDZPT6DahHHkcZb6dcaIP+uTmyG/htFa684a6AHhwkE55gjWbBPxyzrhFVY9L5fwjMCJ+c3PRRw4UAzWBhpcNc1KnfCyDYqAd+pnW9V/AkIYmv17WjpVlH8rq2o03GljkK2IDD8Jt7/dqVqj5FoDa2xFWX8huaWFOhuEQu/bpqnIO2viP4viUFSTG1iosTF8dBoSiYgVSxOQe9dS8bhluv/8RspTAe4I8ufZKpDFG0IC67Ny+bFQx7i4CLoqah8Z0hBjamqzSQJNEqgsgcbWc/96g1BrLWJu6jL0Ii/ucR4j0iMAhUdFpC+l058V3Egz8hnk8TunDySze1Nfmnc4R9/2J7F/2AuP6EJEQ35m7qknKLL580rcWwQGVQwXNTpt/EguCsT09QjD/rMIx3YxHl3tiO8vY48v2xMqRygrjjyA49ylNgQmAe8EET5JIsR+VS+dtbAAp9mMOCQUmU5Mm6U2zEFweqTYhSR6ZbE49FjtejzwxNsrFA9EaOxmShx2Aj2leIsqzsHD4SDmQgbND5wkJL+IMC8x4jqEndg1FhwldhfdhEhedUiSyeRAoJ2Qy0UEBFRdx+5wYjQ5MJodCP8vcoDEDlYR2D1BwF7xlorwkXoiFtd9uwvgMrY8M4g9KhgOuZZsnDgJLACRTIZIEUhOrhU8ICLMq8Kh12m2YCvQIomPJGbGEERV3M5qd/xE3nvr8L/1TkKfeMa1XOUKfHB0GH2r4XOzOyzsSH3RtZ59k9Yi8qhabrqd58ldvYeAe9oR8kS3Wn+LBWPw8BEtr54pQOwv5sKYpey0P8xFe5uroqDWupNGeCD/o3SKvs4i5OkEAu66OqqiwWBn914923foUGVaSkfmBX4BIvzE9acZqe1Uw00O+ubaSZd78GvIXxvS16L4HLcW/IxGHMCnkQ/jqCLH2dtu50F1Pgq7nYNxfjwyt229PKqNrShruz4NVL9eBuGB9/I4vU1Lr2HhJPWqnoNu9docdu7W88Sjwdxzt/sQYhtINv8vm92Sr2GjuoCnI0K46yqXhO6e2Lf5Gj5QF/BkRDADQprW1d3ybWqvSQLukkBj67m6n5DdNWP3tFMvRWk3l3BBv430os+FjELiA+6mRfDjiMoONg6nk0GpuzE5bKxN7M26Sdlkp1sYPCOauJYyVK8dd4FTiIYmMSlPT6wmg5cOve8iFBeMhPJyWrebH3NX0sH/DnrJHyFdyB00W4hfPA6vywhpC6w2Jl/MQLPHDjnwRP9gzmWYOHS+2BU+KZTkOBn9O/nTvbUPEnHp7a3DZCLl5RcQectJXPY2R7/UcOxLDR3+G+T6ubKoDfs5pl6Cp4eM7jEz8JPGu/Iph6btochuZm3izfh6Vu1+KCe4rUtOkMNiJ23wIYSwxYS3u+J5FYNwy7caNn5SUOlAIvBlHU0xsv0PXZUy6dbKh3MqEz//oePAWQN2R+nMW8bI6NdRQc82vki9an/jXQ5CEjO7PbIkXwqt+UxOH0asJIGHZ6vB4SDxrbWsereIX/bqefG5UG7p5/+n2AXQjaw333VRffh0SSZi7FOVwkezFszBeOIoUROnIE9ui7D3Xj6bRpHNztutE1HUwPg4nDWXfOMfdI2eRpB3cpVvWN6G/Wi/O034iJvxu+HaealXNiCMaciSNFpkFXN/hh7P3lbO37aSLPHTfGdoyYOhgTwUXjEX1j2vuftbMZ3Xo5pyAlmyHzHTKuaHCu/C6TMmtu/U8tuBYgQERqEkJkrJbG3B1trJyusaHhmwqllnbSpE9UkhcU+FEHF31d5m90urcot2gxHlmIXYdcVETxuEd3LlvSTQY2TNPInpvAHvnsFEjWhRL2NQGEVjK8rGkGUN+qiXnkvZo2fP8hyS7/Sn2zOVeW6v7D8zy8LYiUoCAz1ZtjABL69/ynGhBpJupCp/hbfuoK6YBcrsKvlsG2naTd00SaBJAjWQQGPruX/KF75eirJ8XfKLj3IsZxk2RzFB3m25LmIkEs9S3rpl6uPsM+TwnKEtu94sJipBystzYlwHk+JDGrLnn8EYIWV2Lz/uLTlP95/fJ/TpF/Dvd4nf7cecVZzW7+LuiDEEbNOi+fJnFP27EzbwgT+3hsnhYHpKJmkmM5IfPLCZnGyYkOQy+gr1NnYd07kMoTxtaXipr7eIm9r70b+jP4Gq42QvnY9vjxsIHzycTaOU6HOs/GdJHIor8tmEfMEDmTNcROYdI8cR5lOa96U0G5iQsZ8WMn9mxlzdc7Rbo2NlZi4DQgJ4MqJ2OSb6X/PJWXoe3x7BRIxqedXXYtWaHHb9omfiuEg6dfChQGdj51EdO47oyNeVzt9PLuLm6xQuQy8quHLOnpDrtvu43mUcqgtL4/vkUhG92vnRv5OC+PCa51AUbc0m//00/O+MJPSZBI4XH2ZV9jx6+vXh1i9NGH7fT8SIcaj92zF5uorYGAnzZsdWOPwKB2jV1FVYs/IIGNCbkCfv+nP+dqOR1OEDSw36pavx8PTkvNHElBRVrWDClUXfcSZ/A4mB99Ei+LEq5auathXT2RziFj2AJOqS0VqDbxSp2SZefUfF4Fw9sTkm9AMPkhe/l/CQ6cxRiV1okq8l1iM+uSaDcFMdIT807eVD2IusJAqXEwovdDo7u37RsX2njmx16Z7x9hbR6wZf+vXxJ0VhYm1WHr38fRl2FcRXNw3vqs2cX5iN5kAxyTOi8WvVsNxl15pLzuovXJQ1in7dCBv0YKWqglGds+IChj35SFv4Ej2lLSJJ7S9jrmy4sRVlQ69nDduvl57TZVnYNEZJaCsZd86oGeXAgsXZHDxczJCBYfTpXTsO1xrO6V9dbU5aFkcNRuY2jyW+kfL5Mkxmxl/I+H/1nf5Xb5Kmyf9rJdDYeq7JILxiqxmtORzNXoTeko5MHELHiDEoZEns0mWxKvcU1/0vDsNhEQ8MDqNr31IFKRx6VJOPY75YzMYbFDxzixTbvMku2oDYmfP+NAg2pA1HZ8vnxbBlqEcux2GxEb90/J/5ZILnZbFSzQF9MS1EMs5/ZSIuTMK8gRXRx4R6J1JL+PkPLQfPFf/pAUuUaOmk/Im+D90AEV3ZNjWT0BYy7pxZUfmbbAX8ljEZs11Dy+AnSQi8xGu4RZPGxoILPBSUxINBV/cclSuVtj7eTKnl4T9LMJ4PaYgc3xqfLlcPWXltWgYXU8wMGhHGwRQDf1w0luPa0C7B22XQdW3pi1cNQkFd3h6l4DXU8vuZYqz2Um9Ps0ipq50b2vohq+agaiuykDb0EJ5+XiSs7ML32s18XfgJ/w15mm5nAshZvQy/G3sTPnAok6ZmkJJqZuqkaNokVzywW9T5qCavxGEwug7RwmFaKPr9e8lZ/RZ+N/Uh/IUhrt99pM7n6/yiWoUUFVuy2Kscg580gZ6xb1Y+pDucpDz/oev3Se88Wetcrq/2FvK/7QVMOVOAp9SDixOWIpMFc0PcEl44kyZEy7I+OQnPKkKU/45f9tz1Keh+zMFyTww/aLw4cNCAvSyCu0VzGf37KujR3ReZTOR61ydezEBpsjQKkfTV5PXHS6lYtXa6vJuEp7T+BlZd1sV48iJZM9fi6e/r4k/19JVXaqbwfyoKP8tAHCIhZlZ7xAHu4bVrbEVZF/k0wDP1MgiFy49PXkx1UfI89m6SC2CmunL2XAnTZmYSHeXF/Dlx1QJ7Vdde098rSmDMuXSyLFbeTU7Cu5HyrgV05KdPpbjSPZa1SmhakiYJNEngbyqBxtZz1WuEv6mgrhhWvRTllVO0O8yczF2D2rAXkYcXyaEvIJf3ZNjxfcTNj0Hu7cnElQlILjuIqfblYVp2gfwgL65f3gXVzMmYUy+6kD69W7dBZ81lQ/oIQiTx3P5rfzRfbUdxy/WEvXgJWfRjdT6b84uIkHjxsDOIZV/kcPN1frx0T0Xuu8vH6/KAHSvzgGnKPGASD3oXeyE6Y6H786G0vu2SB8jmMHEgczp6cxrRin60CR1YwYM1M/MQJ0s0zIrpRnPZ1T1HdqeTZ0+lIPHwYF1yYo1DwOw6K6kvHUIk9yRxVRc8ysJdr1wDdaGFsWOVpcZuCyFGDPx9POnTwY++Hf2JCKw7kobeaOeXE3qXp1WVX5oPJpN4cGNbP5enMSlSetX5ZM05jfFoEZGvJvNp+PscNOxjZNQUmpNY6t2TebvCdXfvK2bVmlyXITFqeESlt8h1mJ693vX7qEkvIG/bDPWKJRgO7Cdy5Dh8OnV1GR9jzivJtlhZ1jKeMEnN5iw8tyd9JCW2XG5OWI1UXDGk0JKlRTnmf8hahRMz45KHsqav+vT3VYhOaHlcqYNuJaTct4bEwPtpEfwIM1MzOVlcwpxmsSR619z7WtO+3V2vqMjGwc/UJGzP4IyHhA/F/vjIRdzUy4/+fRTExlacw5niEqanZpIokzK7WWmEQGMXS6GNI0PTkMdLaDe3caDqr5yjw2IlY+JSrNn5hI94DL8bKqO0GvYXoF5yDg+ZiJgZ7ZDG143rsSr5NraibOw1vkp/9dZzP8zMRH2yhHvmxhIYX7P3c9pMFWfPmRg3OpKund23hn8Tmf5lwxAudp8+dRFvkYi1VYRaN+TAhp5JQ2Oz8UGbZoiFRPum0iSBJgn87STQ2Hrun/IlqLeivHInuBAItds4l/8hThzE+t/Ge193QfSjD53v9OG/z1QEoNhythDfpReJLbQRMaYlDutJcteuxKfr9UQOG80p3U5+yl1NJ/GtRM1Q4rTaiF9yyTu4U6NjdWYuPiIRM5vF8NsBA5/uKuSZ20K4s1v1OUJmdTa7ZizjcOTNnBQ35+aTILZDSl8Jfbr7uzxgUi84ql5MbvEBAr3b0CVqUgXQESFH8oWUXXiLxKxJ7I2omsOukOd4ocRcK2Ol6Hs1+e+m4n9bOKEatoNxAAAgAElEQVTPV/RA2uxODp8vdoV3Hj1jhBTBUoMO/eQuL17nFj6Ia3CrXdO32oUYKeQaHtHx6ykDVlup1zAhXEq/Tgp6tfVFLqsIGKLfk0fO8gv49gph7b1vk21RMS9xDb6eCrIWzsF4vDT/T9wsmZdHplFsdLB8cQJBQZWBP3TbD5C75ktEvnKipw1E9carLmoPIQ9RJJGgMlkYd0FJgkzKm81rx5N3Knc9Kt2PtAsbSpSidwWR6PelkLNsF/53JBP6bI+aistVz2iy8+KiVB7N0tMm30TRE79SmPw7N8YtxEfyf+ydB3RU1dbHf5lJL5Pee0ILvakUBURU7PqwPhsoICICgjQBEQEV6UWaijx79yn2Su+9hQBpM+k9mZbp37p3EmkpM+nPb85arPfMnLrPvXefffbe/38kn+cX83VhKSPDgxgeWP9za9fgTVRZyEE9cUrDH39VcOSoGovRwixjMa5OFgrHJnHtQJmIrlhTWaXIY2+5inGRIQypB+CniaZ7VTclB1RcWJ5H8FAZ8fVwyjXXHIq/+E0Meffs0YHwmaOuMowrU1Uid6cAxhT+Yt2RAA2ZY0sryobMsRnaNFrPHf64iNPfldH/mRDaV0W41DfPw0fULFmRS8cO7syfa1uoaX19On6HEoOR8SkZJHq4sSjRvu97Y+X3ano2Z9RalrePIcKtabz2jZ2To71DAg4JXC6BltZzDoOwniewRHOa4/mr0OlV7Fw6G7PKletedePeK0hk523LwnS6gpE7y3GN8STy1U5kTpuAWa0mbtla/tR/ylnlDm7fcyv8mILvLf0JfuoecfRktZaFGdliOOSsuAi6eXuy/MtcDqSoeeWJSDpF158jVPb7zxR9uAW/O+6hOPJO9q/OpzzQid0RViNH8IB1TcwnIupLYkLNXBe18Cry8MPqQpbkHmeAdygTw7rV+26+k1PA7yUVTIkO49o6yNIv7Ugx9yS68yqiFnTFvb0V6U7wBv55rILtJ5SUq62xet4mCarzZvr192Ly+NrRH+udpI0V1JUmBCoFwRiVF1i9hm4uTvRP8haNw/aR7uKh11xpIv2ZQ+Lva6evw93Tk9fj14v/Xb7tdwq3vIPvsOEEPzaSjz8r4rvvy/jXvf48OKJmkJWiD3+k7PsdSAN8MKlP43VNX8InTBH7+6aghM8KSnggJIARl4AO2bKkAvUhjuUuJcx7IN3Dnr+sSdFHBynbeoqQcdcjGyK4X20vB86qWPllLnNTSnCxmEmftQ4fWTT9ol8TOzmuVPN6Zi4DfL2Z2Er5dbWtRkCs/WtHBX9tq6CouCoH1VvCoBtkXJ9VjOlIiZjTKuS21lRKDUYmpGSIoV3rOsbh2gB0XdslXXtNxSdF5H5bRtzYYEIuAS1qir5t6UOflY98xmqcpBJilr5wGSCW0N5YrEMx5ySmUgOBj8fif0fT86u1tKK0RS4tUKfRBmHmfhXbV+TRYZiMfqPrJqivXo9wgTJtlpzsHAPz50bSsUP9+qgFZPE/P0R1tEF/X28mtfC3cmN2AX+VVjAjNpxePg6v7//8w+RYwD9SAi2t5xwGoQ2PkZBz9+PPX3PowxtRd9QQMqaC2VHX/92yrNLIsz9kEOAuZeY+lWjwCAfLSsWvlP3wLX5338d3vXegKy/jlhWJCHGQQu6gc4AveToDc9MUKE1mRkcEMyzAGqb5/NoMisqNbH4xQaRNqK/krFiM5vhRMUT1wM8BZO5TMWhSKJVRzmJo5J4zFRhN1u2OCXFiWO+gqzxgmwvP8mt5Fs+GdGawrP5D3B8l5SK4xn3B/jxkA6qkPk+LfPIxXMLcCV/Sg0NV3sDTGVpxXoJDslei4A30RZGi49PPS3j834HccVvLQWMLXsPUHJ1oGO45o0RXhSwZHewqGoY3dPVBvTkN5c4ifrr/Z5wGuDMhYpY4/785BP0DiF22lsIiI5OmZiKTSXlrZVyNtBcCkXfesg9QH04GiZaQZ0cgu8Hq0XspVUGaVseb7aKJsRNwwGjW8lfaaJwlngyJ34iT08VnKHvRL2hP5hC9+B7cYq9Gn63rWXv7xwIydhQxKqMcc1cVGQ+/S8egJ4j1s4aeakwmnk5OJ9DFmbVtID/FZLJw7LhGRAo9cuxiDmqXzh5ibuA1fbxF9ETV/mLyVpzDe2AQYc/XbCR/WVCC8O+uID8etRNIqb73157fzy7MpuKUlq6Lo/G0MezPnv7rqis8r9nzN1GZkkHgo7fjf9fl3mfhwiT7ldPoMtTIhoYQPCahWcJqW1pRNpX8GtlPow1CVaGBr5/PJDDBjTtes90rtW1HBRveLhBDRoXQUUdpvASqgdnuCfLjkRb+nnxbWMon+cVtOpKj8RJ29OCQwP+2BFpazzkMQhufl02vKMg4q6PgiSwqO2hZECQlzvdm8bDze1o57xwtZHiiLw9KXMh5LRnXKA/CpkcgnzGJykhv/hyppNcfcYT/Bb7DBxA88m7x8CwgSGbrDNwW6MuT4VYocJXWGpYXHuDCimcFRvq6i8VgIO250SLpXtSbm/jiOQVSZyce2BiHs6uEUm0yu9OXkprZjUz57eQUWY2Dag/YTb19aRfhxmT5HvINWtbH3YC/c/35JWnaSl5KzaKntycz4+o3IEu+VFDyZRaK3gF87OKCUmPlgwiSOXNjTxlDesgIlFlDK9dtzGfHLiWzpkfQo9vVYBX1yaQpfhe4DfectuYapuXpxC4FAJvbfSVcsy2P9HYZqCabuS/o0b+Hy1r4MpUXzhE1bxHu8YlUo/Q9Pz6Ugf1r5v4yabSkj5kLJmd8hvQm5JkHEKhHJpzLJNTVhZXtYxp0qD6Y/Sql2jNcF7UIX/dEcY6CwZs+5hMsOiMJ7z1Waw5nTfIT2j6/NpP+yaVcV1JJyYhdlPc6yqC4dZflKU4/L0eu0/NWxzjRMGyNUlhk4K/tFWzbXkFJqdXr7CuTMniQD0OHyAgLvTxMSvT8jjmIk4uE+E19r5KLUVh7Ff3HKjvyOZt67QIwyJGn0xH+t8/mBJyaMITalrmW/76fwne+wTUunOhFE0Qk3OoizClvRQrqg6V4dJERMSvJrufLlvGr67S0orRnbs1Yt9EGofAOf/FMBnq1iUe2JCK1kUrCaLTw/JQMSktNLHsjhshIR5hhY/e5+oJpTEQwN1VdBDe2T1vb7ytXsVKRx/BAX0ZWnTtsbeuo55CAQwItI4GW1nMOg9CGfc2T61g9XUFgmAvqyec5Y4Th/Ehfnw4kBT/Fm3uKOJ6vYe4NEXQO9hBvyCtTlIRObI/q4BbOWg6RcqOEocvikFokxK6ajpO/D4szczih0tLL25NpseF/5+ydytCw8KMcBnT2ZuJ9VwOSXDllzZmT5Ly5SAQiUXUfy96NBbQb4sOAcaEIqKkCoqjBrCQpeDRRspu4IHrAysW8uWoPWHiQM7nR+US1h6Xtr7NBKmAwC8AyqSIx94ZO8bW20RvM7E9W4b0yBR+NkeUdAihzl9Kng5eYG9g93vMq9LpqlM51q2rOv7Npgk1YSaBaEHINd59SotOZmXa2BC+jiR1PuPHgkJ7IPK0H49KftlL82Uf433UfgSMe4sRJDa+9mUOH9u68+nLN+TfalGSyX1sIphgwWgh68i72XduFLblFDaL1qF52eum3nC/+hHYBD5IQYKU2MRSqyHz+C9wSgoh+7S67JJRdpOfFDZnMOl+Cp95MxsxN+Ad3oE+Vh7S6s+pQ4knRofT3rZ8A265J1FFZOLQKOYF/bKsQ5S6EYAte5+5dPRl6o4w+vbxq9NJWdylQxwgUMgJgkFePy/Mf95YrWaXIp7ePJ9Nj67/8aKo1XdmPNlvPyalyfDq5k/RKy+ZzGUsrkE9djlmrI2rheNyvyHsq+iSTsm9zxAiAqIVdkXrbBoLUEFm1tKJsyByboU2jDUJhTn8sziH7qIbbF0URlHiRJ7e++Vbzwg4Z5MO4MbUDndXXj+N3qwTWZeWzo0zJ7Ko0kZaUS7pWx6xUhXj2mGHDZW5Lzs0xlkMCDglYJdDSes5hENrw5P33nQIO/F7B7Y8HoblehRBa2dMphd6Wnbg5d2TTkcfxcJGw8Y54pBInNCfLyVl0BpdID4JGSvjpwmv4ZPuTuMsX39sGEvzkXbyXU8gvJeVEu7kyPyEKz0sgp7fuK+WjP4p5dGggd/WvP1yy6NMPKfv5e5H3cP/uziKK3C1zIwjsBAey5qI2ZBPjexudgp+8bLWCB0wwcIT8vfQqD5hEamFgZyuvX6doa95cXWXGBTmZlXrWd4zD/wpvkKJAJxpRO08q8S/RMS61jFwfF4pHJojcgf4+NXuPhJyVUWPSRC/nuxtsRzC1YSsbXaVSbxYN6dyPTnOtAn4M9+JAqCfXdPQWjdv2HuUoZryAa0QUMa8tRVjLizPl5OQaeH1BNPFxV3teCz95n/JffsRv+AjKfziOQGC/7ck7+T0qlPnxkXT0aljOjlKXyV7FDPzcO3Jt1Hxx7aqDmeQt+xPZTR0IGTPQLnn8sL+Mv77LFffRlKgkc9RmuoVOINznYvi00OHOMiVvZeW32O1zXr6Bv7aVs22nkvJyqzfQ30/KkMEybhwsIyTYNsOkYlsBBRtSkQ0LJWT05YBHr6RlcVZTyazYcHq0Ys5N0Y4K0tYViGT0Ail9S5a8lR+j2nfi72/YpWNXy07iJSVqQTdcIxr2zNq6npZWlLbOq5nrNYlBeOyLYk58VXoVAnV9c9dozSJQll5vZo0AlOXfOt7/+ub5v/J79TdlZftYwgTEtxYsGpOZp5LTiHB1YXmH+qOQWnBqjqEcEnBIoEoCLa3nHAZhPY+eVm3ijfEZYq2Zb8VR4aZjUuYeolw8eFD6G0dyXdgmf4h+kSYm9+so1hPCcgSEvcqzSkImJPKVZAr910fgbHEhbs0M/jCbeS+3CJlUysLEqKvoBFb/N489p1XM/ncE3eLrD5eUz5mGPktB0Esr+W6eCk9/Z+5bE8Wx/CUUa44T5NmTXuHTL8sju3LZr5w4wdkzJlzlPuit7BVEBLqIRs4N3WR/e8CubLchK59tZUqmx4bT28cLwWDal6wSc/DOZ1eK1QVbd5S6krgLSgJHxeF/a905KPkFBjH3ri6vWmt+MYT9fXP3TEasvRdloCtrE/xRV1rDX0P9XehdsIseij/pvGgBrmER/PxrGVs+KBKNk2euAHIQ+sqcNhFjUSFxK9ahTVaQv/oTKl1d+OTpe1kwqHe9aK+1yULoe3vGsxhMFQyJfxsXqRfFnx+h9OvjBD/dH9+bO9klxtc/ySF0TwGDCrWU3L4b5cCTDI7fiLPkci9Dvt7ApHOZJAjUDHaio9o6IYPBwqEjKhEp9NTpizmoPXt4ctONvvTq4YnUznBKkRLlmUNIfa08k05VcOyZlTpmXFCIdDACKl996Lu2rqEh9TI2F1LwazmJE0MJHNBy3lf10bPkLt6Cc6AvMUunILmEUkSbXEH2wjPWb8bMJDy71U5X05A119SmpRVlU827kf00iUGYdVjNn0tySRzsw8Bn7fP0VQNl3XWHH48+3LIXEo2UXZtr/jf1Q5dEnFuBvuaZ5HRUZpNIPdGa37Q2tzGOCTkk0EYk0NJ6zmEQ1rPxu38s44f3i7hmqIz7qiDeX8jcQ65Bw9rY/qzfe4LThcEMi/uYYQl9RHANYRM1p8vJWXAGabgL5+M3k7BHhmusjNI5k8VQUeGsKRC6d/S8+iZ9yoZMcooNvP1CPD5VoYi1TdNYUkzGlOdwCQtH2f9ljnxSTJe7/fC8eSuK8l/wco3iuqhXRXCR2orBYmZ02jaccGJt1A0cSNaIIaUCuIpQnKVwbZUHrHOsx2Vew5+Ly8TQxpulMsiAXadVaHVW4ygsoMqgTPKi5MXjmDUmkXtQKqv7NrQa5lzI9Rr7tG1IeC35/pYby5iVPo6n147CN19G+BvdOKw0i0ZwisJqBEssJnr4qxk+vCOJYW48NykTsxnWr47D2/ti3pUuMwPFvJm4JbQj+uWFYtsjH3yP7IddaANkJL0+EWcbEVxrksGp/HXkKHfQI+wFQr2vI2fxb2iOZhG18E7c21lzVm0pQtjv08vSmJBcQqDOhHzqZoJjetIt9LmrmguG6LMpGVQYTWxOSsC9CQmXc3L1/Lmtgu07K1Aqrc9ZYKAzNw6SMWSwD0GBjbtpz371NNozFUS+2hWPDlaD6+3sAv4oreCJsCBuD2pdKo3TsxWoU3X0WB2LW0jj1mrLvgt1zJU65C+uwFhURvi0J/Dq0/nvpoa8SgTkYLPSSPDoBHyH2Wdg2DqHK+u1tKJs6DybuF2TGITaMiNfjMvAL9qVu5fYx2NZWmbk+RcycHGR8NbKWDzr0U9NvP5/THdCuoXAQdia4Fsvp2VxTlNpF23UP2YDHAtxSOB/QAItreccBmEdD4UQ7rdiipziPAPPL44mvArRb0thCj+XKxgd1IkPt+swm038u+vrSJy0hHkPoHPIWNFrYj1cFmCW/iIcqygJM7DxqQloLRYmRIVyvd/VN/yCh23UkjQCfZ1ZOyGu3ke2YsdfFGzeiGzYcPYdG0pZlp6+LyvIct2Ii1RGv6iFeLjUbVSd1JSwKOcIfb2CeTH8IsF0Zr4VbVOgYxDCS4UiEMILaJuDugm8hhK+PlrC1sNlUGadqovUiWs7CbmBviTFWENO1YdLyF2SgmcffyKm1e+V+nZrKZ98XswTjwVx+62tewCvaQPOaI6zNud1/rXvfuK+j8T/nkgCH7EerIQ8u1+3K9h5WoVGaoXzDvZ1RqaUknpGx2OPBHLn7RfDgIu/+YLSb78i8IFH8L/DSkOyNCOHhPe/p/uZNNw7xhI5ZwxODQRnyVXu4WT+aiJlQ+kSMpb0Zz/DVK4lYctjSFxtD/k6lqrm3S0KJp0vxRShJHP8ZnpHzCLI82pCcmENy+W5HKhQMzcugi7e9Xu563rQhRC1AwfV/PFXOckpVQa3BDEnULg06NH96hzUel+cWiqU/ZRL0X8y8LsrgqBHY1GZTDx31hohsK5THF6XgKg0dIyGtjMbLBwelYrUQ0KvTS0XSl30wfeU/bALr+u6Ef7CRQAlk8ZI1txTGLK1+N4WTvCT9X+vGrp2h0EoSqBJDEKhoy/HZ6AtNfLwewm4uNePYn2p/De9WyBeyvz7oUDuvrP+lIam2vN/Uj85Oj1Tzsvp4uUhXgy3RhHC+oXw/tbIYWyN9TrGdEjgf00CDoOwYTvWZIry0uHPHVez5fVc4jq5M/YSAIej6iIW5x6joz6clGQ3ronwYkyvSo7lLaPSWIS3aww9w6fgdMGT7PmfIjCsa5I8WX/TcEr9AuqkaUhRaJn3fjbXdPBi6gP1w3vnvbUS1cF9eDw6iz/e9UIWY8JrrJAvJqFv5Fz8PaxhrHWVj4rOs7Usk6eCO3GL79VAFboqUBjRA5Z1MQxUIImvBqURHISPXhck0jJc6dXMW3kO1b7iOjneLp3f2vV57NqjYvaMCLp1bZwxUd/aG/L7b6Vb+ab4I+53epyY2QE4B7kSu7r33yGGgofswouTOaEL5XTff3Mm2wiCszUdpC4QH+sm5poKRZeZjkWnwy0+AScXVwTWyPOaSlzMRp4t+IlgbT6pvh3ZEznMipBiZ7FgorzyPE5OzoRKonng3A5K3bz5vl1/u3oqKDPQvaiCm3Ua9vnrORRgwde9Xa19CJx9BQYjQS7OjUYazcrWo1ZbLySCg5xFgJjBN8iaJYfJUKgj8/kjIjBKzIqe/Fhczgd5RQzzlzE6snW91arUSs7MzsK3lycdZ7QMsE1lejZZL61F4u5KzLKpOAfIrJaJyULO4mS0J8rx7OVH+LROfz//dj1YDazc0oqygdNs6mZNpue2LctFflDNrfMiCU2yL99T8NJPnSHHz1fK6uVxIm2Lo9gngWNKNW9k5jLEz4dxUS3jVb9yhl8VlPBFQclldFf2rcJR2yEBhwSaUwItref+KV/yJlOUl27u+2/mcPaIhocnhtL9knydSrOJMenbsSgCMBZ7M75vCINiZehNFZzIW0OJ9iTOEi86uz6Fctr3OFkk7LvtXr7v40+3jPPMvO0WpLV4Gn4+WMaWX4t4YFAAI26omyPOYjKR/vxYLHo9JdcvJflnJb63/Y7HwG10DX2OCJ8bbHpWp8v3IderWBU7gFCXug2wrEI9fxwrF4FiBGOwf2dvTgdqKPYx8U5SPN5CfOklRfAiZDxzSISfj9vYB4nr5b/XNMGZc+RkZOpZvyYOfz/bvVg2LbYJKm3Jf4sDyp0i/6BsiTOVyRVEzuuCR5L1sCyUwo//Q/mvPxH8xFNoeg4RgXt++roMY7ntE/C2aBhn/BY/1Pwm6ct2aU/bG9dQs725hCdNpznqFMJXzvVfFFzZxThjKVEWI2uc/cl3arl9EV4Vgf9MyA3s2sXjKkTaRgmlhsaKWSfQpauJerM7MyuLyNMbGsQF2dTzyv+ljMz3iogY4U/UA4FN3f1V/Qnfl6w569ClZxP89L343tzv4vP9Xjrlv+ThGu1B1PyuSDxb7nkQJtEMilK43ZgGCDclXQRsJGBIPUK+BhgPCB9awUJXAB8DiwHrzZm1vALMq6Gv24Cf7djIJtNzJ/9bwtFPS+j7eBCd77A/CmPZylwOHlaLOdFCbrSj2CeBX4vL2ZxbyIMhAfwrxD4uWPtGqr32rjIla7PyG4Vk3VRzcfTjkIBDAldLoBn0XJ1idhiEtYinpMDAskmZePtJmbbmalLxhVlHOHXYC4lJyqY74/GuMnTMFhMXij8lo2wrfj8EELBXUJbtKPLvzlePmBn1/lvETngBr569axx5w9Z8tp1QMv3BcHq3t4Yc1la051PIXjQP9y492JP6EJXlRoKnLaFd7DDaBz5k0/tVYqxkfMYuwl08WRE7wKY2QiWjySLC+gu8fKsVeewpV9UYelKNPugzJJjQcbV7lKoHFsJ0nxydhqurE++sb7mwOJsXDrwmn0GWPpPX4zbADh2Fm9JEEu6QsVauP6Foz54h+41X8ejancgXXxL/pqk0sebLfI5e0Ih5mfeF5xO78338ht2C3+13i3Xezy0SQy2figgSQXpMWbkol70Dej2eox/GtZdwVrWvZJT+gLz8Jzoevxm3X5V43N8XtxvrD92tHqW4wsjyd+RMSy3B4KchefTn9I6cgbdr7cTWJjNMvZApgiUsaSfwKNo350trC3lKnh72hbU1fDQo+TqLks8V6O4JZUE3M0me7sxLaFmKh5rmn7oun+IdSjrMCMevV93fhsasv7pt2Q87KfrgB9w7xBL5yjM4Sax7UPZLHkXvpSOVORO1sBsuIbZTFzTFvIQ+mkFRCvHaa4F9QFcg3waDcClwLfCB4NgHugMLgN+BEZesVTAIJyOwFV1ekgE7roiaLmQ054SG31/LIW6AN4Mm1k9tdOW+nb9Qydz5WUSEu7D0jZhmv6RpquemrfTzUV4RW4vKak0daYl5CpEoAg/yNTIvpsbUH43UEnNyjOGQgEMCFyXQDHquTvE24pjWpratyW5Oq1f100dF7NxaxtAR/gyr4TZ+c3o6vx4xEewHa2662tDJUfyFatbPmJwkKMLvJEEuwXm4BOO2FXh270nElJk1CnDG23IyC/SsmxhHQC20DH8f1Kvyz0yDxrD/11hcEy+QNCGZ7mGT6kQUvXTgvyqy2ViQzHDfaEYG2+81EvraWlTKR3nFPBoayF3Bl+eUZC84jfZ0BRFzO+PZpX70wdw8PS9Mk9Oxgzvz57b+IfzKTTJajLyQ+iQeUi8Wx20UgXIyxh0SCc3jNvRF4mo9NFvMZtInPYNZoyF+9SakXtYDvBBO+v3+Mj7+s1g0qPuX7uHpZwfgndgOgfz8mbPp6MxmNnVK+JuKRH34DLlLPxDzCIWDubudxkmZ9hwHsl8m+vNBuJzwIHLebXgk2X4I/P1IOSc/kHNXroqygYfR3ZPBgOgl9VKSzE/LIllTyZJ20US7X0230aa+IJdMRqfQoJh2nNJIV5aN8qWl+RRrk8uJKZlU5hjE/EEXWf2e9sbI11BYivzF5SIFSvQbE3GLtj4vmuNlYqiogIoVObcLHh1bDun00vU0g6IUXlxrXDJ8CQgQmvV5CIU6RVfIeSywERASKjOrfhMMwglVfTZmW5pMz+lUJj4bnY5PmAv3rWwY7cArC7M4m1LJi5PD6NvHuzHr+n/XtjrH+tWESDrUACzXEgJRGk2MOZtOjJsrb7a3D1yoJebnGMMhgf/vEmgGPVenSB0GYQ3iMejNItWETmtm+po4ZAFXh0OtO5rDjjQNQbFq1va9GlijcPO3lP+6l+0DunOuQ1fGbFEjDZaC838wlRYTs3glrqGXH8oNRgsjl6Ti7SFlw6S4eg/cildnU5mWytnOYyk9EkPYQzu46Z5HkUpsP3yvzDvBPlUBM8J70surYTDiJ1UaFmXkMMDXm4lVB0dBrIYiaz6Ws78rsWsv5tjV9UQePKRi2ao8hg2VMXpU6+Zs1TTPbJ2cRYrpdPLoxsTI2WKV3BUpqPeXEPZCB7yvuxjKl//uBpQ7txE6dgI+Ay7n6jt2qoDVX+eKwDMC3+Pkf4WhQC/KsSay4NKtOyj+6Eek/jKiFz2Hc0D9xnX1/AWv9bb0sYQv6YNLqQcJmx9F4ulq87d22Re5dP05m0S1gewxnxPTewjx/lYAnLrKJ3nFfFtU+j+XoyIY7WmTj2LJ17FpcjCvXdc6sPCXytaoMXHk6XTcgp3psbp5wVuE9ee++R80R8/if9+NBD50qzgVfbaGrDmnMGtNhE5oh8/1tqPU1ves2Pt7MytKWw3CmqYthJEeAIRwi71t1SAU5vXN5EyUeQYeeicet0uQj23di8NH1SxZnttm6YFsXUdr1Jt1QUF6pY4NHePwayBgWGPnLbzno5PTMVgs/KdzQr3njcaO52jvkIBDAvZJoJn13FWTcRiENbIRIaYAACAASURBVOzP4W0VfLWhgG79vXlk0tWeFOFDOunnTAo0Ruis4O0OA/GRXjxgG4rKyJy8BJ3EiaUTHubm4CN03eyC5/k4GJwO+7/D79Y7CHrk8ctGT8ut5KXNWfRM9GTmw3WDRpiUFaRPfIayQT4k/zUJLBLuXReCj4/tuUUmi5mx6TvQW8y8HT8Yd0nDvA4qo4nRZ9OvIrkt/S6b4o/l+N0dQdC/bbuF/ubbEj77soSRjwcx/Bb7c1vse93sr31AuYst+Wu5ye8ORgRZ9091qIS8pSl4XeNP+NSLoZjqo4fJXbUEr77XEj5hymWDlW/7g3MffsHnHZ5BYfAhwEdKu8HuHJCqGRsRzNArDD7hmSvY+BXKbYdwi48kct4zItCHreV42kq8XvJFEupGwqp/29pMDA2e+OYFJh8vwuKtJXP629wQvwYPl/ovDw5XqFkiz+UGPx+eayXgBJsXekXFHZvOEvFnKdkjQhj8wMVQ4Ib219h2Fac0nF2YQ0B/b9rV8E1qbP+XtlfuPUH+qo9xCQsk+s3JSFxdEDgaFXNOYizQ4X9fJIEPta5HoZkVZWMMwonACkCIwSu4xCCcI0TbAsJNzqmq0NKv7dy3JvMQCuPuWJ1Hxh4Vw2ZHENHNfvAuIbx/+ksKBNCn+XMj6djBPnAaO9f+j6r+dHIaAvVEaxtiL11QkFapY13HOAJayTD9R22sYzEOCTShBJpZzzkMwvr2Sjh4vzUri5wMHWPmRRJfAwJbZpmOGX8okPmYqUiQMzG0KwN8LhqOire/RvfHAbYN7EnubU7MSXyA1MNbYWkIBr9ylP0+IPCgG/HL1yFxu5h/I3D/vf1jIfcO8OfhG+s27JT7dpP25xoyOnan/IsHiernxNDJ9h1cz2nLeDn7EN08ApgdWXNOY33yqv79+ZQMigxG3ruEd04+/Th6uYboJT1wi7btwLF6XR579qqYMzOCrl1sa2PrHJui3jdFH/Fb2VaeCHmWfrLBYpcWo5n0cYdFz0n8xj5Iva38cGa9nvTnx4j/P37N20hcLxpwOcsXozlxlKCpc/g4I5gdJ5QCMCx0g43D4/B1vtorbTEayV70LpXJ6Xhd24WwyY/+nddV39rkB35Evzwfcy/oMGNUfdX//j1ZruW71amMyFJS0fckpodzuSaqJnyMq7usvigIdXVhVQfbLgRsnlgzVhRCdhf9fo4nNpfinORD3Dwhpax1S863pWR9Ukz044GE39F8UP8mlQb51OWYylVEzBmNZ9d2WAxmshedofKsEq/rAgib1KFFEUVrknwzK8qGGoSCEjgB/AiMvGTejwFCuMNRQIixfQa4vSrP0B6jsEkNwjM/lHLog2J6PRxAt3sbBmwi8IGu31RAn16eTJvSMsi3rfsmNn50tcnE08npRLm5srSVQzVXKfLYW65iXnwkSV4Og77xu+vowSGBppNAM+s5h0FY31bJz2nZ8HI2YTGuIvdg1YZc1uzLMyV8mVzCwPau7PY8xyCfcMaHWsE+1AUlZL2wFIOzlC0TO3NfLAwMekT8LWPRPownLRTe8ztG/9N09nicwMF3/N33uz8V8NuRCl4YEcZ1nerOyUj/5E0u9DhCyaePozvXkZtmhBNpJ9DEF8WpfFWazuOB7bnDv3EH9mXyXA5WqJkfH0lHLw90mWoUM07gGudJzBs1c9XVtBfTX5IjV+jZsDYOP9+WRS6s79kQfhf4BwUewlnRrxPtFv93k4J306j4LZ/gp+Pxvfni5UDu2hWoD+0nfNI0vHr1EeubtRrSnh8rXgbEr94IEgkf7y1m67YyBN6JoT1ljLo1WATsubKYlGqy5q7DkFeM/703EviwNZyvvlL43QHKPz6N8rZcej1pBbmxpXzyVzFen2TQuUJP7hP/Jf76W4mSDbWlqVhn6vlMsnUGNnSKw68GI9fmjlqw4l+lFWzKymf26lLcVSbiN/VF6tMyJPC1LfP8slxKD6pJmheJj500AfaIruDtr6n44wA+g3oTOv5BMee1YEMqyu2FuCV4iWi6EreGRRLYM4/66jazomyIQSjc9ghgMkLis/Cil9axBuHF3gMIJ/C6oIOvQicV9qOpSn6yll/mZxNzrRdDpjQMVMRotDBxSgYlpSYRXCYq0vaohaZax/9aP+laHbNSFfT28WR6bOsa0Z/lF/NNYSnjIkMY4u9Ai/1fe5Yc8/1nS6CZ9dxVwnOEjF4hks/X5nFsl4r7xgRzzU0152nN+F1OZrmehTdF8HLRXmRSF9bH3SByyP218mNi959k/6COFN78O/+KnEGMpwA+B5UXlGIOjslXTeYLm3HRSuiTtBCZuzUnaM57Ci7k6Fj9XCwhfrUfQCv1xew5PQGdkyeFb87E3duZ+9fHIZHat52zFQdI1VWwJLof0W6NAwX4uqCEzwtKGBkexPBAP4o+zKDs+1wCH4vF/07blJ7JJCCMpuLhLmHTuraJMDor/VmUpnJWJGzBRXLx8KNNUZI97xTuHX1EGP7qoty7i/yNa/G5YQihT48T/6zcv4f89avxGTiI0DECaj2I+XYppXgccUKrsdAuwo0XRoQTKLvaKNZnF4hGoVlTScj4B5ANshqadZW8tdtR7Uojd+Qh+tz0Mp4utnFfzd6YyaPbcpC6GFDMfpfB7dbhIrUd4XJjdgGCgTUlOoxrfRv3jNW3xqb4XThwC4e1jEo9r+40I9leTMi4RGRDWjef9ej4dAylJvq8l4DUTiJxW+WiPZtB9isbkPh4EbtsClKZF9Vh39IAV6IXdRPzgdtCaWZFaa9BKHx4PwFuBgYCZ22QkUBxIdBTCB96kw31hSpN6iE0VJr5dFQaHgHO3P9Ww/NSv/+xlA8/KWbIIB/GjbHtu2Ljev+R1faXq1ihyGN4gC8jI1ovD1cQ7rbSCjZkF3BvsD8Ph9qebvKP3BjHohwSaGMSaGY9d9Vq7bMg2piwLplOkyhKZZmRN5/LwMVNwsx1cbjWcPAqUBuY+HMmET4uLL8llnlZB0mpLGdx9HUcTS2k08J3MLk6s/PFMozuxTyT8C4ukothoTlvnkVzpBTl9TspHH4EicWZLmHPEuI1gJFL0nB1duKdKbUbQyazngPpL6G0ZGH+fggF+4bRabgv1460T7EoTXoxf9Df2Y23Yq9vdEL5UaWaxZm5DBaIdiNCyHjuMKYyA3Fv9cE5wLZDpEB4PGW6nKSO7syb0/YQRlWmCqYL4CyuUcyNERDnLxbBkMicdFTMsYpd1QuXUOuem9Rq0ieOReLhSfyqDThJpeStW4XqwF7Cnp+Kdx8BgwKmnM8kR2fg1YhIPviumPPZlfh6SUWwmaSYq0N5NCfPk/P6e1a0xzlj8OhU94Euc+rXGLLLyZj1Jx3jHifa95Z63+YylZFVr6bwqLwCZbcUJE+V0iP8hXrbXVqh+sBxR6Afj4fXn3doV+fNUDlFo2VeWjax7q68rPIh942zePXxF4nXW6voS4wcG5+BR4wr3d5sntw9i8GIfOZqDNkFhIx/ENmg3qgOlpC3PAUnVwlRr3TFLd72i4DmllUzK0p7DcJVgIAuKhiEu2xc+4tVBqHwcWwVg1CY53cvyinL0vPAhjg8Gsj5qtGamTA5A53OzJrlcQTUAMJmo0z+X1SrRuV+PCyIO4JaN08+Wa1lfno2/X29mXQJINz/i41wLNIhgTYugWbWc81qEHYG1lQR+wrJ8+8A8+tRdrUR9goTFeLaXrdxv5rEIPzrmxJ++6yEgbf7cscTNRtYP5wr5YOTxdzT0Z9HugbydUkan5ek0d8jgdAPttP3WArGO/vya78vCXfvyANRgggulso0FVkvnUTibaGkz1uUDDOBE3iYRvDWl73oEuvB3Mcia1y2xWLmRP5q8lX7cJdD6VezKC/24vZFUQQl2scFtkeZx+r8UwyVRTA2RNi6xpVSg5FnUzLEg/Q8rYycRcl4dPMlcrbtfR84qGL56jxuvknG0yNb1yNTkzRSNKdZlbOAvt4DeCpMwI64vBR/Lqf062wCHogmYMRFgzZ76WtoT50gcubLuCe2F8NFMZuseYVubmTr9Ew9LyfG3ZU328WIQC5bfi3k9yMVSCXw2LAghvf1vcpoL/9tH4Xv/lf06EQvHI9LLTe85koDaaM+xMnfldQpWwn26kOvcMFBUXfZcbKC/Lcu0KtMR/5DP9L+5nsI8e5bX7PLfs/R6ZlyXk57DzcWJNbOW2hXp81YeY0ij93lKhHY50aZD+ljDok5ovFvX4PEvXVCJUsPqTi/NI/goTLixzbPe1Hy1R+UfPEbHt3aEfHS0+gzNGS9cgqLzkzYlA54X9u2vAfNrCjtMQhnAQuBB4GvbHw0q0NGBThoe5K3m0TPXTrH3evzSd2uZOj0cKJ6N9zg/+TzYr7dWsqdt/vx2CNt/+LHxn1qlmqbcwr5taScF2PC6Ctr3aiJar2d4O7Ga+3a/ve5WTbE0alDAm1UAs2s565adVN5CAWUg9PAmapbTwHdZFkV2pqArlZbEU7NV7qC7gVmAL2AYzbuU6MVpRCuuOT5DCpKTExdGUNgWM1erVe2Z3G2qJKFN0bRLsCd1MoKZsuP45/vxeT1XyBxd0OzsA/bNR9xjf999K+BID53yVnUh0tx8tqNOu4IRf/24Gx6e3bsv5/h13gw8paaDcILxV+QVvoVLioX/Nf7cqJ8MrIIF+5ZJhB/27eV6/JPs0OZywth3bjOu2nCfMadTUfgNlq6w4J6RxEh49shG2S75/Kr/5bwxVcljHoiiFtvbt2b05qeuz/LfuTLove5O+BhhgcIj+nlRZ+jRT7lGC7h7sQs7/n3npT/+RuF77+L78234dmtO7nLF+PV5xrCn58qdvBtYSmf5BczItifBy4x6v46VsHmnwsxmCxc39WHMbcH4+ZyOUF74ZbvKP95D65RIUS+Oh6p59UXA9qUfLLn/Yhn3yjO3PchTjhxY8I7SJzqztFc+3Uug77KxN1iImfOhwxKWlNvmytlInhOx55NR2M2i4BDrlXk5ja+1y1arcxo5LmUDNydJKzrFIebRELe6nOo9hS3qlGk+LSY3P+WEjcmmJBawtgbIyh9TiHy6SvF5zVmyQs4ufmQNfskxhI9gY/E4H9Pzd+jxozZ2LbNoCgFBCsB6EUowospJFRVoycJIDEa4AKwHXi6qp4A1/sRsKWKe/DSZaUChVV/ENoIxqIQSipYXQLSlEBSL3xEvrNDFo3Wc1eOdfbXcg5sLqT7CH961sC3a+vcysqMTHghQ8x7XrsyDi+v1rk8sXW+rVnv9Ywcjqs0LG4XTWwr87MK3+eRZ9KQOjnxblLbTNNozb1yjO2QQGtKoBn0XJ3Lsc+KqL0r4ZZ0OiAgk1RUVRP+W/AACggb1X+zRbY/AAlAki2Vq+o0WlGe2q/i4xV5dOjhychZNee8lVcaGfdDBv4eUtbeFofEyYl8nZ7J59O5d+tu+hw/R8ADN7N3wHFS1Qe4L2I20Z7drlqGLl2NYtYJnNyNWCwb8HrwFv6jk3EsuRs3DfiRB/vfga/75YihucpdnMxfi9TJg/BNOvILh6PQDKDngwF0/5d9CHGCEng2YycVJgNvxw/CS9o0gBmLM3I4Vapm3qoSnMwQv7EvEg/bDwar1uaxd7+Kl1+KpHMzAmfY8VxdVvWD/A3sVW5jfPgMunoJ9xVXF8Xsk+hSVUQt6oZ7ovX211hWSsbkZ3EODMKzS3cqdvxJyJjxyAYOEn+fnaogVatjcWI0sR6Xc0im5lSy/Ks8iiuMxIW6MuX+8MvySy0mk5Uz7vg5PHt0IHz6k2JY6qWl7Jdkit7bR8ADvcjo/ytFmiP0jZhLgKcVCKmmIkDKvzb/LA+nlKFqn4nr82o6h1Sfg+2T4JLMXA4r1W0eya46D/bS8FbVvmLyVp7D5/ogQie0t2/hTVT77KJsKk5q6fJGNF5xtnOM2jK88C3IWfA22jNpBD4yHN/hN5D96ml0qWp8BgUT8myi3ZdNtozb2DrNoCiFmOv0WuYloEdlVP3bdgmCqGAIPllLGwHKV/hdKO8CN1ZRUZiBI8BrwE92yqFReq5ixwXcYgPEf9Wl6EIlP87JIrKXJzfNsC3Xu7Y5b3q3gD+3VfDIQ4Hcc2fzIeHaKbM2V33KuUxy9AbxgsxDCAFp5TL9vBy5Ts+mTvHInG3X1608bcfwDgn84yXQDHquTpk1lUG4A8gBHr5kNCHZJRO4G9hq484JcUm5VSE4r9rYRqjWKEUpdPDOgmzSTmt5Yno4nWoJnfkzvZxNRwq5JcGXp3oFozWZeTktC3VuEZPWf4GThyuJa2bwbv4k9GZtVf5gzQe43GVnUR8sBZdtSAPl/Kf3LM4q9PzrtlUE+JaRFPwUkTLhDAFl2nMcylmA2WIkSf0vKpd/xVGnl6is9OS+VbH4hNpn0GXqlMxQ7Kejuy/zo6w5bE1RBMSyC9tyeegbJd4DAgmb2MGubqfNkqPI0ouAMjKftqeY3lC8hFyXxqK4t/B3rjmEruznXIq2ZOA7PIzgkRdRSBUL5qJLPY+TswsWIVx09Sak3t6UGIyMT8kgxMVZpGaoydNbrjay6pt8zmRq8faQMPHeMLonXKTkMGkqyX55PfqsfHyHDyB4pPDKXSz5G3ah3Hae8Gk3UZyYzNmi94jzu5sOQbXzEQqG6N6FyfQrqaTw3t9JuudB/Dw62rWf1ZW/Kyzl4/xiHgkN5J7gtnlQNFosTEzJoNRoYkX7GMLcrBECApVI+tiDYh6dcMHh5NyyBziL2cKR0elYjBZ6b05AUgPybIM2papRxbZDFGz4EteYMKIWTqBgXRqCEezeyUcM93a6wiPdmLGasm1LK8qmnHsj+mqwnjOpdWSM+wyLwYRrbACyQe3wHpiAk5c7n4xMxdVbKuYR2htpculahBzwqTPk+MqkrFkRh4tLUx0vGiGxNtbUbLHwxJlUPCQS3k4S7r1bv1QjhC9IiKJ9DREmrT9DxwwcEvj/KYGW1nNN9cUWCHjXVXkEL905ddXflti4nUJi/kZAsCTO29hGqNZgRSk0zlfoWDVNgX+IM1NXxiKR1CyWxbtzOJqnYfb1EXQJ8RBJt48qNTz2ww46HT1H+h296PVgPz5WzCDCvRP3RwkO0ppLNS2Dk4sOk+QdXu/8MmYnKfNHn+d8yQdYMBElG0as3x0czH4FvamcTkEjcf82i6w/znFKP5bgju7cNt9+8JVvSzP4pPgCDwYk8K+AplNKB8pVFC5JoeMFPeEzOuHVy/bDvwBfLiCMentJ2fjWRUPKjmegWauaLCampI3ExcmVJfHv1HpwMpYbyHj2EFJvZ+LW9fnbgCj98TuKP/9YnKNH565ETrdGUv9aXM7m3EJuD/TlifDaw2tNZgsf/1nMD/vLEKKDHx4SyN39/f6eh6GgBMXstzAr1QQ/dS++t/T7Wx7ymd+izyghbt2D6H2U7MqcjI9rLP1jBJDDmsvXO4uJ3XgBmdFEwez/MqDr4gYfFlPUWualZ9PHx5NprQyzXtt695WrWKnIo6e3JzPjLveU5CxORnO0jIiXkvDs3rKhzNocPSenyPHu6E7nBrzrdb0UxnIV8inLRLTaqFefRX0UMQfWOcSN6IXdkMrsu2hq1hfwis5bWlG25NrqGKvBes6s0VP+1zmU2y+gl1cxYkic8OwZxfksP+QFMu5bG493UOP2fPmqXA4cUjP26WCGDqkZpbuNyLJVplF9AZjo4caiNpJT/VFeEVuLypgQFcr1fgJNpqM4JOCQQFuQQEvruaYyCA2AgFKx8gohZgHvVwHE2CLfPwFBi9SPo395bw1WlEI3375bwP7fKrjt0UBuuKtmI0ZrMDPm+zTcpBI23hnPx/lF/FhcTpJKzaOrPkXj7sz7M27i0VANO4vf51r/EfQLfKDONecuT0F9oAS120Fe73A7HaLcefXJKEq0yZzIWykagU5Iq4zDm+kUNAr59Emk5PYj33Qd/UYH02GY/Ur31ezDnNGWsijqWhLdm457KK9IQ/nzx9F7Sui66Vqc7KDByM7WM3WmnC5JHsx9qe3lLOXps3lVPpX27km8UA8xe7UBcalRrM/LRT7TitAZ9Ngo/IZZ+QMXpWdzUjCYbCQG3n1aycbvC9AbLVzXyYtxd4bi4Wb1WmlTMshe8DaYLUTMHIVn9/aiRyB15IdIvV2J2/CwaNTtzJyE1pDP4Lj1uDnX/LyvXpXK7XsLUEYW4vOSjnb1PMt1Peh6s5mnktPEW3EhLKkxXghbPiINqfNqejZn1FpmxIbTy+dycI2KP/Mp2JSG7y2hBD/VdBcotsyzaKeStLfyCb3dl9hagK5s6aemOnlrP0W16xi+t/THvcMA8tdeEEO8oxZ0xTXqoge6of03Z7uWVpTNuRY7+m6UnhPGEUKEhcshIXxUtTsNU0WlOLwBZ9y6xxLxYBJuiUENfkfPX6hk7vwswsNcWLY4ptbLVTvW/I+qelat5ZU2hur5e0k57+QUcn9IgPjPURwScEigbUigpfVcWzIIBWZcwYAUAGUux/S/em+ajLBXpzXz+rPpAugjM9fH4eldc6ji3iwlq/bnc0OMD50S3Xk7pxAfqYTZv+3HuOc4B2/rzLcDI7jbI5ki7W7+FTGXqDpytIQl6eRqFNNPYJTqWdgpgsFJUsbcbz1wVhpLOJ67nHLdBQI9utErYgbG/AIyZkzjkOElzE5uPLAxHrda5lvb46w1Gxmdth1PiTMb4weJeZBNVcp+yqXoPxnsv9aDh1/oISaq21r2HVCxck0et97sy6gmPvjaOoe66h1W7uXd/FUM8R3Og8Ej6+xSuaeI/NXnrwqblb88E322grglq3EOCERlMvFMcjpeUqlI3G7rXmTm61j2ZS4FZUaigoS8wjAiAq0hjhU7jlCw7nMknu5ELRiPWS8ha9ZWPHtEEjHLSjWRXLgZRfmvdAl5lkjZ4KvWotKa+Gz6SQYXaikavoNu/34cL9fG5RfNTc3ivLaS5e1jiKgKx2yKfWmKPhSVOqZdUIhhuys7xF61D6YKA+nPHELq5yLSqDjVEkHQFHO5so/MLYXk/1xO4sRQAgc03e29+vg5cl/fjNRfRsizY8lbckEMSw2fmYRXj5b1gjZEbi2tKBsyx2Zo02iD8NI5Cei5mmNZ5HyZjCUjF4nIpAsuEb7IBrfD5/pEnAPtRx6dvzCL5JRKpk4K45q+rYui2Qx70Kgud5RWsC67gHuC/HgkrG2gsZ5UaViUkcMNfj48F9U0AHONEpKjsUMCDgmIEmhpPWf7ib3uDRJCRt+qopm4tKY9IaOTqlBJBWAahZ3PQ4MVpXBjmnG2ksJsPdfW4W1bvT+PPVkq7u/pzzfaUpEqYo67Gy5z1iHx9uDgK/fzjTaH9k6HCOM0z8S/i/MlxOW1rUcArBBydn4I9yKuXSb3PHfX31XNFgPFmlMEeHRGKnGj7LefSHn/ACmGR4m+xosbpwo2tH3lkLqQpbnHGegdxvNhFwnU7eul5trVgCrrn/Jj8qAEou1AUPvy6xK+/KaEp0cGc3MzICk2dn3fFX/Gz6Xf8GjwWAb6Dq2zO7POJBoQXAGsYyguwlRRjnu8FTCo+nAw1F/G2Ej76AQEo23Nf/M5nqYRPYQT7g6lTwfr4a34018o/e9fIg2F7NZbKX7/MP73dCfwEavjvVB9mKO5SwjzHkD3Gugz9iWrML9xhhCdiZKZf3Jtz9mNFR8f5hXxfVEZz0SGcKN/03mlGz0xIX84p4DfSyp4LCyQO4Nq9phmzT9FZbJS9J65t286w6y++Z+eo0B9QUf3VbG425krXFvfZp0e+bSVGAtKCB79MCVfGDGVGwgaGYffcPu/KfWtoTl+b2lF2RxraECfDdZzdY1VmqnjpxlpJISXEelZhC61yFpdoEPqGiEah17XxCJxqxuVuHqMI8fUvLksl/bt3Hj15agGexsbIJ823+TLghKEf6MjghkWYH90T3MssFBv4PlzmbT3cGdBov0pKM0xJ0efDgk4JPC/axAKoDLZwCOXbKJAaiO3A1Rmrxi5AlboRftKsyjK6ikIsP9jv0/DYAbX9gL+uIVxkSF0+uhHMeQq8N+3kXdzd+ZnH8YPOTd7ZDEi8mWbVqBTaJBPO47K2QmV7FsGLJmD1KvmW9mc5W9w5FAnSsxdGTwljNhr7b99fbfgLL9VZDE+pAuDZE13+KumXNAGu7BorC/jo0IZZMfBf8XqXPYfVDNvTiRJHa8mYrdJmM1YaV3Om5zSHGF61ELi3NvVO1L+ugsodxSKKI2ywTUbe8vluRyoUDM9NpzeV4Qp1juAAHhitvDFjhK+2W3NCRpxvT8jBgXgZLGQt/Jj1AdOIfUPxFQaSNjkoXj3s5LXG82V/JU2GmeJO0PiN+HkdDlQyocfZ9HvOwXKACWBr5mJ8RMQ8htXDlaoWCbPY4ifD+Pa0C20xmQS+TMtFljXMQ7vWlD2yn7IoeiDTPzuiSDoEeHOqvmL2Wjh8MhUpB4Sem1qulDboo9+omzrdjx7JWEs6oFeoW2VcNjGSNBhEDZGepe3NZssfDIqTQQsevideAw55VRsP49yVxqmUoFtA5w8XPC+Lk40Dt07hdZp5AmXrNNfUogAYW31e9500rOvp3VZ+ewoUzI7LoJu3m0jLLsa6MZTImVTUtvL37dPwo7aDgn8cyTQ0nquqTyEAu2EkEMonJSUVdvxIiAghdpCO1EN+T0eWN+A7WxWg/BYnpo3dufiJnNCF27hriA/HjCZkb+4AqmPJ7GrZ2B2c+aptD8wWkw8569iYOD9Ni1DUJ7fP3eMpJJKTM67CH28C363VtNhXezCrNdz/rnnOKCciounq4gIJ3W1D/FQGGti5m4KjZVsiLsBP+emg7CvJmVX3R3CG90t3Bboy5N1gKRcKZypMzLJzjHwzvp4vO0Mg7VJ0I2sNCdjAqXGYlYkbMFVUr/cNCfLyVl0Bo9uviJaFmnaKAAAIABJREFU45VFyKsbk5wuAsQIeXWN4ec7cFbF+q35aPUWerfz5Ll7QvHARPb8jejShXsaX2JWjMc1/OKN9KHsBZRoT3Nd1EJ8LzFwhWdky4xT3CBXUTjoCH2eeRxXaeM9egLH37izGUS4urC8Q8sYVLZs+U/FZfwnt4j6vLSGgkoyJx7FJcKd2OU1U47YMp49dVSplZyZnYVvD0861kKFY09/Ql1dZg6KWWtxcnXGLfFuKk8b8OjuS8SMJLtyfu0dt6nrt7SibOr5N7C/ZtNzP72cReG5Su5dEYMs3Bp+bjGb0ZzMFYFo1AczxXxkoTiH+CAblIjPoHa4hNTsLd+xq4J1Gwvo3dOT6VMbF27eQFm1yWavpGVxVlPJyvaxhLk1DsCnKRdYTYWxOSkBzzZAhdGUa3P05ZDA/6oEWlrPNZVBKMRZCaT0p6qI6YVEuOVVIDOXEtNfSexbvU8zgQVVPE1V8Sp2bWGzKUphFhsPF/BXRoVo2vaN8mJKTBgFaz5Ftec4gY/ejv9dVqfmi+lfkmXyY2xgIEP9bTs0Cvxyryy5wMTzpTihwTn6R2IXL8HpCgJvzakTHH3jV1KN99J+qIz+Y+0LMRTml6NXM0W+l3g3H16Pvs4uAddVWTAiMicdxVigw21JV6aV55Pk6c68BNvCT6oRRn28pWxY2/ZuKDUmNS+mP02ISzivxK6wSW4CXUDGc4cxlRnEvDPnAOshq7ocqlCxVJ5HP5k3k2OEO5PGlewivZhXmFNsICzAhan3hxHmpCFzwjLBJ0jgo7fhf9fFfMH00q2cL/6IxIAHSAwY8ffgikIdF6afIFprRDX1MD2vmdC4iV3SevK5TPL0hjbDdyXcjE89LydXb+CNxGjiruCAvHLh8pnH0WdoiFnWA9fI5r/dz/+1nMzNhUSM8CeqEaTh1esQDvhZc9ehS83Co/NAtGeCcInwEMNgpV62hQM22cPQyI5aWlE2crpN1bzZ9NzB/xSS/FM5108IJeH6q408k0aPal+GaBxWpuT/vR73pFArhUW/eCQeFw0c4Zs+cWomJSVGlrweTXRU/ZdoTSWkttzP+LMCtY2RD7ok4mxHjn1zr2lxZo6ImP56YjTx9XwHm3sujv4dEnBIwCqBltZzTWUQCnMX3CBrgf4CdZ6QmlNFOWG9VrQWgdz3UmLf6r8fA/KAhsamNZuiNJnNjPo+Hb3BQlQXFxa2j8YppxDF9JVIZV7ErpqOxN0Vs8XMK2lvcM5yLbf7RvFEcCebnulD59Qs/SKXCUotYRkqcN5J+Ixb8erW47L2RZ98wK7vwqiwxHPrvEhCG0Dc/nOZnC1F57jHP45HAusPe7RpASK6pZLseadw7+hDxCtdGJWchuC7fDcpwSagFIVCx7SXFHTt4sGcmW0PYfSCNpnl2fPp5XUdY8KtSKG2lKIPMyj7PpfAx2Lxv/PyW/L1WflsL1PyfFQoA5sI6lujM7P+u3wOnlPj5uLEhH4e+K/7FpyElFwL4VMfx6uv1Vup1MnZq5iOr3t7rosS7mKs5effC2j3TipqLz3BK1yIkA2wZak21akOl5oWE04fmf1gFTYNYkelEyoNr2Xk0NHTnfk2XF6UfKWg5IssAh6KJuA+2y477JjOVVXT1udTtF1Jh+nh+NXCjWpP/2U/76Zoy1acg8MwFl6LxMeF6AXdcAlzt6ebNlG3pRVlm1h0I+mV6lpD2k4lu97KJ+k2X655snb6G6EPfV4Fyh1CSHwqxiKV2K2TmzPe18TiM7gdHl3CxAvNH34q5YOPixl8gw/PjnWAlRjMVg7CQBdn1na0hu+3lfKf3EJ+Ki5ncnQY/XztT0VpK+twzMMhgX+SBFpazzWlQdia+9BsBuF7qYX8cqwcZy9YOTSWIFcXMT9Lte8EgY/fgf8dN4jrLqhMY0vWaxzhYaJcvVgaI9jF9ZcvdhTz1c5SnrlGRvR7qWDR4NH3KJEvChG3F8u5aa+wT/EYXgFO/GttQoOQDhfnHOWopph5kX1I8rCdI7C+VRS8k0bF7/kEj07Ad1go89KySNFUigTf4TYgSu7Zp2T1W/kMv8WXkY/XfRipby7N8fv2sl/4rOg97gx4gNsv8abVN1Y116RrrCcxiy8a+CaLhXFn09GYzbzdKR5Pac3ItvX1X9Pvgtfr292lfL69hO6lBTyQlYJHL1+0Rw/g5OZK1PxxuMVFiPDzOzLGozOVcWP827hIrYeATxcl0/dkGXm90+j/4v0imFFTlWp487uD/Ph3G0DYW5KZy2GlmolRoQywwSiv3k+3RC+iF3VvKrHU2s/JF+Vos/T02hiHi2/jPHiGojLkLy7HojOA0xBw8iVyTmc8khofDtzsgqhhgJZWlK2xxhrGbDY9V56j59spcru4bYUoCG1ynmgcCt5Di84oTtk5wBOfG9rhcl0CLywupVJnZvXyOAIDGvcMt5E9aPA0cnR6ppyX09nLg5fj29bF58/FZWzJLeLh0ADuDXZQTzR4kx0NHRJoQgm0tJ5zGIR1bN4RpZo3D+ZCKdyR5MfjnYPQKfJQTF9l9Q6uno6kyuA5Uvo9u4o/5KxkFCVmKWtjryfIpf6b9yWf53L4vJoFI6Pw+VqOancxOO8mdsUTuARbw0IFdMrdk75AbryZbvf60+vhQLsfOb3ZxOj07Uhx4u2EwThfASRid4dVDQTocgFR01xpJn5jH6TeLryXU8gvJeVMig6lv2/9iIyff1XM1/8tZfSoYIYNbRvIa5fK4+OCt9lV8QfPhL1ID+++dolKPv04ermG6De74xZj9YqdVmlYkJFDD29PZl1Bgm5X53VUPnpBTfKa3Vybn82+Xl25Kb4C9de/4RzoS9TC53D2l3EqfwM5ym10D5tMmHc/KvVmto8/QqLKQOWE83S9/ommmo7YTzW9QydPd16xwSPXpINf0VmB3sCkc5nInKW81SEOZxuoJC4NjY57qzfOgU1nLF+5VpPGzOGn03ANcqbnmsZ5E4R55y19H/XhZHDpAIYkQsYlIhtif9h5c+6JPX23tKK0Z27NWLfZDELBuPv06XSE/314cwISOzhkhfWaKw2oDmSKxqH2dK4QkCAWVYA/f5YFEHZLO/498v93LuFxpZrXM3PbHLCWsE9HlWoWZ+aKCNACErSjOCTgkEDrS6Cl9ZzDIKxlz+WVOuamKtClWrFP194WS5CnC7kr/o+98wBvuur++KdJ9957l83rQEVFEBAnbvR1L5ShIi7AiYjoHxEZIiKyxMmLuFBRVFRUHCCCKAhlde/dpkmz0/9zf02gLW3JbtDe58nTkTvPvcn5nXvO+X7XoPptD9G3X074pcOOtP6sdC75TbvwDnqILSolE2P6Myrs+LeAkxbnU6c08OYjmXhVaSmcuguaNYRdXk7MrTdL/df/sJlvlvujbo7lyvmphCe3zUez5tjubqrh+dJdDA6KYWpC23BUa9p3Vke5o5by+QcIGhxBwtSWMNkf6hQsK6nEWk/QwpfL2L5DxawZSfTt43kIo/OKZpCnPcSzaYuJ9rFNWdZ9VkLN/woJvzKR6JtbwFTeLK3iq9oGl0OP5z29EePBCl7ufRrNsaE8rN1K847d+GWlkDRzIpXaHeyueJmkkJEMjLuHXXsaCJy9D52vkYSloUQHO9cLJryXAkhH19zMG/0zrTLCHDmbXbX9X3k1n1XXc21MBNfFWX/BUv1OPvVflLmcokGxt4n9z5USeXYwvR5yLMdUuf1vyhe+C/IgMI4k/IpUom/xHGAfe/bY3YrSnjm6oI3LDEIx103PlVC+V80VL6YQkWr/ZYe+WknjTzlSvqG+XCGJwYAXwWekEjGqt8SJ6vUvBC7ZVNPA6rIqro+N5BoPI4Av0+p4+FChTbn/LjjfPV32SKBHAq0k4G4912MQdnD8BCLiUznFVCsNUtZjZrgfz5+fgragjKLHXkYeEdKSO+jbkkRvajayIm+8lEd4dvyLvFi2h7OCYnk4oesHaoXKyMRFeRK5+Py7U6W+yhbtQ7WtAa+A38lYdjcyPz8OvPA6v/05gvB4I1cu6mvXB+ad6oN8UV/I+Jh+XBDmvPynspcOoPqtlviH+xB8VsuDdYFay2M5RZwUFMB0K0JjpjxaQGmZnteXZRAU5LzwSbsE1a6R2NOpuXfihYz5ma8js9GzaqjRkj/5D7wjfElbcprE7TX5YAG1egOv9U0n3Mc1YVTipj9v3BqajSa+uPpCfs1W4S8z8UjNJvyKiwkecjKR913BD/kT8ZOHMzx9KZ8vPUD/n+oo6VPN8FmXHUNH4Qx5zs0vZZfwkGYm0zvw+B50Z4zZvg+B8HrfgXyajCZe6ZtOpA17oN6voOSZvQQMDCVpxkBXTE/qs/TTOorX1pBySxQJV9gf3m1s0lA4dSHGOvFgPoSgwX2Jf7ivXSHnLlusHR27W1HaMUVXNHGpQbhzTTV7N9Qz5O5Yep/neCix8ExrDlWxfcUeIotLJORjUeRhAYQMy5RQSv3S/j3hiWvKq9lQXc/k5DiGWRGi7ooD1FmfBnN+Y7i3nKX9PA/YzZ2y6BmrRwKeIgF367keg7DdzouHxefySjmk1pCi9KWoRMcNAyMZ0y+SsoXvoNq+l+ixVxB+ydAjLSs0Oawrnk5KwElclvA44/J+xMdLxsqM4ci7MCAEqfictaUM+08Ik69qSbrXlaspfGiXAIgnejyEjTyX78ato1R7FqffHM7AK6PtOqvTCrdSrFOxOG0osT7O8cIZVQby79mBl6+M9NfOQGamwTA0NzN2Xw4BMpmEKGk+1B3OW69v5o7xOYSFyXltsecpokpdOc8UPkSmfx+mJQsWFdtLyXN7Ue9VkDhjAOWZfjyZU0yfQH+edWHYpLiZL3joI/x6x5D87GVs3N7Amu+q8dermVK0AX+lgohrz+fwkC00aA4xJOVFfp5SQe8aA8qxVZx6yZW2L9SKFusra1lXWdslCbwV3ThU5cc6Ba+VVNqF8Cqhx967E2OjnowVZ0gh0q4ohxaWUbddRb+ZSYTaASBlmVPl6k9QbNoGJOObfi7Jz/wHmb9nXbrYIz93K0p75uiCNi41CAu2KflxUTl9Lgzl7HG2RUJ0tdaych2PPpLPIP86rs9sQLO7VMDjSE180yNbUEqHZuId5hy95AK5O6VLC+/ss5lJ9An0vLU+cCCfSr2BtwdkOkSD5BRh9XTSI4EeCZzQKKPduX1OUZTiRnNJcQW/NCjpHeCHLq+ZggYd8y9MJaa2mqLHFyOPDCVt0SNHvINi0TvrPuOXmv8xJPIGBkeOYU7pLv5qqmFW0hn0DQjvVC6f/lrH2u9ruO2CaC4762i90hd30vSHDnl0NrH3ncNns9ToCea6ZRkEhNvuUarWa5hc8DOJPoEsTHMeamTD5gqqVuQSOiqW2IlZbdb55OEicjValvRpAeLprBQUanlsehEn/SeA6Y8dP8TW3Ydsl3I7K8sXcm7ohdwUO86u4RU/VFK5LIeQkTH8MCaC9VV13BIfxRXR9nt+jjcRAfJQvuh7wi7qR8xdLQBHe/ObeHl9Bf611dyT9xm+Bj3cmUFu1vdENY8l4OkwkDWTvCyR0BDH8tY6m58lf/LMUEHfknC8ZTj9ffEZn55TLJ3NmRlJ9A+y/cGsckUOis2VxE7qRehw14Ag/XlfPrpaA6e/kYnc3za+UYvQ1AcLKHla0Lr6IAu9mNQ5g12a9+j0zeqiwx6D0PnSVlbq+fiBAqIy/bjs+RSnDrBwcRnbf1cx4a4YRgzykUjvG388hK5IAJILt6GXRHwfPnoA/r2dZ4w6dREOdvbE4SLyNFqWuTAyxJEpzs4rYY9KzbxeKaT42x8y7Mgcetr2SKBHAkcl4G491+MhbHX6Pq6s5f3KWqJ9vHkoIY6nvikhIdiHhRelUr7gHVQ79hF951WEX9wWQfTT0hcoaPqT65KeJSGgDxvrC3m7+iBjIjK4IaqtodT6sC/6uJxt2Upm3JrIwLSjvGYtXsI/hL+Qpox8/sw7l5gkFaMX2Jf7t7mhhBVV2YwOS+GOGPtCTjv6kBbP2osmW0HSzIHHoBWuKKlkc52CaanxnBHaOYz1L1sbeWVpBaMvDuOOW13zcO3IF8znNR+wse4jbowZx/CwC+3qythkIP/uHXh5y1j+SBwFJj2LeqcSbwUCq10DAjVrd1L36W5iJg4lbFSfI91UK/Qs/LAc+cHD3Ja/SZpT6bhiqg6P5tTv0ihOUTNy3ih7hz1uO62gcdmXS4i3XHow6sp7fNzO7KhwqEnDjNxiUvx8ebFXil3jq3bVUTZ3f5u8WTum0mkTXZ2BP+/NJyDVl5NebAklt7U0G4wUPPQShupqkA+SvMT+Wf8cOHl3K0pb5e+i+k65+OxsbuKy5P2JeeibTNz0ZhZyH+c9Hhw6rGHGrGIS4n1YMDcVmcxLQjrW5tdIuYbCQDQptdLU/LKiJcMw+Ox0vLxPfG+2Rd7jsnMR1BNvDci063vHRWfqSLevl1byTe3xdbar59HTf48EeiTQIgF36znnfeN37w46rCi3NjTyclEF/jIviZNsb5Gat3dXc0WfcK4N1lL8xCt4R4aR9vIjeLXKOTI2G1iRO17ieZuY+TpyL29KdCqmFm4lyy+U2SlndiqZh5YWUF6n5/WpGQS1C+MqfuYnNPu9qfFScKA5i7Nv9qHPlfYBQbxUtpvfVJU8nnAqpwbZF3LafhH6ai0FIjcu2pe0xacdk5NkSaA/HmjHug9qWP9ZHRPHxTBqpOchjC4vW8Bfqt+ZmjSLrAD7jenyRQdRbqth7TWhKAaFMK+3fQ/61n7MSudsoumvEpLnXIF/Rts91xlMrP6yCs3mrVxetg1tgIzikGFkVUZQdbWGITeeZ+0wdtV7MqeIXLWWRb3TiPdzTchlZxN7tbiCn+obHQL0adabyJ24A4zNZKw8A5mfcx9a63aoODS/jOiRIWTeYx9/W9WbX9Pw1fdANHEPjiVkiHM+93ZtuAsauVtRumAJ9nTpkJ4zGJupVxqIDuv8M/fdC6WU/NnEpbOTic5ybo7vrNnFZO/XMOXBeM48o+3lhElroPHnHBq+3IeuuMVrKI8IIOzCfoRd0A95qHPnYo/wHWmjMhoZl51Hsp8v81383W/vPD+vruPd8ppuDee3d+497Xok8E+UgLv1XI9BCOQ0aXgmrwSR+zbNTJo968disqs1PDcymaDV62jamU3MuKsJu/DsNueuXHOI94tnkBp4ClcnPiG9J24+7y/4hRqDhuUZwwmVH4sK2qQxcteCPOIifHh50rGGnrZYQeG0PRiRcYAgRj2Vho+f7aFjxmYTc0r/xNBsYnriafjIbO+jow9a49YaGjaWEXF1ElE3HmvcWDwxp4cE8kha53DjCxaV8ftOFc/NTKZ3L89T+jMLHqRKX8GCzNUEyI56cW398lHtrKNs3n4OZ/hguDqeCyJdZ/yK81c293NMGh1JT13d4S27qLN9v5KGz79igOIgRgSIxClEPZJEcKhrgR6+qK5ja4NKQvgc5EaCeqXRyPyCMry9vHgkNQE/B5AOa9YVov67gbDRCfim2H8uOjpHdb8rqd/VRNSwYEIH2N63SaWmZs2H0iVV2CU3ETP2JFuPq8fXd7ei9BCB2G0Q1jUamLK8kPgIH+aM6zwc9M/3a9j9cR1n3RVD34uc+x216y8Vc+eX0SvLT/q+7yg6QHwvqf8uo/6rfTT9USTRV3j5yKUcQ+E1PFFBaPLUWp7IKWJQSCCPdaEPu/Oc7VAomV9YzoWRoYxL/GeG7XanfHvG7pGArRJwt5771xuECoORxw4XUmcwclt8FJdFR6DQGrn78zzC/eUs7OtNyVOv4h0dTtqiaXh5t83h21H3Kb/WrOWcqJs4I+KqI/u9sjKb7xQlTI77D8NCjoWN31eg5tl3SzirXxAPX9txLlX25B/wqfbsWP7U+afgm3zsQ6sA5xm7L5fjoZY99EgB5eV6Vq/IJDDAOcaqrR+6zuprTBqm5I4lyjuW59IXO9St4GvcO/F3/JtMDvVjXWO9uOYQwVfA8YB6xHwE6EiVdV331DphJOCbehopc6/zyPA0R4XobkXp6Hyd1N5ug1C6pHy1gOoGA8sfSicsqONc9KKdKr6fV0bWyBCG2umd7mytYg6PPllEUbGOmdOT6N+v6/xdXbmChq+zUfxwiGa1+E4D//7xkmEYdEYKXk663HTS3nTZzfYGJQuLyrkkMoyxiZ6XGiEmb+GJtRYd3B1y6xmjRwL/Zgm4W8/96w1CoaQ+rqqjRm9gQmKM9PD0fb6C5TsruTAzlNGbNtK0az8x48cQdsFZx5zNT0qep1C9m+uTnyPev/eR97crK1lYvptzQ+K5L+4/x7TbuL2et7+p5saRkVw9tGOPzHcz8/A/UE9CLxmBMbaDX4hBC3VKCV003S9EApVxZvHLDCLiis6BYKYdKqRYq2N5v3TC2hnSYh46nYk7xucSGenNq4tcA2LiyHrzNIeYVzyDk4PO4J6EaY50hcZo4sVPshm0T8dgF3vFDLW1aA5k4x0Ti3+vo2eyU8NXraY+9zcC4poJiw5xaJ3WNNaZmtnRqCJQJuPUEOeeyU4fRoE/GlVoTc3SLb1AwHWoiPyngiaadc438LW1Bskz4hvlLVhK7Cry8FASp1+LPNB2zlK7BnRzI3crSjcvrwubysz4bseEVm6s5LtdCgnRWiBbd1TU9QY+uCef8FRfrrQzf7WrqW35WcHS5ZUMOjWQx6ZaR1RvatKh+PEwDV/vQ1/eKHXvHRNM2MX9CT2vN/Igz740FfPdUF3HmvIabouP5rLozoHm7NhWpzURl7i378sl1sebxX09Tx87baE9HfVI4ASRgLv1nL3PG54mTrtvTi0LEYahJYRl3q+l7CxrYnqyiYCXVnfqHRT5g8tzx+GFF3dLHHVHc4majAbG5/1IiNyH19LPRebVVtSvflbBT3saeeLGBE7JCjpGnkIxf3hvPv5hcq59NR2Z3L6terJoO7laBQtSh5Dke+w4rtxIS77WE2kJnBJy7Nj5BVoef6qIU04O5IlHrHs4cOV82/f9c8O3/K9qFaMjruGKqOsdGvqvRhVzCsoYEhbMgymOEY0fbyK1H/1J7Qe7iL7zbMIv7n+86t3y/uQD+dIlzKr+GQTJnZuD19GCtiuULCws55TgQJ5I97yzZpmzpkzH7ocLCe7jz4BnnccX2i2b7MJB3a0oXbgUW7p2SM9tf6+KDX800CcrgDGjopAHypAHyKSfMj+vI/rvw0n5qOsM3PhGJj52Itx2tiiDoZkHpxVQU2Ng3vMppKRYb8wJypemP4up/3If6j2l0hBeft4Sn2H4Jf3xTfJMQ0vMc3VpFZtqG44LsmbLYXBF3Un786RoqXcGZOEts++ZwxXz6umzRwL/Rgm4W8/9Uz7xDinK1gdNYzAxYUMePnIvntv7A+q/DhIz8RrCRh0LDlOmPsAHJTNJCzyVqxIfP+a8PlO8g/2aeuaknEmGX1ui32krCimu0nUavrNvYz073q6m/6VhDL7dvhAThVHH3XlbiPL255W0oW4PHdtYXc/b5dXcGBfJ1THHekF//qWRJcsquGx0OLfd7HmgF+9VrWZLwyYmxD/MoOBjvcO2fEH9r7yaz6rrGZcYw4UuzB8Ucyqb/x2qHYUkPXsZAX08MxfklaJyid7l8bQETu3gssAW2VpT97m8Evaq1FLu4Oku9tBaM5/O6lT/3EjukgriRoeRdod9n3tHxj9R2rpbUXqIXOzWc8KY+v1mEUbeSZFxxEBUK01o1Sai+vgTGOuNPFB+xHC0GJDewphsZVBafveywojY+FU9b6+pZviwECbdbR9okgCeEXmGjVsO06xrIbwPPCWJsEsGSD+tmYc793ROfil/KZuY2yuFNA+mdJiVW0x2k4aXeqeS4EIUbHfKvmesHgmcqBJwt54TBuFq8V0K3NiB0NYCSmCChwvUbkXZfl3bipUs+q2cy+UKzn7vfbxjI0lbOLVDYI7fa9eztXYdQ6Nu4fSIK44R0fraPNbV5nBjZBZXRx7N5dLqTYydl0tEsJylD3Sc4/X5E0XU5mm5bE4yURn2ga383FjOkoq/OT80iQmx7vcU7VOpeTavhLNCg3i4A865teuq+fTzeu6ZEMvI4W0NZk84bwuKZ5KjOcAzqS8R6+sYZ95TOUUcVmtZ2DuVRBcr2vz73sdQqyLzjVuR+bsXxdPafbOg0I6JieCGuChrm9lVr0SjY+rhQmJ8vHm5T9ox3nq7OnVRo4I3q6j4qoHMyXFED3N9+K6LluHybt2tKF2+IOsGsFvPmQzNlH1ax0+/N6BqMHJGWgB+JjA2mVpe6pafJq39IaliCTJ/L8lQ9O7AiLQYjc2+XqxZX4tS38zEe2OJjPdtMS79W4xML5+j3srjicWo1KLYfFDKNTTUqKTqPolhLeGkI3p5zPfflIMFlOr0vNE/kwAHwKyOJw9H319eUsn3dQoeS0tgkBsu6hydb0/7Hgn8kyXgbj0nDMIiYArwQQeC/S+wEHAtRr7jO2q3omw/9Cvby/mlSMnTf23G92AusXdfS+h5gzuc4fqS2RSp93BD8mzi/I/lG8zTKHiieDv9/cOZmXzGkT4OlWiY8WYxp/cO5JHrjw1fqy/W8dm0QsKSfLhyfqrdnr1XK/7mp8ZypsSfzJnB7vcUNRmN3JWdR5yvj/Qg3r7MW1jKzl1N/N8zyfRyMsS5o0dKhBBPzbsLU7OJhZlvIPOyP+dMbTQhOKhCveW85mLuPaNCQ97EtfgkhZG24BpHxeCy9vlqLY/nFDEwKIAZGZ3noTpjApZwrVviorgiJsIZXbqsj70zilAd0nLyolT84/+Z+X/OEJ67FaUz5uyEPhzWcx/9VMsHW2q56bworjrn2M9Cs7GZkh0qfnqpnJRTAzj5qsg2BqOxydjmb0M7g9JiWIocWLuL8FaKUFbLy1+GLMBVrLP4AAAgAElEQVTrmP9ZjEhZgAh5BUN+Keqdh9DntwBkyQJ9CBnZRwon9YntvssVU3Mzd+zLlSitVvbPtFss7mj4SVUt71XUMjYhmkuiPDcE1x2y6BmjRwLdLQF36zlhEGqAy4DvOlj8+cAXAtyruwVznPEdVpSif4OpmYmf5xFXUc647z7DJy6K1AVTOvQOtuQP3iXlDU7MWNUmf9AyV6EI7snfgspoYGXmCAJlLchum3Y2sPqrKq4dFsF1I471jux6r4Y9n9Qx6MZITrraPgoAMfa9+T+hNOpZmTGCQHnHqHKu3teHDhZQrtPzege5Yg9Mzaey0sCbKzPxd3KuiqPrqtFXMaPgftL9sng0ZbZD3e1qVDG3oIxzwoJ5wMX5g027Syh9fhPBwzKJnzzCoXm7srE4n8JINjXD6gGZyNvl2Dpr7CajiUkH8gRlIEv7phPiwUTXwouz885cZL5enLYqw+6LIGfJzpP7cbei9BBZOKznDpdoeOrNYgamBTDj1o4vYrRKI+vG5xES78OYRbZz34rLNOFpbO19NFgMSbM3UlVnYPPXDdKDxeCTA/ES9TVmT6XwVqpNmDT2WZVylPhTgi9VeNEs2aZGn2iMYSkQHok8wBwC28rolAnvZGsj1GyASv+XPJ62eS5bn5davYFJB/LJCvBjdlbnlB+ecMa2NShZVFTO6Kgw7kjoCVn3hD3pmcO/VwLu1nPCIDwAvAfM7EDss4BbgF4eviUOK0qxvr8qmpjzcykPbPua2KIiYu+9jtARp3e49FL1fj4seYb0wNO4MvHRTsUjQjZF6ObU+JMZbPbSrfiiks1/Kpj233jO6NuWoFfkenz8QAGqagPXvJJGcIx9IX95WgVPFG2nn384z7TyTrp7HxcVlrNNoWRGeiIDg48iSmq1JsZOyCUqypslL3keotlu1U6Wlc1jaOgobomd6JDY1pRXs6G6XkKxPd/F+YN1n+6mZu1Oom4dTMTlx6LbOrQQJzeenVfCHpWa57OSyQxwzZ3T1zX1vFFWzcjwEO5Jti9fycnL7rQ7VZ6GvU8UE3ZKIH2f8FzgG3fJo6tx3K0oPWHNLRS39hlJRy4pxaXnojzUWhOvT83E37fjyIf1DxbQWKHnxtcz8A1yDejTqjcq+Xazghuui2TMlcdefApdKIzC9oZii7HY2nhsbgl51ZgwmY1J6W+lBrmiGB91CTJaaCsMBKEhER3C2LFtXQIz7mjepLzld/NLyqk0A/S0z60s9NLzUlUlp8QGMTkr3qMvek4EvkQP+Sz2TKNHAi6XgLv1nDAIBZu6MAanAm+ZcwaFlXI7sAAQRuELLl+5YwM4rCjF8Kv+qOTQ9oNM+OFzfOKjSV3wMF6dICBur/2IbbUfMCzqVk6LuLzT2f/cWMaSir1cEJrEeHMe35OvF5FbrmXJ/WlEh7Y1+Cqy1Xw9q4S4/v5cPNN+lMFPavN4rzaHGyKzGNMqf9ExMdve+tOqOtZWHAu3nZun4cmnixl0SiCPTfO8h98va9ezoXYd10WP5bzwS2xfeKsWT+YUkavWuiVRv/zlH1BuzSNxxiUEDnQs79GhRVvR+MPKWsTrjoRoRrsgPEk8PE87XEiJVs+crGQyXGR0WrFUq6pUftNA/utVJF4TQfL1rs2rtGpCHlzJ3YrSQ0ThFD338vpytu5T8sj1CZzeu2Pk6S2Ly8n/VcmF0xNJOMk11DDlFToefqSQ0BA5r7yUhm8nxqmjsm/WG2n8NU9CJ9Xl10jdeQX6ETAoE9+BWeDth1EYnq2MySOGpTA8W+VYSnmWOgeM8tbhsGbP41HD8ih4jzAwRSjsEfCeVsanyLV0FWiOiKi4KzuXRF8fFnaQ5uHoXvS075FAjwSsl4C79ZwwCMUV4QrgLnEDCYjMbKElxHvi//ea/2/9Ktxf02FFKULY7tuYz5ivPiOzqozYSdcTOvy0TlfycclzFKv3cmPyHGL9Oyf/bo/0aTTB2Hk5BPjJWPHQsWFhW1dUcmizgrMnxNDn/DC7JTmreAfZAuE0+Uwy/LsPsOWvxibmFJQyLCyEySlHPTQWPqorLg3nlps8D2F0Vfki/lBu46Gkp+kTMMDufRB5lOOy84jwlvOqi/MHxSQLHvoIfbmCjNdv9nh+rt3KJp7PL+Xs0GAeSnU+Fcffyib+L7+U3gF+POfhoVpi73KXVVD9QyO9H0kg4nT3UsTYfcC7qaG7FWU3LbP9sA7rOdHhD38pWPZ5JRefEcadF3ccFrj38zp2vlvDoJuiOOkq1+XdvrS4jN9+VzH+zhguGGW/vrNmf8QFkeZgJQ1f7kO5vQApXl3uRfBZ6RLZvX9v6/LsRWj30XDYTnIqLbmVTSZK6rXUKfXEGeX4a1uAfOwNh22xZpEAeERepYUqy5Kz2RIgay4d2a2W/3VWrRkaDS2orSHtwG/aOKe76tsMLiR4VP2ifPCN9pY4Vf3ET+l3H7xDZB7tKbXmPPXU6ZGAqyXgbj3XmnaiL3AeIK6mxTXaZuCgqxfspP4dVpSHajS8/sHvjP/hC3wSokmd37l30NCsl/IHvb18mZCx8riAIxYuwIWpQ9DXefPYqiJOyghg+s1tcziMOhPv35OP+Hn9cvtDdZpMBibk/khwJxyITpK5Vd0oDEYm7s8j2c+X+b2PYhOtea+aDV/Uc+/EWEac230Ga2eLmFUwhQp9KfMzVhEobxvWa9XCzZV2KlTMKyxjWFgwk12cPygInHPvWoN3bDDpi6+zZZrdUleA7Yjb6Ahvb5b2c37Y8ILCMn5XqJicHMew8O4DlbBWuHumFaIu1nHqsnR8w7sn59fauXZ3vVaKUjzFC+s5zzwnodMEKra4xRF58Ru6e65OHN9hPSfmUqswMOmVfOIjfVh0b8c5gpZIldQzgxg5xXWRBjm5GqbPLCY+3oeFc1ORWUFb4Qx56quVNHyzH8V3BzEptVKXflnRkmEYfHZ6h7gB9o67tLiCLfWNTE9P5CRz2oQIh22bY9neE2k2NJtMSMA9ZhTYIz/N/+twTq2e6jpMzba830netoFmyVb2lQmDs90Irf82/y54mI8U868ifBdT5xITSLKSgRhlMRJbGY/m/8v97Adys3evetr1SMCTJNCdBqEnycHWuTisKNfsqSZ2xbtkVJUTN/kGQoYN6nQOJepsPiqZRUbQ6VyR8Mhx57qu5jDr6/K5PboPgQXh0u3slUPCuXlUW89YwW9KfnypHEeV8O/KShaU72ZYSDyT47o/j2zS/nzqDAbeHJCJn6zlS37uglJ2/dnE7FnJZGW6Jn/suBvTSQWdScvDuWMJ945kdvqr9nYjtXunrJovauqZmBjLqEjXGr7q7HJKZn1J0JlpJEwZ5dC83dX48cOF5Gt0LOmTRrSvffmyHc21Wqfn/oMFhMhbPLM+bnrQtFdu4kFv51250gPSqUucbxzbOy9PbddKUW4EDgMPmOf6nDkNQvxP5L6PB9701HXYOC+H9ZxlvEdWFFJUpWPxfWnEhh/7udNrTKy9M5egKG+udfF5fO75EvZmqxk2NIRbbowiwo2XISatgcafcySvoeA2FEUeEUDYhf0IPb8v3mEBNm7RsdWfyS1mf5OGRb3TiPdz3necwxPrpINXiyv4qZ0Ba+tYAq1WV2dAV2N+VRvQit+rxd966f+Gxi4sRpC8iMKbaDEajxiQkrfRB58IuctCZ21db0/9Hgm4QgLdYRBaEw+3zxWLdWKfDilKEUoy/62tUrioLCGaDIEsajZcOprjb7UfIl7nRt/GoHAB0Np12a+u55mSHZwSGEX8nmS+3tHAg2PiGDKgrdfi+wVlFP2uYuTUeFIH2++VWlWZzbeKEibHDWRYiOtud4+3bsv78wrK2Nmo4rnMZHoHthh/9z+cT1W1ZyKMFmhymFs8nf8EDmJS4mPWLrPDek8cLiJPo3XLw0D9xr1Uv72dyBtOI3LMKQ7N212NLZQQ9yfHMdSJXrz3Kmr4pKoOd/AcOkNWin1q9j9bQsRZQfR+uPs/s85Ykyv7aKUoy4F7gE/M6Q8VwDzgRXP++1XAqa6cixv7dkjPtZ7nO99W88Vv9Yy7JIYLT+84VPPTaYU0FOu4bnk6AWGu81gLL+Hc+WUoGo34+3tx9RURXHpJuMtyCjvaL/EMoP67TCK7b/qjSEqe8fKRE3xOhuQ19Eu3P6fXciH6zsAsvF2EpuzMM2jJ7R6fGMMFLgRBE17EtgZji6EojEatZDgaaNZ3ka8pA99Is5fxSEhqiwFpMR7lQT2hqc48Gz19uVcC3WEQimuazj51IgBAvGcbHJd7ZSZGc0hRFjVoOTB9KenVFcQ9cBMh53T9MP1R8SxKNNnclPICMX7Hv803NpuYkLcFfbOJ9J97c6hYy0v3ppIQeZRnTEB9f3B3Ht7+Mq5bloHcp32shnVCFYrt/oJfqDFoWJ4xnFB593OZfVBRw0dVddyVEMNFUWFoNC0Io7Ex3ixeeHz5Wbdy59X6VfE971Yu5+KIq7gq6ia7O1YajUzIziPSx1vygB3J97C7x64bVizdQuOWHBIeu5CgQfYDErloeh12+3N9I0uKK7g4Mow7E50Dc64zmbjvQD4qo4nFTvY8uko2ZRvqKFpTQ8otUSRc4bqcLVfN3939tlKUauAi4CfBYABsM/PmlgCCd0XQJtl/u+buhXU9nkN6rnXXu3ObeH5tKYP7BDH1uo4vIH5ZWkHOlkZGPZpA8mmuzWltajLy8ad1fPl1PUYjkm4QueVnnhHk8u/N9iLXlSskonvFD4doVregkwYMiCfs0gEEnZbS5WVx+770pmZu35dDlNABfT1P13V03CzfyVdEh3NLfPfl94tnGeFF1FW3GIqSkWg2FLXCy1htQF9v7PzpVdwQ+Xm1ymFsMRZ9QuV4h4iXzPyz5W+Zt33PXJ71FdEzm3+SBLrDILSGrOxHDxeyQ4py0xd/kPnO+2hiohj48tQuv/ANJh3L88ZJ+YMTM1biZSVh+cKy3WxvrMTnk3TplvD1aZnIWt0WHvy2gW2rquhzQShnj7cuub2jPSnRqZhauJVMv1CeTznTI7Zth0LF/MIyRkWEMjEpFkveyGmnBvLoVM9DGP2g6i2+b/iSu+Ie4IyQc+yWoWXdw8NDmOQGyoPCRz9BV1hH+rIb8Q53PNTJ7oXb0LDKHNqZ7u/HC72cw9G1pU7B0pJKzgwNYkrqieFtO/RSGXW/qej3dBKhA06MvbNhm51etZWiPATMB5YDzwAieXagecArgdVA9z3VOnflDum5L2o/JMk3lVOCBqM3NjN+QR4CN2TllEy85cc+DO//up7tb1Rzyn8jpZc7Slm5jnfX1rDzD4FtB/37+XPHrTGkp/m5Y/g2Y4icbMWPh2n4ah/6ikbpPZ+4EMIuGUDoyN7IAo4f/lmq1THlUCEDggJ4OqNj3ke3L+w4Ax5q0jAjt5jBoUFM9fDvTwHwo681h6NK3sUWQ1EKTzV7G0WupjVFgPR4B8vxkYzFdgaj+L9kSMqkOi3vy5HZeXFvzXx66vRIoDsMQsEzuB0QivVELXYrSnEL9etDL0tk9N4TriP9/I55By2CKWray/rS58gMGszlCYKpw7ryXUMJK3MOw5dJ9E/1Z+ZtbT04X84spuqAhktmJRHb1/4Hwo31hbxdfZAxEencEOUZ9JE1eoPkscnw92NOrxR+2KJg2cpKrrw8nJtv8LxntUUlz3FQvZcZqfNJ8LXf0/ZWWRVf1jRwT1IsIyNcmz9o0hnIHfsu8rAAMl67wbpD6QG1xOdPnI06g5HV/TMJaIdsZ88Un8op4rBaewz3pT19uavNn5PzpQeY01dnSnxmPaVrCbRSlII26XFgEyDi98XfL5tbPw+ca379E0Rqt56r1JXzTMFU8DIyMPBUro8Zy6oPTfyV28TM24ROOlbnVB3S8OWMYpIGBXL+Y+69uNu9p4m311RTXKKTgE3OGxHKDf+NJMyFoaudHZBmk4mmXcXUb9yHem+ZVE0YgyHn9SH8kv74xHYOWPVXo4o5BWUnBA+qZf0WILhUP19ebAUEd6J+gIRBaPEotuQuGjEoTejFzyMvk/S7tcajRRYyf68jxmGLIdnWYBR/HzUw5XgHy5C5iF7lRN2fnnl3LoHuMAhFyI0gWvN0L2BX58ZuRalRqNg5YyXeWh2DX52KrBPeQcvg22o+YHvdRwyPvoNTw0dbfZar9Romb9kF22IYPTiMOy46Gh4nCIAFEXBwjDdjFjsWWjindBd/NdXwTNIZ9AsIt3p+rqwoHvoF0miTycRb/bN4b10Nn2+sZ9LdsQwf5lpDydZ1ibk+mjcRrUnNS1lvIRdsxHYWC2CKCFuMdSJgSkfT0RyuovipzwkclEziYxfaOePuabaosJxtCmUbFD57Z5LTpGF6brGEajuvV4rbw83smbe+3sCue/IJSPblpPlHkXjt6evf0qadohScuSJc9E+zR9CSArEM2Grm1/0niMZuPSdolWb9fIAqrz00x32Mj8yblMJx7Pw1mTFDI7hh5LE5cgLt+n935uIXLOe6Zelu/ywZjc18972CDz6qoVFpIiBAxjVXRTD64nC8uym8T1tQK/EZCiAaDCaEtRp0Rgrhlw7Ev1/cMTLaVNPA6rIqro+N5JpY93hZHT3oQgcKqiRjc7MEBOfqVAdH5+vM9hKliNLYYiwqW4zE9kbjEUPS/L5RZZ0Hso0RKbyMwuMYfNRg9Inwxj/Bp+UV59NjODpzY0/QvrrDIBTewZXm1wkqNsdyCMUXoKpOSXDk8aHpPyx+hlLNfm5OmUu0X8eQ3Z0Jcfz63Sj3BXLrpRFcPuioAt79cS1/vl/LSWMiGHSD/cnrOpORcXk/4uMlY2XGcORWhrO6Y9Ofzy9ht1LNC1kpvLekhj93NzHnuWQy0j0LYbTeUMuT+ZNI8UvniZQX7BaN0mBkwv48t+WOCAj1qte3EnHNKURd3zl/pt0LcmHDjdX1vF1ezXWxkVzr4EOTBeLdkq/qwmk7reu6nSoOzSsjemQImfcc5ep02gD/wI5coChFOIWAjB5iDjkVOYkjrRCdQGRZBFxtBrX53Ix42sKAfrQIcJv/A3oLykkz4M06K/pvXcVug7CsUceMH4pR6kykxjSgSViCtjGYhi8fJCHOyEvjBevUseXzJ4qozdNy7ZI0gqKPHyJp43qsqq5UGflofS2bvm2Q8gvj43y49eZoTh8U2G3GiqFejeK7AzRs2o+xQdypg19GFGGjBxByTsYR2oo15dVsqK4/YahvLBvy5OEicjVaXuubToSP6wCFrDoAHl5JIKoalMJwbDEg2xiMCrNBaTEyzXWOa0R6gV+MMBB9jxqJ4vdEHwlIx8vDUbM9fMtOmOm5QM91uXaRODDUDMv9MPAVYDhhpHV0onYrSlvWqjdpWZ47Dl9ZABMylludP2gZ4743DlFT6sU1N/lyfWaLJ0AYo59OLURRqueqhamEJdoPAvOXqoY5Zbs4MyiWKQkn27I0l9ddW17Np9X13J0Uy0ezaqmpNfDWqky3IslZs8i9ql28WjaXs0NGcHvcvdY06bDOdoWShYXljAgP4V435A9WrvxF4tSKnzqK4MG2XVTYvUgnNcxRa5ieU8zJwQE8mW5/no0IdRLhp8J5sLRvhlPCT520xC67KX6/htKP60gfF0Psha4l53bHetwxRitF2R8QQhNgMqIEAk+14iF8xcr5CINtibkfwdUj0EqtMQi/BvoA02hhXptrbitCVS1lmOCDB5YC64FLAZFvICJzRKirtcUhPVfaqGPuL2VUqPSkR8hJyPyM39YPxdQUytCbv+bm5BuJ8mkL7LR1ZSWHvlMwYko8aWd2LzZPSamOd9ZUS5eJopw0MIDbb4kmJcX9+YWWDWvWG2ncmodAeNbl10r/loe30FaI1+L6On5TqHg2M4k+gfanglh7QJxV7+WicrY2KJmZkUT/oBNn3s5av6v7kYxIldkDqWgxIkU4q6ZMj6ZMJ/0UuZAdFZmvF37xZk9igi8BieL3FsNR5Df2lH+OBLrDIKwyK1HhqhGhNnUdoI7aj3Linr1xSFFaO8Wipj2sL51NVtCZXJYwxdpmUj1h+N25IBeNwcjg29RMTWpBMq3O0bBxejFRWX5cNtsxUI23qw6ysaGQCTH9OT/M/gdrmxZmZeVtDUoWFZUzKiiUzU8rpFveRfM9z3DZVPcpn9Ss5dro2zjfCkqRzpb/ZlkVX9U0MCkpluEuzh8Ucyh6cgPa3GrSXrkOn5jufXCz8kgcqWYQIUr7chGXnq/3bwu2ZEtfn1bVsbaihksiwxjrJMRSW8a3t+6BOaU0/NXEwDnJBGV4lsfc3jW5ul0rRfk98Csw3TymMOruNKOODgdmmmkojjclkbhpif360AxEczyDUHgTxdgCmG2LeQCB5PUbIOK2vzX/TxiNwr3WmhxU8CeKeHlhLFpbHNZzCq2Reb+WcahWQ2ygN5GqJvYf8CVoyDqC0w4wOmIM50dcjo9Xizfw0OYGtq6o4j9XhXPaTZ6R773rLxXv/K+a0lK9lF94wahQrrs2itCQ7nsYFvpdk11B/Zd7Ue0oPEJbsf/kODadHs+scwcSfgJ52iy0Pe7If7f28P/b6hm1JrTlwkBsMRLVFmOxVE9nHkaRsygZh62MRCkENd63BwDnBDxA3WEQCmS2LsheJCnO8nBZOqworVnf1pp1/F63nhHRYzklXFzuWl8q6/U88GoBXpFa/C+uZGXGCLy9ZGx/s4r9XzUw+I5o+o92LOdvasFWSvQqlqQNI9rHsx4sy7U6HjpUSEqtL0UrdZxxWhDTPJBv7Y3yV/hd+QsPJE6nX+BJ1m9wu5qPHiqkUOt8wvWOJtRsMJEz9h1k/j5krLyp28Ko7BYW8GxeCftUaub2SiHN3/Ybf5Ej9cDBAqr1Bhb0TiXJz35PuyPrsLWteJD8Y3weJl0zp7+R2QN9bqUAWylKcaEpDEARqimsmGqzt06kQTwE3C3AKq3s1lLNWoPwWWAiEN+ufxESKjyBwgsoDrOAqHwAEDmNliLyHt8ARGJZg5Xzc4qe0xlNLP29km0lSvyavNAWNpPZtwblqa9gwECsTwI3xtwlff/VFmj5/LEiEk4K4MLpnnPJaDA0s+m7Bj76uBZVk4mgQBnXjonkogvCui2/0LKHApFU8Bm2oa34TwLhowdKOd4nQrjfD3UKlpVUnjA8rlZ+fv4R1Sx0HBZPoqa0xaMovcp1NHfkWOwoBDWxxavYE4LquceiOwxCz5WG9TNziqI83nAfFM+kTHOAW1LmEeVnmzdv+34lCz8qJ6qvjppTS5mZdDp9fcL5cFI+goPwv685Rv5bpVdL/IPJvkHMTxUX155VxAO7SFTX/2nC8CVcfWUEN15nf76kq1b3f4WPUKorYm7GCkLk9gHeWFDaYn28WewG7ikBdFD02KcEnJRI0vSLXSUal/a7rqKG9a24Km0dzELxcVJQANNPEHh3sUahwHc/VEhQbz8GPmfbd4qtMvon1W+lKEX8oLidEx46EaYpPIYiEVPk8AkPoUiDEGGkthRrDcL3ARE9096TKLgPRRGopwOAvcB55rBRyzwECI7I3xcexd+tnJzT9Jz4Pl77dw0bsuslfPGgABnP3+fPBzVvsa/pL2k6pwWfzZjwW/lqQgNyXy9uWJXhcZdNgsz+w49q+WZzA83NkJjgw203RzPoVNfyJlqzX42Nala/v51zd5QSWqeRmvjEhxJ2Sf8W2gr/7snJtGbu2So1s/JKGBIWzIMp7e87rOmhp053SKDZ1CyFmqpbG4k2hKAGmI3EnhDU7ti9Y8fsDoNwMzAJ2N+BCERuhLjVbB3q4hmSajsLpynKzhanN2mk/EE/eRDj00X+oG0kput+qGH9L3UMGSlna1wOV0ekc25hAt/NLXMKrPe3DcWsqtrPZeGp3BYtts3zyqzcYrI3aGAHTL43jmHnHB/Ex52r0DfreThnLMHyEF7IaH2Zb9ssfmtQ8lJRudugxsVNdOWynwm/4j9E3yKeM0+8sqtRxdyCMoaFBTPZjgeQ2Xkl7FGpmZoaz+DQEydktvrnRnKXVBB3SRhpY9vmb514u+i+GbdSlMLYegt4EVhoNgotH4JrgNfMBqItk7PWIPwGEIR5AlCmdXkXyAQEianI0f8ZGGRGQbXUEyA2gupJ3OBYm0fodD23KaeB1Z9WgRouviCUOwZH81fT73xQ/RYCYMvPy48zX5+KPjeQq19KJTTBMz3vRUVaiaZiz94WgJdTTg7k9pujSUrqvvnmqbU8kVPEoKAA7qs2SLQVmuxyaX6yQF9CR/Uh7OL+HhniX6s3MOlAPpkBfjyf1XNRZcuXh6fW7TAE1Ww4dhmC2s5I7EFBdd8Od4dBKPImzjbfVrZf6RnmJHtPh5lyuqJsL4jCpt18Uvo8vYLP5tJ4EYlkW5n7Xim7cpp4+LYoXtLtJNMvhEvWp5H/q5Jz748jY6hjxtHCsr/YrqriycRBnBzoeZ43IS2Jl29pA+TD3P9LIa0byIa72rVibQHPFz1G/4CTuT/pSds2uFXt1aVVbKpt4L7kOM4Nd2xfrZlE1ZvbaPgqm7gHRhByjngOPfGK0mhkfHYeMT7evGKjV9VC/hzt483LfdKQ23hZ053SKnirioovG8icHEf0MNefle5cqzPHbqUox5mNPuHWEkaXCB99xzzWYnO4qK08LJ5iEIp0DpEDeaSIcDFnl1c2lvPLLiXEwMjTQxg/KBYjWr6s/Zhv678g5YuLSdh6Dr3uNnDOef2cPbzT+pPCr3c1SfmF5RV6ZDKkENL/jokkuBvANrY3KFlYVN4mp1mbV9NCW/FLLhjNtBVnphF+6QD8+8TafNHsNOG160jI8o59uXh7efF6f8/zDLtq3f/GftuEoJYeBbU5Xgiqb7SFJuMoEmpAgi/i/ydCWPSJsNfdZRCe1UHYirhaE5aPyH2wn53bPVJ3uUH4a8aC37kAACAASURBVM1adtR9ysiYuzg57CKbV3XPojwa1UZWT8tgWslWalVaLp2bjmCGuH55Bt5+9pNRG5pNTMj7UeINWpUxAl9Z9yXXdyWYLXUKlj5ZKd2pv+2BCKO/KbbwVuVSLgi/nGuib7V5jy0Nph0qpFir49W+6RLthKtL8cyNaA5UkLrwGnwTT1yUSovclvZNJ9IGuVkAfG6Ki+KqmAhXi9up/e97uhjlQQ0nv5QqgQH0FOsk0E5RitBQCw/hd616EAaVCMe0hHBa1zlYaxCKkFHh1hXhoK1LRyGjIqy0Nddvt4aMtp7soRINM94sxjsYDMlwUmwAD58dT6CPnDJdCeu/3Ezw2iGUnfMLcTc0MibqVsK8Hct3t3Yj7Kkn8gu/2lTPR5/UoVabCA6Wcd01kVwwKgy53LbIHnvGt7TZUF3HmvIabouP5rLotvIy1DfR8M0BFN/sx6hoCSf1y4omfPQAgs9OP0Jb4cj4jra15MGv7JdBiLdnPlM4usae9l1LQISgaqsE+mmrPEVLCGqNoUP0ES8fL4lH8QinYisPowC9sTW67t+8R91hEBqPI/B5wONWbIrIlRAQ3yKBrR5YZQajOV7/omsR2vMEIOC+RU6IUOLXmsNxrBjaMR5CawZ4v3gG5ZpD3Jq6gEhf25Lr6xoN3Ls4n9RYX16ckMqqymyyf6znlI9iyBoewtBJjnGPZavrmFWyk1MDo3g8UVySe2Y5WKvm6QdL8I3y4u1FWR43yY+r3+Xb+s+5I3YSZ4WKZ0zbS4PBwN3784n39WFRH9ejqIov7Nw73wUvyFx96wl9M7eypJLv6hQ8lBLP2WHWhX1qjCbuPZCPvtkkUU2EnkAPLoIEeeeduQgY8dM8MD/L9tPvvhYuVpTWGoQCVGYCkNBu5TnAJ+1AZe4Hlreqd5uZ7sntoDLtd8lkambCS3lo9SbiT/WhWKUnJdSXx4YmEB3oQ32xls+mFdGUXszu8cvwlwVwReQNDA+7ELmX5xoKDQ0G3v+ols0/KKT8wuQkX4mm4uSTbE0pte9cWyJFugpjN+kMKH8101YUCoB3kEcEEnZRP8LO74s8tPvA4RYUlvG7QsVzmcn0Duy+edgn/Z5WrpaASWdC0woF9QiwTZlO4mTsqMiDZPhLlBkWJFTz7/E+yP3td4q4eq3d1b+L9dwxyxLXZWLnFiAF8rUpOnNeoSDoPV4R1/Iil2OfmYdJPO2LPl8yc0J11X68mf9J5ICIfEbRl8hZFFxSbkVf62ySOpOGFbnj8JcHMy59mc03HLsOq5i7rowRJ4dw7xVx7FBWsmVOBdE5AVwwPZFEBxXUezWH+aQunzui+zA6vIXf0BNL9kE1s54rQd4H3n0qy2Y5unpNi0tms1+9hydT5pLsZ58xd4ReIyKUiUmuZ2vRlTZQOOVj/PvGkTxL0JuduEXyIJdUMjoqjDsSrMun+6a2gddLqxgeHsIkN/A9OlO6qjwte58oIvSkAPp5EIKjM9foqr7aKUrhfhFoooLCQRhYghBO6K0V5stJW6dhrUFooZ0QYDYiT1AUkWYhLjTb004Iy+mCVhMRqKhi3m6lnehMEIs+LmdbtpKH/hvP5soGdleqifCX8+g5CaSH+fHeuDyE4ej74i/82PgVJkwk+6ZLaKSZAZ6Zs25Za0GBlrfWVLMvuyW/UBDaC2L7hHjXeuRfyC/lT2WTVcjJImxPva9c4jNs+qPoCG1FyLlZhF86EN9k93tk3y2v5vPqeiYnxzHMDakPtn5Ie+p7rgQMSmMbTsXW/IoCUbuj4hMpR4SctngWj4ah+sb4/GvRt7vDILzDHFIj4LrtLcK79yggnqIV5k7E3yJkR0BUWf7Xvn9BbJQHCFI/ARNub3FpyGi+6k8+K3uB3sFDGB3/oM1z/PjnWt7/sZaxF0VzyeBwams0bJhchC7YxJ3LeuMtd+xm5Mmi38jVNrIwdQiJvt2PrtaZgL77voGVq6skH/Li8WnE+noWytrjeXejNCpZlPUW3l72hXq+XlrJN7UKtynRxl9zqVj8o4RcFzNWpAKfuMVCTZIV4MdsK4AMxEPUI4eLpPDc2ZnJZJ1gt9iV3zaQv6qKxDERJN/gmXm/nnqaWilKcfkoQjHFDcIvZlJ4EXIhAF0qzeGcwmN3vCLcRpYbFUEXISCGLfl7gjNQRK4cNo8l8hYtRXAM9m5HTC/G7YiYXnAkCs+hGEcQ2buVmL4rAWz+U8GKLyoZPTiMWy6IZvWuKjbnK/CTe/HgWfFUr6qnfK+aK+aloIotZ13VanI0B6Quh4SM5Orom+1GZT7exjjjffFd8fsOFe+uraayyoBcDpdcFM41V0UQFOQaL+eUgwWU6vS80T+TABt0vK6sgYavs1F8f4hmbQuHQMDJiS20FackuS0K5NvaBlaVVnFdbCTXxop7lp7SIwHHJCChoNaKENSj/IqSl7FUj7ZS33EIqhz8LCGo8UcNRWE0+kTIPc6x4JiE2rbuDoPQMgMR8nk6ICClVgMCDksgoVWYeZS6WqeA/C4FbmxVSbiqCoArgQ2dNBbopnMB8TQkPJL2FpcahL9U/4+d9Z9xXsx4Tgprfclr3XQXfFjG7wdUPHN7Ev1SAtj7eR07360hd2gDt0/oTaa/ffQGYvQGg46787cQ4+3P4rShHv3heOvdKr78ugGugIcvjucsK8MCrZOyY7UUhnoez7+HRN9UnkoVzmr7ytRDBZRo9diaB2ffaFC95nfqN/xN7L3DCB0hnktP3CIe2u7Zn48AmFk9IBM/gQrRRdmrbOK5/FKsNSA9TTK5yyqo/qGR3tMSiDjDcy9yPE1uYj6tFOVnQIbZuCppNVcR1y8MORH5cpUVa0g3X052VFX0L/oRrx+Asa0qCdeNiIQZI8AjzXyIIu++/QWrQCL9P7PxKC5BxWXpe1bMq3UVl+m5GoWB+17JJzHKh4X3pIkcDD49UMd7e2tFNDr/LQ9A9ZOac+6JpdfIUOn93xq38HHNGpRGBYGyIK6KuomhoaOQicR4Dy06nUnSQes/q0WjaZbI7K//bySjRoYikzkvv1DQeghQFn+ZFyv72wf0ZVRpJS7Dhi+zMVQrJYn6JIYRfskAQoZnuZy2Yo+yidn5pRIwmgBI6yk9EnClBEQKhbailaEoGY0tuYv6+o4zz2QBXgQk+RKQ3PblG+Xt0c/C1sqxOwxC8SQiCHL/C+gB4RoRCe9/ACJpvtB8m9nVGsSN6FKzkmtdT0ByC8Un8hA7KgIufCDwOjDdDA8uxn0Y+NVaoYFrcwjXFU2nQpvDbakLifBNtGFaLVXvX5JPdYOB1dMyCfCTseHxQurydfx0XwmXDkxlTKR43rCv/NRYxqsVe7kgNInxsbbyL9s3pr2tZr9Q0gILfhdcPSCCG+M8xyuyv2kPi0tnMzh4GHfGT7ZrifV6A/ccyCfB14eX3JA/KCZZ8n9fof67jJS5V+GXduLf4lryVp7OSGJAUECX+/BSYRm/KVRMSopleIT9lyp2bbYTGu15tBB1oY5TX0vHN8I+j7QTpnFCdtFKUYroExHlIojg2xeRhy5024l3ODreFZcZhGK4acsLKa7WsWRyGtFhLdEbvxQ18tqOCqIL4LRt0OfCUM4edzQUvsmo5LPa9/mp4RuaaSbNL0sKI03z97wc8dYiras3sO6DGn78qVHKL0xN8eWOW6MZOMA5+YUW2gZnXFY1G02odhZS/8U+CTxMFFlQK9qKaOvyrW39oFfq9DxwsIA+gf48m+npuIK2rq6n/okkAWOTyFdsC2yjFgZjiQ6T9tgQ1DaGYorZWEzy5UQzFLvDIBR5FiKERSS5i5AbAXkl8iCEYSZuQkVoiwB76aoIQ/IRYFG7SsXA20BnGP4i3EaE9gilLkJMBZmw+CnGF+6Olm+/4xeXKUqtqYkVueMJlIdxV/pSm28dlGoj4xfmHbl5rSvSsuGRIgKTvfnw7sP0DQhnVrJYrn1lSfnf/KwsZ1r8yZwR7PqcNftm2dLq3vvzaFAYMT0Mp4YH8ni67ca1I+N31fa7ui/4qOYdro66mYsihFPb9vJrfSOLiyu4ICKU8W7IHxS39HkT1kphRZlv3IqXt+fezFsrTQsy341xkVwd07mBW6M3cP+BfILkMgnN1fc43kRrx3dXPaPGJAHK+EZ6c+qrwjnVU2yRQCtFKfLMRQinyPtrX64zg5uduNC7bVfkMj0nhnn7myo2bm9gwqUxnD/oqMiyq9Us+aaUsz5tRh8n47aF6fi2C4Es1OTyXtXr5Gtz8MKLYaHnc2XUjQTJXWOs2HJWuqqbm6fhrXerOXCwBelz8BlB3HpTNHGxjqUz7FepeSavhLNDg3ko1XnE7pqcahoEbcXWvKO0FWekSHyGAQMTbH4+6Uo2wst5274cgmVylve3/9LaWXvV00+PBNpLQApBrTGgLtYd8+rIUJQHyPBP9mnxKpoNxcBkP0T+oiein3aHQShCW0Ri3BoBcGX2EloMQgGnLUJyjkeQZa9BKAh5RfL9aOAr82aL21wRairyLWZ08hFwCz+TGDtftYvPyubSN3goF8cLoDjbyp68Jmb/r5RzBgTzwJh4dv6vmr2f1TPopkhePflvFEa9RBURKLfdQyC+sO/J34LKaGBV5ggCZLb3Ydtq7K+tVBkZf08eiQk+VNyuJ1guZ1m/dI/5EL5dsZRtjVu4L+FxBgadatdCV5VU8m2dggdS4jgn7HgfGbuGaNNIX6Wk4P4PJLjylNlXON6hB/RwsEnN07klDAoJ5LG0zi8M1lXUsL6qjquiw7kpXqQin1hFka1m/6wSIs4MoveU9iCVJ9ZaumO2rRSlyMnraw4ZFXrDUkQ+u9Ap2WYU6+6YprPHdKlB+FeOijnvlXFmvyCmXNv2TJYotHx1fxEyPeTe5csj5yYR6tc2987UbOJXxfd8WrMWlUlJsCyEMdG3cFbIcI8OIxUXa1t/U/K/92qorjHg7Q2XXhLOmCsjCQiw75LNApDlqu8nQ20TDd/uR/HdAYwNLcasT1IY4Rf3J2R4L6eFk9qbB+nsg9/TX48EbJGAZChWG1CXmA3FIt2R37syFIVxKMJPhdHoCYZidxiEIqxThNYI5dneIBSuEuHhOx7ElQgZfdVMM9F6344XMroOELe4Ik6j5VutpXxrRhgV87KmuExR/lz9Ln/Uf86omAn8J+x8a+bSps6GbXWs+a6GW0ZFcflZ4Xw0uYCmOgPXvpLGW8aDbGksY0r8yZxph3cvV6PgyeLt9PcPZ6YDXkabF2VHg/0H1DzzfyWcOTiIikv1FGh0bsuzs2a6c4oep0ibz/PpSwn3ti/08uGDBZTp9Czrm064DTx61syvozrK7QWUL9xM6Pl9iJ0w1N5uPKqd3tTMXdm5+Ircm34ZyDogmRd1Jh/IR2E0srhPGjEeBk5kjUDLNtRRtKaG5JuiSLzqxOJOtGZ9rq7TSlEK96pApxYxbSKqRUSViFAJkQ9fBIgv7fYI2q6enqv6d0jP1dV8gZ9/GoFBHQf86PQmxi3Mw0fuxcopGcjb5dR9NbuEyj1qfr4A/JO9eXxoIokhxyJ1ipzCT2ve4xeF2BbI9O/DDTF3keLn2Z5wkV+4YWM9n22oQ6trJixMzo3XRTHi3BCb8ws/rKxFvMYnxnBBpOsc1M16I8rf8qn/OhvtoSpJ3rIAH0JG9CLsov4O89LOzS9ll7KJF7JSSA/wc9W57um3RwJukcARQ1F4FIWxKAxF8+8dGoqBsg5zFN3lUewOg1AkyQtAmJs7MAiFMSiu34+HZy9AZURC/02tdlWA04j8w65AZSyePmEQtmBCtxRBLiygw4WxaE1xSFF2NcB7RU9Sqc3l9tRFhPvaHvqx+JNyft2rZPrNicQ0wabnSokfGMBFM5L4pbGcVyr+ZlRoEhPtyP9bX5vHutocborqxVURnq1sv93cwKo3qrj26ghqzjLwQ30jj6YlcFpI94NpGJuNPJxzh8SvNTdjhV1eS0vOSJKfDwt620dZYc1Bb12n5v0/qPv4L2LGDyHsgn62NvfY+jNziznQpGF+r1SS/Y994Py5vpElxRUMDgliatqJ6V07vKic2m1K+s1IJHSgc/KWPHZDXTCxdopSHJK7zLnv4kCUAb+ZET3FB0Pop39CsVvP6XTlFOYIMG8TwaFDiY67De8OyOWfX1vC7lw1s+5Iom9y2xzeP9+vYffHdShH+bIlSkeQj4xp5yTQP7rjXN88zSEpjFRctIkw0hFhF3NF5PUEyD37vNfWGlj7fg0//dIonZnERB9GnhvKsHNCiIy0LgpnaXEFW+obeTI9kZOD3bNeKZx0U7bEaygMRVEkdNKLBxA4SKCT2u7tfLOsiq9qGmzihv0nfNB61vDvkkAbQ7F1+GknOYpyi6Eowk4toDYpzkc97Q6DUMBjf2PmUfrADA4j4LZFGI4AmhEM3YJXqasiaCdEDqF4Em75Fm3JPRTEvV3RTlg4my4zI8KJduI6TYT+zDejsllzMu1WlF11rjU2sSJvHIHyCO5Kf9UuQ2HKsgJKa/SsmpLB7reqOfxDI0PujqX3eaEojDruzttCpLcfS9KG2dz/zOIdHNDU80LKWaT7uT5E0ZqN6KzOm29X8dU3DTw4OY6GXkbeLKv2GDjrUm0R/1f0CH0DBvJgUmdRyl2v3mKkXBgZyrhE9+Ryls79hqZdxSTPvhz/LOt4+xzZQ3e1XVNezYbqeiYmxjCqg9v1GTnFHFJrmJ6eyEluethy9tr/vD9fCmk5/fVMhHLpKbZJwEpFKSJMBDCaa3gFbJuyM2rbredEWGTln8tR+e/AhBKZLIDImBsIi7gIr1aooF/8Vsc739Zw7bAIrhvRFvSraIeK7+eXkTUyhPyhcjYcrEekLd97RhxDUzrWPyKMdEvDJjbUvo/a1ESoPJxrom9lcLBnI2KLzTp0WCPRVFjyC0Wwwkn/CWD40FAp19DPr/PP7azcYrKbNCzqnUa8n2P5iLYeHKNCg+L7gzR8sx9DtQjSAu/YYMIu7EfoeX2QB1vv6fuqpl7S1TfFRXFVTE8kg6170VP/xJZAe0OxqViHxhqPYoovYScHEnm2YznUVuo5pwnZgrMs4s1eAASRmVCeArZnmxngRQDNHK+IbwpBSv+3mUZC4CwvNIPMCIJ5S+mIx0nkgJwFPG6G6hagMoICQ7Dd1h1vYPP7divKrvrPU+1kQ9k8+oYM4+I425EnNToTd87LJSrMm0UTU/ng7nwEtO51y9PxDWx5RpletJ0crYIFqUNIsoFDsMloYHzej4TKfXgt/VybjUkr5eq0as/NKWHvPjXz5qTQFGFiZl6Jx3h4fm/8hTcqXmFU2Gj+GyMAC20vK0oq2VyncOtNat6970n5I5lv3orM17qba9tX5v4WOxQq5heWMSI8hHvbwZ3nqTU8kVNMoq/wxKZ6/LnvSHr6BgO77s6XchVOmi/YeXqKrRKwUlH2GIRmwepqSilcOVXCEPe/sD8a3/2St9DPP4OY+HH4BwiGKSiq0vLIiiJ6Jfrxf3eKIJ+jRaQ6fHhvPuGpvlz5Yiqbchp4488q6WHhhoGRXN03otPPo6D1WV/zP4mqQpTeAQMkNNIEX89Hrywq0rLl50Z+/rWROjP8fYC/F2edGczwYaH06+t/TEjppP351BkMvDMwC+8Owt5tPe/21JfQSf8okjgNBRK1KF4+ckKGZRJ2yQCrUKl3NaqYW1DGeRGh3O0GoDR71tnTpkcC7paAxVBsKtJJSKfCUBShp61RT2PODyVjgmPOASv1nNOW3554R8R+COOu3kzEa8tAwogTQDBDzO1XmSknWhOIdMTjJExoQUtxvTmXUBiggnZijw2Du8Qg/Kn6HXbVf8H5sRMZGDrKhum0VD1QpGbm2yUM7hvEtckhbFlUTtrZwYx46Gjo6fs1OXxcl8dt0b25LNz6UMPtykoWlu9meEgCk+IEc4dnl7sn56FUGnlrVRYGr2buzM4lysebJX27P9T1k+q1bKr/lNti72FI6Ei7BPnQwQLKdXqW90snTKASuLgY6pvIv2cdvqkRpL4oKM7+OUVhMDJxfx7xvj4sakffsay4Qgo3HpsQzSVRx0tt9kyZ1P+h4uCLZUSPCCHz3h5+L3t2yUpF2WMQthJu498/UblxBc1GPUGDB2PIqkOrEXe0XoSGn09U7I3IZEESH2Gd0sjKhzMIDmjrXP1wUh7qOiM3vZmJt5+MP8pUvPxbOVpjM+f9P3vXAd5k1f1/2U2Tpm2696KUvfcS/CMiKgqIipMlIiIfICg4QURFhoiIiCwVxYUTFVEQkL13KdA26UqaNGmzd/J/7puW2ZHdFnKfp8/3Ye4499z7vuc9957z+6ULMKFzDJj18PldNuTiG/l6lJmLQQcDd0YMwzDhKITQQzzZBgFtY7c7cOasHrv3aiiSe4vFCXcfE81E/35hGNAvDPFxbJAc56fO5zcZ+0ZkNJdUUeGk6t2Xr5Ddh+TEIfzuVuD3SK8TobrMZMbMS0UUBRChAgqWoAaCGqhbA9c6iuxIBniZ3r3XXLRzPlsS4hC+0UBv5K23wGcj+qcjvziEm4vnQm4qxNNpHyKc5f6H27YjVdi4vQIP3yFExAETSo7pMGh2AlK6Xs2byzNU4c3So+jAFeKVpC4ua2eNLBc71aV4Ia4d+oa5n9vo8kA+qKjR2PDMlEIkJ7Gx5D3njcjMS2KUmSwUcEgYs3Ejuj4uW4Rz+hOYk/wOUkPcJxEmFAjP54mQzGFjSXZgbnx0J0ogWfQ3hSgXN4VEfd9apQagZ02rDAiq94fWasNzeSIwaMCqnAyE3gB931w0UPK9AmVbKpE2PgZxQ/wHONFc9OGJnC4ayqBDeI1yTTYdKiQnYP7pK9i0leAktwR/SE9Uqn6C3aYFgyFAVOwT+HpvDnad0uB/I+LQu831oaAkZJSEjg6dn4TYHGfuYGGVCe/vK0Ol0Yb2sVzM6JWAUFbd4ZQ2B8kh/wtbld/D5DAigiHEQzFPoTOvZ7O58dcb7Dh4SIv/9qqRm3cVD69ldgg69grF90Il2kQ1PSfKpjdDs+cydWtokRC2L4ARyaVy0Ak4GTPi+nxHq91JPRHJZGJVq8Y/vPXkXRFsE9RAc9WAi3bOZ9MjDqETmqoep7catc1ng/qhI587hEabFmsKn0EYMwpj0z7yyFCt/q0cu05rMPPeOOQtLgc7lI6HPskAg3n1YtbmsGNS4R6YHXaKfoJDb9g5IvkgU8V7obSa8GnGAAgYNwNv+EHHHneZe8GA+QtL0asnH9OnOp3XFcVS7Fdpm0Qe2CuiKVBZK/FB5udg093X5X9VGnxcUo67heEYlxiYXD7lT6eg/PY4op/qgYhhTf+G2N3NU3MTOCs1Ht0Ezjj8Go7CIcJwjA+Qnt2V25X6ee+WQXVKjzYLk8HP8u4E0ZXxbsU6LhrKoENYvfgaqwJfiKcjgpWARyLnQLJlKUySfDAFUYgd8Ry02Ae16l+q9gX53dj0X38M7BiGyfddfxB6+iclTn6rRLenotFm2NUb+gq9BYv2SVCsNiNVwMZLfRMQHVp/7lyVVYktFV/imPYANS7J4X4o+mkkcQJzqOar56JcZsHefRrs3quGTGZ1dssAYtowMX5IDDq0DwWDnGI1oUJuMgxnylC1PRf648XOJCEGHfye6dStYUjL2CvfPNPyRJBZrPiiTWaz43ttQioPihLUgNsacNHOud1vXQ3IW4okTf14DRiMzzoPYEc+dwjztUfxu3QJWoUNwJC4KR5N5eXPiiCWmfFyeyHOfK1EzpBw9Bx/s8OwXHoaB7UyvJzQCZ15DXOqlZi1mFV0EFkcARam9PBItkA22v6PCus/l2P0SCFGjXBSOtR83D8eF4X7GzFZXWfTYnbhRMSxEvFmGkl7db/UOC8zUuLRM9y7JGJXR5cs2wndYTGS3rwH3NZN+4bY1TldW2+nUo01ZTLcHx2Bx+OjQTg3SVgu+TBZ3CIFKSGuAyN4Mr6/2pDDnOPPFMJutKPrxizQrzkc8teYt1q/MTExqKgg9LkNHmaSTUIeyIZP2ZqHkryyc18XvYQKcxEeS1kEISMB8j8/AwkjpTHZiL3vOTDThJBL16FKI8e7W+cinGfFxy9kgMG4iiJadkqPf94tQ0Y/PvpXH+7VqE5vseGDg1KckRkQGcLAS30TkRHR8HN6QX8G38k3QmoppdBIB4TfhXuFo8FnNG2gtBu3DHm2CQDN5h0K5B0zAmZnDUJfQRBKSUhpWmrD+gj0VrSUaygAGgJEY9c5heZkRFFk9/w+GXi3tBxndIY6UZ8DLW9wvKAGbhcNNIZDSKghiBdCeAg3A9h6AwVEc9C9V4aytgnukX+Ok6o/MTh2Mtp4kFdmsTowdnE+wrgMPKBgQX7JiHsWJCMm++YbgX/VpfhUlot7wlPwdAwBd62//F4lxpcVlzAqMgOjo7Iaqt7ov6/fKMf2HSrMmBaPnt2dDtNZrR5vi8rQJ5yPaSmN59BcNJzH8tK30IXfCxPjp3ukq5oT1GvDGz3qyI1GomnfwyrTInPDExTv1K1WSk1mvHipCC1DQ/BWZjKOa3R4XyxBWx4XrzfjXBZjuQWn/ycGrwUHbd++HrTjVltDf81n3rx5mD9/Pume0Ba5UqjKt0Dxys4dq/wN+xRfoWvE/egb/TiIA1N1eCsU/34NOByI7DMCkf1HQl25HQu/46FYmYwZd3+Btjn3g8fvRt0YGTU2fPdMIQQJLDz4wc057yTEcN0JOf4VqcFh0DC9Zzw6JzRMLUTCSHertuN35Q8UGimPzsd9wtHoFz4YDFrz8ucplGRJFYYowyE7bsGpM3qiXqqkpbIpIJq+ffiICPd/rrk7e95uskK7r4DiNDSLCesXQOdzIOqehO/aRmFihwx0EzS8lu6MGawb1EBQA3VroDEcQiINoZZ4tJqgngSR/wrg02bE3+SVoaxtOb4uehkVLy7Z9QAAIABJREFUZjHGpq2AgOU+UlCBxIhX1pega3wI4v42IiyOhQeX146KqLAa8bxoLxJZoViW1qfB5+Od0uM4bVBiflI35HCbPrDG/IUlyL1gxNJFqUhKdIZkknywiRcKkcBm4YMbgEMaVIAPK/xb9Se+r/gcw4WPYKhwhNs9V5gtmHpRjFQOG+8HKH/QpjWhcOLXYMULkLacRMXdeoXcCBJgGaPdjg2tsyjU0VNaPWamxKNHgG5h/aFVxX4N8leUI25oONLGBia82B/zaOw+A20oG3u+1eN7ZedI2OgG0VTwmZEYl7byCt2ELv8Eyn9ZAbvJAF52N8Td/zy+36/AT/uNuKf9H+ibvR+h/M6IiRsHFjsWP04TQSuz4tF1GWDzbnbWiKP5c14lvj2nBAlBGt85BndlupYrq7Gp8ZviO+xT74ADDiSyUzA6+mnkhLZrIkvQsBgfFElwSK3DW5lJaBnKRWWVFfv2ayik0qJi5w0coQTs2CEUd/QToEvnULDZTYd6hqyfMU9G5RlqD4sAmwN2GqBrH4+c+zuC2y7BoxSahjUXrBHUQFAD12og0HbuxsB28rW+sBrlkziFI5vJ8nhlKG+cY03+oIAZjbHpH3mkgh0nVPjsDznu54TAdtSIDqMi0Wn09bxO13Y8u+gAis06rEjri1hW7US/pL7JbqPoJtg0OtZkDADjGg4pjwQNQKNnphTAYLBTCKPX5lK8kCdChcWK9a0zwW0kgJBNsk+xX/0vnkuYjfa8rm5rY3elGp+UyjA0KhxjEwLzga8/J0HZgm3g985A/P88Q0V1e6KN0OB9cRmOa/SYkhSLVaUyCJlMfJSTBkYjwbj7QgXiL+Qo/0OFzOfjEN2/eYXE+WL+vuoj0IbSV3J72Y/Xdu7H0gUoMZzDyMTXkRx6NffYrCiF5PvFsFRKwY5OhqbvTCz4SY+2qVY81fsTWCzloNHYiIwegTObekJ8UI+7XktEQru6Sdf3FWvwydFyWO3A/S0jMKZdFOguPruEzP57+ee4bMylVNaJ14PiL4z24HDWS5273Xzu5WIUGk1YnZOOCNbVW0DiaInEZuzZq8a+/VqoNU4Adl4oHb178dG/bxgIKE313nZ7XH80sCr1uPDHGVh2XUKY1kINwUoMp8JJBQNa3JLRKf7QY7DPoAY80UCg7dy1PITkhpAQ0ZOvFBI2+gmA3Z5MohHaeG0or5U5X3sYv0uXoU3YQAyOm+zRdNb+KcM/x9R4oIwBi9KGBz9IhSChbsCSTRWXsLVKjAkxrXBXeN3cTCd0FVgkOYle/FhMj+/gkWyBbKRSWfHsVBFSU9h4/53rwQKWFUlwWK3DvIwktOLV7QT7U95Fxa9CbMrH22krIWQ1nL95oyyflJRjd5UGL6bGo3s1+Ik/5SV9V249C8WmI4h6rBsih7f393CN1v8v8kpsLleAz6BDa7PjkVghRsQ6c1Cbazn/Zgm0eUa0X5YKbvVteXOdS2PKHWhD2ZhzvWZsr+3cOfVO7JCtQRvBIAyOffa6adkMWpT/8hH0hafg4IThHetLsDro+Gx6EvTqrahU/AKHwwLpoWEo2NYHXcZEod0D9ZOV51YYsGS/BDqLHT2TeHi+exzYLh7+Ufm22oP4UbEJlVYFmDQWBkfch7sjHwCnCdNUTMgtoKgnPm+TWadzZ7U6cOq0nnIOj53QwVqNRRMfx6IoLIhzGBvTNFIBio0mzMkTY2ihGoOOl8N4UUbtGxqXRTmF4UNagZ3U9COVmsgzHBQjqAGXNRBoO0ccQjEAAiVGcgi/qQ4X1bsscdOo6LWhvHYau+UbcUq1DXfFTkFrAYmmdb+8tqEY8ssm9M0HorM5GLag/nyhM3olFpYdR3deDF5M6FjngJ/L8/CnqhjPxrbGIEHT5wU6d16PBe+WoU8vPqY9f32u4I8yJb6TKfF0QjTuaQROObvDjhkFY8GkMbEkY51HJ7NT80QgtBOEPoMfIPoM6Ue7qVyPxFeGILRD098D7j89zha5OgPmF5ZS/59gr3ycExiOR0/lbaidw+bAsXEFoDFp6LI2A7R6+Noa6ut2/z3QhrKJ6NtrO0eoJ9aKJoNBY2Fi+mowb0BVdthtVE5h1eHfsdk+BufQFnMeTUCnLB7MZgnk0g2QnlPj3OcTEdtBisEvtQOTWb8zUKYx4719ZZDprMgWhmB2nwQIOK7nBZrtJvxd9Ru2V/4Ki8NM0VSMiH4M3fh9PXpn+3MtdTYbJuQWukVBRPh5DxzSUs7hpcumK+K1ac2lgGhI3j2X23ghpSa7HU+fL0Asi4kVOekwFlZA9dcFygY5LM5bTm77RETc3RqhXZJBI/GwwRLUQFADXmsg0HaOOIRjq1FGnaQ0zbN4bSivnfZXRS9BYS6i8izCPLg1stkJoEwB2pQ6kCQDeoyLRqu76zeaFocdEwt2gQ4aPsu8A8w6QkFnivejzKLHx+n9EMVs+pD12/6uwsYvKvDwKCFGPnj97c4JjQ6LxBIMiAjDlGT3eR693arl5jLML5qJFiGtMDPZVXyKq6PKzBZMuyhGeggb77UIHFS6+MUfYSlVIWPNGDAETX8PeLpOZrsd43ILSAoL+oXzMbURwYc8ncO17fRiE86+XAxBey5avXrrOvK+0FVDfQTaUDYkT4B+94md+0PyAS7rDmFY/Ay04PesVXT16d34/Y9j+MU2HANji/Hs+P6gMZhOIBr5Yfz2PyE4gip0n7kKwphHEB455EpOYm0dqoxWLD4gwWWlCXE8Fl7um4DEMPcofpSWCvyk+OoKTUVmSEs8HD3WI+5Yf61XocGEufnF6BwWipfTEt0epkxixn97NdizTwOFwnltyGbT0KMbjwKjadeWC3ojHCRNuVCIKqsNX7TNArM67NemNkK96xJU2y/AWqGlZGVG86kbQ8GgbDDCbl3b5PbCBhsENeCBBgJt54hD6Lz/r7+4j6rSUI++/d0nhpKIpLepsbZwEgTMWIxNX+GRlMUyE15aU4wheQDTDoz+JAMhgoZPRBeVncQJfQXeSOqKNtybQ3FkFgOmifchhc3D4tTeHskW6EZrN8jwz041Zv4vHj26XU/JUGWxYnKeCKkhbLwfQIeqRgckHGmtdDnuCB+CR2LGu62aXZVqrC6V4Z6ocDwdoPxBu9GCgnGbwIziIX3lw27L3NwavFlQgjy9EQsyk5Ed2rw/MGQ7VBB9JkfCg5FIebTufOLmtkaNIW+gDWVjzLGWMX1i5/K1R/C7dCkyed1xX8KLdU6tNO8SXvyBhliU46W0v5EwciYYoQKq/i8zRVCVWdF91ntg8bTghGQgJn4CQrgt6uzPbLNj5ZFyHC7Vgc+mY1bvBLSKdj9V4JIhl8ovLDGLKJqK3oKBFCiYoIGbykCs4WGVFsuKpV5z0trtDpzPNVBANIeOaGEyOWFKhZEM9OsbRoHRJCW551B7M//5BSXI1RuxPDsV8Zzrx3XY7dAdL6ZuDQm3ISk0NgNhA1og4p42wXBSbxQfbHtbayDQdo44hG+6oPGmDtvtE0NJ9HBZewh/SD+oNcfCBT1RVfacVuOHr2ToJgaSu4TizpdcOyncVlWMjRV5eCAiHWOibzasf6tKsE5+AfdFpOGJ6GxXxWnUevPeLsGFPCOWvZ+KxFpyKCdfKITaasPGRiC9JWh2f1b+iMdiJlLw5u4WQkZPSOlnpSYEDI7bkFeO0jf/AK97KhJe/D93RW529SUmM6RmCzqHNX+488I1Msh3qpE9Kx6RNxyONLuFaWSBA20oG3m6NcP7xM5ZHRasK5wMi92EiRmrEcKomzt15qoClFXaMYu2GNERLCSMmg1OXBr2ripHwR4N+s9ggBW3HkbDJeIGQBDxf4iKfRSMOvok6MFfn1Fg66UqMOnAlG5x6JPiPrgSCfffr96JXxXfQmvXIITOxbDIURgYMZRKAWissrWiEpukCjwZH417o32TV2c02nH4KAkp1eDcecMVCovMDA7uGxaBXj34fr81rOHanZOWgE71vIvNpVUUOql692U4TM4bztBOSYgY1pYKK21KgDmNtUeC4wY14KoGAm3nbkQZdVXOplbPJ4aSTGqXfD1Oq7ZjSNxUtArr59E8P/9bDskPKiSqgAH/i0N6b9cMntSsx/Si/UjnhOG9lJtDeZZKTuGITo5XE7ugfWjTB9cg4UXPTCmkTjc/X5tZq9FaJCrDCa0eCzOTkRXgG6DVkiU4rTuK2ckLkBHinoNN5kboJpQWK9a2zgCP0fANsEeb6YZGhCOqYsNBCEd3hnBUJ190GewjQBo481IRDEVmdFqVDraw8T5aAzRdvw4TaEPp18m43rnP7NxO2Wc4q96BO2Mmol09h2Gfb5fjzyMqPJp4Bu2k34LG4iDuvikoEeXg8MYKdBwtRIeREdCodqFC9jXsNi0YDAGiYp9AWHj/Oh2A7fkqbDgpB7n3Iuijw1tGeOQs6G1a/KHcgl2q7bDDhlhWAh6KfgrteJ1d16oPa64vk2O7UuU3kLEKhQV79zkpLMokTtTPlGQ2Ro0QUmGl/gon/VmuxDflSoxNiMZQF/L9CTWSeudFqLadB0EqJYWdEoGIe9qC3y8TdHbw/efDbRfs6hbVQKDtXNAhvGEjbSqaBaW5BOPTV4HP9Mzpemt9MVL+NoHNoeGRNRlgusExNE20DzKrAavT+yOCybkindVhxzMFu2GHA2szB4LVDOgmqqqsmPyCiCLjXbSw9hy7b8sV+EleiYmJMRgsdI2rylfP/uuiF6C0VmBp5gaEuIlaJzVZMP2SGBkhHLzbInAE4+Wr90Kz6xISZg8Gr2vgxvWVzm/XfmxGOwUow4pkoPOqjNtVDT6bd6ANpc8E964jnzmEpYZcbCmdj8SQ1ngoue4goROXdVj0rQQ9W/EwNvEwFLsI7pwDaPEk9m/JuS4CxmZVQyHbDLXqX2qWIaGtERM/HhxO7e+pYxIdVhySwmRz4M50ASZ0jgHDw/w4ibkUP8g/R67hNDV229DOeCj6ScSxXYvO8W5ZrrZ+T1SGk1o9FrVIQVrIVfvtq/5r+iEHkmfPGfDDT0rkXTRS/5kgeT80QohuXX3vGB5UabG8WOp2eoTDaqe4DKt+PwdTfgUlJ8l7F9zVCuF3tQIzwv2QYV/rMthfUANNVQOBtnNBh/CanaC3qrBW9CwiWPF4Km25R3uEhMS8OicfrcVA1h1h6Puce2Ap6+UXsF1VgimxbTBAcNWYnTdU4q3SY+gcGo2XE5vHzdCZc3osfK8MfXvz8cKU6xFGa5Rbk3MxOFKAiUmBS1U12PR4sXA8YljxmO/BWu9UqrGmTIZ7oyLwZIL7dBUebS4ARXN+gVmkRPqqR8AU1s0B5mn/wXb+0YAm14Dc+aWI7M5D9osJ/hnkNuo10IayiajWK4fQYbXCplaBKYyCw2HHRvE0aKwVGJv2EQSs2jlUTRY7Ji4tBJtFw2czMmDIPw7prythM1pw6Owb4AiYGL0647rbPYM+D3LpOphNReTzHxFRwyCMHgV6LYduhZVGvL9fgkqjjXIKn+kS49FNIVkf4iSd0R/HloovILeUgw4GBkUMpUJJuYzAvCtnXhSjzGzBhgBx65I5nz5rwA8/Kq4glJID2BrH0FchmoUGI+bml6BLWChe8gAshyK7vyhD1R/noTssJosFEjMc1i8LEcPagJPq2eF7E3kug2IENeAXDQTazgUdwmuW8aLmALaVf4i2gjvxf7GTPFpgqdKMr2cXIVqHBol7axvgmE6OxZJT6MOPw7T4qxxzmysu45cqEcZG52BoRPO4Gfrzryp8vqkCj4wWYsTw2l/4NUidWVwOFmYFbl75hjwsLX2TIjyelDDT7bVeWSzFXpUWs1MT0FUQmPw2AvGdP/ZLMPgcpK9+1OMPJ7cnG2zgtQYkv1ei+EsFksdEIbEB7javB7sNOgi0oWwiKvXYIbTIZSh97y3KGUx6ZR717tiv2Iyjlb+gT9QYdIt8oM4pLvy6FGcKDVgwNhnZSSEwV5RA8sMSHD00EjpDIh54l4/wjOsP/BwOG1TKbVBUfA+H3QgmMwrR8WPB43e76b0l11nwxq4Syil8jISP5tTPbdjQWlgcFvxb9Sf+VP4Ik8OIMEY4hkc9gt5hA0H3Y2QNOQwm9AwhdBo+a53ZkJg+/Z04XKfO6PH9FiXyC5zUFelpHDw0UoiunUO9thV6mw3jcwuRyGFhWXaaV7JbZBoqz1C18yIcBmfYK0VbMawNQjsS2opb5bPUKzUFGwc1UPPcBuyBCNhAfl5bjw3ltXL9K1uHM+q/cXfcC8gJ6+uRyHsOVqFweQUQSsNTazPdfrkZ7VZMKNiNUDoTn2YMAL0a4nlO8SGITBosT+2DeHZgTjs9UsA1jT5bL8OOf9WYNT0e3brWDlxADNnEC4Uw2x3Y0CbzCqS1t2M31H6Paju+ka/HvcKHqD93CpH5+TwRKq02rGudgdAA5Q8S/qeSub8htGMSEucOcUfkYN1G1sDlD6VQHtCi1WuJELRrHs9vI6us3uGDDqF7q0OQIIvfeBnmkmIkznoFoe06QGEuwVdFsyBkJ+PxlMV1Og2/HazEVzsUeKi/EA8NcB7sERL7f+ftQ5k4C61a/oj2E+8DN7XNTUJZLQpUlH8JreYg9VsovzNi4saBxb4+GoTcFM7bXUqFj07vGY9eyXUD3bg6c5W1Er8ovsFBzW6qSSonE6Ojn0YWN8fVLtyqR/LJp+SJEOjDzWuFJLbp5Ck9vv9RiYJCp2NIwGeIY9i5o3eO4aTcQujtNnzRJuvKd4lbCrqhsl1vpmgrqkieocxJW8FKDKeQSQlCKZ0TzDP0Rr/Bts1fA4G2c0GH8Jo986V4JiotZZiQ/gl4TM9OKTctK4H9sBFhPUMxYoZn+QsLSo/hnKESC5O7IyskHFVWEyaL/kMsk4sV6Z45qo3xaLy5oITKb1i+JBXxcXVDZL9dWIqzOgPeb5GCVD/mXVyrg82ytfhP/Q8mxc9EJ34Pt9RDkC9nXCpCJpeDdwJ4q0lOVOVr9iHywQ6IerSrWzIHKzeuBk5NE8Ekt6LLugwwQwMDQNS4M/bv6IE2lP6djcu9e3XwqT1yENKPlyOkRUskvTqfcgC/LpqDCrMIY1LeQwwnvVZBigiN0mfF1O0guSWsKRf/qcTBtQokxe5GWtJOxAwZh/DOtaM167QnUSHdAIulHDQaC5HRIxEpvA80OutKf8fKdFhyQAImnYY3BiQhO8o3NDMi42V8J98IkekyNVZ3fl88GP0YIpm+pX65oDNgXmEpegn4mJ5ae4qEyyvtZUXiGB4/qccPPypRKHI6hlmZTsewUwfPHMPX84txyWDCypZpiGZfXTcvRQVFW3G0CFW/n4cxr5zqjs7nIPz/chB+d+vbIjWCpKDILRY8HCv0+jbX2/UItm86Ggi0nQs6hNVrr7NWYZ1oMiJZiXgybZnHO2LtpHyw1Q50fSkObbu4hi5642C/VorwteIyRgszMUqYiT3qMqySnceQ8GSMj2nlsWyBbEjd/E0uhMXqwMbPakcYrZFnk7QCWyuq8FxSLO6IdPJc+bssKXkDBcaLVP4gySN0p/yjVGFtmRz3R0fg8fjA5Q/K1h2A+u8LiJ8+CPxetX+8uTOPYN3AaMCituHEpEKEJLLQYZl34VaBkbjpjxJoQ9lENOKVQ+i8JZwDc0kREmfNRWi7jjheuRV7FZvQJeI+9It+otZpknf5lBUiVOlsVB4hn+s80FCKTNg6pxgx6UZkR74L2G0I7zIE0YOfokjsbyx2uxlVil9RqfgFDocFLHYiYuLHIZR3NTXiz8tV+PxUBQQcBt4elIxYnm8cD0JTcVizFz8rvobaVgU2jYO7Ix/E4Ih7waL7hs9vT6Uaq0pleCA6AmMCaBfq25tk7Y4d11HgMyKxmaqa3YJD5Rh2aO+eY7iyuBx7VRq8lp6Idnz/RDkY8+WUY6g9VAjYHACDjrDeGQi/tw1CMgJnawP5vJvtdjxzoRAmuwPzM5OQExoE2gmk/pvyWIG2c0GHsHo3XNTsx7byFWgvGIxBsRM92iNKkRFb55RAHQJMXpcFJsMz9YpNGrxcfAgtQ8LxVnJ3rJCewX5tOWYndERXXu3J/x4J7MdGykorpkwTISOdg3cX1J8buK9Kg49Kyt1GMPNUfPJxMKtgAhxwYGnmerfzSlYUS7FfpcXLaQkB5ccrfn0rTJfkSPvwIbDiPDts8FRnwXaea6DqhA4XF0kQNSAMWVPcA5nyfNRbu2WgDWUT0aZXDiGZg/bIIUg//gAhLbKR9Opb0NkqsV70PHiMCIxL/7jOd+Env5Vj92kNpo+MR6/WznBOu9WBzeMKwGDTMHyeBeU/LoNNr6JCR+NHTL9CYn+j7sxmCeTSDTDonIigfEEfRMc+CSbLGZWz8aQc2/JVSAxj4a2ByeCzfXejbrQbsE35M3ZW/Q4rrIhixmBk9JPoxOvu9c3MDzIlyF9jIGY3tD+JY3jkmI66MSwqdjqGLbNDMHqkEO3acl2aeyDnZ1XoQCiW1DvyYNc55Q1pHUfxGRJ0bRqd3tCUm83vpzQ6vCuWUPL2Cw/D1JSgjWg2i+dnQQNt5zzzWPysBA+699pQ1vAyDY2bhpZhfTwQAdi7XoaC7WrIshmYtcBzaHnqRFb0H6psZqzJGICZ4gPQ261Yl3kHQujNI67+9Bk93nm/DP37huH5yfW/4MpMZsy8VIRWoSGYl3k1JMmjRXChUYVFhjfE0yjuQcJB6E4ha/NcnghqKn8wE1xGYAyTw2ZHwbhNoDEZyFj3mEsG3J15Bev6TwOlPyhB/tLGRSPubt+QVftP2ubRc6ANZRPRitd2jrolfHMOzMVFSHhxLnjtO+Kn0rdRbDiLEYmvISW0Xa1T3X9OgxU/l+POTgJMuvdq/t8frxej4pIJDy5PBZerhnTLEpjKRWCGxyDhodngxNZON0TeozrNIcjLP4fNWgk6nYvIqAcRLhwK0NhYsl+C41I92sZwMbdfIhVG6ssiM0vxo2ITxUNLSg63LR6KfhpJnNrldWXsVSXl2FOlwSvpiejgpxs0V+Sor47dftUxLC5xOlo5LZ2OYds29TuG/1Vp8HFJeUAjY+xGCzR7LlPopBapmpKXHIaG39MGgoHZoIf45gbZW716035DmRx/KVVUF0wasConAwKm7w5BvJEt2LZxNRBoO+fbt2zj6c5rQ/mFeAaqLBJMTF+NUKb7H23kRfvNs4WwaOwwjeJj0mj3whBvVN3q8nPYpZHg/og0/FYlRltuJF5Paj55Y7//WYUvv67AmEei8MB99edjEnS2cbkFIJtxfetMnySs17cVT2qPYI10KfoJBuMxN2+Da5zXFlwO3g5g/qCpuBLFs38Gt008kt64p/GetODIbmsgb1EZVCf0aLMwGfws3+RFuS3ELdYg0IayiajPaztH5qE9ehjSlcvAycpG8mtvIVezG//IVqNN2EAMjptc61Q1ehsmfVAIoYCJlVPTrhxIHd4ox4VtKvSfFoeMPmGwW0yQ/f4JtLkHnST2w6eC37J7neqz2wwUEilBJAXsYDAjKYoKNn8A5u8ph6jKhDvSwjC5a6xfDsFy9afxvfxzSC2loIGGAeF34V7haPAZ7kdgzC8oQa7eiOXZaYjnNG1HhXyvHD7iDCUtKXU6hq1zQjB6VBTatK49ZPGS3ojXC0rQQ8DDzNTAUuc47A7oTxSj6o9zMJyTUvLSQ9kQ3NkS4UNbgxXtPQhRYzzj5GBk2kUx5BYrlXt6UK3F43FRuD/GMwyLxphDcEz/aSDQdi7oEBIDaVVivWgKIllJeDJtqUerKzmjx98Ly1DBA9pNicGQrt6RrO/XSLGi/CyYoMEKBx6LaoHhkc0nb+zTtTL8u1uN2TMT0LVzw7QMbxaUIE9vxAfZqUjg+Cano66F/EO5BVuV3+ORmPG4I9w9tM6/lSqsK5NjeHQEHgtgnojmv3yUf7wHEfe2RfST7oHgeLShg418ogFi8En+oM1gR9cNWaCzbpVXrk/U43EngTaUHgvq24Y+cQivuyWcOQfMti0p/l0GjYmJ6Z+CWUdO3avri5EvMWHps6lIina+o/P3qLFvlQxt7o1AtyedOV5kz1ce+BnK3d9S/xYOeBiRfUbU69CRMFKl7NsraKQsdjzoYY/h3SMxUBpteKStECNa+YerzuawYo/qb2xV/gCDXQcenY/7hKPRL3wwGDTXb2qmXCDI01Z82TYrYGjZ3m4v4hgePKzFlp+UKC1zUkC0bc3FQ6OEaJ1zvWNIomImXShEaggb77fw/CbVW5lNIgV1Y6jZVwDY7ACdBn7PdCqcNCS7eaTU1Oig2GjC7MvFFDLtc0lxmHW5CLEsJpa3TPP7wbi36xBs738NBNrO3SpfJ14ZyjzNXvxVvhLtw4dgUMx4j1Z53yflyN+twalkYPIMJ1+TN0VjM2NS4R44qjtZlNITaRz3Ty29kcGbtq/PL6aIclcsTUNsbMOnpRvL5NimVGFaShz6hPt3np9JluGE7jBmJs1DC657ID3Li6TUKd7ctAR0DGvY0fVGh9e2lX9xGKo/ziFu6gCKzDdYmocGTDILTk0Tg5fFQduFgePZbB7a8VzKQBtKzyX1aUuv7Ny1kmiPHYb0o2XgZLZA8usLKP7dS9qDuCd+OrL5vWoV+ttdCvy0rxJP3RWNYT2cUTRVpWb8+mIRYluFYOi868P9tRePovy3lXCYjeC36oXYeyeDzq7fLhoNBVDIN8OgO0P1r7B3w6pLD8Bko+GFHnHom+I/26CxqbFV8R32qndQ+eWJ7BSKpiKnjjDaa5VksTvw1Pl8RLGYWJnTfA5ua+ZAHMP9B7X48WclyiROx5DkFpJQ0pyWTseQOPoTcgthgwMbW2f65cbWnafFWqWHavsFqP6+ALvGiaRKHEJyaMrrngZagNI53JH5xrq/yCuxuVyB0bFCjIoVouaWeU5aAjrADS4fAAAgAElEQVQF8PvCmzkE2/pPA4G2c0GHEMAO2RqcU++s1xjWt+RWkx3fTy6E0eTAztbA2jmZ4LC8zy17vfgILplUiGSwsSq9f6O/gF3d9sRwjH+2EHabAxsaQBit6XNXpRqrS2UByU+YJ54OmUWKpRnrwWW4jpZG5jX5gghamzN/MCSABqdk/p8w5kqRumQE2MnuhzS7unbBer7VgGK/BvkryhE7JBzp45vX6bVvNeHb3gJtKH0rvce9+cwhdN4SzoW5WIyEmS+jPMuGrZIlyOB1xf0Js2sV8EKxAfO+KEXHzFDMHeOkVCKhfN9MKIDDDjy64WY0aZO8GJIfFsNaJQM7Lh0Jo2aBFd4wWqRedwYK2WaYjAXI07TEpuInwKDT8Vr/JLSK9i8KY7FJRIWRXjbmUnNsE9oRPcL6owOvG0LotTu0NVREbXhcvJGR5PECN3ZD4hjuO6DFlp+VkEqdjmH7dk7HsGU2F3MvF6PQaMLqnHREsJoGnoHdbAWJoFH9eR7mkipKZmY0nwolJSGljFD/Rhx5s2Y1kVHvZqUgg8vBfpUGK4rL0TWMh9lpgQ3L9WYewbb+0UCg7VzQIQTwuXg6VBYpJmasQSjDfdqDwv0a/LeiHGXhQEVXNpZM8k04xQ/KApC/gWEJmBzX1j87zg+9VigsmDpdTBHivvOWa7ciYoMJL+cXoz2Pi1f9aFBNdiNmFoyjOKjeTl/p1uxLjGYqpCObG4IFWf4Hv6kRjnx0FU74CgRYJnPjE7cUwppbC9AMKxd9WQHp71XInBKL6AHuv1ua4ZQDInKgDWVAJtXwID5zCMlQ2mNHIP1oKTiZWUh4bR7Wi56D2W7AhIzV4NaSQ2e1OfDMB4Ww2RxY+2IG2Eznoedfb5Wi/LwBwxenICKFc9MsbHoNpD8vh0F8jkIejR85E9yUhiMzaoBnFPJvsVeagt+kw8FjmjGvvwApQv++f6lQb90h/FixCUprBTUnFo2NDryu6Mbviza8jmDRrka+1CBFDowIw+Tk5o8SSdZ43wENtvxUiXKZ0zHs2D4U1r4OnAs3YF5GElrx/OuYN/w4XF+DrJn+dBkVSaM/VUr9SAthQjCoJSKGtmlyyNwkBPfZC4WIYDKwKiedOvC32h14/qITtO4jH/M9uqvPYP3G10Cg7dxt7xCS/IEdsk+ht6nxYOJcj3bAjkVlKD2hx5E0oBVB1RzuG4NACOk3KS5hZGQGEtmBC0/0SAnXNDp5Wof3FkswoF8Ypjzrmi6sDgfGns8Hl07HmlYZfrsNJSTF75e8hvahXfFcYu0n4XXNf7tChfUSOR6MicSjcb4lNa5P5wRdTTx9CxUOk7zgPm+XJ9g+gBrInVcCzQUj2i9NBTep6Z5UB1AlPhkq0IbSJ0J734lPHULyAU3dEhaJkDDjZRxOOIYz6n8wKGYC2offVau0S3+Q4EieDq+MSUSHTGd0xdFNFTi/tQp9JseixcDaDz0cNisqdnwJ1bG/ADoDMXdPQHinO6n2VocdZ/VKdAiNqjVvyuGwQV21C1+clmNfRTdEsSswq+MFpCQMB5Pln7zCmsmT74ML+rM4ot2HU9ojMDmM1E9cOg+d+T0okvtsbhvsqNRQueWEWHxkrH9l8n4bud4DcQz/26ehQkllcquzYSYwckQkHu4UOBvousTOmgSEjdwYkptDh8UG0GjgdUtx5hm2ivPb94U7ctagtv5fpADPJF1F7v2mXIGf5ZUYEROJRwL4neGO7MG6gdFAoO3cbe8QerusRrWNChcFh4Y/shx44q5o3Nvz9g7p2/pHJTZtVuDxR6Nw/72uo2W9crkYBUYTVvrxZGyfaie+kq/BPZEjcH/UI24t/wdFEhxS6wIOK649WAjp8l0IH9IKMeN7uyVzsHLjacBhc+DY+ALQ6DR0WZdB/W+w+EYDgTaUvpHa61586hASabTHj0K6Ygk4GVmgz34SW8rmITEkBw8lz69V2H+Oq7D2Tzll454c7Az9FB3QYM+H5cgZEo6eDYRFq07ugPyv9U4S+65DET34SXxXWYifKkV4Orol7omoO7rGajNi8X/ncUohQHpoIcalfY2o6CGIjBoOBsP/B6Zmuxln9cdxRLMP53QnKB5DUsIZkWA5HsFFfTKeT4pF/8hbLxLAanVgz14NNv9UAY3STs27S6dQPDRSiMwM7/ASvH4q6unApjZSOYYk19CmMlA1WYnhCOufRf01Jjrph8VSHFBpMTs1AV0FV/ev3GyhkEfDmQwqH5VJC9oNf+6Rptx3oO3crbLTfG4oXd0kuduqcGRjBYwtWNjJteCNJ5LQJq1phVK4Ohdf1Vv9WTl27dHg5RcT0LmT64Z6TakMOyvVmJUaj24C/8BIfyvfgN2qvzAxfjq61AGeUJseCDUGCe/Q2+0UNQYngMS4is3HUPnLacRO6kvlRARL89CAXmzC2ZeLIWjHRavXmm9eUVPUtp8MZRsAHwEgpy4kGWktAOIZ2erRwTwAb9bx+ysA3q3+bSOAp2up1xrABRd17HM7R24JS+bNhUksQvz02fghYiPUVjnGpq2AgHX11qJGPlmVBdM+FiMlho3F1akRGpkFP00TIzqLg2EuACcZinMh/fED2PRqsDLaY2GPvlDbrUhm87A4pVe9tzdGqx3zdxehsMqKTuGnMCrxe8oZjIx+AOGRQ0GvAyHVRf26XE1v0+GU7gjlHOYZzkJtfgQWezukcL9D/4gsdA/riwS2f8NaXRbWhxXPqvR4+7cysA/RYK5yQt517cLDQyOEyEi/OVzYh0N71RW5JdTsL6AcQ1O+MwSYFELjFDagBfg90igai0AVEhE1KbcQFocDa1tn3PQ98b64DMc1ekxPiUevcP98CwVqrsFxPNeAn+xcnQIFHULP14pq+cerxajINyG3EwOFNhvWv5iB0BDXoaq9HL5JNn/1zWLkF5jw0QdpiIluGGG0ZhI1lA6jYiIx2k+hEstK5lNgAW+mLkMc2wmM4EqpgYfOCQ3B/MzAGvqyd7dTOREp7w4HJ6Pphum4osfbqY5spwqiNXIkPBCBlDENA2ncTrrxdq5+MJQklOEcgPMAFgEgUL6Eg+gDAK/VIy95Gdz4QngQwMsAOgM4Wd2WOIQ9AYy7oS/yuzMOseHic4eQDKk7cRSSD8ktYSaKn++Ao1U/o7fwEXQXjqhVohmfiCFRWrDqhXSKl5A4ld9NKoTFYMeYjVlgEHbtBopFJYfkhyU4wmHg+17O0FFSFiR3R3ZI/ZRNlQYrXvu3BAqDFfekFKBvGFEt4TAUQhj9EAQRd4DmBl1EQ7I29LvaWoVX8ktQYQlBBOc90GlaqkkSOxXdwvqiG783ompxrhvqtyn+rrRYMSVPhAwWG/9XGo6ffq2EQuG8Ke3e1ekYpqU1XceQyGkuraJCScmfVaGjZKexGOB1T0VY/xYI7ZDod4TSc1o9FojK0JkfipfTb/4OOaHRYZFYguYOUtQU93BzkskPdq7e6Tf85m4e2vOLoWxo6uoyM36eWQR+HBPfxVgRJ2ThwylpDTW7pX8nHwfjJhVQc9ywxj1o6hri2y5hoXgpzXVnzVWFEtlmFU6E1WHGB5mfg05zHQl2m6IKGyUVAY/rJzKLnv0GNp0ZWZ8/ARrz9j5scHWtm0K9wjUyyHeq0WJmPIQ9gqe8vlwTPxhKkkD+EgDyAldXy0r+TW4A46/5b65M43dnphXI7V9NIV5LOwDdXOmgjjp+sXPOW8JXYBIXgjtjPLbw1iOSlYgnUpfWelu38S85th1VYfJ9sRjY0Rke+c+7ZSg7pce97yQjKtO1EEK72YjXcv9CAY+HtmVFOJeYijsFiZgUSy5q6y9FKhPe3FUCg9WBZzux0YqzBTrNIaoRi52IqJhHwAvrEbBcsQm5BSDUE0uz+TimPYCj2v0oMxddmURmSEt04/dBV35vhDG94yhuSDf+/J3slafPF4BFp1FI2xaLg+Ib/vlXJZSVzov0Ht15GPmAEOlN3DEkYG2GXCnlGGoPieAwOMFzGOFc8PtkQDCgBdjpQr/soS8lFfhdUYUJiTG4S3jzfiARSdMviiGzWLG0RSqSQgJ3e+nP/RPs2z0N+MHO1StA0CF0b32uq33yewVOb6lE4l1hWCvVoFdrPqaPJN8Ot2+RV1jwwgwxsjI5WDjfNYTRGm2Z7XaMPV9Axc5/0irD50pUWirwmngqUjmZmJPyjlv9LyuS4LBah9fSE9GO7zpVhVuD1FLZqtRBNOU7cNKFSHnvAW+7C7YPoAbOzimCXmRGp1XpYAubBkR7AKfv16H8YCj3ACgD8Og1gpOENjGA4QB+c3FC5ApfAuBtAG9d06bJOoRERt2JY5B8uBic9EzsHe+A3FyIR5PfRWzIze/h45d0eP87CXq34eN/I5z27sS3Cpz5qRK9Jsag5WDXHJ5ikxaziw8ihhCeb/0Ki+5/jCKCX515B7jMhj+AT5XrsWhfGchHDKGjyOCXQSH7GgY9uegFOCFZiIodg1Ae8cP9V3Q2G8XPl8xhY0n21RzIMlMxBUZzVLMfCquMEoAOOnK47aiQ0o787uDSA2dLfKWB2ZeKUGwyY22rDPCrDyjNZjt27lbjl98qUVntGBKU8YEDBOjbmw8er2kfZNpNVuiOFVHOIYVQaneGwxKKJyrfsF8WmFGup780pOuZF8UoM1vqxUuo4SgcGhWOsQlByqKGdHor/u4HO1evmoIOoYe7iJyU/fQ/MbQyKyLGReLro5UYMygKD/RxHUTFw6GbdLMTJ3VYtFSCgQPCMPkZ1xBGr51QjbFZ3SodEUzffkSf0R3HJ5L30TtsIJ6Mm+yyHslp3aQLhTBW5w+yA5g/SIyUZPEOCAZlI/bZfi7LHKzYuBqwmew4Nq4ArHAGOn/i+8ONxp1d44/uB0NJvthXVd8IXjtBElNGbgkXuzjrSQA+BUCSfS/d4BA+TEA1ia8C4AiAVwHsdrFfUs0vN4TVHaNk/qswiQqgmDUIh0L+ReeIe9E/+smbxDOa7ZiwtABcDh1rpmeATqeh6KgWu5ZI0WKQAH2evTn3sLY5rpdfwHZVCR6PysadFRVYWXIMJ1Iy8aioEPf3HQMGt+Fb9R2FKnx2XA4ei44Fg5KRwGdRpPaE3N5kLKSG5fI6ICpmDEK4/nkOCw0mzM0vRuewULxcS2QLFeVhukzlGx7XHoDapqLkYtJYaBfaGd3C+qBdaBewA5T/6MZ+q7XqUrEERzQ6vJ2ZjBah198GU47hLjX+3qFCaZnzxo3FoqFndx7lHLZpzaX2S1Mu1ioDtPsLKOfQVKhwikoDuG0TKOeQ3yMddK7rqTA3zrWGszIthI1FLeoGUSLUE1PyCsGm0bGqVTpCAvjd0ZTX53aSzQ92rl71Ne0n0/WV95uhrEsEWZ4B294sRUzLEBR0Y+G/MxrMfTQBHbN8d4rk+vSbTs3ffq/EV98o8MSYKNw3zH3n+OOSchA45jlpCegU5ltdblP+jF+V3+Ch6KdwZ8Qwl5UmNprw8uVitA4NwZsBzh9U/nACyh9OImZ8L4QPuTYCzWXxgxUbQQOaPANy3yxFZHcesl8MEgz7egn8YCjJ1yvhoVl+g6wlAL4AQABiXCk7CfAkwdq4ofL/SPpSdY4iOe5/sboOOeU57ErH3jqExDGp1lutw+lOHoNk+WI4WqVg20MlCGVEYFz6x7WG1i/YVIpzYgPeHpuMFkkh0Cut+GGKCJGpbNz/fsM8vEa7Fc8V/gcrHFiV3g9hDDZOSy/hHa0YqRXlmHLiEBJGvQhObMMpGF+dqcBvF6sQx2NRTqGAw4DDYYdWcwhK2bewWKTUfPmC3hDGPAI227dRPIdVWiwrluJuYTjGJdZ/k2N32HHRcI5yDk/qDsNg11OyhdC41I0hobHICW1H3ZQ21bJJWoGtFVWYmhyHfhFhtYpJ9trlfBMVTnrgoAYGo/PGLTaGiTsGCHBH/zBER3nuVAVKN4S+gso33JsPm9K5VjQOk8o3FPRvAW77BLd5gX+vqMKXUtfSTz4qlmKfSotJibG4U3jrodcGah2b6zh+sHNBh9Afm+HgOhku/q2mYLY/E6lQIjfj0+npCOf59lbLH7L7s89Vn5ZT8NRzZiegUwf3Hbo/KqrwhbQCj8QKMcLHfE7rpSuo3I7pia+jZWhbl9VQI5M/wW7qEkayZAd0R4uQ9Na94LZ07eTd5YkFK/pNA4SMnpDSJz8iROKIW4eXzG8Kc7NjPxhKXziExPMnDiQBlFnSwJRIrCCJbTwFgIDQ1FZuQjAlH9qeFLvdgd82yBEZw8KA4bUf1FG5hG+9ClNhAU7OSUMZU4wHE19Famj7m4b8ZX8lNv+rwOgBQozq79zf3z9XCKPKhjEbMsHk1J+f/Y+qBGvlF9A/LB7PxzlDOsn408X7UG41Ytq2HxBnNCB22LMIa9On3imTCI7lh6Q4XKpDTlQIXu2fCDbDOb7DYYW66l8o5VtgsxHgWAYEEXdCGDMKTKZv6KG2VlRik1SBJ+OjcW+0631aHBac053EUe0+kOgVi4OcFwB8hoBCwCbOYUZItlu57p7sDXfb1IC/jY4VYpQLNtpotOPQES127VYjN8+Jn0SYFDq0C8XAO8LQrQufukVsysVht8Nw7pp8Q5MTSIcRyUVYn0wKqZST5tp7fkFhKc7pDFiQmYzsG25Yb9TBBZ0B8wpLkRnCwTst3EvBacr6DMrmmgb8YOfqHdiXT6EnkN3pAJxxHdeXb2/I5WhIewG9IbRZHRT3oNVgx/CP0vDsajEi+Uysmkamc3uXV94oRkGhCSuXp3l0ApirM2B+YSl6CniYkerbm5UFRbMgMZfg/YzPwGfUfrJZ2+otEUtwVKPD6+mJaBvA/EEii+j572BV6pG54XHQQ5r+iertvfuvzv7yCimU+7XIeTUR4e2bX55QU19HPxhKEjL6cTXNxLXTdydklNwCElRScq1V7IIOyXj3A2j4Ss3Zmcd2rqLMjJVzi2E2OXDPE1Hof1/tTqHu5HFIlr+P8kHRONa3Aq3DBuCuuCk3TUUkNWHOumLkJIdg/tNOkNWdiyUoOabD0PlJiM2pm3qJOH5ziw9BZNbiraRuaMm96kT9rCzEN8p8DNabMfCPL8mMEdHjXkQNegw0et23ZmabHW/tKcVlpQl9kvmY2iPuOpJ7u92IKuU2VCl+hd2uB43GQYRwGCKi7geD4d3zub5Mju1KFV5MjUd3D+mSjHYDTumOUvmGufrTsFcznQiZ0RQQTfewfhRqaX03vC7sN59UOaPVY6GoDAMiwjAl2b20EInUTFFS7flPjcoqJwgNn09Hvz5hGHSHAGmpTRuhlMhrN1qoQ1oq3/B0GbVHSWGnRjrzDftmgSmsfU/pbTY8k1sIHoMBkhZDb4BjkDwrJDqpyGTGwsxkZDXgQPpkgYOdNBkN+MHO1Ts3XzmEnkJ21ziEswDsu0ZSQhRz2Y1V8dhQujHGlao1+RIp3XhIHhOJ1zeWoGt2KGY/7HtkTE/ka6w25BR63DMFoDNoWP9phkfGS2+zY3xuAWJZTKzI8Z2DbbGbMaNgLASMCLyTQVKFXCvk9PmZC4Uw2x1Y1zoDgcwfJKS6hZM2U4ntqUtqh4B3bRbBWoHWwKlpIphkVoqQntnEARUCrRtfjOcHQ0lAZUoBjLlGPnIkT6AiXQWVOQCA3DQOcHGOK6sdwobjIp0demXnCnMN2PheGSwmB+59Khp9h918m+W8JXwNutJ87JjNBJ3OwsSM1WDRr/9QJ+/F5z4UQaO3Ye1MJ9XS6R+VOPmdEt2fikbrWvqu0cklowqvlxxBGpuP91J6XmcnlFYTpor2gs9gYokjAhW/fAS7UQtuWlvEP/g/MELrDpurMlrx+r8lkOutGNEqEo+0vZmix2bVoFLxC1SVf8HhsIDO4CMy6kGERw7xmMPwPVEZTmr1WJSVgjSu9w6N1qbGce0hyjkkFEk1JZ6VRIHRkJzDGJZvw15d3K9UNVk1cXrL0BC85WEKhc3mwKkzeurW8NgJHWzVTJ/NCYiG6MJaqYdmnzPf0CxWOtVIo1GhpCSklISWXnuQe1ClxfJiqVvO9HaFCuslctwREYbn3HTA3VnXYN2mpwE/2Ll6J+krh9BTyO4ah5Cckm71Yjm8MpTujrv7AwnEh3S4Y3o88lg2rN8mx6j+kRg94PbmiJPJLJj2ohjZLUKw4E3PufoI3LLUbKEcMHKS5otSZCzEeyVz0Ta0E55PnONylzWAAY3BB6Q/XYqyd7aD3y8T8VPvcFnmYMXG1YBFY8OJZwoRkshCh2Wufus3rszNbXQ/GEpiw0gOIVkwTbU+yEElQQp1hXaixpaR67RPXNAnuUIjnIcnAIx0ob7XDiHpIP+cHl8sksBiduD+cdHofffNTqHu1AlIPliEU4/zUJqhw9C4aWgZdnPY5se/llO58zNHxaNHKz5KT+mw410JMvuFod/Uum+OVpWfwx6NBBNjWmFw+M12YnHZSRzTV2BmfAd0tgKSH5fBXC4CUxCF+BEzEZJIKCJrLyVqM97YVQK9xY7JXWMxML12B9JiqUClfAvUql3EzwaTcBjGjEZY+AC3OQxnXhKjzGTB+taZCK0OVXVxPRusRpCxa2gsik1Xg6nSOFmUY0huDyOYroUqNjiYixXIYcCT5/PBZzDwqQ/QwFUqK/bu1+Df3RqUlDrDZkkIaY9uPOrWsDkA0RCZTWKlM99wXz5slQanb8hhgt8zjeI35LaNxydlcuyp0rhFOG+w2fFcXiFsDuCTnPQryK4uLlewWjPWgB/sXEAcQk8hu5udQ2jW2fDdZBEYLBoeXp2OdX9XYOdJNWaNTkC3lu7nzDXjvXqT6OSkb/EyCe4cKMCkCZ7nuy0vkuKgWuvTEM0D6l34UrYaQyKG48Hox1xWe00CuKv5Ei537ELFyl9OQ7H5GKKe7I7Ie/0Lne6COMEqLmqg6qQOF9+TIKp/GLKedy+kysUhbvtqfjCUJMqFOGhnq4npCY/gsmqQmWuJ6UnkCkEGnXDDIpBTpgUASJw7iXC5thCQGXLguak68iUawIxq4vq+AI66uKA+Ofi8fEaPL96XwGpx4IEJMeh51/U0EdQt4YLXUETPx7FHgfTQLhieSCgZry97z2qw8pdy/F9nAZ4ZFguj2kYR1AsSWXiwjoMQjc2MKaK9YIKGTzL6I4R+c879Ua0MS6Sn0Tk0Ci8ndobdYoL8z8+gObcXNAYLMXePh6DjoDpVdkamx3t7CYMIMLdfItrF1h0SajaVQiH/BjoNAX0lHIZJ1RyG3V2KcCHOEeHlC6HT8FlrsmX8V8rNZRQYDcmDl1kIswkBv6ShBbc1uvP7oBO/p1upEN5IOuOiGBKzBRtaZ4LrIye4Bohm1x419h+4CkQTE82kEEopIJropp82QeUbnpE4+Q2PiOGoyTcUhmJfqygcaxeDhXe0d+vwYG2ZDP8o1W7nqXqzxsG2ja8BP9i5eiflqxtCTyG7axxCYkDJMRfpZ3M1HLfziMW14hND6cpQl3aqcWCNDC3uFKDPpFjMXVeMQqkJK19IQ7Sg6b+sXJmjp3UIB9Hm7xR46vFoDBvqenL9jePV8O88GR+Fe6PdRyqtTf4f5F9gp+oPjIubSuVjuFoWi8twTKPHmxlJaM2rOy/G1f7cqSdd/i+0B0VIfH0oQtv6Np/SHTmCdd3TQOkWJUq/VyJtbDTivHgO3Bv19qrtJ0NJ8uBJGGdvAASBZG015UR1QBulYxEAcq009gaNnwRA4CyH1rISBJv/awDdCdAiAIKsQcJLCWjMQTdWzmd27uIpHb5cLIHNCox4Jgbd/+96p1B3+gRKly/Czpl0WENomJDxCbiM62/bVDornl0uQnQ4Ex89n0Y5UD9OE1FUTI+uzwA79Obojq2VYmxSXMKQ8GSMj2lV69StDjsVNqqymbEyvR+imCEU4Izq2F+o2PElYLdB0HkwYu4aCxqjdhC3f0VqfHpMRtFRvDUwGUmC+nkNjYZLUMg2w6AnZwIAh5uN6Jgx4PLIlqi7KC1WTMkTIYvLwcKswIB+EF2Q20LCcUhuD6uszlBFOhhoHdoB3fi9KcTSELr/7NUiURlOaPV4LysF6T4Ik71RwwSI5vBRLYVSmnvhKhBN+3Zcyjns3rXpA9GQOZF8Q+1hsTPf8GwZaNWYUIRXmNwa8vtmgBnRcA5rDdJ5ApuFZdlNI5fUjfdWsKqHGvCTnatTGl85hJ4itJGvXMLFtB2AGsDAaoQ28m93WLh9ZigbWre/5pegPNeIIW8kITonBGMX51/hY2oKCd8Nye+L3412I7ZUfAG1VYUxsROuhKysXF2Ovfs0eOWlRHTwAkjjlEaPd8Vl6BfOx9QU3+RKfFj6NvIMZ/FaymIkclwz3OT0d2JuISwOBxUOxAowf5J4+hZYpGpkrHsMDJ73uSm+WPtgHw1rIG9RGVQn9GizIBn87Ot5uhpuHazhigYCbShdkSkAdXxq5/JO6LBpqdMpHPlsLLoNuurwOW8JX8exlpdR1A0YGDMeHcKH3DTFmgPRZZNTkRjFxu7lUogPajHk9UTEt73+Q5e8T2cWHYDUosfilF5I4dTNM/h1xSX8WiXGw8JMjBRevXkzFOVC+tNy2PQqhCRlI37EDDDDag+Z/OasAj/nVSI2lEnRUYSH1I8ATuas152CQvYNzCbi9wOhvI4QxjxaJ4dhDQpkLwEf01N9Y6vc2UeExqLAmIcjmv04oT0Ird0Z7cy6wnHYF21DO/uc43CjRI5tCpVboY/uzOvautJyJxDNbgJEU016TwHR9A7DwDsESE9rHrZxS14JJLsvYVCuAhxJdVQ6nYbQDomUc8jrlgo6p+49+kZBCS7qjXg1PRHtAwxu5+naBdt5p4FA27nGdghr09Zz1QTBnaohuWur4zM4bneWS1thwY9TxeBFMzFyRRrEMjOFtNYhk4tXxiS501WzrSszS7FGuhRlZgcjDaUAACAASURBVCeInoARjonxM9CC2wpzXiuGSGzCqhXpEEZ6Tr9BCFkJEXwyh40l2a4C8NWtUgqpq3ASCJLbB1kbwaC5JluBwYhX8kvQjsfFaxmBXV+73oyC8V+BGRuG9BUPNdv9crsJTvbaiWdFsOls6LohE3R2/fD7t5t+fDXfQBtKX8ntZT8+dQiJLLnHdPh6mQR2OzDquVh0GXDVKdSdPonz376HA+OABE5LjE4h6ZTXF0I9QSgonh4SjXu6R+Dcb5U49pUCXR6LQrsb6C3O6BVYWHYCOSERmJ/crV5VlJl1lPMYy+RieVqf69AYrRollVdoKrsMBi8C8SOmg5ty820jcUA/OlyOAyVaZAs5eH1A0hU6ivoGpzgM1QegkH8Lq4UELQEhoa0pVFIevytotKvP9J5KNVaVyjA8OgKPxZNI4MYrNocNefqzVEgp4Tgk9o6SncZFB343dOP3QevQ9i7bv/pm8qeiCp9LKjAmLgoPxPgmiqchzRHQulOn9fh3jxrHjl8FoslI52DggDD07RMGfhMG8HrpchGKjGZ80CIVQokGahJSuq8ANlV1vmEIE7yuqeD3TgevYzJorOtv2Ak/M+Fp7iHgYaaPEdgb0n3w98bRQKDtnK8cQl9AdtdonDC7kv5IjsZ6F5fB54aytnHP/FKJE5sVaPdAJLqMicK/J9X49HcZhveOwGN3Nq4xcFFPXlUjXEkby1dSZLqtuR0gZEVjn3onFaoyUvgkNszKBptFw9rVniGMXivclAsiVFqt2NAmEyF07z6qVdYqzBVNRjI7Ha+kvueyDn6rqMRXUgUejhVipAt8Sy537EJFQ64UpfP/BK9nOhJm1J0v40JXwSoB1IBJZsGpaWLwMjlo+45rN9EBFO+WGSrQhrKJKM4vdu78ES2+Xi6Fww6Mfj4Onfo5KXmo0MQFr2H7PfnQRwJPp32IcNb1ObHnxQa8takUnbNC8fKjiZCe02P7gjKk9eThjhnXh7kvk5zCYZ0cU+PaoV9Yw7dp80qO4oKxCq8ldkG70OtvAR1WC+R/b4T65A6AzkD04KcQ3mXITXl/hI7i7f/KcFFhRK8kPqb1vJ6Oon7HkHAY7kSV4o8r5PZMViwihEMhCB8IOiMUW2RKfC9TYmJiDAYLrw+7bcw9Q1C1z+lP3cRxyKPz0Znfk3IOSe4h/Rrn1h15T2h0WCSW4M5IASYleY4X4M6Y19ZVq234bx8BolFfB0TTnQDRDBCgbRsu6AGO6KlvLnKzBS9cFCORQ0I+rwKNOWx2irqChJTqjhVdyTekc1kUQim/VwZ1g0hjMmC22/F8ngg6mx0rc9IhZLl2sO2pjoPtGl8DgbZzvnIIfQHZXaN94lnJAYwHsMHFJfGLobx2bGIcf51dDFWJGcOXpCIimY0Nf8nx11HV/7N3HdBRFl372d53Uze9E0LovTeliQXbp9hFRCl2EBWx/oodbIg0FQsqoqICgjTpIL2HENKTzWZ77+U/824iBBN2N9ndBM2ck+P3sfNOuTPve+fOvfd58MTNCRjUOXBeuwDn1GaqkbCUjdo1WK/5EV54MTbqRkyInUgpk936rfhB+QXsGjH0H89Ebi4Hr73U8oPwu+U1OGw04/+yU9CR37JciDOW41goexMDRMNxfyOcWk0J+u1yGY4aLXg1KwV5Ec4f1P1+GqqvDiBmYm/E3NyjzeyF9oFcXgKa/Sac/0AO6VgJMieTu632Eg4JRFpRhmMOzWgzbHru1F8mfP+hnKJUm/hYAroP9ukz88nj2HHoTZwfBgyMvg39Y29tMGyX24spC0qo55bPzAacXnw3uQRCEkXz8QXaII3LhkfL9kBAZ2JR1jCwAjBEdhpkWKQ4gyHCRDyW2Diolv7YNig3fQ4S9yrqNhzx46aAzmqYL2iwu/HCn5VQmF24MS8Kd3YN7vKWeAwtpqPQaTbAaiGYQwCNzqOMwlWOEdhtcOH5zGR0b6NhfCTF46T5EBVWWmA5Dncdx6GEEY0+woHoKxoCgloaTMqLzO7AzKIKtAb69qXnsuISOwgQzZ59JlitHurnOAJEM0yMEcNFiG8DQDT11BHXx0XhniY8yRS/4dEqmPaVwnK0Cl6nL3WZLmBD0C8DwkFZ+DWOhd+0BvxPGkP9tZd/twQiredCZRC2FLL74lWdVgfbTU7BJwJc7rApyvr+1aV2rJ9TiZhMDq5/y2fwvPRlFc5V2fDB9HQkxlw+aT3AebS5ala3BV8qPsEJ82FwaFzclzCdumG8uJTaivDBtt9Q+92NiOl3Fq9NH4BYVssOwz8qNCB/k5PiMTa2ZTevm7VrsUa9ErfE3oPR0dcHJGM3lT9YAo8X+Cw/G8wI3zbWLtoJ485iJD03BoKezafwCGiy7ZVCJoGKb1SQr9Mha7oU8SOa5kwLWYf/0YYirSjbiJjDqudO7DNi1Ue1hEYNEx9PRLeBQspLWPDhc9hyfTkk7mjc13HRPwyHd3+oweEiM+belYxuWXz8Oqsc+monbl+aBa7YF/b2o6aE+rshKgN3x+UGJE67x41pZTvh8nrxaeYwCBmNg7bZqotQs+Z9uI0acBKzkHjLTLAkDfWPzOigOArNTg8e7h2Pq7Oap1PstgroNRtgNOymeAyXux9EGbLwVqodGZLOQRlVAQkhxJXMbhOOmQ5QYaXnrKepC17KgGJK0Uc0mPIcpnD8p2m4PD7qCeKl+iSEfMEtma7d7sFfB01UvuGZgrowTBrQtQuP8hr27SMAu5VC+Ou5Kl/KSqGMaH/FY3VSHkMCKmc+VgW4fIYuTcjBgdxolHVLwOxxPcAMES2Xv/G0/946Eoi0nguVQdhcyG6SC0iuIgkpPQGVIYS+hAvqdwANryIvvx5hVZSk60Nfq3BmvQ59741F5+uiQZGwv1dChSV8NiurQY5D62yd0Pda46jCkpr5FMS1lJWEqUmzkMRu3Dj5/hcZfvnJAv416xA76DQeTHwcnfjdmj2oQwYz3quowVXRYkxtYUgKCXM9YNyNx5Kfp1DYAinFJHm7pArdBDzMjXD+IBlfxew1cFTqkLn4DjCj/CuQQObUXif8Eih4tQrGAhu6vZcOXuq/85Io/FL030OkFaX/EUWkRtj13LHdRqz+pBbEgXfnE4no0l8Iy6njWK15E/pk4PaU15HI69BgspsO6fH5H0rcMDAKd4+Kw+5PalGyy4hRzyYhpZcAbq8Hj5XtgdZtp/IBE1j+URXrO1iuKMAWQzUmxeXhmqimI09cZh3kaz6ErbIAdJ4IiTc9Dn5mQ/1zWmnBG7tklAn03JBkdE8IfByXrq7bZYBetwVz5JkwQIhX6K+Ax01DVMx4CMWDm01yH5FdVNcJSaU4YtpPhZWSS936QnQ8MQz7CAdDym46tPexwjIonS581Tkb7BamdYR63gSIZkcdEI2mDohGIKBj6GARBg0QomMuN2IhpTaPBw8VlIJNo2FJfhaY5MYliOK2OGA+VOHzHJ6QAW6fcegRcRA9KIsKK+V2koLWxtYgiCm2V21CApHWc8HtzMsvW3Mgu+8AQMh/yZUhOfVW1MFzzyM8n0HskrAqSmL8/TSjDDa9G7cuygQ/molqlQOzllQgP52Ll+/993lwjpr+wle1n8LutaG7oA/ulz4CHqNpBfrxIjkVsjFs2imcTvie4ke6KfYujI66vlm3pmqni4qXz+Ry8FaHloWgzqt4BtWOCryVuRhiZmB0GL8ptfi2Vo2J0hjcHOHQDI/DhZJJ34Ah4SHr04lBvAbtVVtTAl63F4cnlxD8d/T5LBu0CHuVW3Puke470ooy0vNror+w6rn6Po/uNODHTxWUUXjXU4nI7yPAjtWP4URvFbrYemFU12cbDE+udeLJReXIkLLx9kPpKNiow8EVKvS4LQY9bo3BAZMCC+Qn0IMfiznJvYISZbHNgLlVB5DBFuKttAGX1SVetwuqP1dCf3ADiJszduRdiBrQUP/sLDdg0SEFeEw6/u+qFKSJm49Q6fR4cd+ZYkQzXJjD/RJ2m484nsGQQBw9GpLoMWAGqG+CEkoYKqudCorC4pBxL6oc5X/3kM7JRj/hEPQWDUQ0M7ZBz/NKq3HSbMV7HdKRym2bl18UEM1JC7bvMODQRUA0YhEDfXoLKK9hty68sHoO6y+3B0uEeLyFqOlukx2nd55D1e5idCjTgU5CmMiei+ZBOCCTCivl5hLjMJRH+zBsuPYmA5JApPXcv2XXhFVRyo5bsOVNGZK68zDmeR/aZD0p7/j+Etw/pmXhkQHtjAhVIvmCv6lXYZPuV8qouy7mf7gm+ma/yefPzq1AeYUDiz/ORCFjH75VLIPT60Bv4UDcI50GLj04+H0SqjT1bBnMHje+zM9pdsimy+vCU8X3g88Q4u2sJQFL8c0yGY6bLCHJYQy407qKtvNKVL2wDvzeaUh+ZnSwj7fXbyUJWCrsOPVMJURdeMh/MbKotK005VbrNtKKstUm2rDjsOq5i7s6vN2An5coQJwOd89MQhznOL5lfwSOnY4pnb8E45LwzScWlaNW68TiJzLhrHFhw4tVSO3Dx9WzkzGv+ghOWjV4OqkH+gqC05UUQnTlX6hwmPBGan9kc/2HYRtP7YZiw1J4XQ4I8wdCeu000NkX9M/qM2r8VKBFHJ+J169KRZQfOoqm1r7G7sBTdXl0L2Ymw2YthE7zex3JvRegMSESD6a8hhxuVhvZQv6HUeOoxmHjXspzqHASak1QZ4Ecbif0FQ1CL+FAiBhi1JOlz05PQh+xwH/DrVzDYHRj334jDh0248xZK9x17KIcDg09uvMpbsNePfkhRypdWq3ANq0Bj6YmYGhUy7EmCHouAaix6q14ReMB41AlrKflBAWKkjAzhk8ZhsRzyOkQ16wL+VZeqvbu6yQQaT3XbhAGsPXqQ2CGzJAipw6W++stKqz/S4cZE6QY3s2/kgqgm1avYnIb8YX8YxRYT4BH5+OBhMfQVeD/Rpfcwt0/pQTkw7pskQ9htNJehqU186F2KZHMTsPDibMuG37S2OTfKKvGCZMVb+akIauZ5LfV9grMq3wGnXjd8HgKobz0X0jOCskfJJ9XKn8wyBAP/z1cvoZ+81koP9uH6Ft7IvY2//JvaX/tz4dGAso/DShdokDShCik3RUccEVoRvDfaSXSirKNSDZiBiGZ76FtBvy8VAHC/X7PrCQcNT0GRZIF4ww3I693w8iFzzcqsemwHjNukGJwJyG+e6AEXBEDQz6UYmblPsQyOfg4Y2izUis26CrwpeocxohT8aC0cTL7S9fHXltGUVO4dAqw49OQeMsssGN84Y/EyFx4sBZ7Kk3IiebgpeEp4DCDR7I+bjTjzfIajIwSYVrqBfRVp0MBvfYPGHR/wuOxUH1StBXR4yEQ9W1AW9FG9lWjw6BQZu1lVL7hYdNeaF1qqh4ddCodhOYZi106Ce5NjMN1cYFF3rSV+ZrMbhw7ZsHBwyYcO2GB3e4zpsgFSOd8HuU57NtbgLjYxvNWA50HkeGMwjLoXG4s7ZQFEbMhlUSg7Vxab41Cg1UKDa6LjcK9SXFw6awwHyiDcV8pbGdrUZcaCma8EMKBPs8hJyu23ThsrsBb6blI67l2g9DPQjttHqye6gsFuW1JFlhcn+J4bWU1TpdZ8e5DaUiTNj/spJX22T+6bYkBJ6txYOYzFeiUx8UrL1wIn73UwJyU8Ci6CXoHPOXv5Cr8qtLh4WQpro5pntFNcgdJDuGoqOtwa9y9AfVdZLHhxZIq9BDyMSczOaBnQllJsWwPDFvPIXHW1RD2uwBRHco+2tsKvQRKlyug3GJAh5mJiOnfNOl26Hv+77UYaUXZRiQcUYOQzPnAFj1+Wa4Ek0XDkIkFKOz0DdLO83HT2OUNcpYOnTPjvdU1GNJFiMduSsTa5yqgLXMAr3nwO63iHwTzwcjT6HZgeukusOkMLM4cRv03kOK2miD/9SNYS0+AzuEjYcKjEHTw6R+n24vXd1WjUG1Dv2QBnhqYGLSxulmjx2cyJW6TxuDWRtIKPG4rDPod0Gs2NqStiB4HUdRVYFwmBSOQ+UWyDokcInmGxGt4xPQXjG49HO58mJx3I4VTirsShZRuZ9OvvLOQw+HBqTNWynNI/ognsb5kZ3Eow7BfHwFSU9lBG1SlVhvmFFchj8/Fq9mhSy3S1aXU8Bh0LMrLbJDD6dJYYPqrjMo5tJ3z8WiSwkoUUV5DYhyy06ODnksk91t7Xz4JRFrPtRuEfnZeyW4jdi+sRdZQIYY9euGGccqCUjhcXqyYnQ3GFR6vTYymlYqlzQ7xPHDIhAUfyjH6ajGmPNCQk6i5IahkWfbrTfigUo6xMRJMTg4u1Kh+WdeoVmKzbi3uk87AQDHBLPJfflVq8V2tOqKkuxePqvL5tbCXqJCx8Daw4toNC/8r1jZqnHquEpYyO3p+kgl2bDtHVDhXJdKKMpxzCaLtiBuE1Hd4kx6/fU6MQkBy5xfgZhTjTvMMxPS/8D212j0U/QSfS8eSJ7Pw1zIlirYZcPoeNao6mfBJ5lBEMZtvLHwkP4m9plo8ktAFw0QN+Q0vJz+vxwPNzlXQ7vuVCnyMGfY/RA+5mfLSGe1uvLi9CnKTE9fnRuGe7sF59VfKVVir0vkNBbwcbUVUzDiwLgPcEsTeiFhVt9dNIZT+qT2OndpBYNKLIGZ/SaGQdxf0pcJK8/k9wKRded9AEu1UdN6Gg4fNOHjIjFqF82+5JkhZlOeQGIeBgtLU81TelRCLCfEEezF05YMKOfYbTJieIsWI6MYvzJ0qk8843FsKe7Hq785ZyRLKMBQR4zD1yvLuhk6Cbb+lSOu5doPQz54guYMkh3DUc0lI6emLk1fonHj8k3J0SObg9QdaBnjSmlvS7XXhZ9VK/Knf0CIQmJ9/0eCHnzSYdF8crhnT+MclWJAaIhe53YEniyqQy+PitZzm3a4R/kHCQzgn7S2kcS7wYl1O7vWhqq9lpyKXH1zuY0vX0+vyoHjS1yDEtFlL72y/xWupQCP0vMfhwaFJJWCJGej5aWb7uoVZ7pFWlGGeTqDNt8ggLLDKEMcUIp4VfLTF3g06rPtSBTrLBcm9X2JIhR6DJi9s4CV89esqFFTY8MbkVLhO27F/uRLnR+gQczMbTyYGhu7clCBOWtSYJzuKfG4UXk7tG6i8/q5nKjyA2nWL4HXYIMjtA+n1j4DB5aOG0FFsr4LJ4cGDveIxJjtwOor3K2rwl8GMV7NTkBcgV+6ltBXESBUI+0ASMx48ftunrbhY8HaPB/efKUEU043+UX9Q1FQOrw8LUEgXYYjkagwTj0EMKzhDO+jFDdMDJNyzqtpBeQ2JgVhSegHnMFBQmrnFlSi22sMCvHPaZMFrZTJ04HHweo7/c6hTYaRoLIjn0F7qC/8lhRiEVM4hMQ6TA9//YRJ7e7MXSSDSeq7dILzM9rPqXPhxehk4Ygb+tygTdIZPXAfOmrDgJzlG9xZjyviGHrErZTcbXDosl3+I87YCCOiiFtFEfPSJHHv3m/DinGR06dw0EmkwNBZEjiR5+sGCUhBOwBWds4MO6SFtzCmdToW3vJ/zJVg0//kAhF/pwYISEKfv8vxsMCKcP2gv16Dy2V/B65aMlLnjrpTt9J8fp7HQioKXqxFFbo9nB+7B+M8LrpkCiLSibOYwQ/1Ysw1CuVOPOZU/gE1j4Omka5HLbZpOoKlB716vw+9fq0BjOZA16ktMzL8RogGD/67+yx4Nvt+uwcSRMRieLKB4e5U5VlwzNwVd+C0j0Sa64InyPVC6bPggfTAS2cFTRjhUVaj5aT6cmhqwYpKQdOsssONSUaCyYt6uaopz9pnBSeiZGBhAyvPnK1Fis2NxXiaiWMF5w+ppK/TazXC7tL6DOScdUTHXXjG0FWTM08+WQu9y46suOXB77ThpPoKDxj04bTkKDzzURXM3QR+MkIxFHq+rX3C6UL8woWxPpXbi8BFfWGkgoDRapwvTC8sgZTHxYceMkF8SEoP16fMVqLY7g8ZZcNTo/zYOHRW+/UftwYwYn+dwYCZYicFfHIVS3u1ttYeMNncPNFtRXq7Dgt91OPiVCvnjJeh3/4WQxVXb1VizR4uHro3HqF5X3o0KyQVYVvM+dG4NCKz0Q4lPtYhI/pnnK1BR6cCShZmQSC6vGBsS3XNwb8J0Com0qfJqSRUKLLZm3bAZ3QY8W/owxZ34Yvp7Ae2tQosVL5dUo5eQj2dbIX/QsL0IisW7EXVDV8Td3S+gMbdXan0JyH/XoeIrFVInxiD55pYdflt/Nm1/BO0GYXBrRLgAVyh3YbPhFFg0BqZLR2GwKDCC+It72vGbGn98qwWNbcf45HUY8sbsv72EpTU2zPm8iqJiuvcWMXZMrYWX5cWkzzqAHgKOtJ80JVitKcGN0Zm4M7YhF2Kg0nDbLFCs+wTmosOgsThIuH46hJ0GYneFkQKa4TFpeGVkKjIk/sNbycUhoZ74snN2sw/7Xq8LJsN+Cp3UbiuhpsFgiCGOHnNF0FbU6+cPctORyLlAPaFxqrDbsBV7DNuoC1lSCJfxcMkYDBSNAJ8RmNEd6LpGul5ToDSEJz6/kw+UxpHtxbdWNa6JkWBSM1Ne/M1rg1qHL2tUuDpajIebydfsqNZRXkMCSOOs9q0VKZzs2L/RSlnx7akr/tYiHL9HWs+1ewgvs4rrn6+EusSO695IRWz2hdDBt76X4VixhQqNyU6KbEhhSzfdbv1W/KD8Ai64MFA0HHfETwGb3nwOIbebIIwWg8ejY9mi7ICGR/IKN2rXYL3mR3jhxdioCZgQe0ejt4df1Sjxu1rvN0+jsY4LLafwoex1imR3cuLjAY2tHr3r7oRY3BDimP9ABqBcsR/6jQVIeHwERIMDk2cg7bbXCa8Eij+WQ73HhLznkyHpHrz3Iryj+/e1HmlF2UYk2KKLT7XlDHZbNfhee4L67t4W0x+3RBPUy+COAV+s3IyitVlgMGy4+38WdLq5PyUe4sWb9kEZzDY3ht7rhOMDD6KrOLj5gwyIEv1HZ/iTscppw2PluxHFYGNh5lAwCFFiMwrJ6dPu/QWanasJ5iiiBk5A7Ig78HOhDqvPaBDD89FRkP82VSxuNyYXlCKFw8L83JYDfxFvj816ro624gA1LoABkWRIm6atWFxVi+06I+ZkJKGH6J9GHqF9IukiO/WbUGwrpMTJpnHQXzQUwyVjkcppueyasQVC+sjlQGmQCAzvJ8INg6KaBUrjb6BmtxszzpZR1T7tlAk+sUibWcgedFTpqHxDYiA65Ya/W+J2SoB4ZC6FWErntvxdbuYQ/3OPRVrPBacJ2u5ytEhRNjYtfbUDv86qgCSZhQnz0xsozWkflMJodWPF7BywmFeGCJ1eJ2UIkhs7Ohi4Le4+6oMc7GHgUllVyxyY9WwFBdX8Uh1HY6Db5JT5KL6o/RhWj4WihSBGm5DRkKdnp9aARdWKv+GVA22b1Num+x0/qr7CjbF3Ylz0jQE9Wk+2Oy8nFTm8yBv7VS+vh61QgfQFt7TH8we0Ym2j0vEny2GXO9F7eRaYwuYr5bYxm7Y/ikgryjYikWbrObOjBvsrnweDzgYz6gEs15yAzevEUGFHPCy9Cmx64CGPclsRPv96K0xbR4NNs2Pyq1lI7+i7BFn4qxy7T5nAHKpE/lEu0vaLMOzxBGQNbjn/Gmn/TdlRHLeoMTupB/oEyWl46Rqazx9F7dqF8NjM4GV2Q8KEx7D0jBU7K4zIiuLg5REp4DZBR1FmteO54kr0EvHxbEZokaivJNqK+gvUB5LiMC728uAkBMmcGIYkpLQ+1zCHm4cRknHoKex/RYLQXLqn6kFp9h8yYcM+PaC7UKM5oDSBfHfqeQ4nJcXhGj9rEEh7pA5lHJZrKK8hMRBdShP1KI3DpLyG4hEdQIzElp4fAx3Pf7VepPXclWHN+N8NzVaUTTV9dJUaJ9do0XNiDLpfFAKmNbow/aMyZEjZePuhdP8jawM1CH8QCREts5+HmBGFKYlPogMvMD4nf8Pff8CEDz6WY+xoCSZfFFbr77n63xUOOZbK50PmqEQsMx4PJ81EGucCiW+lzY7Z5yvRWcDDS1nBkX1/XbsY+4zbMSPp2YD4FEn4DwkDInmDy/OzIp4/6PV4UfLANwRnANmf3wPaFY5eG+geuNLruUxuHJlSCm4SC93fv/JvvK+E9Yi0omwjMmm2niNesbOqFajUbwKDxkVc3MNYpi2CymVCR24ilVcoZvACmiY5LH5V8RSqNnaG+c+rwWF7MOWVdKRkc7HzpAGLflMAOQaMEvDBWclE5+ui0Pfe0ACL7DfV4gP5SYrgnhDdt7Q4NHLIf14Ah7ICTEkc4m+aifnnOTijtKJPkgCzBjVOR3FAb8KCSjnGxUjwQJjCAT0eGwy6HdBrN8Dp8BHEM1nxiIq+ps3QVuzTG/FhZS3Gx0pwf1JgSOAWtxn7jTso47Ce+F7MkGCIeBSGSkYhmhnb0mVt9ecpjsqyGnS18dBFxms2KE0gE6mntkjlsPFuh7SQG2nkXGItkMO4vYhCLPU6fLQchMZCNCIX4uEdwIy9skOAA5Fza9SJtJ5rNwgbWWXyAqx5ohwmpQu3fJQBofSCi/xIkRnv/FCDkd1FmHbDBTLa1tgsgfRZZC3AcvkHVBx/FjeXyheMYoYux+nHnzX4cY2GMgaJUdicYvPY8I1iMY6Y9lPAL3dJH8YA0TCqKQIo88CZEooc/rN8H+l9oOWtyudRYS/BG5mLAprzWbMVr5RWo7eIj2dCfOsbyJgdMj0qZv5M3bylvnJtII+012kDEtAdN+PcmzWIHSpETh01TRsY1r96CJFWlG1EmM02CMn4iSFXqv0F5zWrQAMDabGT8bVJg2K7AlKmGM8kX4dUdmC6Yb96Nf7S/AT256NRWToSPAEdD76QAl4cHdM/KgcETsy9IQFH52qQkM/FuJebhxJ9qdxdXg/FtCQtAwAAIABJREFUSWj2uLCohVQW9W17HDYofl8CU8E+0Jgs8Mc8jHdr0yEzOTG+gwT39/inobNOpcU3cnVESNl9tBXHoNNugNV8kho2jc6FWDKSAqFhsVsP2K7eGGmOziSpI2etJynDkIDRkDBmQnrfQ9AXwyXj0JF3ZaGuXrxXv5Ap8YdGj6kpUlxVRwnhD5Smd08BlX8ojWcGdc4h/dajmZJLc3J5Hq7isTgoryExDm1FSl83NIDfPQWiER0g6JsOOjvwaINwjfPf0m6k9Vzgp+u2LeEWKcpLp1Z71oo/XqmGtBMX17zSUJH9vFuDH3ZoMGlsHK7p13b5W4jy367fiJ9U38ADN4aJR+O2+EkhD8sg3kHiJXx5bgr1MWtuIePdoluHX9TfUophpOQa3Bp3Dxg0Jl4orsR5q51C6kpgBxa/TriSZpZMovIV3slaFtAHtp4z6N7EWFwXF1rOoEDkYtxTgtqPd0AyvjPi7x8QyCPtddqABKp/0qB6tQbp98chcXzb/Sa0AVGFbAiRVpQhG3jLGgqJnqs2bMcZxVJ44UFmzB343c7FfnMxeHRCDzEOPfj+I190jhrKSygwsMGfPxIn3cPBE9IxarYYH22pBXQcvD81DX8+XUWlw93xRTboIYp4+Fp1Dut1FRSwDAGYCUUh+kd3YD3Uf64kljMcvW7C+87+MDo8uK97HK7Nbfhefy5TYpNGj1npiegnjhzgBkVbod0Io34XvF7CkUeHUDwQ0bE3gsONfHRCqHIp1U4Fdum3Yq9hG0weI7WkBAxuuHgM+ouHgUe/cvKyyV56/Fw5lE5Xkwi0TYHSkHlHRzOQn8dDJ+qPi9QUtt93Z7vWgMXVCgySCPFEWvAIws15hwgYjWHHeRh3nYdba6WaoAvYEA3JhmhkLjhZsQGdu5rT93/lmUjruXaDsJGdtW+ZAkVbDRg4JR4dL/F6zf+xBgcLzXj1vhTkpTXfAArnhnZ47PhWuQyEcJ5JY+GO+MkYLL4qLF0+/VwFxdWzbFEWRKKW506dtZzEZ/KPYPYY0YGbjymJT2C1woHNGgOeTEvEQElgyrfGUY3XKmYhl9cZT6W8FNDcXyutxmmzFW/mpCKrFfIHVSsPQrf2FKTTh0I8IngEwIAm2V4p5BI4964MusMWdH4tFcLcyOedhnxCV0CDkVaUbUQkITEIyVyU5qM4Lv8AHq8dqeJxOMnIxxrtYdBBw6T44Rgr6ep3yqsq56LWXoxBnwHF6v/hhKEn6AIvyoeY4KwR4YFxccB6E2oLbJjwXjqiUpsPXnbxYKocJjxdsR9JLD4WpA8K6aHTUnYK8l8+hMdqhDxjKJbwroHTA4zNluC+HnFg1hm1b5XJcMxkwds5acjg+Uck9SvMICtQtBXazdBpN8Lj9hlQfEEPRMVOiDif4UMFJbB6PPiqcw5l/G89aiA2Ncb0EQe9Nk6Pg4oU2qHfRKW4kEII7weIh1PGYTLHP99ekKIMefX6NJccHgfzAuAHJKA0J09bceq0BWcLbSgrt1Pyqy8CAR15uVzqwp0YiVmZHDAvwa5weDwUxYXN48EnhAaFGTkvndftgeVENQx/FsF8uBJwe6ihs9OjqXOMaFgOGOJ2vdicjRZpPdduEF6ySm6nF6unlcJl9+C2xVngXAIQ8ejCMqj1LnwxOxtcdvNQzpqzMQJ9RuVUYGnNAlQ5yqhY/IcSZyKTmxPo40HVc7l8CKNCIQNLFl7I+wuqkUYqq51KLJO/T4V7RjFi0JP/FH5W0nBTfDTuSAgsv+CQcS8+r/2I8jTeHj/J75DIB5VwHrJpNCzLz2oW56HfTvxUqH59I6ynapD2zo3gpAcWutXSPtufb5kEyG3wsWllIHmEfYgXpA1+E1o2w7b5dKQVZRuRQsgMQjIfva0YR2rehtNtQIJgALT8sViu3AkXPBgv6Y5744ZcljfuuG4jdqhWIKcoBh2/1+BI/hwcPyaAm+eBLJWO7t34uMrOxpn1OgyZLkXOiNDxmr1YeRBFdj1eTumDfF5oozmcehWVV2iXl6AspjtWJd4Go4uG3BgOnhyQhFg+EzOLyiGzO/F5fjb4jNY7B3g8dhh0f0KnWQeXU+UzoLg5lMdQICIIsuEf24vFlSiy2jE3Jgnf/6FBscxH4D6urwT3j41rti6tsJVQhuEh0x4QUDxSyAUv4TQkYaUkeqgtll+VWnxXq8Zt0hjcKg1ej1usHhQVWSnjsKDQiuISO5zOCxYim01Dbg6X8h526sSj/jeXS0c9IvtEaQxubka/oZCl22ADiXQy7CiCo0zja5JBg6B3GmUc8numgtYEUFMo+v+3tRFpPdduEF6ygyoOmLB9gRzp/QUYObMhwbTJ6saUBaVIjmVhwbTIh2f42+xnLMfxhfxjmD0m6sM5JeEJiJjNy+vz1xf5vbLKjtlzKtGlMw8vzgkO8MVf+w6PA98rP6OSz72eNGgdU9FDyMecALkBf1V/jz+0v+Bu6cMYIr7aX3c4Y7bi/0qr0UckwOyMyBOLU/k9U76lErazV9wDWiseMvwKq73C3xKwq5w4/mg5+NkcdH2j7d9e/1uWLtKKso3ILaQGIZmTxSHHYdkbsLoUiObmgxd9Lz6s/RNGjw29+Bl4PHEsFUraWLG49PisbDq4Xh5GvGHG3tFDcFA5BJL9YriZgDoPmHNVAvYsrEXeWAkGTA4MdCQQWW8zVGOpogDDRUmYkdAlkEeCquNxOqD84zMYT+6AnhWN1R2nocwpgJjDwGP9E/COsgZcOrk8bBvUQPV8hlr1b3DYK6i5sthJiIq5AWLJMNDogaVaBCWkusoflddg7yEz6OcAjwfISeZAqXPBYHHj6p5iTBkf7zfk8XL9mtxG7DNsxy79ZqhcCqqqhBGNYZLRlG6XMEN7IdAcGVz8zMslVSi02IImi2+qX2IMFpfYcLbQZyQWFtlgtfq8cKQQponMDA7SOrCxXWhETCYDC3tmNtsQb+n865+3l6lBeJWNu0vgMfkuCRgSHuUxJBQW7NT29Ap/so60nms3CC9Zke3za1Bx0IyRMxOR3r9heOLJUgvmfSvD4C5CPH5TZOK0/W0Y8jsxJjbpfsNv6u+p/LtRUdfhpti7wKC1PITzcv3v+8uIDxfW4poxEky6L3TKvr5PMq+dhs34QfEN1Pa5YNPcWJ7fISDexEWyd3DKcgTPpM4LyEP6o0ID8ndfYhyujYv8h8qpNKH8sdXg5MQhbd4NgSx7e502IAHNfhPOfyCHdLQYmVNaD9yhDYgiokOItKKM6OSa7izkBiHpyu7S4WjNOzDYSyBkpyE1/lF8oNgDmVOLdHYsZiddi3hW4969X2VvodxyDIN3p+OzLgOhjRJjwp+dcWqbBS4mcP2kWJQtVyOuAwfXvh66CxOrx4Vppbsofbc4czj4jNB7i4j+MRzbCtWWr+B0u7Ep807sZXUiGBrwxgFZKWy82cF/vmUk9w4Zs8V8DMQwtFkKfIdwZjTFZSiOGg0GI7S5eKU1Nrz1Sw30GjfIEtw1Mhbj+0WhRuPE6yuroTW5MbSrCNNvkILRwhxSAkJDLr2J1/CM5VgdCA0DvYT9Ka9hDrdT0CGqoV4bg8uNqWdLEcVkYFFeZljGQ6gtyiscdQaiFWfP2aDX+5A/60tcEhM98/l/5yHGxYbvQsCfDL1ONxVKSryGlmPVVI4uKeSsQ3EbDs4CQxD5sGt/424Lv0daz7UbhBetut3kpsJFmRw6FS7KYDUUz9p9Wqzcpsbdo2Jxw8C2cStl81jxde2nOGo+ABaNjXukU9FPNCQie3n1T2r89IsWUx6Ix+irw+eJLLYW4tVSFRyeBHQVrcIjKdP8QlO/UPYoCN3G+9krwKb7/9i8WlKFAosNb+WkIbMVckJMB8ohX7AN4tF5kE4ZHJH1a++k5RKoWKmCfK0OWdOkiB8ZupC4lo/s391CpBVlG5FmWAxCMjeXx4bj8vehthwHhxmD/ISnsUxzCietlZAweBQtRS73n5eghcbd+KN2IXieodhK74RsmRqvDboVn7xTA/lJO5g8IJ9GB0nFuPOLbDBCyNu7uPYMthtlmBLfCaMloUExbWydHWoZatd+AntNMY5F98Mv0glweWmIjmJg/vAM8FnhD8tszv6zWYsow9BsPEQR3dPpPIijxyAqejyYrJadXxwuD37epcVv+7TwkPN9LHD11SI83PEC8rpc48DrK2VQGVwY0EmAx25KBJMRmiOn0inHLv0WynNIIqJISWanU4ZhP9FQcOmtk7O2S2fEJ1W1GBUtxkMpkbkgJJcANXIn5T3cfcqAM4W2BvyHRDbxccy/jUOSh5icxAqLsepvn7o0FgqEhoDROGV6qjqNxYCgfwZlHPK6JLXTbV0kxEjrudC8nf52Qfh/D4miPLdFj/3LlcgdJcagh/75Mn+0Ro69Z0yYe1cyumWF9qatOSKqdciwVL4ANY4qxDGleDhpFlI5kQtlXfBRDQ4cNOOVF1Koj004y0eVFdird0DI+hqxbDnFpZjLy2+0S8Jz9HTpg0hgJePljAV+h0XyBycXlIBLp2Npp9bJH1SvOgLtmuOInzIYktF5fsfcXqFtSKDg1SoYC2zo+m4a+Gn+Lx7axqiv/FFEWlG2EYmFRM81NReP14XTiqWoMe4Eky5A98RZ+MWkwBbDabBoDMyQjsIgUUOwK6fHhuWlU3HSOxIapGHiT1sxauh4mPIG4M0XKyHSAllMGjguL657MxWxWaE7qJ+z6vBS9SHkcMSYl9Y/rEvkdbug2bMG2r0/Y3vqcGwRjwGcQJKQhZmDEpEmbrvvvsMug069Fgb9TkLkBNCYEEuGIyr2BrDZwadHFFXbsHhdLapVTnDZNIwZKsFaoQ79JQLMTG/YnlLvxGvfVEOhc6FPLh9P3JIIdghzyEhqyWHTXsprSDAHSOHSeRgoGkEZhwns5LDui0sb/7BSjn16E2anJ6GPOPLcfISm69HCMmi1btzvikNNsRNnz1lRWeVoAFQjFjF8OYh1SKYZ6RwwQmSsByJwYsTazilgJCil+0rhtfpyRJlxAoiGd6DyDVkJokCa+lfXibSeazcIL9pOG1+pguKsDeNeSUFCIxQKMxeXQ6Z2YvnMLAh54Q3H9LfLT5gPY0XtQhAPYWd+DzyQ8BgEjMAQOP21HejvM58th0zmxPLFWRAKwiuPjWodVtSo0ElQBoV7OehgULQUBDTmUm7C89YCLKh+Fb2FAynD0V85bbLgtTIZ+okFmHWJQvP3bKh+l729GZajVUiddz24OaEPvw3VONvbuSABwld6eLLvENLn8+z2m80Ibo5IK8oITu1yXYXVICQdk4PaefX3KNX9CjqNha7SR3HEzcXXqt0EQBK3xwzAzdF9Gnxzf6pZjNXmbAi8wOw3V4AXL0XavPfw5OJKeE+5kaUDCC1994kx6Hlz8CAbTQmEjJWgjVY7zXg7bQAyOOE/QFqrzmHl8QPYkjoQiUUKyGlScBg0PNxHiiFp4e+/JfvQ5dRAp9kAvW4LvB5CE0CDQNSPAqDh8vwDz9mdHqzarsaGA3pqL3TP5uGha6XgCOh4+GwpMrhsvN1ICK3G4MLr31ZTZyfyzKz/JYETYq8q2QsElZQYhkeM++CCixJVJ143DJeMRTdB77Cn0Li8XjxcUAqn14vl+Vng0FvHc7y6Vo2flFpMiIvCXYnkzQMIzUXhORvOniUhplaUlNrhvijKlMeloWOuj+aCGIk52RywIwSQ5rE5QSKkjDuKYD0t/3uLc/MTIb4qF8L+GaBzWy/ktSXvXEufjbSeazcI61bMpHDi58fLIYxn4uYPM/5xuLPaPZj8XgniJEx8/GhouI+as1lIHP3vmp/wu/Yn6vFrom/G9TG3XRYNrjn9+HuGJDoThFGJmIFPPw4dwmhT/RZarHi5pBr9RAIMij6L75Wfw+V1or9oKO6Kf6hBWOgO3R9YpfoCN8TcjvExt/ibCn6oVeNnpRb3J8VhfGzk8wfJAEunfw+33kYByrQTu/pdsjZRwVJpx6nZlRB15iH/pdCCKrWJCbbhQURaUbYRUYTdIKyfZ4VuI86qvvQdquMmQcnKw8fyTbB5nRgq6oip0qspryEpS+R78KfJii5MGSb/UAHrmVOQPjQDPxg6YcthA0YaWOBXOGHh0nAHoZ+IC93hbp22HN+oi3CNJA2T4iMTWbGoXIadRgvu3/cFZO4UbIkfAy9ouCZHgnu6X6CmCOee0ZbbceQ7NYVzkDNSFBRoi9tthkG7BTrN73C7fWF7PH4XRBPKCkH3RkMJT5dbsHS9ErVaJwRcOu4dHYcR3UVUXWKMTS4ohQderMjPbvR5ncmFN76ToULhQOcMHp65PSlsKO1GtwF7DX9SIDQalw95lSCuEy5mAkITLqC9+ovlXkI+ng0Q/C4ce0TjdFFeQgGDjkV5WWA1krtpt3twvpgA1fiQTIuKbLA7LiCZEtaKnGwuOnX0IZl27sQDhxN+A9epMPq4DXech0vlCwWm8VgQDcykuA25HaWtEuoajnUKpM1I67l2g7BuVU6s0eDYKg263RyNXhP/SW1wttKKV76qRr88AXXD1RqFhEISr+Apy1FwaTzclzADPYX9WmMoqKi045nnK9GtCw9znwv/Ydjm9uCBghLEsphYmJeJMlsxlskXUHmCqexMPJw0E3EsX5jvSsVS7DFsw7Skp9Fd0NevfF4pqcJZiw3vdEhDOjfyoT8unQVl01ZRvD3p79zkd7ztFdqGBJTbDShdrEDSDVFIu9t3E9teIiOBSCvKyMzKby8RMwjJSOSm/ThV+wk8Xieyom8CSzAK78p/h9plQh43CbOSxoNP5+CRst3Qu+3oix/wgGcqtG+8B1ZCImon/R8W/KzA0Ew+xGstsAEwSJl46OVUSGJDAwKjdzkwo2wXeHQmPs0aBlYEaBbq883nsfSgbViMczQpViffATOdh46xXDw5IBExvNDMr7Ed4XZ4sG5OJfTVvjC7qHQ2+t4dh+QewaWxeDwOGPU7oVOvg9Pp88xwOJkUl6FQPAA0GgPkIvzbbSpsPmKgfu+TK8CD4+MRI2o4vznnK1FqszdJxE6eNVrclFFYKrcjN4WLOXckgc8NX2QRuTwnwHI7dJtQYD1BjZ8JJnoJB1Bew2xux5AaF1/XqLBercPkpHiMjQ0fpoLfrwSA+RU1OGgw49HUBAyN8u+5JhRihP+QIJkWECTTc1aYTBeQTDlsGnr04GNAPyF69RSAzwuvcUiib6ynayggGvNf5SDANKSwksQQjciFeHgOmDGRD8kNRPahrBNpPdduENaFyfw2qwJ6mRM3zk+HJOWfMNsbD+qwYpMKt4+IwS1DQxf2EujmkdkrsUQ+HySZmuTGTU2ahUR2+A2xpsa3d58RHy2qxfhxEtx/T2RCHOu5n5Z1yoKIyYDRpcfy2g9RZD0DAV2IBxIfo8Jn36l8gQofeS3jI8TWGYlNzcNelz/Ip9OxpJXyB81Hq1Dz9mYqdj5hxrBAt0R7vVaWQNlyBRRbDOjwZCJiBkY2XLuVp97q3UdaUbb6hH0DiKhBSDrUWAtwrOZduDwWJItGICnmLiyQb0KxXQEpU4yxksH4Rl2KDKYVKa6VGB53P+KXHYH19EmIJs3AzH3JVHrF6LOAVedGIYCYRBYeeikF4pjQGE0Lak7ggFmBxxO6YrAo/OjfjxSWgXhhviZE7BYdFOuXQFZRiu+S70Q1NxUSDgNPDEhE5/jw5NUfXqnC6bU6SPN8+ZgKAiJCQFV68NHnnlhEB5nL7PV6YDYeoABo7DZfCDyTJYXMdBu+3ZUKlcENEZ+OB8bGY1BnYaNG1AcVcuw3mPBqVgryBE3P22xz463vZSiqtiM7kYM5dyZDxA+fUVj/3hK8BeIx3GfcAavHQv1zKjsDwyVj0DdEIDQzz5VD5nBiYccMxLFD5wVvzrfnpMmCeWUy5PG5eDU7eMAlgmRaLSNIpjacPmPFsRNm2Gw+DyLxHnbr6jMO+/QSQCQK7/q5LQ6Y9pZSxqG9SOkTB40Gfo9kiIbkgJsnBTO+8X3ZHNm1pWcirefaDUIA6hIb1j9fhdhsDq5rgkvs07W12HHCiGcnJqFXh8jeTBw27sM3isWwe+3oIeiH+xKmg0cP7jYw1Jt81Y9qrPlVi4cmx2PUVZG5Dfu4Uo49ehPmZiajm9A3f7fXjV/U32Krbj1ooOGGmInYqF0DOuiYn/253xvA+g9nf/E/E+JDLbOm2tOsOQ7NqiOIu38AosZ3jlS37f20UAKn5lTCUmpHj4UZ4IQwDK6Fw/pPPB5pRdlGhBpxg5DM22ivxBHZm7C7NYjj90Qn6aNYqtqN/aZi0JEOD3iYGpuAk+p5SODkYILtPlTPexksaQK+6PI0zlbZcY+XC80pG2j5XBQU2BCXxMIUYhRGt9woPGpW4e2aY+jGi8HclN5hXSqnx4v7zhT/HalSZ6XDcGQz5Nu+w7qYMTgY1R8kSu+urrG4LjfKrw4KZsDKc1ZsfLkaTC4dEwiQVSwTlQfNOPytGka5k5yTkXOVGD1viwE/SNmS0E+r5RSqZRvw8/5MHK3wybJPjg5TruuIaFHTKMrfydX4VaXF9BQpRkRfHm2ZeB3f+UGGggob0qVsCqRPImj5PghEjnaPDYdMe7FTvwmV9jLqEQJCM0A0nDIOk9jBG0+kjRq7A08VVTSZRxnI2EJZx+P1YmZRBeQOZ0ginxwOD06esuKvgyYcPmqG2ezzHpI0yc75PPTvK0S/vgJER4V3HR3VOh+34a5iuHUkD9ZXGFE8cHPjwc2VUv8llBb/htSbSOu5doMQwIblp6HcwkHSHWbEjfbdtl1aPl8pgVLFxCNTNBCS7PkIlWLrWWzTb6CMnetjbse46Bsjni/Y2FTnf1iDg4fMePXFFOR1DM9N6KX9rlVpsVKuxl0JsZgQ3xA2+6BxD75RLIHT66Aey+HmYVbqq35XaVWtGmuUWjyQFIdxrZQ/WLNgG8wHypHyyrXgdboA2+138O0VWk0CbrsHRyaXgClkoOfi8PBNtdrkroCOI60o24hIWsUgJHO3OVU4XPMWzI4qiDnZ6JH4DL7RnsFWA/G2OPBAbCpqDV9A56zBfenvw/zh55SX8OCY2fi1TIKbBTzY91vR595YHDljQ8FhM+JTWJjyYgpELTxEksPvo+W7oXHZ8VHGEEhZ4dNH9Qf/zgIeXspqGKHjUFej9rdPsM8iwW+EmoLOwoAUAab1SQAvBCAqLrsH656rhKHGicHTpOhwEc2N2+XFuc16HP9JA4fJAyaHhi4TotH5uiiwuIGH9x0qNGH5BiV0ZjfEPBuu7/EzOiefAY3GgTh6FKJirgWL9c/w+D+1BiypVuDm+GhMTPhnys2l7w8BqHlvdQ1OllqRHMvCC3en/CMMNZzvXD0IzU79Zhw27aPwCEjJ5XWm0El7CPqCQQvcuFmv0uFruSrg+YdzbvVtr1dp8bVcjbExEkxODl0UFwkvPVNgxYFDJuoMqDf4wjnJZURuBy4G9BOgX18hpPHh85J63R6K09ByvAq2IiXs5Rr4+E/qCoMGTkYsuB3jwe0QT+UeXolexEjruf+8Qehxe7Fi6gkwLFwceeYdOIXmf7yrXjcT2p9eBI1jQfSNb0fiXW7QB48uwOSER9FF0CvifTfWIQknePTJMmi0bny2OAuCMCOM1o/hlMmC18tkGCQR4om0f4YGVdnLsbRmPlQuBfVRnxg/2a+8XiqpwjmLDe92SENaK+QPkgGWPb4aLqUJ2Z/fAzovfB9Rv8Jor+BXAnaFE4ptBqh2GODUuhHVh4+OsyMLbe53kP+BCpFWlG1EpK1mEJL5O90mHK15DzrbWfBYCSjkTsIWoxp0qOCBGr04fHDtf2BA9K3ooelOeQlrk7rhY+FE9OGxkXDAgeyhIgycKsXKBTUoPGqBNJVNeQqF4paFna1SF2ONthS3Rmfhtlj/iJnNXc/jRgveLJdhZJQI01L/eXlH0VPs/glnDu/Ht0l3QsuOQbKAgVmDU5Ai/mcqSjDjOPiVEgW/65HSi4+rn0lq1PPoMLtx8hctCjbo4HEBvGgGet4ei5wRlweeMZjdWLFJSdFqkUIAYwhwDIehhFa9Hkb9dngpo4kBkWQIBUDD5lzwpp0xW/F/pdUYLBHi8UZ0c2PzJFyGH/wkx5HzFiREs/Di3cmIk0Re/5ncBuwz7AAxDtUuBTVUCSOaAqAZKhmFKKb/FKHXSqtx2mzFa9mpyOWHjlolmP1xaV2Ty43phWUgbBKf5mWBxwj8YiDQfslZsLDIhgMHfcahSu1DdyUlK5OD/n0F6N9PiJTklu19f+Px2F2wl6goKgtiINqKFBRI38WFIeH5DMR6L2J2HOicwI1+f2MIx++R1nP/eYOQJGivWbcDTjUD7JvLG11TjVKArT93R2K6FsPGnw3HujfZJpPGwhDxVYhjtR3P0aEjZrz3fk3EAGXqhWNyuzGloBSJbBY+6Ng436LFbcJuwzb0FQ5GTCM3mRcL2ubx4MEzJRAwGFjSqXW8PG6THaVTvqWSpTPevzWie6u9s8Ak4HF5oTtkhmKrHoaTdWEqNEDSk4+0O2PBT488EFFgI//31oq0omwjkmyRQeg2mEAXCVoUwuj2OHCydiFk5sP4HnfDAzaeTOyIJYrNMHpsiIYafZkaTM54H7L5b8J86iTe6fwKXHYGrj4NSJJZuHFBBpwOD1bOl+PccQsS09l48IUUCFpgFCqcVjxevgcxTA4WZgwFnbgrwlA2a/T4TKbEbdIY3Cpt2lCwVhWidN1yrOKNQKGwEzh0L6b1S8SgVP8AH40Nu7bAij/+rxpsPgkVTQffT/6lUeHE0e/VKNvrM/Ci09noc08ckrs3TDUhnrJ9Z0z4YpMSRosHsWImHro2Hj1zGqbFuFw66DV/QK/dBI/Hd2kuEPahAGh4/Dwqp3JGYRlyeBzMy0kLWPIutxcf/SKhmov3AAAgAElEQVTHgbNmCsGdeAoToyNvFJIBExCaAstx7NBvxmnLUXjhpVJPCDAdCSfN43Vt9N2xuN14qKCUOkcs7pQZtr0XsFAvqrioqhY7dUY8mByPMTHhTe0he4nQWRDj8K9DZsjlPq8rKakpbCqklOQdZqSzW/QNCkQOZCzkkv1iA5HyIrov9SLG/G0gEkORKW1buYiR1nPh+WoGsmKhrdMiRelvKFuO6KkwipuHRGPiSP/hEP7au9J/n/d2NRVPPuuJRCo0IJLlscIyKJ0ufJ6fDX4Lb7zqb3sHioV4Mj38YASNyclySgbZ639AODgLiY+PjKQo2/vyIwFbjQPKbQYodxjhqguLYccwEXeVCPFXidvzBltxB0VaUbbiVC/uutl6zut0oXLuQjCiREiY+j8wW4CCSEBIvpOtxm/WWOSgFE8l9YeLnYl3ZOshc2rBhwlPJ45HlpyL6tdfwuqMe3GcnYcbKxlw6t2447NsyrAhRuHX79bg/EkrkjLYGHN7LDLzueA2E2Tk9eojOGXV4LmknugpCA/q70q5CmtVuoDQGz12C2o3fYl1Mhq2xV4NL42O8dki3N1DCmYjVABN7TGnzYN1z1bCWOvE0EcSkD0scKNSWWTDoW9UUNYBz6T05KP33T7gGa3Rhc82KnHonM/AG9NbjDuvjgP/MvQCHrcVet026DTr4XZpqOe4vDxIYibgkcooiuLgs/zsoF4Xt8eLRb/VYs9pE6KFDMooTIkLr0fJ3wDVTgV26bdir2EbTB4jVZ2A+Q2TjKZI7/mMCwbzfr0JH1TKMTxKhBmNeI399RXO34ssNrxYUoV0whGZkxZ2Q6x+LsQgq6py4K+DZiq0tKLSl8pDilTKpHIOifewQw43KMqUlsjqby9inQfRdk4Jt/5CHiJpmyHhNjAQqVzEVvQiRlrPtRuEAeyw5RsU2HLEgJm3JqJ/p8gaQAEML6JVZDUOzHymAnGxTHw4PwMMEo8QwbKgogYHDGa8nJWC/MugmQUypPpE+NaEidauOwX1NwcRe1dfRE/oFsiw2+uEUQIepxfaAyYqLNR4uk5Z0IGo3gJIrxZTXkFaEIe5MA71P910pBVlGxF2sw1Cp0ID2dsr4KxWgM7nIv6BGyEc2rPZB8TnK/9Cid2Ia7EWSVChi3QqJIIBeK1qJcqcVvBowNyU/4G/8HPsLqfj58RbcY2RBXqZE2NfTEZiF5+nihiFX75dg5K6d4049pKzOMjuzEN2Fx4y8njg8gMLddtjlOPj2lMYIJDiqaTuYVmy9ytq8JfBjFezU5DHDyxX0VR4ALu3bcaq2BtgZfDRUQw8NTQT0QFSU/z1hRKFf+iR1leAkbMSg14zcjivOGDGEQI8U+sDnuH34GIz7NC5vZBGMTH1eim6ZAQOVOf1umDU76aQSZ0OGSXrhZ6nIPfGYlleGkSs4KImSOjhst+V+PO4ARIBgwKaSZcG10Y4FtzpdeKo6S8KhKbEdo7qgk3joJ9oCOU1TONkod4L92RaIgZK2tb5kKz9nOIqlNnsQe3ZUMuyRu6gQkoJKE1xif3v5mOiGZRTgRiHnfJ4ET1P/u1FrDcQSS5imbqhF5FOchGJF9GXh0jyEZkJPv7NSJRI67nIzCr8kmu2ogxkaHO/qESxzI6PHsmANKp1whkCGWck6qz4WomNm/S48/ZY3HhDQ2CXSPS/RqHBKoUG9yXG4dq4lpHIv1hciSKrHfM7pCOF2zo3kvKPd8C0pwTJc8eB3609Fy0Se6ixPqzVDiokVL3LCJfRh6DGjmdSRmDcCDGIZ7C9tB0JhElREojfjwEMAqADsBwAQabyoSY0XjIBlDby0yoAd1zy7zcCeJ1gVwAg+P6kbVIv0NIiPedxOKFZ9Qd0v+8hBBYQ9O8C6ZSbwRAHd4gtthkwt+oA0tgCPClx4IxyKbxwo0PMHYgTjcCL5QtQi0SKuP4hbxeI3vwSb2c/h+4mOlJLPZSHqutFuoMYhcf3mFBy2kIZhgbtBXGTc1dKts9AzOrMQyYhyG6CA83hcWN62S7YPG6Kk1DMCP03/fnzlSix2fFpXiaiWYF/E1xGDc6u/xqfu3tCxk2BmO7EU0MykC+9vBEmP23Bptdk4Ih8oaK8FgDwEOCZI79pcHqNFnQn4KIDzJ4c3DI9CcJLeAUD35AemE1HoFP/is9M/VCAzniE9S26xvaAQNQPbE7gOo2AA634Q4VNh/UQ8uiYe2cyspLaRj4ekQdBJSXUFQeMu+Hw+oyaTHZHFJjuhsPDwLIQRC0FKvdg6m3T6LFUpsRQiQiPprV+6pFK7aSMQxJaevacjXyKqCIS0dG3txD9+wnQrQsfTGbkzROPg+QiqqkcxPpw04vRTMk4KS9iHVANCTPlZMeCzg2PXRAmPdfk9om8xIPZyYHXbZGivFw3JJxh0rslYDNpWD4zK2I3A4FPPXI1rVYPZjxRBqfTg0UfZkHcgpyP5o76qNGMt8trWhyeYXV78GBBCcRMBqXcI3Xjc+m8y2f+DKdMj6xld4IhajvKr7nrcyU953F4oNlvgmKrAaa6kCoaA4jqS7yBEoi78dq9gW10QcOgKMnt1mkAZwAQ5DCCTDIfwPsAXriMGOoNwqcB7LmongrA+Yv+/1AA2wEsArAGwLUAZgG4BsCmAMUcEj1nLShB7SICZKUFQyxA/EO3QNivS4BDABbXnsF2owwPxOVhXFQaVOZjOC5/H26vHWmSsTjnUGKftRblyAE5640vsOHIwf5wOpMwsBTIGCjEiCcbD9Ent/ZquROlZ6woqfszXmQgEph7YiAS45AYiRnEQLwIRXOFshAb9ZW4NzYX10U3nmce6ERtDg9+26eF1uhGTjIHHZK5eE1dBRL89lXn7KB1Bpmb6tAmfHlKh0PiPqB7Pbgzj4/ru6Y02pbT6sHaZypgUrow7PEEZA0OPFT00jkSY4tEORGSeZfVi54GOhJqPPC6fcAzvSbGInv45YFnLic3MrcVVefwh56B22g/oAfdRwTPYidRuYYCUR8qtJRGu7y3l7TzzVY11v+lo0JXCU8hIbFvS8XqtmC/cSflNayyM2B0TAWHXoabpLUYJhmDOD/cx5GeC8FKmHG2DA6vB4vysqgzT1spOr0Lhw77wkoJ36G77i6Ix6OjTy8+BUjToxsfnMuEMYdzLpQXUWVuYCDay0guou/SmCrEi5ge7fMgEgMxNx6sEHkRw6DnLiuudoPQz26qVNgxe1klumTy8OLdrUcEH85NH2jbm7bo8fmXSgwfKsKMqa1z06RzujCtsAzpHDbeyU0PdOj/qHfMaMZb5TVBoaI1u7MmHvTYnCh54BswYwXIXHh7qJtvb68JCVgq7JQRqN5thLuOT4mTyPJ5A4eLwGrBLXy70CMjgTAoyjkAngFArAhD3SzI/38FALFe6v/t0gnWG4Q3AFh3mdn/Qc7HAK6+qM7vAAhpGzEWAykhMQhJRx6rHapv1sOw9QDVr2h4b8TdfwMYfsLwTW4nZpTtomiQiBeOT/d5yfS2YhypeRtOtwHg5OCsvRg87mDstnNg97og3ZwCjbIrxp4BhPFM3PIxEZv/QhmINc6/jUNiKBp1FzyIlIGYUxdi2pkPWqYTLygPIpUtwLtpA4M22upHdLrMgiXrFVDoLqAmUr8xAHYMDeM6SJCbzEWHFG7QdAkOVRXWbtiENdxBFDVFX5EVj1zVGTxWw4P6/uUKnNtiQMYAAYY/GXyoaP1c5BoHNRfC+0ei3ScMisYtw6JhV7tx5Ds1yvfXAc9k1AHPdAs8dPTiFawH3LklhoFRrEMwGw/DUcf1R+rRGSIIhL0gEPYFX9gddHrjhh5Z8x92aLBmjxZcNg3PTkxGfnpg4bn+d1ToapBxLqouxC4dEwLmBnCYe6j3ojO/JxVO2oXfs01QhJEZr5ApsVGjx90JsbjhEsqu0EmkZS2ZzG4cOUo8h2YcP2mB0+lzHXLYNPTowafyDnv3EoDfRIRAy3oP/GnKi1iqvgiwRgm3ltDvXCgMMRfiqzsi9o4+gTfcSM0w6LnLjqfdIPSzXDtPGLBorQLXD4zCPaPCk6jeoh0ToYfJx2/2nEpUVTsw79VU5GS33q3d9LOl0LvcWNE5G2xyImhGqQcHmJIcj9FhRt9qanjWwlpUv/w7BP3SkTRrVDNm0f5IoBJw2zzQ7CPeQD3M533hPoRmKrq/kDIERZ3bvYGByrIt1AuDotwJgCRDXRzmSW6cCPT0BABrm5h3IAYhSYYiyBSPA1h8UTv3AfgCAIGr1Acg15AZhPV9mY8WQrH0R7i1RjBjJJBO/x/43UhEa+Pld10FvlKdwyhxCh6S5jeoZHHKcVj2JszOWpwn+Iw0JsYmv4EFtVuhqWYD2/thTLEHLAsdty/NArcZESaUl40YiKd9HkRiIJr0FxmIDMCT6oQ204yJ/TIwuGss2EHw8BGv4Lfb1FTYIin98gQY1lWEUrkdJystOC+zA5fYiDEiJjqkcChPFvEiZidxwPHDO0joKY79uR5LVMnQsaKRCCNmDc9CWrwvDUJ2woItb8goGU14L71ZsiJ5eRsO6rFquxoOl5cigZ9+vfQfYZiE7P7Q12oQABpSCK1Fn7tiEZUWXA7fCZMFb5TJGkTvOJ0qyjA0mw7DaiYO+Lq1ojHB53elPIfEg8hk/ROxdc1uDVbt0FDRWU/floTu2c0zVAN4r5pd5ZnzFaiwOfB/2RIU2XZgt34r9G4t1V4sM54CoRkkvgoiBrn3ab1SZXPg6fMVkLKYFEJ7uFB4QzVDm82DY8ctVM7h0eNm2Gw+45DJBBVOSjyHvXvxIRbSQWvmGTBUY6W8iGpzAwORGIxR13ZG3N39WtRNGPTcZcfTbhD6Wa4vNympj+pjNyVgSJfmh2y0aFe0gYdPF1jw2hsy5GRzMO/VwGGlwzH0t8tkOGqyYF52KnKayfkzt7gSxVY7FuSmI5kT+lyTQOat23gGqhV/Iea2Xoi5tWcgj7TXCVIC5lIblFsNUO0xwmP1KRVuMgvxo8SIGyYGqxmH0iCH0F49DBIIg6IkBGQknJN4BC8uBIKR/Nu7TUyj3iAkIaLkVEva+Q7AXAD1EHYkN5Gchq+qCxutb4qcFoiLrj/w/+ydB3hcxfX2f9urtOq9WXIvGGOwwY3eTA02EBIIvRMCIYQkpAFJCIEQIKGE0EJCh1BCCc0YVzDuvan3sitt73e/Z+6uLMmWrNWq2Hx/zfPss9LuvXNnzp29M++cc96Xb+Iw05ADQnHNsMtD63Pv4lq1SW6C5bTjSP/emSj3y6sWC587albTEPTwx8LZlOgOnA/9ITsbGh9gp79CdqmekP4DCpOO54Hyt6l6+2imVyvJb1dy8s9yyT+yp7RBHP0/4BDRptaGKECUw0x3eHF3A4gqFRSM1e8LMS0ar0fbR/jZVuEVfL+FVnuIJIOSK8/I5LhJXTT0a+wuHq5pYq7SzNSgkb0NPvbU+6htDezLgxINFF44QYgyLl8nexAFSMxN1/S6CG+t3MFfV9ezW1eCVgpwTVmEYydO5L07a/FYQxx/ew7FsweW4ynaUN8W4Kn3m9lT70eQcV8wL43z5qSi7oMETiae+drNupfbcLWEoiLjJyczfXFa3HmLLYEgt+6uZoJRzz2lXRqFnTdNCnvwuDfjdq7F7dqwT75CfK/Tl+4LLdXqivd5dkXo6L8+a0OjUnDbohxmjhv8mElknPV2TlsgyC27q8nTaXh4XDQ8ORwJsdm9Tg4n3eUVP3lQo+aopGNZkHwaY/TjEvZaD7bd91TUscPj42fFuRyZdPjYsb9+BQKSzGovwOG6DW7crjATIzWcIq3FovJTM+NU0udNo6xMJ5MdHqr0n+79EF7ESCCMyjywTZX9bTEM89xBzT0KCPsZjff8q04Otfjz9UWHnAq5vx/OcH7/l8caZQrhm67PYsG8Q7vb9Vqzlbdb20nUu+cJS1yzowKLWsUThzB/sPmpFTiX7iH3p6dgOurQguzhHDsjXXfYI2Fd5ZSZQj0xRjOFRkHasVFvoHmi/rCYNEbaLv8/XW8YJkohmnUn8Mh+dqoTKWPAL/qwX24M/Ik8QBkDAXfF8gIFiYwoc4EVwAxgY7d6xgJ7gNPjzCMcFkDY2R7XV5tpefYdJKcHTU46WTdehGFCVx7eNo+N+xrWM05n4b7Cvne+Q5KPpfX3st1fgRk1Fxbej1aTwy1PfUPaniymNELu+SpO/e6YIR+SMrCp8fLHFVvRV+pJrzbjiZFEiYt1AkTBYCpyEAVADEWQc+s+XR+NCp41wcRVZ2SSYu5JGvN+Wzv/brJyWU46Z2V0EaoJr2J5o4+99X721vtkoNju6slDZNIr5TzEzjBTARKTYvIaQa+bl/63go/DY2RpitPXu1GVmyiZY2bBrQOTQxK6fv/9qp23ltsIhaEsV8cNZ2dRGCdjZzgYYdcndjb/x0bALaHWK5h6biqTz0pB3U8eVzgS4QfbyzHLur4Hv7eRSBifZ5fsORQAMRhs3jcW1Or0fZ5Dg3Eyn27w8Nz/WmVg+6PvHD5M759Y7TzX2MrZGSlcmnNg9FhToF4Wu//K+SU+Kbo3VKAtkcNJBUupro+Q2SH/UcQqXGV38lhtMzOTTNxZLB5b377i3LiHhhc/QtUQZbbtLBsUY/lAdRw6i5HSUh1jS/UyQCwboycp6fDJmRyoxYdhnjtoE4YSECbC0Na9cSL2T+yWiqDb/vIx9u/UsEyUIhH76ocqkCLw/E9KR0wvZaCDZriPt9pC/PD2KkwmJY8/UoJWm1iY5lC1c40julN7cmoy1+ZnDbja9U43f6puZK7FzA8LBzbhDvhiBzmh5mfvEqiyUfLExajTDr9wmKHs63DXJRaC7nK/7A0UYFDyR72BhkJt1Bs4Lwm1+ds7MQy3/b5t9Q/DRJkoIOzNdDfGvI3C7S/cbokCQuGZ/E33C4hxnmiJSAKkRFDE8v56qyfU4aTl6bfwrN+JcBOlnHs86YtPQSFCzZo285WrhZuyJrMg+eDskSHJzzOV1xKIBJigMHBM3k9ZvlTNR8sVzKmA5oktzLg9mVMsUxPtzkHPe7x5K8udTVyXOZEpjsyuENMd3h4AUamCgAmcelCkKfjeBZnMndY7rfxzDa18YrNzR1EOxxyEmVXOe3SGZIAoPIgCJFY0+QkK5NmtCAF2EWoqwKEIN22s2cbba/RMX6UjpAtz2s+1FPQDrLrXV9Xk56n3W6hq9qNRK7jo+DQWzkpBlYBMjt8VlkGhkLsQw8aYFiOemZ90UKKt23dX0xgI8sKkUvRx6gQLewnpiqjncB0+r9gjidpKoTRgMk1nQ+08XlySInsubzo3m3lTD3201h+rGtjo8vDrMflMPkjurV/y8Y1zpQwO6wJV0XlJaWR20gIZHOZoR4abIiRFuHl3FY5QmL+OLyZDOzzMmMPxg/btqcH66sd4t5XL1etK8ki96DSs7WF8L7+Nyu3CpTbzlmI+e+hpT6F7KAPEUj1jy3SUFOsOGUnNQG0zDPPcQZswVIAwUYa27o27DrhX6H8eLoCw0Rbg9idrmFCg557LDwyBGOjN/bYe/9qbVt5+t53zzk7hkosPfR5lZ2hKmUHH78sG7ln7d1Mb77d1cF1eJicdovzBSDBM+RX/QmXWU/LUxaMeqwR/HCF3WCaHESQx3pqo+K1SpyDtODNZJ1swjdWN2jZB2x7Opw3DRClCPR+PSUF073p/IaO9mSkzFjp6NfAc0BkyKryHX3Y7YcRCRqWQj6ZvHkOts5A547qD/ibEAt25dC2tL75PxOtHW5SD4frzuS1SjkGp5omSeWgFkuqnLGt9kY32DxFbdhkKDZnaW3nghVRO3xbBn+RnyV1fstAynUsz5gw5+cZ2bzv31q9jvN7CvQVd3kyRV9dSF2DXZg/Ll9lx1odQdXPmqTUKCsfGWEynGOW/NbEN0E4AIAS+iw0DCwUTnjtBUCdCODtDTRttYg+iqxgiMG8PaPywdga4853cUGjl6AUnHZSdUwDNt1faeHdVu0x+OKFQz/VnZZGXPvhUCGdTkPWvtFH9dVS8PrVEy9GXZpA7tfcNzMHYqNMSoZAdj2uDnHsoQkwjMYmHTbXTeWvtIiIRBVedoefUmQOf+/sbs/F+L5g7r91RiVah4O+TxqCOQ5dO/K4qfXtY5viE9c6vCMWSUScYprDAchpHmGaiEontw1g6o6u+k5nKxdnpw3iloanaX9OE7fVPcK8V5M+gycsk/aJTMc2aui93UA55f/49XCujwReRY2dRMX4+e2skyit8NDT2/J2JlMPCAu0+gCi8iAUF2hHVQIzXOsMwzx300kMFCBNlaOtsnACUQvXzZzHtp8PCQ7h6u5NH327m9KMtXHm6mOP/7xXB9HTLbVU4nGEee7iYzIxDv6skHqzX7KwkIEV4fnJpXA/j7neuU0vqkXFF5Byi/EFfZRt1P/8vxiPzyfvZaf/3BtYgeizuv2t3NDdQyEZIgehusrFES+ZJFtLnmVHHwrEGcZnRUw9jCwzDRClIZeqBS7p1W6w4a/ohlenNSmLXrBW4KkYa00kq80Pg791OuEwQAI4EqUzIa6V26S8J+9pJm3wxaRO+0+/dDba20/LUG3i3VSCplHx+4hjM58znsuyJ/Z4rDmjxVfBq3S9IUWeQE7LKeXZvv/9Lpm7SkuxT8NVdq7ElOTjKWMIPc07FoBw8gOlsmHhG3F6ziqaglz8XHUe+titnanOFh6c/aKHNESLZoGTRUakkeRX7SGq8MeZhUZcAiAVjdRSN0/OZyUljdph/HFOKMU7v18EM5fKGZXDY6UlUrfCQY4W6FNjciXVUEXJ0Hcw/IocJZemU5eoxdAvdFN7Hpz5ooa41gE6j4JIT0zntaMuQk4a07PKy9t9ttO2JEnLJxDOXZpCS3/OedbJZ3l6Yw+whEGmXpABe99ZYaOk6Ntfk8fqaiwhH1Jw/cxmnHKWXcw/1BpGbN3KRS2sdbh6qSZyl3Bl2sNqxVNY1tIbEoyIqeC8kKzI02WSos8gU77H/0zSZaBSDX3uJvMcf7q6W02X+NqFkwGunuH74Q3BQoKkN25uf4Vq5SdZMVWekkLb4FJLmz0Ah4r57KT1D3jPIvvki9OOKcLvDVFT6ZXBYXuFnb4WP9m5SNrLttQrGlOgYW6anbIyOsjI9WZmHPh9xGOa5g96doQKEiTK0dTbubyK8GrgtJvJ7WABCkVfw3uoOOQb/hOmHNm9uCH5jCVWxYpWTvz3ZzNFHmfjJ7YdP3PnvKuvZ6vbywNhCivXx79a6w2Gu2VFJqlrN4xO6ktcTMs4gTrIv2U3r0ytJPf+IQVMTD6IZ36pTg86wLBzfusSBty7mDdQrSJ+bROZJyZhKR72B36obOojGDsNEKTY1RQ6hSJoTjKCiCG1BEbVyMNmJ3npxA/AkMB2ICrKBkJ0QK5lTup0gZCoEreSIyE74OyqpW/ZbImE/2UffQlJh/5eNSBIdH6+i+aUPUYcklGX5FNz8XbR5/W+QyppyNXfQHmzgzIwrqLa+xPI1C9EsO4aidphwZj2vnVBHQ7CDYm06d+aeRYZm6EIB322v4hXrXs5KKeKyjPF4/BL//qyNJRujuYLHTjJz1emZJJu6FpjCg9hcG+gRYurrBhDFealZahkgFo7Ty++5xTpUgxTRrl3r5ouHGtFZVGRdnUqlLcC6vQ6sdrHZ1bVME38VZGrlMFOxLv58g0MG2tPGGLh2YRZZKYMHDX39LMX9FBIVQqpCJp5RwriTehLPfGTt4J+NbVySnc55QyxvEIlI+H2VfL1tN//4dAwhSc2Z0z5k7rhVqFTJGM1HybmHRtO0PiUtBvHI6XHq0/UtLGl3cEtBNvNSEh+zUkRiu2eTHE5a7tuJV+opX9B5USFnkapOJ10TA4rqGHDUZMvA0ag0xR0J82B1A+ucHm4rzOHYIQDtQ2VTUU/IZsf2nyU4vvhG1vpTWcykfudELCfPlsPW+yv7h7ynnn8CaYtORiHoSbsVmy0kA8S9FVGgKACjx9NNW1BI8ZiVshdRECkKgCiAosXSfxv6a+NAvh+Gee6glx8qQJgoQ5to3BHAV7F3QehcebiEjP7+5Xq2VHp54JpCirPjBx0DueH9HStCCyOhngO1v3OG8vv77q+XfzB33p7LlMk9w0QUOtUho/ztDPu8MT+L41PjB+vrHG4erGlkniWJWwoPjZaiuD8tz67G8elOcm4/EfPs+DS5hvK+7l+XFIoQien+DOd1EqnbXSlyA+3Y1rj3tdFUppNzA9PnJKEaAK18ItcfPefws8AwTJQiSkXEJW2NCdOXAg/HSGa6C9MLsXkR9inCQUUReX5iRShE6QXSWBADlkJjcFE3y3UK04vNz3diwvQCcI6oML27aQONq/8kROHIn3s3hoye0hG93em17lZe2LyKy97eRXqNDYVWQ/olZ2A5/bh+n/9rbP/hK9vrHJP6HSYaj+A/a95j57uLmNYAhclfc/Rfz+fRts/Y6q0jWWXg9pwzmGQ4eH5ivKOxPeTn5qoVmFVqbpRm8syHbViFV9CokkljBCDsrwiA2FofYMdOD2+utZLcoIDWnrmAGq2C/FLdPoAoiGqSBqBl6nOGee8nNfjsYU66K5eCGV3ezB2NTh5eUovTo0Xn9qILKHAEuySfhLfwslMyOHF673mP/fUvke8F8czOjzvY8na7TDyjMUSJZyYtTGFzwCvn55+Umsx1CeT3x9ueLZUeHny9gUAIzpy+mbllb+2TtFAoNBhMU6OspbKkRRcBULz1H+w4AYxv2lVFRyjM0xPHkDSEQu/usIu2YDOtwWbagi20hWLvwWbahZc9llu5f/tETmJvnkXxmQCSKkXXpscGp5sHqhvlvEeR/3g4lLDDTfu7S7F/sppIMITSpCflnONJOWPuAWzH/bVXDnn/QoS8/5eIL4C2JJfsmy5GV9Q3X4T4nTc1B+DHjHIAACAASURBVNlbHvUiijVvVbWf0H4SM5kZ6n0gUXgThVdRP4zrj2GY50YEEA4mIV9Mrl/HRIHj0XTqrUNDTiojBtV1j1Ti9Uu8cGdZn3TN/Q3ORL8X129/dwu2NzfAIQSEB2u/0qQlaX4ZySdPQFc4tA/d/uy2ssPJX+uaOSPdwhW5/e9Wd9b3r8Y2PrB2cH1+FicOAEj2156Bfl/7q/fx72ml+LHFaLIS32Ec6HV7O96118fOe+v3hV4ORZ3DUYfKoCR9fswbWHJoNmiGo1+jdQ7cAsM0UYpcPwHYjgM6YukLAvB1p4wUrBBLgStirRa6hQLYCfE+oZ4tQkxfBn4PROPrusr5wO9ix4qNT1H3qwPo/aDmuXdabeTrtJS2raZ907MoNSYKjr8XbdLBF4X3N2xgk8fKjzOnMnbJDjmUi3AYw5RSsm64EM1BPEH2YDP/rP4RyeosLi9+lFZnLb/6k4+5e5Xo8/ZwyrkRko8/nX+1reRj+xZUKLk8cx6nJk+N2+NxMPv9sXojG1dGoDL6jJ0z2cwVwis4wJDyXW4vv6ms59hkM9enZ1K710fNbh+1e3zU7vXj28+7kJqp7gEQc4p1qPvwIi57rImqVS7GnpDEnBsO3KR0+MM8uqycbQ4lurCPs21LyZ16Mg5tLrMnmklPHlmvRae9BZDdIohnPukknlFTsiiZh7JsTDEb+NUwg40dNV4eeK0BXyDCeXPMnDWjAo97LR7XRqRunjadvgxT0tEyONTqCgc9riq9Pn5eXtenvMYAfs8DOjQYCWILtkaBogCMoZYu4BhsJhDLtdy/UiUq0jUZpMfCUNPV2bzVMh57SMUfyrIoNcS/oT6gBsdxsOTx0f7Bcjo+WC6DN4VOI4PAlHMWoDIPjmgv2GKj+ck38O2oBLWK9ItOI+Xs+f1uYnU2OxSKUF0jwGFXuGl9Q0+JGZE6WpAv8hFj4aZiY6ig7996HCbpccgwzXN9NmOoPISJAkIxmQqa7/Gx3dV4AeGQsq/1Zp02R5Bb/lpNaY6OP1w9ssnLkjdI8xPLcH9Tg0KrQpN9aACDcKu73BKpqWrZfd6jRCDQ6JDd+qLox2XKwNB8bAlK/fCFrXS2ocEf4Md7agb8UP7Z3lqqfH4eG19M1iFi2YqEJSqu/DcKjYoxz3xv0BPUQB8y3Y8XnsFtv6iVCVn0uRoUgwx9Gkxb+jpXnawiY36SLBsx6g0cDgt/++oc6YnyMLFQwoDQHgpx484qxNParFJyZLiRSbXvUqr0UXjCfTLZTG+lOejhtupVpKp0/LVkLiqFEn9VA81PvE6gpgmFQUfm5eeQdPzMPp9jb9T9mkbfbi7Mv4dcwwR+9VwFpZ9KqHQ+iq69n2NnPopen8EXju082/IlISROSp7MlZkL0HTzbAz0Hmwsd/P4+004XRHUeolbz8pj1sT+vYK9XWdZu4Mn6ls4NyOF7+0nMSB7ERuC1O72URMDisKr2J0QVuQiCi+iHGo6PhpqmpyqpvprF1/+pQljuppzHyxE2wdQFYznr29u5p29Lrl5823LWDxGQ+b8RSh1g1s4D9Su+x/vaAqw/mUrNWuixDP2HKhdqOKBU0uGfW4TLK73v9IghwOfNTuFS08WRClhvJ6dMimNYC0NBUUAW7SoNZkYTUfIYaUG4xRU6oGvrd5qsfFGi21YwmITvReyVyxsjwLEUAwwdgLHYAv2cHuPqr2h+XhDp6NTrSJbtzzqXewMR5X/jv5vUaUOOeGTaIgUCGL/eLXsFZRcHlkPxnLqbFLPPxH1IEJw97efHPL+0Upsr34sex71E4rJvvEiWVYnkeLxSlRW+Sgvj+YiCrBotfZ0I2o0CpnJdM6xZs48XWQEJF5Gep4bKkCYCEObQA0VsbCc52MmK4rRdAugKEJuOnM5+rNowhNlXxWv3eXioTebOOnIZK47a+DSBv01uK/vAw12Gh/6nGCDXQaCOXechK5I6B2PbHE6w9z0oyo5T+GJx8ZgNByYsB2ye3Eu24vj890Em6J5GUqDBvO8Miwnj0dXktiPLp6eignyyh0VcnbFc5NK40qid4XCXLuzknSNWqZdPlQCpv7admrvfAfDlBzyf3VmPN0dtmMa3m2n7hUrSZP0TPx1/iGzybB1cLTi/y8tMNIT5WFixITnuaAUQcjtrOhwst7lJhyLekwN2jgqWMuZU06nwHQgWHq5bQ/vdVSzOK1UfnUWsbiyvvkZHe99KZM+GGdOIuvaC3pdzG22f8LS1ueYZjmNEzOvkjXy6p+zkeKFjB//GVNSiGPK7sOozWG3t4m/NH1Ee9jDOH0OP845g1T1wES03b6wLGa+dFN0+aAp9hCc0cbjE+aQru4KtxzIPe0EAfFq3/o8YWr3+GWA2AkUD8hFTFOR7ZAQQojTL89gyqmWPr2InW1d1+jmb1/V45WUlHrKWWx9n4KJ07HMOAVd9qFNPWje6WXdv9poK/cTVsPYezOZX9r7RsNAbN/fsZWNPv7wSgNOr8RpMy1ccXrGvvWAAEqBQN0+cOiXJS06iwKdvkQOLzUap6I3TkSp7D/y5O7yWsq9fh4cW0jhAPgL+uvHcH4fkPw9QlDr/DY+aj0GBSHSdQ8RVkR1EvcvaoVGJriZbJzOrKT5FOoGB/IjobCcHyjyBMPtDlnaJun4o0hbdMpBIw0Ga5tAXbO8ieWvqEeh05Jx6UKST5k9JOudjg6Rj9gJEKMg0e2WOPN0C5dfGn/0Wm99HOl5bqgAYSIMbQI699y26GkRITgihHvjKQlPlH1V/sYyK28tb5dzDcRDZiSKa201zY8vJ+INYpxRQPbNwm3e/wNqONr23w/aeelVK6edbOGqKw4+qMVD17ejGfvnu3CvqUbkPYqiK80g+eTxJM0plYHiUJffVNSxy+Pj4XFF5MXBFrrW4eKhmiYWpCRxU8Ghyx90LNtLyxPLSTlrChmXzRpqs8Rdn68pyJY7a+QF3dQ/FWHIGzqWv7gbMXrgqAUSsMBIT5QJNHE4ThmSeU5sjK12uFje7mC3tyuqVcj4CIKMORYzFrWaYETipsrluKUQfyuZS1ovYMq7q5qWJ18n2GRFmWQk6+rzMR8raAG6ijfs5NnKG9AqjVw95kkqG0K8/Ps6SmyQPfdVFGduRaNMYmr2DWSaZmILuXm48SP2+ptJVZm4I/dMxurje15v2OvmHx+2YHOGsZhUXHNmJnvSG2VQe1FaKRd0A7UDuUFP1jXzZYeTX5TkcUQCoWzCi9jWGJRDTEWoac1uL+q6oJx8KhZBQpZdeBHzxsS8iIKwZrweS9qB4aBNriAPr6yjxhVGKwU43rqUOR2rMOeWYJlxKuZJx6HUHJpnuVgLfPx8Ey2fuGmbrOC6X5RgHsIcu77umZD0+N3LDdjdYU6YnsR1C7N61Y0WkhZezzaZuVRIWoSCbV1VKtQYDBOiANE0FZ2+FMV+Hur2YIgbd1WRpVHz6CHcVB7I2O3r2L/VNrHC7uLavAxmJIV6hKFag13hqG6pyy+Tqy2QgeEx5rmkaeKXIBOeOsEYan3jU0ItNrlJ5mOnkXbhqWiHMde0e98FGLW9vYT2t78AScI4fTxZ1y9CPcTSY+I30NwSlMdfVubg1r0jPc8NFSBMhKFNPOn2pzkTWZ+vAL8AlsRyC+MZ+0MyUXa/kEhYXrfHw31XFMiiscNZxI/F9sZG2t8WGsaQesF00hbPOKgA7HC2R0xet/2kmpbWEA/eXyjHRMdbwk4fzuXlOJbsJlAn0nBAoVOTNLdUBocCJA6VZ66T4vrWgmzmxBFm8M/GVj6y2rkhP4sTDmH+YOuLa7B/uI3sWxaQNK8sXtMO6XHiobXrDw04tnjJvzCN/EUj74Ue0g6NVvZ/ygIjPVEeJsYd1DxXe/dm1CladKVmdKUm+b1V5eKjzR+wXl9Gmza68SdiQQToydKH+cS5m1nmDH6cKwhTey+SL4D1lY/kEDB5oTdnOplXndcjB+i/DQ9S6VnHObl3Umw4il/dXcGEygj5uXsxHfMCjti+WKbpaCZmXI5KlcpzrV+y1LlTDhu9JvMEjk/uW+5CeAVf/LSNLzdHF69CuPzyUzNIMqpoDHhkCYpMtZ5Hi+fGFU2yf0/vqahjh8fHUEkVVa5ysvyxZplVNP3EZOqq/DJY7C55IdpgSe/GaDpeT16JTgaO/pDEWztsfLi3Q6YYSA/bObPpPSa4d6HSm0iatkAGh9r0oSHoGcj4D/okXv5RJQp7BM+NBm44fmSIS+rbAvzupXraXWHmTjHLAvYqZd9LXDEHhoLNeNxbowDRsxUpHA3JlX8HSqMcVtoJEDXaPL5od/J0QwtnpFm4Ig6m3YHYbaSP7cyLLdXr+MPYvtOiOkI21jlXs8a1nFq/SKEWvLcKxhomMTtpPjNMszGoeg9bFjYWGoK21z5BeOlEMR45gbSLT0M/zDmmfdnTV15H8+OvEWxolclrMq86X35mDdW6dCjv40jPc0MFCBNlaNvfdvHmEO5/3qAmyt5u4E2PVdLhCvPCnaVoNcOnbxN2+Wn+25d4NtajMGjIvnk+5qMF8/mhK+s2uHnw4UamTDLwq18k9jCXvYa7W+RwUtdXlUQCUa+htiQNy0nj5bBSlXFwu5hL2x08Vd/CORkpfH+/vI7erHfX3hqqfYFDmj8o2lV3z0f4djRR9OfvoM0fXIx5oqOkbbmTiseb0edrmPrHIpSaoXoUJNqi0fNGLRC/BUZ6ooy/ZcN6ZMLzXNgZpPL6tchJhN2KOl2LpkiNN7KctnQ3O446ia+MGdjDnTw6EtPMes7JyGCqyXBQMOXZsoeWJ9+UqeNVqUlkXbcY04wJ8tV2O1fzv+ZHGWc+jjNzfsRfX2jA8j8PhnyYYf0lviPNtJ9vxBtqQanQUpp6AcUpZ/GpYwcvtq1AIsKZliO4NCOax9i9rN8T9QoKIJAivIILszh6fM8w03vq1rLD18HdeUcxzTjwza+bd1VhC4b41+Qy1AcBGfHcfW9HiHd/UkPAJXHar/LImRJdTIuNWGtTMOpBFGQ1e3yyBEb3XESVGvLHCMkLHcUTDKSM1/Dydivrm6JyBRNp4fTqV8n0R/PmDMVTSJ5xCubxx6AQJ49Q2bHMzjdPtOLIhnn35nKkZWBhv4k2s6k9KIPCNnuIWRNM3PqdnLgJAaOyFtV4PVtkkOjz7CAS6RI1V6nTeFn6PluC2dxVkMSMlPi81p19EWuisN0le9ODTW0Em20Em8XfVvldFF1pAfqxBejLCtGNLRzSfLr9bSrac9feWmr8AX5fWkCZsX/HR4O/ljXOFXzjWiGznooi9BGnmWbKnsMpxumoFNFxJp4H1lc/wV9eK/+vn1BC+ndPxzBpTKK3d8jOEzmM1lc/xv7RSjlCSngrBTBUJY/MOI23IyM9zw3lKjARhrb97XJYAEK7O8T1j1TJej8PXSfSGoen+GtsNP15CcFmJ5p8C7l3nIw2b2TCUw/Wo/sfbGDTZg8/vjWHWcckloTfvf6w249zRQWOz3cRqIlGCQuyHPNxY2QiGkFIk8juTLXXz13ltUwzGbi7n90mZyx/MFPkD044dLkWESlC5dUvId5Ln/9+3IxXQzkChZ7flh9XE3JKTPptPkkTBTniaBm1wLfHAiM9UR4mlkkYEIr2S/4w/moP/goX/gq3/B6o97I/k70yFZzFZlam+ajPMdKQq8FrUJKqVjHHksT8lCSK9dpen9lht5e2f/4X57L1ssmST55FxqVnIekU/KPyeiRCXDPm76zaFKTywVaEvvupx7yLZ8M3GI6ZiWdxAVXOD4gQwqTJY2LmVTSRwiNNH+OUfEwx5HNbzhkkqfQIYfcXP2tjWcwrOH9a1CtoNhwoXL3M0cATLduZY87m1pxpA7qdIv/yB9vL5dxzIeY9mCIW4Uv/3ITQHZxwuoXZVx48HcPvlaKMpjGAKN69ri5Un5mn4dyrMnGkR/jn5jZEOKlKASca25hX+Qbqjnq5uSqTheTpJ5I8/SQ0KcPPiSD6+eYva/CWB6k8R8ld3y1BL272CJQ2uwCFDQhwOGOskdsX5aBVD/zakhTA592D1y0A4hZc3hrul34m9+AXyj9g1OXGvIeCoGYSSpUREe0VsjmigE8GegL0df0d8Uf1c/cvnRp7Ije3e1GnW9CVFaIfG33pSvNRDmHe4idWO881tnJ8ShI3DiCNRugn7vHuYI1zORvcX+OTojmIZmUSC9pmMPGjIOxolD/TleSR9t3T5RDNRNZ5wzlkPNvKaXnyDUJtHahSxCbWIkxH9R2JMJxt6XVcCBrT7kKkw9yAoQSEw9zUg1Y/qIly/5o3lbu5/9VGxARz87kD2wWK1wjOVRW0/H0lEX8I0yzBfDR/WPLs4m1P53GNTQFuv7OGtDQ1f324GJWYXYaoiEnCX94mh5M6V1bIfRdFW5BC8knjSVowdkA5k6FIhCu2l6NXKvnHxDEHfdissbt4uLaJE1KSuGEAD74h6vq+agT5TvVtb8kguOC+s4e6+rjqq3iymbYvnbKO35hrh39xEFejRg8atcAALDAKCAdgrNihbUEbaeqUHqyBkk+ARDf+cjeubVX49ljBITxoPZ/7rjQVVdkq6nM11OeqodjI7FwLcy1mMnpha3Z9s43WZ96WPSLqrDSyb1zMirSP2eH8klOzbiSbufzz9irS3bDwFybc//4jwZZmtAVFWG6+jL3Sf7F6N8stzxH5Spbzeax1JTUBK5nqJBZ6TuW9T32yVzDVLHIFs5i5n1ewu4X8UpgbqpYh5ownS+ZjVsWf29PoD3D7nhomGfX8prRg4IbvdkbFcicrHm/GnKXmnD8VoRmghpmYQ62NQRkgblzhZO+W6EL8yHlmTv1eOitaXby104YvFMGiU7Eo282U6v/h3btO9oSI+2osO1ImoTGWibSUgQOleA1grfDx/t11BAyguTuZK8pGbq6xOUOyp7DBGmTaGAM/uTAX3SAjvTbYbTxQa2O6qoVLHG8SamkHmxpsGmjXoGg3EGlXyCRBvRXByKvNTpcZLjXivfPvnHQZjCBF8Nc24d9bi6+8Fv/eOgL1LbH7FqtRoUBbkNUFEssK0BbmoEgwT9MblrhxV6VMMvXkhMTyPQNSgC3udWzb9QXZ7zdTtjO6wWzPlHCfN45J888hU9e3BmC842m4jhPSF60vvo9z6Vr5EsknHk3GZWejjMNjOlxt6qx3pOe5oVvtD7dlDl7/kALCd1baeHWpjR+cmsHCWUMb0ickB6wvr6Xjg23ynJv+3ZmknDvtsNk5efGlVj78n52LF6fxnfMGHloT7zAQ0hoCFDqW7MJfEQ09EDIMptnFWITXcGJ2XDb5RXktFV6/zBqaeRAZic58w5vys1hwCPMHRfhs0yNLsZw2kcyrhNzZyBbHVg87f9eAxqJi2p+LUJsP3E0f2RaNXm3UAgO3wEhPlANv4bCckfA8F5CC3Fn7B0yC2CXzYsr0vUe+tO9+j4bNb/NK8iXkNuo4z5FDuNJDsMF3QIesqUrZexgqNpA3PoUjpqaRlNSVbx52uGh55m3ca8Rcp0B56gQ+PO5j8pOn8p38u/nznRVk1kpMuyKdI+ZpaHryUbzbtqA0mcm++VacRR52tb6IP2xDrTRQmLqYd+0a1q7QQXU0lWHBtCR5nu7NK7h/g59p2cFnjnquyBjPGSnxR/5scnq4v7ph0JuJHluI9+6sIeCROP3X+WRPGlxkhgCHW75y8cGLbTjbw+gMSk67OI3x8028tt3GsppoPuXYNB2XjdWQUbkcx6YlhF3RKB11cgbJR54kew7V5uHREV76RBM1y1xUzoJLbshnvHFwfR7Ir0pEev3+5QZqWgJMKtJz4/kBWj2fkJd8IqmGaChzX0WEFArvXqjZSiAW1lld3UikxUaqw4VC6h30YQxDWhBSI6izU9DnF2MsnIKxcBKq5KS41jTd2yR5/fgq6vCX1+HbWyuHX4as9h7NFt5F3Zh8dGUi3LRQDjdVZ6fFfa1nG1r41ObgspwMzsoY+Ho30NSG7Y3PcK3aJIPXUJqOjacEWD6lkkhseVGmn8AxSfOYaT4Ok2rwUWcDGQfxHutet52Wp/8T3cTKTCX7xgsxTO5iVo63nqE8bqTnuVFA2Mvd+8tbjXy9081vLstnUtHQPcDCDh9Nj36Bd1sTQtQ959YTME5PLEdvKAddZ10+nyRLTQQCEo8/UoLFMjI5B77KNjnXUPYaeqMx+5o8i+w1TBZew+S+Y9ufrm9hSbuDO4pyOCa57wfNnXtqqPUH+Nv44l53tIfDnr3V2fbKWjre3ULW9XNJPlHIb45ckQISW35ai78pSNmt2aTPGbgG08i1dvRKoxbo2wIjPVEeJvciYUDYFmznkabn2OuvkgkhTkmey8Xp52DejwxCgIx3tr3Ga/oMjnLVccfkC1FpTUieEP4qN75YqKm73EWkuYuhtNM+rgw12jEmssdbMJaZ0RYb8WzYSuvz7yK5fbizwmxc1MKiuX/hP8/64AsPpqP0LPppAZFwGOvrL9Px8QeC0YOM716K6eQTqGh/i5qOj6iuH8+qtRfg8RnB4IOjt3H+lGIuSpsdF1FMuc/B3XVrKNaa+WNh/JTzn9rsPNvQyoVZaSzKSmyTVNh1yZ8aqd/gYdJCC8f8YHB09N3Ho88j8fkbVlb9zy47AQVT6XlXZ+JNjfDCxjYqOqL36YTiJC6elIKmbhP29Z/irdoSrUapwjT+aNlraCieGjeQiOc3IfIl37ytmnAgwu6b1dxzXDGaQeZgxnPdfePRG+b3L9dT2RQgM72W0xb8E702yISMy8jXHC8zXsp5fCK0s6mNQJOVkACCtp6gq/s1lalJaHMy0GSnocnOiHr8stKIpPrwsydKUuPZTiQWRinOVamS5fBSgzHKYKrRJu4tDbU7ogCxvBbfXgEWaxFerh5tNBvRlxXIeYhyPmJZAWpL7+ujGp+fn+6tJVerkRnb4w3rFMDU9p/PcXwhcpMlVBYzqd85CcvJsxAgtTnQIOcbipc1FM1pVaFiqukoOd9wqmmGnH840BIJRxA50WFnCOHaVKVqUSWph4SEMexw0/LsO7i/3iJvYqUsnEvaxaejPESa1SM9z40Cwl5G44+eqKa5PchzPynFqBuakApfeRtNDy8hZHWjLU4j946T0GQdXgvyz5bYeeb5VubNMXPLjSPv4pd8QVyrK7Ev2Y1/T2v0zqiVmI8plsGhYUruAT/6zsn6gsxULsruXffQEQpz3c5KsrUamSr6UJaG+z/Bs6mewvvPRTdm+HQae+tj3WtWGt5uxzLDyPif5sb94D+U9hq99qgFerPASE+Uh8ldSBgQivaLvJ8vHKt5xfoeLslDssrM99PPZ0HSrH3PAgFcflb7NdUBF9fvfZfxpizy5v4chfLAzcGwO4Sv0kXtLjvWPQ401V7S2jvJaLospsnTo8lTEKxfRbCxBkkRIbiwGM+4S9jxiJVgmpKrn+jaiXesXEbr8/8gEgqSNHcBhouu5NnPG/l6RzR3blzJeqYd5+CjiIQ3EuQoYwm3ZJ+CUXVwNmyZRKP2a2oCLv5QMItSfXJct/Wlpjb+29bBLQXZsixHImXvUgernmohOVfD2X8sRD1E64rubWmo8vPes61yOKlIPTrm5GROvTiNr9s8vLrNisMfxqBWsnhSKqePTUHqaMax8XMcm5cieWOajWm5MglN8tQFqIyJ9dX1jQ3JHSJpfiYKlYKt/21n/UtWWkuh8NYULoqDAC4RG3eeEwmFCNvdhBwu3NZqKio/YtuGEiIOPVkqB4WavejalajcfUTHKBSoM1J6hHba05J52B8gJTeDeyf17zWKREL4vOX72Et9nt1A129Do8nGYJomg0PBZKpSJ2Zr0WeRtyjyFbtAYi3+qgYI9fwtCq9XlKwmRlozRuQjRon9OiW87i7JY1o/sirC8y8E5e2ffCWLvQumztRzT8By+px99XW/f+J3V+HbzdfO5ax3rcYjueWvDUoTM83HMks/j+LAGCRnOAr0HOIV6vm3+Ez+LoTk6plnGUWaCtSpWtRp0ZcAiZ1/d/9Mqe1/PS/aKyQyWp9/R97E0uRnkX3zRegHGS6eyJge6XluFBDud5cEffXVf64kJ1XDIzcNDXhwLN1D67OrZX0+87xSsq6di1I3Mt63eAehPFneXUtNbYD7flPAuLH9M07FW3cixwnCHTnXcHk5kjuaiK3JTormGh4/FnVKlJltr8fHLyvqmJFk5K7i3im2v7K7eKS2iRNTk7l+hDRveuuzsHHV9a8Sdgco++elCcf9J2JPT62fbT+rRaFWMO3BInRZA9+ZS+S6o+eMWmA4LDDSE+Vw9CGBOgcFCDuv5wi7ZFAowKEoE/SlchhpkS6P3d4Ofl2/lmKNiZvK3yVoryKpcAFZM2/sdwMpIElsbHKyY4cVT7mL3MYgeY0h0jo6SVBEmF0VEbahIIzSlE6l+1g6yOCsp8aiS+7a5fdVlNP41z+zNZDFu7kX4FIYSUtSccEJzajMzxKUnLgVGXyhnE5LOECeJoWf5C4kT3vw0Mf/ddTwQttuTk0u4Oqs+MgjHqlp4iuHi3tK85mQQMijuy3Ie3fWEvJJnHFPPpnjhy7qaP8xJJhK1y118L+XrTL5jClZxZmXpjN+tpG3drbzcbldpKqRl6Th8umZTM82IoUCuHeuwb7hU3x1u+QqFSqNrGcowKE+f1y/914GJuEIbf+uxP5RVF5AU2Ag8/Ix6CYl8/Yd1XiaQ6y/GG4/s5DiARCjiHlTeMBEKJ/w4ITtThnwCWASEp/Jn3e9i0V8fyWijBBMCxHJ0JBWNANDXkFXXl9WKgp1z/XZu63tvNJsTdhLLEk+vJ6dMYKarQT81d2aqECnL0FvmIBGm4tWm4tGl4tanY5iP0bd/vrV+b0Axf7qJtl7KEJNhdRCUOQjdi8iH7EwWw4zrc3N5F9GHcWlBdzeB0FfTpShcQAAIABJREFU2OOj44Pl8iviC6DQaUhZOI+UsxegMnWNaZGXLIM6ZygG7mIAzxEk6PBjb7fitjvBIaH36NH545c1U+iUqJI0qJLVqJI1ctpVuD1IqD1A2N7FCNuXnZRJ6oMDx1Qt4hgxvwgPaPPf38S7eQ+C/SrtgpNIPe/EEV23jfQ8NwoI9xs526o93PfvBo6dZOa2CwbnJRNCmK3/XIPj052gVJBx6TFYzpwc18M13h/+UB23Y5eXe35Xz5gSHX+4t+CwaaMUCOH6qkoGh76d0YlG7AaZZhbJDKXqKTlcubMSi1rFkxN7pzN+rqGVT2z2Qe3wDoWdhXe46ubXZc+g8BCOVBGMpjt+W49rt4/Cy9LJPWt48kVGqj+j1xm1wEhPlIeJxYcEEHb2Zbe3kmdbX6M6UI8SJWemHE97qIxVrlauyZzICVojdV/+kpDXRtrExaRNWhy3GVyhMKsdLlZ0OKlp85DXFCK/McT4FonMmkZMThGuKASqxY79JEDosSqiXDYipFClICRFCEogiX8JYTBqUImNVCUEI06CuJGUEm6lHq84R6kgR2fBrDEgr6NVCtlDJT5XyN9DWAlfe1vkz+Yk56BSK6PHxs4Xx3ceK5+rUvCS0cvKfIVMupGqGdhGrgAzn93fQONmL1POSWHm9+MX847b2L0c6HaE+fgVK2u/cMjflkzSc95VmQQtCl7Y2MrW1igZzdG5Ji47IoNsc3SD0N9Sg2PDZzi2LicSiB6jzSqSw0mTpsxHqesdzApvcfNju/FsEqGWXlDYIBJNhzHNSiN4ZBZLn7biToW627TcW5pHxOmRwV1IBnn7Abt94M8le/r293b1ZRuFTosy2YDf6MRvcCGZJJIzJ5CWPQNMJt7aGGCtVYshO5lzTvkPIeU6NEoz03J+SIaxb63NTg/a/WWFjDHED2D6amcoZMfr3obXsxWPezOhYNsBhyoUGjTabDQaARBzokAx9lKpLANeowlA5++WjyiAYrg9Oj46S0Cjxliaj3lsUSzctACFzkDHByuxf7qSiNcrhxjrx05FWzSdiF9zgDcvEthP2+YgA1nSR/CYPDiMDrwmL16jF41FR3ZaPkWZYzFZkmTgF32pUer65jyIhCRCAhzaAoTbA/J7yOaPvgvAKP8fIBLsI/8z1k6FRhH1MIpQ1FQNkmsv3h2rIRxCk5dL5hUXYJiUj2KQJEXx/L5Hep4bBYT73ZUPvu7gX5+1ccmJ6Zw3J/GFc6jdQ9NfvpC1+FQWPTk/OhHD5MEBzHgGUKLHPPK3Jr762sUN12ZxwoL4QmkSvVai5wXqO2Rg6Fi2F8kZzYtQZ5hZNT2LL6Zk8OAxE0jpZcL+yZ4a6vwBnphQQtoAJ/RE29rbee51NTQ++DnJJ44j6/p5Q1n1Qetq+dRO1bOtGMfomPK7gugiabSMWuBbbIGRnigPE1MNKSAUfQpHwnxiX87r1g/wRkSI2RloFUr+XnI8BpUGv72aumW/JRLyknXUjSQXHz9gUzQHgqzscLK8w0ljILqLb3L6+c7yDUxYvx0FEpIiDZUuDSIKJElBMCSeUQqUnS95DScWgzJ6i7Ggdn/v77Pevo9eIx5W99XHGvj+zdNQagZGwrX7MztfPdOKJV/D2fcXooojZG3ABj7ICdW7vLz7bCtNNQGxjmfewhROuCCVzTYv/9rcRqsnJOf0nT0+hfMmpKKPyTNIAR/O7StxrP8Uf3NMjFyrJ2nKPBkcajOLiASC8stf66Ll77sItQgAaQXTOgg5IZyNQj2OiM8HCj8hdYhg0I9S7UMXOjD/tNduKBSokoxyfpoq2Rx93/e3Sc6L6/xcmWSiMbiSXa3/IhzxYtYWMS37ZpJ0XZFewVCER99uYu1uN5kWNd9buAan9B95DIxNu5gxqecdALSEZJVIOUlRq+Q1RLw5dvHeR7FpEAo24/cJ/oZGgrFXINCEFI6G8u5fFEoDWm1ODCB2B4s5qAZA2iJyJTs9iNXbK9BVN6KP/Ua7ril+I50gSpAxCUKe3oXolWbhuVPHvHhdnrzuXr1OcCc+6wRVbcEWvnGukMNKW4JRuQqxSTXZOF3ONzzCdDRa5eB0q0WdsrfZHYqBxRhI3AceO0FkAEnkJ/YoLkBI6QhCJvEsmYzCPB5Nui4amioAZHoMRMbCVtUZOlTGgW0gHXCfR2Un4v0Z9ThuyCbKv73bzIqtTn5+SR7TS3sf9P210LurmaZHviDc7kVXlkHuj09CnX54CV5274OtPcQPb6/CYFDyxKMlaEd40urPnvt/L0JvXd9Uy7qGgqBHFLGLHD4ij6LTJmOckb+PTrsjFOKGnVXkaDU8cojzB21vbsD25kYyrzoWy2liV3z4S6A9xJY7agh7Jab8vgBT6aENBR7+Ho9e4f+CBUYB4dDe5faQnQcaPqMqIHRwK5hmCHJl5oXkabPwNG+iYfUD8qI5b87PMGYNTMevs6VyLpHXz+e2FpZ12AhhZtLyds77cilmASQOVVGpovNF7KUQyEmplMP1IiJ0zBFGGVGiMGjQFZqj8lBqFQpxnniXX+poKNm+/1UE/Qq2f+wkHFYy+ew0zLn6Hsd2nh89J3p+z5e6R31y/XK7em7oCbsKD5pgxuwEaSKypvPvkC/Azq/tbF3ZAcEQZpPEtJl60rJgT5OLylYXCvG5QqLMrCJNBZFgtC5Rp+TxEHa5iPj90Yk2EnsleL8kVDiSDaRlpKBPTUKdbDoQ7KXEgF6SMS5pDH+onW0tT9Pm2SCPUwHsytIWo4yJpHdvaigc4Yn3mlm13SWHIV93XgttoSeQIn6yTMcwNftG1MqutZ/YyHi8rpmTU5O5doRTTsJhlwwQo0CxKQoW/Y0Egk1EpN5DY5WqpJg3MQoYuzyL2SiVfc//YuPm9l1VlHY4ud3mx/7hdiS74HJwoUrORzdmpkyio5RDNruBPfF3UhQEDnazWYzlan+FrG+41rkSlxQFxHqFgRnm2cxKmsc4w+Qe0jkJDsODnhYJCm9jF0CU/7b68G5dR6BGjDEJFBkQmdEnODbPyyDnlnGDat5Iz3P/v7gKhgwQ/uTvNdS1BXj6tjEkmwa2GygGswgPFWGihCU53y3zymNlOYXDubzxHytvvd3OOQtT+P4lIxPSMlT2CDTa2fjhFrSrqjC7YzHkWh3qTBMqgwpXOExTIEiySkXWIWKK6uxrsNkhezYL7jsL/bjEWcYGYru9jzRh+8pF9kILxUPIbDeQNoweO2qBobbASE+UQ93+BOsbsnlu/+tLkQi316yiOeglU72J1lAVatSck3oy56eehr9mBS0bnkapMZK/4B50yYUJdiF62uu1v2WHJ4zRexk5fwSlpondxUFUAQmVRkJbJJGeoiRNoSBVqcSiUGBqtxFZvQKlz486IxPT9KNApYZwmFDQi929B7vfRks4mUhYhSmiplSTgVZSIMLJBIupmJdFKkeTLxqGmK7QoJQi0e9CYYQsVOdxg+rgcJ2sUHQBT6USkSsWCYR6atUN17VVSnnBH0FcT7x0EBJkKAKoeuQQYF3JGLQF+SgNOlzrvybc3kLSvHmYjpqHY0kHno3Ci6imxaRh3VXp3DJn8B63JudqdrSKvFIXRk0OU7NvJkV/8IW4yLV8+oMWlm52YjGpuG2xgrbwX/AGmzFp8jgy9w5M2mjI66O1Tay2u7izKJeZyYfHxr5Ya4ZDHQSDTQT8nV7FKGAUYFG+P70UlTqtG0Ds9CwK4ChkvtT8sbIe7TIb53zuRhGIoBtnJvumsWhzhy/vta/hGo6E2O7ZLIPDze61BCPR9V2KOo1Z5nmy5zBPN7jnUCI/FX91I82Pv0agpgmFXkfK2aehK50UzWXsFq5qnJ5Cypm5iVxi3zkjPc+NAsJut8sflLjiwQpSzWqeuLVkQDdS7MgJ4hjnl3tlZkwBBIWe3uFeQqEIt9xWhd0R5tGHisn6FpKN7HB7+d3eWs6udXH0O3Xgsu4nq3z43AVVmpHiRxah1A4ulCCeHrWvc7PnwUa0GWqmPVSEaoAiyPFcY/SYUQscCguM9ER5KPrYyzWHDRBu8li5v2EDE/Up/DL/SD7o+IL/2P6HPxIgU53GFZmLKaneTvuut1EbMig44Xeo9QPXLOvs0xb7Z3zR+gxTk06h5q4TwRfhkymQNUGDYZqCpkgIr3RgLpJYsKQ57WS0NJLtcTHumNkU5eWQr9NiVqmwebaxvuUFPgil0EgKRoWC23LO5AhTz/zyt2wVvGGr4LyUEi7JGHuAqWWvW1jim3Y7T1Q3cqrRxClLHTiXCWIOCcsZ2ZjnpcUAZigKJmVAGX1v2OCkcnkHplQlUxYmoYgIIBqKHhN77Tun++edwHS/Y8U50eO71REOy/T+So0GhVa81PK7oMg/4H9N9DvxslojbFvnx+FUEFGpmTDLwpQ5KdT5JT6sclPnjxBWqZg9JoVzJmfJ2pLyuTEReykYpumvG/CsCYIiBIWfQeouVLpUMs+8XJawUKjUBBrqqf31z2TbFt77R7R5+TjWtVP1570YpZDsbHTPTeWIH5RFCUIGWAJhJztbn6fJtUo+s8hyBuPSL0GljC/HT2yCvPBxG5+ss2M2KLnzolTcyqiXUaUwyJ7CdNMxXLejkmAkwjOTxqCL2WCATR3RwyMRSc5JPMCzKDyMQTF+e8uhU6D2FeF4+xSMu81ISrCcqyH1vEI0+syEyW2GquNeycNG1xo5pHSPdzuRWB9ytQVMMkxjgnGq7DnUK0cGuAp2Vdubn9H+3pfyZoxx5iSyrr0AdYIsxH3ZaaTnuVFA2O1O7Kn38asX6pg5zsSdF8WP7INtLllSQgisq1KN5P74xBHzAA32B7fqKyePPd7MzBlG7vxx7yydg73GcJ/vCUtctaOCY3Yqmf+mhFIbJhIIo01T8d53oVob5oGyQiyHgadWZdKNiMc47JPkUNGANSRLTKQcdXjsbA73WBit//+GBUZ6ojxMrDpsgPDPjZv4xt3KD7OnMjcpmuveFrTxz7a3+Ma9Wf5/pnEqCxvd6GrWoEspJX/+r1GqEwtB94VdPFN5PVqlgYJXfkvbFh9l16Qz95Ro3r4AZB0CWPkD1PuDsfcADYEg1mDv3g+LSkWeTkOuTo0+vIdt3q1UK7Sg8LLIUsLizLP2hVu2BX38sHoFKSotfyuZh6oPNsf329r5d5OVy3LSOSsjFeeKVlr+UUHEL2GamUrWTWNRmXpu7jmbgvz3rhrCoQgLf1dA+pjEbDScYy4YkFj2XgdfvttOKBghI1fDuVdlMmaKgc8q7by+zYY7KGHSKLl4Sjonj0lGpVTIrJGND+/Ct9OJKkWDumANftsyFJkaIkQ9OCqTBfOkORgKJ+LfWUn7O++gKxtHwd33yKBy9ycdVD5XS6HSjVaKoDCqSL+oEMupOXGHHLa6N7Ct5e8Ewh3o1elMybqBdOPAQ5nFOHtpiZX3v+rAoFNy10U5qE0fyNqXcjFexnO2CcwwG7mr5Nu5Puo+joQcRjDQLIefduUrNuFfr0F6bxZ4DVjT4bXzU7go/29kKKxEyW3y0emL0OmK0epL0OmLB5SrOJRj2RZsY61rpaxv2BCo3Ve1yDks1pcx0TCVCYapjDGMT0jncCBt9e6upuWJ12XZD2WSkaxrvoN59sDH4SggHIjV+z92SCbKT9baee7jVhbPT2PxgvgEaD1bG2h6dKkcCqifmE3ObSfsk0Tov9mH/ojf3FfHrt0+fn5nLtOP+PaChp9urOLMv4YwuWHir/Jo/cKBdYWLxnxYcZWaB6cMzON76O/M4FpQ/c9Wmj+yk3asmbG3Hb5kRoPr5ejZ/1ctMAoIh+7O20I+bqlaiVml5vGS+Wj2A0cb3Nt4vvVNWkJtaBUaTrZrmF1TTXL2THKPvSNh78H7jQ9R4V7LkZvupuoNAyqNggmnW5h6bir65L7TLHxhiXp/gL1rvqJyz15a0zOwFZfRojN0U3rrbp8wKNxYVE6Ot5QwxphGnk7LS9btbPZauTN3OjNNvQvFdzJU31GUwzHJUWFvf62Hpod3EWz0ocnWkXP7BHQl0blTMDp/fG89LTt9HLEolSMvHFmt2YGOCmtTgPeeb2PPJhHyCUfMMbPwsgwwKnh9m5XPKx2yL6bIouXKDDOGZ6oItfnRlZrQT9mD/dM30GRlk/+r+/C3VMgMpe6963qGsEbU4AhhmjKLtNMXoU4r4MNf1uOs8mEY42ZqlQ9FBLQFBjIuH4Nxmshj7b2EJC+72v5FvWOJfEBe0vFMyLgcjSoxvgf5nkUivLncxlvL29FpFLIzIDtzB1uaH2eVfwFbw/O4PCeJMzOyB2rew/74sCdE2/OVOJdHWU7Np5r48kSJtz1mTtRXcZb6Sxk4SmFBqtKzqEX4qb44BhKj7xptTsLPg0SMZQ22ssu7lV2erfK7IyxYbqNFo9BSpp/ARGMUIBbqxgxL7qHkC2B9+SPsn0RlfMzzjiTzinNR9aPnGE9/R3qeG/UQdrsrIqZ8yUYHP7kwl6PHHxwcyTuYH2zD+tJa+eFnOWMSGZfOQhFj6YrnZh/qY6qr/dz1y1pycjQ8/EARSkG9/S0t/324iuw1IdTzjBx1Sx5SQGLNb2tRVgRpn6HmtJ8WDzk72OFqKle5j+2/rENlUDLt4SK0KcMfnnq42mK0Xf9/WmCkJ8rDxIpDsvG5f1/esJbzVntln+GT4viAFODd9s94r+NTgpEQGQE4u66DmdknknHElQk9W/e4vuKjpkco088hZ9n32Pk/O+FABLVeweSFKUw+KwVtP3n87k0baH7qr0heD7ojZ6K88nqaFKp9HkXhXazxefBH+hCkVgRJViuYn5xBvk4jA0XxShYELsAfqxrY6PLIESbF3eQGJE+I5qfKca+xIWjqM68uJfmELHZ82ME3L7aRWqJl4e8KUamHZ051iIWwcwWTk0/ApE6cDV30Uaxltn7t5oMXW3HYwugMCk69KJ3Zp1mocQZkmQrFZjsXfu1AFwbNrDTSZtto/sejKPQGCn91H9r8gn3DKuS04ancgq9+F7663QTa6noMOSFd4dEex/oVJxFIirB9UZhrVgXQ7o4KlguZioxLi9Fk9fSs2rzb2dr8JL5QK1qVhclZ15JlOnrIfprvrW7n5SVWNCoFP16cw/giB3fsraNDSuUy43PMz7+WZF3v0lZD1ogRrMizzU7LE3sJWQOo0rRk31CG8YgUOoIhbt5VhUGllFlVNQoF4VA7fn81fl+1rKEo3kVI6v7hpwqFDq2uQPYganXF8rtOV4RyEIA9XpOIcdwYqIsCRO9Wdnu345Oi0imiGJUmOaxU9iAap5KtyUvoudVXezyb99D81BuEbQ5UaclkX78Y4/Tx8Ta/1+NGep4bnqfVoEyQ0MlDMlH+/NlaKpv8PP7DEtKT+15ES74gLU+vxLWqUg7/y7x2DskLDsxDSKgnI3jS08+2sGSpgx9cmsHC0xPPBxnBJvd6KccOLzvvqcdjBPdvUjmrOLor+/zOJgr+4sJih/wL08hfFJ/X91D3ZzDXF+LA2+6uxVMVoOSaTLJO6Xu3dTDXGT131AKH0gIjPVEeyr52u/aQzHPd+xKKSPywaiUdYT+PFs8lS3PwHJymQCvPt73BJs8OuZppHT6+n3IGJWMXDdhEISnAM1XXI8gjrin5O5JTy5Z32hFSDVIItCalrN038YwUNAfJfw40NdD4yEMEmxrQ5OSRe9tP0OZ0hfeJhWKVt52/NHxGS0iHKmIgAy1BRR62UO9AMUmllIFhnS+AW5J4blIpRlXPY0W99g8baXupWiYd1M9OZ9l6JSL18aw/FJJaHF8u20ANJ4D05y1PE5A8pGkLWJz/W/QDkBro63p+r8Tnb9pY9VGH3Ie8Eh3nXpWBeZsV22vR0LxPpxpZPcnIgj1LmbtnGUW3/hjTdMG02HcJe120vvMirrXLUKYbiGglIkE/u6ouxmqfxp550Hicm1+sLYd1OUgOhQyyU87JI/XcfCLaMHutr1Jt/0gGINmm2UzKuhqtaujlsf73TQcvfNImdMj5wdkZPK9oI0vl4GzNn1AqNEzOuo68pPkDvWWH1fFis9z2Wg0dH0TlHcxz0sm8qhSVuWvN+0hNE185XNyYn8Xxqb3bWZL8BPx1PUCiAItSNxDW2XG1JivmSYyGnQqgKD4bagmP7oYWkjo1/op93sNy325CMWIacZxFlSoDw06AmKoevDc/7PLQ9sJ/ca7YgPnYI8i57XuDuvcjPc+NAsLY7RJUxJf/qRyTXsXfb+ub+SrY5KDx4SUEatplDbz/x955gMdVXG//t71qVVa9u0hybxgwNgY3egCDgVD/1BBaCKGEFDqkEGqAkEYg9NA7mFBs3CDGxr1Isq3eV7ur7f1+z9yVZdmWLMkq2Hya59nnrlZz7505t8y8c85538xb5qIfcXgxc4oue7xRrruxUiZfefqJQkx9ZFTt110+gDvHwhKbb68mUB/m44WQOsvMDXnxEMmbyqqI1Ia59AUFUkBi1I0ZWGcKVrQfbmn40EHNS62YS/SMvVvIb/xQHvEf7jUb7lnfLTDUA2XfWzgoeww4IFztaebRxo1MNVq5PfvAE/vdPRJAaLV3A883v4Y95kEXjXGm/kjOzL8ElaJvjNqfN/2Nre6lLEi/RvZ2ieK1hdn4toMdS11IAmglqphwZjIlCyzd6vhFfT6a/v4kvg3rUBqMZFz7M0yT9u5PWIryz8bFLPNWoiTGTHbgVx/L2lAGx5hyyVYnduQrCr1EQSQiSqpGzVMl3acc+Le7aHy8jKgzjAc1qtPymHjJwIfph2NBltmeZ0t7uGSyJhtHuJ5MfRFnZd+BppdkKj3dmQ1VQVm7sK7Mz7G4GUUQhU5JyjWj+EQb5KNdbqJKNVZFiMuOzmd6tqnHib0g26m9/06Clbuwnn8RhsnjsW/dxZfP5hFFwdJrlYxxfceide9D8xHQMh1iakgI4zx1NfYJa1CrTIxNu5xM86wez9dTHw/0/yXrXTIDqZgcSdNg4ZQkZui+pqz1FRmQCvKa4tSLu5S06M95h2LfYKWXpqfKCdX6UZpUsmc7Yeb+89etXj/3VdQx2qDjgVG9Z/KMayq2EAxWEwpUyV5FsQ2Hm/brntBSFN7DuDdxz1Y5QPfxvicUUQ67AqUdIaZC3mI3OY2om67JkkNLRYhpsWE8pn4ssni+3YKhpBBVP1lph3qc+6HMFvs9UFY2BvnVv2qYNNLAby6I0w3vW7zraml66iti3hCGidlk3ng8qoRDL2G8Ny+Wjz5x8uIrNhbMs3DV5UMjgdCbdvW1Tu0brdS/5cA00cD9Z/jJ0Wt4pKgAezjCdaWV5Oq03OG2UvZQAwq1QgZJ5tGH5zXryTbB5jCbbqtGikhMeDAfQ27/hVx7Oufw/4ct8H1YYKgHyu+jj12cs9/j3L7H/F3dd2zy2w+YR9dd3wOxIK9WP8tn4S3EFApyVVauyryEMYZRvTZXjW8z79Q/QK5hPGfn3LnXfq7GEBvfdLBrpVuOTDOmqJh4Vgqj51q6DMWUYjHsb7+O48N3QaHAes75JJ16xl7gQUxYP2vbzL9ty4RTj5E42cXRpCol/lw4F5Uy7iURDJTNoQgNoZCsYZulO/C7dMvrLQTeriKRMEqjiozrR2M6YuAiUmzBKj5pfAJHuA6jKpETM64nS1/CO3UP0Bgsp9A4ldOybkHVhe5ery9Gp4ohW5Bd925F3RLAjZIVxiRmXpRO+qpHqW9o5bPZF7NZG583TEw3cNnkNHIsB7ZRsLaGmrt/JRPL5N3/J7SZWax/o5WNbzmwjYXVi+C61s2M2LWaQK2NkGsWupp4yF0wuw5DyU4sJfkYcorR5xSjMg7e4u7KLW6efK9Jvu/OXJDMBUdbafVtZmPjnwnH3CTpS5ic+Qt06sMjskrktjo/qKf19RqIShgmJsohompr115s8ZzctqOG2mCIP4zKY0SncOmDuZ9iUX8cJMrhptXtQLEaSQrucziFnIcYJ6+J5yXKBDbqlAFfBPBFvZT7t3WEmIpw091FgYJcXWEcIBomMMpQgu4AGo4HY5Pe7DPU49wwIGy/KmJV6O8fNXPmzCQumLv3iol4mBzvbEAIi4sXRNIZE7GeP61Xgqm9uehDXUdo8PzitmqamsP86fd55OcNTmjLYPfLXxeSvYPCCyZkFW521MlA8LlxI1nj8vJUbRMnpiRyRXYajR87qX7BhiZRxbjf5aJL7TvN9WD3pz/HFy/wsgcbaFvvI/vsZHLP63/4Q3/aM7zvsAUG0wJDPVAOZl/6cOwBBYQNIZ+sPZiq1vNEwSyU+wie97ZdW3f8hxc8S6g0xwHB8QlHc1HqmVhUPU/YY1KM5ypvwBt1cHnBkyRo9vdWOGtDrH+9lerV8Rwzc7qayYtSGDE7ocu8d8/qb2h65q9IoSDmo2eSfsVPUer2HuO2+et5rOETXLEAGrIJk8A56m9ZkLGIJEPf8n6cdSE+/FUNIs5y7nEq/Evi3pDkhTmknJfXrygN8V7f2PZfVrS+RFQKU2CczAnp12FUx1MB/FE3b9beIwPFsQnHsSD92n5PnAM73DQ8UirrqmmLElifYeWbFXHbZygqOX7kBibd/TM22sO8sMFGvSeMSgEnjUrknHEpGA/A5m1//20ZtOuLS8j51d1Ew/DuL6rx2SN8czGoitTcna+kvPkvuMNVGHfmk/7ePJR20d8YWDdD5tegDqBJyUafW4whtxh9bon890CFIPqiUa76ooLYmnia3KUnpnLKkUn4wzY2ND6KK7gLnSpZBoV9vV96+1wNVL1wU4Cmp3cQKHXLobjWiwpIPDGzx/tycauTfzfYmJds4eqcgXcaCHkM4TmUPYmyN1F4FSuJRFr367pSZd4v5FSrzUWhHLh5XFvEQal/S0eIqT0SJ9oRRYWKEfriDoKaQv2oAy6+rCn1MK7AgFHft4iJfTs+1OPcMCCEr89BAAAgAElEQVRsvwLPLm6R9WhuOjuTGWPjbGKiRH0hmp9ejndNNQqdmoxrZ2OecXgzVq7b4OXBhxsYW6Ln7jv2JIMP1AtoKI4jQPr2++pwbw+Qd6GVrDOSeaiqgbVuL/eNzGGJw80Sh4ub8jKZkWiWE+crn2mh5QsXxgItY+/N/UHp8rWucrPziSb0WRomPJiHUtsNicJQXJzhcwxbYJAtMNQD5SB3p7eHH1BA+KKtjI+c1fw4ZRRnpRw8WYZ4t7Zs/DfL7EtZnGXBowaT0sj51tOZb5nZI7PfStvLrHV+gEmVzHFplzLadHSXE/vWigDrX7dTty7OiJmYrWHyuSkUHG3eb3IbrK6i4c8PEWm1oSsoJPNnt6BJ3ZtJ1BZ280jjJ1QEQ0AmeVRwAl+QY5kn69lpewNooxKf3FVL684g0y60ygyp3jV2eQIe80UxjLeQcWMx6sS+T1wF2Pui+e8yE6sSFTOtFzI16ZT9WBzdYRtv1N2FJ2LniKTTmZV6UW/vp/3qybIaf9+JFJawzEsn7YoRMlHe5uc/Z/FiHXYpCyHFN+vUJOadk4JKq2DxDidvbbPjj0gk6lScP8HK8QUJXS4wCB3F2vvvIFhVSepFl5J0wilUrHKz/IkmIllKPrs8xjjNNxyt+VD2wgktQIMyg7bPG+W8t5gvBtoIyvyNxIwrkOlJ24tSb0afUySDQwESdVmjUGoObrH7mzYPj9c0MsFjoPSrAOGIxAVzrZw5M5loLMS2lmepdy9FgYoxaZeRa1kwYGD0oC/ePjuK59K9pJmWFyqRAjGZGTbj+iK0Ob3T6hOg+NrtlfJR/zqmEKOqf+Cmt/2KRj2d8hLjXkWRqyh1yv/bDdPUGisqpRGFyoBSqUepFNs930VYqvy7/P996xhQKPWyrMa+CwnyOy3cRJl/M9sFQY1vC56Yu6MLOoWeUfqx5OumkKQuRiFZsYUjNAfCbPyfj9ayKOmFKp646ODfq+JkQz3ODQPC9kt81/O1lNUGePzafDJT4iudoTonDQ9/QbjBhSbTQuYt89Dl9Y/Rq7cPxWDWe/CRetat93HTDZnMOHoP+B3Mcw70sZu/bKPyHy0yuBv3uzyUagVvNtvlz+VZqXzc2kZTKMw/xozoYIyLRSTK/lCPa4ufpCNMFN3S8yrZQLd7MI4X8UTZeEs1kbaoLLlhGX/wFNyD0b7hYw5bYKAtMNQD5UC3/yCPN2CAMBSLcm3lcgKxKH8pPJYk9cFNnHf3Q6z2N3zzCK3N37G0IJdV5qCcnzNSl8+VaT9mlD6/2y6HYn6WtjzLdvdyuY4Ifzw+7XISNV17JZpL/TIwbNwSZxBMLtAy5TwrudOMe03som4XjU//Gf+2LSgTEsi64WYMJWP3akcwFuavTUv4Ju4A43iWMIoKNMoEGRTmWOYckEZ/07t21v3HTmqRjpPvze3wWIYbAzQ8VkqoyocqWUPmTcUYSnpPglLn38anTU/KIC9Rk8nJGT8jQ999KG5rqJa3au8hEPNwrPUSpiWf1qdbTCywtgqikffq5dy51P8rJPFkISGgwLtxHQ2P/QlJraX++LtY+gUE/RKJKWpOuzSV8UeZaAtGeXVzK19VxSfNhYlaTi9JZkaOWdYv7FyCNVXU3PMbFCqVHDoqZCs+vruC1rIY204JUXGEhuvSKpmdPn8v20fdYexv1ND2WTyUU5Ojw3xCBElbTqCujGDDLiThctxdlCp0GYVIeSXsyBnBJrOR8oifs1NGMM/SdVrQ7l2frm1imdMtLygnOJT86fUGgmGJs49N5tx2SbJa1xdsb3kOiSjZCXMYm3YFKuWhkaYRcYZkvUzfWgcoIfmsXFLOyukzC/4/6pr50uHCqlGTrlGTpFGTrFaRrFaTLP4W39t/MyiVgwaKJSlKKFTfKS+xWgaN0aizT/d515VV+4FGASRRGHCRhJNE7DEzNVEVNVGwRdR4YgaiknieO5FPineI8CiLJmkh+Zgofz22pF/tG+pxbhgQikCEmMTlD++SX+b/ukVolSjwrK6k6enlSIEIxml5ZFw/GyEqfriXxqYwv7itiqQkFU8+Woh6kGixB9NOIWdEFl2P+mKMuz+3IydwrcvLQ9UNTDQZ2OT1k6fT8lDR3hMRAZ623llLoCFM5ulJ5F90+BEC7Wvbin800/Kli9Q5CYy85oenlTSY99LwsQ9PCwz1QHmIWGnAAOFXrnr+2ryVGeYMbsocGCHlWCRA3fL7CDp34SiYxrtpSnYGqxD5OAsss/ix9XTMB6Cfr/FtYknLv3CGG1ErtByVsoipSad1G5rVsNnHutdasZXH85BSR+uY8mMrWRMMHRNT4ZGy/ecl2j5fDCoVaRddimXuCfvlFd5Ru4KdQXGcRmZqPRSHvkKJ8HgVMcp6DhbdyP08ho7qIB/9ukb2Tv7owTwSs/cGA7FQlJZnK3AvbUHEVKaKUL1T4iCruxKToqy2v823jrdlQD0mYTZz0q5AKyaoPZQGf5mcjxmRQnKOodi3NyXmj8pEI961Djn/MfPnxRgnx3PjQvV1skcv5veTce2NJBw9E5c9wkcv2tj0dVybrniKkdMvS8OaqaHcHuD5DS3ssLdfE6OaU0cnMbfQgkGzJ2rF/t5b2N95A13JGKRrZ7Jl4xe0/PVKJEOUz67VkJmklXPXNF2QogWrvNier8S/1SWff7dMhTpFRaCxQpa7aGrcxfpYgC1p6exKzyaq3Nu7dWFIySnZE1Anpu13PUT+6E+3V+CLxvhnO7tsaa2fP/6nAX8wxmlHJ3HxfKu8nzNQzoaGRwlGHfI9MjnzZgxdhD335joMVB3PGjst/9hJ1BVBk6mX81n1RT2Hb3d1fqH3+UBFHY5ItMfm6RSKDsCYJAPGvYFjSvtvAwkcJSmCyE8UzKbxT6Dju7T7747/7/5fAPG/cDSAI6rCHtVjj+mxSwk4BQCUknCQjAsLMbr3ihrwoFG0EVaE8TckwNpkCCshNYh2xmaOSGzi54U39mi3A1UY6nFuGBACdbYQt/y9mrH5eu66KBv7a+twvLdRvk4p50wh+ewpPcZb9+uqD+HOgkhGEMqce3YKi84auKT3IewCO55oxL7KQ8bJiRRcticMqLVdP2d3W05OSeSy7P0FhwMNIbbcUUvUG2PENemkzen9yu1Q9rM353Jv87Pt3jrUFhUTH8lHkzA0YR29adtwnWELDJYFhnqgHKx+9PG4AwYI76hZzY6gizuzpzHeOHDjQCTgpParO4j4bCQWn8nG3HxebX0fT8yHRWXmIutCjks4qltQJKQo1jjekz8xIrKswry0n5Bt6HqlXYR2iRDSda+34qgUoZ+QMVbP1POtpJfsAVGuZUtofuFfEIlgOX4eaZdcgUK9Z3W/zO/krjoRmhkkRiXjdakcK20gHNrVcYlEzphZl49Zm4dJmc/aB7Npq4Lpl1gZd1r3kUOuL5toea5CDsM0z7CS/tNRKA37v6dF6KfwCtYHStEo9ByfejmFCccQiIXwx8J7b6WwTHxzhKkQs2oPSVqF9zs+bHhYBuE/yrqNQtOUA95i4eYADQ9tJ1TjR5OlJ+u2MWiz43aLejwyGAw3NZJ8+llYF/14r2OVb/Tx/rMttDaGUWsUzFmYzHFnJKNSw+YWPx+WOdnQFA/vNWqULBhh4eTRSaQY1AigvuuhX1E/rRb/KOGUVBL9+EaaV6Xinqli+bwoi9KSOTej61x4cd29/7Nje6mSiC0k58YpT01jw1wF34Zt7AzGwaIoJgnygo1I+k3y32XMRULNqeu/5rimRgz54zo+6qR0yv0B7tpVxwSTgTtG7PEk7moI8PtX6/H4Y5wwzcLlJ6fJzoNgxMmGxsdxBrbLnuVJmTdiNQ7MIktf3g8C2Lc8374AAVhOyJAXIZT9zGMTbQjFYjgjURzhiAwOHZGIrFcof2/fOiMRPFFB1XTgolUo9ngXOwHHPd7GOJg09tPjGBZe73CYlnCEllAEW8f3sBzeKeaLewKO929zkkrCqpawqsKkqEKkKAMkK70k48YpBVgT1vJd2ExoUypsj+f0JpWUY534Dg6li1naQi4q+GNP5jjg/4d6nBsGhMCKzW6eeq+J0ycbmLtpC74NdSiNWjKuPw7TEb2n3O3XlR+CnYPBGNf9vJJAIMZfHi8k6TAULHeu88rkKdoUtQyAhPj67iIGiZ9ur8QVja9m3ZyXyVGJXYfEurb4KP19vVyv5Lc5WMb1vAI7BJeoT6foLLkx8oYMUo89uFXAPp10uPKwBQ4BCwz1QHkIdFk0YUAAYUXQxa9rVpOjMfFw/owBD/MKuWqpXXYXsbCPtKlXo8g7SgaFS1xfy2Ycox/FlWnnkafboxW4r30doXrZW1jr3yL/a7xlHrOsF3artydCHgXpjGCtbKuLhw1mTzYy9ccpWEfGAZN/RxmNTz5KtM2JfnQxmTfcjDop7gkTY8et1d9QF/aSrm6lOWIjU5PIxQlWtKEy3MFqfOEGJJmbFDxfzsXz5Xw0BVXkXfMJCYZczJo8dLo81OpMUCYQkCIdQC5c4Sfhb21obBKBDNh+VQxHxm6gF8YRbpVzliIyh4kOFFqCUs9eGZNSx8LkIzgpcSLadobUba5lfNb8NGqFjrNz7pBlKboq/q1tNDxWRswdwTApkcwbizu06IRMRP2jf8S/ZROmI44k8/pfdEmiFw7FWP6Bk6XvOoiEJdlLeMYVaRRNiqctVLcF+ajcyYpqtyC3lMlnjs1L4OicCpy+vxEhgMamYFLBzRiNU3n3pmoiwRhfXw1tachewnx915FZAhCXtzmof7+GrP96UUcUOBNjLD45RNMUDdPNaUw3peH0fcZa5zuy13ma+RQ+d61lq2IBMdSctGE1s8vii/+iqC1Wvph8Bp8ljeKiRB0/ys3d6/moaQ7ywCv1tHmjHD8pgZ+eli5HlsWkCGW2l6mWtRIVFFkvpDDpRwP+bHX3HhLSJyJvNdIcRJWkkRcdTFOHPr2pAzjKgDEOHB3tWwEYd3/vDXDUCODYEZIqwlXbPY7tvwkAqVIoZLDXEo6DvJZQHADaQmEZrHYH+ATwEWGvaVo1aRo1qVqNvE3TamSZGfHRimTZTsURCbLc3cBSVz31YR/4VCi+TkOy6dHrFFx/egZHlsTnm+6IS74nEjX9W2wb6nFuGBACL35uY+3SWq6xlaJ2+tDmJsn5gtqsH5aotxChF2L0M2eYufH6gddJGuxJUzQQY9Ot1YRsEYpuzSJ5umm/U/6hsp4NHp+sryjyBxPU3XvMmr9oo/KfLajMSsY/kIs+89CI/++tHevetCM+lkkGSn49cAxrvT3/cL1hC3xfFhjqgfL76uc+5x0QQPiP5m186arj0tRiTknqPrevP332tWymfuUfZHiTfcztGDMmU+rfxbMtr1MVqkOJklOS5nBOyikYuqFzFyBN5BUKhk1/1IVBZeFY68VyKGR3YZci/aNihZsNb9rxNAt4BflHmph8XgrJeToiDjsNTz5CcNdOVMkpZN14C/oR8dy8jxxVvNhazgJLNm3RSr71VmBQaDg5aTKCDdUXC+CJtBGsjpLxxEQklcSGG5bgsnoJoyKMGkkeebouer+Kc94sZExpIiFNlHcXVrNxsmO/yiqU6JUaDEoNeqVWboO8lT97fndGvCxzlxJDIlWdwPnWo5lpLpa9VmsdH7Cy9WX0ygTOyb2HFO3eOXNtn8e9lgKlJZ6SRerFBSgEWmsvLS/9Ww6z1eblk/vb+1DqDyzVJLyEH/y7hbL1cY/gmCOMHDkvkeLJRlkixO6PyOQzn+9qwxeJT9NzEsqYra1g7EvLMY8eR/Yv72DbJ22sebEVzRgN7y0KM8qo4/6RuR0ENSL3VcikrPG2sNbbgqs9ZzDJoeDsz4yMbMd2+jEJpF5awDfmN2SWVq3SyBlZt8ueZkHS80rDK2zlBGJoOEvSM7+6An/1VoKNlTx13HU0Jmbxiy8eJUOt2M+D2GAP88DL9djdEY4ZZ+b6MzJQt9uu3r2crc3/JCaFyDDPYHz6NagHUa5ACsdofaNGlpQQ6EeEz6ZfNRKVpe8kRv153vu6727g2BkkCrAo/90JTLp74XHs6tziThZ5j51BngB7MvjTxEGfuhcazREpxjqvTQaB63yt8rMmSlZrCo4VFgIBGJWl4+dnZ5KeNPA2H+pxbhgQAi88uIbp67eglWIyg2j6Ncei1A/8xe3rQzOQ9cXg+us7a6isCnHvnTmUFB9+HrHqF200fuQk+SgTRTdndWmeVxttvGdzUqDX8uDonic7u4+pz9bI+Yhq0+ERctlZcmPCQ/noM35Y9+tA3vvDx/rhWWCoB8pDxIL9BoS+aIRrK5fJ3Xm6cDYm1eC9N1zVy2he+zQKtYHc4+5Bl1hAVIry37blvN76EX4pQLIqkf9LPZsZ5qndgrxA1MPK1lc6BNmFZuHctCtJ1nbvYRQEYkLYXgjcC0kDgdNGzDQz+ZwUzFZoef4Z3CuXoVBrSL/iahJmzsYVDXFtxXL0ShV/KTiWD9u+4037t3tdekVEway/HYOlMYEtp22j6phq+f86hQqdAjREUUthlJIfDWH57/gnIm/1ko5xKydQ8Gk+CklB5YxdrD15KQatjnmp/0exeRoaharXnqW6kJ1XWr9mrTfOBjlCl8ZF1plMMOay3PYi65wfYVZbOTf3PhLUVqSohO2FSto+bZTddQI8WObuTd7TtvQLWv79T1QJFnLv/t1+7KzdPQtijrH1Wy8f/ttGm7C5CNe0qJg8y8zU4xJQp21mQ+O/2dJSxBbbbDyheKpGts/GrO1fMnfudBLnnMAHv6zGVR+m7mI1GwojnJeeRKo+xLfeFjb6WglKcS+tCgVjDcmyF1B8UjV6fFva5PzCULUPSSFRfeQmak7cwmlFt5CmK+hoeql7JW80vcE2TpKvzqLkEZyTMpJmj5ufVzWTEfFz67pXZIAoI632IjyIIsTUmzqRx9bk0OKSOKLIxM/PzkCrjnuUXMFKOa/QH2nGpM1lSubNmA5wrx7suyVY44uLzFf55BDk1MtHkDA7tdf3zsGedyj3E2GfAiTuCVeNh6nuDleNSFKXHr4UAfgOUkZH9K8m6GGpu54V7kbaovFQdItKw7GmLEKbE/jia698V5w8PZGL5qeiGSQujqEe5/6/B4SRNj+l176BJhbFesF0Us6Y8IN6oHY/vKVlfu6+v47CAi1/uD/vsOujtyLIlt/WoNIp5VBRETLaVVnn9vJgVQNnpiZxQWbPhDEi1Kj84Qac3/mwTDRQfHu2zFh6KBdZcuP+OtzbAuReYCX7zKEPDTmU7TPcth++BYZ6oDxELNpvQLjYWcO/baXMtWTz0/Rxg94t+7Y3sW9/E7UhhdzjH5C3ojgibbxke5eVHkHLBxMNJZyYOJtJxrHoumFqrPdv58uWZ7CHalGiZnrymfJHfQBmx2goRtkXLja96yDQFkWhhFHHW5h4VhLR9Z/LhDNCPzDp5NOwnnshjzVvYbW3mRszJjAzIZNSfwPVodYOb539HWj8MEryWDUzf52KUa1Fp9DsJ7EgWFf9kRY8wRo8oWrcoWr5uzdcL5PFRHfmUfD6yei8Rly5DXgvWUNiRgZmbT5mXZ68NajF5L538kFCV/El20p2Bptle04x5nNhygy2tb1GqXuFnIt5duIdOJ6sx7+5DZVFTebNJRjG7J0/7y/dRt2fHpCPkXP7nRiKx/T5HhGho9vWelm3zCV7DGPtaWWmjEYypqxn8rFmJo44j7UNUT4oc1LpjBPQJPmdnDo+gwnRZFY83AipsPgqiZhaAm09KCPoFSqmmKwyAJxiTMXcxYJGOBzkm3dfw/pxLlq/AYVRifW8fBJPyNzLC7q57Qvea3mXrZxCFC1nJheSQirPNdg4zZrEJVmpRANeAjXbZe/hbg/iboDYJll4TnEVtlgK49L83LwwDVNahjy3Ckc9bGx6klbfBtRKAxMyrifdNL3PtuxqB1lk/uMGWv9TDREJwzgL6deORpM2OKSHoXBMBjwDpfE4IEYYhIOIxbJVnkaWuOo7clCVKJhqtDLHks1IKYmn329hS6Ufg1bBT3+UsZdE3SA0aVh24iCNetADpQgx2frxDtwROGZh17H2B9mmQ2q3J55uZNXXHq6+Mo15cw6vUFjxAhQkML5dQQquSCPjxO7bL1Ypt/kCjDbo9osB7+6CRP0xtt5di786RPoJFvkch/LLTzCKCmZRQ76W8b+PS24Ml2EL/P9kgWFA2PerLd6Nt9V8Q23Iy+9zj2KkfvDJtMQ5m7/7K+7qZWgTC8idfQ9KzZ7olM2+Up5teYP6cFzMXaPQMMk4hummiUwzTiBRvXdedFSKyF6v1fa3ZDbNJE2W7C3MM044oEHCgRiln7ax+X0HIW8MQTpZND+R0eMbaXvhz8S8HgzjJ9J4xf/xkKOUiYYUfpszba9j2nYG+OTOWlQaBac/lE9Cet+9q96wg8+b/0KVfzN6l5kZ/zkTY3UqUaOf5nMX4y+KexxFUSkMmHW5JAiQqM2TCW3Ed42q67x4YetvPDt4tfUbmiMumVjmuIRiksJr8dVUcvTLi9DbTGjzjTJ5zL4AItzSTM19vyXmdpN++dUy+U5/S03jZpZ8/g21a4vxNMQ9usJxM3qSkamzExg73Ui5K8jby7exlfi4rlDFOGJVlPRqDdsX+Ng1w0iyJsZPcpKZYExBuw9jaOc2BqM+Pmh4iPrANtJCIzhu1SX4v2iTMZw2z0DqpSNkbcjd47u4lz6xfSKDwgg60mIFtIQU3DUih3Gm/aOo9gaI22htbOa52GU0kUkhFVye+BFJBUWyF1GfN4aq2FdUON+VmzgyeRGjUhb1GuR3ZftwS5Dmv+6IM6yqFVgvyCfplKwBJz0Uuovflnr47Ls2tlUHKMrRc/ECKyW5h19k2YHuYZGHus3vYIm7ntWeZkLt3udsjVEGgbMTskhW69ha5eeJdxpxeqPkp2tlvfJs6+CnGA31OPdDmUkeNCDs7wvvcNjf6Yxw/U2V6HVKnn6iEJ2ud6uOh0rfGj92Uv2CDXORnrH35gz4y0/0M9gSlkGn0PLLvyyVzJPjZAOHWgk7I7Lm4L6SG4daO4fbM2yBwbTAUA+Ug9mXPhy7X+OcmPjcW7eWUToLv8s7qg+n7V9VKRahftUf8LdskXMJs2b8EkWnSX1EirDas4E13k2s823BHwvEgQEKivUjmG6aJAPELO2e0Ma2cLOsXVjlWy/XLUk4ltnWSzCqD7zYGfJF2fqRk20fOwn7JRncFc1Wk1LxDDSUo8rI4KGrz8IuRXiiYBbp7eA1Gpb48Nc1tNWGOPrKNEpO6PuiqvBwLpa1BVuxqNM5OfNGMtQjsb1cRdsnjXJYq+pHAXzzy/GEq/GEaohKce9Z56JTpbR7EXNRK40oZQ+lGqVCK38XVPlf+x0s9tTjkyKMLUvk3NcK0AbVtE1opuTnM9Aad+8n9lXJshK1v7ubUG01iSecIstz9KdEY0HKW1+lum2xfJhM80xS/JewaWWU9SvcuJ1xwhy1HoxTYjRMctCUGgCbBZxmTC4Fsz+VZNCz6VoVdZYoP81JZ25y94sYvqiL9+r/QEuwgnTdSM7M/pWcd7qvTIUyQY0214gu34g2z8iOpP/xheELNmpPIhIsEqfkubGj0OxDKtKVPQRAtO0o5ZHPlVR7zeRSw6WK5zEo4vewOiGF0PgManJ3EFWESTVOYWLGDd2C+u5sLovML7dhe64CwSaqLTCScUMRuryB1RxucoT5Yl0bSze4cfni10inUcgajKIcVWLi/LnWIQFD/bn/etrXFg7wlbuer1wNNEfieqbC+3xMQgZzErIp1ifKiwYCML6/ysFrX9mRJJg3xcJlJ6ai7SSh0tO5+vP/oR7nhgFhf67WYbLvW+/aeeMtO6edksQlF/YcRnkodStoC8uag1JEYvwf8zDmDU5YhOizpzzAtvvq5HMV355F0pT9SWu+b9t0J7nxfbdr+PzDFhhKCwz1QDmUfTvAufoFCJ9o3MQqTxPXpI+TV7+HskRDXuqW3U3IXYulcD5pU67qMgpDgMOt/h2s8W5kjWcT9k7C07naTI4wTZQB4ihdvgwYd3j/x7KW5/FGHeiUJpmJdLxlbo9emIArypYPHGz/tI1oSEKtV5CXtp205jf4asEklhwzibOTR3CeNU44892rNja/5yRrooEFv+kbgZcgpFnjeIf/2d+Uw0WLzTOZm34VOuWeybz7axvNf9+JFIhhnJIka8cpzaqOsFN3qEoGiJ5gNd5ww155bd1dx6CkIrBqDpMXj0cpKVg2p5byeasoVjaS3Yn+Rkg+CHpTRSgmA0tNQnIcXCo1KNHEtzLo1KBSaFC0b3f/tu9WPJtVzk9kVlaN0szYtCvJTDhGbqY/FmGd28aq7+zUfR1Bt1WPMtK+QJ0UISWplGK24Dr7Eqo+DpK3HWoKYdPxoEtV8NjkfFK0+3tmhWTHu/W/xxGuJ8cwjh9l3bqXfXfLVDjeryFUE5AlQPYt7qQAdWkWmtJjpI3WcuL40eiyDb0Sc/cFovzxtQbKagPkJUW5btR3qBs2EWyqEBS2hMzQNAvCFtCG9JRETiM1bzbqpHiI6YFK1BWm+ZldeFfb5UWDpDOysZ6Th6ILUCLFosRCAaRQgFg4GP8eDsjb+Pf4b7Fwe51QkHAoyGa7hZWteZT64mSDSmKMVe/kKOUaRkjlbLXM51PPUTiC4j6ABdMSWTQ7mURT16k7Q/lu6e25BCGRICMSBDGCmGj3HTBGnyS/D2eY09G3M/WKY7p9UZ5+v4l1O30yKL7y5DSOmzT4URWd+zPU49wwIOzt3XSY1otEJH52cyVOZ5THHiog8zAiHxEv8fKH4vl9WQuTyTu/a02igbw0thVudj3VJMtZjL0vZ1ABaF/b7VzvpeyPDWhSVEx6uACV8fDy9Pa1v8P1hy3QnQUGYaAUCan/3/cAACAASURBVHVPAmLm6gSeAe4VcmwHuApHAtcBQgFcIKwa4BXgQSDuIoiXe4C7uzjOKUDchdK7ctCAsC0S4rrKOGGKIJPRHSDsrndN6XutsK+F2qV3Eg06sY6/kOTiMw54EPH+rwjW8K13I2u9m6gOxWWCRElWWWRwKD5FujzWON6S2SRFbGCWvoR5aVdh1fUsGeVzRNj8noOyz9uIRUCjjZCkXcFLvxhBgjLKX4rnY98VZvFdtTJoFKGi5tTeh4q6I638t/Ep6gLbZBmIOWmXMzbh+C5BQKjOT+NjpYRq/ahTtWT+ogT9qP3DQ6OxEN5wHd5QAzEpSFRoEsaELuHuT4hYKIritVRU/0tGUsdYdW4Fn4wXt7UCPT4mK5s5UiUhSSEiPhfRSABJuMa0SpHh2PeL28UeqcapjE+/Gj/GDlbQTT47u9XfNAol40khZ3sy7tVKakv3eEMzTTamLCyi6h0nkk9i5XxoSwGjUckV49OYkWvuYIkUEiXv1P9O9ryOMB3BKRk/3y+vVNxLjrJ3sW99HV1SCZbUhSg9uYRqfLIGo7xt8stEP3sVlQJtjgFtrgFtvkn2yImQW3F99gVygVCMh95okHPMclO1/PbCbCyaYEcOoqduC3X5lXhzQRGBtDWQ5EyRw0uDusnE1GmkjQjGwVw7aAuWSXg+MyMJiQNzCP2MnSiTWrsAePH9pHbW1d5cQJEDuUaazhqm4yYOdCy0MV0hflmDRelBqY2zy8aCfsKSmq85hmXMISDp0Kvh9JnJ/GhGMroh8pj1pl/71hEyOwIECoIYr3jI5feHjuMtWRyfkE2Wdn8va3ldgMffbqTVFSHbquEXizLJG6QczQP1aRDGuQOacBgQHswddhjt881qD48/2cjUyUZuv3VoV4X7ayb7Nx52PN6ILlPDxD/lodQODQCqfb2V+rcd6NLVjHsgD43l+2celSU3bqsm1CIkNzJJnt51Hkl/bT68/7AFDgcLDPBAKViZhODd1nYwJ9xCjwCPAXccwB4PiygqoVwElAOTgPuBz4FFnfYTgPAmQUq3z7G2AW19sPdBA0KxOi68g4FYhJMHSWqiN/0IOHZSt/w+pGiQjCNvJCF3Zm92k+s0hW0dnsPtgZ2yt00UvULHFNM4irQZ2DzLaAtVoUTF1KTTOCplERplz1ElHltYZiTdudSFSCOK6EOUzfOwQNqMZ908XI0Rjrk6naJ5vfcQCHmDz5v+RiDmIVVbyCmZNx6QGVWeeAeiNP9jJ55VrXK4ZNplI7DMT+/Ri9TZiBFniMZHywiUuVGlaMm6tQT9SDOVwRb+3byE7cEWuXqOWsfFtnwSnnoWpcFI7l0PoM3KRhDi7AaXUSkk66ntAZsCfHb+bff3UByYynVDeJVZVMSyWOO1sSPY1uGNMSnVTDWlcqQpjclG614eGXtTmO+WOlnzQTWuSDwkN0UF6VFQpKn49IQokbjEpCxuf+roRCZmOfi05Q8EYm5ZjmRB+jVyCGznIvpj2/QibTuFRuCeok8pImXMORjSxWMLN27aha4xyDT7f5GaJ2Ft0pLXrEbn3t+bKFg9RbipyEsUWzn8NN9IVKfisbcaZa9SZrKGOy7KJjVxzwJCxO9hZ+0LVLFM9vYllkF4yRFU1JyBhIq8jC/IzViCIqaGhtnQGm8bKZshexmo2g2wz1Oj0OhQavQotDoZxMnfxW/iu1Z8j29R69jutrK8wcrGJoPsJ1YgMTEH5o7XMHWUAY3eINcXDLziPSvsF2yowFv+Ld6yNTha7CyV5rCaowSnLomaIGdNkThhTjEq7eDn1fXmheGOhljpbpSBYGXII+8iWGkFGZHwBk4ypqDqgrBJLBx88m0bL39hQyhezBpv5ienpqMfornnvn0b4HGuR9MNA8IeTXR4V7j3d7Vs2x7g9luzmDr50AuB7M66EW9UDhUNO6OU/DabxIkDGyt/oKsqSGx2PtGEAKTmEj1j7shBqfl+H5Xql200fuAk+UgTRbd0LblxeN+pw60ftkDvLTDAA+WvgV8Cgpfe1d4K8bcAciKGavdv+zZQxN/b9vnxauDvQCFQ1f4/cZwbkHkT+1UOGhD266wDvLO3YS0N3zyMQqkme9ZvMaT2ncnSFfWwzrtFBogbfdsJSnFqeKHhl6tJRhOuIxEvaWorc9KuoNA0tVe9cDWGZA3DipVukBTEVFGUURUZhSFO+P0YlL3IK4vEQqxofZmNbZ/K55ySeAozUy9EreidZ1FMSoUshO3FKlknUEgJpF01EqWu54XJYIWXhoe3E2kNoRtllsGgOnnvSfoSxypeal2Bl/h8YNx2GxcXnsTICTN6ZaN9K4n2NoR9bA842e6Pf3bnZYm6qWp9hzTEGEMS6h6YU/07d/Ddvf9kB9OpVEwlKwjCT9WsV7BrnIQ/F/ztGF+lDJBh/Zb5I3WcmnP+fqHCIn+1+bu/4a5ZgVKbQPbMXxHxtWDf/hYhl3Dogy65CPfo87jPaaDEqONIw2ts9ZayjTPwY2AO6VzszyNSE5A9iULuQWylYDt9aieDqJI1aPKMbPZLbPBLBKw6rr4yn6z0vRclWn0b2VD/JPZPZuFbeSwKZQylMkY0omZUQSNZjhgxlwaFMYbl1CiGCToU7eBuf7AnvJUHXixv80bkvECRH9jsjHvJEk0q5ky2MH+qpU8aemFHI57ytVRt2caHDaPYLE2Uj5euaGZhXiXTp+ZjGj0VlX5o55si30/Ikgi5iDWelg5PdL7WLIPAYxMysai6B6wi7PdvHzazutSLRqXg0hNTZdv0FNJ7UA9NL3ca4HGux7N+v7PcHpvX6wo/iIGy173tZcXqmiC//E0NGekaHnsoH2UvhDh7eehBr1b5TDPNn7uwHpfAqOsyBv18+54gFoqx7d46vDuDchtGXtu3VdqBbLC3MsiW39Sg1CmY9EhBt5IbA3nO4WMNW+BQtsAAD5RCmE/EI57fqc9CxFQAOhHX+EEfbCHCSFcDwvX1dft+w4BwHwM6d32KbcNzKDVmcufcj9Z88ItcoViITf5SmZTmO+9m2qLujrOZCJNCiMn6Ys7M+AkJmt6lHbRU+3nh+XLSthhRKfxM0T6JZVSaLE1hHDu+29vBHqpjceOfsYWqZVH4EzKukcMYD6YEyt00PlZGxB6SPVEihFSb3T3Lo+d/rTQ9vUMGKgnHppJ29ahuo2rKGpfyouttqhSFhBQ6GUjPTxzPouTpJKoPvPgalWJUBT3tANBBaaCtQ6ttdz8LtOY4CDSnUahN6POk2vbGqzg/eg/NuKnUjryKnW86RJojO9uzJxUZEoER9TSlZRDRq4WkIrPyEjitOImCxDj4ikWCNK5+HF/TOtQGq7z4oE2IR0kJr5e3fjX27W8TclXzZcpcvrTOZ5ExyFmFRTJTabm/gu2ciQ8DR5nSuTFzQgeYFYvGkZZgHBxWt39qfYTq/bAPThR/qjJ0GAtNHUQ2ynQDq15ron5dFIXBR/rFH1KSdQ41DwbIDHqF8xDj1CTSrxmNupOHsS/3kcy4Xh2QmUJXb/fIHi9RxhcYWDDNwpElZtTCcP0oUZ+LTd9u5vW1Snb54+tdI9nJSarPGF2QhKlouvzRJPZ3Laz7RjaGfXzlqucrdwP2SDzsWHiiZyVkygQxI3Q9338VjUEee6tBBssZyRqZRXREZs+RBd21yh91oVeaewTqPZl+gMe5nk4n33c/hDIMCLu4is8818znX7q45EIrp51y+GjVuUv9bLu7DnWC0Bws+N5CNkOOCFt/W0vIHvne9P7EwLP1jlq8QnLj8lQyTjo02U9/CC+R4T4cPhYY4IFSiLc93e4R7GwEb/tvD/XBMje2h5oKhBMXhYt7GkXoqUjiErFwm9tDS9/uw3Hj81hBdfcDKSKMz7njIzSmDHKPvx+VrvfhmLtNIOwhRUNyCGosEiAS8bMjWM3aQCnrwhU0SXucuzopyhjJwuxwLqPCOpSRELFosH3fztsAsUiID9ImszE6ieNd2zl+XSnh0ngOo3HSVKznXoAuT6wZxIssyu5ewlctzxORguQaxnNixvWY1XHdxYMtglCk8cly/JvaUBhUZFw7CvNRe4NaMUY43q7F/matHIZoPT9fJh7pzrMRC4Wo++O97EjYyfrTlTSTT4NiBAEpgkGh4YzkaZyaNBmdMu7RFOHGO4IutvsdsvevPNCGX9qTZyhC8cSke4whGeEBLNEnknAAT0xvbCHaWHPPrwnX18kSGOs3jKNylYeE8XpW2fxomtqnrgoJ80gNtZlRHBkSkgompRs4daQBa/mfCTpK0STkkD3z12iM+4OSODBcw73NEWo06fys6gnyTQkklJzBZ9GPqQzUsl2xEK9kkAHuzzMnInIfuytSOCaDQgESA9Vedn7Xhro5QFJ4f2+isGBQo0E11oYnewPmjSXo6jOIKhRUSCYUY5KY+8tstMaePcOd2+PxR1m2yc3n37VR3xoPMTXplRw3KYEFUxPJSR34sE5x/6/Z1sYrnzfR4I63dxIbOEHxGckKJ7qMwjg4LD4SbXp+nxcIurL3Jl8rb9sr2BYQr1X51meCIUX2BoqQ5APJk3R+br9Y5+L5/9oIRyWZRfWaH6Vj1PfN5p3bV+Fdy+fNf2d68kKmJp3am9u92zoDPM712JZhQNijiQ7PCl5vlOturJRj94XUhNl08Df4UFogFpHY8qsa/LUhRl6XTupxfZ8kDGR7hXdu2921xIISo2/OJOWooc3d2y25YRqtY9x9uYMiuTGQ9ho+1rAFhsICAzxQilnTbcDj+7S9FngB+E0v+yTCSzcCHwOXddrnYhFRBawDhLDeTwExUxB5hn0BhT8oQCgm48KDIzw1uuTRmHNmdAPOBFgT4C2AFAnGQdzubXR/WYaOyR7QolOxxaJlg1WHTbMnZFMfjVHiCjHOFWS0J4Rur/m6AoVKS5MxgycKT0Qdi5AedJId1WLdUou1qo70Vif546aQtvBcYkkGvmz5J+WebxCMnTNSzuWI5DNR9lJUvqd7SwA++1u1ON4StyMknZYl688p1Eo557Dprzvw/s+OQq8k82dFmI7oHoTKupD/+Avur1egH11M4zVTWeV8DaUiCY1pIcs8FTKljFmZzGh9Ib6Yil0BV0f4nTi/TqGkSJ8kgz/B0DhanyiTFQ10Cewsp/aBu1Dq9aTc8iAf3ecmFotSecOHlHvPIHVTDOtWDV5XHJyq9Ar8edCSKxG0QqaigTnmMk6avQibKkp5sImmUBtHm0cxUr9HwsQZjnBNaSWpyii3NT5LuC0e6a1MKeTrPD/1MTflirNxSXpZpPwXmZN6BTbEMYS9n//MxtJvHBTEYpybpce9yok2GMGsjKCK7b3AE8ivp+HU5Tg/vBh/bRLJBVrm/yobY/KB2TzFeXbUB2UQuGqrB6EjKEpRjk5mBD1mrHlIpBKiMYkl6128scxOmzeKWhFjpm49s4Mf75HiSEzt8Bwa8sagUPWdqVQQxDzdtIUYEmlqvQwCj0vIIq2TxmlP96MgAXrmk2ZWbPagUsJF81M55ci43MTBlHio+Evt5FbxUPHj0von3zLA41yP3Tq4nnd92INhaROxFyJ5X2TOimUvoU4rqMLuBAS3cm/LD2qg7G2nD1Tv40+dvPCSjXlzLFx95Z6X30AcezCPUfe2nbrX7VgmGOTcwYN9OAeyjY41HsofaUSpVTD2nhxMI+LMW4Nd9pLc+H0exoKDD2EY7LYOH3/YAkNpgQEeKAcCEIpld0EmkwuIGEHHAewhxt1VgIj/m3KAevuxk/6QPISi3yKsr27F/QQdO/p0+yiUGhRqPUqVFoVKh1Ktb9/q4luVDoW681ZPldLOJ6yligBtaDuEFzSoGKcbwXTjBI4wTyZZa+0Yd/7RtJVV7joCXQRTqSJRkt0uVBYXGlUrKcooJ6WezqSEcQf0IvWpo50qe9c5aHqqnJg3in5MAqkXF9DyTAXBSi/qdJ0sNt+TLp3j4/dpff0V1ClWcu/+HSpLIoubX+Jr9zb8ikLCqjE0ymF3e6aGBsEGarTK4E+AwEJdQo95gAfbx333s73+Ms6PP8AwcRIbMo7D9mEm4fFbqb9oNFt8Wk5OsjCj2cS6ZW62rfUSaZeTiJoitBWo8RRANDmAlFoJ1lpQReWezbWM43zrDCwqA1/aXfyjvpmTUhK5LMuKyHGVcwzbKgmqYHWRhlaNmnLFItokPRMNKdyaJTyo+5LXSARF3qAvSsAXw++NtW+jrNzgpmGTnyKXyHMVJDlqQmkqJE8ErSuE0RdCZVGTe3kDvpQ38Pu8OF+5kNDO0QjH5om/yceSvb9nzx+MsXJL3BtY2RTPo9VrFRw7Ie4NLOxH2GN/rqFo14f/c/DhN05Zw9Ckg5NyGzgy8BnRhrKOQyv1JkyjpmIqno5xxGSUup6F77901fHP5m0yHc5P0sfITKHKPoK4mhYRItooe1CtFrUcIlqUc/DzOluwStYYtYdq5VDxBRk/ZaRpen9MKO87wONcj+0ZKEB4sCxtIs/iEmB5ew7HiHZ6bjGYilyMePZrz2UYEHayUSwmccvt1TQ0hvnjA3kUHiZAwl8fYvPt8UTviQ/loc8c+NCGnm+lrms0vO+g5pVWNMkqxv8ub9Dz+GTJjYcbcK71kXVmEnkXDF4M/sHaZHi/YQt8XxYY4IFShHb+pV1monOXehsyKsbRV4ETgFnA9l7YRXgkhTyFcFv1luf/BznOCY1CT+1KJCnaDuR6Bno9kWh0Z3/hldzs+oKvbK/SLEVwKxJwoCfQTkojJpmjdQVMN09iumkiOdpM2cvTaN/Jlq2v0SAYK43p2JOLqIkECaj2J4oRN0OGxkCO1kSOxkSu2LZ/Ouuc9eIe2a9KuDlA4+NlBHeJWzNeDOMsZN5UjMpyYNIa7/q11P/5YezpVtquuZodRtV+BDDieKlqHTlaPY2hRpoijSJolPGGHC62zmKEPu1gmn3Q+4jQ0eq7b2fT+AYqpmmwPHETSmcix/w6gz9qWvDFYlyZoyeosLGtuZKa1VFUG/PRV+9JkRGpbQIYBgpijBoRpsa0Aa/WgUmp49yUo9jclsoat49f5WUxRqOXgZzfG8VRvY3WHV/jdLspT9Dji5hxBCYQDmhJDOvJjyYQ8kn4BQBsB3/dRXQLn61Ylhd+O8Hxat/HImqNQgazgrNo9hkWiheso8r5Ac2vzSOwaRIqU5BZt+goHDda3rOqKSjnBq7Y7CYQinsDC9K1nHBEIrPGJ2DQDQ0je08X1uGO8OZyO1+ud8nC7mmJas49Rs9ExWb85WvwVW2GaPs0X6XGWDgRU9ER8kdt3j/N6RNnNc/bymTGUBG+e5S5786OZRtd/GtxiwxUp44yct0ZGST0MSx3d7/F+2R922JW2l4hRoQ8w0ROzLgOk3pgUrQGeJzr6XINWA7hwbK0ddVAMagKL6FYZf2uxx7EK/wgB8pe9n2/ahs2+fjDn+opKdZz751iwfrQL2LQ3f5APe4tfnLPTyF7Yf9yLwa6x7Im1t+bsS11YxypY+zdOagG8aVr/5+HHY81osvQyOB4qCQ3Btpuw8cbtsBgWGCAB0pBKlMHXNCprULErrqXpDJ/BgS7qBi7VvSyv7e2A0Kx6vX/NSDspb0GtJo34mSF7UVKPStlDhCjbhxoR7DRX44tsse5m6VJl7UOpxjHUqzJwbn5FZprv2RDHrSaxQTfTE7NeEKb2mixJmHLSsOWk4GjmwhKq1pHrtZMtsYobwVQFIDR3AWw7K7DgvDM9kIlrs+bsCzIIO2ywm7F0wXzYlXQzeamStZvWUNVdioe896kMeL8Iu/PG1hOOLSaEfpcFmb/VhahX+ut4JXWr6kPx/O0jk0o5scpR5OmGZpUjpgU5b87H6GM71D7YVzNrVS/kISUHebba+qxhQpA4QHtKlBIKCSJzJiSkeHx6NZl0Po/LW5bPB44pgKhuy6pQSukNaIBlJEYyoAJVUCBKiRryB9U0eoVGIwq9CYleqMSQ/tWp1cSLAsQqA6hUENsip6lrQEE4eylp6ZxxDizXF+lgjVfuvjoRRuhgERWgZazr7USTVzF2pecuFdOBU0I6cQKtoeLqWiI+3I0agUzx5nlsNDR2bpDIqKqKwPW2UK88qWNteU++d8jM3VcNN/K2CwFvooNspyFd+c6YoE9Cx267NGYi4+Mk9JYs3nPUcl/7Dtl7/vNmZNk+ZK+lFA4xnOf2liywYVwKJ4/x8rpxyT12bu4+5ziHfJZ81+p9m1AiZqZ1guYmnRKv4lkOvdpgMe5Hs01UB7CgWRpmwasbRcI/qbHHsQrDAPCToZ66NF61q7zceN1Gcw8RqSsHPqlZamLir81Y8jTMv4PeSiFSO4hVkR+Y+nv6nBvC5B8lInRN2UOSk5fxNcuueEYesmNQ8zkw80ZtkCXFhjggVIsaAqPnZCd2E1RKQDbfT3IToi2iX0fAM4D3url5dodMipiwMV419syPM711lK9rFft28iSlmdpCzfKwvFHJS8i2TiZ77xbWOvdRGUonrcnik6hZaQ2g1iwHJOQtPDHOMqZQ9GUW8ADrW/+B+9aQTALsRGFBM86m9aCXOrCXupDXmrDXprD/g5Nvs5NTFRpOzyKu72JYpssQmK7CYeL+aMIPbzORRDA7JQJYJwyC2iZ37kXAYxSkhipT+ySACYUC/BO3f00BXfKzKinZd4sa/oJVtEvXVt5076atqgfNUpOTprEwuQjMKsOPsyup0vkjLh5veFflAXr8ccS8YZMBHQajv7XkVgrU9h8+lbqpmYRjSUzMrCW4+1LGJ87h5yxP+6wmYiWqioN8N1XLjZ945HBVlclppVQGyNYLQZMJnUc1HUCeOLvQGg7mxSfEzKGqdNMw2FKIt+k4NdFx5Cg2z+aKeCKsvTRBpq3BzCmqJh7WzbWETr+u7aNZxe3yHlrP1uYyYyxe3gJHM1h3vxbMxVb/TJInH9OCoUztHz2QiW6dXp58WJjLgQKXcyfauakaaMw73MP9GTXvvxfeMAaAuWk6grQKvt/rbdW+WWNv50N8dzfqaONXDjPKou+S9EI/prteMvXyJ9IW1zRR1yxJdOP58sRRehQcFvWFCaYescWvLuv9a0hWWi+ujlEslkl231cQc/hqd3ZajdxjGATTdbkcHLmz0jTCaWhgS0DPM712LiBmnX3l6VN+LdFZqkIGX1S5E0DQhRnf2qmrrs0PFC226W5JczPb6ki0aLiqccLUR+CwGrfSxh2Rdl4cxVRb4yx9+aQUHzwD2qPd3w/K4i2br2jhmBzhOyzksn9cd9eTL05feW/mmn+zIV1dgKjrh96yY3etHG4zrAFvk8LDPBAKeJ7hCi9YP8UYZwjgUfbSWY6C9OLRLevgCvb+34h8DLw73btwc4mEQz5cQXw+D4CLIpQUiHO9ZN2kfqFwPt9sOPwONcHY/W2qiCDWON4lzWO94gRxarNZ17aVWQZimkJ21nv28o672Y2+rYR6TQlSQsrKWrzUOyVOHLURaQUzEMQobS+/jKBslL59PqSsaT++CL0I+OhfgKwCc2+upBX/tSKbdhLQ8hHtAuoaFSq5bDT3SBxd/ip0PYTeVPeaFiWfRAMoKUBJzu7IIDJb7STt32nDAKPPO9yDOruQ0t9URdv1t6NM9zAOMtc5qdd3QGu/LEQHzrW8aFzPUEpIodcnp08nROTBPNm/0hlIlKUqmAr5YFGdgSb5G1TeG/5T3UkRlaDh5LwOLTPjUNjUjLlF438PppMTKHkN7paxhcv6Payh4IxGRyKsExbNMJGp58VLW5kaCJmwoooysQWZuWZuXzkeIxd2Kk5UMHbtffgI0ZF6GRatBnkBdu4NSmX9OwjO7xDbXUhvvxTA+6mMNaROubemoUxZQ95ylcbXbLmnSjX/iid4ybt8bgKELvyEyefvtpKLAIBgyDKgcwATKgVXlAF5pM/xnzsKlIM4xmZcg4phrG9vd17Xa/Wv5XlthdoCVZiUiUz03o+YxJm99sDJrzW32zz8J8lrbLMg1jvmDvZwjnHpZCSELeRiMgKNVfjKV/DfyJtLM/NRx8K8n/LP2VEIIBx9DTMxdMxFExAqT0wUBXn+vuHTfhDEhMKDdxwZgZJ5r4T2Yh27UscM9FyAsemXoxGOTj8DgM8zvV47QcKEPY3KX8xcFJ7a4V3UDCw7abs7rETwx7CPSZ6+T82PvjIyaKFyZy7aODBSm8uRl/r7HyqidYVbtJPTKTwiqHNUehrW0V9f12IrXfWEvXFGHl9BqmzB84LK0tu3FOH2vz9Sm4cjF2G9xm2wFBZYBAGSkGK9lR7ZIqIj3umXS6iczhnJbC0E4OoAILd0chd3g4UhUn+JXgsACFFIRY5RSrE74FP+mivYUDYR4P1pbrQEFzS/Ax1gW3ybhMs8+UwMOEFWNz0BE3BSgIKC2bDNCojdmpDe3jvNDGJooiJIzMWMDVhMglbamh941VC9XEPo2n60VjPOR9tZtd6ixEpJnsPZYDYDhL/X3vnASZXWf3/z/S2ve8mu+k9IYTQu4ACgoCgIqKov59dQf8ISFERKQqIwM/eEQsiUtUIIkgJARJCCEkI6WWzve/s9PZ/zp27yWSzZWbL7M3u+z7PPDM78957z/28d+fM9573PUeeJbIYShx6X9xuMlNodRwSccw125inZ/+UBDCeR/6G77lnsddMZ+pN38HsGDrK0x1p4ZF938YX69BS54sQSG0dUR+PtK/mv92bSWhZHnO1BC0n5MxJa/qd/NhviXrZHmzSHpL9c3eohUhKKQs5nhM/JeYEpxWcxkL3dMpq22m647uYXW4aF36H7StDVMx5lYYzOvlX6bnMcjm4debUtGzoFR1XbtlNa1eMo3GxrsFHLJZce2eyRlhaZeeDM6uYW+Q8KErbENjKE/W3E0zE2Rs5jwZbCZWBVj7XuoHquRfQ3baIF+9rJuKPU3Osh5O/cgOrswAAIABJREFUXI61n+Ulq97x8pMnm7T6gJ85t1Sb9tncGdGKx0sReX97jNJacAZA9PZ7PlzI3GoHL9/fRCycIP+0N3Ge+Tgmc4JC5wJmFX2IQtfCEU8b7Yo0sbL1T+zwJaPdedYyuqPJn+NljpmcWnIFVa75mfxr9dtXMqHKOsjHVrbTE4jjsJk477gCPnB8obYGUoTjb1re5bnuOnJMFq7qDlH87loCe9+B+IGvZbMrF2teMdbcIv2RfJ1wF/HIphz+szGZSOjikwu55JSiYdfjHqvEMYOBHAM/N+i4GUUQzgFk0Zg8yx1ZmUgsi/ODA1g/4bOvDee/LRyO8+Wv7sYfiPOje6dTNESq4uEcY7S36Xrbz5Y76rVkLUvuqcE6zMW9o23XUPvrWu9ny531SGbx+d+aQu68kUc1U0tuzPhiGaWnZWedxlDnqj5XBIxGINuO0iDnrwThGA+EVszb+6L2gzgY9+Ky5BONB4kkQkxxLuB9FV8h15q80doa6WC9/x3Wdq5mU2gHIfOBn1MVtlKWuhYwd1eUkr+9jKWlHZkDmHfaGRRdeAnW/PTqycqP4tZocH9EUaKJIhpFKPriUSRS2Jv9UwRglc2zXxB1Pf9vWh78rZZJVDKK2orTX3PVGtrLo3W3EIr7OLXkkxxZcO4h5PeF2/lz66u86Zf7JDDTUcblxSeyyD3loL7+eJgduviT6J+IQJl6mtok2jjbWU6NLY8m339IRHdR45zFBZXX4bBIUD3ZWh56kK5nVhA/YTprVl5OLOrgzGv9/DS/mB2BEJ+oKOG8kvTY1gZDXLu9VhOSt8+qJhSN81xtC0/saqC7Q9ZZJsVhkdvMaTX5nFydy5S85NTQWv9Gnmq4k3AiQYP5MvbGHZQH2rng2Qbq1pwLCTOLLypg2UeKB11W8sZWH/c91kA0JiUinGyvC2pxYomaHT3HwxlLc+l8J8zzj7QTi8GMBU7OOKeANb9sJuyLU3l8ANcFv8cf10uSOOdpwrDItThjYRiK+3mj/XHWdf5LS5CSYy3mpOKPMTfnRBqD23ip9UGaQslswLM9x3FSyeXk2zJP6tL3QvIFYzyxqoOnV3dptQDzPRZNvG2Zso9VvkYKLHZuqjqKakdyam0s6MO/Y502rTTUsItoTzuJaLLeYm/rSBTwl8RHqWMqbnxcan+KBQVdWHOLseYVYdHEowjHQv25CMl42t/07LFOHDPYV1q2/dxoCcKRZmlLZSLrOHYBnwF+m+b3v3KUcuv6pW5+/qtmjj8uh699RUpiGbvFQnE2XrtXm345HjX+Rkqn6d9d7PltC9Y8C4tum4qjbPAsb0Mdr/7xdvY93E7eIhfzvmmMkhtD2aw+VwTGg0C2HeV4nGM/x1R+LksDIVFByRz4jvcFLb39cUUf4ujCDw5YWzAU6mbNpp/ydnAbW3PtNLoOTEmzmazM6clnxup6Zm/2UeqzUnjO+drD7BrejUQRrsFEDJe5/6lv/s2bqL/7dmR+5JRvfAvXnHkZk6sLbOaJ+juIJaKcU34lc3MlKfyhbZO/jj+1rWJnKBlFOso9nSM909gZbNamf9aF2w+aDGvBzDRHsSYAZzsqtOdKWz5d0SaeqLtDi0ZNcy/l/RVXHzIVLxYMsOeBLxEvD9Cw9RR2rTubyiUu5l1dzA0792HFxF1zaii3D+2Ln2zp4KGmNj5cVsQlZQcnsVvZsYsHt2+nu7UA/AcyRs4ocHBydQ4nVOfSGV/PioYfEsVEq/V/sT2Vx4xV+ZjMMWYd/ThTl7ZRNP8SPFXHDjrNcv1OP/c80kA4mqAo18IZR+bzniPztHIIva1hT4i//ayJht1h7A4TZ55fQNN/u7UoYtWRLuZ/Zg97/I/h09e85jvnMKvwEordS4cUhvFEnHe6/8ur7X8lEOvS1tIeXXgBywrOP4i/CKMtPatY1fZneqLtWiIVKbx+dNFFOMwHJyrK+GITsd8V4a8vtrNygzd5veRGyF3Ww3ePXUil48BNgb77lv+FeKCHqLedqLeNtdsD/O6tYgIxK9MdLVzmWUGOv5ZEeKD4UnKPJpvj4ChjXhGRPCev5LxGnWm3njjmUpYVnDfiabPp8sm2nxstQTjSLG19+chq0p/r0cJ02E16Ryn/FDfdvI+du0LcfNMUFswfnqNJB/Zo9al9qJWGJzspWO5hzjUVQ35xjdZxR3M/u3/XQvMzXbim2rXC8Rb38NI9BxvCbLhOL7lxVzXOSuOU3BhNXmpfisBoEMi2oxwNm0dhH5Pez40Cw4x2IWvGpMC8JNUYqokP9u59kZb1v6PLFGF3RQ27K6exKbwLX/xARKywM8ac7WHmN1g56ogPUH7a2Zisw1vT1J9NkeZGam/5JnFfD2X/+wXyTjl9KNMH/Hyn7w3+2XAPJsxcUPUNatxSMvrQpq0L69nOX9peozl68Nq/EmuuJvrmOMq15xmOUux9hKxEJEV8+mOdzMk5gfeVfxmLpOVMaYlYhMY3foyv/nVt7lhinYdN4W/S3Rjj9GsqeL06xKMtHSzxuLhx+tA3VG/euY8t/iDfmzWVGa5Dp9LKFNYVnW/xaMNGwh1lmDumEA8lhY/8cF5U6mJOWRP14Z+T9+hF2LbMJeyOsevyVj5m/y+OVlmSDPbcqRTOv4ScKccNKCRqm0O0eaMsmeHGkhJpTj3/aDTBfx9r58UnOojHYc4CJwUdMbyNEUpmOXjPdRV0Wdays/1ResLJ3xJ5jlnMKrqEEveyfn9fSaRT1gm2hiWhMizIPZUTij9KjnXgLO+ReIg3O//B2o6niCZCWhT9hKJLWZh3+oA3TNK9AGWN7W2bNrL1dTM0JX/Dzqt28vEzS4asExiNJXj4hTb+/loyI+4Hji/g0tOLsVqSMice8idFY3ebLh6TAjLaLc/J16lZTpsqYcPREHZCTjcc+Rrk9VhTRGNvlLF3qmqRNnXV4inAJAtVR6Fl28+NliAcSZa2vtjkVpYsvpcIoay9SKdNeke5bXuQb92yj5pqO3feXm14ceXfE2LjDbVasfclP6jBUTL0Hb10LoRs90nEEmy9qwGZQpp/pJu511Zi0r+A0rVFfkhsua2ebim5cWkRVR80VsmNdM9D9VMEskUg244yW+c1xHEmvZ8zyDgMakbYW0/TGz8i1LkLs81D8ZH/S2NxhZacRqaY7gwlf6xLs0QTzGg0saxoGcfMO4dqe+WIfHc84Gffrd/W1i4WnH0eJZdJmeeRtU3dz/Nc8y+xmRxcPOXblDtnDbhDEVEvdG+mPdrDLEeZJgALrANHd2RHksXyqfrva9NTF+edxeml/3OIsIhHAjS8/kMCLRuwespwtS7A++8XCcw4l3WbTyKnzMr7767hm3v3sS8U5gtTyji9cOAlF95ojM+9u4sCq4Wfzps+KPO2aA9/al3FKu82CORS6J1DtKMMbyiB0w/HrEyQ22UiXNRN02dCbMjzUW51cY1MQdzyOMH2ZIKhpDC8mJwpx48owrRvR5BHftJES30Et8vE/Hwr/sYIeVU2zrqhCk+JhWbfG5ow9Ib3aMfOc8xkZtHFlLqXa+faGW5gZdufEMEvrdI5T1sbONjY9h10iRKuavsL73olHgQl9mmcUnIF1e5Fw7rggvEodzesZ1OgQ0ukdHFoAU++2K1lBpV23HwPl72nmIqiQ2+Wt3dHuf+JRrbUBvE4zXzxA+UcPXfw664/I+OREMHuRlZ1PcI78SSb2V1TWbK7FLo7NfEY83cNfn4mM9acAnIXn0Lx6amVjDLHkm0/N1qCcLhZ2n6gF59/HRBZL6mSrtPfW6qvJUyH4qR3lD/+eRMrX/HymU+XctYZ+ekwG7c+iXiCd769D9/2EDWfLKHi3PTm/I+bwUMcWMpEbP52HYF9YcrPzWfaJzNLjNPyYje7ftasRRkXfd+YJTeMyl7ZNTkJZNtRGoTypPdzBhmHIc2QaFbbO3+hc/s/kz/Ip59ByZIrMFuddEa7tYylb7a+wdvBLfjtB0ohFCdyODJ/KUvdC1jsnovbnP5Mn0Q8TsP9P8C//k3cS5ZS+f++MWqRijXtj/Nq+8M4zbl8eOotFNqrhmSQTgcp+/GPhnu0SNPyggu1BDZ913HFQt3Uv3onoY4d2POnUXXiDZhNTmq/9Q0izU3srPwmjbucHHVZMY73uvj2zn24zWZ+MKeGQlv/kdeXO738ZF8TZxbm8dkp6a2D2xyo53ctL7E33AYJE8fVHkvhnwow+6C1DN48AXAFyC+GttxOSvJNfHvKUeR07qD93b8RbEsKQ1vuFIrmXUzO1BOGLQwj4TjPPtzOKys6tboM8wos0BnDVWjRRGFhjUPL1NniX8sOEYYhWYUFLls1flsZ2/xvaRl1c60lnFR8OXNyRKQOTw40BXfwUuvvaQhu1Y4x03MMJxdfToE9/WVLki33+w1vsS3YxXRHLjdWLSPPYkcyrr60wctfX2yj3RvTSnW896h8Lj65iDxPMrPt2zv9/OjJRryS5K/SwdcurqCsYHgBhnQSx8h6xWhPRz9RxgORx1hPB/nLz6H0vQPlHEvnv0PWkmpjMryBSe8QB/UazQMNJ0ubpK+6UheCErOXuLV8g35P1mxncD6T2lF2dUX58td2Y7eb+en903E6RydcnQH/jLo2Pd3Jngda8cxysPDWqWNSyy8jg0ahc7ApopWjiHrjTP9MKWVnpSfKpYzFhq/v0bbTSm6MQnKaUTgdtQtFwNAEsu0oDQJjUvs5g4xBRmb4mtbTvPanxEJd2HKqqDjmKhwFB+qVyfqtTe/+l9WbV7C52EddlZWE/sNc1trNc83SxOGR7oXU2AefBtn61z/TueIpbBVVTP3WrVg8mUdIBjo5ERfyo39919PkWUv50NRbBp1WmA6kbT2v8UzjjzRhIslLlhdecMhmEX8r9a/cQaSnHmfxPCqPvw6LPXlegS2bqfveLYSc1azzfh6zzcRF907jkWAH/2rr4tg8D1fX9J/V9f7aRl7t6uHamkqW56XPSWoy/qdrE8+/uJP5j8zHErXgPDnKlPMrWbFnOzvbPCSk8r2m/KI4i4N8df4MjizKI9C6ifbNIgxlAhza9VAkEcOpJw5bGO7aHODRnzXR3hxlqg1yImBzmznj2krKFyRvJsjYNfvW8nrrA9RGW5H8nGZMLMo5gZNLP4dtFGpJyjFkPF9p+xPeaCtmLCwtOIdjCy8+KClQf9dFdyzMHfXr2B3yMseRz/VVR+KxHCzoQpE4/1rdyZOrOrTyEZKF9MITCpBMpY+t7NDWHJ59dL42tdQ2jFJro504JhGPaYluhiqJMdT/Sbb93GgKwqHObSw/n9SO8vGn2nn4kXbOPTufT348s+jUWA5Kf/sOt0V5+5o9xEMJFn+vGve0sanfku3zkuN5Nwd497Y6EnLH7oYq8pcMvdB6x0+aaHvZS9lZeUz/THp3Ksfj3NQxFQEjEci2ozTIuU9qP2eQMcjYjGioi+a1P8Pf9BaYrZQs+hj5s849KCIj0b2eNa+x5x8PsSWvm62z7eyY56HHfiC9fqElPykOPQtZ4pqHx3LAv3hXvUzTL3+C2e1h6rdvG7C8RcbGp2wgP5qfafoxW3tWUWKv4ZIpNw/5Y3+g48k01Oebf6X9kJeaj4vzzzyka9hbp4nBaKANd8VRVBzzVczWg38vtPzxAbr+8zR1RZ9kT/0cZp2ay/IvlHLttr20RKL8v+oKjss/UPhdDhJNJPjc5l1EEgl+vWAGjgzWe4nw2fBEB2893E7ClODdc7ew64Q9THeW8KniU9jX9RLP791He8dyOn3V+8+pKs/Ke2ryOWFqDm7/1mTEsDVZ3mSkwjAUjPP0n1p5/dluJI+sPMxWE6d+tZyaY3KQKKxkCG3XE86UmvPIj3drhb899qnMKryYci1COPJAgtTpW9e5QqvrGUkEtYjy8cUf1kq4mPupVdkRDXF7/Zta5tyFrkKurVw6YKIkYdXti2llKqRchZTrkOaym/jceWWcsHB45b980U6ebf4Ze/3r9cQxl7GsQP4/R85jJP9vsm22/ZwShCMdsXHePhZLcOXVe2hvj3Lv3TVUVhg7Gcm2exroWOOj8gMFVF+efhrsccac9uF7p39aPGYt+umqGng8ujb42XK7XnLjBzVY9SkQaR9MdVQEJimBbDtKg2BWgtAgA5GpGSKmunY8TeumPyPVxt3lR1K2/ItYHQfPJElEo3S98B86nnyMiLebhqlO9r53PlunW9ke2avV/ZNmxswsRw151lwsviCRDZuwROPkL1mOu6QCm8mGZDdNPmzYzDbsva9T3zcdeN9qsmrJXpLb2pAI5SHTNxNRnqq/k9rABqqcC7io6gas5sx+c7zZ8Xdt/ZpEkc6uuFKbrti3BTt2UL/q+8TDXnKrT6HsqM9j6iejajwUZO83ryPY3MV6y02E/Gbef9tU6ivi3LG7XlsjKFNHcyzJ6YXSNvX4uXV3Pcty3HxjevpTX2ORBK/+opmdK71YHSZOvaqCyCI/v2t9ia3BRm3fJ+fMZTq72dnzHJboDPb2XM6eJjMEk4zkB/eCUpdWwuIIZy2h7Y8SaH1H+8zmqSB/9rnk1ZymTS3OtG1b7+exXzRjao9SrpeucH3oTRqWPqbtSsZL1gmWOqbTHtigTSXtDCansXpsU5hZ9EEqcoYfrUy11xft4NW2h3nH+6LEKCmyT+WUkk9o2WN7W2skyG31a2mMBDjSXczVFUdgNx8Yp8HOv7E9rGUk7fTFtBqOVcWZXYO9+97lW8t/mn+h1RottE3hnIorNT5Gadn2c0oQGmXkh2nH6jU9/PD/Glm6xM0N16X/5TbMw41os/bVPWz/YSOOMiuL767B0k+x1hEdwCAb92ZPdVTYNFFoyz30Sy4ejrPh2lpCTRFmf62CouMPvotpkFNRZigChiSQbUdpEAhKEBpkIIZrRqhzN41r/k+bAmlx5FO2/Et4yg/8SO7drySH6Xj6n3Q+/Q8SoZBWjN1+wfvZc3w1b4e3sd6/mc7Ywdk8h2vTQNtJuY1eQXlATNqwmsx0RxqJJYLkWPKpcs7GbpL3rdqziEmPxUWptZhSW5H2XGTN10Tsq+1/4Y2OJ7XSBudVXM00z6Hn7m/eQMPr95CIBrVIasmSTwwardFKbNx5K83WE9ne835KZjs497tT+Xl9My92ejm9IJcvTBWJlGx/aGjln22d/E9lKe8rTm9pR7A7xn/vaaBlSxB3sZUzrqukSJ/dJFHDl71b+XPbKjpjfpwmG4vtYayhVyi0luN3fpoXWzqxd+bh6s6nK5gMbdnMJpZVuDmmoJOa1seItG7Q3pdERLLmNH/m2djcmd00D/hiPPVAAztfDiKTZSXGFTt9DSdfPovZOcccHJVOJGgPbNKSz3QEk9FKt62SmYUfpCL3pH4jepleY82hXbzc8iB1+v6nu5dxcsnHCVPIbfVvajU2j/GUclXFEmxZjMhJJHNl2x95u+vf2iktyXuvZpfNbKwZa9n2c0oQZnqFG6z/rd+rY9M7Aa69upLly9KfC5/t04j549pU0Uh7jLk3VFKw1Li2jpSNJM3Zfm+jFgnNXehi3o1V2hSO1Fb7UBsNT3ZQsNzNnGtGlllupPaq7RWBw41Ath2lQfgoQWiQgRiJGfFokNYND9K9+3ltNwWzz6N44Ucx9Vk3JZ9FOztpf+pRul94Dqk1YC0qouiDH8Fz4sk0BZvZ+5v7CTTWYV9+FJ5zziGaiBJORPTnKJFEhEhCnntfJ/8Ox/v26dsvuZ9IPLp/nzFt9dnwmohBt8mCOeHDjZmluScwwzVXF42FFFkLsZos9NS9TuMbP9KiqEULL6Vw7kVpJTtp+cNv6fzPs2y0fR1vTwEnfamM8hM9fH37XrqiMW6aXsWSnOQU26u37qE+HOHHc6dRkka9ws66MM/fWU9Pc5RiKe9wTSXuwkOT1fjjYR5rX8O/Ot8mRpw8U5wpiU3MsuURcl3Bs92N5JhsXOZcxI7GOK/V9eCPJMWh22Zmdl6chL+JaKAdE3Etmmh1FWHPqcTiyNHEnbwp78v3n/asvyefSfS4M1KHiDDTLg+F/53B1LAVuR0dnm3FcrFby9fQmzsmdT/BWBtdgS2EYi2aPTazm0LXPHId1VjNFqpybcwudFLitqY1HqlXiQjmHb41rGz9o1ZjMkgR75ouxJ+wcHJOBV8sX4gli2IwncQxw7vKR3+rbPs5JQhHfwyztsd9dWGuuX4vpSVW7r9nGuYB6tdkzaBBDtRbr6/4pBxmXZl+9ikj2D4cG2LBOJu/U4d/d4jS9+Qx/XOl+79I/XtDbLqhFpPVxJJ7Dt+SG8PhorZRBEaDQLYd5WjYPAr7UIJwFCAaZRc9da/RvO5XxCM+HAUzKD/6Suy5/c/yCTfW0/a3v+B7Y7Vmvn3KVKyFxfg3rsc5dz5TrvvmqNYy7I+RJMA5ICyjtIcb+UfjvfjiPczPPZ15uadpAlSEpDfWQ2u0g5ZIG83RNvaFavEnIgMmTBR5U4iTvJ5uCiIxphQtZWrpckqtRZTaiinWBeNAYxcPytTRa2lvdrIx/Hkt26YkmHkz5Ofe2kZKbVbunlNDh6wr3LaXGqedu2bXDHkp1L/t58X7Gon440w7PkcTmlb74GvL6sId/L7lZd4OJMuLFNLG0bYoVvdH+VdXPR6zVcuiWW3L5a1GPyv3enmz0UdUXxM3pFFpdjCHoPo1mNeMtl6w3QNrzoTYCIJg+Q4Ls4uczC5yaM+zCh24belN84wmIvy79Rke6oIITirZzhXF0zmi4KxDak6meYoZdRvtxDEZHXyYnbPt55QgHOZAGWGz3z7Qwr+f6+LyjxbzgfOk8ocxW8+2oFZmQoq2H/HDGmz5o1eE15hnnLQq3B5l0021RDpiVH+8mMrzC9FKbty8D9+2EDVXlFDx/sO75IaR+SvbJi6BbDtKg5BUgtAgAzFaZkgWzaY3fqxlnjRZHJQe8Slyp50+YBQmuH0brY/8meCW5BQ/a3EJ1TffgSVv4Jp7o2Vrf/uRaMvf6r5DOB7gtJJPaZklU5sUMV/ReC97/G+RYynl5LIvEjJZaIm2a2Kx97k5UEcnwf0ZVvseSwRjkbVAE4gl2jTUpFBMPhdRYi0kvPld6u+6jW2Jy2gJLWLJRYUs+2gx9+xtYE23j/cX51Nis/FgYysXlRby0fLiQdFs+XcXqx9oIRGHJR8s5MgPF6WdEV2iYm/4dvH71pdpjfZoEb95liAzc85nRVcdLrOFGyqXMdeV9P/+SIxmXzS5QjSRXCkaj0boaVqHt3YV0UArCUyYnYXkVB2Pu2IZWJx0R1p4u+tZGoM7NLFdYp/O4vyzyLOW6fuCXS/10PGEF1sCQjaY8ukCSuY5tc8lAZ4cLZZI4I0F6YoG6IoFaAu10BpuxBuPEErYsQRySPgLCAUKSWgxx2QzkaDME2NmgZk5xW7mlxQyLd+NpZ/ghJSU+F79OvzxKIvt3eSFH9HimoW2Kn194ZEZRx/TvbaNnDhmsHPItp9TgjDdK8pg/fyBOF+6ahexGFqpidx+1qkZweR4NMGmG2sJ7A0z4/NlWrRsMrWeHUHevaWOeCShTQ2VLKt7ftuCZ6aDhbdNjJIbk2k81bkag0C2HaUxzloyyB+oWWcQm5QZIyQgkYuOLY9rJQnkB7IULi898rP7yyv03b1cA/716+hZ/SqF512oRQvHs9UFNvNE/R3EElHOKb+KublSkA9CMR9/b7iL+uAWLanIRVU3HlKqQs69beOftHqNMYsDx7GfoyevRBeK7bRE22iNdGjPbdHO/Ul1+hOMkoW1oCNM/uYIFf/6LCasLLgtRm5ZAXft9hGIJ7RIYXMkyndnTmGuu/8aj1L7bu0fWtn8ry5JCssJnytj1qnD+90Sjkd5on01T3auJYYZF3GWeI5jta8bp8nC9VXLmK+LwoHGUBj5m9bTuWMFgebkOsOIw8nuGeVst9WSIE6+rYKTiz/OTE+y8Hzf1rQ7yNO31GEKJER2Ez4thO2CFlropjnSTWvUS1xPWDTotRQ3QyAPm9+DzZdL1F9EOHJwdk+rKUKZp42peT1My48yq9BGt83Db7pChBIJLiqYxqXFs2kL7+Xl1j9QG9ioHbLGvZRTij9OseNAdtbRuK4PThxTxTkVVxkqcYwShKMxygfvY9I5yqef7eSBB1s5/dRcvvDZAwumRx/tyPZY/2QH+x5q09bSzf/W4HWURnYk427d/loP2+9rxOwwaau8peTGotur8cwYwdwN456uskwRGHMCShCOOWJ1gCwTCLRtoemNHxH1t2J1l2hTSF3F87JsxfAOt6NnDSsafygxLC6sup5iezVP1H+f1vBuyh2zuKDqelyWg4VDIh6l+c1f4q19CbM9l6oTvoGzaPaABkQTMdq1aagiFCXCmBSMvX+3RaUeXfKGyaz/HsO8506kceF23vzYPyG6CKJnaZ+JYDmvfDtlEmnUo4wltkItGU4kEOel/2ukbp0fR66Z079eSfn8/oVjJqRqg/X8sO6PNCSSuRNKrdNpiTpwmMxcV3kki9xFae3O37WbtbW/ZoNtO1ELWGOwKFjN8qpPYi2aS0vUS3M0KfKaI/K6i5aIl6ZIF9FAgmN/cTyFLTmEgZ3FPey79G3CVV4KLW5KbXmUycOqP+uvC6xuGsKdbAs1sT3QyLZgA/siIs71FrGDPw9XIA+zL5egv5BYvM9vG2sU3GGq3DtY6l5LVV4PeY5cHJYivMTYGtiCL+7Vrp8FuSdzYvHHcFtHNnvqcEkcowRhWpd+Rp0mlSCUO4Rfv34v9fURvnfrVGZMzzxFcUZ0h9k52Bhhw7V7tXkJi++qGbQEwzAPcdhsVv94O/sebtfsrTi/gJqPZ5Y97LA5UWWoIpAFAkoQZgGyOkTWCcTCPlre+jU9da9qE/KKFnwomVglzXT8WTc45YAbu57j+ZZfYTM5cVvz6Yo0MdW1iPP2Me8GAAAgAElEQVQrr8FuPlhUxWNhGlffj79xrZY4perEm7DnTRmR+UnB2EndtjfYuuKvBDd9CVMgn8bPv87e6VtpDZwO8WowvwP2Zw86lkxJLe+uYtEf3oejIQ9TRYjyr3iprCrcPyVVsqiOpPVE2/lF7V1sjBURwCOjS4JS7CYz11Qu5Qj3wFNYk4lZ3uS51oc1kRfChTteTsIXpdNipcPmpMc2cOkFl9lOmTWXMlMBxb+finmjkyhQZ4bjLi7gzIuKsWRQ0D0YD7Mz1MKOYBPbgs1sDzXRHu1J4hGlGPJg9RdiC5QR8OVBQGxLjVzGKXC0UOreR6lnHyXuWnA2026KI0spZZVmhdnDVNsU3LYSnNbigx52cxFBr4dQIIHTZcbpNmN3SsKc5DHGM3GMlI6RzLfWwiIcU0cW7cy2n1NTRkfyHz5O227Y5Of279czZ7aTW28e3+kiAyGQL7Atd9TTvSHAlA8XMeWS9O6AjRPSMT+s8Nj35zb8+8LM/moFFuf4Fz0d85NWB1AExohAth3lGJ1GprudVDc+M4UzUfqLr/DufZGW9b8jEQvhLJ6nRQszLUEwHjxWtz/Ga+1/1Q4903MM55RfeUidwljET8Ord2nrJqUoe9VJN476uTU/8Ct2PtfI1shlFFTbOf/71bTGwvypsYnl+WESJlm/eCC66N9hYtaDp+Lo8dAyaw/rPrqCqEviaMmmJb2x5h+0drFs/xrGoZPe9O6nO9LMw7XfYUfcST2zCCNRsHJtVd5VFUcw35mTjPJpEb5uLdpXH2qmNtyEPyEr9vr/3WBJJCgIB7VHcTxBVe50asqWUe4s06J+OWbHfrEkeQxW/76VLc90aeJrnyS+mengQ18qp3zq8Or5yfm1R31sDzZpjx2hJrYGfEQoS556vAX8CVzBUpzBUkI+D/7gwediM8co87Tjcu3C7NpBjrMOpx9y24uhvYhAWzFBedYfcYlMpjZTHJszitkZIubwYXIEcbjiFOd4cLstOFzgcJs08eh0WXB6LLjcVlxuG26PHZc8XA6sFgcW4UX6GVV7RaBvzWv0rF1D3NdD3mlnUPbpz43o3zDbfk4JwhEN1/hsfM99DaxZ6+MrXyjn5JMOnoYxPhYdetTWl73s/EkTzik2Ft9Zc0jZBaPYqexQBBSBw49Ath2lQQgpQWiQgciGGWFvvTaFNNS5S6tNV7bss9r6QiM3EbPrOlcQjvs5tujiQ2rZRYOd1K/6HuGuPTgKZlJ14vVYHMNbnzcYB6njuOfGa1nfdCHd8Zkc++kS5p/d/xTEXau8vPKzZm2df82ZDko+GqQ10SsW+5+S2vfYqUlvUoVib9Kb3rIamnAK1/Fo3S1a8ha//TTeCUv0tHfZj4hQqS8pD8nKeqA5iFJhK6TaUZmc2pkyxVOmfIZa36Fz+wr8TeuSItbi0Irc588655DstTJOG5/qZN1DbVpAr0ES29hMvPcjRZx0XsGIM9Y/31XHr1o2azHB0/IKMOHVool7w+0H1oGKoPMWUNA6BVtzAdFmG2avCWsP2PSHaYAl0zZXCFdxOxaXl1jQSTTkIBp0Egk6SYRHsBTHFMfiCGGVhzOI1RnG5oxgc8kjht0Zxe6Kaw+HM4ENHxZ/G+buJmyxHuwEsSUiuMqnUDTjeKqWXTKif9ds+zklCEc0XNnfuLU1wpVX7yEv18KP75uOzWa8IYx4Y2y4eg9Rb5wF35lC7ijMwc8+aXVERUARMCqBbDtKg3BQgtAgA5EtMxKxCG3v/EVLuiItb9oZlBxxBWarMZeJDMYl4mum/pXbifiacJUuovK4azDbRr4+b6BjSkmObXf9jvXhL2F3W7jo/uk4U5LviSh6+7EO1j/SrtXmO/qKEuafkz9gpstD1zD2ZklNrmUcKulNsZYltVibguoxWdjlfQFLIsDsnPexOpRHU8RKnANixkEXBeymkjrOLD6DowrOSas8g9xI6NzxLy3KnIiJwDRpWUkLZr8fV8mig85v+wvdvPrLZi2TaosZ2uIwbZ6TD32xjOKK4UULV3Tu5cHWrVgw8dWKJSyzl9DREqWtMUJTQ5Dd9V7t2dsSJ9ZuwxTv/zds3Jkg7IFIjolIjjxDNCdBSYWFOVUerS5iZY6NeCJKfeBd1nU+Qzghk2kLmGs7BXs4h2AgRtgfIxJMaOtDw9ozRIMQDcmzmVjQRCxk1h7xkEV7JMIjmR6cAHuMkiM7ufrqkd3AybafM56aGN437aRxlA/9tY0n/97BBy8s5NIPDZ42eXgoR77Vzp810fqil9Iz85jxWX3KwMh3q/agCCgCikDy7ndyrchE8V/pjuqk8XPpApks/XxN62le+1NioS5tmmXFMVfhKJh+2Jx+qGuvFhmMBTvwVB1LxdFXYrLYxtz+5t/+knXPeWiKHce8s/M57tOl2jFj4TirftnMrpU9WJ0mTr2qgqlHJRO+DLdFE1FNFB4op3Eg6Y3UYuyIdg2RJTUPM258sTxCVElp++R3HQmm2KwscedwlEdKbuSRY/HgNjsxD1LQPRby0r37eTp3Pq1xl2bPn0bBrHPJnXrSfv771vp48f5GYuEEoXwLu7pi2Bwmzr28hGPPyksrWhgOxmlvjrBiex2v72nF0W5jpreQcCt0tUW18hb9tbwiC3llFiwlYYKFPXQUtrMvtxFfYTcJZwwkq6k/D3ugEJPPSVQS1vTJajrc8Rp0uwSYI2CKJp+H88hZFONb148sKVS2/dxEcaiTwlGGw3G+/LXd+HxxfnTvdIqLRnIXY0z+jeje6Ofd2+qxFVi0outWT3pFS8fGGrVXRUARmIgEsu0oDcJwUvg5g7A2nBnRUBfNa3+Gv+ktpB5C8cLLyJlyLBZHPmbL8KI52ThJyZ4qawbjER9508+g9MjPYBpEyIymTTG/n503fJPVTZ8mbnJy/l01OPMsvHBPIy1bg3hKrJxxXSWFNSOYZpimwSIYWw/KktrGnuBOtgc2E8REOKW+X3KX+YDkiJBHb0Q4BjRpK/9MtJBrcZJj9pBr8WgiMbfPa3kvx+TE0rqd2O6XsbTv1o4i10z+zPeRP+O92pTdlq0BnruzgbAvjmuanfW1YeJxmL3ExcWfL6OgxEbQH6OtKaJF+tobI/tfy3veDrHr0Cb37fKLrRRX2JKP8uRzkTzKbNgdh66JjCfi1IU7tEQ1vWsSD5lq6s/H5s/BGnEQJ4IZEzm2AlwWF2aTCYvJlHxGXpv3vyfvJx/m/c9WsxlryntW+UxkeEszsdq9RPfuhVAAUyKh1aF0VE7BNWMGzpppWJxO7a6kHEvONfV1mdvKjMKRRfKz7eeUIEzzn9kI3V5a2c1Pf9HMscd4uPqqSiOYdJAN8XCcDdfVEmqMMOur5RSfYMz1jYYDpwxSBBSBjAhk21FmZNzYdVaCcOzYHhZ7lmmOXTv+ReumP0vl8v02m21u7Ue+xVGA1Zl8tjjzscp7zgLtM6v+bJLiellqvsZ1NK6+V5u6KNlSixZeOmbFxwc6Jd/bb/HmXS+wK3o+xTNthLwJelqilMx28J5rKnEVZI9HfzZKnbx/NvyQGDFyrVUcUXgh+Y5p9MR8eGM9dMV62BMKsTMUpyli1+oZSjNpeUIbSLBX8mqmPaLOuAlXNIo7Gscdg3xHKYX5s8jrrCb046nEOyw4Z8fZ4zPT0YAWLZSHv1tS0PQj+sxgLU7QVRAgURzjlBmlzJ2SQ1G5lfwyC1Z9WVNvSRBtDykRw9T3Dw4kHvgrGI+wJ9zOrlALW/21bAs1EEhkJzGfKZ7AnCApIC3W/WJSRKgmPKVYhi4+RRRqf2NioauKK0rPTHtc+hfU2Z0JowThiIYruxvfdHMtO3aG+NaNVSxa4M7uwdM42r6H26h/vIP8ZW7mXleZ9S/+NExUXRQBRWACEFCCcAIMojqFYRMIde6mY9tTRANtxIJdSPQwEQ2ktT+zLUcXi0nRmCoWRTzuF5H2vBGVu/DWrqRp7c8gEaN48SconHNeWvaNRaeGX/2Clc8vJpBIJm+ZfmIOJ36hDKs9O6JiqHPaF3gHb6SVubknDrpOMJKIs87XyiveRt70tyJ/S8uz2FjiymGu00aOOYwv4cerCUpfUljG9Wf9dSAe7NckZ2cOxzx4EbnNxXRMaWRHdSOe1UeAKU6kqEt7RIvluZNIcWfy74IZYJkmtSYAKZfSNdTpZv55HCwRW/IRtmINy2sH5qgDEiJjtNictl+TJhR1aaOLRkn6o723v6+87hXW+jWQ8pnpoH69Mim5j+T+9aZr1uT+Dxa6ntIYt590aebnmrJFtv2cEoQjGq7sbbxjZ5Cbbt7H1Cl27v5eteHElr82xKbrazFZTSz5QQ2O0rFfH5A9+upIioAiYCQC2XaUBjl3FSE0yEAY0Yx4NKStMZRMnvIcC3UeeB3s/Tv5nEw2MlQzYbHnpkQYk+Lx4IijLirtuQdNA5WkJq1v/x5MZsqO+gJ5NacOdbAx/Tzm87Hhuvt4t+0cqkp3MXvOHqz5+VjzC7Dk5WORZ/lbf212jP0U0pGesD8WZbWvWROHGwOSvTPZKmxuTsop56TcCqrs/a+LlAQ5IhS7Qq001b9Cc9Pr9ER78FtMBE2lOJ+6BOveYsKlPWy/4iXCJd1oYTKtJWWDrAvsjM0mkCjBTJhSy7s4IhHMYSvmsA1z2KK9FgHX+zr5mRVzpJ/3whZMen95PrCNRet/uLWcUyJc/OUFIzI7235OCcIRDVf2Nv7pL5p4aaWX//lkKe87S+aWG6dJXZvN36mjZ2uQ6k8UU3leoXGMU5YoAorAhCOQbUdpEIBKEBpkIA5nM2TaaSIaJBrq1KKLmnAUAakLyaj+XjLy2HnQ1NQBz1vWXdnzNLFotjoItm3BZLZRcezX8FQuNwQu/6YNNP74h8QDQ0dSTU7nAbGYpwvHfBGO+VjyCnThmBSSZvv4r9/sjIZ4taeJld5GdoSkZEWyzXTkcVJuOSfmVFBoHVjkJuIxfA1rtLIVwfatxKI2tr52OR11s3EVmKhamkM0lCAaihMNxomEEjT7AkSCcawRM46ImfjBVTJGZcwtNpOW9MfqMGN1yGt51l87zMjnok/3C5nUYF6vdJV5nIk4sa5OIi3NRFtbSET0GyImtALytrJybCWlmOy2Q4Mtsv8+Sumgv/XPdJ+0XzOXznYy7ficEXHItp9TgnBEw5Wdjbu7Y1oyGavVxM/+bzpOgxU1b362i92/acE9w8Gi26ZiskyUyyo746uOoggoApkRyLajzMy6MeutBOGYoVU77o+AiMd4xK+Jxv3Rx4OEY2rksUubHirNbHVRecJ1uEpGFiEZ7VGRAuLRrk5i3V2aQIh1dRHVXncR6+4kKs/a6y7ifl9ahze7XHqUUReLmnBMRh4lCtn7Wv7OhnhsDPt5padRixzWR/zaOcgvskWuIk0cHucpx20ZOOIWbN9O544VeGtXs2PNB2jeNbigt9hN2DSh1le49fN3H0HXK/ZkjWJS6Jn3C0CLw5RWhtOBBikRixHYspme1a/hW7uamFcXyhYL7oWLyTnmeDxHHYMlZ2SiLa2LZJidsu3nJsov9wntKKXMhJSbOOe9+XzqimTaZKO0cHuUDV/fSywYZ9EdU/HMGFlWJaOcl7JDEVAEjEsg247SICQmtJ8zCGNlxjAJJBJx4uEeLdpodRVjsRkvz0EmpxYPhzURsV847heSXQeLShGPaUQdNaHsciejjPl6lDFluqrF7QGLBZP+6Pe12dJPHysmixksVkxmebZoUS4R87tDXlb2NLLK20RHTNb4gc1kZpm7WJtSusxdgl322U+L+Fvp3PEM9Wu3EglGsFjCxOxxnpp2HLsLSimKd3DFvn+QFw9jdRdjc5VidZdgcyefre5SbPLsKiZbiYz2i8A1r+F7o48IXLCYnGOzJwKF/0FRw0wuPr1vtv2cEoTDGKRsbhKPJ7jq6j20tkW5584aplSN//SE1PPfdm8DHa/7qDivgJpPlGQTjTqWIqAITFIC2XaUBsGsBKFBBkKZoQikEtDEoy4YtShjdzL6mIxA6q+7k0IyEew/ocuoEhVRqIlDEYsWElYLu6rLWT+3ho2zqgg4kr8jneEoS2pbWFbbypxWL+ZUQWpOEac2Ez53gl/Mr2ZPjocpgR4+s2cNrlgXsYSPRGKwabiyFjVfE4aaSMwpw+Ypxeo6IB7N1uEHEhLxuB4JfBXf2jVadFdrZjPuhUvIOeY4PMslEji8rPfxWFi70REL9xCP6M/a3z5ikZ4+n/mSf0d6yKs5ndKlnxrRsGbbzylBOKLhGvuN31jbww/ua2TJIhc3XT9l7A+YwRE61vrYdncD9hKrlkjGYrCprBmciuqqCCgChxGBbDtKg6BRgtAgA6HMUASGSyAeCh48VbU38hjwQzSGrOcjFkOiXfLQXsdjJKJSqL3P+719Bv08CrE4iVhUywQTtZjZNmsq6xfP5t05NURtyemjOV4/R7yzgyM27mBKQ+uBdXmAz+XggY+dS0NlCdW1TVzxl2dwhVISE0niTQkIuwZ5HkxtRMxaQhlT1IYp5sAcd2JOuDDjwWL2YLK6MdvsmGw27dH7OtzUkIwEpohA18LF5Mp00BQRmFw3G9BEXSyii7Z+BN5Bgk/vl8h4caQJKQOTW3MqpUd8criXibZdtv2cEoQjGq6x3/j2O+vYsDHANV+r4OjlxpnrLFNEZapouC3K3G9UUrCs/2xWY09IHUERUAQmG4FsO0qD8FWC0CADocxQBA5HAhJNk4rzIg4TsTj+SIg3Am2sCraxMerdn6m0HBvHmzwcn3DhjCW429RCvTnG/IiFL3WYcYSjWmKWRCSiP8LE97+W98IkwvpzJEJM/iZI3BIgYQmTsEW0B444uBJJIdn/zNUkZim5KMsh5SHByN5nSWQjdRKrK7HVVGItKSBBOCWadyBiJ4llMmqSKMmWg9meg8XuQSvXYu/9OwezzXPg7/395H33QRl3Mzpmn87Z9nNKEAIrvrobghleLCMZ5Qy27eqOY7ebWLzQdUimowx2M+pdwx0x/LtCFB2fw+yvVYz6/tUOFQFFQBEYiEC2HaVBRkIJQoMMhDJDEZhoBCRT6Ws9TbzibWJb6EAtQYfJQigR09Yd/r+KIwZcczhcHiJS45EwUV87UW8TEV8TUX8r0aA82omGO4lFOknE0ymVcqgVku02KepErPWKOk+K2Ov9LFXgSVRSfnOPr0TKtp8b37Md7hV06HYjcpQvXLYdd2+JldGzacLvyZprZvHdNdgLDr8aMRN+cNQJKgITmEC2HaVBUI7IzxnkHJQZioAiYHACjRE/q7yNWhkLyVR6rKeMqyoWYzWlFGXP8jnImr1ooJWov4WIv0UTjVJ7U4veieAbIJpnthgr70Ym2LLt55QgBHa/6SMWNWaE0GoxUVJiHfc7Ff1dxPYiK9acweL8mVz6qq8ioAgoAukRyLajTM+qMe+lBOGYI1YHUAQUgV4CsvauLRqi2Oow5G/QiT5S2fZzShBO9CtKnZ8ioAgoAhOMQLYdpUHwKUFokIFQZigCioAiMNYEsu3nlCAc6xFV+1cEFAFFQBEYVQLZdpSjavzwd6YE4fDZqS0VAUVAETisCGTbzylBeFhdHspYRUARUAQUgWw7SoMQV4LQIAOhzFAEFAFFYKwJZNvPKUE41iOq9q8IKAKKgCIwqgTGyFEuBH4EnAB0Ar8GbgFiQxifD9wHXCTlkIF/AFcBbX22uxC4DZgD7NT3/XAGYJQgzACW6qoIKAKKwOFMYIz83IBIlCA8nK8WZbsioAgoApOQwBg4ykJgE/AOcCcwC7gHuBf45hCInwHmAtcAkp1Mtm8CTknZ7mTgBeCnwOPA+4GvA+cA/05zCJUgTBOU6qYIKAKKwOFOYAz83KBIlCA83K8YZb8ioAgoApOMwBg4yhuA64BpQLeOU/7+DiCFVnvf60taoomrgNOAl/QPjwVeB94L/Ed/T0SjDTgjZQcrgDxAxGI6TQnCdCipPoqAIqAITAACY+DnlCCcANeFOgVFQBFQBBQBncAYOEoRc/XAR1Mg1wB7gAuAvw8A/7vA53TRmNpFpoRKJFCigA7Aq08j/XlKpyuA3wFFwIFK0AOPshKE6j9AEVAEFIFJQmAM/JwShJPk2lGnqQgoAorApCAwBo6yWZ/OKRHB1ObTo4R3DwD2r0AZcHqfz/+p/30eIGsTZTrqe/Rpo71djwFWAxJRXJPGwClBmAYk1UURUAQUgYlAYAz8XNYE4XAW5ItD/JK+1qIKqAX+rK/BCGYwoMpRZgBLdVUEFAFF4HAmMAaOMgJcqyeHSUWzD3gQuHEAXs8CIholoUxq+yMwEzgROAlYCSwD3krpNBvYBpyd5jpC5ecO54tW2a4IKAKKQAYExsDPZUUQDndB/g/0u6N/0B3jEcCt+rqLSzLgphxlBrBUV0VAEVAEDmcCY+AojSgIJVp5c+o4JRKJw3nYlO2KgCKgCCgCaRIYAz+XFUE43AX5JUBrHwtlPcYvgOn6+o100ClBmA4l1UcRUAQUgQlAYAwcpUwZ/YleCiKVUDpTRkv16aCp2/U3ZVSmlb6Y0klNGZ0A16I6BUVAEVAExoLAGPi5rAjC4S7I78+4XicpU21eTROyEoRpglLdFAFFQBE43AmMgaMUH1YHXJbCphrYm0ZSmc8ClX2Y7gCe6JNU5kr9Zmdv108AD6ikMof71ajsVwQUAUVg9AmMgZ/LiiAc7oL8/oyTgr5S+0kcrOw3naYEYTqUVB9FQBFQBCYAgTFwlDLLRdYQStkJyQgqTeoKShbRdMpOSM1BWSco7Wg9SUzfshMW4KwU/FLAvkCVnZgAF6Q6BUVAEVAERpnAGPi5rAjC4a6/6GucON63AanP9KkM2CpBmAEs1VURUAQUgcOZwBg4SlkHL0XpN+pJzSQhzA/1JDOphem369M+/zeFn9QYnNOnML3czOyvMP2P9cihFKYXwakK0x/OF6KyXRFQBBSBMSIwBn7usBGEdj2ZzFRgOdAxiOVqsf0YXYBqt4qAIqAIGJ3AGDlKyZQtgk2KzXcCv9ZLTsRSeOzWS0ek3rCUKJ/MavkgYAYk8iczXfquj5dMpLfp4nGXvu+/ZMBa3fjMAJbqqggoAorA4UxgjPzcgEhMowRruAvyew8vdjwEyBQbSdH9boZ2KUeZITDVXRFQBBSBw5VAth2lQTgpP2eQgVBmKAKKgCIw1gSy7edGSxAOd0F+L8/7AckuKoKwdx1GJqxVLu5MaKm+ioAioAgc/gRGy38dLiSUnztcRkrZqQgoAorA6BDImp8brQMNd0G+4JJtZRrNR4BHR4ffsPYizna0eAzLgEE2MqptRrVLUBrVNqPapZgN77/WqONpVLuMfJ0N7wo4vLYy6nVhVLuMfr0alZtR7TLyeCpmmX+XKmaZMxtwi9ESQMNdkP8x4E966m2pPZjaJG13yyie61C7UhfWUIQO/VwxU8wyJ5D5Fuo6U8wyJ6C26I+AUf+XjGqXkQWEkW1T45n5949ipphlTmAUtxgtQSgmDWdBvtRg+uQA5/NpXSiO4ukOuiv1z5g5acVMMcucQOZbqOtMMcucgNpCCcLRuQbU90/mHBUzxSxzAplvoa6zzJkNuMVoCsJRNGtcdiWZS+VhxGZU24xql4yhUW0zql2K2fD+8406nka1y8jX2fCugMNrK6NeF0a1y+jXq1G5GdUuI4+nYpb5d6liljkzJQhHkZnalSKgCCgCioAioAgoAoqAIqAIKAITgoCKEE6IYVQnoQgoAoqAIqAIKAKKgCKgCCgCikDmBJQgzJyZ2kIRUAQUAUVAEVAEFAFFQBFQBBSBCUFACcIJMYzqJBQBRUARUAQUAUVAEVAEFAFFQBHInMBkF4SzgWuBE4BFwMvA6ZljHPUtPgx8AlgO5ANbgB8AD436kTLb4YeAq4F5gAfYA/wBuAsIZ7arMe89RecmduYCPWN+xIEP8Cngd/18/EXg5+NolxzaClwD/C9Qo5d6eQT4f+Ns1wvAaQPYcCLw6jja91HgOmAu0AU8B1wP1I+jTXLoi4Dv6v+fYsuPgB+Og03pfK+K75EatPI/UAKsAa4C3hoHeyf6IdMZj/FgoPzcyKgrH5c+P+Xn0mfV21P5ucGZpfO9elj5uckuCC8Efgy8BiwGmgwiCOXH7i7gCaAVeD/wdf0Hk/zIG6/2eaAaWAt0Asfq2Tx/A3xlvIwa4Lh/Bs4Ayg0kCMWeQIq9O4Hmceb2R53TLcC7+vhKCZkbx9kusSGvjw0idpYBlUB0nOy7AHgS+AnwuG7LbUCHfgMnPk52naTf0Pot8FfgOODb+g2v+7JsUzrfqyIGe+2T605uNMn3iXwPN2bZ3ol+uHTGYzwYKD83MurKx6XPT/m59FlJT+XnhuaVzvfqYeXnJrsgNAO9P+D+pt+pNkKEUO6YixBMbfLlL5HMGUNfp1ntcTvwZaAQkJowRmin6mL6DuBuAwnC8Y5U9h2bc4C/A0uBd4wwcIPYYNeFwsN6VGm8zP0LMEcXf7029DpPEbGbx8mwZwA3cErK8e8BpJ5rRZYj+EN9rzr1m29in4h8aRLJ3w38AvjmODGcqIcdajzG67yVnxs+eeXj0men/Fz6rHp7Kj83NLOhvlcPOz832QVh6pAbSRD2dynK1FaJRDiGvk6z2kPu7Itd8oPOCILQArypT9GUKKZM1RxvIdY7ZXS87eh7YUgkSaYkn53VK2Z4B+sVXTKN9KXh7WJUtnpUv/kh0d7eJj/OXtSnnY+XsJbZDRK17BVYYpuM69P6rAexbzxaf9+rwk6m2S7Qo9K9dkl0U25OyFR51caGgPJzw+NqJD+nfFxmY6j8XGa8pLfyc5kxmxB+TgnCA4NudEf5mB6ZWJLZdTomvcUhiTA9CpCpGPLlIVNajdAkWnklIJwuN5gglOmhxcAOfW2XREPGs8ka0KcA+R64Ql9PKM5iE5sAAAsdSURBVAJCpv+O93q4vlwkQi7RL1nnOJ43Hs7To8+y5lKmdEv07ddABDhzHAdT1jJ+D/h+ig3vAZ7XI6rjtVa1v+/VLwH/p3+HxFLslZteUmhYbi6pNjYElJ9Ln6tR/ZzycemPofRUfi4zXtJb+bnMmE0IP6cE4eEhCOWH5rPA/wAPZHadjknvYEqk8kF9Wtp4rZ1KPUERW9uAjwMrAKNE5iRScwywGpAfGbJYWwSY3HW+d0xGKL2dhvSphOsBmV4rEUxJECRruI4fZ+GVegYyFVLEtAhoI9x4kBsNsm62N1q/SnegEpEerybremXK5SUpBnxDF4g36eM7Hrb15yjFHhF/BX0M+gzwK52r0ZJUjQe7sTimkQWh8nNDj7jycUMz6ttD+bnMmckWys+lz21C+DklCI0vCKcDrwPyo/OD6V+fY9pTIoPyI12SQEhiCIneyF3/8W4SBZEIkiThkWYUQdgfF1kLdxZQmrKONdv85Ee3PKYBbfrBe6c/im0yrc8I7VJA1jSIqH5jnA2SqJtEVX8K/EtPWiRRLRHRwiw14pVNUz+rZ6z9AiDOSf435WZNmZ7NMzVymE27JoSjzCawMT6WUQWh8nPpDbzycelxSu2l/FzmzJSfy4zZhPBzShAaWxAWAa8AXn0dkD+zazQrvSXS9XtAUvDKVMjxalI2ZB0ggkayFkr7mL6uairQ3ifD53jZ2XtcSbkuaxtmAZJtdDyarDuTY0uyot4mC6UlE6qUohjPjLapPCSbp2SflGQu491kfaokjpG7p71NyrDINSfROZnaPR5NIs/3AyII5bV8V0iEUMZQEsuM18yCCTGVZjwGdIyOaURBqPxceoOtfFx6nPr2Un4uc27Kz2XGbEL4OSUIDwy60RylROD+o0cg5Af7eJcnGOjfQ36obwDeq9ub2b/R6PWWGmwiHAZqMsVPpqQZpUlNR6n3N1MvMTIedkmtP8mEJdNDe5sIQpkSLHUIJUnJeDdJeiMOXaaySjR6vJsIrZv17LWptgz0frbtlWy/cgNEytbM1+v79U3ekk2bBltsL/ZJjdXeJv+jR6qkMmM6PMrPDQ+vEfyc8nHDGzvl5zLnpvxcZswmhJ9TgvDAoBvJUUoRVal1JtO+pAi3rIszapPahDKNZbwjhJLCXJx2apN00xIlkSmkEglL/fE53jxlCqSsmZE6ieO1/lKigFJ/UKaM9pY5kbIr/9UTuKwcb0gp037Hs6RDKgaJDkrx9MtS3hTBJdlFP6KLfANg00yQrJ0SvZQahePV+vte7U3HLSVhJEOxNLkBJmsgf6nKTozpUCk/Nzy8RvBzyscNb+yUn8ucm/JzmTGbEH5usgtC+RHSu95MklVIIWy5+y9NkpKM1xRN+VEka4K+qiciSb00ZVqkLJIejyYZKCVquUlfKyU/NIXbP/REKeNh02DHNMoaQsnCKgll3tan88maOEl8c9U4T8uU630jUJeSVOZOffqjRHyN0OSak0yeEjkyQpP/SUkEJI/eNYQSuZQ6iXJDwjdORkqU92RdrMq4imCVZEbynlx32WzpfK9Kwd5v6cllegvTH6eX7pCIsGqjRyCd8Ri9o6W/J+Xn0mc1UE/l44ZmqPzc0Iz69lB+bmhm6XyvHlZ+brILQlnILlOr+mtSAF7uWI9Hk+NK1MZodt2qJ7YRblE96iZ1/iRCKGn3jdaM4iwlg6esL6vWSzxINOk+4A8GACaRXSkBIPX9ZPG9RKZlumiHAWyTO+INunAYr6QofTHId6as0/uivv5TMotKJFW++MdrLajYKLX75P9QopUScX4ZuF6fzp3toUzne1U43qhzlMyJkixIbpDIDS/VRpdAOuMxukdMb2/Kz6XHabBeyselx1D5ufQ49fZSfm5oXul8rx5Wfm6yC8Khh1z1UAQUAUVAEVAEFAFFQBFQBBQBRWCCElCCcIIOrDotRUARUAQUAUVAEVAEFAFFQBFQBIYioAThUITU54qAIqAIKAKKgCKgCCgCioAioAhMUAJKEE7QgVWnpQgoAoqAIqAIKAKKgCKgCCgCisBQBJQgHIqQ+lwRUAQUAUVAEVAEFAFFQBFQBBSBCUpACcIJOrDqtBQBRUARUAQUAUVAEVAEFAFFQBEYioAShEMRUp8rAoqAIqAIKAKKgCKgCCgCioAiMEEJKEE4QQdWnZYioAgoAoqAIqAIKAKKgCKgCCgCQxFQgnAoQupzRWBoAt8BvgJIIfW5wMf0wvNStDyb7SOAG3igz0FfAFqBD2XTGHUsRUARUAQUgQlDQPm5CTOU6kQUgUMJKEGorgpFYOQEUh3l+cDfgRnA7pHvOqM9/E0Xpaf32WohEAG2ZbQ31VkRUAQUAUVAEUgSUH5OXQmKwAQmoAThBB5cdWpZIzCWjtIFBNI8k4EEYZqbq26KgCKgCCgCikC/BJSfUxeGIjCBCShBOIEHV51a1gj0OkqZkvnfPkfdA0zX36sB7gLeBziBl4GrgC3659JvF/Bx4GzgAuAN4CzgCuBzgET75P/2LeBa/XPZXKaJfrLPsW/R7+r2N2X0DOB7wFKgC3gUuA7o0fchUUY5l/cAXwbOBZqBHwA/zRpZdSBFQBFQBBQBIxBQfs4Io6BsUATGiIAShGMEVu12UhHodZQzgc/qoulioAEIAeuAIl3EtelCzA9cD0zT1x1KFLBXEDYCjwFPADHgeeDbgLy/A7ADlwEfBhYBO4FZwK+AAuBLOv19gDz6CkLZRmx6Vhd31cD3gdeAc/oIwu3A74HX9WN+GjgOWD2pRlidrCKgCCgCk5uA8nOTe/zV2U9wAkoQTvABVqeXFQLpTKW5VRdqc4B23apCfZ3hjcBPUgShCMEPDmK5GZDHRuDPwHf1vgNNGe0rCP8CLAfm64JTNpeENA8DJwKvAr0RQrFbxKg0G1AP/EYXs1mBqw6iCCgCioAiMO4ElJ8b9yFQBigCY0dACcKxY6v2PHkIpOMoRWTJ9FGZDpra/q2/L5G33gihRBl/3affAuAOXbCVpXz2p5R9pisIJaIofWWKaG+zAEFAxOndKYLwFGBlSr9VekSy73lMntFWZ6oIKAKKwOQjoPzc5BtzdcaTiIAShJNosNWpjhmBdBylZPicPYAFz+nrBHsFoawdlEylvS0XeAdoAu7VBaSINxGNMqWzt5xEuoJQpqd+E7injz0yJfV3wA0pgnCJHons7apKWIzZZaR2rAgoAoqAYQkoP2fYoVGGKQIjJ6AE4cgZqj0oAuk4SlmDJ4JLpmD2bV49sUyvIPwA8I+UTpKE5hlAooTvprwvCWjWDkMQSoTwEeAbKfsaKEKoBKG6vhUBRUARUASUn1PXgCIwgQkoQTiBB1edWtYIpDrKgcSbTPeUdXoisAYqIzGQILxQTzAjSWtEBEqTtX6v6NlBeyOEsp5Q+hzf58z7RvUe0tcQisCUpDXSJEHNX/tZQ6gEYdYuI3UgRUARUAQMS0D5OcMOjTJMERg5ASUIR85Q7UERSHWUkjVUCtLfB0jyFskmukEvGP8mUAf8SH8uB07T1+iJSBtIEEo/mRoqUUYpWzFVLychiWUkM2ivIJTkLxL1u1zPLioJYOQxUJZRiTr+TN/fnfr++2YZVYJQXd+KgCKgCCgCys+pa0ARmMAElCCcwIOrTi1rBFIdpRz063p9wSm6MOutQ1gF3A68Xy8PIWUpJGGL1APcNIgglH2KUJMagFJeQtYjSskKSQrTmiIIS/TSEyIyJYPpYHUIz9ST1Egdwu6UJDN96xAqQZi1y0gdSBFQBBQBwxJQfs6wQ6MMUwRGTuD/A0besoP6bagMAAAAAElFTkSuQmCC\" width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM automl_output_info;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM automl_output_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "ax_metric.set_ylabel('Metric')\n",
+ "ax_metric.set_title('Validation metric curve')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Validation loss curve')\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss,metrics_iters FROM automl_output_info WHERE mst_key = $mst_key;\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " iters = df_output_info['metrics_iters'][0]\n",
+ " \n",
+ " #ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
+ " ax_metric.plot(iters, validation_metrics)\n",
+ " #ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, validation_loss)\n",
+ "\n",
+ "plt.legend();\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Show each trial"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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2RqD9imjoxF+j6J5dNZvd7M1A/nMeAqKRT/brrnXKaNmNdJGAF5r4ukx7gP5kq0TKGoIs2koHrn0CGa+eJTXbckE0OtdvJNa0bUq+fUvzpGF3dOI/CaXAgEI1EMAXfgz19i64GcWMRjZmrNOU0azz8xsyminHmJT5OmhHoXzyFin4s9sm0G/iHV/8OYt3UpmEto3ovP1WTxFZq1k9XebveLf++WjX0K2Txn1S2m/RM1PGW2P199XPJXS32c8eugR0E7FWm+d8otmm53hSpF8Zff3Ygi9jMQbqHUyfcOZRZtZ78HQacqo2+ydYMtOGc3a4X0OXDxC7c1xvMSo20sD83F/G6hNZW644YbnrLPOOje86EUvWrbOOut0OtrtGX1qxYoVk3/1q1+tv2LFih133HFHz4J6RsEQjvBXyK8rI+APehsov8nzG0Qbu2zBcfaQbNdHv2Xz2rmseCcq7w6aHemQ/bzICKGv9Yeud+jypjLZLqXdNpXxegh/SPqtZXb2nf9OPPXF89nzBi2bk+9dySxI+TLcpjJ+c+i3hNmB9X5j6k1lPHWkfVOZ0RjCjJvFJv+GNovTU1w8n98LtzNB8qiYp8R2mgpiDtkUGE8HchvMqn26T/66/wrrDL1RS7apkEf5bOKcj6KG0KbVwuqF4173mZXdw1Qlm87MEGabylggPC3YI4D5ko/PP/dCf3858MsKP8e5dvspEIBAvQTQhT9PP/TMmFi64IwWMRjZpjI2dP4czTZ7s1Gy0fPvbZg8U8Ofoe1TH/0561k+/rz3F3VvQGL9aN8szDM8bORsQNo3KGvXVI8Oee26Rx3zxctLsh25/fLQ17QXLwFwO7LpcsMZwmxTGU+t9IvsrGS7rmZLUKw5fnb76IyNi19cZhu4dOLj7yCeDeVNeDzbZ7hSJF/Z/b0YQo/oejaU15/5pXS+DGcI3W7vS+C1f97LINsh1DOp3I9dby+byvj7nV885EvG2u0xT0/fdd3txSbdL29tQkcaZa730yxy7TfffPNzx48f/+MXv/jFj0yZMiXbpLBrFE8++eSkO++8c4O1a9fuvP3229tkP6NgCCMncIAf57nf2VbR/qPuNDffHyAeUfKccU8n8oespwLaQPoP3tMDex0hNHJ/2FpovCDZHzyu19MKOx07kX2Qe2qrP9D9oe23oBY1jzy1G7Rspy2PePpD0eLoN34jHTvht2te02dx9fREz633Bi02Wi7DnWNYdMqoTVc2b7/b0Qk2wB6xtGD7zadF3rE5F5466/+2wcoWcmdGyebev/OubtmxExZ8v8X1lwCPPLrY3DvfNmU2m+bpUT4vgLdoFDWEfo6n95iL+5E3CvIbZU9dsTBZlPJTZByH33y6WNBtuL0xkOPxyKbXa+aLTWD2JaPIVNwB/lOm6RCojAC68GeU2aYYMXTBz7PBsDmybnUqHo3x5613A/WUPRtWL6GwFlgDvE4sv0bbG3hZ023ErIm+zi8ZPWXS6/6sB9ZPfyb7xZ7rsx7bDPvFpb+cehbOSEf8WJP9EtAzONq/AHt/gey8Xeuo1+zZcFlb/eLSLzAdiw2oy3CG0FrhF8l+kekpvf5eYr1xm/PHTtjo2TD7hasNqb8DeDMXt8nFLz2tq9Ycb6jimLx/gLXSI7DWQL+A9TOGK86XNdjr+ToVv+jO1lX2YghtZj1S5Je17oM23I7Xmj3SofX+XuQXvtZix+8XBtZQH7VR5NgJ58szhfx9zzN82kesvGOo+4VNp2di+fpOm6G4j2XHYGTHf1X2AZVyRRjClLNDbMMR8Aes/7htrDpN+8vu9fQAi5R3ufQImkcUvemJFzX7XKMyhtB1u95/DR+q2cH0NgrtB9P7Whs0vzGzGFhQ/GHvaaH+sG83hDYeXtDsKTH+kC96ML0Fw6LqYX6/ofMBuG5rVkZrCM3PImbTme3U1Sk/2U6r3rnUm/24OFcWcH/gZ2+Abaj8xcBv4bLi35mLp1h6tNQCnx1anB819GHy3tXMa0Qtwjblng7iD/GihtDPNGPHaBH19FYbXou7WVqI2tdM2CQ6Pk8BtqH0G2yztlg57/mSbb7jqULZ5kf8RUMAAvUSQBee5usv+3XrQva0kY4x8C6gNoUu78ptsuYv5NlnvE1OVmy4HL8/m218PAroF4PW7a+EL/I2Zv68thZ79oZfDloPbCI9IpcdXTBcj8s2NMuWObRfa4Nj/fa0RZtNz4Kx4bBW+KWfN3HJdvgczhC6XrfDWpEdTG/DYjObP5jebbB2u/02gn5+do6fv29kM5LM0LNbbOrMwd9BbIo9KtdtSmm+bSPly2Y9e1nbiyH0M7wm0yO01ku3p/1g+k7TSbPYrJudDqZ3ztvj9/c3r5f0jKr2g+nbZ4tl92ZnPHqHWfPsVPySwHG4T/W6e2u9n241144hrBkw1UMAAgNJIPui4ZcPXhdBgQAEIACB9Ah4lNEvBDvNMkovWiIygU7Le4qQ8QsF77LuQ+nz51hn93q5jU23l+z4RfNAFQzhQKWbxkIAApEIfD0c+eFRz/ZzkiKFwGMgAAEIQGAEAp794lFKTwH1sRWU9AmUMYSePeDNezzN1rOVOhVPVfborPeIyPZkSJ9GRRFiCCsCSTUQgMDAE/B0Ik+XsZh4pz+/YfSaJgoEIAABCEAAAtUQ6MUQWo+9ptBLTTy11MtyRjqWo5ooG1YLu4w2LGGECwEIJEsgO0fSG9x4IyBv1uP/pkAAAhCAAAQgUA2BXgzhZ8IeBl7j6bWNnjZK6UCgjnMIvYDUZ4p4dyFvsuEFsiMtzPTOSV4Y6h0gvTW/dxo6ou3LVKfdgNwkb6Lhhasu3mTCO1K1F+9u2L6tMB0CAhCAAAQgAAEIQAACEIDAwBO46aabDpk8efLRm2+++YqpU6cuHzdu3F94r6GhoXHLly+fev/996+zcuXKE2fPnp0dvfIMfj52wgt0vcOTt5H3trM2etm2vJ1g+7Br7x7lf7zjn7ep97+9XbJ3fsqKh3nbi3el8k6K2XWZITy8bYdF7wbobWYpEIAABCAAAQhAAAIQgAAEIJAjYLP385///OCJEyd6Z3zv4t/pOEGbxBWrV6/+/A477PDFTqbRVfpGn4PmM95cvMOeD9z09rDZz9rhe62N//GuPx5VdPG2vN6a3wd/envfTsW/8zxgj/x5BNAlM4S+v9NBoiQeAhCAAAQgAAEIQAACEIAABDoQ8PTRWbNmTZ88ebJ34H1GWbly5ZqFCxcu2WeffYad/dnuJG3yfGjmnuEgzU7gbeZ8jorP9sqKXamnjnq6qf/pVHxWmw/g9NkuvrZKQ9hteiodBwIQgAAE+pNApzeh/dnSP7cKnevn7NI2CEAAAn9JIJrOdXrQ42GUsNtZXJeGNYA+YDIrPhB6RTjItNPaPz/H28deK+m9ufuyEUJPEfXB0T7424eH+lBsH2xatAwNDaGVRWFxHQQgAIEmExg3riVd0YQyEVboXCKJIAwIQAACdROIrXOdBNU7+1wo6agujfU6w78LU0ZXhWteI+mGcF7Imzrc99fBDLaPPG4WzN/lYYrqXEkfl+T/f0cPsBHKHmBxKQQgAIEmE4gtlImwQucSSQRhQAACEKibQGydK2MIXyzptrAjqdcbelMZG8iXhw1q8iOHGa8vSHp3WJvoXUaHKx8M54H5HJJbulzo5x6b/x0jhHV3TeqHAAQgkAaB2EKZRquFIUwkEYQBAQhAoG4CsXWuzJRRM/CBkd6N1BvSrA3nhHjTGBvF/dsgeTrp7yV5qukHCgD0+kRPHT1Q0pcLXO9LEMqCoLgMAhCAQNMJxBbKRHihc4kkgjAgAAEI1E0gts61G8KZkhaOsKlMxsAbyWwdzJvXAD4k6ROS2s+3eLOkH0h6g6SrCwCcLmlxMJ1fKXA9hrAgJC6DAAQg0A8EYgtlIswwhIkkgjAgAAEI1E0gts7ZEK4vaVlomM8DPGGEYyc6MXifpDPDMRJ/arvgAkleV7h5GE0cieEhkjzF1FNQ/3eki8PvEcqCoLgMAhCAQNMJxBbKRHihc4kkgjAgAAEI1E0gts7ZEF4RDqbfUpKPhjij7WB6HxDv3UE9hdPF00S9C+h1klZL2lXSYWE6qM1fvkyR9IAk//yfOsDzWkAbUh9W73MPvfnMEZLmSfqbHmAjlD3A4lIIQAACTSYQWygTYTWiznkt/erVq72GIpGQCQMCaRHwZ8fEiRMVPkPSCo5oIJAjEFvnbAg9jXOncMj8eeHIifzhhfdKmp9bG7iepEskzZY0NawbPFHStztkcq9wrev3LqTtxUdUeFTSU09dl6erfl2S63uyh54xolD2UBeXQgACEIBAwgRiC2UiKLrqnA3gkiVL9NBDD2EGE0kWYaRLwJ8fG2+8saZPn44xTDdNAx9ZbJ3rl3OcMIQD/6cDAAhAYFAIxBbKRLh21bnFixe3zOCmm26qddddN5FwCQMCaRJ44okn9Mc//rFlCjfZxPsYUiCQHoHYOochTK8PEBEEIAABCAxDILZQJpKMjobQo4N33nmnNttsM2244YaJhEoYEEibwNKlS1um8EUvehGjhGmnamCji61zGMKB7Wo0HAIQgEAzCcQWykQodTSEq1at0t13362tttpKkydPTiRUwoBA2gRWrlyp3/zmN3rBC16gSZMmpR0s0Q0kgdg6hyEcyG5GoyEAAQg0l0BsoUyEVEdDmH2xxRAmkiXCaAQB/m4akaaBDjK2zmEIB7q70XgIQAACzSMQWygTIYQhTCQRhNF8AhjC5uew31sQW+cwhP3eo2gfBCAAgT4jEFsoE8GHISyQiOOOO05nnXVWa9dVl/nz52vXXXfVrbfeqm233bZrDYcffri+9a1v6d57vbF6sfLggw/qnHPO0f7776/nP//5T91U9JnFntLbVY7ltttu00033dTbjTVe/fjjj+vAAw/UFVdcoT/96U/6yle+0uKcz1OVj7/gggv0/ve/X8uWLdOznvWsjlVjCKskTl11EIitcwNvCNcuX65Hr79GTy5aqCkzZ2nanF01fqpPwBi7klpMVcVTVT3OTJV1VZHp1OKpok1ZHam1rcp4qqyrSuYp1ZUio9hCmUg+MIQFEtFuCB999FHdcccdevnLX66pw2h7GUNo47Xddtvpmmuu0dy5c5+KrugzCzSn50tSNISf+9zndOyxx+rCCy/U8573vNZ61yeffFIPPPCAdthhh57bONINGMKRCJX7fYpaUK4lzbgrts4NtCF05150/FFatfhBae1aafx4TdpkhmYee9KYmcLUYqoqnqrqycxgSnmrsm2pfUyl1rYq46myrtTyVlU8qTKKLZRV8RxlPRjCAgDbDWGBW1qXVGkIiz6zjutSNIT/8A//oJtvvrn1T4yCIayecqpaUH1L06kxts4NtCFcevk8Lbn4ImnNmqd7wIQJmr7vftpwt93HpFekFlNV8VRVj5NSZV1VJDm1eKpoU1ZHam2rMp4q66qSeUp1pcootlAmkpO+NoT+En/QQQfJ0zDzx2fcfvvtramenm7oUaUzzjhDt9xyi1asWKFtttlGJ5xwgt70pjc9laIiU0Z95MA//uM/6jvf+Y7WX399ffjDH9bDDz/8jCmjf/jDH3T00Ue3ppz6v2fOnKl99tlHn/70p1u7uXrK41/91V/9RdfwMSCdpoz67LtPfOIT+uY3vyk/3yOLJ5544jNi9yijD0vfe++9W88xi9e+9rX6j//4D22++eaFumEnQ/jLX/5Shx12mH7yk59oypQpeutb36rTTjtNz3nOc56q81//9V91/vnn6/7779cGG2ygV77ylXJOfLald7L95Cc/2Yrdo3o+v+81r3mNLr744hF3tvVU2vvuu+8ZsZtRtzx5tPXss8/W97//fc2YMaNl1J2rrLgNjvVnP/uZPBK79dZb64gjjtB73/vep67BEBbqKj1dlKoW9NSIhl0cW+cG2hA+cP65WrbgWmlo6OluMm6cps2ZqxkHHDwmXSe1mKqKp6p6nJQq66oiyanFU5Cow5MAACAASURBVEWbsjpSa1uV8VRZV5XMU6orVUaxhTKRnEQxhGM1LcwmyQbl3HPPba3/yoqN0Ze+9CX97ne/0xe+8AWtXbu2dXbc+PHjW6bh85//vK677rqWcXIpYgjf+c53tkzbZz/72Zbh8ZRGH0EwceLEp9YQes3hV7/6Ve2yyy7aaKONdNddd7Xqftvb3qYvfvGLLXP63//93y0jYgOz/fbbt56/4447djSEvs4G9KSTTmoddWCTd9lll7Wmm/oZLjaEjsPm8+Mf/7iWL1+uj370o61plfPmzSvUDdsN4eLFi1um6SUveUmrzscee6xlTJ/97Ge31hna3Hoq54c+9CGdfPLJeulLX6qHHnpIV199tT72sY+1pnfadJv9v/3bv7VMsM/vczz+2XDTcB3wL37xCx1zzDG65557WmsHM0bd8mQ273vf+1qG8xvf+EbrnhtvvFGvfvWrW/f+53/+Z8tg2lCvs846+tGPfqR/+Zd/aeXqPe95T+saDGGhrtLTRalqQU+NaNjFsXVuoA1him88Uoupqniqqsd/z1XWVcXnQ2rxVNGmrI7U2lZlPFXWVSXzlOpKlVFsoUwkJ7UbwrGeFvaOd7yjZbR+8IMfPIXc5m+33XZrbUCSLzaG/mePPfZorUv78pe/XMgQZiOONhbvfve7W/fYJM2aNUvTpk3ruqnM6tWrWyNkBxxwQGtkykaq2xrC9hHC//u//2sZLZsbmx0Xx/6yl72sFfsPf/jDpwyhDZRHH21CXTwiamPmEcaRzJevbzeENn822QsXLmy1z8UGy8b161//estEeYTUo6A2uJ2KTbDzcOqpp5b6U+g0atnNEH7qU59qGVAXj0w+97nPbW1IYzPaXjzSuGbNmpaZ/fWvf90ysS4YwlJpGvamVLWg+pamU2NsnRtoQzjW4tep26UWU1XxVFVPS0gTW/uZWjxVfpyl1rYq46myriqZp1RXqoxiC2UiOandEI71lz4bFBsmj0B5WqKnOnrqokcA58yZ05rO6GmcV155ZcvA2BC4eHRwwYIFhQxhZhY8+uYRpqzYHNooZbuMum6PPnp08re//W1rimpWbD48klXUEHoEzu3ybpvrrrvuU/Ucf/zxrVFK/9zFI4QepXT7snL55ZfrzW9+c8vw+JkjlXbz9frXv741DdVmNl880uepox7dPO+88/SRj3xERx55ZMtge0RywoQJT13uET6PBnqE8S1veUtrdC78DY4UTuv3vRjC66+//qkRU9+78847a8stt9TXvva1Vl2e2usNai699NLWqLENoYuNtfuHC4awUFp6uihVLeipEQ27OLbODbQhdN9oTY9ZMF8rFy3UZO8yusvcMdtQJuurqcVUVTxV1ZNi3qpsW2qfWam1rcp4qqwrtbxVFU+KjGILZVUsR1lP7YZwrKeFeaTO68Y8Kub1hF63ZiPg0S0btNmzZ7eOEvCol83Reuut99Rau+yYhZGmjHqkydM2PcqXLzZEnsKZGcLTTz+9tTbNJuh1r3tda8TO69Y8GpUdYVHUEHoq5mc+85lW7PnikbsPfvCDLbPptX3ZGkIff5GVXo+waDdfnir6xje+UWeeeeYznu0RQptCT8v0aGVmfu+8886WGT/kkENkw2pj6PjMzMbW0zVtvszG01mLlF4MYfvxIO1MPIp8ww03yCOJXkPqUU+bVRvE7KgRDGGRrPR+TYpa0HsrmnNHbJ0beEPYnK5BpBCAAAQgYAKxhTIR6rUbwrEeITTnv/3bv22dVXfVVVe11q/ZAHgDFK/h87RFrxv0KFVWfMagjVZRQ1h0hNAjU55G6qmlWfE6NZubXg1hLyOEHs2r0hB6hHCTTTZpbQCTL/kRwvzPFy1apIsuuqg1EuvRQxvDfPFIpY2sc9Kei25/J1UZQhtTvwRoj8trTm3mMYSJfFIRRiUEYuschrCStFEJBCAAAQjEIhBbKGO1a4Tn1G4IU5gW5nVsnr55ySWXaM8993xqQxHvLPqKV7yitU7MJtDFo1XeMMVr8YoawqJrCD1V1fXaBGbFu5l6t9PMEHYzqd3WENqM/v3f/32rOo94un6vkcuvIazaEHqU1SNoNnreUdXFI53epCVbQ9ip39l8e6rqv//7v//Frx271zN61PCf//mfR/zzqMoQPvLII60daL1eNNt4yC8DvJOpPxMwhCOmggsaRCC2zmEIG9Q5CBUCEIAABBghzPeBlStXtnam9GiaNzoZbRnraWFe2+fdRp/1rGe11vh5d0oXbzbjaaIe7fKukjYCXkvm9nuKY1FD6Lo86ui1ap7Kudlmm+mUU075i11GvZ7OZsgjYWbrUTOvU/R6wswQesTKu3W6vkMPPVSTJk1qTWvtNM3Tu4x6FMtHJrg+7zLqXUfbdxmt2hBmu4x6emV+l1FPgc12GT344INb7fA0Uh854ZjM+H/+53+01157ybuyel2hTbKNoEcwvbbSUzfd3pFKVYbQz7GRdZu8M6x3mvUUYP+/pwBjCEfKBL9vEgEMYblsdXxzWq4q7oIABCAAgZQJxBbKRFjUPkKYSDu13377tQyY1wraQGUlv4bP5/Jl5wR6LV8vhtAbk3jtng2ajafXBdpQ2Ohkawi9ntHrCr02zcVnA9ocvf3tb3/KEPrnjtNr7Xyfd8Uc7hxCG7L2cwg9CpeVOtYQum7vXOpzCG3g/NLAm8l4jWR2DqFHLm1QvRuqTa6Nt9cHendPFxtmTzn1dFGvN7S5POqoo1pGuEip0hDefffdsoF1W/wiwDukegdW70KLISySDa5pCoHYOscIYVN6BnFCAAIQgECLQGyhTAT7wBjCRHgTRh8TqHpkvY9R0bQxIhBb5zCEY5RoHgsBCEAAAuUIxBbKclFWfheGsHKkVDioBDCEg5r55rQ7ts5hCJvTN4gUAhCAAAQYIXxGH+CL7eD8SXi6pv/pVnyGYezicwCz8yDbn+0vtPnzDGPHNtzz+LtJKRvE0okAhrBcv2ANYTlu3AUBCECgcQRiC2UigBghTCQRYxWG1+Lldz1tj8Mb3njHzZjFz/Nur53KFlts8dSazJgxFXkWhrAIJa4ZSwKxdY4RwrHMNs+GAAQgAIGeCcQWyp4DrOcGDGE9XBtTqzeuyTZO6RS0j7GoYqfZXoB4x1XvANupTJkyRdttt10v1UW7FkMYDTUPKkkgts5hCEsmitsgAAEIQGBsCMQWyrFp5V88FUOYSCIIo/kEMITNz2G/tyC2zmEI+71H0T4IQAACfUYgtlAmgg9DmEgiCKP5BDCEzc9hv7cgts5hCPu9R9E+CEAAAn1GILZQJoKvoyH02Xc+m62qg+kTaSthQKBWApkh9JmLkyZNqvVZVA6BMgRi6xyGsEyWuAcCEIAABMaMQGyhHLOGPvPBHQ2hd3i88847tdlmm2nDDTdMJFTCgEDaBJYuXao//vGPetGLXpSda5p2wEQ3cARi6xyGcOC6GA2GAAQg0GwCsYUyEVpdd9NevHixHnroIW266aZad911EwmXMCCQJoEnnniiZQY33nhjbbLJJmkGSVQDTyC2zmEIB77LAQACEIBAswjEFspE6HQ1hB4l9O6TNoXdzoRLpA2EAYExJ+DPD5vB6dOnMzo45tkggG4EYuschpC+CAEIQAACjSIQWygTgTPiebs2g6tXr8YUJpIwwkiPgD87Jk6ciBFMLzVE1EYgts5hCOmCEIAABCDQKAKxhTIROCMawkTiJAwIQAACEBglgdg6hyEcZcK4HQIQgAAE4hKILZRxW9f1aRjCRBJBGBCAAATqJhBb5zCEdWeU+iEAAQhAoFICsYWy0uDLV4YhLM+OOyEAAQg0ikBsnbMhvErSTpKWSjpP0vGS1oxA7aWSTpe0i6QnJP2XpCMkPZa77wJJ7+tQz0sk3Zn7+QaSzpC0l6Txkr4r6VBJD/WQOYSyB1hcCgEIQKDJBGILZSKs0LlEEkEYEIAABOomEFvnbAivlHSypK0knRqM3jHDNNQG7q7wz2clbSzJ//5xMHXZrTaEr5H0/ra6filpRe5nP5T0QkmHS1obYnlA0pweYCOUPcDiUghAAAJNJhBbKBNhhc4lkgjCgAAEIFA3gdg6Z0M4TdKjoWFHSjpO0qa5n7W3+ZOS/M+sMKro379d0nckvUrSTeEGG8JtJc0eBppHJm0kXyfpunDdqyXdKGm3YFaLMEcoi1DiGghAAAJ9QCC2UCaCDJ1LJBGEAQEIQKBuArF1rn0NoU3efZL2lHRZl8ZeLMkneb4+9/t1wtRRTzf1Py5FDOEJkg4KBjT/uHskXSLpsILAEcqCoLgMAhCAQNMJxBbKRHihc4kkgjAgAAEI1E0gts512lTm8TBKeEqXxl4qaYqkt+R+PzFMA/2WpH1zhnAfSavD9T+TdLSka3P3fVPSDElz2571vfD/exQEjlAWBMVlEIAABJpOILZQJsILnUskEYQBAQhAoG4CsXWukyG8X9KFko7q0livM/y7MGV0VbjGawVvkHSFpDeFn31U0kpJd4QRRY/27RA2ovlpuMbX24B6Q5l8+ZqkLSXtXBA4QlkQFJdBAAIQaDqB2EKZCC90LpFEEAYEIACBugnE1rkyhvDFkm4LO5J6vaE3lbGBfHlY85cfOczzWlfS7ZJuyRnAsobQzz02X/nQ0FDduaF+CEAAAhBIgEBsoUygyQ4BQ5hIIggDAhCAQN0EYutcmSmjZnBA2I3UG9J4Z9AvhQ1lbBT3HwbS2WEDGq9VdPGUUa9H3LXtHqaM1t3TqB8CEIBAQwnEFspEMGEIE0kEYUAAAhCom0BsnWs3hDMlLRxhU5mMgTeS2VrSg5KWhHMDPyHp3GEgnRUM4RbhGm8q8wFJm7Xd8xtJ32ZTmbq7G/VDAAIQaB6B2EKZCCEMYSKJIAwIQAACdROIrXM2hOtLWhYa5rMAbdKGO3aiEwMfQH+mpOdL+lMXSFPDesJfSNo7XJMdO+EzBxeEn/mYCm9Aw7ETdfc26ocABCDQQAKxhTIRRBjCRBJBGBCAAATqJhBb52wIvY7PB9N7E5fTJJ0hKX8w/d1hZ9ADQ+M9TdS7hfrcQO8g6ume3jDGI30+asLFh9d/V5I3h/H90yV9TNIrJb02d1ahr/XB9B5pzB9M71FHDqavu7dRPwQgAIEGEogtlIkgwhAmkgjCgAAEIFA3gdg6Z0N4tSSP1C3NbRSzJtfQeyXNz60NXC+cEeiRPI/6ed3giWGKZ3abp5N+Pawr9LESKyT9JBxn4d1I82XDsB7xnZLGByN5aJiGWpQ3QlmUFNdBAAIQaDiB2EKZCC50LpFEEAYEIACBugnE1rlOm8rU3cY66kco66BKnRCAAAQSJBBbKBNBgM4lkgjCgAAEIFA3gdg6hyGsO6PUDwEIQAAClRKILZSVBl++MgxheXbcCQEIQKBRBGLrHIawUd2DYCEAAQhAILZQJkIcQ5hIIggDAhCAQN0EYuschrDujFI/BCAAAQhUSiC2UFYafPnKMITl2XEnBCAAgUYRiK1zGMJGdQ+ChQAEIACB2EKZCHEMYSKJIAwIQAACdROIrXMYwrozSv0QgAAEIFApgdhCWWnw5SvDEJZnx50QgAAEGkUgts5hCBvVPQgWAhCAAARiC2UixDGEiSSCMCAAAQjUTSC2zmEI684o9UMAAhCAQKUEYgtlpcGXrwxDWJ4dd0IAAhBoFIHYOochbFT3IFgIQAACEIgtlIkQxxAmkgjCgAAEIFA3gdg6hyGsO6PUDwEIQAAClRKILZSVBl++MgxheXbcCQEIQKBRBGLrHIawUd2DYCEAAQhAILZQJkIcQ5hIIggDAhCAQN0EYuschrDujFI/BCAAAQhUSiC2UFYafPnKMITl2XEnBCAAgUYRiK1zGMJGdQ+ChQAEIACB2EKZCHEMYSKJIAwIQAACdROIrXMYwrozSv0QgAAEIFApgdhCmQt+G0lnStpJ0lJJ50k6XtKaERo4W9JJkvxvl5slHS3pxh7AYAh7gMWlEIAABJpMILbOYQib3FuIHQIQgMAAEogtlAHxRpJul3SHpJMlbSXpVEmnSzpmmDTMlHRrMIG+3uUISdtL2k7SfQVTiCEsCIrLIAABCDSdQGydwxA2vccQPwQgAIEBIxBbKAPeT0o6UtIWkh4NP/P/Hydp09zP2rNxiKSzJT1b0iM5c7lE0oclfaFg+jCEBUFxGQQgAIGmE4itcxjCpvcY4ocABCAwYARiC2XAe52k30vaN4d7Vhjh21PSZV3S8BFJn5O0nqTV4ZpJkh6T9DFJ5xRMH4awICgugwAEINB0ArF1DkPY9B5D/BCAAAQGjEBsoQx4HwzmzSOC+fJ4GCU8pUsanhumml4o6cRwzaclvStMGV1cMH0YwoKguAwCEIBA0wnE1jkMYdN7DPFDAAIQGDACsYUy4F0V1v6d0Yb7fkk2e0cNk4ZXSPqupOeFa/4gaXdJt/SQOgxhD7C4FAIQgECTCcTWOQxhk3sLsUMAAhAYQAKxhXKUhnAzSZ5u6s1osvWCH5L0Skk7S1rYJYUeiTw2/7uhoaEBzDZNhgAEIDB4BGLrHIZw8PoYLYYABCDQaAKxhTLA8pRRbw7jYybyZaQpo6dJ2lvS1pI8yugyWdKvJV0q6dCCyWCEsCAoLoMABCDQdAKxdQ5D2PQeQ/wQgEDfEFi7fLkevf4aPblooabMnKVpc3bV+KlT+6Z9VTUktlCGuD3K9ztJ78m1w0dKeIRvuE1l5kny0N4ebe3v9vNumDCEVXUg6oEABCCQOIHYOochTLxDEB4EIDAYBGwGFx1/lFYtflBau1YaP16TNpmhmceehCls6wKxhTI83sdO+PxAHzuxLPzscEknjHDshKeJvjWMEK4M900JI4TemdTTR4sUDGERSlwDAQhAoA8IxNY5DGEfdBqaAAEINJ/A0svnacnFF0lr1jzdmAkTNH3f/bThbt5/hJIRiC2U4bk+mN7rAG8LB9NvKcnTQb3JTP5g+rslXSvpwHDfDpJukHR52KXUumsT+EZJs3vYWAZDyJ8ABCAAgQEhEFvnMIQD0rFoJgQgkDaBB84/V8sWXCvlNw4ZN07T5szVjAMOTjv4yNHFFspc87aRdJaknSQtlXReOHIi5+J1r6T5kvbP3feGsEHMtuFnt4b/93VFC4awKCmugwAEINBwArF1DkPY8A5D+BCAQH8QYISweB5jC2XxyGq9EkNYK14qhwAEIJAOgdg6hyFMJ/dEAgEIDDAB1hAWT35soSweWa1XYghrxUvlEIAABNIhEFvnMITp5J5IIACBASfQ2mV0wXytXLRQk73L6C5z2VCmQ5+ILZSJdEsMYSKJIAwIQAACdROIrXMYwrozSv0QgAAEIFApgdhCWWnw5SvDEJZnx50QgAAEGkUgts5hCBvVPQgWAhCAAARiC2UixDGEiSSCMCAAAQjUTSC2zmEI684o9UMAAhCAQKUEYgtlpcGXrwxDWJ4dd0IAAhBoFIHYOochbFT3IFgIQAACEIgtlIkQxxAmkgjCgAAEIFA3gdg6Z0N4VduZSsdLyp+p1KnNL5V0uqRdJD0h6b8kHSHpsXDxBEmHS3qbJJ/b5PJzSUdL+llbhUMdHnCjpB17gI1Q9gCLSyEAAQg0mUBsoUyEFTqXSCIIAwIQgEDdBGLrnA3hlZJOlrSVpFOD0TtmmIZuIOmu8M9nJW0syf/+saS9wn3PkrRI0ldC/TZ9H5b0Rkk7B3OYPcK/83O/lXvmMkm39wAboewBFpdCAAIQaDKB2EKZCCt0LpFEEAYEIACBugnE1jkbwmmSHg0NO1LScZI2zf2svc2flOR/ZklaGn75dknfkfQqSTdJ8gih6304d/PkYCKvkfT+3M9tCD8i6axRwEUoRwGPWyEAAQg0iUBsoUyEDTqXSCIIAwIQgEDdBGLrXPsaQpu8+yTtKemyLo29WNImkl6f+/06Yeqop5v6n27le5ImSnozhrDurkT9EIAABPqTQGyhTIQihjCRRBAGBCAAgboJxNa5TpvKPB5GCU/p0thLJU2R9Jbc723yVoRpn/t2uc/33Buu8YhgVjxC+JCkDcOIo0cavf7wTz3ARih7gMWlEIAABJpMILZQJsIKnUskEYQBAQhAoG4CsXWukyG8X9KFko7q0liv9/u7MGV0VbjmNZJukHSFpDd1ue8ESR+X9DJJv8pdc0EYjVwsabakT0m6R9KrC2xu85SpHBrqtDdN3emifghAAAIQiE0gtlDGbl+X52EIE0kEYUAAAhCom0BsnStjCF8s6TZJ54WRRG8qYwP58rCBTH7kMOO1R1hjeJikM0aAuLukeZLeKenbXa71Osdj87/DENbdNakfAhCAQBoEYgtlGq0WhjCRRBAGBCAAgboJxNa5MlNGzeCAsBupN45ZK+lLYUMZG8X92yB5oxlvJPNVSR8qANAxeZMbH2vx6QLX+xKEsiAoLoMABCDQdAKxhTIRXuhcIokgDAhAAAJ1E4itc+2GcKakhSNsKpMx8EYyW0t6UNKSsA7wE5LOzUF6oaQFkn4iae8epoD62InT2kcBh4GPUNbdM6kfAhCAQCIEYgtlIs1G5xJJBGFAAAIQqJtAbJ2zIVxfkg2Yizdz8Vq/4Y6d6MTgfZLOlPT83GYwm4WzCf8o6Q1hF9Ii/Dzl9PuS3hGmmRa5B6EsQolrIAABCPQBgdhCmQgydC6RRBAGBCAAgboJxNY5G0JvBOOD6bcMo3Je45c/mP5uSddKOjA03tNEj5Z0naTVknaV5LWBH5DkDWJcpoZRQRvE94bRw4zdk5J+Ef7noLCRzJVhlHH78GxvOuMD7NcUBI5QFgTFZRCAAASaTiC2UCbCC51LJBGEAQEIQKBuArF1zobwakk7hSMfso1i8kbMR0XMz60NXE/SJcHI2fh53eCJbRvA2Aj+tgssn3Po37t45NAjkt6oxkbTo4mu2zuNPtIDbISyB1hcCgEIQKDJBGILZSKs0LlEEkEYEIAABOomEFvnOm0qU3cb66gfoayDKnVCAAIQSJBAbKFMBAE6l0giCAMCEIBA3QRi6xyGsO6MUj8EIAABCFRKILZQVhp8+cowhOXZcScEIACBRhGIrXMYwkZ1D4KFAAQgAIHYQpkIcQxhIokgDAhAAAJ1E4itcxjCujNK/RCAAAQgUCmB2EJZafDlK8MQlmfHnRCAAAQaRSC2zmEIG9U9CBYCEIAABGILZSLEMYSJJIIwIAABCNRNILbOYQjrzij1QwACEIBApQRiC2WlwZevDENYnh13QgACEGgUgdg6hyFsVPcgWAhAAAIQiC2UiRDHECaSCMKAAAQgUDeB2DqHIaw7o9QPAQhAAAKVEogtlJUGX74yDGF5dtwJAQhAoFEEYuschrBR3YNgIQABCEAgtlAmQhxDmEgiCAMCEIBA3QRi6xyGsO6MUj8EIAABCFRKILZQVhp8+cowhOXZcScEIACBRhGIrXMYwkZ1D4KFAAQgAIHYQpkIcQxhIokgDAhAAAJ1E4itcxjCujNK/RCAAAQgUCmB2EJZafDlK8MQlmfHnRCAAAQaRSC2zmEIG9U9CBYCEIAABGILZSLEMYSJJIIwIAABCNRNILbOYQjrzij1QwACEIBApQRiC2WlwZevDENYnh13QgACEGgUgdg6hyFsVPcgWAhAAAIQiC2UiRDHECaSCMKAAAQgUDeB2DqHIaw7o9QPAQhAAAKVEogtlJUGX74yDGF5dtwJAQhAoFEEYuschrBR3YNgIQABCEAgtlAmQhxDmEgiCAMCEIBA3QRi6xyGsO6MUj8EIAABCFRKILZQVhp8+cowhOXZcScEIACBRhGIrXMYwkZ1D4KFAAQgAIHYQpkIcQxhIokgDAhAAAJ1E4itcxjCujNK/RCAAAQgUCmB2EJZafDlK8MQlmfHnRCAAAQaRSC2zmEIG9U9CBYCEIAABGILZSLEMYSJJIIwIAABCNRNILbOYQjrzij1QwACEIBApQRiC2WlwZevDENYnh13QgACEGgUgdg6hyFsVPcgWAhAAAIQiC2UiRDHECaSCMKAAAQgUDeB2DqHIaw7o9QPAQhAAAKVEogtlJUGX74yDGF5dtwJAQhAoFEEYuschrBR3YNgIQABCEAgtlAmQhxDmEgiCAMCEIBA3QRi6xyGsO6MUj8EIAABCFRKILZQVhp8+cowhOXZcScEIACBRhGIrXMYwkZ1D4KFAAQgAIHYQpkIcQxhIokgDAhAAAJ1E4itcxjCujNK/RCAAAQgUCmB2EJZafDlK8MQlmfHnRCAAAQaRSC2zmEIG9U9CBYCEIAABGILZSLEMYSJJIIwIAABCNRNILbOYQjrzij1QwACEIBApQRiC2WlwZevDENYnh13QgACEGgUgdg6hyFsVPcgWAhAAAIQiC2UiRDHECaSCMKAAAQgUDeB2DqHIaw7o9QPAQhAAAKVEogtlJUGX74yDGF5dtwJAQhAoFEEYuucDeFVknaStFTSeZKOl7RmBGovlXS6pF0kPSHpvyQdIemxtvveIekzkraWdE+o++K2azaQdIakvSSNl/RdSYdKeqiHzCGUPcDiUghAAAJNJhBbKBNhhc4lkgjCgAAEIFA3gdg6Z0N4paSTJW0l6dRg9I4ZpqE2cHeFfz4raWNJ/vePg6nLbrVZnC/pHEmXSHqrpMMkvUXS5bn6fyjphZIOl7Q2xPKApDk9wEYoe4DFpRCAAASaTCC2UCbCCp1LJBGEAQEIQKBuArF1zoZwmqRHQ8OOlHScpE1zP2tv8ycl+Z9ZYVTRv3+7pO9IepWkm8INNnqTJL0+V8G88DybRRePTNpIvk7SdeFnr5Z0o6TdglktwhyhLEKJayAAAQj0AYHYQpkIMnQukUQQBgQgAIG6CcTWufY1hDZ590naU9JlXRrrKZ+btBm9dcLUUU839T9TJC0LUz/PzdXz95K+IunZkh6RdIKkg4IBzT/O00s9qugRxSJlzIVylip2iAAAIABJREFU7fLlevT6a/TkooWaMnOWps3ZVeOnTi0SO9eMIYF+zltqbUstnjHsdjx6lARiC+Uow63q9jHXuaoaQj0QgAAEIDA8gdg612lTmcfDKOEpXUK9NBg+T/3MykRJKyR9S9K+kraRdLukXcO00ew6jyD+VJJHAX8m6ZuSZkia2/as74X/36NghxlTofQX3UXHH6VVix+U1q6Vxo/XpE1maOaxJ2EKCyZwLC7r57yl1rbU4hmL/sYzqyMQWyiri3xUNY2pzo0qcm6GAAQgAIGeCMTWuU6G8H5JF0o6qkvkXmf4d2HK6KpwzWsk3SDpCklvkvRaSQskvVLSL3P1vEDSryW9Oawj9PU2oN5QJl++JmlLSTsXpDemQrn08nlacvFF0prcXjwTJmj6vvtpw912L9gELotNoJ/zllrbUosndl/jedUSiC2U1UZfurYx1bnSUXMjBCAAAQj0TCC2zpUxhC+WdFvYkdTrDb2pjA3ky8OaP48c1m0I/dxj83SHhoZ6hl3VDQ+cf66WLbhWyscwbpymzZmrGQccXNVjqKdiAv2ct9Tallo8FXclqotMILZQRm5et8dhCBNJBGFAAAIQqJtAbJ0rM2XUDA4Iu5F6QxrvDPqlsKGMjeL+uSmjngp6bQ5apymjXo/oqaX50qgpo4x+1P1nUU/9/Zy31NqWWjz19ChqjUUgtlDGatcIz8EQJpIIwoAABCBQN4HYOtduCGdKWjjCpjIZA28k4/MFH5S0JJwb+AlJ3kQm21TmI5K+mIP2/yRd0LapzAckbdYG9jeSvt2UTWVYH1X3n0U99fdz3lJrW2rx1NOjqDUWgdhCGatdGMJESBMGBCAAgTEmEFvnbAjXDzuCuuk+C9A7fw537EQnRO+TdKak50v6U7jAx05MkPTG3A0+dH7DcKC9f5wdO+EzB73m0GV22HCmUcdOtHZQXDBfKxct1GTvMrrLXDaUGeM/piKP7+e8pda21OIp0j+4Jk0CsYUyEQqMECaSCMKAAAQgUDeB2DpnQ+iNXXwwvTdxOU3SGZLyB9PfHaZ9Hhga72miR4dzA1eH6Z4+HsIjfR79y0p2MP1ZYbTPB9PbcHY6mN4jjfmD6T3qyMH0dfc26ocABCDQQAKxhTIRRBjCRBJBGBCAAATqJhBb52wIrw4jdUtzG8XktsvUveHoCK8NdFkvnBHokTwftOd1gycG09fOx7uHfiZMLf1tOM7iP9su8ojh6ZLeKWm8JI8iHhqmoRbljVAWJcV1EIAABBpOILZQJoILnUskEYQBAQhAoG4CsXWu06YydbexjvoRyjqoUicEIACBBAnEFspEEKBziSSCMCAAAQjUTSC2zmEI684o9UMAAhCAQKUEYgtlpcGXrwxDWJ4dd0IAAhBoFIHYOochbFT3IFgIQAACEIgtlIkQxxAmkgjCgAAEIFA3gdg6hyGsO6PUDwEIQAAClRKILZSVBl++MgxheXbcCQEIQKBRBGLrHIawUd2DYCEAAQhAILZQJkIcQ5hIIggDAhCAQN0EYuschrDujFI/BCAAAQhUSiC2UFYafPnKMITl2XEnBCAAgUYRiK1zGMJGdQ+ChQAEIACB2EKZCHEMYSKJIAwIQAACdROIrXMYwrozSv0QgAAEIFApgdhCmQt+G0lntp3de7yk/Nm93dq6t6RPStpW0hOSfibpbyQ9XhAOhrAgKC6DAAQg0HQCsXUOQ9j0HkP8EIAABAaMQGyhDHg3knS7pDsknSxpK0mnSjpd0jEjpOAfJJ0l6bOSrpbkul4f7nukYPowhAVBcRkEIACBphOIrXMYwqb3GOKHAAQgMGAEYgtlwOvRvSMlbSHp0fAz//9xkjbN/aw9G9Ml/VbSP0v6j1GkCkM4CnjcCgEIQKBJBGLrHIawSb2DWCEAAQhAQLGFMiC/TtLvJe2bS8EsSfdJ2lPSZV1S849hRHFjSStHkT4M4SjgcSsEIACBJhGIrXMYwib1DmKFAAQgAIGxMoQPSjonjAjms+A1gB4lPKVLar4q6aWSzpd0tKTnSLpZ0sck/biHdGIIe4DFpRCAAASaTABDWC57CGU5btwFAQhAoHEEYgtlALRK0hGSzmgDdr+kCyUd1QXkDyXtHKaUeorpQ2Hq6WxJW0t6oGAC0LmCoLgMAhCAQNMJxNY5Rgib3mOIHwIQgMCAEYgtlKM0hJdL2k3S7pJ+EOqaFqaaeqOZT3VJn0cdj83/bmhoaMAyTXMhAAEIDCaB2DqHIRzMfkarIQABCDSWQGyhDKA8ZfRsST5mIl9GmjJ6saS/lbSupBW5G6+U5B1GffREkcIIYRFKXAMBCECgDwjE1jkMYR90GpoAAQhAYJAIxBbKwNabyvxO0ntyrGdKWjjCpjLZSJ8N4fLcvVdJ+lMwi0XShyEsQolrIAABCPQBgdg6hyHsg05DEyAAAQgMEoHYQhnY+tgJryH0sRPLws8Ol3TCCMdOeK2gD6HfQ9K8cN8GYcro5yR9pmDuMIQFQXEZBCAAgaYTiK1zGMKm9xjihwAEIDBgBGILZcDrw+R9KP1t4RiJLSWdFjaZyR9Mf7ekayUdmEvLtyW9RtInJC0Jm8psI+mFkh4umD4MYUFQXAYBCECg6QRi6xyGsOk9hvghAAEIDBiB2EKZw2sT541gdpK0VNJ54ciJNblr7pU0X9L+uZ89KxxLsU9YS/ijcOzErT2kDkPYAywuhQAEINBkArF1DkPY5N5C7BCAAAQGkEBsoUwEMYYwkUQQBgQgAIG6CcTWOQxh3RmlfghAAAIQqJRAbKGsNPjylWEIy7PjTghAAAKNIhBb5zCEjeoeBAsBCEAAArGFMhHiGMJEEkEYEIAABOomEFvnMIR1Z5T6IQABCECgUgKxhbLS4MtXhiEsz447IQABCDSKQGydwxA2qnsQLAQgAAEIxBbKRIhjCBNJBGFAAAIQqJtAbJ3DENadUeqHAAQgAIFKCcQWykqDL18ZhrA8O+6EAAQg0CgCsXUOQ9io7kGwEIAABCAQWygTIY4hTCQRhAEBCECgbgKxdQ5DWHdGqR8CEIAABColEFsoKw2+fGUYwvLsuBMCEIBAowjE1jkMYaO6B8FCAAIQgEBsoUyEOIYwkUQQBgQgAIG6CcTWOQxh3RmlfghAAAIQqJRAbKGsNPjylWEIy7PjTghAAAKNIhBb5zCEjeoeBAsBCEAAArGFMhHiGMJEEkEYEIAABOomEFvnMIR1Z5T6IQABCECgUgKxhbLS4MtXhiEsz447IQABCDSKQGydwxA2qnsQLAQgAAEIxBbKRIhjCBNJBGFAAAIQqJtAbJ2zIbxK0k6Slko6T9LxktaM0NDZkk6S5H+73CzpaEk35u4b6lLHSklTwu+eL+m3Ha67WNK+PcBGKHuAxaUQgAAEmkwgtlAmwgqdSyQRhAEBCECgbgKxdc6G8EpJJ0vaStKpkk6XdMwwDZ0p6dZgAn29yxGStpe0naT7ws927FDHZZJ+JGmvNkN4ePh5dssSSXf3ABuh7AEWl0IAAhBoMoHYQpkIK3QukUQQBgQgAIG6CcTWORvCaZIeDQ07UtJxkjbN/ay9zYdIOlvSsyU9En65kSSbuA9L+kIXSK+S9NMw8ucRQJdshPDtkr47CrgI5SjgcSsEIACBJhGILZSJsEHnEkkEYUAAAhCom0BsnWtfQzgrjPDtKcmjeZ3KRyR9TtJ6klaHCyZJekzSxySd0+W+0yQdJGmGpCcwhHV3JeqHAAQg0J8EYgtlIhQxhIkkgjAgAAEI1E0gts512lTm8TBKeEqXxj5X0u2SLpR0Yrjm05LeFaaMLu5wn5+zSNK1kt6b+302QujRRY84PijpG2E94vIeYCOUPcDiUghAAAJNJhBbKBNhhc4lkgjCgAAEIFA3gdg618kQ3h/M3lHDNPYVYYrn88I1f5C0u6Rbutzz18EMto88bhbM3+VhiupcSR+X5P9/Rw+wEcoeYHEpBCAAgSYTiC2UibBC5xJJBGFAAAIQqJtAbJ0rYwht4q6TdEduveCHJL1S0s6SFnaA5HWF7w5rE73L6HDlg2HaqU1nN4PpdY7H5isZGuq2qWndKaN+CEAAAhCISSC2UMZs2zDPwhAmkgjCgAAEIFA3gdg6V2bKqNcC7i1pa0mrApDJkn4t6VJJh7ZBmijp9+F3HygAcJMwdfRASV8ucL0vQSgLguIyCEAAAk0nEFsoE+GFziWSCMKAAAQgUDeB2DrXbgh9pIRH+IbbVGaeDZikPdpgdPv5myX9QNIbJF1dAOB0SV6HeICkrxS4HkNYEBKXQQACEOgHArGFMhFmGMJEEkEYEIAABOomEFvnbAjXl7QsNMznAZ4wwrETnv751jBCmE3/9EHzHiH0zqSePpovF0h6k6TNJa0tANDHWvgZL5f0vwWuxxAWhMRlEIAABPqBQGyhTIQZhjCRRBAGBCAAgboJxNY5G8IrwsH0W0rydNAz2g6m9wHx3h3UUzhddpB0Q9j4xUdMuA6bwDdKmt227s9G8QFJNoX/1AGe1wLakPqwep+F6M1nfMi9Rxv/pgfYCGUPsLgUAhCAQJMJxBbKRFihc4kkgjAgAAEI1E0gts7ZzHka506Slko6Lxw5sSbX0HslzZe0f+5nnv7pTV22DT+7Nfy/r8uXvSRdEuq3iWwv+0ryqKTXI04N01W/Ho6zeLIH2AhlD7C4FAIQgECTCcQWykRYoXOJJIIwIAABCNRNILbOddpUpu421lE/QlkHVeqEAAQgkCCB2EKZCAJ0LpFEEAYEIACBugnE1jkMYd0ZpX4IQAACEKiUQGyhrDT48pVhCMuz404IQAACjSIQW+cwhI3qHgQLAQhAAAKxhTIR4hjCRBJBGBCAQP8QWLt8uR69/ho9uWihpsycpWlzdtX4qV7FNrYlts5hCMc23zwdAhCAAAR6JBBbKHsMr67LMYR1kaVeCEBgIAnYDC46/iitWvygtHatNH68Jm0yQzOPPWnMTWFsncMQDuSfAI2GAAQg0FwCsYUyEVIYwkQSQRgQgEB/EFh6+TwtufgiaU1uL80JEzR93/204W67j2kjY+schnBM083DIQABCECgVwKxhbLX+Gq6HkNYE1iqhQAEBpPAA+efq2ULrpWGhp4GMG6cps2ZqxkHHDymUGLrHIZwTNPNwyEAgX4gkOoahH5g26kNsYUyEY4YwkQSQRgQgEB/EGCEMOeD+yOlQij7JJE0AwJNI5DyGoSmsSwaL4awKCmugwAEIACBbgRS1u/YOscIIX8nEIAABEZBIOU3jKNoVtK3xhbKRGDw4jORRBAGBCDQPwRaM3wWzNfKRQs12buM7jJ3zDeUMd3YOoch7J8+TUsgAIExIJDyGoQxwBHlkbGFMkqjRn4IhnBkRlwBAQhAoC8IxNY5DGFfdBsaAQEIjBUBRgjjk48tlPFb2PGJGMJEEkEYEIAABOomEFvnMIR1Z5T6IQCBviaQ8hqEfgUfWygT4YghTCQRhAEBCECgbgKxdQ5DWHdGqR8CEOh7AqmuQehX8LGFMhGOGMJEEkEYEIAABOomEFvnMIR1Z5T6IQABCECgUgKxhbLS4MtXhiEsz447IQABCDSKQGydwxA2qnsQLAQgAAEIxBbKRIhjCBNJBGFAAAIQqJtAbJ3DENadUeqHAAQgAIFKCcQWykqDL18ZhrA8O+6EAAQg0CgCsXUOQ9io7kGwEIAABCAQWygTIY4hTCQRhAEBCECgbgKxdQ5DWHdGqR8CEIAABColEFsoKw2+fGUYwvLsuBMCEIBAowjE1jkMYaO6B8FCAAIQgEBsoUyEOIYwkUQQBgQgAIG6CcTWOQxh3RmlfghAAAIQqJRAbKGsNPjylWEIy7PjTghAAAKNIhBb5zCEjeoeBAsBCEAAArGFMhHiGMJEEkEYEIAABOomEFvnMIR1Z5T6IQABCECgUgKxhbLS4MtXhiEsz447IQABCDSKQGydwxA2qnsQLAQgAAEIxBbKRIhjCBNJBGFAAAIQqJtAbJ3DENadUeqHAAQgAIFKCcQWykqDL18ZhrA8O+6EAAQg0CgCsXUOQ9io7kGwEIAABCAQWygTIY4hTCQRhDE6AmuXL9ej11+jJxct1JSZszRtzq4aP3Xq6Crlbgj0GYHYOoch7LMORHMgAAEI9DuB2EKZCE8MYSKJIIzyBGwGFx1/lFYtflBau1YaP16TNpmhmceehCksj5U7+5BAbJ3DEPZhJ6JJEIAABPqZQGyhTIQlhjCRRBBGeQJLL5+nJRdfJK1Z83QlEyZo+r77acPddi9fMXdCoM8IxNY5DGGfdSCaAwEIQKDfCcQWykR4YggTSQRhlCfwwPnnatmCa6WhoacrGTdO0+bM1YwDDi5fMXdCoM8IxNY5DGGfdSCaAwEIQKDfCcQWykR4YggTSQRhlCfACGF5dtw5WARi6xyGcLD6F62FAAQg0HgCsYUyEWAYwkQSQRjlCbCGsDw77hwsArF1DkM4WP2L1kIAAhBoPIHYQpkIMAxhIokgjNERaO0yumC+Vi5aqMneZXSXuWwoMzqk3N2HBGLrHIawDzsRTYIABCDQzwRiC2UiLDGEiSSCMCAAAQjUTSC2ztkQXiVpJ0lLJZ0n6XhJue2fOjZ5tqSTJPnfLjdLOlrSjbmrL5D0vg53v0TSnbmfbyDpDEl7SRov6buSDpX0UA+wEcoeYHEpBCAAgSYTiC2UibBC5xJJBGFAAAIQqJtAbJ2zIbxS0smStpJ0qqTTJR0zTENnSro1mEBf73KEpO0lbSfpvvAzG8LXSHp/W12/lLQi97MfSnqhpMMlrQ2xPCBpTg+wEcoeYHEpBCAAgSYTiC2UibBC5xJJBGFAAAIQqJtAbJ2zIZwm6dHQsCMlHSdp09zP2tt8iKSzJT1b0iPhlxtJWiLpw5K+kDOE2+ZGETux88jkjyW9TtJ14YJXh5HG3YJZLcIcoSxCiWsgAAEI9AGB2EKZCDJ0LpFEEAYEIACBugnE1rn2NYSzwgjfnpIu69LYj0j6nKT1JK0O10yS9Jikj0k6pwdDeIKkg4IBzT/uHkmXSDqsIHCEsiAoLoMABCDQdAKxhTIRXuhcIokgDAhAAAJ1E4itc502lXk8jBKe0qWxz5V0u6QLJZ0Yrvm0pHeFKaOLc4Zwn2Aap0j6WVhneG2u3m9KmiFpbtuzvhf+f4+CwBHKgqC4DAIQgEDTCcQWykR4oXOJJIIwIAABCNRNILbOdTKE9wezd9QwjX1F2PzleeGaP0jaXdItuXs+KmmlpDskbRJG+3aQtIukn4brrpBkA+oNZfLla5K2lLRzQeAIZUFQXAYBCECg6QRiC2UivNC5RBJBGBCAAATqJhBb58oYws3Cej8bvWy94IckvTIYuIVdIK0bRhZtGjMDWNYQep3jsfnnDA0N1Z0b6ocABCAAgQQIxBbKBJrsEDCEiSSCMCAAAQjUTSC2zpWZMnqapL0lbS1pVQAyWdKvJV0ajozoxsmb0bxdktcqunjKqEcPd227gSmjdfc06ocABCDQUAKxhTIRTBjCRBJBGBCAAATqJhBb59oNoY+U8AjfcJvKzPObSknt6/u6/TzP7KxgCLcIP/SmMh+Q5FHHfPmNpG+zqUzd3Y36IQABCDSPQGyhTIQQhjCRRBAGBCAAgboJxNY5G8L1JS0LDfNZgDZpwx074Wmibw0jhF4j6OJNYzxC6J1JPX20U5ka1hP+Ioww+prs2AmfObgg3OTD7r0BDcdO1N3bqB8CEIBAAwnEFspEEGEIE0kEYUAAAhCom0BsnbMh9Do+H0zvTVw8HfSMtoPp75bknUEPDI33xjA3SLo8HDHhOmwC3xjOHPQawQ3CpjPeHMb3Tw9HUnid4Wsl3ZQD6YPpPf00fzD9gxxMX3dXo34IQAACzSQQWygToYQhTCQRhAEBCECgbgKxdc5m7uowUrdU0nnhyIk1uYbeK2m+pP1zP3tD2NTFB8+73Br+39e5rCPp65JeFY6VWCHpJ6Fum8l82VDS6ZLeKWl8MJKHhoPui/JGKIuS4joIQAACDScQWygTwYXOJZIIwoAABCBQN4HYOtdpU5m621hH/QhlHVSpEwIQgECCBGILZSII0LlEEkEYEIAABOomEFvnMIR1Z5T6IQABCECgUgKxhbLS4MtXhiEsz447IQABCDSKQGydwxA2qnsQLAQgAAEIxBbKHPFtJJ3ZtszieEn5ZRbDJcjLIn4qyWvxfQTTd3vIJoawB1hcCgEIQKDJBGLrHIawyb2F2CEAAQgMIIHYQhkQbyTp9rBbtjdi20rSqWEN/DEF03BQ2Mn7ORjCgsS4DAIQgMAAEoitcxjCAexkNBkCEIBAkwnEFsrA6pOSjpTkc3QfDT/z/x83wlFNGWobyrskfSJs4MYIYZM7IbFDAAIQqJFAbJ3DENaYTKqGAAQgAIHqCcQWytCC6yT9XtK+uRbNknSfpD3DObzDNfYsSZtL+idJv2WEsPp+QY0QgAAE+oVAbJ3DEPZLz6EdEIAABAaEQGyhDFh9Pu45YUQwT/rx8LNThsH/snB+r/+9GkM4IB2VZkIAAhAoSSC2zmEISyaK2yAAAQhAYGwIxBbK0MpVko6QdEZbq++XdKGko4ahca2kG8OU0+djCMem3/BUCEAAAk0hEFvnMIRN6RnECQEIQAACLQKxhXKUhtBTTG0iXxjWHhY1hF6beGw+5UNDQ/QACEAAAhCQtHb5cj16/TV6ctFCTZk5S9Pm7KrxU6f2DZvYOoch7JuuQ0MgAAEIDAaB2EIZqHrK6NmSfMxEvgw3ZXSSpHsknSbpK+Emrzu8JaxFnCdpWcGscexEQVBcBgEI9DcBm8FFxx+lVYsflNaulcaP16RNZmjmsSf1jSmMrXMYwv7+m6F1EIAABPqOQGyhDAC9qczvJL0nB3SmpIXDbCqzoaSHh0nAbyS9oGCCMIQFQXEZBCDQ3wSWXj5PSy6+SFqTOwJ2wgRN33c/bbjb7n3R+Ng6hyHsi25DIyAAAQgMDoHYQhnI+tgJryH0sRPZqN7h4VzBTXNHUeQTMVHSLm2Z8bXfCGsOrw5rC4skD0NYhBLXQAACfU/ggfPP1bIF10r5afTjxmnanLmaccDBfdH+2DqHIeyLbkMjIAABCAwOgdhCGcj6HME7JN0myQfTbxmmgnp9YP5g+rsleROZA7tkpOgawvbbMYSD08VpKQQgMAwBRgir7x4YwuqZUiMEIAABCNRIYIwMoVu0jSSfJ7iTpKXhgHlv/pKbt6R7Jc2XtD+GsMZOQNUQgMDAEmANYfWpxxBWz5QaIQABCECgRgJjaAhrbNWIVTNCOCIiLoAABAaFQGuX0QXztXLRQk32LqO7zO2bDWWcw9g6hyEclL8c2gkBCECgTwjEFspEsGEIE0kEYUAAAhCom0BsncMQ1p1R6ocABCAAgUoJxBbKSoMvXxmGsDw77oQABCDQKAKxdQ5D2KjuQbAQgAAEIBBbKBMhjiFMJBGEAQEIQKBuArF1DkNYd0apHwIQgAAEKiUQWygrDb58ZRjC8uy4EwIQgECjCMTWOQxho7oHwUIAAhCAQGyhTIQ4hjCRRBAGBCAAgboJxNY5DGHdGaV+CEAAAhColEBsoaw0+PKVYQjLs+NOCEAAArUTaO18ev01enLRQk3xzqdzdi2982lsncMQ1t49eAAEIAABCFRJILZQVhn7KOrCEI4CHrdCAAIQqJNA1WcjxtY5DGGdvYO6IQABCECgcgKxhbLyBpSrEENYjht3QQACEKidwNLL52nJxRdJa9Y8/awJEzR93/204W679/z82DqHIew5RdwAAQhAAAJjSSC2UI5lW3PPxhAmkgjCgAAEINBO4IHzz9WyBddKQ0NP/2rcOE2bM1czDji4Z2CxdQ5D2HOKuAECEIAABMaSQGyhHMu2YggToU8YEIAABIYhwAhhGt2DN6dp5IEoIAABCNROAENYO2IeAAEIQAACPRBgDWEPsGq8FENYI1yqhgAEIJASAQxhStkgFghAAAIQMIHWLqML5mvlooWa7F1Gd5nLLqORuwaGMDJwHgcBCEBgrAhgCMeKPM+FAAQgAIEYBGLrHGsIY2SVZ0AAAhCAQGUEYgtlZYGPriJefI6OH3dDAAIQaAyB2DqHIWxM1yBQCEAAAhAwgdhCmQh1DGEiiSAMCEAAAnUTiK1zGMK6M0r9EIAABCBQKYHYQllp8OUrwxB2YNdas3P9NXpy0UJN8ZqdObuWXrNTPjVp3wmjtPNDdBDoRCC2zmEI6YcQgAAEINAoArGFMhE4GMK2RFS9q18iea40DBhVipPKIBCNQGydsyG8StJOkpZKOk/S8ZLWjNDi2ZJOkuR/u9ws6WhJN4b/nyDpcElvk7RN+NnPwzU/a6s7d4LjU79xPTv2QB2h7AEWl0IAAhBoMoHYQpkIqzHXudRGmqo+9yuRPFcaBowqxUllEIhGILbO2RBeKelkSVtJOlXS6ZKOGabFMyXdGkygr3c5QtL2kraTdJ+kZ0laJOkroX6bvg9LeqOknSXZHGbFv3M938r9bJmk23ugPuZC2UOsXAoBCEAAAqMgEFsoRxFqlbeOqc6lONL0wPnnatmCa6Wh3HvlceM0bc5czTjg4CrZN7YuGDU2dQQ+4ARi65wN4TRJjwbuR0o6TtKmuZ+1p+QQSWdLerakR8IvN5K0JJi+L0jyCKHrfTh382RJd0m6RtL72wzhRySdNYrcj6lQjiJuboUABCAAgR4JxBbKHsOr6/Ix1bkUR5pSjKmu5JetF0ZlyXEfBMaWQGyda19DOCuM8O0p6bIuKGzePidpPUmrwzWTJD0m6WOSzhkG4fckTZT0Zgzh2HY0ng4BCECgqQRiC2UinMbUEKY40pTiqGVVfaWq6bn9zKgq1lXWU1XeqoyJuppJILbOddpU5vEwSnhKF4TPDdM5L5R0Yrjm05LeFaaMLu5y3xRJ94apoTaVWfFcj4f1GSmiAAAgAElEQVQkbRjWMX4nrD/8Uw8pHFOh7CFOLoUABCAAgVESiC2Uowy3qtvHVOdSHWlqfQFfMF8rFy3UZO8yusvcxu8yWrWJ60dGVf1RVVlP1XmrMjbqah6B2DrXyRDeL8lm76hh8L1C0nclPS9c8wdJu0u6ZZh7TpD0cUkvk/Sr3HUXhNFIG0lvUvMpSfdIenWBzW2eMpVD+TUEzcs7EUMAAhCAQEECsYWyYFh1XzamhpAvu3Wn9+n6UzXf8Qg080nkrZl5SzXq2DpXxhBuJuk6SXdI8npBlw9JemXYMGZhB7h7SPLI32GSzhgBvo3lPEnvlPTtLtd6neOx+d9hCFPt0sQFAQhAoFoCsYWy2uhL1zamhtBRM9JUOnc93Zji9NyeGjCgF5O3AU18Tc2OrXNlpoyeJmlvSVtLWhU4eMOYX0u6VNKhbWxeFTaS+WowjiOhc0ze5Ma7nXoqapEy5kJZJEiugQAEIACB0ROILZSjj7iSGtC5SjCmXwkjTennqFOE5K2ZeUs16tg6124IfaSER/iG21TGo3de9+dRv3zp9PMXSlog6SfBRI50vmFWn4+dsPF8xijgMElDKFPt0cQFAQhAoGICsYWy4vDLVofOlSXXsPuYntuwhIVwyVsz85Zq1LF1zoZwfUk2YC4+TN5r/YY7dsLTRN8aRghXhvu8YYxHCL0zqaePunhq6Y8l/VHSGyQ9URD6WyR9X9I7wjTTIrchlEUocQ0EIACBPiAQWygTQYbOJZKIGGEwPTcG5eqfQd6qZzqoNcbWORvCK8LB9FuGUTmv8csfTH+3pGslHRiSsoOkGyRdHo6YcB02gT503pvCeGOZqWFU8PmS3ht2Ec1y+qSkX4T/OSjcc2U4x9CH2/vZ3nTGB9gXHVFEKAf1L4Z2QwACA0cgtlAmAhidSyQRhAEBCECgbgKxdc5m7mpJO4UjH84LR07kjZiPipgvaf9c4z3i5+mc24af3Rr+39e52Aj+tgus+8Lv/WvX4xHJF4eD7D2aeEnYaTQ79L4Ic4SyCCWugQAEINAHBGILZSLI0LlEEkEYEIAABOomEFvnOm0qU3cb66gfoayDKnVCAAIQSJBAbKFMBAE6l0giCAMCEIBA3QRi6xyGsO6MUj8EIAABCFRKILZQVhp8+cowhOXZcScEIACBRhGIrXMYwkZ1D4KFAAQgAIHYQpkIcQxhIokgDAhAAAJ1E4itcxjCujNK/RCAAAQgUCmB2EJZafDlK8MQlmfHnRCAAAQaRSC2zmEIG9U9CBYCEIAABGILZSLEMYSJJIIwIAABCNRNILbOYQjrzij1QwACEIBApQRiC2WlwZevDENYnh13QgACEGgUgdg6hyFsVPcgWAhAAAIQiC2UiRDHECaSCMKAAAQgUDeB2DqHIaw7o9QPAQhAAAKVEogtlJUGX74yDGF5dtwJAQhAoFEEYuschrBR3YNgIQABCEAgtlAmQhxDmEgiCAMCEIBA3QRi6xyGsO6MUj8EIAABCFRKILZQVhp8+cowhOXZcScEIACBRhGIrXMYwkZ1D4KFAAQgAIHYQpkIcQxhIokgDAhAAAJ1E4itcxjCujNK/RCAAAQgUCmB2EJZafDlK8MQlmfHnRCAAAQaRSC2zmEIG9U9CBYCEIAABGILZSLEMYSJJIIwIAABCNRNILbOYQjrzij1QwACEIBApQRiC2WlwZevDENYnh13QgACEGgUgdg6hyFsVPcgWAhAAAIQiC2UiRDHECaSCMKAAAQgUDeB2DqHIaw7o9QPAQhAAAKVEogtlJUGX74yDGF5dtwJAQhAoFEEYuschrBR3YNgIQABCEAgtlAmQhxDmEgiCAMCEIBA3QRi6xyGsO6MUj8EIAABCFRKILZQVhp8+cowhOXZcScEIACBRhGIrXMYwkZ1D4KFAAQgAIHYQpkIcQxhIokgDAhAAAJ1E4itcxjCujNK/RCAAAQgUCmB2EJZafDlK8MQlmfHnX1IYO3y5Xr0+mv05KKFmjJzlqbN2VXjp07tw5bSpEEkEFvnMISD2MtoMwQgAIEGE4gtlImgwhAmkgjCGHsCNoOLjj9KqxY/KK1dK40fr0mbzNDMY0/CFI59eoigAgKxdQ5DWEHSqAICEIAABOIRiC2U8Vo27JMwhIkkgjDGnsDSy+dpycUXSWvWPB3MhAmavu9+2nC33cc+QCKAwCgJxNY5DOEoE8btEIAABCAQl0BsoYzbuq5PwxAmkgjCGHsCD5x/rpYtuFYaGno6mHHjNG3OXM044OCxD5AIIDBKArF1DkM4yoRxOwQgAAEIxCUQWyjjtg5DmAhvwkiYACOECSeH0CohEFvnMISVpI1KIAABCEAgFoHYQhmrXSM8hxHCRBJBGGNPgDWEY58DIqiXQGydwxDWm09qhwAEIACBignEFsqKwy9bHYawLDnu60sCrV1GF8zXykULNdm7jO4ylw1l+jLTg9mo2DqHIRzMfkarIQABCDSWQGyhTAQUhjCRRBAGBCAAgboJxNY5DGHdGaV+CEAAAhColEBsoaw0+PKVYQjLs+NOCEAAAo0iEFvnMISN6h4ECwEIQAACsYUyEeIYwkQSQRgQgAAE6iYQW+cwhHVnlPohAAEIQKBSArGFstLgy1eGISzPjjshAAEINIpAbJ3DEDaqexAsBCAAAQjEFspEiGMIE0kEYUAAAhCom0BsncMQ1p1R6ocABCAAgUoJxBbKSoMvXxmGsDw77oQABCDQKAKxdc6G8CpJO0laKuk8ScdLWjMCtdmSTpLkf7vcLOloSTe23fcOSZ+RtLWke0LdF7dds4GkMyTtJWm8pO9KOlTSQz1kDqHsARaXQgACEGgygdhCmQgrdC6RRBAGBCAAgboJxNY5G8IrJZ0saStJp0o6XdIxwzR0pqRbgwn09S5HSNpe0naS7gs/20XSfEnnSLpE0lslHSbpLZIuz9X/Q0kvlHS4pLUhlgckzekBNkLZAywuhQAEINBkArGFMhFW6FwiiSAMCEDg/2/vTKBsKco7/uMhIDE+UYG4AOIS9wVDNBpBQcUlRlyPYoIrUXGJSyIuaGTXuKJRFHJECYqKiUkIyjGKCILR6IkBd+OGPFzgkYigIiDv5XxSc2yHOzN9+3bVre7763M4hzdT/fVXv6/u/Oc/VV0lgdwESutcGML1wGWpYy8FDgVu1vja8j4fCBwD3AT4afrmjYFLgOcD70xfC6O3FfDARoDT0vPCLMYVM5P/ATwA+HT62r3TTOM+yay2Ya5QtqFkGwlIQAIjIFBaKCtBps5VUgjTkIAEJJCbQGmdW/4O4S5phm9f4NQVOvuXwBuBGwC/Sm3C+P0MeHGaEdwGuDwt/Ty2EecpwHsaZvJw4FnJgDYfF8tLY1YxZhTbXAplG0q2kYAEJDAnApuuuILLzv4UV264gG123oX1e+7Num237ZRNaaHslGT/N6lz/TM1ogQkIIEqCZTWuUmbyvw8zRK+YQVCtwC+CpwIHJXavBp4fFoyuhG4c2qzd1o2uhTqXsDngZgF/ALwIWBHYK9lz/po+vcjWlZJoWwJymYSkIAEShMIM7jhsIO5euPFsGkTrFvHVjvsyM6HvKaTKSwtlKV5rfA8da6SQpiGBCQggdwESuvcJEN4YTJ7B6/S2d3S5i+3TG1+BDwcOC/9+37AOcA9gXMbcW4HfAt4aHqP8BNAGNDYUKZ5vQ+4DfDHLYErlC1B2UwCEpBAaQKXfvw0Ljn5JLimsV/Zlluy/X77s90+IR3TXaWFcrrssrVW57KhNbAEJCCBugiU1rkuhvDm6X2/rzXeF3xeMn9h4C4AchvCeM/xkGbpNm/eXFclzUYCEpCABH5N4KLjj+Xyc86C5s/pLbZg/Z57seMznj01pdJCOXWCeW7QEObhalQJSEAC1REorXNdloy+GXhsOkri6kRw6zTzd0p6b3BpyWgsBT2rQXnSktEdgFha2rxcMlrd0DQhCUhAAt0IOEPYjduyuzSEvWA0iAQkIIH6CczbEMaREjHDt9qmMrFTaEzHLX+/r/n1pU1lYgOa4xrYnwycsGxTmWcCMevYvL4D/KubytQ/YM1QAhKQwFoERvQOYfyx821Tnt0bfwh9bjpKKd7B3wC8Px2x9Mu12DW+ryGcApZNJSABCQyZwDwM4Q3TjqDBLc4CjJ0/Vzt2Io6ViDMF47D5qxLsMIDxbmDsTBrLR+OKYye2BB7cKEgcOr8dsPzYiThzMN45jCsOu48NZzx2Ysgj2dwlIAEJNAj8epfRc87kqg0XsHXsMrrHXp02lImQpYUydSOOV4oN1eJ1iWnO7o1duWMjtfcmnbw7cEQ6VulxUwwSDeEUsGwqAQlIYMgESutcLBmNjV1C3GITl1gO+pZlB9N/Oy37PCCB3R34XNoUJg6djxhhAsP4hZlb2lhm6WD6t6fZvjCRYTgnHUwf5rJ5MP3FHkw/5GFs7hKQgATyESgtlKknrwDirN5bTXl27/bpnN4mkDhuKVbP7JqOemoDS0PYhpJtJCABCYyAQGmdCzN3xrLlL7FhS2MrOM5PR0c8rcH3QWlTl7umr305/fvMZTWI3UOPTLOJ30vHWXxwWZuYMTwaeAywLu1e+oIJArpaeRXKEQx+uyABCUigDYHSQply+jTwQ2C/Ro5tzu6d1KWl9+ljI7bPtulzvKoxls3T+jyTsiU7m0lAAhIYFIHSOjdpU5lBAUvJjkYohwjfnCUgAQmUJFBaKFPfYuVKrIqJP5o2r7XO7p2EJv7oGX8IjffnI26baxQ61/f7pG3A2UYCEpDA0AiU1jkN4dBGiPlKQAISWHACpYUy4Y5dtQ9Kr1U0K9Dm7N5m+3hH/0tAbMTWXHmzVlVHYQj73nF2LWh+XwISkMAQCZTWOQ3hEEeJOUtAAhJYYAKlhbJHQxhHNJ0O7ATE+/g/WaWMozxvt+8zKRf4Y2DXJSCBERMorXMawhEPJrsmAQlIYIwESgtlYhhLO48BDlvGtO2S0dDbD6QdtO8HfGPK2jhDOCUwm0tAAhIYKoHSOqchHOpIMW8JSEACC0qgtFAmzLGpzA+AJzWwtzm7d6n5W4HYXTSOVFo6ZmmaCo7CEPoO4TQlt60EJLCoBErrnIZwUUea/ZaABCQwUAKlhTJhimMn4h3COHbi8vS1Nmf3RtO4N3bcfgLw4Y7YR2EIo+99nknZkaW3SUACEqiaQGmd0xBWPRxMTgISkIAElhMoLZTp+XEwfRxK/5Upz+79M+Ak4IR09mCzO98BNras8GgMYcv+2kwCEpDAwhIorXMawoUdanZcAhKQwDAJlBbKBqU7A2+f8uzeMIJPXYH005NRbFMIDWEbSraRgAQkMAICpXVOQziCQWMXJCABCSwSgdJCWQlbDWElhTANCUhAArkJlNY5DWHuihpfAhKQgAR6JVBaKHtNvnswDWF3dt4pAQlIYFAESuuchnBQw8NkJSABCUigtFBWQlxDWEkhTEMCEpBAbgKldU5DmLuixpeABCQggV4JlBbKXpPvHkxD2J2dd0pAAhIYFIHSOqchHNTwMFkJSEACEigtlJUQ1xBWUgjTkIAEJJCbQGmd0xDmrqjxJSABCUigVwKlhbLX5LsH0xB2Z+edEpCABAZFoLTOaQgHNTxMVgISkIAESgtlJcQ1hJUUwjQkIAEJ5CZQWuc0hLkranwJSEACEuiVQGmh7DX57sE0hN3ZeacEJCCBQREorXMawkEND5OVgAQkIIHSQlkJcQ1hJYUwDQlIQAK5CZTWOQ1h7ooaXwISkIAEeiVQWih7Tb57MA1hd3beKQEJSGBQBErrnIZwUMPDZCUgAQlIoLRQVkJcQ1hJIUxDAhKQQG4CpXVOQ5i7osaXgAQkIIFeCZQWyl6T7x5MQ9idnXdKQAISGBSB0jqnIRzU8DBZCUhAAhIoLZSVENcQVlII05CABCSQm0BpndMQ5q6o8SUgAQlIoFcCpYWy1+S7B9MQdmfnnRKQgAQGRaC0zmkIBzU8TFYCEpCABEoLZSXENYSVFMI0JCABCeQmUFrnNIS5K2p8CUhAAhLolUBpoew1+e7BNITd2XmnBCQggUERKK1zGsJBDQ+TlYAEJCCB0kJZCfHOhnDTFVdw2dmf4soNF7DNzruwfs+9WbfttpV0yzQkIAEJSGA5gdI6pyF0DEpAAhKQwKAIlBbKSuB0MoRhBjccdjBXb7wYNm2CdevYaocd2fmQ12gKKymsaUhAAhLQEPYzBjoJZT+PNooEJCABCZQkoCFsT/vSj5/GJSefBNdc85ubttyS7ffbn+32eXj7QLaUgAQkIIFiBErrnDOExUrrgyQgAQlIoA8CpYWyj5x7iNHpD58XHX8sl59zFmze/JsUttiC9XvuxY7PeHYPaRlCAhKQgAT6JlBa5zSEfVfQeBKQgAQkkJVAaaHM2pn2wTsZQmcI2wO2pQQkIIFaCJTWOQ1hLZU3DwlIQAISaEWgtFC2Sip/o06G0HcI8xdmUZ7g5kSLUmn7WQOB0jqnIayh6uYgAQlIQAKtCZQWytaJ5W3YyRBGSr/+Rf6cM7lqwwVsHbuM7rGXG8pkqtVYTdPY/7Aw1rplGuaGLUCgtM5pCAsU1UdIQAISkEB/BEoLZX+ZzxSpsyGc6ane3JrAmE3TmJcej7lurQevDasjUFrnwhB+ErgvcCnwLuAwoLEd2XUYHQocsgK5g4HXpu813mD/rdZXAdukr+wKfG9CrJOB/aaojkI5BSybSkACEhgygdJCWQkrda6SQqyUxphN05g3Jxpz3Sr/yJjeKgRK61wYwtOB1wG3Bd4EHA28apUcdwLiv+b1aOBlwD2Bc9M37jMhxqnAZ4BoH9eSIXxJ+vrSLZcA355ipCiUU8CyqQQkIIEhEygtlJWwUucqKcRKaWiaKi/QCumNuW7DrIhZB4HSOheGcD1wWcL/UiBmAG/W+FqbynwUuA1wp1Ua3wv4fJr5ixnApiF8JPCRNg9aoY1COQM8b5WABCQwJAKlhbISNupcJYVYKY0xzzSNeVnlmOtW+UfG9FYhUFrnlr9DuAvwfWBfIGbz2lw3BX4EHAkcvsoNbwaeBewI/EJD2AatbSQgAQlIYDmB0kJZSQU0hJUUYqU0xmyaos9j3Zxo7HWr/GNjeisQKK1zkzaV+XmaJXxDyyqFyTsOuD3wrZX6BWwAzgL+vNFmacloLBG9CXAx8AHglcAVLZ8fzRTKKWDZVAISkMCQCZQWykpYqXOVFGK1NMZqmgaAfqYUrdtM+Lw5A4HSOjfJEF4InAjEBjFtrjOAGwG7r9L4/skMLp95vHkyfx9PS1T3Su8ixr8f1ebhqY1COQUsm0pAAhIYMoHSQlkJK3WukkKYhgSGQqDG4zRqzKnGepbWuVkNYRi6MJCxocwbVwH6TuCJ6d3E2GV0tes5wDuA3YDzVmh4nZ1ON29eaVPTGstsThKQgAQk0JVAaaHsmmfP92kIewZqOAmMmUCNS2FrzKnWMVBa52ZdMvrCtCvprdKS0Elcrwf8EDgFeGYL8DukpaMHAO9u0T6aKJQtQdlMAhKQwNAJlBbKSnipc5UUwjQkMAQCNW6WU2NOtdaytM4tN4Q7AxdMsanMZ4GrgVgSutL1UOBjwIOAWF661rU9sBF4BvCetRqn7yuULUHZTAISkMDQCZQWykp4qXOVFMI0JJCbQB/LKms8TqPGnHLXsmv80joXhvCGwOUp4TgPMHYKbXPsxNKGMM8FYknoStcJwEPS2YWbWoA5MMW7B/ClFu2jiULZEpTNJCABCQydQGmhrISXOldJIUxDAjkJ9LWsssbZuBpzylnLWWKX1rkwhJ9IB9PHOYJxNMRblh1MHwfEx+6gsYSzeb0cOAKI9whjl9BJ1zbARUCYwhdNaBDvAoYhjcPq4yzEmGk8CDgNeNwUIBXKKWDZVAISkMCQCZQWykpYqXOVFMI0JJCTQF+mqS9j2Wdfa8ypz/71Gau0zoUhjGWc9wUuBd6Vjpy4ptGp84Ezgact6+i5wI+Bh60C4NHAv6T4n5vQbj8gZiV/H9g2LVd9P3AUcOUUYBXKKWDZVAISkMCQCZQWykpYqXOVFMI0JJCTQJ/LKms8TqPGnHLWs2vs0jo3aVOZrrnP8z6Fcp70fbYEJCCBggRKC2XBrq32KHWukkKYhgRyEuhrhjBnjsbOT6C0zmkI89fUJ0hAAhKQQI8ESgtlj6nPEkpDOAs975XAQAi4rHIghcqcZmmd0xBmLqjhJSABCUigXwKlhbLf7DtH0xB2RueNEhgWAZdVDqteObItrXMawhxVNKYEJCABCWQjUFoos3VkusAawul42VoCEpDAYAmU1jkN4WCHiolLQAISWEwCpYWyEsoawkoKYRoSkIAEchMorXMawtwVnWP8Pg42nWP6C/loa7aQZe+902MfR6WFsvcCdQuoIezGrdVdY//MtIJgIwlIoBoCpXVOQ1hN6ftNxJeS++VZIpo1K0F5/M9YhHFUWigrGTUawkyFWITPTCZ0hpWABDIRKK1zGsJMhZx3WLctnncFpn++NZuemXdcl8AijKPSQlnJONMQZirEInxmMqEzrAQkkIlAaZ3TEGYq5LzD9nmw6bz7sijPt2aLUum8/VyEcVRaKPNWrHV0DWFrVNM1XITPzHREbC0BCcybQGmd0xDOu+KZnu9fPDOBzRjWmmWEu0ChF2EclRbKSoaPhjBTIRbhM5MJnWElIIFMBErrnIYwUyHnHdZ3IuZdgemfb82mZ+Yd1yWwCOOotFBWMs40hJkKsQifmUzoDCsBCWQiUFrnNISZCllDWA82raEK0+VgzabjZevJBMY+jkoLZSXjTEOYsRBj/8xkRGdoCUggA4HSOqchzFBEQ0pAAhKQQD4CpYUyX0+miqwhnAqXjSUgAQkMl0BpndMQDnesmLkEJCCBhSRQWigrgawhrKQQpiEBCUggN4HSOqchzF1R40tAAhKQQK8ESgtlr8l3D6Yh7M7OOyUgAQkMikBpndMQDmp4mKwEJCABCZQWykqIawgrKYRpSEACEshNoLTOaQhzV9T4EpCABCTQK4HSQtlr8t2DaQi7s/NOCUhAAoMiUFrnNISDGh4mKwEJSEACpYWyEuIawkoKYRoSkIAEchMorXMawtwVNb4EJCABCfRKoLRQ9pp892Aawu7svFMCEpDAoAiU1jkN4aCGh8lKQAISkEBpoayEuIawkkKYhgQkIIHcBErrnIYwd0WNLwEJSEACvRIoLZS9Jt89mIawOzvvlIAEJDAoAqV1TkM4qOFhshKQgAQkUFooKyGuIaykEKYhAQlIIDeB0jqnIcxdUeNLQAISkECvBEoLZa/Jdw+mIezOzjslIAEJDIpAaZ3TEA5qeJisBCQgAQmUFspKiGsIKymEaUhAAhLITaC0zmkIc1fU+BKQgAQk0CuB0kLZa/Ldg2kIu7PzTglIQAKDIlBa5zSEgxoeJisBCUhAAqWFshLiGsJKCmEaEpCABHITKK1zozGEuQtjfAlIQAISqIrAWPSrLdTNbRvaTgISkIAERkGgmM4Ve9AoylK+E/ELwBhrNNZ+xQixb+U/J308cax1G2u/+qi5MeZPYMzj077Nf3x1yWCsdRtrv8b+e1eXMdzpnjGajU4gKr1prB/gsfZr7D+YrFulPyhWSWvMNRteNcx4OYExj0/7NszxPta6jbVfY/+9q9inSENYDHWnB431AzzWfo39B5N16/QxnutNY67ZXMH68F4IjHl82rdehkjxIGOt21j7Nfbfu4p9ADSExVB3etChQPw3tmus/Yo62bdhjtax1m2s/RrmKDPr5QTGPD7t2zDH+1jrNtZ+jf33rmKfIg1hMdQ+SAISkIAEJCABCUhAAhKQQF0ENIR11cNsJCABCUhAAhKQgAQkIAEJFCOgISyG2gdJQAISkIAEJCABCUhAAhKoi4CGsK56mI0EJCABCUhAAhKQgAQkIIFiBDSExVC3etDjgb8C7gDcAPg+8F7g9cBVrSLU3eh6wEuAA4BdgI3APwIvrjvtNbN7NHB4qtsPgbcBb17zrvoa3A44CLgvcBfgbGCvRpo3T+PzIcBtgZ8AZwCvAKLfNV9r9S1yPx+41bJOXATcrOKOtelX1O01QNTtRsC3gDcCJ1XcL1MbL4Ex69xYNS5Gozqnzs3rp5I6V4C8hrAA5Cke8WxgZ+C/gEuBe6ddK48Hnj9FnFqbvg94IHAY8I3U1zsDB9eacIu87peM07uBDwF/BLw6Gau3tLi/piaPAt4OfA64KxBmqGkI/xSIPr0L+E/g99L4vH5q/7OaOrMsl7X6tmQIP5MM/dLt8YeYLw64X+tSrW4K/A3wYyB+IT8QeBzwzxX3zdTGSWDMOjdGjYtRqM6BOje/n0dr6bc610NtNIQ9QMwc4ijgecCNgThHZqjXw4BTgXsAXxtqJybk/e/A7wB7Nr73JuDpaWZpSDO78UN1U+rHPwHbLzOE2wFh+n7V6OvtgW8CTwP+oeK6rtW3JUMY/Y5Z7KFca/XrjsDXgX3T52+pX2FyY6bwiUPpqHmOmsAYdG6sGhcDT51T5+b5A0idK0BfQ1gA8oyPiCWkR6YlpEM2hDF7FsvVHjojj9puj1m0Y9KS0aXcoo8fS2bqrNoSbpnPJEO40q0/T2P0tS1jz7vZSn2LJaNDM4RNlpP6dTfgS8ADgE83GsdS30uAJ8y7GD5fAmkp+tB1bqwaFwNUnQN1ro4fVepcpjpoCDOBnTHslsA2wB8AsQTlw8Bfzxhz3rfH+5D/BsSYewoQ71qEaYqlsLW/f7Yau58CYYT+ttFo7/Ru3XOAY+cNvuPz2xrCuwPnpWWIMU6HcK1mCOOPFvH+7hXAJ9LnLsbuEK5J/YrP22eBXwLPTL/YPRY4Dnh4GqdD6Js5jo/A2HRurBoXI0+dU+dq+QmkzmWqhIYwE9gZw8Yvb2EI4zoxLT9cWso3Y+i53X5l2hgnzENscHHDtFlOvADuXB0AAAdeSURBVNN0nwEvh433PWNmKd7HWrpelgziK1Nf5wZ9hge3MYSxjOOTwC3TJjRXz/C8kreu1Le3pvcnLwTuBBwCXAPELFv8QlT7tVK/Yrn5KY1lzVGnWNLspjK1V3Tc+Y1N58aqcTEK1Tl1rpafRupcpkpoCDOBnTFszAzGe2mxqUxsUPJ+4Lkzxpz37fEuXfwXuzj+b0rm/kAsqXxwMhbzzrHL82PWJWYBY5OO+EEVNQsTv2PafbM5c9gl/rzuaWMIXwe8MC1HjE1mhnK16Vv0JTbWOTe9UziEDYIm9StMe5jB26TNnC4G/gR4Qdo1MGbpvSQwDwJj07mxalyMDXXu2mX36tw8flL89jPVuUw10BBmAttj2FheGZt1xLa73+kxbulQ8Q7Cd9ORBkvPjl9WY2lebOIRRzUM8YplTzGzFIYw/v8XQMwQRn9iFuaEIXYqmdvlm8o0uxJ/oIgdSZ8EnDywPrY1hNGtr6a/jsfnsPZrUr9iM5kwhLH5T2wis3R9IM3qxpJfLwnMm8AYdG6sGhdjQ51T5+b9M2Lp+epcpkpoCDOB7TFszFJ8GdgHOL3HuKVDncm12zbH8tClKwxhLBuKcwhjY5YhX7Esbyfge0Ds7PiFtOwwjtcY4rWaaYrlsbGBQhjfOM9uaNc0hvAryRA+dQCdnNSvl6alr/FeZPNa6esD6KYpjpDAGHRu7BoXw06dG86HT50DdW6K8aohnALWnJrGmU2xJHHoM4QxCxjnD8aS0djdMK444+5T6d2mc+bEN8dj40zCO6Szm3LELxFzJTGJmsUywxiTLyqRSIZntBXKpSWjsdPv32XIo++Qk/oVx0p8MP2RIo4HWbpiVjf6d5e+kzCeBDoQGIPOLZLGRYnVuQ4DveAt6ty1s7rqXMtBpyFsCapQs/hFO2YBY5labGYRh8HG7qIfAfYrlEOux6wHYrblB41NZeIdtJhBi9nPoV4x47lHetcs+hhLKOPYifhabPc/pCveW433y+KKcRf9iY1V4jotmfnYsTI20YkdVJvHoGysfEnzWn2LnWH3T5+12PU2ZnlfBcRGEbsBl1VayLX6FUu94nMXS5kPB6JOj0hmPs43fUel/TKt8RIYq86NVeNiJKpz134e1bn5/FxS5wpw1xAWgDzFI44AHgPsmg7/jnfu3pNmY4ayg+Nq3Y1ZzphpiZez4wX8eLcplov+ZApGtTXdPdUndqWMnWDPBl6elvnWluta+cS4iyWvk65bpxndGI+TrnjPNQ6nr/Vaq2/xy9zRQLxTt13a+Ch+cT248mNR1upXmPf43MXRKPEHpuhnvIscRvDvB7y7b63jzLzWJjBmnRujxkVF1blrx7U6t/bnO0cLdS4H1WUxNYQFIPsICUhAAhKQgAQkIAEJSEACNRLQENZYFXOSgAQkIAEJSEACEpCABCRQgICGsABkHyEBCUhAAhKQgAQkIAEJSKBGAhrCGqtiThKQgAQkIAEJSEACEpCABAoQ0BAWgOwjJCABCUhAAhKQgAQkIAEJ1EhAQ1hjVcxJAhKQgAQkIAEJSEACEpBAAQIawgKQfYQEJCABCUhAAhKQgAQkIIEaCWgIa6yKOUlAAhKQgAQkIAEJSEACEihAQENYALKPkMAKBDa3ILM3cOYK7e4IfB3YBzi9RaylJh8EdgL2mOIem0pAAhKQgASmJaDOTUvM9hKYAwEN4Ryg+0gJJAL3aZDYFjgDOBL4aOPrXwMuW4HY9YHdgNXaTLpVQ+gQlIAEJCCBEgTUuRKUfYYEZiSgIZwRoLdLoCcCvwtcDjwdOKFFzDCDv2zRTkPYEZK3SUACEpBArwTUuV5xGkwC/RHQEPbH0kgSmIXAakJ5IPBOYHfgrcAfAq8GTp2wZPQAIP67E7AJ+G/gJcC5jeScIZylUt4rAQlIQAJdCKhzXah5jwQKENAQFoDsIyTQgkAbofw2cAxwHvB/wJUTDOHhwAXAd4GYRXwy8MhkEDekPDSELQpiEwlIQAIS6JWAOtcrToNJoD8CGsL+WBpJArMQaCOUMVN4XOMha20qsyWwDvifNMP4eg3hLCXyXglIQAISmIGAOjcDPG+VQE4CGsKcdI0tgfYE2gjlzsCFaxjCuwFHAfEi/w6NtscDf6EhbF8QW0pAAhKQQK8E1LlecRpMAv0R0BD2x9JIEpiFQBuh3Bq4ehVDeOO04+j307uGsUQ0Np45EfgisL+GcJYSea8EJCABCcxAQJ2bAZ63SiAnAQ1hTrrGlkB7Am2EcivgV6sYwn2BU4BbA+c32v0I+KSGsH0xbCkBCUhAAr0TUOd6R2pACfRDQEPYD0ejSGBWAn0I5ROB2DDmFkCYwLgemMzgSRrCWUvk/RKQgAQkMAMBdW4GeN4qgZwENIQ56RpbAu0J9CGUO6UNZM4EjgZ2TcdTXM8ZwvaFsKUEJCABCWQhoM5lwWpQCcxOQEM4O0MjSKAPAn0IZeQRR0zEbqJhBr8JHAQcAcSRFb5D2EeljCEBCUhAAl0IqHNdqHmPBAoQ+H9koGOE5RjkuAAAAABJRU5ErkJggg==\" width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM automl_output_info;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Trial')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Validation Accuracy')\n",
+ "#ax_metric.lines.remove(ax_metric.lines)\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Trial')\n",
+ "#ax_loss.set_ylabel('Cross Entropy Loss')\n",
+ "ax_loss.set_title('Validation Loss (Cross Entropy)')\n",
+ "\n",
+ "validation_metrics_final = df_results['validation_metrics_final']\n",
+ "validation_loss_final = df_results['validation_loss_final']\n",
+ "iters = df_results['mst_key']\n",
+ "#iters = [x - (iters[0]-1) for x in iters]\n",
+ "\n",
+ "#ax_metric.plot(iters, training_metrics_final, label=mst_key, marker='o')\n",
+ "ax_metric.plot(iters, validation_metrics_final, marker='o', linestyle='None', markersize=4)\n",
+ "#ax_metric.plot(iters, training_metrics)\n",
+ " \n",
+ "#ax_loss.plot(iters, training_loss_final, label=mst_key, marker='o')\n",
+ "ax_loss.plot(iters, validation_loss_final, marker='o', linestyle='None', markersize=4)\n",
+ "#ax_loss.plot(iters, training_loss)\n",
+ "\n",
+ "plt.legend();\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Show best by trial"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "20 rows affected.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n",
+ "3 rows affected.\n",
+ "4 rows affected.\n",
+ "5 rows affected.\n",
+ "6 rows affected.\n",
+ "7 rows affected.\n",
+ "8 rows affected.\n",
+ "9 rows affected.\n",
+ "10 rows affected.\n",
+ "11 rows affected.\n",
+ "12 rows affected.\n",
+ "13 rows affected.\n",
+ "14 rows affected.\n",
+ "15 rows affected.\n",
+ "16 rows affected.\n",
+ "17 rows affected.\n",
+ "18 rows affected.\n",
+ "19 rows affected.\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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X6Q2pTakFiRvL+2P/eRoYB6G0CbDRtUjR9651aNffsmYocUjXiwfHzQfwhCWO9g+3nzFDDpKsTB0lCfZO9poQxj3nJZbQ+iPCfcsJre67ui+vF7R05f8XHi6q59dP/MWB/caevqsxc4piyF0J4qft7jX0e2yWHr6Stbe66a8HLjoCk0AbUAbkn8AabUhrcHIYgjdDhsAfze4E9bX8HvUBs7aH3ce+nvD3xnx94aXQVhnbbTiDVgq/VH7/vzuLz1yop41hOX0yR2MaQ1h3FZ3cPu7y/fmfzYt1pfkZnTuGPYmfv69+XipThoTkzZebktyU5lYE+M2llvKUs0QupO83JEUeRpCt9da7/Wi1mN3UrsTPGkak89IYaeMepro+uuv/9rQoUM79+nTp3Qk9T+e8w8++GD18ePHL/nXv/718XLTRzGEK7Tet998NdH3Bh5+yXrEximep58cbs9C0aNUNkMeCYqPhPCUTa9xq+V59AvR9fiP1j1iHpGxUSxdo+b/9uYzXiCcTO5V8tz7WgxhlimjcU+Vd+XyCFoy2YR47V3SoHmE1VMuy005LX3xdjRl1CJq82tONiZOHR1sn1b0baLd4+apFuWObvDvPd3YL10n98R5+qTvs9y0lJiHDb2NfUdrJpzXI5Cux2sKkqLo33ltntuU1hB6Coqfb5v15FQKj+R5pDdpCN0j7Y8ST1Wu1uvsqU/uKXXHh1l50xwvbve0JhIEik4AbUAbks9oWm1IazA8pd6jL6VTRj3K5VktySmjpX8r/lawnngkzaOH1v5yyfpj3feMDGtFR8kmwcYrnooY5/UonWeX+H3uUaR41ktHdXU0g8W67tlI5aaMxmsG4ymj5a5hI+zpoW6TR0PLJX8T2Ax6XwHv++AdTCultPFy+ayG0BvF+HusnOnryCx6FNQzhfz9Uvot5+mrfmb8u1qmjPo+/Mx4Ezl/j7jTwN8n3rTGRrQ0xXzKbUTT1Hf4888/v1b37t2f32CDDeZ0797dndgdpgULFnR98803V12wYME222yzzX/sYVDLB3i1a/L71iLQkejHPWMeTvfuiE7uPXNPm+e3ez1Y6UiHe6XiB82jNKUfzX6p+mceRfQfopMNmY1ZPAUgC8F4kbHXP3r+fLm57a7PL3G/FP2SiZPnhvtn7iGqxRDaiNogpNlUxmshfC2vR/DREHHyC92CZRFMGsJ4Pr7n15e+EDvaVMYx81TWOHnNp0Wm3KYytY4QuoPAYuQ4VzqbJ17PEYtRvKmMNyjypjKlJjLeMCbeVMYdBt5CO/mSS24q462ubbBKF9LHawY8JSStIbS4enMf7/Yary90PDzdxm1NGsJYzN2J4VHKpIFMti/J33P7/Xx6FNjPmheokyBQdAJoA9qQfEYbbQjjTWVKd2+Md+CMpyXa3Pj9XDoC4ul77jiNP9TLfW94YxFPK/SmYKVLG0r//ryBmNfzu1M5uZGe88XnEFqXbCJKjyywafTmKNZ2d4R2ZAjjTWWsoe74jDeaszm2Fvn3NjzWyHL3ZJ2JZ6pYw71mzzqU3LjGbY6PS7Ch8Vr4SilPQ+gZZd7XwPdUmjoyhPGmMqUdqP4mcAdwlk1lPAjh74/kzqHWd3c42Fz6G8wjhH7flUuFPXbiL3/5y8CVVlrp2Q033HBWt27dOlpi035fCxcuXPmNN95YfdmyZdt98pOfTPJo/z2GsOiSnH/7YtH31DbPVXfyS9Qf3H4J2sB5SoanZsQpXmTtD3J/6PrBci+QX24ePXEZJ/fa+cXml6jXqnkUxh/FNoLJjTq8RsNTJL1W0T15frC9NXOaD2eLhYXKRtSp3Lx8/9wLs/0B7/nnnmLqqQoe3bI59QYmtRhC1xuvqfRCZN9nfLZf6bETfgH55eORTI+C+YXuzWjM2P/fxilp0Lxg2y89x8UvVTPxSJM5dnTshIXTi6u9Ts8sbJIrHTtRqyGMR86S2zmXPqmxAU4u9I6F3vdg425B9PPhkUuLe3zshBfVu+fXI3d+vtxb7J5Z34t7fGMx9n15eky81bYZen2mNz+wQKY1hPEL372S7uiwCfcz6TUlfjaShtDvTG+D7c4Qd5R4BNEfKu5d9OhzaY9tfNix+ViUq03Fzf8vnitAIB0BtAFtSD4pWQ2htTFeFpKsx+vZPbvGa/7dGep3uzf88JIKv8Ot08ljJ2yKrBU2AjZMLu+1ZzYNTv5WsUb4XeyZG15HaI2xUXIHnkf9kpuKVHr6rRfWYo8WlTu7L+7kdEeoR/jcRi8F8Wwpa5Pb6aM0rD8dGUJf3+sRvXTEHeNeG+l63GnrbwLff3zshL8VvH7QHc/+pnA+d4L7m8yzcDz91BrlTmB/b7k+d367TebjbzNvsFduM5qYQ2wI/Q1mDSyXXLeNa9YRwvhb0ffhJRMeQHBb/d3VkSH0N6TXV/rYCX8DeaTRGuvvxmrHTvjoDLfVGu5v0tJO+Pj+4l3E/d/J77/S+/dOpf52jWc7pXt7BsiFIQwAeQW7RLmtxT1K4j86/8H6jyZ5zl+Mx6Njfql5xDA+ANYf9DZG8YJVD8v7JeeXvBd8e6TLR1l42ml8YLzr80vOO1B6TryNZXwmT6WD6UtDFPc0elSodNpfnNdtdO+dX9wWDQuMTaIFxi/BWg2hRce9WBYeG4mODqa38Hmai9dAuD02PC7rHjS/2JMGzaNwzmsDbfNqJmkOprdolR5MbwNZ7mD6Wg2hxcEmrtq0YcfDou9F47EgeS2HBdfPTbwjqQXCvbPJqTh+mXtKjXv3bMLcm2dj7N1K40XfrtcjhL5f57FoeAMAj9iWrk3o6GPGrD2l07vIeR2GPy7ccWCBdIyShjB+Xt02dyjY8FnkbMBtXkvXVTi/hc3GNd78aAV7xXC7LUoAbUAb6jGElR57mxabFCdrsXU5PpjeZsv6kjyY3h3UHn1zh5uNoLXTa/3cwebvk/gQeXfSuSPVH+7+3oh33vb3RqUppaVtdKeuy1b6+LdWezaT3+fWGH8r+fou530K4rZUM4S+rtvrjk93IiYPprexjZPv2Xsi2NSalXXcptWG2rNUXM7tdaem/149EmZe1jubSH8bJc+QLheTasdOuIy/XXzQe1ZD6E2ArNH+XrRhtk6XHkxfbjqpr+my1t5yB9PHx104X+nB9I6Pv22cx7H39cslP0veDd7Pib8lSkdYXcYzepzHu6eWO6Oxqa92DGFT8XNxCEAAApkJ+GPBPZUWneS5ipkrogAEIAABCORGwEtj3LFYaaZRbhem4poJlNsEME1lnp3kkWQPYLiDt1yymfSsI3f8djTKmuZ6Dc+DIWw4UiqEAAQgkBsBC4mnFnl6UGEPtc3t7qkYAhCAQGsR8CZvHo3zKCCp+ARqNYTxxnhe3uLpyqXJs4U84uspvN6gp3AJQ1i4kNAgCEAAAv9BwGsfvB7kuGjtg/9/cpoLyCAAAQhAAAIQqI9AFkPoKatehuOptZ6G7OUq3hOhJRO7jLZk2Gg0BCCwghHw+kOvK/XaQq+Z9NpaUrEJeE2spwh50yufv+o1xv5o6OjMSK+D9TpgdwB4cwfvNlhts7Z4Z2Qfv+MyJAhAAAIQqI1AFkPoTQi9QZ+nf3ptv/cNiM/Mru3qTSzFOYRNhM+lIQABCEBguSTgzY+82YU3vfJGDN5MwEe2XFXl3EjvKujd/3wUjz82fKxNR4bQhtHX8GZU3vgBQ7hcPk7cFAQgAIH8Cbz00kuHde3a9fRBgwYt6NGjx/xOnTolj8Jqb0BbW1un+fPn95g4cWL3RYsWXbDlllvGGz9+pIHVejLzvxuuAAEIQAACEGguAe8ye3I0lSg+99P/7bPQvJtg6VmgydbG5096dzuPMHakq941z1vGe22Kd1rEEDY37lwdAhCAQMsSsNl7+eWXD+3SpYvPO640Q8UmccGSJUuu2WKLLW4pZxoNAEPYso8BDYcABCAAgQYReDo6s8tH38RpSLQLnXeZS24FX+mS1Qyh6/MopI+d8ZE9GMIGBY9qIAABCKzIBDx9dMiQIf26du3qY7Q+khYtWrR0/Pjx0/fcc8+Olj8sN4bwP4ZIV+QHg3uHAAQgsAIQaGSHpg+79jliHhFMJh9w7J/5TNBqqZoh9DmVHmn0rnU+YzWrIUTnqkWA30MAAhBYvgg0Uuc6JBPsQjnHp62tDa3MmTHVQwACECgEgU6d2qWrkfrljQZOknR1yQ16nd89kk5LceMdGUKvLfTh2B+T5MO3azKE6FyKKJAFAhCAwHJAIAedwxAuB88FtwABCEAAAhGBHIQyT0PozWb+FhnLS6NbSGMIPTI5Khl0DCF/AhCAAARWDAI56ByGcMV4dLhLCEAAAisGgRyE0lNGb4iOmUhCbMSU0cOjEcYtJC2KKvf01I0kbS/J17AhrZaYCVONEL+HAAQgsJwQyEHnMITLybPBbUAAAhCAgOeKNn7KqDeVmRSdHRkzHixpvKR6N5XxNFTvAFcp7SfJ51ZWSxjCaoT4PQQgAIHlhEAOOochXE6eDW4DAhCAAATyMYQ+dsJrCIdKmhNBPlHSuSmOnYhjUmkN4XqSBpUE7vuS1pV0qKR/RusKq8UWQ1iNEL+HAAQgsJwQwBDWFkiEsjZulIIABCDQcgRyEEofTO8D41+NDqYfLunKaJOZMxKA3pb0VLRTaPzjXST1krRz9PNvRb94MTq2ohzfNGsIS8uhcy33pNJgCEAAArURyEHnOmxII3dpq+2OG1MKoWwMR2qBAAQgUHgCOQnlxpKul7StpJmSbo+OnEie3TRW0pOSDkxA8s88sliaDop2E8UQFv6JooHLOwFvyLRkyRKxMdPyHuni35/1q0uXLvHSh4oNzknnKl+v+OhStRBDmAoTmSAAAQi0PoHQQlkQYuhcQQJBM1qHgA3g9OnTNWPGDMxg64RtuW+pNaxv377q169fRWMYWucYIVzuHztuEAIQgMDyRSC0UBaEHoawIIGgGa1DYNq0ae1mcMCAAerZs2frNJyWLtcE5s2bpylTprSbwv79+5e919A6hyFcrh85bg4CEIDA8kcgtFAWhCCGsCCBoBmtQcCjg2+88YbWXntt9e7duzUaTStXGAIzZ85sN4UbbLBB2VHC0DqHIVxhHj1uFAIQgMDyQSC0UBaEGoawIIGgGa1BYPHixXr77bc1YsQIde3atTUaTStXGAKLFi3S6NGjtd5662nllVf+j/sOrXMYwhXm0eNGIQABCCwfBEILZUGoYQgLEgia0RoE4g9uDGFrxGtFa2W15zO0zmEIV7QnkPuFAAQg0OIEQgtlQXBhCAsSCJrRGgSqfXC3xl3QyuWVQLXnM7TOYQiX1yeN+4IABCCwnBIILZQFwYghLEggaEZrEKj2wd0Kd/G1r31NY8eO1SuvvFK2uUcddZR+9KMf6b333lO3bt2q3pKn0K6//vr69a9/rZ139tGp0qBBg/Ttb39bF198ccXyf/vb37T55pvrj3/8oz7zmc9UvU6c4eabb9bAgQO12267faRMmmumvkiKjKGvl6JJqvZ8htY5DGGaqJEHAhCAAAQKQyC0UBbkxjGEBQkEzWgNAtU+uFvhLu677z7tu+++eu2117Txxj4q9f/S0qVLtc466+grX/mK7rzzzlS3U84Q/vWvf20//mDw4MENN4T/9V//pS233FK33+5jXf8vpblmqhtKmQlDWB0UhrA6I3JAAAIQgECBCGAICxQMmgKBghJYHgzh3Llzteaaa+r444/Xeeed9xHSTzzxhL70pS/p8ccfb//fNKmcIUxTrtYRwkqGMM01G5kHQ1idJoawOiNyQAACEIBAgQhgCLMH450jv5u9UIuU6NS5s/rts79W3ebTLdJimhmCwPJgCM1pr7320l/+8hf961//+gi27373u3r00Uc1efJkde7cWZMmTdIZZ5yhJ598sv04A4/47b333u0/i3exTDtl9LrrrtOll16q999/XzvuuKMOP/xw7bLLLh+ZMnrZZZfp/vvvb29Xjx49tM022+jKK69s39XVyZ+3IU0AACAASURBVFNL//SnP32kzT/84Q/bp6eWM2g/+clPdP755+utt97SWmutpQMPPFCjRo1qvzcnjzIefPDBevXVV3Xsscfqueee05AhQ9qnun7961/v8JFKYwirXf+DDz7QCSec0D7d1v/fbTQTT4t1Gj9+fLtxf+qpp/Thhx+2j976Xs8+++yybav2fIbWOQxhiLcS14AABCAAgYYRCC2UDWt4fRXVNWV09GEH1nf1opZetkxtixZplW2204DDjilqK2lXEwhU++BuQpNquuTDDz+s3XffXS+99JK22GKL9jp8pIYNiaeT2rw5eRTv3nvvbTdiPnfRZzCec845+sY3vqEbbrihPU8aQ/izn/1Me+yxh4488kh5DeMf/vCH9nonTpz4EUNoU+b22JTNmjVLN910U3sbbOhWXXVVvf766+3t3mijjXTqqae2X99HLHh6aqlB+9WvfqVdd91VBx10kPbcc8/2es466ywdcsghuv766z9iCDfddNP2n3/sYx/T1Vdfrd///vcaM2ZM+3mTlVI1Q5jm+vvvv397DGzwzH7ChAl65pln/m0IP/e5z8nTeE8++WStttpqeuedd9pZVFqbWe35DK1zGMKa/jwpBAEIQAACzSIQWiibdZ8l163LEBbkHhrejIXjx2nCWaeo52af1MDvndzw+qmwdQlU++BulTvzfdiAeETQo3JOjz32WLtZ8wjcdtttV/ZWlixZIq9BPOywwzR79mx16dIllSH85Cc/2T665dHHONmo3XXXXRU3lbERWrhwofr376/bbrtN++yzT3vRSlNGSw2a1xn26dNHv/vd7/59zQsvvLDdFNp42ezFI4R33323bM6cpk6dqgEDBujWW29t51OrIUxz/Q033LB9ZNKjpeVS9+7d9dBDD7WPGqZJ1Z7P0DqHIUwTNfJAAAIQgEBhCIQWyoLcOIawTCAWT5uqcScdo+4bbKRBp44qSKhoRhEIVPrgHnvCUVo2b15TmrhSz54adsX/jnhlSSNHjpTXDHrHUb//9ttvv3Zz5pGx6H2oZcuW6aqrrmo3Ts63YMGCf1/C+YYNG1bVEJqZp3/ecsstHzFYNofeKTS5y+izzz7bbti8QYynlsbJ0zzjaZJpDKFHO33NG2+8sX3kL07xoe0///nP20c5Y0P47rvvtpvAOHmN5RFHHFFxaqbzdTRCmPb6nrr7/PPP65RTTmmfRuvdWpPJU2bnzZvXPq10hx126HCTHpfDEGb5C0ifF6FMz4qcEIAABFqaAIawpcPX0MYvnTNbY44+RN2GDtPgcypvm9/Qi1JZSxBYngxhvIGMRwQ9gheboOR0RI8eemrmaaedps9+9rPt00ZtYI455hj985//lEe4qk0Z9bRQrz30NNXkURF//vOf29cIxobQBvMTn/hE++ikTZxH8Lp27aqddtqpfZ1f3K40htBrH23YfvGLX3xkLaDX4XnqqY2gDXFsCOfPny+PxsWp2nTQaoYw7fW9btAG+IEHHmgfmbQhvOCCC/Stb32rvSlmZ/aPPPJI+xRaH9NxxRVXaPvtty/794IhzOc1giHMhyu1QgACECgcAQxh4ULStAa1LV6s0Qfvp5XXGqChl1zdtHZw4eIRqPbBXbwWV25RfMSE19d9/vOfb1/j53V2m2222b8Lbb311u3r6nwuYZzuuOOO9pG+tIYw7QihR/OOO+64duPj0T2nuOxJJ52UyRDGI3Reg2gzGadKI4SNNoRprx+3q62tTf/4xz90ySWXtJtDHwmywQYb/LvdjtULL7zQbh5tyD3l1ea8NFV7PkPrHFNGW+mNQFshAAEIQCCeIrW86FfaiNLxWYHU29/9tjr37KV1r70lLUvyrQAEqn1wtxoCj/Q9+OCD7aNyNnjetCWZvNmKTaFNYJw8ddGbwqQ1hC7nkS2PunW0htAjX9691GsT4x1M77nnHh1wwAHtUyrjEcJyJtXXKB3V22qrrdrXEPoIjThddNFFOvPMM/9jDWGjDaGvl+b6pc+LjZ431CkdTY3zeTTVG838/e9/bx9NxRCG+YtDKMNw5ioQgAAEmk4gdM9p02/4fxuAzlUIxDtHHay2RQs14tZ7ChIqmlEEAsubIfQxCzaDfv9591CbpWTykQceZfPRD8OHD5cNmst4emcWQ2jT6ZFI7zLqaaPexbN0l1GPTnqHUR9r4Q1nXnnllfb1izNnzmzfdCU2hN78xYbUJnWNNdZob5f/t9Iuo54a6imYNlG+P48Ylu4yWqshtOnzrqzJtMoqq2jnnXdWvMtoR9ffdttt27lssskmfhm3b2TjTXDefPPN9umy3uTHazs9Sus2Xn755e27jHq30W7dumEIA70UEMpAoLkMBCAAgWYTwBA2OwLFuv7YE4/WkunTNOKOe+UzCUkQMIHlzRD6ntZdd932DWNsNHyEQzLNmTNHRx11VPsaNr8jPa3URzn46IcshtB1XnPNNe3nEHrdnEcZXW/pOYTedfTcc8+VN3nxqOK1117bfi2fvRcbQq9ZPPTQQ9uPa/BoYkfnEP74xz9uX5Pncw29RtJG05vTlJ5DWKsh9FrB0uQzE91Gp2rXt+G2AbTB9qio7/m8887Tpz/96XYDePTRR7evsfTIYa9evWQD6VFOG8hyqdrzGVrnlpcpNxhC3v8QgAAEVhACoYWyIFjRuQqBGH/myVo0YbzWveEOde7VqyDhohnNJlDtg7vZ7eP6KzaBas9naJ3DEK7YzyN3DwEIQKDlCIQWyoIAwhBWCMTEC0ZpwVtvaugV12vlvv0KEi6a0WwC1T64m90+rr9iE6j2fIbWOQzhiv08cvcQgAAEWo5AaKEsCCAMYYVATL7iIs175e8afMFl6rbO4IKEi2Y0m0C1D+5mt4/rr9gEqj2foXUOQ7hiP4/cPQQgAIGWIxBaKAsCCENYIRBTbrhaH774vAadcZ66r/fRw6ILEjua0QQC1T64m9AkLgmBfxOo9nyG1jkMIQ8nBCAAAQi0FIHQQlkQOBjCCoGYeuctmv30HzTwxFPV8+P/dy5bQeJGM5pEoNoHd5OaxWUh0E6g2vMZWucwhDyYEIAABCDQUgRCC2VB4GAIKwRi2n13a9bjv9aAI7+nVbb6VEHCRTOaTaDaB3ez28f1V2wC1Z7P0DqHIVyxn0fuHgIQgEDLEQgtlAUBhCGsEIgZP39AHzzyc6058jCt9tkvFCRcNKPZBBYvXtx+pICPFvA5cSQIFIlAbAh9fIiPsShNoXUOQ1ikp4O2QAACEIBAVQKhhbJqg8JkwBBW4PzBrx/VjPvvVb99D1DvL+0SJhpcpfAEfHj4G2+8obXXXlu9e/cufHtp4IpFYObMmZoyZYo22GCD9nMjMYSNiT9C2RiO1AIBCECg8AQwhIUPUdAGzvrDE5p29+1a45v/rTV2+0bQa3OxYhOYNm2aZsyYoQEDBqhnz57FbiytW2EIzJs3r90M9u3bV/379y9736F1jhHCFebx40YhAAEILB8EQgtlQajR8VkhEHOee0bv3XK9en9lN/Xbc5+ChItmFIGARwmnT5/ebgr9/0kQKAIBa5jNYL9+/cqODrqNoXUOQ1iEJ4M2QAACEIBAagKhhTJ1w/LNiCGswHfu317Wu1dfptV3+LL67/+dfKNA7S1JwGZwyZIlmMKWjN7y1WjrV5cuXSoawfhuQ+schnD5es64GwhAAALLPYHQQlkQoBjCCoGY/8brmnTxuVp1289orUOPKki4aAYEIACB2gmE1jkMYe2xoiQEIAABCDSBQGihbMItlrskhrBCIBaMHaOJZ5+qXptvqbWPPbEg4aIZEIAABGonEFrnMIS1x4qSEIAABCDQBAKhhbIJt4ghzAB90XtTNP6U49Rjo020zilnZihJVghAAALFJBBa5zCExXwOaBUEIAABCFQgEFooCxIIRggrBGLJrJkae+xh6rbucA0edWFBwkUzIAABCNROILTOYQhrjxUlIQABCECgCQRCC2UTbpERwgzQly1cqHcOPUArDxiooRdfmaEkWSEAAQgUk0BoncMQFvM5oFUQgAAEIMAIYZIAI4QVngfvIDn6O/uo8+q9te7VN/F3AwEIQKDlCWAIawshQlkbN0pBAAIQaDkCoYWyIIDQuQ4C8c4RI9W2bKlG3HxXQcJFMyAAAQjUTiC0zjFCWHusKAkBCEAAAk0gEFoom3CL5S6JIewgEGOPP1JL3p+hEXfep04rrVSQkNEMCEAAArURCK1zGMLa4kQpCEAAAhBoEoHQQtmk2yy9LIawg0CMP+1ELZo8UcNv+oFW6tGjICGjGRCAAARqIxBa5zCEtcWJUhCAAAQg0CQCoYWySbeJIcwAfsJ5Z2rh6Lc07Kob1aXPGhlKkhUCEIBA8QiE1jkMYfGeAVoEAQhAAAIdEAgtlAUJBiOEHQRi0mUXaP5rr2jIhVeo68B1ChIymgEBCECgNgKhdQ5DWFucKAUBCEAAAk0iEFoom3SbjBBmAP/udVdq7ssvaNBZF6j78BEZSpIVAhCAQPEIhNY5DGHxngFaBAEIQAACjBBiCDP8Fbx3+02a88xTGnjyGeq58cczlCQrBCAAgeIRwBDWFhOm0tTGjVIQgAAEWo5AaKEsCCB0roNATPvRXZr1xG804OgTtMoWWxUkZDQDAhCAQG0EQuscI4S1xYlSEIAABCDQJAKhhbJJt8kIYQbwM376E33w2C+05sFHaLVPfy5DSbJCAAIQKB6B0DqHISzeM0CLIAABCECgAwKhhbIgwWCEsINAfPDYw5rx0x+r/37f0epf/HJBQkYzIAABCNRGILTOYQhrixOlIAABCECgSQRCC2WTbpMRwgzgZ/3P45r2wzvVd4+91eerX89QkqwQgAAEikcgtM5hCIv3DNAiCEAAAhAIP0K4saTrJG0raaak2yWdI2lpB03pKukCSdtI2lJSd0nldNX1fFPS0Oj3b0q6TNL9GQLNCGEHsGb/6WlNve1G9fnq7uq7x14ZsJIVAhCAQPEIYAhriwlCWRs3SkEAAhBoOQI5CGUfSa9Jel3SJZJ8bsEVkq6SdEYHgHpLGiPpBUldJO1QwRC6nolR/TaYe0g6WNK3JP00ZQDQuQ5Affjyi5py3RVafced1f/bB6ZESjYIQAACxSSQg851eKOMEBbzOaBVEIAABCBQgUAOQnmqpJOjEbzZ0WX932dLGiAp/lm5FllH2yQdFY0wptXVP0maIWm3lIHGEHYAat7rr2rypedr1c98Xmt99/CUSMkGAQhAoJgEctA5DGExQ02rIAABCECgFgI5COXTkiZLSs41HCJpXGTYHk3RzqyG8BFJnnK6c4q6nQVD2AGoBe+M1sRzT1evLbbW2kcfnxIp2SAAAQgUk0AOOochLGaoaRUEIAABCNRCIAehnCrpxmhEMNmkudHPvN6vWkpjCD2tdBVJu0q6IzKgv6hWcfR7DGEHoBZNnqTxp52gHptsqnVOOj0lUrJBAAIQKCaBHHQOQ1jMUNMqCEAAAhCohUAOQrlY0kmSri5pj9f93SPptBTtrGYIvfHMc1E9S6IpprekqDfOgiHsANaSD97X2O8doW4j1tfgM8/LgJWsEIAABIpHIAedwxAWL8y0CAIQgAAEaiWQg1CGMIS9JG0kyRvReITQBnJ/ST+uwMHrF0clf9fW5qWKpHIEls2fr3cOP0hdBw7SkAsvBxIEIACBliaQg85hCFv6iaDxEIAABCDwEQI5CKWnjN4QHTORvFajp4wm675T0o6SvFYxTWKEsANKbcuWafR39lGXNfpq2JUOJQkCEIBA6xLIQecwhK37ONByCEAAAhAoJZCDUHpTmUmS9k5ca7Ck8TluKhNPMV1ZkqeQVksYwiqERh92oDqt1FnDb/TyTBIEIACB1iWQg85hCFv3caDlEIAABCAQwBD62AmvIfTB8XOi650o6dwUx07Ezau2hrD0NjxV9DOSbDzTJAxhFUpjjjtcS2fN1Ig771P0MZWGK3kgAAEIFI4AhrC2kCCUtXGjFAQgAIGWI5CDUPpgeh9K/2p0MP1wSVdGm8wkD6Z/W9JTkkYmoO0iyesDfXyEf+7D5p1ejI6tsMn09NCfSBod7TL6DUk+Pd0H5t2cMgDoXBVQ475/vBZPmazht9ytlbp1S4mVbBCAAASKRyAHnevwJtMeoFs8Uh9tEUJZ9AjRPghAAAINIpCTUG4s6XpJ20qaKen26MiJpYlmj5X0ZGTm4h/7ZzZ9pekgSXdJWj2q16OBa0d123x655NfZUCCzlWBNeGc07RwzDsads3N6rK69+4hQQACEGhNAjnpXEUYGMLWfE5oNQQgAIEVlkBooSwIaAxhlUBMuuQ8zf/naxpyydXqutaAgoSNZkAAAhDITiC0zqUxhOtFayvca7qJpD9K+kKKW3OvqM902l3SSpIek3SMpBklZb8u6XxJ60t6J9rl7f4U9SezIJQZgZEdAhCAQKsSCC2UBeGEzlUJxLvXXK65f31Jg86+SN2HrVuQsNEMCEAAAtkJhNa5NIbQhs3TaJ6X9HFJ76U0hL+V9DFJXpi/LFqX4bKfTWDxFBpPv7lR0kOSviLphGgtxuMZ8CGUGWCRFQIQgEArEwgtlAVhhc5VCcR7t1yvOc89o3W+f5Z6bOgZwCQIQAACrUkgtM6lMYQe3bOhc/qppH4pDKFHE5+V9HlJ3s7baWtJf5b0JUlPRD+zafSW2zskwuU1FatFu6+ljSJCmZYU+SAAAQi0OIHQQlkQXOhclUBMu+dOzfr941r7uJPU67+2KEjYaAYEIACB7ARC61waQ5i8i7SG0Ft1HxJt150s7ymhHgn0KKC3APP23p5GmtxlbX9JP5C0hqRZKREilClBkQ0CEIBAqxMILZQF4YXOVQnE9Afu08xfPaK1Dj1Kq27rCUgkCEAAAq1JILTO5WUIH5C0ZpmRxF9GYdlVkudzvCZp+2jaaByxrSS9EI0oetvuNAmhTEOJPBCAAASWAwKhhbIgyNC5KoF4/5GH9P7P71f/A76r1bffsSBhoxkQgAAEshMIrXN5GcLfSZobbSiTpPAjST7faTtJn5b0jKTNJf0tkcmb2LwlaSdJadcRIpTZnzVKQAACEGhJAqGFsiCQ0LkqgZj5u19r+r13q+9/76s+u3ytIGGjGRCAAASyEwitc61qCM+WNCqJt62tLTttSkAAAhCAQMsRCC2UBQGEIawSiNl/fFJT77hZfXb7pvp+c8+ChI1mQAACEMhOILTO5WUIPWW0fzQdNEmh3JRRH2HxVCITU0azPzeUgAAEILDCEAgtlAUBiyGsEogPX/yzptxwlVb/8i7qv88BBQkbzYAABCCQnUBoncvLEHpTmYMlrV2CYLSkX5RsKnO0pFsS+faTdBebymR/eCgBAQhAYEUgEFooC8IUQ1glEPNe/bsmX36RVvvc9lrzO4cWJGw0AwIQgEB2AqF1Li9DGB874TMHvU7QaUtJ3iSm9NiJzpKSq799gH1vjp3I/vBQAgIQgMCKQCC0UBaEKYawSiAWvP2WJp5/plbZehsNOOK4goSNZkAAAhDITiC0zqUxhD2jA+N9Nz4uwmcExuv3fGbgPElvR9M+RyZu2WcMrl9yMP3UCgfT++B7jxz6YHofZL9zhg1lfEmEMvuzRgkIQAACLUkgtFAWBBI6VyUQCydN0ITTT1LPTTfTwBNOLUjYaAYEIACB7ARC61waQzhM0pgKt7KupLHRvyclHZjI51G+qyR9Q5IPt/fIn88cnF5S1+6Szo/Mo6/jDWN+khEdQpkRGNkhAAEItCqB0EJZEE7oXJVALJ4xXeNOOErd199Ag04/pyBhoxkQgAAEshMIrXNpDGH2uwhfAqEMz5wrQgACEGgKgdBC2ZSb/M+LonNVArF07lyNOXKkug4eoiHnXVqQsNEMCEAAAtkJhNY5DGH2GFECAhCAAASaSCC0UDbxVpOXxhBWCUTb0qUaPXJfdenXX8Muv64gYaMZEIAABLITCK1zGMLsMaIEBCAAAQg0kUBooWzirWIIM8Iffcj+6tS1m4Zff1vGkmSHAAQgUBwCoXUOQ1ic2NMSCEAAAhBIQSC0UKZoUogsjBCmoDzmmEO1dN5crXf7j1LkJgsEIACBYhIIrXMYwmI+B7QKAhCAAAQqEAgtlAUJBIYwRSDGnXKcFr83RSNu+6E6rbxyihJkgQAEIFA8AqF1DkNYvGeAFkEAAhCAQAcEQgtlQYKBIUwRiAmjvq+F48Zq3etuVedVfUoWCQIQgEDrEQitcxjC1ntGaDEEIACBFZpAaKEsCGwMYYpATLzoHC14858aetm1Wrn/milKkAUCEIBA8QiE1jkMYfGeAVoEAQhAAAKMEJYSwBCm+KuYfNWlmvf3v2jwuZeo25ChKUqQBQIQgEDxCGAIa4sJQlkbN0pBAAIQaDkCoYWyIIDQuRSBmHLztfrw+We1zmlnq8fHNkxRgiwQgAAEikcgtM4xQli8Z4AWQQACEIAAI4SMENbwVzD1rts0+8n/0drHn6Jen9i8hhooAgEIQKD5BDCEtcWAntPauFEKAhCAQMsRCC2UBQGEzqUIxPSf/Egzf/OY1jriWK269bYpSpAFAhCAQPEIhNY5RgiL9wzQIghAAAIQYISQEcIa/gref/hnev+hB9X/oEO0+ud3qKEGikAAAhBoPgEMYW0xoOe0Nm6UggAEINByBEILZUEAoXMpAjHzt7/U9B//UP323k+9d9o1RQmyQAACECgegdA6xwhh8Z4BWgQBCEAAAowQMkJYw1/BrKd+r2k/uFVrfONbWuPr/6+GGigCAQhAoPkEMIS1xYCe09q4UQoCEIBAyxEILZQFAYTOpQjEnBee03s3XqPeO++qfnvtl6IEWSAAAQgUj0BonWOEsHjPAC2CAAQgAAFGCBkhrOGvYO4//qp3r7xEq33hi1rzwINrqIEiEIAABJpPAENYWwzoOa2NG6UgAAEItByB0EJZEEDoXIpAzP/XG5p04dlaZZvtNOCwY1KUIAsEIACB4hEIrXOMEBbvGaBFEIAABCDACCEjhDX8FSycME4TzjxFPTfbXAO/d0oNNVAEAhCAQPMJYAhriwE9p7VxoxQEIACBliMQWigLAgidSxGIxdOmatxJx6j7Bhtp0KmjUpQgCwQgAIHiEQitc4wQFu8ZoEUQgAAEIMAIISOENfwVLJ0zW2OOPkTdhg7T4HMurqEGikAAAhBoPgEMYW0xoOe0Nm6UggAEINByBEILZUEAoXMpAtG2eLFGH7yfVl5rgIZecnWKEmSBAAQgUDwCoXWOEcLiPQO0CAIQgAAEGCFkhLDGv4K3v/ttde7ZS+tee0uNNVAMAhCAQHMJYAhr40/PaW3cKAUBCECg5QiEFsqCAELnUgbinaMOVtuihRpx6z0pS5ANAhCAQLEIhNY5RgiLFX9aAwEIQAACVQiEFsqCBARDmDIQY088WkumT9OIO+5Vp86dU5YiGwQgAIHiEAitcxjC4sSelkAAAhCAQAoCoYUyRZNCZMEQpqQ8/syTtWjCeK17w+3q3GuVlKXIBgEIQKA4BELrHIawOLGnJRCAAAQgkIJAaKFM0aQQWTCEKSlPvGCUFrz1poZecb1W7tsvZSmyQQACECgOgdA6hyEsTuxpCQQgAAEIpCAQWihTNClEFgxhSsqTr7hI8175uwZfcJm6rTM4ZSmyQQACECgOgdA6hyEsTuxpCQQgAAEIpCAQWihTNClEFgxhSspTbrxaH77wvNY541z1WO9jKUuRDQIQgEBxCITWOQxhcWJPSyAAAQhAIAWB0EKZokkhsmAIU1Keeuctmv30HzTwxFPV8+ObpSxFNghAAALFIRBa5zCExYk9LYEABCAAgRQEQgtliiaFyIIhTEl52n13a9bjv9aAI7+nVbb6VMpSZIMABCBQHAKhdQ5DWJzY0xIIQAACEEhBILRQpmhSiCwYwpSUZzz0oD54+Gdac+RhWu2zX0hZimwQgAAEikMgtM5hCIsTe1oCAQhAAAIpCIQWyhRNCpEFQ5iS8ge/flQz7r9X/fY9QL2/tEvKUmSDAAQgUBwCoXUOQ1ic2NMSCEAAAhBIQSC0UKZoUogsGMKUlGf94QlNu/t2rfHN/9Yau30jZSmyQQACECgOgdA6hyEsTuxpCQQgAAEIpCCQg1BuLOk6SdtKminpdknnSFraQXO6SrpA0jaStpTUXVKppnaWdKKkr0ryNZxelnS6pBdT3GoyC4YwJbA5z/9J7918nXp/ZTf123OflKXIBgEIQKA4BHLQuQ5vDkNYnNjTEghAAAIQSEGgwULZR9Jrkl6XdImkEZKukHSVpDM6aE5vSWMkvSCpi6QdyhjCVSRNkPQDSU9IapN0lKQdJW0XmcMUd9yeBUOYktTcv72sd6++TKvt8CWtuf/IlKXIBgEIQKA4BBqsc1VvDENYFREZIAABCECgSAQaLJSnSjpZ0lBJs6P79H+fLWlA4mflEFhDY5PnEcZyI4SrSfogUdgji/+S9AdJB2XgiiFMCWv+G69r0sXnatVtP6O1DrX/JkEAAhBoLQIN1rmqN48hrIqIDBCAAAQgUCQCDRbKpyVNlrRX4h6HSBonaTdJj6a4d7uOcoawUtFfRqOKO6WoO86CIUwJa8HYMZp49qnqtfmWWvtYz9glQQACEGgtAg3Wuao3jyGsiogMEIAABCBQJAINFsqpkm6MRgSTtzk3+tllKe49iyHsJmmspJ9KOjpF3RjCDJCcddF7UzT+lOPUY6NNtM4pZ2YsTXYIQAACzSfQYJ2rekMYwqqIyAABCEAAAkUi0GChXCzpJElXl9zjREn3SDotxb1nMYTnSjpF0ickvZmibgxhBkjOumTWTI099jB1W3e4Bo+6MGNpskMAAhBoPoEG61zVG8IQVkVEBghAAAIQKBKBBgtlSEO4q6RHJJ1QxoCWIvYaxlHJH7a1ebkiqRqBZQsX6p1DD9DKAwZqgZllpAAAIABJREFU6MVXVsvO7yEAAQgUjkCDda7q/WEIqyIiAwQgAAEIFIlAg4XSU0ZviI6ZSN5mo6eMbhVtJHO3pCNr4MkawpTQbJxHj9xXnVdbXetefVPKUmSDAAQgUBwCDda5qjeGIayKiAwQgAAEIFAkAg0WSm8qM0nS3ol7HCxpfAM3lfmYpGckPSfpm1XON6yEGkOY4SF854iRalu2VCNuvitDKbJCAAIQKAaBButc1ZvCEFZFRAYIQAACECgSgQYLpY+d8BpCHzsxJ7pPb03ptX7Vjp2IsXS0hnBtSc9KmiLpi5Lm1cgSQ5gB3Njjj9SS92doxJ33qdNKK2UoSVYIQAACzSfQYJ2rekMYwqqIyAABCEAAAkUi0GCh9MH0PpT+1ehg+uGSvPDMm8wkD6Z/W9JTkpInne8iqZeknaOffyvi9GJ0bEWPaFRwmKR9Jc1IcFwo6a8ZuGIIM8Aaf/qJWjRpoobf9AOt1MNhIEEAAhBoHQIN1rmqN44hrIqIDBCAAAQgUCQCOQjlxpKul7StpJmSbo+OnFiauG8fFfGkpANLfuaRxdLkA+c9V9FGcEwFdj7n0L9PmzCEaUlJmnDemVo4+i0Nu+pGdemzRoaSZIUABCDQfAI56FyHN4UhbH7MaQEEIAABCGQgEFooMzQtz6wYwgx0J112gea/9oqGXHiFug5cJ0NJskIAAhBoPoHQOochbH7MaQEEIAABCGQgEFooMzQtz6wYwgx0373uSs19+QUNOusCdR8+IkNJskIAAhBoPoHQOochbH7MaQEEIAABCGQgEFooMzQtz6wYwgx037v9Js155ikNPPl09dx40wwlyQoBCECg+QRC6xyGsPkxpwUQgAAEIJCBQGihzNC0PLNiCDPQnfajuzTrid9owNEnaJUtfAQkCQIQgEDrEAitcxjC1nk2aCkEIAABCEgKLZQFgY4hzBCIGT/9iT547Bda8+AjtNqnP5ehJFkhAAEINJ9AaJ3DEDY/5rQAAhCAAAQyEAgtlBmalmdWDGEGuh/88mHNePDH6r/fd7T6F7+coSRZIQABCDSfQGidwxA2P+a0AAIQgAAEMhAILZQZmpZnVgxhBrqz/udxTfvhneq7x97q89WvZyhJVghAAALNJxBa5zCEzY85LYAABCAAgQwEQgtlhqblmRVDmIHu7D89ram33ag+X91dfffYK0NJskIAAhBoPoHQOochbH7MaQEEIAABCGQgEFooMzQtz6wYwgx0P/zLS5py7eVafced1f/bB2YoSVYIQAACzScQWucwhM2POS2AAAQgAIEMBEILZYam5ZkVQ5iB7rzXX9XkS8/Xqp/+nNY6+IgMJckKAQhAoPkEQuschrD5MacFEIAABCCQgUBooczQtDyzYggz0F3wzmhNPPd09dpia6199PEZSpIVAhCAQPMJhNY5DGHzY04LIAABCEAgA4HQQpmhaXlmxRBmoLto8iSNP+0E9dhkU61z0ukZSpIVAhCAQPMJhNY5DGHzY04LIAABCEAgA4HQQpmhaXlmxRBmoLvkg/c19ntHqNvw9TT4rPMzlCQrBCAAgeYTCK1zGMLmx5wWQAACEIBABgKhhTJD0/LMiiHMQHfZ/Pl65/CD1HXgIA258PIMJckKAQhAoPkEQuschrD5MacFEIAABCCQgUBooczQtDyzYggz0G1btkyjv7OPuqzRV8OuvCFDSbJCAAIQaD6B0DqX1hBuLOk6SdtKminpdknnSFpaBdkmkq6S9BlJ8yQ9KOkkSR8mynWV9H1J+0taR9IkSfdKulDSwpQhQShTgiIbBCAAgVYnEFooC8ILncsYiNGHHaROK62k4TfekbEk2SEAAQg0l0BonUtjCPtIek3S65IukTRC0hWR0TujA1yrS/pX9O9SSX0l+X+flbR7otyVkg6T5Lr+KumTkjzh/1ZJx6YMB0KZEhTZIAABCLQ6gdBCWRBe6FzGQIw57nAtnTVTI+68T9Ezk7EGskMAAhBoDoHQOpfGEJ4q6WRJQyXNjrD4v8+WNCDxs1JiLud/Q6JRRf/+a5IekbSVpJeiAlOiEcETSkzivpLWShkGhDIlKLJBAAIQaHUCoYWyILzQuYyBGPf947V4ymQNv+VurdStW8bSZIcABCDQPAKhdS6NIXxa0mRJeyWw2OSNk7SbpEcr4LpfUn9JOyR+3z2aOurppv7nNEPSNZLOTeQ7S9JRktZMGQqEMiUoskEAAhBodQKhhbIgvNC5jIGYcM7pWjhmtIZdc7O6rN47Y2myQwACEGgegdA6l8YQTpV0YzQimCQzN/rZZRVwPSzJXXI7J37fRdICST9NGExPQ/22pD0l/V3S5pIekHRTiUnsKCoIZfOeWa4MAQhAICiB0EIZ9OYqXwydyxiISZecp/n/fE1DLr5KXQesnbE02SEAAQg0j0BonUtjCBdHG8FcXYJloqR7JJ1WAZfXGe4TTRl1HU6fkvS8pN9J+nL0M7fBI4RHJ+qxAT0yQxgQygywyAoBCECglQmEFsqCsELnMgbi3Wsu19y/vqRBZ1+k7sPWzVia7BCAAASaRyC0zuVpCDeU9Gq0I6nXG3pTGRvIzSQ9kRg59HpE7zLqaaL/iH5/nqRro5+Vi4brG5X8RVtbW/OixpUhAAEIQCAYgdBCGezGOr4QhjBjIN675XrNee4ZrfP9s9RjQ2+WToIABCDQGgRC61waQ+gpoz7EJ17zF5OsNmXU+b4T7Ua6mqRl0c6h3lDGRvFASf2i9YkeDbwtEaJDJV0fHUPh61dLCGU1QvweAhCAwHJCILRQFgQbOpcxENPuuVOzfv+41j72JPXafIuMpckOAQhAoHkEQutcGkPoTWV8NuDeCSyDJY2vsqlMnN0byawvycZuerSJjEcEb5a0taQ/R//7YqL+Sj+vFBmEsnnPLFeGAAQgEJRAaKEMenOVL4bOZQzE9Afu08xfPaK1Dj1Kq27r45BJEIAABFqDQGidS2MIfXSED5P3sRNzIownRhu+dHTsRDniB0QH3A+T9H50rISPnfA5hLckChwebWTjXUanpQgdQpkCElkgAAEILA8EQgtlQZihcxkD8f4jD+n9n9+v/gd8V6tvv2PG0mSHAAQg0DwCoXUujSH0wfQ+lN7TPL0j6HBJPkzem8wkD6Z/W9JTkkZG+DxN9HRJHmFcIml7ST5r8GBJdyUQPxT9zmsCvYbwv6LdS38b7TyaJhoIZRpK5IEABCCwHBAILZQFQYbOZQzEzN/9RtPvvUt9/3tf9dnFxyCTIAABCLQGgdA6l8YQmpxXY3tN37bRIfO3R6ZtaQLrWElPRmsD/eNekmz2tpTUIzKUF0j6RUkobBy9ocw3JA2Mpqf+XJI3lolHJKtFD6GsRojfQwACEFhOCIQWyoJgQ+cyBmL2H5/U1DtuVp/dvqm+3/TJViQIQAACrUEgtM6lNYRFp4dQFj1CtA8CEIBAgwiEFsoGNbveatC5jAQ/fPHPmnLDVVr9y7uo/z5esUKCAAQg0BoEQuschrA1ngtaCQEIQAACEYHQQlkQ8BjCjIGY9+o/NPnyC7Xa57bXmt/x5uUkCEAAAq1BILTOYQhb47mglRCAAAQggCHkGchAYMHbb2ni+Wdqla220YAjj8tQkqwQgAAEmksAQ1gbf3pOa+NGKQhAAAItRyC0UBYEEDqXMRALJ03QhNNPUs9NN9PAE7xhOgkCEIBAaxAIrXOMELbGc0ErIQABCECAEUKegQwEFs+YrnEnHKXu62+gQaefk6EkWSEAAQg0lwCGsDb+9JzWxo1SEIAABFqOQGihLAggdC5jIJbOnasxR45U10FDNOT8SzOWJjsEIACB5hEIrXOMEDYv1lwZAhCAAARqIBBaKGtoYh5FMIQZqbYtXarRI/dVl379Nezy6zKWJjsEIACB5hEIrXMYwubFmitDAAIQgEANBEILZQ1NzKMIhrAGqqMP2V+dunbT8Otvq6E0RSAAAQg0h0BoncMQNifOXBUCEIAABGokEFooa2xmo4thCGsgOuaYQ7V07oda7457ayhNEQhAAALNIRBa5zCEzYkzV4UABCAAgRoJhBbKGpvZ6GIYwhqIjjvlOC1+b4pG3PZDdVp55RpqoAgEIACB8ARC6xyGMHyMuSIEIAABCNRBILRQ1tHURhbFENZAc8Ko72vhuLFa97pb1XnV1WqogSIQgAAEwhMIrXMYwvAx5ooQgAAEIFAHgdBCWUdTG1kUQ1gDzYkXnaMFb/5TQy+7Viv3X7OGGigCAQhAIDyB0DqHIQwfY64IAQhAAAJ1EAgtlHU0tZFFMYQ10Jx81aWa9/e/aPC5l6jbkKE11EARCEAAAuEJhNY5DGH4GHNFCEAAAhCog0BooayjqY0siiGsgeaUm6/Vh88/q3VOO1s9PrZhDTVQBAIQgEB4AqF1DkMYPsZcEQIQgAAE6iAQWijraGoji2IIa6A59a7bNPvJ/9Hax5+iXp/YvIYaKAIBCEAgPIHQOochDB9jrggBCEAAAnUQCC2UdTS1kUUxhDXQnP6TH2nmbx7TWocfo1U/tV0NNVAEAhCAQHgCoXUOQxg+xlwRAhCAAATqIBBaKOtoaiOLYghroPn+wz/T+w89qP4HHaLVP79DDTVQBAIQgEB4AqF1DkMYPsZcEQIQgAAE6iAQWijraGoji2IIa6A587e/1PQf/1D99t5PvXfatYYaKAIBCEAgPIHQOochDB9jrggBCEAAAnUQCC2UdTS1kUUxhDXQnP3U7zX1B7dqjd33aP9HggAEINAKBELrHIawFZ4K2ggBCEAAAv8mkJNQbizpOknbSpop6XZJ50ha2gH6rpIukLSNpC0ldZdUTle/JOk7Ud0++8D1np0xpBjCjMCcfc4Lz+m9G69R7513Vb+99quhBopAAAIQCE8gJ52reCMYwvAx5ooQgAAEIFAHgRyEso+k1yS9LukSSSMkXSHpKklndNDU3pLGSHpBUhdJXqRWTldd186Snpe0l6TLMIR1PAAZis79x1/17pWXaLUvfFFrHnhwhpJkhQAEINA8AjnoXIc3gyFsXqy5MgQgAAEI1EAgB6E8VdLJkjx6Nztqkv/bo3gDEj8r11rraJuko6IRxnK6upKkZVHh6ZKuxxDWEPgaisx/601NumCUVtlmOw047JgaaqAIBCAAgfAEctA5DGH4MHJFCEAAAhDIi0AOQvm0pMnR6F3c7CGSxknaTdKjKe6lI0OYLI4hTAGzUVkWThinCWeeop6bba6B3zulUdVSDwQgAIFcCeSgcxjCXCNG5RCAAAQgEJRADkI5VdKNZUbt5kY/8xTPaglDWI1QE36/eNpUjTvpGHXfYCMNOnVUE1rAJSEAAQhkJ5CDzmEIs4eBEhCAAAQgUFQCOQjlYkknSbq65J4nSrpH0mkpWGAIU0AKnWXpnNkac/Qh6jZ0mAafc3Hoy3M9CEAAAjURyEHnMIQ1RYJCEIAABCBQSAI5CGURDaHXL35kSKutzUsVSVkItC1erNEH76eV11xLQy+9JktR8kIAAhBoGoEcdA5D2LRocmEIQAACEGg4gRyE0lNGb4iOg0i2lymjDY9e+Arf/u631blnL6177S3hL84VIQABCNRAIAedwxDWEAeKQAACEIBAQQnkIJTeVGaSpL0TtzxY0ng2lSnoQ5ChWe8cdbDaFi3UiFs9+5cEAQhAoPgEctA5DGHxw04LIQABCEAgLYEchNLHTngNoY+dmBO140RJ56Y4diJuNmsI0wYwcL6xJx2jJdOmasQd96pT586Br87lIAABCGQnkIPOYQizh4ESEIAABCBQVAI5CKUPpveh9K9GB9MPl3RltMlM8mD6tyU9JWlkgs0uknpFB8/759+KfvdidGyF/9NGc6vo53dK+o2kByR5SuqvU3JuYw1hSlIl2cafebIWTRivdW+4XZ17rVJbJZSCAAQgEJBADjqHIQwYPy4FAQhAAAI5E8hJKDeODozfVtJMSbdHR04sTdzOWElPSjqw5Gc2fKXpIEl3RT90/h+UyeNzDoelxIUhTAmqNNvEC0ZpwVtvaugV12vlvv1qrIViEIAABMIRyEnnKt5Ap3C3luuVEMpc8VI5BCAAgeIQCC2UBblzdK7GQEy+8mLN+8ffNPiCy9RtHS8NJUEAAhAoNoHQOochLPbzQOsgAAEIQKCEQGihLEgAMIQ1BmLKjVfrwxee1zpnnKse632sxlooBgEIQCAcgdA6hyEMF1uuBAEIQAACDSAQWigb0ORGVIEhrJHi1Dtv0eyn/6CBJ56qnh/frMZaKAYBCEAgHIHQOochDBdbrgQBCEAAAg0gEFooG9DkRlSBIayR4rQf36NZv/2VBhz5Pa2y1adqrIViEIAABMIRCK1zGMJwseVKEIAABCDQAAKhhbIBTW5EFRjCGinOeOhBffDwz7TmyMO02me/UGMtFIMABCAQjkBoncMQhostV4IABCAAgQYQCC2UDWhyI6rAENZI8YNfP6oZ99+rfvseoN5f8ikhJAhAAALFJhBa5zCExX4eaB0EIAABCJQQCC2UBQkAhrDGQMz6wxOadvftWuOb/601dvtGjbVQDAIQgEA4AqF1DkMYLrZcCQIQgAAEGkAgtFA2oMmNqAJDWCPFOc//Se/dfJ16f+Vr6rfnvjXWQjEIQAAC4QiE1jkMYbjYciUIQAACEGgAgdBC2YAmN6IKDGGNFOf+7WW9e/VlWm2HL2nN/UfWWAvFIAABCIQjEFrnMIThYsuVIAABCECgAQRCC2UDmtyIKjCENVKc/8brmnTxuVp1289orUOPqrEWikEAAhAIRyC0zmEIw8WWK0EAAhCAQAMIhBbKBjS5EVVgCGukuHDcGE0Ydap6bb6l1j72xBproRgEIACBcARC6xyGUNLYE47SsnnzwkWZK0EAAhBYQQms1LOnhl1xfV13H1oo62ps4wpjCGtkuei9KRp/ynHqsdEmWueUM2ushWIQgAAEwhEIrXMYQgxhuKebK0EAAis8AQxhzY8AhrBGdEtmzdTYYw9Tt3WHa/CoC2ushWIQgAAEwhHAENbGGqGsjRulIAABCLQcgdBCWRBA6FyNgVi2aJHeOWR/rTxgoIZefGWNtVAMAhCAQDgCoXWOEcJwseVKEIAABCDQAAKhhbIBTW5EFRjCGim2tbVp9Mh91Xm11bXu1TfVWAvFIAABCIQjEFrnMIThYsuVIAABCECgAQRCC2UDmtyIKjCEdVB854iRalu2VCNuvquOWigKAQhAIAyB0DqHIQwTV64CAQhAAAINIhBaKBvU7HqrwRDWQXDs8UdqyfszNOLO+9RppZXqqImiEIAABPInEFrnMIT5x5QrQAACEIBAAwmEFsoGNr2eqjCEddAbf/qJWjRpoobfdKdW6tGzjpooCgEIQCB/AqF1DkOYf0y5AgQgAAEINJBAaKFsYNPrqQpDWAe9CeedqYWj39Kwq25Ulz5r1FETRSEAAQjkTyC0zmEI848pV4AABCAAgQYSCC2UDWx6PVVhCOugN+myCzT/tVc05MIr1HXgOnXURFEIQAAC+RMIrXMYwvxjyhUgAAEIQKCBBEILZQObXk9VGMI66L17/ZWa+9ILGnTWBeo+fEQdNVEUAhCAQP4EQuschjD/mHIFCEAAAhBoIIHQQtnAptdTFYawDnrv3X6T5jzzlAaefLp6brxpHTVRFAIQgED+BELrHIYw/5hyBQhAAAIQaCCB0ELZwKbXUxWGsA560350l2Y98RsNOPoErbLFVnXURFEIQAAC+RMIrXMYwvxjyhUgAAEIQKCBBEILZQObXk9VGMI66M342f364NGHtObBR2i1T3+ujpooCgEIQCB/AqF1DkOYf0y5AgQgAAEINJBAaKFsYNPrqQpDWAe9D375sGY8+GP1+/ZB6r3jTnXURFEIQAAC+RMIrXMYwvxjyhUgAAEIQKCBBEILZQObXk9VGMI66M36n8c17Yd3qu8ee6vPV79eR00UhQAEIJA/gdA6hyHMP6ZcAQIQgAAEGkggtFA2sOn1VIUhrIPe7D89ram33ag+X91dfffYq46aKAoBCEAgfwKhdQ5DmH9MuQIEIAABCDSQQGihbGDT66kKQ1gHvQ//8pKmXHu5Vv/iTuq/30F11ERRCEAAAvkTCK1zGML8Y8oVIAABCECggQRCC2UDm15PVRjCOujNe/1VTb70fK366c9prYOPqKMmikIAAhDIn0BoncMQ5h9TrgABCEAAAg0kEFooG9j0eqrCENZBb8E7ozXx3NPVa4uttfbRx9dRE0UhAAEI5E8gtM6lNYQbS7pO0raSZkq6XdI5kpZWQbKJpKskfUbSPEkPSjpJ0ocl5fpKulCSV3qvLmlc9N/3pESOUKYERTYIQAACrU4gtFAWhBc6V0cgFr07WeNPPV49NtlU65x0eh01URQCEIBA/gRC61waQ9hH0muSXpd0iaQRkq6IjN4ZHSCxsftX9O9SSTZ9/t9nJe2eKLeapOcik3i5pOmSbEAXRsYzDXWEMg0l8kAAAhBYDgiEFsqCIEPn6gjEkg/e19jvHaFuw9fT4LPOr6MmikIAAhDIn0BonUtjCE+VdLKkoZJmRwj832dLGpD4WSkdl/O/IdGoon//NUmPSNpK0ktRgYsl7SFpU0nza0SMUNYIjmIQgAAEWo1AaKEsCB90ro5ALJs/X+8cfpC6DhykIRe675kEAQhAoLgEQutcGkP4tKTJkpL7NNvkeVrnbpIerYDzfkn9Je2Q+H33aOqop5v6n9N7kq6WdFEdYUEo64BHUQhAAAKtRCC0UBaEDTpXRyDali3T6JH7qkufNTTsyhvqqImiEIAABPInEFrn0hjCqZJujEYEkwTmRj+7rAKWhyV1k7Rz4vddJC2Q9NPIYK4r6R1J3vLLo4c7Spol6YeSvi9pUUrkCGVKUGSDAAQg0OoEQgtlQXihc3UGYvRhB8nPzvCb7qyzJopDAAIQyJdAaJ1LYwgXRxvBeBQvmSZK8qYvp1VA4nWG+0RTRl2H06ckPS/pd5K+HG1S4zWF3mTmJ5J+LGmzaEMZb2LjqalpEkKZhhJ5IAABCCwHBEILZUGQoXN1BmLMcYdr6ayZGnHnfe3GkAQBCECgqARC61yaN2KthnBDSa9GG8N4vaE3lbGBtOF7Iho53E7SnyT9WdI2iaCcFRnNNaIppqXxcn2jkj9sa2srakxpFwQgAAEINJBAaKFsYNPrqQpDWA89r3P5/vFaPGWyht9yt1bq5glMJAhAAALFJBBa59IYQk8Z9YT7eM1fTK7alFHn+060G6l3El0m6dZoQxkbxQMlbRTtXuqNZbwBTZx8TMUfJX1C0ispQoVQpoBEFghAAALLA4HQQlkQZuhcnYGYcM7pWjhmtIZdfbO69O5dZ20UhwAEIJAfgdA6l8YQelOZSZL2Ttz2YEnjq2wqE2f3RjLrS7Kx9JESM6L1gTdL6ippTmQavWYwTp+V5Ot+PDryohpxhLIaIX4PAQhAYDkhEFooC4INnaszEJMuOU/z//mahlx8lboOWLvO2igOAQhAID8CoXUujSH0yJ0Pk/exEzZvTidKOrfKsRPlKB0QHXA/TNL7UYbHot1Ivb4wTp4S6mt6yqjPI6yWEMpqhPg9BCAAgeWEQGihLAg2dK7OQLx7zeWa+9eXNOjsi9R9mPe0I0EAAhAoJoHQOpfGEPpgeh9K72mePph+uKQro6MikgfTvy3pKUkjI7SeJnp6NNK3RNL2kk6QdLCkuxL4t5b0jKR7o01lPE3Up8aeJ+mClGFCKFOCIhsEIACBVicQWigLwgudqzMQ7916g+Y8+0et8/2z1GPDjeusjeIQgAAE8iMQWufSGELfrd+c10e7gs5MbBSzNIFirKQno7WB/nEvSQ9J2lJSj8hQ2uD9ogy+naJzCDeJppZ6raHzet1hmoRQpqFEHghAAALLAYHQQlkQZOhcnYGYds+dmvX7x7X2sSep1+Zb1FkbxSEAAQjkRyC0zqU1hPndcWNqRigbw5FaIAABCBSeQGihLAgQdK7OQEx/4D7N/NUjWuvQo7Tqtt67jgQBCECgmARC6xyGsJjPAa2CAAQgAIEKBEILZUECgSGsMxDvP/qQ3v/Z/ep/wHe1+vY71lkbxSEAAQjkRyC0zmEI84slNUMAAhCAQA4EQgtlDrdQS5UYwlqoJcrM/N1vNP3eu9R3z33V5ytfq7M2ikMAAhDIj0BoncMQ5hdLaoYABCAAgRwIhBbKHG6hlioxhLVQS5SZ/ccnNfWOm9Vnt2+q7zf3rLM2ikMAAhDIj0BoncMQ5hdLaoYABCAAgRwIhBbKHG6hlioxhLVQS5T58MU/a8oNV2n1L++i/vv4FCwSBCAAgWISCK1zGMJiPge0CgIQgAAEKhDISSi9m/Z1JbtpnyMpuZt2aYu6RjtibxPtqN1dUiVd/Xp0pNL6kt6R5LrvzxBkDGEGWOWyznv1H5p8+YVa9bNf0FojD6uzNopDAAIQyI9ATjpXscEYwvxiSc0QgAAEIJADgRyE0uftvhaduevzdkdIukLSVZKS5+2W3k1vSWMkvSCpi6QdKhhCb2npY5lujI5j+kp0Lu/Okh5PiQhDmBJUpWwL3n5LE88/U6tstY0GHHlcnbVRHAIQgEB+BHLQuQ4biyHML5bUDAEIQAACORDIQShPlXSypKGSZkdN9n+fLWlA4mfl7sY62ibpqGiEsZyu/lbSypFhjOv4laTVJKU9/wBDWOeztHDSBE04/ST13HQzDTzBISdBAAIQKCaBHHQOQ1jMUNMqCEAAAhCohUAOQvm0pMmS9kq0Z4ikcZJ2k/RoinZWMoTdJM2RdIykmxP17C/pB5LWkDQrRf0YwhSQOsqyeMZ0jTvhKHVf72MadMa5ddZGcQhAAAL5EchB5zCE+YWLmiEAAQhAIDSBHIRyajSd0yOCyTQ3GiW8LMU9VjKEXpvo6ajbR9NG46q2iqaabi2ZG+B+AAAgAElEQVTpxRT1YwhTQOooy9K5czXmyJHqOmiIhpx/aZ21URwCEIBAfgRy0DkMYX7homYIQAACEAhNIAehXCzpJElXl9zLREn3SDotxT1WMoSflvSMpM0l/S1Rz3qS3pK0U8p1hBjCFEHoKEvb0qUaPXJfdenXX8Mu9/5BJAhAAALFJJCDzmEIixlqWgUBCEAAArUQyEEoi2gIPVo5Ksmnrc1LFUn1EBh9yAHq1LWrhl9/Wz3VUBYCEIBArgRy0DkMYa4Ro3IIQAACEAhKIAeh9JTRG6KjIJL30sgpo1+Q9FSicqaMBn1q/vdiY445VEvnfqj17ri3CVfnkhCAAATSEchB5zCE6dCTCwIQgAAEWoFADkLpTWUmSdo7cf+DJY1v4KYyR0u6JVH/fpLuYlOZsE/cuFOO0+L3pmjEbT9Up5W98SsJAhCAQPEI5KBzGMLihZkWQQACEIBArQRyEEqfQeA1hD52wjuCOp0oyVtRVjt2Ir6NasdOdJa0Y+KeH5Pkcww5dqLWB6GGchNGfV8Lx43Vutfdqs6r+tQPEgQgAIHiEchB5zCExQszLYIABCAAgVoJ5CCUPpj+dUmvSvLB9MMlXRltMpM8mP7taNrnyETbd5HUS5IPmffPvxX9zjuH+tgKp/hg+usl/UKSD6a34eRg+lofghrLTbzoHC14858aeuk1WnnNtWqshWIQgAAE8iWQg85hCPMNGbVDAAIQgEBIAjkJpY+HsGHbVtJMSbdHR04sTdzb2OjoiANLfuaRxdJ0UDQlNP757pLOl7S+l7JFdf8kAzd2Gc0Aq1LWyVddqnl//4sGn3uJug0pF7YGXIQqIAABCNRJICedq9iqTnW2tyjFEcqiRIJ2QAACEMiZQGihzPl20laPzqUl1UG+KTdfqw+ff1brnHa2enxswwbUSBUQgAAEGk8gtM5hCBsfQ2qEAAQgAIEcCYQWyhxvJUvVGMIstCrknXrX7Zr95BNa+3unqNdmPhqSBAEIQKB4BELrHIaweM8ALYIABCAAgQ4IhBbKggQDQ9iAQEz/yY808zePaa3Dj9Gqn9quATVSBQQgAIHGEwitcxjCxseQGiEAAQhAIEcCoYUyx1vJUjWGMAutCnnff/hnev+hB9X/oEO0+ud3aECNVAEBCECg8QRC6xyGsPExpEYIQAACEMiRQGihzPFWslSNIcxCq0Lemb/9lab/+B7123s/9d5p1wbUSBUQgAAEGk8gtM5hCBsfQ2qEAAQgAIEcCYQWyhxvJUvVGMIstCrknf3U7zX1B7dqjd33aP9HggAEIFBEAqF1DkNYxKeANkEAAhCAQEUCoYWyIKHAEDYgEHNeeE7v3XiNeu+8q/rttV8DaqQKCEAAAo0nEFrnMISNjyE1QgACEIBAjgRCC2WOt5KlagxhFloV8s79x1/17pWXaLUvfFFrHnhwA2qkCghAAAKNJxBa5zCEjY8hNUIAAhCAQI4EQgtljreSpWoMYRZaFfLOf+tNTbpglFb51HYacPgxDaiRKiAAAQg0nkBoncMQNj6G1AgBCEAAAjkSCC2UOd5KlqoxhFloVci7cMI4TTjzFPXcbHMN/N4pDaiRKiAAAQg0nkBoncMQNj6G1AgBCEAAAjkSCC2UOd5KlqoxhFloVci7eNpUjTvpGHXfYCMNOnVUA2qkCghAAAKNJxBa5zCEjY8hNUIAAhCAQI4EQgtljreSpWoMYRZaFfIu/f/t3Qm0XVV9+PGvhCkJBMIQAiQBEooCUkpxQnFCqTNV/i7UMiiNzKMyCQgIRkRknqFAKYoWKUUErVZqwalUkSJ/hKKGkJAEQgghhCEJGbp+eN5/3f9b7+Xue+85+557+z1rZSnv7eHszz7v/d7vnn32eWExM448iLUnbc2ks84poUWbUEABBcoXyB3nTAjLn0NbVEABBRSoUCB3oKxwKK00bULYitYwZVctX870z+7HWuM2Y6tzLy6hRZtQQAEFyhfIHedMCMufQ1tUQAEFFKhQIHegrHAorTRtQtiK1mrK/vGz+zFi1Gi2ueTqklq0GQUUUKBcgdxxzoSw3PmzNQUUUECBigVyB8qKh5PavAlhqlSTco8ddRCrli5lyjU3ltSizSiggALlCuSOcyaE5c6frSmggAIKVCyQO1BWPJzU5k0IU6WalHv8hKNZPv9pplx3E68ZMaKkVm1GAQUUKE8gd5wzISxv7mxJAQUUUCCDQO5AmWFIKV2YEKYoJZSZddqJLHtiFttcfi0jRq+XUMMiCiigQF6B3HHOhDDv/NqbAgoooECHArkDZYenW1Z1E8KSJGeffQZLfv8oW51/GWttvElJrdqMAgooUJ5A7jhnQlje3NmSAgoooEAGgdyBMsOQUrowIUxRSigz94JzeOnBB5g47eusM2FiQg2LKKCAAnkFcsc5E8K882tvCiiggAIdCuQOlB2eblnVTQhLknzqiot44Vf3suUXz2LkttuV1KrNKKCAAuUJ5I5zJoTlzZ0tKaCAAgpkEMgdKDMMKaULE8IUpYQyT19/Nc//9N/Z4viTGfX6nRNqWEQBBRTIK5A7zpkQ5p1fe1NAAQUU6FAgd6Ds8HTLqm5CWJLk/G/fyKIf/YDxRxzLem98S0mt2owCCihQnkDuOGdCWN7c2ZICCiigQAaB3IEyw5BSujAhTFFKKLPgtltYePutjJt6KGPe/q6EGhZRQAEF8grkjnMmhHnn194UUEABBToUyB0oOzzdsqqbEJYkufBf7mDBzTexyb6fZsM9P1BSqzajgAIKlCeQO86ZEJY3d7akgAIKKJBBIHegzDCklC5MCFOUEsosuvsu5t9wLRvt/Qk22utjCTUsooACCuQVyB3nTAjzzq+9KaCAAgp0KJA7UHZ4umVVNyEsSXLxvb9g3lWXsuEHP8Im++xbUqs2o4ACCpQnkDvOmRCWN3e2pIACCiiQQSB3oMwwpJQuTAhTlBLKvPjAb3jyoq8zZo89GXfA1IQaFlFAAQXyCuSOcyaEeefX3hRQQAEFOhTIHSg7PN2yqpsQliT58qOPMOerZ7L+bruz2SFHltSqzSiggALlCeSOcyaE5c2dLSmggAIKZBDIHSgzDCmlCxPCFKWEMktnzuCJM05m9C67svkxJyTUsIgCCiiQVyB3nDMhzDu/9qaAAgoo0KFA7kDZ4emWVd2EsCTJZfOeYtZJxzJy+x3Z8qTTSmrVZhRQQIHyBHLHORPC8ubOlhRQQAEFMgjkDpQZhpTShQlhilJCmeWLnuPxYw5lnW0mM/GMsxNqWEQBBRTIK5A7zpkQ5p1fe1NAAQUU6FAgd6Ds8HTLqm5CWJLkymXLeOzgA1hr/OZsdc6FJbVqMwoooEB5ArnjnAlheXNnSwoooIACGQRyB8oMQ0rpwoQwRSmhzKpVq5g+dV9GjNmAbS66MqGGRRRQQIG8ArnjnAlh3vm1NwUUUECBDgVyB8oOT7es6iaEZUkCjx0+lVUrVzDlqhtKbNWmFFBAgXIEcsc5E8Jy5s1WFFBAAQUyCeQOlJmG1awbE8JmQi18//HjjmT5gmeYcv23eM0aa7RQ06IKKKBA9QK545wJYfVzag8KKKCAAiUK5A6UJZ56J02ZEHaiN6jurFOPZ9mc2Uy+8nrWGDmqxJZtSgEFFOhcIHecMyHsfM5sQQEFFFAgo0DuQJlxaKvryoSwxIl44sunsXT6H9j6witYc+xGJbZsUwoooEDnArnjnAlh53NmCwoooIACGQVyB8qMQzMhzIQ957yzefmhB5l09vmsvcWWmXq1GwUUUCBNIHecMyFMmxdLKaCAAgrURCB3oKzJsL1DWOJEPHnZBbx436+YcPo01p28bYkt25QCCijQuUDuOGdC2Pmc2YICCiigQEaB3IEy49C8Q5gJe961V7L45/ewxYmnMmqHnTL1ajcKKKBAmkDuOGdCmDYvllJAAQUUqIlA7kBZk2F7h7DEiZj/zRtYdNcPGX/Ucay36xtLbNmmFFBAgc4Fcsc5E8LO58wWFFBAAQUyCuQOlBmH5h3CTNgLbr2ZhXfcxriDDmfM296RqVe7UUABBdIEcsc5E8K0ebGUAgoooEBNBHIHypoM2zuEJU7Ewu/fzoJbvs0m+x3Ihu99X4kt25QCCijQuUDuOGdC2Pmc2YICCiigQEaB3IEy49C8Q5gJe9G//Svzv3E9G3/8U4z98F9n6tVuFFBAgTSB3HEuNSHcAbgU2A14DrgWOBNY0WRYOwIXArsDLwG3ACcALwxTL34rfxf4DfCGNLJXS/nJaQtYFlVAAQV6WSB3oKyJlXGuxIlY/MufMe+ayxn74Y+y8cc/WWLLNqWAAgp0LpA7zqUkhGOB3wEPA18DpgDnF4neF1cz5A2A3xf/zgU2BuJ/fwl8dIh66xZ9jAJmmxB2fjHZggIKKNCPArkDZU0MTQhLnIgX7r+Ppy45jw3e8z423f/AElu2KQUUUKBzgdxxLiUhPBk4EdgKeL4YYvz3l4DxDV8bPPqoF/8mFXcV4/sfAb4HxJZe9w2qcBrwV8B04PUmhJ1fTLaggAIK9KNARYGy3ZUw8eHnRcUHnWsAdwJHAwsa7CPWngIcAowrPvyM+PijFubHhLAFrGZFX3r4IeaeO4313/YONjvo8GbF/b4CCiiQVaCiODfsGFISwp8Cc4HGNRWR5M0E9gLuGKb1m4FNgT0avh93AWPpaCw3jX8DR7QXdyHfWQRSE8Ksl52dKaCAAr0jUEGgbHclTKBFUrcdcDywslhJMw94e4NoJH/xIerpwAPAfsAngLcBv06UNyFMhEoptmTGdGafeSqjd30Tmx/1+ZQqllFAAQWyCVQQ51Z77ikJ4dPAFUUwa2zsxeJrXx+mh9uBdYD3N3x/TWAJ8E+DEsx4tjDuPk4FbvAOYbbrzY4UUECBnhOoIFC2uxImnquPxyDiw8z48DSONwH/CewJ3AWsDTwDXAzESpiBI56VfxL4cOIEmBAmQqUUW/bkXGad/HlG7rgTW55wakoVyyiggALZBCqIcx0nhK8UG8HEkpjGI57zu7FYBjNUJ/Gc4d8US0ajjTjeDNwL/LhYHhpfizuItxWfsManqiaE2S43O1JAAQV6T6CCQNnuSpizgIOLxycaIR8r4tpxwPbFEtHGpDHKfhWIW1PrA8sSZsGEMAEptcjyhc/y+OcOZ53J2zLx9Gmp1SyngAIKZBGoIM51LSF8HfBQsSNpLJWJTWUigdy5+NQ07hzGHcNYPhNfjw1n4khJCKO9MxpHtmrVqiwTZCcKKKCAAt0VqCBQtrsS5jvFM4HvGiTy/eK/PwTsAtxf7NIdH4gOHPHYRCwhjYTxvxNETQgTkFKLrHz5ZR477EDW3mICk84+L7Wa5RRQQIEsAhXEuY4TwgiUlw965i8abbZkNMr8bbEb6Zji2Yprig1lIlH8DHBYcYdx14ZPSGN5agTIdxd9DNxdXN1ADJRZLk87UUABBbovUEGgbHclTKx2iVg4eOfsbwKTgbcCsenMQuCY4vVNA4D/UjxSEc8RxrLTZodxrplQC99ftXIl06fuy5pjx7L1BfFnh4cCCihQH4EK4lzHCWEspZkDfKqhpYnArCabygwUj41k/gyIxDKeo4id174AXFXszBZBcrhjfyACa7PDQNlMyO8roIACfSJQQaCsMiEM9ZuA9xRx9LfAvsAFxSqZeA6x8c7hwCy5Eqbi63X6oQcS19LkK6+vuCebV0ABBVoTqCDOdZwQxsP28TL5eO3E4qK12E0tnp1Y3Wsnhur408UnpFsDzwLbAhMGFYxkcZtie+5HgHiusNlhQthMyO8roIACfSJQQaBsdyVMLBmN3bRjRUvj0bhkNL4eZWLn7YFyTwDXFRuzRbx7PGFqjHMJSK0UmXHsYaxY9BxTrv/Wq4mhhwIKKFAXgQriXMcJYWzHHS+lj2We8WL6WAYTn2zGJjONL6b/I3BPsVNodBrLRGPrrrjDuLwIhPGA/UHFc4LDnVjKM4SD6xoo63IFex4KKKBAxQIVBMp2V8LEB6MR0zYfNOR4n+53gYh5jUd8ABpLSB8Fji2+P7jucHrGuZKvq5knf55XnpzL5Kv/gTXWiU3RPRRQQIF6CFQQ5zpOCKOBeGHvZcVD8c81bBSzoqH1+ITz7uLZwPjy6GKXtTcAI4uE8itFkFzdSZkQ1uNa9CwUUECBWgpUECjbXQkz8NqJeOfgzwusiHnxbsGB104MZRiPUsRGM7HDduo7D0wIS74anzjzVJbOmM7WF13FmhtuWHLrNqeAAgq0L1BBnCslIWx/RHlqGijzONuLAgoo0HWBCgJluythwiJeTB/PyTe+mD6WoDa+mD6eh18LiNdRTAI+1/D84AuJoMa5RKjUYnO+9mVefuR3TDrnQtYen3qjNrV1yymggALtC1QQ50wI258OayqggAIK1E2gokDZzkqYoIlbSxcCHwPWAO4Eji42URugi+fnTymexV9UrJSJ/45N1lIPE8JUqcRyT15yPi/e/2smfOmrrLt1PMrpoYACCtRDoKI4N+zg+uUpagNlPa5fz0IBBRSoXCB3oKx8QGkdGOfSnJJLzbvmchb/8mdscdJpjNp+x+R6FlRAAQWqFsgd50wIq55R21dAAQUUKFUgd6As9eTbb8yEsH27IWvOv/F6Fv3kX9n8mBMYvUu8DtlDAQUUqIdA7jhnQliPefcsFFBAAQUSBXIHysTTqrqYCWHJws9851s894PvsdkhR7L+bruX3LrNKaCAAu0L5I5zJoTtz5U1FVBAAQW6IJA7UHZhiEN1aUJY8kQ8e8dtPHvrzWx6wFQ22CM2hfVQQAEF6iGQO86ZENZj3j0LBRRQQIFEgdyBMvG0qi5mQliy8HM//iHP3HQDG++zL2M/+JGSW7c5BRRQoH2B3HHOhLD9ubKmAgoooEAXBHIHyi4McaguTQhLnojnf3Y3T193FWP32puN996n5NZtTgEFFGhfIHecMyFsf66sqYACCijQBYHcgbILQzQhzID+wn2/4qnLLmCDPT/ApvvGm0E8FFBAgXoI5I5zJoT1mHfPQgEFFFAgUSB3oEw8raqLeYewZOGXHnqQueedzfpvfxebTT205NZtTgEFFGhfIHecMyFsf66sqYACCijQBYHcgbILQxyqSxPCkidiyR//wOxpp7HeG9/C+COOLbl1m1NAAQXaF8gd50wI258rayqggAIKdEEgd6DswhBNCDOgL5szm1mnHs+onXZmi+NOztCjXSiggAJpArnjnAlh2rxYSgEFFFCgJgK5A2VNhu0dwpIn4pUFzzDzuCNZd9vtmPDFs0pu3eYUUECB9gVyxzkTwvbnypoKKKCAAl0QyB0ouzBE7xBmQF/x4ovMOGIqa0+YxKRp52bo0S4UUECBNIHccc6EMG1eLKWAAgooUBOB3IGyJsP2DmHJE7FqxQqmT92XNTfZlK3Pu7Tk1m1OAQUUaF8gd5wzIWx/rqypgAIKKNAFgdyBsgtD9A5hJvTpB3+a16y1FpMvvzZTj3ajgAIKNBfIHedMCJvPiSUUUEABBWokkDtQ1mTo3iGsYCJmHH0IK158gW2vu6mC1m1SAQUUaE8gd5wzIWxvnqylgAIKKNAlgdyBskvDHNytCWEFEzHzpGN5Zd5TTPm7b7x6p9BDAQUUqINA7jhnQliHWfccFFBAAQWSBXIHyuQTq7agCWEFvk+ccTJLZ85gm0uuYcSYMRX0YJMKKKBA6wK545wJYetzZA0FFFBAgS4K5A6UXRxqY9cmhBVMxOyvnsmSRx9hq3MvZq1xm1XQg00qoIACrQvkjnMmhK3PkTUUUEABBbookDtQdnGoJoQV48+98Fxe+u39TDzra6wzaauKe7N5BRRQIE0gd5wzIUybF0spoIACCtREIHegrMmwvUNYwUQ8ddWlvHDvL9jylC8xcrvXVdCDTSqggAKtC+SOcyaErc+RNRRQQAEFuiiQO1B2cajeIawY/+kbruX5u+9i88+dxOidd6m4N5tXQAEF0gRyxzkTwrR5sZQCCiigQE0EcgfKmgzbO4QVTMQz//hNnvvhnWx22NGs/+a3VtCDTSqggAKtC+SOcyaErc+RNRRQQAEFuiiQO1B2cajeIawY/9nbb+XZ225h0wMPZoN37lFxbzavgAIKpAnkjnMmhGnzYikFFFBAgZoI5A6UNRm2dwgrmIjnfvQDnvn2jWz8yf0Z+/4PVdCDTSqggAKtC+SOcyaErc+RNRRQQAEFuiiQO1B2cajeIawY//l7fsLTf38NG33046/+81BAAQXqIJA7zpkQ1mHWPQcFFFBAgWSB3IEy+cSqLegdwgp8F//qP5h3xcVs+P4Psckn96+gB5tUQAEFWhfIHedMCFufI2sooIACCnRRIHeg7OJQvUNYMf6LDz7Akxecw5h37sG4Aw+uuDebV0ABBdIEcsc5E8K0ebGUAgoooEBNBHIHypoM2zuEFUzEy394lDlfOYP13vxWxh92dAU92KQCCijQukDuOGdC2PocWUMBBRRQoIsCuQNlF4fqHcKK8Zc+MZMnTjuJUTvvwhafO6ni3mxeAQUUSBPIHedMCNPmxVIKKKCAAjURyB0oazJs7xBWMBGvzH+amScczbqv3Z4JJ59RQQ82qYACCrQukDvOmRC2PkfWUEABBRTookDuQNnFoXqHsGL8FS8sZsaRB7H2pK2ZdNY5Ffdm8woooECaQO44Z0KYNi+WUkABBRSoiUDuQFmTYXuHsIKJWLV8OdM/ux9rjduMrc69uIIebFIBBRRoXSB3nDMhbH2OrKGAAgoo0EWB3IGyi0P1DmEG/Omf3Z81Ro1im0uuztCbXSiggALNBXLHORPC5nNiCQUUUECBGgnkDpQ1Gbp3CCuaiMeOOohVS5Yw5e++UVEPNquAAgq0JpA7zpkQtjY/llZAAQUU6LJA7kDZ5eEOdG9CWNFEPH7C0Syf/zRTrruJ14wYUVEvNquAAgqkC+SOcyaE6XNjSQUUUECBGgjkDpQ1GHKcgglhRRMx67QTWfbELLa5/FpGjF6vol5sVgEFFEgXyB3nTAjT58aSCiiggAI1EMgdKGswZBPCCidh9tlnsOT3j7LVeZey1iabVtiTTSuggAJpArnjnAlh2rxYSgEFFFCgJgK5A2VNhu0dwoomYu4F5/DSgw8wcdrXWWfCxIp6sVkFFFAgXSB3nDMhTJ8bSyqggAIK1EAgd6CswZC9Q1jhJDx1xUW88Kt72fKLZzFy2+0q7MmmFVBAgTSB3HHOhDBtXiylgAIKKFATgdyBsibD9g5hRRPx9PXX8PxPf8IWx5/MqNfvXFEvNquAAgqkC+SOcyaE6XNjSQUUUECBGgjkDpQ1GLJ3CCuchPnfvpFFP/oB4484lvXe+JYKe7JpBRRQIE0gd5wzIUybF0spoIACCtREIHegrMmwvUNY0UQsuO0WFt5+K+OmHsqYt7+rol5sVgEFFEgXyB3nTAjT58aSCiiggAI1EMgdKGswZO8QVjgJC//lThbc/E022ffTbLjnByrsyaYVUECBNIHccc6EMG1eLKWAAgooUBOB3IGyJsP2DmFFE7Ho7ruYf8O1bLT3Pmy0194V9WKzCiigQLpA7jhnQpg+N5ZUQAEFFKiBQO5AWYMhe4ewwklYfO8vmHfVpWz4wY+wyT77VtiTTSuggAJpArnjnAlh2rxYSgEFFFCgJgK5A2VNhu0dwoom4sUHfsOTF32dMXvsybgDplbUi80qoIAC6QK545wJYfrcWFIBBRRQoAYCFQXKHYBLgd2A54BrgTOBFU2GvAFwEfBRYA3gTuBoYEFDvbWBLwAHAFsCc4CbgLOBpYmkJoSJUK0We/nRR5jz1TNZb7fdGX/Ika1Wt7wCCihQukBFcW7Y8zQhLH0KbVABBRRQoEqBCgLlWOB3wMPA14ApwPnAhcAXm4zlR0C8zfx4YGVRfx7w9oZ6FwCHFm39F/CXwDTgGuCYRCsTwkSoVostnTmDJ844mdG77Mrmx5zQanXLK6CAAqULVBDnVnuOJoSlT6ENKqCAAgpUKVBBoDwZOBHYCni+OPf47y8B4xu+NnhYcTfxl8A7gZ8W33wT8J/AnsBdxdeeKu4IHjcoSYwH1jZLtDIhTIRqtdiyeU8x66RjGbn9jmx50mmtVre8AgooULpABXHOhLD0WbJBBRRQQIGuCVQQKCOZmwt8smFQk4CZwF7AHcMM9izg4CJpbCzyGHAbMJAAxvLRi4EoP3CcDsT6xHGJkCaEiVCtFlv+/CIeP/oQ1tlmMhPPiFW8HgoooEB3BSqIcyaE3Z1Se1dAAQUUKFOggkD5NHBFcUew8VRfLL729WHO/ztFQjf4bebfL8p/qPjfWIa6H7AP8FtgFyDqXjkoSVwdkwlhmRdRQ1srly3jsYMPYK3xm7PVObFK2EMBBRTorkAFcc6EsLtTau8KKKCAAmUKVBAoXwHi4bHYHKbxmA3cCJwyzPn/GIikMTaUaTy+CUwG3lp8MR7PiDuERzUUigT0iBZcTAhbwGql6KpVq5g+dV9GjNmAbS6KHN1DAQUU6K5ABXHOhLC7U2rvCiiggAJlClQQKKtOCON5xNhlNJaJPgjsDHwZuKT42lA88fziGY3fiMTFoxqBx46YyqoVK5hy1Q3VdGCrCiigQAsCFcQ5E8IW/C2qgAIKKFBzgQoCZSwZvbx4zUTj6FOWjG4KvHsQWeOS0U2K5xPjbuDfNZQ7BLiseA1F9N/s8A5hM6EOvv/4cUeyfMEzTLn+W7xmjXh7iIcCCijQPYEK4pwJYfem054VUEABBcoWqCBQxqYy8W7ATzWc60RgVsKmMgcBmw8a43Tgu8WmMgO7jsb//rqh3HBfH47LhLDsC6mhvVmnHs+yObOZfOX1rDFyVIU92bQCCijQXKCCOGdC2JzdEgoooIACvSJQQaCM107EM4Tx2onFhUO8VzB2BU157US8c/DnRb03FInfwGsn4mkbrgYAABRfSURBVLUS8dqJeA/h1Q3GhxUb2cQuo/MT7E0IE5DaLfLEl09j6fQ/sPWFV7Dm2I3abcZ6CiigQCkCFcQ5E8JSZsZGFFBAAQVqIVBBoIwX08dL6R8qXiwfG8LEy+Rjk5nGF9P/EbgHmNoAES+m/7NBL6aPJaCNL6aPV1DEstJ4JjCeIfyLYvfSqBs7j6YcJoQpSm2WmXPe2bz80INMOvt81t5iyzZbsZoCCihQjkAFcc6EsJypsRUFFFBAgToIVBQodyie6YuXzT8HXFskbSsaxvw4cDfwmYavbQjEuwo+BsTDZ3cCRwPPNJQZU2weE2W2KJan/nOxsczAHclmtCaEzYQ6+P6Tl13Ai/f9igmnT2Pdydt20JJVFVBAgc4FKopzw55YbIWdckSgvBRoDJRnAo2Bcqh2diwC5e7AS8AtxbKcF4rCI4pPVT8MRB9x/AY4ddCzFs3O0UDZTMjvK6CAAn0ikDtQ1oTNOFfhRMy79koW//wetjjxVEbtsFOFPdm0Agoo0Fwgd5xLSQhjKc3viuU08XLdKcD5RaLXuJRm8Og2AH5f/DsX2BiI//1lwzub1gOeAP4euAuIPbWPBN5bvL8pksOUw0CZomQZBRRQoA8EcgfKmpAZ5yqciPk33cCiH/+Q8Ucdx3q7vrHCnmxaAQUUaC6QO86lJITxsH28Qyketn++GEL8d7wjaXUP20e9+DepWH4TVT8CfA+I37b3AXGHMJbSLGygWbtIIv8dOLA52aslDJSJUBZTQAEFel0gd6CsiZdxrsKJWHDrzSy84zbGHXQ4Y972jgp7smkFFFCguUDuOJeSEMZ23HOBTzacfiR5M5tsx30zEO9n2qOh3rrF0tFYbhr/hjviHU5rAu9rTmZCmGhkMQUUUKAvBHIHypqgmRBWOBELv387C275NpvsdyAbvjf1T48KT8imFVDgf7VA7jiXkhDGbmlXFHcEGyen2Qt7bwfWAd7fUCmSvCXAPw1KMBvbjTrx4H6UOSrxajBQJkJZTAEFFOh1gdyBsiZexrkKJ2LRT/6V+Tdez8Yf/xRjP/zXFfZk0woooEBzgdxxLiUhfKXYCCa23248ZgM3AqcMM6x4zvBviiWj0UYcbwbuBX4M/NUw9eK9TycBfw482pzs1RIGykQoiymggAK9LpA7UNbEyzhX4UQs/uXPmHfN5a8mg5EUeiiggALdFMgd56pMCF9XvNNpYOvu2FQmEsidiw1kGu8cDph/qHjG8Lji/U/DzUU8vxjvc/p/x6pVsR+NhwIKKKBAvwvkDpQ18TQhrHAiXrj/Pp665Dw2eM/72HT/1O0LKjwhm1ZAgf/VArnjXEpCGEtGLx/imb9mS0ZjIv+22I00No5ZCVxTbCgTL/9tfI9TlI2NZmIjmX8AjmjxKjBQtghmcQUUUKBXBXIHypo4GecqnIiXHn6IuedOY/23vYPNDjq8wp5sWgEFFGgukDvOpSSEsanMHKBxDcVEYFaTTWUGRhsbyfwZEIllvKh3AfAF4KoGju2AnwP/Aeyd8H7DwZIGyubXliUUUECBvhDIHShrgmacq3AilsyYzuwzT2X0rm9k86NikZKHAgoo0D2B3HEuJSGMV0ecULx2YnFBczwQz/qt7rUTQyl+unjB/dbAs0WBzYt3Ez4FvKfYhbTVGTBQtipmeQUUUKBHBXIHypowGecqnIhlT85l1smfZ+SOO7HlCadW2JNNK6CAAs0Fcse5lIQwXkz/cPE8YLyYfjJwQfGMX+OL6f8I3ANMLYYZy0Tjt2rcYVwOvBuIj90OAm4oyows7gpGgrhvcfdwQGkp8F/NyV4tYaBMhLKYAgoo0OsCuQNlTbyMcxVOxPKFz/L45w5nncnbMvH0aRX2ZNMKKKBAc4HccS4lIYyz3gG4DNiteMn8wEYxKxqGFK+KuLvh2cDRwG3AG4BI/OK5wa8A322oE4ngjGFY4j2H8f2Uw0CZomQZBRRQoA8EcgfKmpAZ5yqciJVLlvDYoZ/hNeuOZORrt6+wJ5tWQIF+Fxj1+j9nwz2H2jszfeS541xqQpg+gu6UNFB2x91eFVBAgewCuQNl9gEO3aFxrsKJiJ3KZx53JMufjW0OPBRQQIH2Bca8Yw/G/e3B7TcA5I5zJoQdTZeVFVBAAQVyC+QOlLnHN0x/JoQVT8TKl1/ilWdi7zsPBRRQoH2BEaNHs+ZG8ba99o/ccc6EsP25sqYCCiigQBcEcgfKLgxxqC5NCGsyEZ6GAgooULVA7jhnQlj1jNq+AgoooECpArkDZakn335jJoTt21lTAQUU6CmB3HHOhLCnLg9PVgEFFFAgd6CsibgJYU0mwtNQQAEFqhbIHedMCKueUdtXQAEFFChVIHegLPXk22/MhLB9O2sqoIACPSWQO86ZEPbU5eHJKqCAAgrkDpQ1ETchrMlEeBoKKKBA1QK545wJYdUzavsKKKCAAqUK5A6UpZ58+42ZELZvZ00FFFCgpwRyxzkTwp66PDxZBRRQQIHcgbIm4iaENZkIT0MBBRSoWiB3nDMhrHpGbV8BBRRQoFSB3IGy1JNvvzETwvbtrKmAAgr0lEDuOGdC2FOXhyergAIKKJA7UNZE3ISwJhPhaSiggAJVC+SOcyaEVc+o7SuggAIKlCqQO1CWevLtN2ZC2L6dNRVQQIGeEsgd50wIe+ry8GQVUEABBXIHypqImxDWZCI8DQUUUKBqgdxxzoSw6hm1fQUUUECBUgVyB8pST779xkwI27ezpgIKKNBTArnjnAlhT10enqwCCiigQO5AWRNxE8KaTISnoYACClQtkDvOmRBWPaO2r4ACCihQqkDuQFnqybffmAlh+3bWVEABBXpKIHecMyHsqcvDk1VAAQUUyB0oayJuQliTifA0FFBAgaoFcsc5E8KqZ9T2FVBAAQVKFcgdKEs9+fYbMyFs386aCiigQE8J5I5zfZMQ9tQse7IKKKCAAp0K9Ev8SnVYlVrQcgoooIACfSGQLc5l66gvpiX/IOIPgH6co34dV1whji3/z0kZPfbrvPXruMqYc9vovkA/X5+OrfvXVztn0K/z1q/j6ve/u9q5htuq04/JRlsQNa3Urz/A/Tqufv/F5LzV9BfFak6rn+es92bDMx4s0M/Xp2Przeu9X+etX8fV7393ZfspMiHMRt1WR/36A9yv4+r3X0zOW1s/xl2t1M9z1lVYOy9FoJ+vT8dWyiWSvZF+nbd+HVe//92V7QfAhDAbdVsdfQmIf/129Ou4Yp4cW29erf06b/06rt68yjzrwQL9fH06tt683vt13vp1XP3+d1e2nyITwmzUdqSAAgoooIACCiiggAIK1EvAhLBe8+HZKKCAAgoooIACCiiggALZBEwIs1HbkQIKKKCAAgoooIACCihQLwETwnrNh2ejgAIKKKCAAgoooIACCmQTMCHMRp3U0ceBzwOvBUYDM4FvAOcCy5JaqHehNYHjganAJGA+cAvwuXqfdtOz+yhwVjFvc4FLgQua1qpfgW2BE4DdgB2BnwHvajjNzYvr86+AKcBC4CfAyUCMu85Hs7HFuT8ObDVoEPOA8TUeWMq4Yt7OBmLeNgD+AJwH3FTjcXlq/SvQz3GuX2NcXI3GOeNct34rGecyyJsQZkBuoYtDgInAb4DngDcVu1ZeBxzZQjt1LfpNYA/gTOC/i7HuAJxS1xNOOK+3FYnT9cB3gDcDpxeJ1UUJ9etU5K+By4B7gdcDkQw1JoQfBmJM1wL/CWxWXJ/rFuVfqNNgBp1Ls7ENJIS/KBL6gerxQcz9PTyuNYq52hg4DXgKiD/IDwX+D/DPNR6bp9afAv0c5/oxxsVVaJwD41z3fh81i9/GuRLmxoSwBMSKm/gKcAQwFoj3yPTq8X7gDmBn4OFeHcQQ5/0jYBTw9obvnQ8cWNxZ6qU7u/FLdWUxjn8CNhmUEG4IRNK3vGGs2wGPAp8B/qHG89psbAMJYYw77mL3ytFsXK8DHgH2Kn7+BsYVSW7cKfxErwzU8+xrgX6Ic/0a4+LCM84Z57r5C8g4l0HfhDADcoddxBLSacUS0l5OCOPuWSxXe1+HHnWrHnfRLi+WjA6cW4zxh0UydU/dTjjxfIZKCIer+mJxjX41se1uFxtubLFktNcSwkbLoca1E/Ag8E7gpw2FY6nvM8A+3Z4M+1egWIre63GuX2NcXKDGOTDO1eNXlXGuonkwIawItsNmRwDrAH8JxBKUW4HjOmyz29XjecjvAXHNHQDEsxaRNMVS2Lo/f7Y6u0VAJELnNBR6d/Fs3WHAVd2Gb7P/1ITwz4HfFssQ4zrthWN1CWF8aBHP774M/Lj4uYtrtxeOocYVP2//ASwBDir+sNsbuBr4QHGd9sLYPMf+E+i3ONevMS6uPOOcca4uv4GMcxXNhAlhRbAdNht/vEVCGMeNxfLDgaV8HTbdtepLi41xInmIDS7WLzbLiWea3tLDy2Hjec+4sxTPYw0cJxUJ4qnFWLuG3kHHKQlhLOP4N2DLYhOaVzroL2fV4cZ2cfH85Gxge+AMYAUQd9niD6K6H8ONK5ab396wrDnmKZY0u6lM3We0v8+v3+Jcv8a4uAqNc8a5uvw2Ms5VNBMmhBXBdths3BmM59JiU5nYoORbwOEdttnt6vEsXfyLXRwXFCfzDiCWVL63SCy6fY7t9B93XeIuYGzSEb+oYs4iiR9X7L7ZeOewnfa7VSclIfwacEyxHDE2memVI2VsMZbYWOeB4pnCXtggaKhxRdIeyeDkYjOnp4EPAkcXuwbGXXoPBboh0G9xrl9jXFwbxrk/Lbs3znXjN8X/36dxrqI5MCGsCLbEZmN5ZWzWEdvuTi+x3dxNxTMIjxWvNBjoO/5YjaV5sYlHvKqhF49Y9hR3liIhjP//EhB3CGM8cRfmhl4cVJHcDt5UpnEo8QFF7Ej6KeDmHhtjakIYw/pd8el4/BzW/RhqXLGZTCSEsflPbCIzcHy7uKsbS349FOi2QD/EuX6NcXFtGOeMc93+HTHQv3GuopkwIawItsRm4y7F/wX2BO4qsd3cTd3Nn7ZtjuWhA0ckhLFsKN5DGBuz9PIRy/ImADOA2Nnx18Wyw3i9Ri8eq0uaYnlsbKAQiW+8z67XjlYSwoeKhPDTPTDIocZ1YrH0NZ6LbDyG+3oPDNNT7EOBfohz/R7j4rIzzvXOD59xDoxzLVyvJoQtYHWpaLyzKZYk9vodwrgLGO8fjCWjsbthHPGOu38vnm36eZd8q+g23kn42uLdTVW0n6PN4YJJzFksM4xr8tgcJ1JBH6mBcmDJaOz0e0kF51F2k0ONK14r8Y/FhxTxepCBI+7qxvh2LPskbE+BNgT6Ic79b4pxMcXGuTYu9IxVjHN/uqtrnEu86EwIE6EyFYs/tOMuYCxTi80s4mWwsbvoncAnM51DVd2MAeJuy5yGTWXiGbS4gxZ3P3v1iDueuxfPmsUYYwllvHYivhbb/ffSEc+txvNlccR1F+OJjVXi+EGRzMeOlbGJTuyg2vgalPk1X9LcbGyxM+x+xc9a7Hobd3m/CMRGEX8BPF/TiWw2rljqFT93sZT5LCDm6UNFMh/vN72ipuPytPpXoF/jXL/GuLgSjXN/+nk0znXn95JxLoO7CWEG5Ba6+DLwMWDr4uXf8czd3xd3Y3plB8fVDTfucsadlng4Ox7Aj2ebYrnowhaM6lZ012J+YlfK2An2Z8AXimW+dTvXZucT110seR3q2Ka4oxvX41BHPOcaL6ev69FsbPHH3IVAPFO3YbHxUfzhekrNX4vSbFyRvMfPXbwaJT5ginHGs8iRCF7Tw7v71vU687yaC/RznOvHGBczapz703VtnGv+811FCeNcFaqD2jQhzIBsFwoooIACCiiggAIKKKBAHQVMCOs4K56TAgoooIACCiiggAIKKJBBwIQwA7JdKKCAAgoooIACCiiggAJ1FDAhrOOseE4KKKCAAgoooIACCiigQAYBE8IMyHahgAIKKKCAAgoooIACCtRRwISwjrPiOSmggAIKKKCAAgoooIACGQRMCDMg24UCCiiggAIKKKCAAgooUEcBE8I6zornpIACCiiggAIKKKCAAgpkEDAhzIBsFwoMI7AqQebdwN3DlHsd8AiwJ3BXQlsDRf4RmADs3kIdiyqggAIKKNCqgHGuVTHLK9AFARPCLqDbpQKFwFsaJEYCPwGmAd9v+PrDwPPDiK0L/AWwujJDVTUh9BJUQAEFFMghYJzLoWwfCnQoYELYIaDVFShJYD1gMXAgcENCm5EMLkkoZ0LYJpLVFFBAAQVKFTDOlcppYwqUJ2BCWJ6lLSnQicDqAuWhwJXArsDFwBuA04E7hlgyOhWIf9sDK4H/Ao4HHmg4Oe8QdjJT1lVAAQUUaEfAONeOmnUUyCBgQpgB2S4USBBICZR/BC4Hfgs8CywdIiE8C5gFPAbEXcT9gY8UCeITxXmYECZMiEUUUEABBUoVMM6VymljCpQnYEJYnqUtKdCJQEqgjDuFVzd00mxTmRHAGsDvizuM55oQdjJF1lVAAQUU6EDAONcBnlUVqFLAhLBKXdtWIF0gJVBOBGY3SQh3Ar4CxIP8mzaUvQ74rAlh+oRYUgEFFFCgVAHjXKmcNqZAeQImhOVZ2pICnQikBMq1gVdWkxCOLXYcnVk8axhLRGPjmRuB+4H9TAg7mSLrKqCAAgp0IGCc6wDPqgpUKWBCWKWubSuQLpASKNcClq8mIdwLuB3YBni8odyTwL+ZEKZPhiUVUEABBUoXMM6VTmqDCpQjYEJYjqOtKNCpQBmB8hNAbBizBRBJYBx7FMngTSaEnU6R9RVQQAEFOhAwznWAZ1UFqhQwIaxS17YVSBcoI1BOKDaQuRu4ENi6eD3Fmt4hTJ8ISyqggAIKVCJgnKuE1UYV6FzAhLBzQ1tQoAyBMgJlnEe8YiJ2E41k8FHgBODLQLyywmcIy5gp21BAAQUUaEfAONeOmnUUyCDwP8RhV0h+mBu1AAAAAElFTkSuQmCC\" width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM automl_output_info ORDER BY mst_key;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "best_so_far_acc = []\n",
+ "best_so_far_loss = []\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT mst_key, validation_metrics_final, validation_loss_final FROM automl_output_info WHERE mst_key <= $mst_key; \n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " best_so_far_acc.append([mst_key, df_output_info['validation_metrics_final'].max()])\n",
+ " best_so_far_loss.append([mst_key, df_output_info['validation_loss_final'].min()])\n",
+ "\n",
+ "df1 = pd.DataFrame(best_so_far_acc,columns=['Trial','Validation Accuracy'])\n",
+ "df2 = pd.DataFrame(best_so_far_loss,columns=['Trial','Validation Loss'])\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Trial')\n",
+ "#ax_metric.set_ylabel('Accuracy')\n",
+ "ax_metric.set_title('Best Validation Accuracy')\n",
+ "#ax_metric.lines.remove(ax_metric.lines)\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Trial')\n",
+ "#ax_loss.set_ylabel('Cross Entropy Loss')\n",
+ "ax_loss.set_title('Best Validation Loss (Cross Entropy)')\n",
+ "\n",
+ "validation_metrics_final = df1['Validation Accuracy']\n",
+ "validation_loss_final = df2['Validation Loss']\n",
+ "iters1 = df1['Trial']\n",
+ "iters2 = df2['Trial']\n",
+ "\n",
+ "#ax_metric.plot(iters1, training_metrics_final, label=mst_key, marker='o')\n",
+ "#ax_metric.plot(iters1, validation_metrics_final, marker='o', linestyle='None', markersize=4)\n",
+ "#ax_metric.plot(iters1, validation_metrics_final, marker='o', markersize=0.5)\n",
+ "ax_metric.plot(iters1, validation_metrics_final)\n",
+ " \n",
+ "#ax_loss.plot(iters2, training_loss_final, label=mst_key, marker='o')\n",
+ "#ax_loss.plot(iters2, validation_loss_final, marker='o', linestyle='None', markersize=4)\n",
+ "ax_loss.plot(iters2, validation_loss_final)\n",
+ "\n",
+ "plt.legend();\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pred\"></a>\n",
+ "# 6. Predict\n",
+ "\n",
+ "Now predict using model we built. We will use the validation data set for prediction as well, which is not usual but serves to show the syntax:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "30 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>class_name</th>\n",
+ " <th>class_value</th>\n",
+ " <th>prob</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.77083427</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.76736474</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7637215</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.76102996</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7710857</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7592268</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7686342</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.76880336</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7689748</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.77831817</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7722787</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>47</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.76963973</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7690843</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>60</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5698719</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>65</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.71937233</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>69</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.7692964</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>75</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.8146459</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>79</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.5106894</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>80</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.7508131</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.77062976</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>105</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.9310901</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>107</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.9202143</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>110</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.9461502</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.53245807</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>130</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.5506391</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>136</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.58871216</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>139</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.76892227</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.94731</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>146</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.7525016</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5657851</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'class_text', u'Iris-setosa', 0.77083427),\n",
+ " (2, u'class_text', u'Iris-setosa', 0.76736474),\n",
+ " (3, u'class_text', u'Iris-setosa', 0.7637215),\n",
+ " (4, u'class_text', u'Iris-setosa', 0.76102996),\n",
+ " (6, u'class_text', u'Iris-setosa', 0.7710857),\n",
+ " (7, u'class_text', u'Iris-setosa', 0.7592268),\n",
+ " (8, u'class_text', u'Iris-setosa', 0.7686342),\n",
+ " (10, u'class_text', u'Iris-setosa', 0.76880336),\n",
+ " (18, u'class_text', u'Iris-setosa', 0.7689748),\n",
+ " (19, u'class_text', u'Iris-setosa', 0.77831817),\n",
+ " (28, u'class_text', u'Iris-setosa', 0.7722787),\n",
+ " (47, u'class_text', u'Iris-setosa', 0.76963973),\n",
+ " (50, u'class_text', u'Iris-setosa', 0.7690843),\n",
+ " (60, u'class_text', u'Iris-virginica', 0.5698719),\n",
+ " (65, u'class_text', u'Iris-versicolor', 0.71937233),\n",
+ " (69, u'class_text', u'Iris-versicolor', 0.7692964),\n",
+ " (75, u'class_text', u'Iris-versicolor', 0.8146459),\n",
+ " (79, u'class_text', u'Iris-versicolor', 0.5106894),\n",
+ " (80, u'class_text', u'Iris-versicolor', 0.7508131),\n",
+ " (82, u'class_text', u'Iris-versicolor', 0.77062976),\n",
+ " (105, u'class_text', u'Iris-virginica', 0.9310901),\n",
+ " (107, u'class_text', u'Iris-virginica', 0.9202143),\n",
+ " (110, u'class_text', u'Iris-virginica', 0.9461502),\n",
+ " (120, u'class_text', u'Iris-versicolor', 0.53245807),\n",
+ " (130, u'class_text', u'Iris-versicolor', 0.5506391),\n",
+ " (136, u'class_text', u'Iris-virginica', 0.58871216),\n",
+ " (139, u'class_text', u'Iris-virginica', 0.76892227),\n",
+ " (145, u'class_text', u'Iris-virginica', 0.94731),\n",
+ " (146, u'class_text', u'Iris-virginica', 0.7525016),\n",
+ " (147, u'class_text', u'Iris-virginica', 0.5657851)]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('automl_output', -- model\n",
+ " 'iris_test', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'attributes', -- independent var\n",
+ " 'iris_predict', -- output table\n",
+ " 'response', -- prediction type\n",
+ " FALSE, -- use gpus\n",
+ " 13 -- MST key\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_predict ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(3L,)]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id) \n",
+ "WHERE iris_predict.class_value != iris_test.class_text;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Percent missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>90.00</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('90.00'),)]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from\n",
+ " (select iris_test.class_text as actual, iris_predict.class_value as estimated\n",
+ " from iris_predict inner join iris_test\n",
+ " on iris_test.id=iris_predict.id) q\n",
+ "WHERE q.actual=q.estimated;"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/Train-multiple-models/Define-model-configurations-v2.ipynb b/community-artifacts/Deep-learning/Train-multiple-models/Define-model-configurations-v2.ipynb
new file mode 100755
index 0000000..fbe5ee5
--- /dev/null
+++ b/community-artifacts/Deep-learning/Train-multiple-models/Define-model-configurations-v2.ipynb
@@ -0,0 +1,2025 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Define model configurations\n",
+ "This module generates model configurations using grid search or random search.\n",
+ "\n",
+ "Once the configurations are defined, they can be used by the fit function in Train Model Configurations. By model configurations we mean both hyperparameters and model architectures. The output table from this module defines the combinations of model architectures, compile and fit parameters to be trained in parallel.\n",
+ "\n",
+ "This utility was added in MADlib 1.17.0. Improvements were made in MADlib 1.18.0 including support for custom loss functions and custom metrics.\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#define_model_arch\">1. Define model architecture table</a>\n",
+ "\n",
+ "<a href=\"#load_model_arch\">2. Load model architecture</a>\n",
+ "\n",
+ "<a href=\"#generate_configs\">3. Generate model configurations</a>\n",
+ "\n",
+ " - <a href=\"#grid_search\">3a. Grid search</a>\n",
+ " \n",
+ " - <a href=\"#random_search\">3b. Random search</a>\n",
+ " \n",
+ " - <a href=\"#incremental_load\">3c. Incremental loading</a>\n",
+ " \n",
+ "<a href=\"#load_model_selection_manual\">4. Create model selection table manually</a>\n",
+ "\n",
+ "<a href=\"#custom\">5. Custom loss functions and custom metrics NOT COMPLETE</a>\n",
+ "\n",
+ "<a href=\"#load_model_selection\">6. Load model selection table [deprecated]</a>\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "The sql extension is already loaded. To reload it, use:\n",
+ " %reload_ext sql\n"
+ ]
+ }
+ ],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g9d9f756, cmake configuration time: Thu Mar 4 23:11:53 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g9d9f756, cmake configuration time: Thu Mar 4 23:11:53 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"define_model_arch\"></a>\n",
+ "# 1. Define model architecture table\n",
+ "The model selection loader works in conjunction with the model architecture table, so we first create a model architecture table with two different models. See http://madlib.apache.org/docs/latest/group__grp__keras__model__arch.html for more details on the model architecture table.\n",
+ "\n",
+ "Import Keras libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow import keras\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture with 1 hidden layer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_4\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense_14 (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_15 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_16 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 193\n",
+ "Trainable params: 193\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1 = Sequential()\n",
+ "model1.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model1.add(Dense(10, activation='relu'))\n",
+ "model1.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model1.summary();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_14\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_15\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_16\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_4\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model1.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 2, \"batch_input_shape\": [null, 3], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"new_dense\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture with 2 hidden layers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_5\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense_17 (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_18 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_19 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_20 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 303\n",
+ "Trainable params: 303\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model2 = Sequential()\n",
+ "model2.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model2.add(Dense(10, activation='relu'))\n",
+ "model2.add(Dense(10, activation='relu'))\n",
+ "model2.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model2.summary();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_17\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_18\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_19\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_20\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_5\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 44,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model2.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_model_arch\"></a>\n",
+ "# 2. Load model architecture\n",
+ "\n",
+ "Load both into model architecture table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>model_arch</th>\n",
+ " <th>model_weights</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>__internal_madlib_id__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>None</td>\n",
+ " <td>Sophie</td>\n",
+ " <td>MLP with 1 hidden layer</td>\n",
+ " <td>__madlib_temp_61202069_1614901986_7314581__</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>None</td>\n",
+ " <td>Maria</td>\n",
+ " <td>MLP with 2 hidden layers</td>\n",
+ " <td>__madlib_temp_12006647_1614901987_43673839__</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1340 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'MLP with 1 hidden layer', u'__madlib_temp_61202069_1614901986_7314581__'),\n",
+ " (2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1835 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Maria', u'MLP with 2 hidden layers', u'__madlib_temp_12006647_1614901987_43673839__')]"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS model_arch_library;\n",
+ "\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Sophie', -- Name\n",
+ " 'MLP with 1 hidden layer' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Maria', -- Name\n",
+ " 'MLP with 2 hidden layers' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT * FROM model_arch_library ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"generate_configs\"></a>\n",
+ "# 3. Generate model configurations\n",
+ "\n",
+ "<a id=\"grid_search\"></a>\n",
+ "## 3a. Grid search\n",
+ "\n",
+ "The output table for grid search contains the unique combinations of model architectures, compile and fit parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 1, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
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+ " (9, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
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+ " (11, 2, u\"optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
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+ " (13, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (14, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (15, 2, u\"optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (16, 2, u\"optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128')]"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.generate_model_configs(\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " $$\n",
+ " {'loss': ['categorical_crossentropy'], \n",
+ " 'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ], \n",
+ " 'metrics': ['accuracy']}\n",
+ " $$, -- compile_param_grid \n",
+ " $$ \n",
+ " { 'batch_size': [64, 128],\n",
+ " 'epochs': [10] \n",
+ " } \n",
+ " $$, -- fit_param_grid \n",
+ " 'grid' -- search_type \n",
+ " );\n",
+ "\n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note that above uses the same learning rate for the two optimizers. If you wanted to use different learning rates and different parameters for different optimizers (common):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 1, u\"optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (2, 1, u\"optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (3, 2, u\"optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (4, 2, u\"optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
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+ " (6, 1, u\"optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
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+ " (10, 2, u\"optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (11, 2, u\"optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (12, 2, u\"optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (13, 1, u\"optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (14, 1, u\"optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (15, 1, u\"optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (16, 1, u\"optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (17, 2, u\"optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (18, 2, u\"optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (19, 2, u\"optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (20, 2, u\"optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128')]"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.generate_model_configs(\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " $$\n",
+ " {'loss': ['categorical_crossentropy'], \n",
+ " 'optimizer_params_list': [\n",
+ " {'optimizer': ['SGD']}, \n",
+ " {'optimizer': ['SGD'], 'lr': [0.0001, 0.001], 'momentum': [0.95]}, \n",
+ " {'optimizer': ['Adam'], 'lr': [0.01, 0.1], 'decay': [1e-4]}], \n",
+ " 'metrics': ['accuracy']}\n",
+ " $$, -- compile_param_grid \n",
+ " $$ \n",
+ " { 'batch_size': [64, 128],\n",
+ " 'epochs': [10] \n",
+ " } \n",
+ " $$, -- fit_param_grid \n",
+ " 'grid' -- search_type \n",
+ " );\n",
+ "\n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"random_search\"></a>\n",
+ "## 3b. Random search\n",
+ "\n",
+ "The output table for random search contains the specified number of model architectures, compile and fit parameters, sampled from the specified distributions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "20 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.0347167931002948,decay=4.746966178774611e-06)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01062006045632861,decay=1.1876016717166215e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.0006995070125407458,momentum=0.9844790514730665)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.07439975848075757,decay=1.7976337634506005e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.09030450672567254,decay=1.340890767690431e-06)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01357387578284614,decay=2.3014993523846666e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.00010336714004241796,momentum=0.9711372680116186)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.00011116485234161093,momentum=0.9664752194346332)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.0003071392825766392,momentum=0.9697893478568044)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.03540256307419597,decay=2.7490870549984347e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.00026429087119428287,momentum=0.9702132562449013)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.04882317737663686,decay=8.006807036282709e-06)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.0005040379351745158,momentum=0.9863934944304705)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.00037668508410008814,momentum=0.978821521218891)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.016426175651771575,decay=1.6439282808391488e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.00046988338854109496,momentum=0.988290883937812)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.0005402557401986037,momentum=0.9795021324622476)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.012596752275640428,decay=1.2801865417619381e-06)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01055415187064375,decay=7.646989120220466e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.00014021314734214438,momentum=0.9663397507032889)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ " (19, 2, u\"optimizer='Adam(lr=0.01055415187064375,decay=7.646989120220466e-05)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (20, 1, u\"optimizer='SGD(lr=0.00014021314734214438,momentum=0.9663397507032889)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64')]"
+ ]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.generate_model_configs(\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " $$\n",
+ " {'loss': ['categorical_crossentropy'], \n",
+ " 'optimizer_params_list': [ \n",
+ " {'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']}, \n",
+ " {'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}], \n",
+ " 'metrics': ['accuracy']}\n",
+ " $$, -- compile_param_grid \n",
+ " $$ \n",
+ " { 'batch_size': [64, 128],\n",
+ " 'epochs': [10] \n",
+ " } \n",
+ " $$, -- fit_param_grid \n",
+ " 'random', -- search_type\n",
+ " 20 -- num_configs\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"incremental_load\"></a>\n",
+ "# 3c. Incremental loading for more complex combinations\n",
+ "\n",
+ "If it is easier to generate the model configurations incrementally rather than all at once, you can do that by not dropping the model selection table and associated summary table, in which case the new model configurations will be appended to the existing table. Here we combine 2 of the previous examples in to a single output table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 1, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
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+ " (8, 1, u\"optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (9, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (10, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (11, 2, u\"optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (12, 2, u\"optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (13, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (14, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (15, 2, u\"optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64'),\n",
+ " (16, 2, u\"optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128')]"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql \n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.generate_model_configs(\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " $$\n",
+ " {'loss': ['categorical_crossentropy'], \n",
+ " 'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ], \n",
+ " 'metrics': ['accuracy']}\n",
+ " $$, -- compile_param_grid \n",
+ " $$ \n",
+ " { 'batch_size': [64, 128],\n",
+ " 'epochs': [10] \n",
+ " } \n",
+ " $$, -- fit_param_grid \n",
+ " 'grid' -- search_type \n",
+ " );\n",
+ "\n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now add to the existing table and note that mst_key continues where it left off:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "36 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.00020031615564004395,momentum=0.9724038009180801)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.07529364006470769,decay=1.463102386655202e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.017537612203171578,decay=9.268965340542783e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.02436723652830891,decay=2.7036693659868636e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.0009162225178908051,momentum=0.9636373679078051)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>22</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.00020973934011486018,momentum=0.9810505351311615)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>23</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.00011669504881554843,momentum=0.9563917160422619)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>24</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.019887949889421844,decay=1.3512689688436213e-06)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>25</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.06844958467546351,decay=1.0949453143707621e-06)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>26</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.0279719469411538,decay=3.116565475127251e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.0005340915863494089,momentum=0.9846555995292319)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.00037236518835129966,momentum=0.9750593509631483)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.0001703149580002491,momentum=0.9516827304557754)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>30</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.0003205092574897573,momentum=0.9745610627224451)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>31</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.000638802198775629,momentum=0.9896674744988915)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.0007145264848827797,momentum=0.9859303213231139)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.053811310244363884,decay=5.1052295876998844e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.0617217468673046,decay=2.0871014466512653e-06)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.013012045649000626,decay=5.7173240691732966e-05)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>36</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.000575290475361327,momentum=0.9883738353302843)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
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+ " (35, 1, u\"optimizer='Adam(lr=0.013012045649000626,decay=5.7173240691732966e-05)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=128'),\n",
+ " (36, 1, u\"optimizer='SGD(lr=0.000575290475361327,momentum=0.9883738353302843)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=10,batch_size=64')]"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT madlib.generate_model_configs(\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " $$\n",
+ " {'loss': ['categorical_crossentropy'], \n",
+ " 'optimizer_params_list': [ \n",
+ " {'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']}, \n",
+ " {'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}], \n",
+ " 'metrics': ['accuracy']}\n",
+ " $$, -- compile_param_grid \n",
+ " $$ \n",
+ " { 'batch_size': [64, 128],\n",
+ " 'epochs': [10] \n",
+ " } \n",
+ " $$, -- fit_param_grid \n",
+ " 'random', -- search_type\n",
+ " 20 -- num_configs\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_model_selection_manual\"></a>\n",
+ "# 4. Create model selection table manually\n",
+ "\n",
+ "If you want more control over the content of the model selection table, you could use grid or random search to generate a large number of combinations, then SELECT a subset of rows for training.\n",
+ "\n",
+ "Alternatively, you could manually create the model selection table and the associated summary table. Both must be created since they are needed by the multiple model fit module.\n",
+ "\n",
+ "For example, let's say we don't want all combinations but only want batch_size=4 for model_id=1 and batch_size=8 for model_id=2:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "Done.\n",
+ "6 rows affected.\n",
+ "6 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_arch_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (2, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (3, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (4, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
+ " (5, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
+ " (6, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1')]"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table_manual;\n",
+ "\n",
+ "CREATE TABLE mst_table_manual(\n",
+ " mst_key serial,\n",
+ " model_arch_id integer,\n",
+ " compile_params varchar,\n",
+ " fit_params varchar\n",
+ ");\n",
+ "\n",
+ "INSERT INTO mst_table_manual(model_arch_id, compile_params, fit_params) VALUES\n",
+ "(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),\n",
+ "(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),\n",
+ "(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),\n",
+ "(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'),\n",
+ "(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'),\n",
+ "(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=8,epochs=1');\n",
+ "\n",
+ "SELECT * FROM mst_table_manual ORDER BY mst_key; "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create the summary table which must be named with the model selection output table appended by \"_summary\":"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_arch_table</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>model_arch_library</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'model_arch_library',)]"
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table_manual_summary;\n",
+ "\n",
+ "CREATE TABLE mst_table_manual_summary (\n",
+ " model_arch_table varchar\n",
+ ");\n",
+ "\n",
+ "INSERT INTO mst_table_manual_summary(model_arch_table) VALUES\n",
+ "('model_arch_library');\n",
+ "\n",
+ "SELECT * FROM mst_table_manual_summary; "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"custom\"></a>\n",
+ "# 5. Custom loss functions and custom metrics\n",
+ "\n",
+ "Define custom functions using the utility \"Define Custom Functions\". Psycopg is a PostgreSQL database adapter for the Python programming language. Note need to use the psycopg2.Binary() method to pass as bytes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import database connector psycopg2 and create connection cursor\n",
+ "import psycopg2 as p2\n",
+ "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
+ "cur = conn.cursor()\n",
+ "\n",
+ "# import Dill and define functions\n",
+ "import dill\n",
+ "\n",
+ "# custom loss\n",
+ "def squared_error(y_true, y_pred):\n",
+ " import tensorflow.keras.backend as K\n",
+ " return K.square(y_pred - y_true)\n",
+ "pb_squared_error=dill.dumps(squared_error)\n",
+ "\n",
+ "# custom metric\n",
+ "def rmse(y_true, y_pred):\n",
+ " import tensorflow.keras.backend as K\n",
+ " return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1))\n",
+ "pb_rmse=dill.dumps(rmse)\n",
+ "\n",
+ "# call load function\n",
+ "cur.execute(\"DROP TABLE IF EXISTS madlib.custom_function_table\")\n",
+ "cur.execute(\"SELECT madlib.load_custom_function('custom_function_table', %s,'squared_error', 'squared error')\", [p2.Binary(pb_squared_error)])\n",
+ "cur.execute(\"SELECT madlib.load_custom_function('custom_function_table', %s,'rmse', 'root mean square error')\", [p2.Binary(pb_rmse)])\n",
+ "conn.commit()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "16 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.001)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=64</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='SGD(lr=0.01)',metrics=['rmse'],loss='squared_error'</td>\n",
+ " <td>epochs=10,batch_size=128</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ " (12, 2, u\"optimizer='SGD(lr=0.001)',metrics=['rmse'],loss='squared_error'\", u'epochs=10,batch_size=128'),\n",
+ " (13, 2, u\"optimizer='Adam(lr=0.01)',metrics=['rmse'],loss='squared_error'\", u'epochs=10,batch_size=64'),\n",
+ " (14, 2, u\"optimizer='Adam(lr=0.01)',metrics=['rmse'],loss='squared_error'\", u'epochs=10,batch_size=128'),\n",
+ " (15, 2, u\"optimizer='SGD(lr=0.01)',metrics=['rmse'],loss='squared_error'\", u'epochs=10,batch_size=64'),\n",
+ " (16, 2, u\"optimizer='SGD(lr=0.01)',metrics=['rmse'],loss='squared_error'\", u'epochs=10,batch_size=128')]"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "SELECT madlib.generate_model_configs(\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " $$\n",
+ " {'loss': ['squared_error'],\n",
+ " 'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ],\n",
+ " 'metrics': ['rmse']}\n",
+ " $$, -- compile_param_grid\n",
+ " $$\n",
+ " { 'batch_size': [64, 128],\n",
+ " 'epochs': [10]\n",
+ " }\n",
+ " $$, -- fit_param_grid\n",
+ " 'grid', -- search_type\n",
+ " NULL, -- num_configs\n",
+ " NULL, -- random_state\n",
+ " 'custom_function_table' -- table with custom functions\n",
+ " );\n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_model_selection\"></a>\n",
+ "# 6. Load model selection table [deprecated]\n",
+ "\n",
+ "#### This method is deprecated and replaced by generate_model_configs() method described above.\n",
+ "\n",
+ "Select the model(s) from the model architecture table that you want to run, along with the compile and fit parameters. Unique combinations will be created for the set of model selection parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=4,epochs=1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']</td>\n",
+ " <td>batch_size=8,epochs=1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ " (6, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
+ " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (8, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
+ " (9, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (10, 2, u\"loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
+ " (11, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
+ " (12, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']\", u'batch_size=8,epochs=1')]"
+ ]
+ },
+ "execution_count": 55,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " ARRAY[ -- compile params\n",
+ " $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,\n",
+ " $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$\n",
+ " ],\n",
+ " ARRAY[ -- fit params\n",
+ " $$batch_size=4,epochs=1$$,\n",
+ " $$batch_size=8,epochs=1$$\n",
+ " ]\n",
+ " );\n",
+ " \n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb b/community-artifacts/Deep-learning/Train-multiple-models/MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb
similarity index 100%
rename from community-artifacts/Deep-learning/MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb
rename to community-artifacts/Deep-learning/Train-multiple-models/MADlib-Keras-model-selection-CNN-cifar10-v1.ipynb
diff --git a/community-artifacts/Deep-learning/Train-multiple-models/MADlib-Keras-model-selection-MLP-v1.ipynb b/community-artifacts/Deep-learning/Train-multiple-models/MADlib-Keras-model-selection-MLP-v1.ipynb
new file mode 100644
index 0000000..4ae9eae
--- /dev/null
+++ b/community-artifacts/Deep-learning/Train-multiple-models/MADlib-Keras-model-selection-MLP-v1.ipynb
@@ -0,0 +1,6279 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Model Selection for Multilayer Perceptron Using Keras and MADlib\n",
+ "\n",
+ "E2E classification example using MADlib calling a Keras MLP for different hyperparameters and model architectures.\n",
+ "\n",
+ "Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this workbook we use the well known iris data set from https://archive.ics.uci.edu/ml/datasets/iris to help get you started. It is similar to the example in user docs http://madlib.apache.org/docs/latest/index.html\n",
+ "\n",
+ "For more realistic examples please refer to the deep learning notebooks at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#class\">Classification</a>\n",
+ "\n",
+ "* <a href=\"#create_input_data\">1. Create input data</a>\n",
+ "\n",
+ "* <a href=\"#pp\">2. Call preprocessor for deep learning</a>\n",
+ "\n",
+ "* <a href=\"#load\">3. Define and load model architecture</a>\n",
+ "\n",
+ "* <a href=\"#def_mst\">4. Define and load model selection tuples</a>\n",
+ "\n",
+ "* <a href=\"#train\">5. Train</a>\n",
+ "\n",
+ "* <a href=\"#eval\">6. Evaluate</a>\n",
+ "\n",
+ "* <a href=\"#pred\">7. Predict</a>\n",
+ "\n",
+ "<a href=\"#class2\">Classification with Other Parameters</a>\n",
+ "\n",
+ "* <a href=\"#val_dataset\">1. Validation dataset</a>\n",
+ "\n",
+ "* <a href=\"#pred_prob\">2. Predict probabilities</a>\n",
+ "\n",
+ "* <a href=\"#warm_start\">3. Warm start</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g14a91ce, cmake configuration time: Fri Mar 5 23:08:38 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g14a91ce, cmake configuration time: Fri Mar 5 23:08:38 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"class\"></a>\n",
+ "# Classification\n",
+ "\n",
+ "<a id=\"create_input_data\"></a>\n",
+ "# 1. Create input data\n",
+ "\n",
+ "Load iris data set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "Done.\n",
+ "150 rows affected.\n",
+ "150 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>attributes</th>\n",
+ " <th>class_text</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>[Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>[Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>[Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>[Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>22</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>23</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>24</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>25</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>26</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>30</td>\n",
+ " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>31</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>[Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>36</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>37</td>\n",
+ " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>38</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>39</td>\n",
+ " <td>[Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>40</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>41</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>42</td>\n",
+ " <td>[Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>43</td>\n",
+ " <td>[Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>44</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>47</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>48</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>49</td>\n",
+ " <td>[Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>51</td>\n",
+ " <td>[Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>52</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>53</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>54</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>57</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>58</td>\n",
+ " <td>[Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>59</td>\n",
+ " <td>[Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>60</td>\n",
+ " <td>[Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>61</td>\n",
+ " <td>[Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>62</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>64</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>65</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>67</td>\n",
+ " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>68</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>69</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>70</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>71</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>72</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>74</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>75</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>76</td>\n",
+ " <td>[Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>77</td>\n",
+ " <td>[Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>79</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>80</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>81</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>83</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>84</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>85</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>86</td>\n",
+ " <td>[Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>87</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>88</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>89</td>\n",
+ " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>90</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>91</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>92</td>\n",
+ " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>93</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>[Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>95</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>96</td>\n",
+ " <td>[Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>97</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>98</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>[Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>101</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>[Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>104</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>105</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
+ " <td>[Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>107</td>\n",
+ " <td>[Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>[Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>109</td>\n",
+ " <td>[Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>110</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>111</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>112</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
+ " <td>[Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>115</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>116</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>118</td>\n",
+ " <td>[Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>119</td>\n",
+ " <td>[Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>122</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>[Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>124</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>125</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>127</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>128</td>\n",
+ " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>129</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>130</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>131</td>\n",
+ " <td>[Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>132</td>\n",
+ " <td>[Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>133</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>134</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>135</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>136</td>\n",
+ " <td>[Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>137</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>138</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>139</td>\n",
+ " <td>[Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>140</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>141</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>142</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>143</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>144</td>\n",
+ " <td>[Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>146</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>148</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>[Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>150</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (2, [Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (3, [Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (4, [Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (5, [Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (6, [Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (7, [Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (8, [Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (9, [Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (10, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (11, [Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (12, [Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (13, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (14, [Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (15, [Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (16, [Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (17, [Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (18, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (19, [Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (20, [Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (21, [Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (22, [Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (23, [Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (24, [Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')], u'Iris-setosa'),\n",
+ " (25, [Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (26, [Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (27, [Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (28, [Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (29, [Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (30, [Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (31, [Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (32, [Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (33, [Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (34, [Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (35, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (36, [Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (37, [Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (38, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (39, [Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (40, [Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (41, [Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (42, [Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (43, [Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (44, [Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')], u'Iris-setosa'),\n",
+ " (45, [Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (46, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (47, [Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (48, [Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (49, [Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (50, [Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (51, [Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (52, [Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (53, [Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (54, [Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (55, [Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (56, [Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (57, [Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (58, [Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (59, [Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (60, [Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (61, [Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (62, [Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (63, [Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (64, [Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (65, [Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (66, [Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (67, [Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (68, [Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (69, [Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (70, [Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (71, [Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')], u'Iris-versicolor'),\n",
+ " (72, [Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (73, [Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (74, [Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (75, [Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (76, [Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (77, [Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (78, [Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')], u'Iris-versicolor'),\n",
+ " (79, [Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (80, [Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (81, [Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (82, [Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (83, [Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (84, [Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (85, [Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (86, [Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (87, [Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (88, [Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (89, [Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (90, [Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (91, [Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (92, [Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (93, [Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (94, [Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (95, [Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (96, [Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (97, [Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (98, [Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (99, [Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (100, [Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (101, [Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (102, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (103, [Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (104, [Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (105, [Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (106, [Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (107, [Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')], u'Iris-virginica'),\n",
+ " (108, [Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (109, [Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (110, [Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (111, [Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (112, [Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (113, [Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (114, [Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (115, [Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (116, [Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (117, [Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (118, [Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (119, [Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (120, [Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')], u'Iris-virginica'),\n",
+ " (121, [Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (122, [Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (123, [Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (124, [Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (125, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (126, [Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (127, [Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (128, [Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (129, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (130, [Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')], u'Iris-virginica'),\n",
+ " (131, [Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (132, [Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (133, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (134, [Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')], u'Iris-virginica'),\n",
+ " (135, [Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')], u'Iris-virginica'),\n",
+ " (136, [Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (137, [Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (138, [Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (139, [Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (140, [Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (141, [Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (142, [Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (143, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (144, [Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (145, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (146, [Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (147, [Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (148, [Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (149, [Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (150, [Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')], u'Iris-virginica')]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql \n",
+ "DROP TABLE IF EXISTS iris_data;\n",
+ "\n",
+ "CREATE TABLE iris_data(\n",
+ " id serial,\n",
+ " attributes numeric[],\n",
+ " class_text varchar\n",
+ ");\n",
+ "\n",
+ "INSERT INTO iris_data(id, attributes, class_text) VALUES\n",
+ "(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),\n",
+ "(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),\n",
+ "(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),\n",
+ "(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),\n",
+ "(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),\n",
+ "(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),\n",
+ "(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),\n",
+ "(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),\n",
+ "(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),\n",
+ "(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),\n",
+ "(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),\n",
+ "(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),\n",
+ "(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),\n",
+ "(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),\n",
+ "(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),\n",
+ "(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),\n",
+ "(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),\n",
+ "(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),\n",
+ "(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),\n",
+ "(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),\n",
+ "(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),\n",
+ "(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),\n",
+ "(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),\n",
+ "(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),\n",
+ "(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),\n",
+ "(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),\n",
+ "(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),\n",
+ "(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),\n",
+ "(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),\n",
+ "(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),\n",
+ "(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),\n",
+ "(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),\n",
+ "(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),\n",
+ "(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),\n",
+ "(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),\n",
+ "(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),\n",
+ "(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),\n",
+ "(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),\n",
+ "(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),\n",
+ "(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),\n",
+ "(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),\n",
+ "(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),\n",
+ "(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),\n",
+ "(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),\n",
+ "(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),\n",
+ "(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),\n",
+ "(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),\n",
+ "(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),\n",
+ "(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),\n",
+ "(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),\n",
+ "(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),\n",
+ "(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),\n",
+ "(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),\n",
+ "(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),\n",
+ "(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),\n",
+ "(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),\n",
+ "(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),\n",
+ "(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),\n",
+ "(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),\n",
+ "(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),\n",
+ "(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),\n",
+ "(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),\n",
+ "(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),\n",
+ "(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),\n",
+ "(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),\n",
+ "(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),\n",
+ "(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),\n",
+ "(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),\n",
+ "(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),\n",
+ "(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),\n",
+ "(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),\n",
+ "(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),\n",
+ "(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),\n",
+ "(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),\n",
+ "(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),\n",
+ "(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),\n",
+ "(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),\n",
+ "(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),\n",
+ "(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),\n",
+ "(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),\n",
+ "(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),\n",
+ "(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),\n",
+ "(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),\n",
+ "(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),\n",
+ "(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),\n",
+ "(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),\n",
+ "(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),\n",
+ "(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),\n",
+ "(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),\n",
+ "(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),\n",
+ "(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),\n",
+ "(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),\n",
+ "(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),\n",
+ "(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),\n",
+ "(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),\n",
+ "(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),\n",
+ "(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),\n",
+ "(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),\n",
+ "(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
+ "(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),\n",
+ "(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),\n",
+ "(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),\n",
+ "(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),\n",
+ "(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),\n",
+ "(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),\n",
+ "(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),\n",
+ "(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),\n",
+ "(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),\n",
+ "(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),\n",
+ "(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),\n",
+ "(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),\n",
+ "(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),\n",
+ "(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),\n",
+ "(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),\n",
+ "(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),\n",
+ "(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),\n",
+ "(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),\n",
+ "(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),\n",
+ "(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),\n",
+ "(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),\n",
+ "(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),\n",
+ "(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),\n",
+ "(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),\n",
+ "(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),\n",
+ "(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),\n",
+ "(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),\n",
+ "(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),\n",
+ "(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),\n",
+ "(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),\n",
+ "(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),\n",
+ "(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),\n",
+ "(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),\n",
+ "(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),\n",
+ "(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),\n",
+ "(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),\n",
+ "(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),\n",
+ "(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),\n",
+ "(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),\n",
+ "(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),\n",
+ "(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
+ "(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),\n",
+ "(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),\n",
+ "(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),\n",
+ "(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),\n",
+ "(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),\n",
+ "(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),\n",
+ "(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');\n",
+ "\n",
+ "SELECT * FROM iris_data ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create a test/validation dataset from the training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(120L,)]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_train, iris_test;\n",
+ "\n",
+ "-- Set seed so results are reproducible\n",
+ "SELECT setseed(0);\n",
+ "\n",
+ "SELECT madlib.train_test_split('iris_data', -- Source table\n",
+ " 'iris', -- Output table root name\n",
+ " 0.8, -- Train proportion\n",
+ " NULL, -- Test proportion (0.2)\n",
+ " NULL, -- Strata definition\n",
+ " NULL, -- Output all columns\n",
+ " NULL, -- Sample without replacement\n",
+ " TRUE -- Separate output tables\n",
+ " );\n",
+ "\n",
+ "SELECT COUNT(*) FROM iris_train;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp\"></a>\n",
+ "# 2. Call preprocessor for deep learning\n",
+ "Training dataset (uses training preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>attributes_shape</th>\n",
+ " <th>class_text_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[60, 4]</td>\n",
+ " <td>[60, 3]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[60, 4]</td>\n",
+ " <td>[60, 3]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([60, 4], [60, 3], 0), ([60, 4], [60, 3], 1)]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('iris_train', -- Source table\n",
+ " 'iris_train_packed', -- Output table\n",
+ " 'class_text', -- Dependent variable\n",
+ " 'attributes' -- Independent variable\n",
+ " ); \n",
+ "\n",
+ "SELECT attributes_shape, class_text_shape, buffer_id FROM iris_train_packed ORDER BY buffer_id;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train</td>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>60</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[3]</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train', u'iris_train_packed', [u'class_text'], [u'attributes'], [u'character varying'], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 60, 1.0, [3], 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validation dataset (uses validation preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>attributes_shape</th>\n",
+ " <th>class_text_shape</th>\n",
+ " <th>buffer_id</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[15, 4]</td>\n",
+ " <td>[15, 3]</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>[15, 4]</td>\n",
+ " <td>[15, 3]</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[([15, 4], [15, 3], 0), ([15, 4], [15, 3], 1)]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('iris_test', -- Source table\n",
+ " 'iris_test_packed', -- Output table\n",
+ " 'class_text', -- Dependent variable\n",
+ " 'attributes', -- Independent variable\n",
+ " 'iris_train_packed' -- From training preprocessor step\n",
+ " ); \n",
+ "\n",
+ "SELECT attributes_shape, class_text_shape, buffer_id FROM iris_test_packed ORDER BY buffer_id;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_test</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>15</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[3]</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_test', u'iris_test_packed', [u'class_text'], [u'attributes'], [u'character varying'], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 15, 1.0, [3], 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_test_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load\"></a>\n",
+ "# 3. Define and load model architecture\n",
+ "Import Keras libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow import keras\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture with 1 hidden layer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/tensorflow/python/ops/init_ops.py:1251: calling __init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 193\n",
+ "Trainable params: 193\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model1 = Sequential()\n",
+ "model1.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model1.add(Dense(10, activation='relu'))\n",
+ "model1.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model1.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model1.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture with 2 hidden layers:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_1\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense_3 (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_4 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_5 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_6 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 303\n",
+ "Trainable params: 303\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model2 = Sequential()\n",
+ "model2.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model2.add(Dense(10, activation='relu'))\n",
+ "model2.add(Dense(10, activation='relu'))\n",
+ "model2.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model2.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_1\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model2.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into model architecture table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>model_arch</th>\n",
+ " <th>model_weights</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>__internal_madlib_id__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>None</td>\n",
+ " <td>Sophie</td>\n",
+ " <td>MLP with 1 hidden layer</td>\n",
+ " <td>__madlib_temp_4017958_1614991901_4240024__</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_5', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_6', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_7', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>None</td>\n",
+ " <td>Maria</td>\n",
+ " <td>MLP with 2 hidden layers</td>\n",
+ " <td>__madlib_temp_28416680_1614991901_72274844__</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1340 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Sophie', u'MLP with 1 hidden layer', u'__madlib_temp_4017958_1614991901_4240024__'),\n",
+ " (2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1835 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, None, u'Maria', u'MLP with 2 hidden layers', u'__madlib_temp_28416680_1614991901_72274844__')]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS model_arch_library;\n",
+ "\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Sophie', -- Name\n",
+ " 'MLP with 1 hidden layer' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_6\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_7\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Maria', -- Name\n",
+ " 'MLP with 2 hidden layers' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT * FROM model_arch_library ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"def_mst\"></a>\n",
+ "# 4. Define and load model selection tuples"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Generate model configurations using grid search. The output table for grid search contains the unique combinations of model architectures, compile and fit parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, 1, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4'),\n",
+ " (2, 1, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8'),\n",
+ " (3, 1, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4'),\n",
+ " (4, 1, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8'),\n",
+ " (5, 1, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4'),\n",
+ " (6, 1, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8'),\n",
+ " (7, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4'),\n",
+ " (8, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8'),\n",
+ " (9, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4'),\n",
+ " (10, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8'),\n",
+ " (11, 2, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4'),\n",
+ " (12, 2, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8')]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS mst_table, mst_table_summary;\n",
+ "\n",
+ "SELECT madlib.generate_model_configs(\n",
+ " 'model_arch_library', -- model architecture table\n",
+ " 'mst_table', -- model selection table output\n",
+ " ARRAY[1,2], -- model ids from model architecture table\n",
+ " $$\n",
+ " {'loss': ['categorical_crossentropy'],\n",
+ " 'optimizer_params_list': [ {'optimizer': ['Adam'], 'lr': [0.001, 0.01, 0.1]} ],\n",
+ " 'metrics': ['accuracy']}\n",
+ " $$, -- compile_param_grid\n",
+ " $$\n",
+ " { 'batch_size': [4, 8],\n",
+ " 'epochs': [1]\n",
+ " }\n",
+ " $$, -- fit_param_grid\n",
+ " 'grid' -- search_type\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM mst_table ORDER BY mst_key;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is the name of the model architecture table that corresponds to the model selection table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>object_table</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'model_arch_library', None)]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM mst_table_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"train\"></a>\n",
+ "# 5. Train\n",
+ "Train multiple models:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit_multiple_model</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
+ " 'iris_multi_model', -- model_output_table\n",
+ " 'mst_table', -- model_selection_table\n",
+ " 10, -- num_iterations\n",
+ " FALSE -- use gpus\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_selection_table</th>\n",
+ " <th>object_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>warm_start</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>None</td>\n",
+ " <td>iris_multi_model</td>\n",
+ " <td>iris_multi_model_info</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>mst_table</td>\n",
+ " <td>None</td>\n",
+ " <td>10</td>\n",
+ " <td>10</td>\n",
+ " <td>False</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>2021-03-06 00:51:48.452654</td>\n",
+ " <td>2021-03-06 00:53:20.221035</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[1]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[10]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', None, u'iris_multi_model', u'iris_multi_model_info', [u'class_text'], [u'attributes'], u'model_arch_library', u'mst_table', None, 10, 10, False, None, None, datetime.datetime(2021, 3, 6, 0, 51, 48, 452654), datetime.datetime(2021, 3, 6, 0, 53, 20, 221035), u'1.18.0-dev', [1], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], [u'character varying'], 1.0, [10])]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View results for each model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[90.2427790164948]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.983333349228</td>\n",
+ " <td>0.201789721847</td>\n",
+ " <td>[0.983333349227905]</td>\n",
+ " <td>[0.201789721846581]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[88.9964590072632]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.134730249643</td>\n",
+ " <td>[0.933333337306976]</td>\n",
+ " <td>[0.134730249643326]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[88.7690601348877]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.402144879103</td>\n",
+ " <td>[0.933333337306976]</td>\n",
+ " <td>[0.402144879102707]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[90.9196391105652]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.416792035103</td>\n",
+ " <td>[0.933333337306976]</td>\n",
+ " <td>[0.416792035102844]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[89.534707069397]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.908333361149</td>\n",
+ " <td>0.19042557478</td>\n",
+ " <td>[0.908333361148834]</td>\n",
+ " <td>[0.19042557477951]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[89.273796081543]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.181902274489</td>\n",
+ " <td>[0.899999976158142]</td>\n",
+ " <td>[0.181902274489403]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[90.4800100326538]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.824999988079</td>\n",
+ " <td>0.303107827902</td>\n",
+ " <td>[0.824999988079071]</td>\n",
+ " <td>[0.30310782790184]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[89.7936120033264]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.808333337307</td>\n",
+ " <td>0.300039559603</td>\n",
+ " <td>[0.808333337306976]</td>\n",
+ " <td>[0.300039559602737]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[90.0158791542053]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.658333361149</td>\n",
+ " <td>0.869387447834</td>\n",
+ " <td>[0.658333361148834]</td>\n",
+ " <td>[0.869387447834015]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[91.1929490566254]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.558333337307</td>\n",
+ " <td>0.84612262249</td>\n",
+ " <td>[0.558333337306976]</td>\n",
+ " <td>[0.846122622489929]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[91.7660541534424]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.341666668653</td>\n",
+ " <td>1.10138702393</td>\n",
+ " <td>[0.341666668653488]</td>\n",
+ " <td>[1.10138702392578]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[91.5026919841766]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.341666668653</td>\n",
+ " <td>1.10163521767</td>\n",
+ " <td>[0.341666668653488]</td>\n",
+ " <td>[1.10163521766663]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(6, 1, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [90.2427790164948], [u'accuracy'], u'categorical_crossentropy', 0.983333349227905, 0.201789721846581, [0.983333349227905], [0.201789721846581], None, None, None, None),\n",
+ " (3, 1, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [88.9964590072632], [u'accuracy'], u'categorical_crossentropy', 0.933333337306976, 0.134730249643326, [0.933333337306976], [0.134730249643326], None, None, None, None),\n",
+ " (7, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [88.7690601348877], [u'accuracy'], u'categorical_crossentropy', 0.933333337306976, 0.402144879102707, [0.933333337306976], [0.402144879102707], None, None, None, None),\n",
+ " (1, 1, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [90.9196391105652], [u'accuracy'], u'categorical_crossentropy', 0.933333337306976, 0.416792035102844, [0.933333337306976], [0.416792035102844], None, None, None, None),\n",
+ " (10, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [89.534707069397], [u'accuracy'], u'categorical_crossentropy', 0.908333361148834, 0.19042557477951, [0.908333361148834], [0.19042557477951], None, None, None, None),\n",
+ " (9, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [89.273796081543], [u'accuracy'], u'categorical_crossentropy', 0.899999976158142, 0.181902274489403, [0.899999976158142], [0.181902274489403], None, None, None, None),\n",
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+ " (12, 2, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [89.7936120033264], [u'accuracy'], u'categorical_crossentropy', 0.808333337306976, 0.300039559602737, [0.808333337306976], [0.300039559602737], None, None, None, None),\n",
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+ " (8, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [91.1929490566254], [u'accuracy'], u'categorical_crossentropy', 0.558333337306976, 0.846122622489929, [0.558333337306976], [0.846122622489929], None, None, None, None),\n",
+ " (11, 2, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [91.7660541534424], [u'accuracy'], u'categorical_crossentropy', 0.341666668653488, 1.10138702392578, [0.341666668653488], [1.10138702392578], None, None, None, None),\n",
+ " (5, 1, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [91.5026919841766], [u'accuracy'], u'categorical_crossentropy', 0.341666668653488, 1.10163521766663, [0.341666668653488], [1.10163521766663], None, None, None, None)]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_info ORDER BY training_metrics_final DESC, training_loss_final;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"eval\"></a>\n",
+ "# 6. Evaluate\n",
+ "\n",
+ "Now run evaluate using model we built above:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>loss</th>\n",
+ " <th>metric</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.194916069508</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0.194916069507599, 0.899999976158142, [u'accuracy'], u'categorical_crossentropy')]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_validate;\n",
+ "SELECT madlib.madlib_keras_evaluate('iris_multi_model', -- model\n",
+ " 'iris_test_packed', -- test table\n",
+ " 'iris_validate', -- output table\n",
+ " NULL, -- use gpus\n",
+ " 9 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_validate;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pred\"></a>\n",
+ "# 7. Predict\n",
+ "\n",
+ "Now predict using model we built. We will use the validation data set for prediction as well, which is not usual but serves to show the syntax. The prediction is in the estimated_class_text column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "30 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>class_name</th>\n",
+ " <th>class_value</th>\n",
+ " <th>prob</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.9999999</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.9999999</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>30</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.9999999</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>31</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.9999999</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>49</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>1.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.99069124</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>64</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.9864196</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>70</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.9983382</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>76</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.9991603</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.9974559</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>84</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.60661113</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>92</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.9940832</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>98</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.9987955</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.7598468</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.8414144</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>107</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.715776</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.9163472</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.5081183</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.85080105</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.9842195</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>125</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6804195</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>127</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.81555897</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.92707217</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.7158722</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>148</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.55272627</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.7662018</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(3, u'class_text', u'Iris-setosa', 1.0),\n",
+ " (10, u'class_text', u'Iris-setosa', 0.9999999),\n",
+ " (12, u'class_text', u'Iris-setosa', 1.0),\n",
+ " (14, u'class_text', u'Iris-setosa', 0.9999999),\n",
+ " (18, u'class_text', u'Iris-setosa', 1.0),\n",
+ " (20, u'class_text', u'Iris-setosa', 1.0),\n",
+ " (30, u'class_text', u'Iris-setosa', 0.9999999),\n",
+ " (31, u'class_text', u'Iris-setosa', 0.9999999),\n",
+ " (49, u'class_text', u'Iris-setosa', 1.0),\n",
+ " (55, u'class_text', u'Iris-versicolor', 0.99069124),\n",
+ " (64, u'class_text', u'Iris-versicolor', 0.9864196),\n",
+ " (70, u'class_text', u'Iris-versicolor', 0.9983382),\n",
+ " (76, u'class_text', u'Iris-versicolor', 0.9991603),\n",
+ " (82, u'class_text', u'Iris-versicolor', 0.9974559),\n",
+ " (84, u'class_text', u'Iris-versicolor', 0.60661113),\n",
+ " (92, u'class_text', u'Iris-versicolor', 0.9940832),\n",
+ " (98, u'class_text', u'Iris-versicolor', 0.9987955),\n",
+ " (99, u'class_text', u'Iris-versicolor', 0.7598468),\n",
+ " (102, u'class_text', u'Iris-virginica', 0.8414144),\n",
+ " (107, u'class_text', u'Iris-virginica', 0.715776),\n",
+ " (114, u'class_text', u'Iris-virginica', 0.9163472),\n",
+ " (117, u'class_text', u'Iris-versicolor', 0.5081183),\n",
+ " (121, u'class_text', u'Iris-virginica', 0.85080105),\n",
+ " (123, u'class_text', u'Iris-virginica', 0.9842195),\n",
+ " (125, u'class_text', u'Iris-virginica', 0.6804195),\n",
+ " (127, u'class_text', u'Iris-versicolor', 0.81555897),\n",
+ " (145, u'class_text', u'Iris-virginica', 0.92707217),\n",
+ " (147, u'class_text', u'Iris-virginica', 0.7158722),\n",
+ " (148, u'class_text', u'Iris-versicolor', 0.55272627),\n",
+ " (149, u'class_text', u'Iris-virginica', 0.7662018)]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('iris_multi_model', -- model\n",
+ " 'iris_test', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'attributes', -- independent var\n",
+ " 'iris_predict', -- output table\n",
+ " 'response', -- prediction type\n",
+ " FALSE, -- use gpus\n",
+ " 9 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_predict ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(3L,)]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id) \n",
+ "WHERE iris_predict.class_value != iris_test.class_text;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Percent missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>90.00</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('90.00'),)]"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from\n",
+ " (select iris_test.class_text as actual, iris_predict.class_value as estimated\n",
+ " from iris_predict inner join iris_test\n",
+ " on iris_test.id=iris_predict.id) q\n",
+ "WHERE q.actual=q.estimated;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"class2\"></a>\n",
+ "# Classification with Other Parameters\n",
+ "\n",
+ "<a id=\"val_dataset\"></a>\n",
+ "# 1. Validation dataset\n",
+ "\n",
+ "Now use a validation dataset and compute metrics every 2nd iteration using the 'metrics_compute_frequency' parameter. This can help reduce run time if you do not need metrics computed at every iteration."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit_multiple_model</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
+ " 'iris_multi_model', -- model_output_table\n",
+ " 'mst_table', -- model_selection_table\n",
+ " 10, -- num_iterations\n",
+ " FALSE, -- use gpus\n",
+ " 'iris_test_packed', -- validation dataset\n",
+ " 3, -- metrics compute frequency\n",
+ " FALSE, -- warm start\n",
+ " 'Sophie L.', -- name\n",
+ " 'Model selection for iris dataset' -- description\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_selection_table</th>\n",
+ " <th>object_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>warm_start</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>iris_multi_model</td>\n",
+ " <td>iris_multi_model_info</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>mst_table</td>\n",
+ " <td>None</td>\n",
+ " <td>10</td>\n",
+ " <td>3</td>\n",
+ " <td>False</td>\n",
+ " <td>Sophie L.</td>\n",
+ " <td>Model selection for iris dataset</td>\n",
+ " <td>2021-03-06 00:53:31.218406</td>\n",
+ " <td>2021-03-06 00:55:25.621208</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[1]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[3, 6, 9, 10]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_test_packed', u'iris_multi_model', u'iris_multi_model_info', [u'class_text'], [u'attributes'], u'model_arch_library', u'mst_table', None, 10, 3, False, u'Sophie L.', u'Model selection for iris dataset', datetime.datetime(2021, 3, 6, 0, 53, 31, 218406), datetime.datetime(2021, 3, 6, 0, 55, 25, 621208), u'1.18.0-dev', [1], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], [u'character varying'], 1.0, [3, 6, 9, 10])]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View performance of each model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[31.5490398406982, 64.223620891571, 97.8899219036102, 113.156138896942]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.991666674614</td>\n",
+ " <td>0.177691921592</td>\n",
+ " <td>[0.824999988079071, 0.975000023841858, 0.933333337306976, 0.991666674613953]</td>\n",
+ " <td>[0.508709609508514, 0.290052831172943, 0.217903628945351, 0.177691921591759]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.20564225316</td>\n",
+ " <td>[0.833333313465118, 0.966666638851166, 0.933333337306976, 0.966666638851166]</td>\n",
+ " <td>[0.516587793827057, 0.316147029399872, 0.228292018175125, 0.205642253160477]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[30.4000718593597, 62.9767029285431, 96.690801858902, 112.145288944244]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.908333361149</td>\n",
+ " <td>0.203085869551</td>\n",
+ " <td>[0.933333337306976, 0.808333337306976, 0.958333313465118, 0.908333361148834]</td>\n",
+ " <td>[0.372362315654755, 0.304766088724136, 0.11820487678051, 0.203085869550705]</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.190864190459</td>\n",
+ " <td>[0.966666638851166, 0.833333313465118, 0.966666638851166, 0.933333337306976]</td>\n",
+ " <td>[0.347199022769928, 0.290798246860504, 0.110275268554688, 0.190864190459251]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[30.875373840332, 63.4593389034271, 97.1958589553833, 112.702126979828]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.883333325386</td>\n",
+ " <td>0.692279815674</td>\n",
+ " <td>[0.533333361148834, 0.616666674613953, 0.875, 0.883333325386047]</td>\n",
+ " <td>[1.08197057247162, 0.851473987102509, 0.729827761650085, 0.692279815673828]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.674779772758</td>\n",
+ " <td>[0.600000023841858, 0.666666686534882, 0.899999976158142, 0.899999976158142]</td>\n",
+ " <td>[1.05298256874084, 0.817528009414673, 0.710631787776947, 0.674779772758484]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[29.8903229236603, 62.4677069187164, 96.1764039993286, 111.539803981781]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.925000011921</td>\n",
+ " <td>0.176520362496</td>\n",
+ " <td>[0.833333313465118, 0.925000011920929, 0.774999976158142, 0.925000011920929]</td>\n",
+ " <td>[0.324734181165695, 0.182637020945549, 0.468331128358841, 0.176520362496376]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.2585529387</td>\n",
+ " <td>[0.866666674613953, 0.866666674613953, 0.866666674613953, 0.899999976158142]</td>\n",
+ " <td>[0.341204434633255, 0.261798053979874, 0.45467621088028, 0.258552938699722]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[31.7836039066315, 64.4592599868774, 98.1328208446503, 113.377946853638]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.891666650772</td>\n",
+ " <td>0.797108471394</td>\n",
+ " <td>[0.341666668653488, 0.491666674613953, 0.916666686534882, 0.891666650772095]</td>\n",
+ " <td>[1.09786474704742, 0.967048287391663, 0.838281869888306, 0.797108471393585]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.800795376301</td>\n",
+ " <td>[0.300000011920929, 0.433333337306976, 0.933333337306976, 0.899999976158142]</td>\n",
+ " <td>[1.07609903812408, 0.962578594684601, 0.834975183010101, 0.800795376300812]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[30.1456639766693, 62.722916841507, 96.4333670139313, 111.892151832581]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.816666662693</td>\n",
+ " <td>0.734887838364</td>\n",
+ " <td>[0.850000023841858, 0.958333313465118, 0.966666638851166, 0.816666662693024]</td>\n",
+ " <td>[0.335647404193878, 0.0894104242324829, 0.0672163665294647, 0.734887838363647]</td>\n",
+ " <td>0.866666674614</td>\n",
+ " <td>0.665323019028</td>\n",
+ " <td>[0.866666674613953, 0.966666638851166, 0.966666638851166, 0.866666674613953]</td>\n",
+ " <td>[0.320426166057587, 0.154994085431099, 0.204012081027031, 0.66532301902771]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[32.0452349185944, 64.7241299152374, 98.4015560150146, 113.899842977524]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.791666686535</td>\n",
+ " <td>0.772948563099</td>\n",
+ " <td>[0.316666662693024, 0.349999994039536, 0.725000023841858, 0.791666686534882]</td>\n",
+ " <td>[1.01266825199127, 0.905348658561707, 0.807280421257019, 0.772948563098907]</td>\n",
+ " <td>0.866666674614</td>\n",
+ " <td>0.740880072117</td>\n",
+ " <td>[0.400000005960464, 0.466666668653488, 0.800000011920929, 0.866666674613953]</td>\n",
+ " <td>[0.964996755123138, 0.868514597415924, 0.771895349025726, 0.740880072116852]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[30.6602540016174, 63.2428169250488, 96.9531948566437, 112.484740972519]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.691666662693</td>\n",
+ " <td>0.501820206642</td>\n",
+ " <td>[0.658333361148834, 0.658333361148834, 0.658333361148834, 0.691666662693024]</td>\n",
+ " <td>[0.654709756374359, 0.581917643547058, 1.33844769001007, 0.501820206642151]</td>\n",
+ " <td>0.766666650772</td>\n",
+ " <td>0.457984447479</td>\n",
+ " <td>[0.699999988079071, 0.699999988079071, 0.699999988079071, 0.766666650772095]</td>\n",
+ " <td>[0.592061340808868, 0.525563180446625, 1.17788350582123, 0.457984447479248]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[31.0910878181458, 63.7646949291229, 97.4185988903046, 112.939773797989]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.666666686535</td>\n",
+ " <td>0.50052946806</td>\n",
+ " <td>[0.433333337306976, 0.641666650772095, 0.649999976158142, 0.666666686534882]</td>\n",
+ " <td>[0.850135624408722, 0.611121952533722, 0.509139358997345, 0.50052946805954]</td>\n",
+ " <td>0.733333349228</td>\n",
+ " <td>0.459399551153</td>\n",
+ " <td>[0.466666668653488, 0.699999988079071, 0.699999988079071, 0.733333349227905]</td>\n",
+ " <td>[0.802468597888947, 0.571285247802734, 0.492577910423279, 0.459399551153183]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[29.6670269966125, 62.2440509796143, 95.9554150104523, 111.311369895935]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.733333349228</td>\n",
+ " <td>0.821944594383</td>\n",
+ " <td>[0.341666668653488, 0.341666668653488, 0.658333361148834, 0.733333349227905]</td>\n",
+ " <td>[1.06431686878204, 0.996406197547913, 0.869706034660339, 0.82194459438324]</td>\n",
+ " <td>0.699999988079</td>\n",
+ " <td>0.852133929729</td>\n",
+ " <td>[0.300000011920929, 0.300000011920929, 0.699999988079071, 0.699999988079071]</td>\n",
+ " <td>[1.09268116950989, 1.01670277118683, 0.891825795173645, 0.852133929729462]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[32.5322558879852, 65.2217888832092, 98.9477097988129, 114.400418996811]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.683333337307</td>\n",
+ " <td>0.455871999264</td>\n",
+ " <td>[0.725000023841858, 0.683333337306976, 0.683333337306976, 0.683333337306976]</td>\n",
+ " <td>[0.383917421102524, 0.457853585481644, 0.455943495035172, 0.455871999263763]</td>\n",
+ " <td>0.600000023842</td>\n",
+ " <td>0.488439053297</td>\n",
+ " <td>[0.800000011920929, 0.600000023841858, 0.600000023841858, 0.600000023841858]</td>\n",
+ " <td>[0.388951361179352, 0.50080794095993, 0.487448841333389, 0.488439053297043]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[32.2720308303833, 64.9502189159393, 98.6836059093475, 114.134181976318]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.675000011921</td>\n",
+ " <td>0.452209770679</td>\n",
+ " <td>[0.683333337306976, 0.675000011920929, 0.683333337306976, 0.675000011920929]</td>\n",
+ " <td>[0.492754250764847, 0.469423890113831, 0.571796059608459, 0.452209770679474]</td>\n",
+ " <td>0.600000023842</td>\n",
+ " <td>0.464268505573</td>\n",
+ " <td>[0.733333349227905, 0.766666650772095, 0.600000023841858, 0.600000023841858]</td>\n",
+ " <td>[0.438488334417343, 0.390993624925613, 0.690678656101227, 0.464268505573273]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(4, 1, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [31.5490398406982, 64.223620891571, 97.8899219036102, 113.156138896942], [u'accuracy'], u'categorical_crossentropy', 0.991666674613953, 0.177691921591759, [0.824999988079071, 0.975000023841858, 0.933333337306976, 0.991666674613953], [0.508709609508514, 0.290052831172943, 0.217903628945351, 0.177691921591759], 0.966666638851166, 0.205642253160477, [0.833333313465118, 0.966666638851166, 0.933333337306976, 0.966666638851166], [0.516587793827057, 0.316147029399872, 0.228292018175125, 0.205642253160477]),\n",
+ " (10, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [30.4000718593597, 62.9767029285431, 96.690801858902, 112.145288944244], [u'accuracy'], u'categorical_crossentropy', 0.908333361148834, 0.203085869550705, [0.933333337306976, 0.808333337306976, 0.958333313465118, 0.908333361148834], [0.372362315654755, 0.304766088724136, 0.11820487678051, 0.203085869550705], 0.933333337306976, 0.190864190459251, [0.966666638851166, 0.833333313465118, 0.966666638851166, 0.933333337306976], [0.347199022769928, 0.290798246860504, 0.110275268554688, 0.190864190459251]),\n",
+ " (2, 1, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [30.875373840332, 63.4593389034271, 97.1958589553833, 112.702126979828], [u'accuracy'], u'categorical_crossentropy', 0.883333325386047, 0.692279815673828, [0.533333361148834, 0.616666674613953, 0.875, 0.883333325386047], [1.08197057247162, 0.851473987102509, 0.729827761650085, 0.692279815673828], 0.899999976158142, 0.674779772758484, [0.600000023841858, 0.666666686534882, 0.899999976158142, 0.899999976158142], [1.05298256874084, 0.817528009414673, 0.710631787776947, 0.674779772758484]),\n",
+ " (3, 1, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [29.8903229236603, 62.4677069187164, 96.1764039993286, 111.539803981781], [u'accuracy'], u'categorical_crossentropy', 0.925000011920929, 0.176520362496376, [0.833333313465118, 0.925000011920929, 0.774999976158142, 0.925000011920929], [0.324734181165695, 0.182637020945549, 0.468331128358841, 0.176520362496376], 0.899999976158142, 0.258552938699722, [0.866666674613953, 0.866666674613953, 0.866666674613953, 0.899999976158142], [0.341204434633255, 0.261798053979874, 0.45467621088028, 0.258552938699722]),\n",
+ " (1, 1, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [31.7836039066315, 64.4592599868774, 98.1328208446503, 113.377946853638], [u'accuracy'], u'categorical_crossentropy', 0.891666650772095, 0.797108471393585, [0.341666668653488, 0.491666674613953, 0.916666686534882, 0.891666650772095], [1.09786474704742, 0.967048287391663, 0.838281869888306, 0.797108471393585], 0.899999976158142, 0.800795376300812, [0.300000011920929, 0.433333337306976, 0.933333337306976, 0.899999976158142], [1.07609903812408, 0.962578594684601, 0.834975183010101, 0.800795376300812]),\n",
+ " (9, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [30.1456639766693, 62.722916841507, 96.4333670139313, 111.892151832581], [u'accuracy'], u'categorical_crossentropy', 0.816666662693024, 0.734887838363647, [0.850000023841858, 0.958333313465118, 0.966666638851166, 0.816666662693024], [0.335647404193878, 0.0894104242324829, 0.0672163665294647, 0.734887838363647], 0.866666674613953, 0.66532301902771, [0.866666674613953, 0.966666638851166, 0.966666638851166, 0.866666674613953], [0.320426166057587, 0.154994085431099, 0.204012081027031, 0.66532301902771]),\n",
+ " (8, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [32.0452349185944, 64.7241299152374, 98.4015560150146, 113.899842977524], [u'accuracy'], u'categorical_crossentropy', 0.791666686534882, 0.772948563098907, [0.316666662693024, 0.349999994039536, 0.725000023841858, 0.791666686534882], [1.01266825199127, 0.905348658561707, 0.807280421257019, 0.772948563098907], 0.866666674613953, 0.740880072116852, [0.400000005960464, 0.466666668653488, 0.800000011920929, 0.866666674613953], [0.964996755123138, 0.868514597415924, 0.771895349025726, 0.740880072116852]),\n",
+ " (12, 2, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [30.6602540016174, 63.2428169250488, 96.9531948566437, 112.484740972519], [u'accuracy'], u'categorical_crossentropy', 0.691666662693024, 0.501820206642151, [0.658333361148834, 0.658333361148834, 0.658333361148834, 0.691666662693024], [0.654709756374359, 0.581917643547058, 1.33844769001007, 0.501820206642151], 0.766666650772095, 0.457984447479248, [0.699999988079071, 0.699999988079071, 0.699999988079071, 0.766666650772095], [0.592061340808868, 0.525563180446625, 1.17788350582123, 0.457984447479248]),\n",
+ " (6, 1, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [31.0910878181458, 63.7646949291229, 97.4185988903046, 112.939773797989], [u'accuracy'], u'categorical_crossentropy', 0.666666686534882, 0.50052946805954, [0.433333337306976, 0.641666650772095, 0.649999976158142, 0.666666686534882], [0.850135624408722, 0.611121952533722, 0.509139358997345, 0.50052946805954], 0.733333349227905, 0.459399551153183, [0.466666668653488, 0.699999988079071, 0.699999988079071, 0.733333349227905], [0.802468597888947, 0.571285247802734, 0.492577910423279, 0.459399551153183]),\n",
+ " (7, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [29.6670269966125, 62.2440509796143, 95.9554150104523, 111.311369895935], [u'accuracy'], u'categorical_crossentropy', 0.733333349227905, 0.82194459438324, [0.341666668653488, 0.341666668653488, 0.658333361148834, 0.733333349227905], [1.06431686878204, 0.996406197547913, 0.869706034660339, 0.82194459438324], 0.699999988079071, 0.852133929729462, [0.300000011920929, 0.300000011920929, 0.699999988079071, 0.699999988079071], [1.09268116950989, 1.01670277118683, 0.891825795173645, 0.852133929729462]),\n",
+ " (11, 2, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [32.5322558879852, 65.2217888832092, 98.9477097988129, 114.400418996811], [u'accuracy'], u'categorical_crossentropy', 0.683333337306976, 0.455871999263763, [0.725000023841858, 0.683333337306976, 0.683333337306976, 0.683333337306976], [0.383917421102524, 0.457853585481644, 0.455943495035172, 0.455871999263763], 0.600000023841858, 0.488439053297043, [0.800000011920929, 0.600000023841858, 0.600000023841858, 0.600000023841858], [0.388951361179352, 0.50080794095993, 0.487448841333389, 0.488439053297043]),\n",
+ " (5, 1, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [32.2720308303833, 64.9502189159393, 98.6836059093475, 114.134181976318], [u'accuracy'], u'categorical_crossentropy', 0.675000011920929, 0.452209770679474, [0.683333337306976, 0.675000011920929, 0.683333337306976, 0.675000011920929], [0.492754250764847, 0.469423890113831, 0.571796059608459, 0.452209770679474], 0.600000023841858, 0.464268505573273, [0.733333349227905, 0.766666650772095, 0.600000023841858, 0.600000023841858], [0.438488334417343, 0.390993624925613, 0.690678656101227, 0.464268505573273])]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_info ORDER BY validation_metrics_final DESC;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot validation results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%matplotlib notebook\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.ticker import MaxNLocator\n",
+ "from collections import defaultdict\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "sns.set_palette(sns.color_palette(\"hls\", 20))\n",
+ "plt.rcParams.update({'font.size': 12})\n",
+ "pd.set_option('display.max_colwidth', -1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "7 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "/* Put everything inside the global mpl namespace */\n",
+ "window.mpl = {};\n",
+ "\n",
+ "\n",
+ "mpl.get_websocket_type = function() {\n",
+ " if (typeof(WebSocket) !== 'undefined') {\n",
+ " return WebSocket;\n",
+ " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+ " return MozWebSocket;\n",
+ " } else {\n",
+ " alert('Your browser does not have WebSocket support.' +\n",
+ " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+ " 'Firefox 4 and 5 are also supported but you ' +\n",
+ " 'have to enable WebSockets in about:config.');\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+ " this.id = figure_id;\n",
+ "\n",
+ " this.ws = websocket;\n",
+ "\n",
+ " this.supports_binary = (this.ws.binaryType != undefined);\n",
+ "\n",
+ " if (!this.supports_binary) {\n",
+ " var warnings = document.getElementById(\"mpl-warnings\");\n",
+ " if (warnings) {\n",
+ " warnings.style.display = 'block';\n",
+ " warnings.textContent = (\n",
+ " \"This browser does not support binary websocket messages. \" +\n",
+ " \"Performance may be slow.\");\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " this.imageObj = new Image();\n",
+ "\n",
+ " this.context = undefined;\n",
+ " this.message = undefined;\n",
+ " this.canvas = undefined;\n",
+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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j6yzACX86NKqwsM5D9T8oPKdHAkkP0D80LprIeT06dbBjyM/XiSW7Jd+9M7STtg/2nRt5cebxJEPqenTzxu0lgTNHp2MFk8qEn4I6VraXtoJugIy8Agtb3w/yNtFBk4hAWrvA660hZAKgmSJpIxz4/pxnnzjwH/j/ydpY6EiJDmk9YvBYxgum6SZbTwhhLTCEisqYrpf0pLHSGdMO0HXGrqwtFV4u0mrHyOjUckx7QSjuvIRPdfantuSypdvSEjeaJ3mW1X+nYSaFwnuEkKGFuce4r4lJrSuOkbZpcwkrDyc8CaYsvGAyHEYmp7yca+JKKN+8gETYggEvIyA0IlNgNrTTiilE51dJFPHM08s3R1p+eJTFcYS4HfcMe0EvU64bgzAwstlHqJ5lmG+QZI4envwIo96hm8G6e3DcwHfnPOCkIX6xB7J1NmW4rmaZwM+VeAFOj1keGahTqK+ZWwD+xmBrpnULYxsTR3Hsw7PKfRwoj5xx0LIPpk/l8H/qLfsuq+ljLReMrAM61Bv0gOGQff43pLWTwbSEVFGvfyx8EV3ghD6AlXRpysI8IPBGwUSK0aoItlyVphOgeSKuXZoQeMHkR8pPlRm7j9PCCHHYb+0ctE90J6Yni6sLRPTsy4JGi1cPJjzPSM/7Lx144e9JSEkMbG7bZCouJqYnsqBH1Ca6WlR4zszxwAx/uQySkz4fpPvC0iieIvKwvW0v7XkDam9UCHwfQPrkVC3TEzv6htCe38kcMSfiojWSBJ7KjsqrpZWQ2d7iuG0qch520lyS7kpLy2f9htOEnMqVRIx7lHeinLfUfnzYOAuIaQcVNZcY1oC+d7EWeFFAOWgZZIEm1ZpRqgjZszT2V4UWVd+90QdgYBAwP8QEDrx0JowCIlSOrE1l38SO2eJ6Ul67IWuvvRkYlCwdPmMwkthXugx3y31OXUe9TnJG4kgLwTtuXh5BnHMpdzarmRUb+pbpp1gYDF6qdCjiqTV8ZKQ+oUXnrw459iM/ElvJXqcOKaDctVCyCc21D8c3zEyfEs56XHFnxMzEmDqURJDXhzz0r2l5dD/fvuERBCEUGwCgYBAQCAgEBAICAQEAgIBgYBAQCDQRREQhLCLLryYtkBAICAQEAgIBAQCAgGBgEBAICAQEIRQ7AGBgEBAICAQEAgIBAQCAgGBgEBAINBFERBRRrvowotpCwQEAgIBgYBAQCAgEBAICAQEAgIBkYdQ7AGBgEBAICAQEAgIBAQCAgGBgEBAINCFEVi3bt1NOp3u/szMTH14eHijSqU6LJWXzWZTNTY2hufn54cZjcY5w4YNe9UZZCLtRBfeSGLqAgGBgEBAICAQEAgIBAQCAgGBQOAhQLK3fv36G0NCQhhZnxHWnfE6kkS92Wx+YejQoYuckUbOXBDCwFt/IbFAQCAgEBAICAQEAgIBgYBAQCAgEADdR7OyspJ0Op2mJRxGo9GSm5tbNmHCBKY/abUECyE8zEQq9odAQCAgEBAIBDUCwaK/XF0koedcRUrUEwgIBAQCwYGAYnpOsYF8vC42m03oSh9jLLoXCAgEBAJ+gYBKJamuYNFfrmIq9JyrSIl6AgGBgEAgwBFQWs8Fi0IVijLAN74QXyAgEBAIuIqA0orSVbl8XE/oOR8DLLoXCAgEBAL+goDSek4QQn9ZeSGHQEAgIBAQCLiEgNKK0iWhfF9JEELfYyxGEAgIBAQCfoGA0npOEEK/WHYhhEBAICAQEAi4ioDSitJVuXxcTxBCHwMsuhcICAQEAv6CgNJ6ThBCf1l5IYdAQCAgEBAIuISA0orSQai+AKYDGAlgIIAVAE5tR+jjAdwC4GQAGQDyALwPYC5Dgbs04aZKghC6AZaoKhAQCAgEAhkBpfWcIISBvFuE7AIBgYBAoAsioLSidID4AgAvAVgD4GgAxS4QwmcADAfwDoBdAI4B8BiAXwBc7MbyCULoBliiqkBAICAQCGQElNZzghAG8m4RsgsEBAICgS6IgNKK0gFiNQCr/PdPASS5QAhZp6zFMk0FsAhATwD7XVzCdgkho22bzWaaEl3sMrircZ+EhIRA3i/BPVkxO4GAQCCoEFBazwlCGFTbR0xGICAQEAgEPwJKK8pWEHWVEDprTjfSbACjAKx2ccVaJYQkgGVlZSgvLxdksAWY3CuJiYlISkoSxNDFjSaqCQQEAp2PgNJ6ThDCzl9zIYFAQCAgEBAIuIGA0orSB4TwdgDPAUgHUOLi1FslhKWlpRIZTEtLQ0REhIvddY1qDQ0NKCoqkkhhcnJy15i0mKVAQCAQ8AgorecEIQz4LSMmIBAQCAgEuhYCSitKLxPCNACbAXwP4Ho3Vs4pIaR1cMeOHUhPT0dcXJwb3XWdqlVVVRIpHDBggLASdp1lFzMVCAQ0AkrrOUEIA3q7COEFAgIBgUDXQ0BpRelFQqiTg8lkAhgKoLKN1XsYwEOOP3f2NtBkMmH37t3o06cPdDp2L0pLBIxGI/bs2YO+fftCq9UKgAQCAgGBgN8joLSeE4TQ77eEEFAgIBAQCAgEHBFQWlF6iRBS334A4EwAJwLY4eaqOrUQ2smOIIStoykwcnOnieoCAYFApyOgtJ4ThLDTl1wIIBAQCAgEBALuIKC0ovQSIXwBAKOLkhCudGe+cl1BCD0AjU0EIfQQONFMICAQ6DQElNZzXZoQ2mxW1OWvQvXeZTA1lEIbkYzY3mchKnMUVCpGFxdFIOAeAlaLGZs3vY2txhWoDzcgsjEUR+tOxjGDr4NaE+JeZ6J2l0eA+6ls+duoLVoBm8YAlSUU0WknI+nUwNpP/NburFuFLdXLUGMqRYw2GYNiz8KAKM++tUorSi8QwnsBPA5gAoDPPNzYQU8Iv/zyS8yePRs7d+5ERkYG/vvf/2LatGkewnWomSCEHYZQdCAQ8BkCVpsV6+pW4Y/qZSg3lSJRm4xTYs/CsKhRUHfhs7jSeq7LEkIeUIrWLkB9QTZgs6eVUgEqFSIzhiPt+NsFKfTZr39wdszD+5erb8eBlArY+JvFP9am/3QrScCFIxcIUhicS++TWXE/7f/odlhCK5o2Ef8wvZwN0BgS0OOywNhP/Nb+ULQAe+qzYTuYwk8F/q9P5HCck+b+t1ZpRemwwAzhOU7++90AYhze+TFITAOA3QB+BzBZrnclgPcAvCXnHnTcL3sAlLq4gYKaEP755584+eSTMWnSJEyYMAF//fUXHn30UcybNw933nmnixA5ryYIYYfgE40FAj5DgGTwzaIF2FSfDauDflBDhcGRwzEp7fYuSwqV1nNdlhDW5q1E8fqFDmTQYb+r1Egdeguiu5/ks18C0XHwIbBp/WKsiP4ZNifGZZUVOLn2TAweaj8jBt/8xYy8i0DJ/xajpupnwJmzghWIiTsTKaf7/37aUbsSPxcvdCCDh3BSQY0zU2/BEdHufWuVVpQOK8tE8jmtrHQvAPvkP8sdIoiSCF7XSpuJMlF0ZfMENSE8++yzwRQRK1asOIjF3XffjSVLlkgRQjsSMEcQQle2l6gjEFAegezalVhavNCBDB6SQQ01rk29BcPd1A/Kz8I3Iyqt57osIcz/fTb0Ff+2sooqhCX0Q+boR32zyqLXoETg3dXXoyJJ32TJaVmsQNwB4KRPo4Jy7mJS3kfAelwdEC9bBlt2bwNUjWHocxW5hn+XT/Jno1Df+rc2PawfLs1071urtKL0E4R9TghtVivq1qxC9a/LYCorhTYpGbFjzkLUiFFQqX37jCI1NRW33nqr5DJqLz/99BPGjh2L5cuXY/To0R4vgyCEHkMnGgoEfIrAM/mzsbcN/dA7rB/ucVM/+FRgBTtXWs91WUKY88PNsOhbj/itCUtAr3MWKrj0YqhAR2DRpitgiKJPXyvFCqTuVSOukH9UiC1SQ2sMll/BQF89/5PfdooeCG9DLoMKfS9n0Er/Lm/k3IgGS3WrQkZqEjC5l3vfWqUVpZ8g7FNCSDJY9MoC1K/PBqzyMwqV/Ixi6HCk3Xy7T0lhbGws7r33XsyaNesg3L/99hvGjBmDV155BTfddJPHyyAIocfQiYYCAZ8icG/Ozai2tH4Wj9Mk4Ak39YNPBVawc6X1XLCcRp0qyrbWrclCuEt+lHN4zbCEAcgc/YiCSy+GClQEeJCq+vl7fJz8Lgx8UeSs2Hlis984FRJ03ZAW2hepYX2RFtYXibruUKs0gQqFkNuLCOx5/zrYwgzOLc60EBq16H3ZUr9NtM2ceTvr6C76ilN30SaoVEgP649LM9371iqtKL24rB3pyqeEsHbVShS/sfAQGXSUVK1G6g23IHqUe6697kx26NCh6NmzJz777FDMnblz50oEcc6cObjvvvvc6a5ZXUEIPYZONBQI+BSB9i2E/XGPm/rBpwIr2LnSeq7LEsI23xACiM46DalDb1Rw6cVQgYiAIS8XhUtfQfagHBQdJd8vOPmt4hvCUbVjkH7UaBTpd6PYsEv6b625rNm0Q1ShSAntJZFDiSSG9kVUSKLfHvoDcc0CQWZ95R4c+P0R2GzGNsWNSB2M5MGTpQjJ/lQYSfS30sXY37BJFsseEae5lAH4hrAzYXaLEO67+zZYGxjjxrViNeidk0F7c7Ua6tAwlzpTR0Sg5/yXXKprr/T6669LVsBXX30Vl1xyCbKzs3HttdeipKQETz75ZDPLoVsdi7QT7sIl6gsEFEPgr5o/8HaJcw8R8YZQOkwqxtMUG8jHu8ttC2HzKKNy6L6DuDeZc+L6jUfiwMtFtFEfL14gdm8zmVDx9ecoWv4V1l9sRVUmEIV4RJZaUZJYLQWDlIKBtBNltN5chWLD7iaSqOd/98Bka2wGSaQm/qAFMVWyJvaGTt2WL2EgIipktiNQV7AWxetehM1ihMocCptathI6RBlVG6KgToyCuaEIKk0oEgdeIaXM6ex0OYwYx9QSq8o/gMlmQJw2HWOSp2Bz9TI5yuihb22ARhntzI3qW0LY2D55VIczyGr7xRNCaLFYcMcdd0iEkP8/IiICtBAy9QQDy1x//fXtD9xKDWEh9Bg60VAg4FMEfq38AZ+Wv33YGCSDIsqoIISebD63CSEHaZ6HsAzaiCTpUKUOjUNx9nOwmuoRmXECUofdCrVG54lcok0QItC4aydKlryGCuMBrLscaIwD0kL74byM6QhDxME8hA3hBkS4mYeQB+pK4wEUGewEcTfKjbmwNVFMqfAgnaDLPGhBpDUxQXI19W3QhyBcSr+aEl0sq/d8j7It70qm5sSjr0JMr7Eo/33pwTyE0GuA3WZE9RqNlOsno3LHZ6jc9Y0ULZmBsFKG3AhdTGanzKvcmI//lSxCkX4XaPkbGj8ew+MvQohaJ31rD+UhLEOMNikY8hAqjbNbhNBd4fIfnw39nl1UjIc3VakQ1rc/Mu93z7XXXRlYv7KyEvn5+ejVqxd27NiB448/Htu3b8cRRxzhSXdSG0EIPYZONBQI+AyBIuMBPJk3CxabBecmXIJtDZuQo98lnXeuTp6KETGndulzjXAZ9WzreUQI2xrKWHsABavnwlxfgtD4vkgfOR0hobGeSSdaBQUC1sZGlH/6oRSBr6yHDRsnqGHSWdEvaiTOTLlZOvj6opisepQYciQrIg/btCjWmSuaDaWVXE17y66m/aT/RoUk+EIc0acPELBZLSjb/Daqc5ZBpdYiddhtiOp2wmEjWRoasH/6f8G9mPXEfOjS0mGo2oeSjYtgqMoB1CFIGPAfxPe/ACp1iA8kPbxLi82MdZVfYW3FF7DCjOTQXjgj5UYkhzJDg2+K0orSN7Nwu1efEsLOfkPoDA3mJGSSeuYo7EgRhLAj6Im2AgHvI0C9MS9/NnINezE+4TKMTfiPNMiiwvn4u34tpnV7CH3Dj/T+wAHUo9J6rsu6jLqyJyyGGhSumScFnwmJSEHGyJnQxXRzpamoE2QI1G/eiNK33oC5ohz5w3XYcqYZNpUVw+IvxMiECYq76pEQNrmY7pasiSX6vTDZ9M1QZ/RGEkP7e8TU0N7QqplZa4UAACAASURBVF17AxRky+fX07GaGlG09gU0FG+CJjQW6SPukax9rZXKb79C+acfIGrEiUi76b9SNRLKqt3fo2L7x7BZTdDFdJeshWEJfX06d15Q/K/kNZQb86BRaTEiYQKGxI3zeWAkpRWlT0F0vXOfEsJmUUZpJeQfBaOMrlmzBitXrsTgwYNRU1ODDz74AEw7wX875phjXEfJSU1BCDsEn2gsEPA6At+Wf4LvKz9D77D+uKvbQ9DIwfR+qvwKX5V/gIsTr8Hp8ed6fdxA6lBpPScIYTu7w2oxomT9K6g7sBpqbQTSTpiGiOSjA2lPCVk7gICltgal7y9F3eqVktPmviu7YXvvA1BDg9NSbsDAmNM60Lv3mtLVtMKYLwesaSKKFca8w1xNGcXUHtGUbq7xum5d2iXDeyvgWU/mxnIUrJoLY00udNGZSB85A9rIlDY7Y/CP/dPvAPdm98eeRmhm94P1jXVFKN34GhrL/pGci2P7nIPEoyZAHeLdiwBarVeXf4RN1T9K7q2Z4QOlt4JxujTPgHCzldKK0k3xfFXdp4Sw6WJBzkP4G/MQlkGblITY05TJQ7h+/XopqAzdQ9VqNU4++WQ89dRTGDRoUIfxFISwwxCKDgQCXkNgn343GF1Uq9Livqy5SNYe0hs7G7bihYLHMTRqJCan3eG1MQOxI6X1nCCELuwSvn+p+OdjVP77JaDSIGXIFMT0ONWFlqJKoCLA91x1a/5E6ftvw1pbCyTFY/vkFORod0KnjsC5aXehe0THDyq+xMdIV1P9nmbvEetb5PvRqsKlIDUMVmO3JkaGMBu6KL5GgC6eBauflvKhhicfjbThd0Gji3Rp2Kpl36Ps/aWIHDoc6f+d1qwN927Nvl9Rvu09WE0NkncDv1kRKd7Zr7kNm/FryeuoMZdKvwsnJV4tXYzIyssl+TtaSWlF2VF5vdTe54TQS3L6XTeCEPrdkgiBuigCBqteejdYYirClck34KTYM5oh0WhpwN05k5AUkoJHey7ooig1TVtpPScIoRvbrWbfbyjZ9AZgsyB+wH+QcOSlirsKuiGuqOohAqbyMpS+vRgNmzdKPejOPAVrTipAkWk3YkKScX7GTCTqOidwh4dTOtis1lx+yNVUvxslhr0w2wzNumWaC6a7sLua8m2iVh3a0aFFewcE6gvXo2jtAtgsBsT0OE1KHeHOmz+r0YjcWXfCXFGBzIefQFjP3ofha26sQOnfS1BfuFb6WXTWaCQNugYaXZRHa6G31GFF2VJsr/1Dat8n8niMTp7YKW9VlVaUHgHm/UaCEHqIqSCEHgInmgkEvIzAh6Vv4o/qZTg6YghuTp/h9CLxkf3TUGwqwNO9XkOUprXkzl4WzA+7U1rPuUoImWHtRQAjAVQBeAMAw41Z2sFwIIDnADCbLWNafwJgOoA6h3ZvAbjOST98TbrDxTXyelCZ1sZtKNmCIikCaQOiMkch5bibRARSFxfJ36vRXar615+l91k2vR7a1DToJl6EZeGfodpUjNTQPjg/fToiQuL8fSouy2e1WcDokMX6pryIfI/I6Kl0A7QXRoxM1GU1e4+YoM0QlyEuo9y8YtWeH6UAMsQ44ajLmwLA8K2Wm6V6+S/Su9aIYwYjY9osp61pLawvyEbp32/CYqiW3igmHztRip7s6pjsY1fdGvxe9hYaLdWI0MTh1OSJ6Bt1eNAbN6fgcXWlFaXHgnq3oSCEHuIpCKGHwIlmAgEvIvBP/d94qfBJRKqj8UDWPMS2cpZ6q/hlZNeuwK3pMzEwcogXJQisrpTWc66cQug/tg0AH6XM5cUwgPky0XugDXgZkvNf+c/TABIB8L+rAFzYghDyZDGxRV/MaNw8SkbrgylGCCmCsSa/KQJpQynCEvpLQSA0oV33FiOwfsWcS2ssOCClktDv2gmo1Yg753w0nj0Q35e9AIO1Hn0ih+Os1Fu7hKXMYG2QgtRIuRHlHIkNFt4DHSrMg0iCfOg9Yt+gIsq+2NN0PS/bshTVe36UIommDL0Z0ZmjPB7KZjZj/73TYC4tQbf7H0F4vwGt9mUx1qFs67uo3b9cqhOZPgzJx05CSHjbkWgZvIgJ5nPq10vtjoo+FSclXY0wjWdWRo8n26Kh0orSW3J3sB9BCD0EUBBCD4ETzQQCXkKg3lKHx3Ono9pSiSlp0zAkanirPS+v+hEfl72F8xIuxbiEi70kQeB1o7Sec4UQ3gtgBoAeAGpkSPn3hwHwJaj931qizXb8kyVbFfnz8wF8DeB4AOvkBrQQMkrLsA4sl6KEkHKa9VVSBFJD5R5oI1ORzgik0RkdmIJo2hkI8FBd+f3XUpJ5mM0I7dELKZOmYm98npRTzQoLhsadj1GJV3RZi5j0ntJcLlkPpbQXkqtpDsw2Y7Mliw5JanIzDW1Ke5ES2stnqTg6Y690ZEyrWY+itS+ioWg91Lpo6RIpPLF1AufqWDV//oGS1xci/IijkDHzwXatfvRwKNn0upRORx0S3pTrsOeYw/Y2yevWml/xZ/l7MFobEROSgtNTpvjNu1mlFaWr6+HjeoIQegiwIIQeAieaCQS8gADPEIuLX8CGujU4IfoUXJd6S5u9MhfhvPwHJbfSWzJmekGCwOxCaT3nCiHkg5ECAJc7QEqStx/AeADftAL1RwCSAYxx+DlD3dF1lO6m9gy3AUkIOSer2YDi9S9LLllqbWTTIS+pa+dNCaRfO/3ePSh5cxGM+blQabVI+M+liD1rHLKrv0B25WdScu3Tkifh6BaPngNpjr6SlTmEmGrgYOoL/W5UmuhqeqgwEmtiaFaz94jx2vQuR6z5lq9w9dMwVO+DNioDGYwkGuWdaJx0c869/x6YCguQMeN+RBzVfuAYktOK7Z9IaSrothqWdCRShkyFLipdWrxKY4EUNOaAfjtUUGFw3DgpnYQ/vSNVWlH66vfIzX4FIXQTMHt1QQg9BE40Ewh4AYG1tSuxpPglJIQk4f7uTyNcE9FmryarEdP2TkSEJgpP9Xy13YtOL4jol10oredcIYQlABbKFkFH0Orlf5vXCpJfAWAkirEOP2emZLqBfupAMEkIJ9DoJtdnBIT7AfzuxgopbiG0y8ab9PJtH6Bq1zdNEUiPuxExWae4IbqoqjQCDNtf8fknYKRG5tqidSX5+ilQpybhf8WLsLNuJRh9c1z6negRcazS4gXseAZLA4oNe5rlR+SbM8cSqo4Eg9Qcyo/YDxFB/GjcUL0fhXQvb6xAeNJRUtoaT4O6tLYx6rLXoGjh8wjt3ReZDz7msvLUV+5ByYZFUsoLurDGHXkR9iYB2ZWfw2IzIUmXhdNTbkRqGF8J+FdRWlH6yewFIfRwIQQh9BA40Uwg0EEEKs3leDx3BvTWBtyR8QD6RzC0SPvlqbz7pKT1j/V4EYla2pa6XlFaz7lCCE1yIJjnWyxHPoClAO5rZZn4zvBK2WWUfbDwreAaAD8DOEv+NyYaoe8Z3yhy1e8GMFQORJPt4hboNEJol6865xcpcANsVsQfcTESjrjE5YOZi3MU1byAQMM/W1Cy5HXp3ZU6PAKJl12FmFNOg95Wj+8K56NAvwOMsjk+fSaSQmkIF8VTBOgmUmsubQpWI79HpKspyYZjoTuiPaIpo5smh/YMClfT+uJNKMp+HjazHtFZp0hWOHciibqKO62EeQ/dC2PefqTfOR2Rg/n5dK3YrGZU/vs1cvZ/hi0ZFtSEQ8qxeULCJTgu/nxoVLzD87+itKL0EwQEIfRwIQQh9BA40Uwg0AEEmB/5pYInsaNxC06POxcXJ13jcm8flCzGipqfcUPanTguaoTL7YKpotJ6zpeE8AgAW+WIpHxvyKAyJJA0ufzSwnLouIa0JTOIzd8tgs841mF/Dzn+Aw+fnV3qi/+WD4CNiO5+ElKG3AiVRtvZYonxGQ63vg5lH76L2hVyUI3jjkfyNRMREp+AKmMRvi58ClWmIsl6xUiiIhefb7aN5GpqyJXfI+6WrImVJnqkHyokJEmhPeT3iE3pL+IkV1NXPle+kdvdXqv3LkPp5rekC6KEIydIaWp8KX/9pvUofH4edFk90f3hJ6BSq10S2Ww14q+KT7Ch6jvYYEV8PTDogAqZWecj4chL/DaCstKK0iUwfV9JEEIPMRaE0EPgRDOBQAcQ+K3qR3xS9hbSdZmYlfkEtGqdy72trlmOd0pexRlx5+OipKtcbhdMFZXWc66csOgy+rLDmz873u25jLLeJDkaKUNwWgG8JgeUIVG8vo2F43gMQOOqiabTLYT2uRiqc2UXsXKEJR6B9BPuhiY0Opj2aEDNRQq7v+4vlL6zBJaaamhiYiUiGDmsKex+QeMOfFs4H3prLXpHDsPZqbdBq+ZTV1GUQoD57Zq5mup3S+vhWOhqKkU0dciPGK7xv98ryYV863uo2v0doA5B6nE3SZdDvi7c5/mPPQjD3t1Iu+VORA1v/0Y1v2Eb/lf6OqpNRdCqwqTAST3KgfJ/PpCsmtrINKQcN1VydfW3orSi9JP5C0Lo4UIIQughcKKZQMBDBAqNB/BU3izQSjij++PoHtrLrZ4KDHl4PG86+oUfhbu6zXarbbBUVlrPuUIIGVSG0SKucAC5O4DcdoLK2KvzdN0PAIllGYByAEya9Wobi/aSTAgZ2dSV4jeEkMKa9ZUoXD0Phqq90qEqfdQs6LwURMIVMESdJgTMlRUSEazfICfmPvlUJF12NTRRTSHzd9b+iZ+LX4EVZgyJOxcnJl4Ftco1y4rA2HcIkNzUmEua3Exld9NS477DXE1jtakSQbSnvkiiq6mq8yzyDNZSvO5lKRG8WhuF9BF3Kxpkiu7QBU/PgTY9A1lznmnVSmiw1GNl+XvYVvOrtIg9I4bgtOTJiNYmSX83NZShdNNiNBRvlP4e0/N0KRqpRtt2IADf7YjDe1ZaUSo5tzbGCmpC+Omnn+LZZ5/Fzp07UV9fjx49euCaa67BjBkzoNO5bllwhp8ghH6yg4UYXQIBegLNy58tvQEcn3A5xiY4ZppzDQISybv3MhudCvN7v9klz2ZK6zlXCCFTRzCZPMmZ/dr+HgCPtpN2wtmqMwE9E9z3BFDRyrYIl98T8jRykWtbB35FCClz0+HwJdQXrvNqmHkX8ejS1SRC8fuvKP/oPVgbGxCSnIKU66cgYmBTBEb+fG3lF1hT8bEURXF08kQcE2t/0tqlofPbyVPBlBn2S2kvmP6CRJEuvo5FjRAkS66m/Q5aE0kafemqaR9fSkPDSKIHL4FmHozaqRSo3NcFcx9D445/kDLlFsSceHhwqz11a7G89E3UWyoRpo7G6OTr0T9q1GEYSalG8v9E6ea3YTXWQhMWj+RjJyMqoyPZgbyHhNKK0nuSd6inoCaEixYtQl5eHoYOHYq4uDhkZ2fj4YcfxuTJk/HSS7wj9rwIQug5dqKlQMBdBL4t/wTfV36G3mH9Ma3bwx6TuefyH8Eu/XY8mPWM5Hba1YrSes4VQsjE9Az4QjdPJqbvDeBZAAwy45iYfrccGXSyvGh0E2W0UFoYGUH0NDlgzBQAjCzKwuT13wJ4FwDb84r6LgBDAJzokKuwvX3gd4SwiXi0dB+7GdHdOS1RfIWAsbgIpUtekw7FUKkQd9Y4JFx0KdShTW6gJBa/lryG7bV/SG5y56TdgZ6R3G6iBBoCdDW1B6uxWxP11rpm0yDpYZRMKappKIliH68nVTfU5KFwFSOJliEscQDST7in09zEG3ftxIE5D0mXID2efBaqkKagMPXmKvxeugS76/+S/j4g+iScknQtwtuJ8Gox1KB0y1LU5a2U2kV1G4GkY65HSFhcp24XpRVlp0720OBBTQidYXz//ffj5ZdfRmVlZYcudgQh9JMdLMQIegSYQ3B+/kPQqrS4L2sukrWep1j6vOw9/FL1Da5JuQkjY04NeuxaTlBpPecKIaSMfETCK7qRcpL5N+SUExaHCewDwIgd9reBkQC+kBPO0+pHQjkHwJcObXhKf19+V5gip6RYLffNaKSuFr8khHbhpQATfy+Rcn4lHHUZ4vtf2CHl5iooXamezWJB1U/foeKLT2AzmaDLzJISzIf17nsQBhKI74qexYHGfxCpScD4jJmSRUmU4ECAVq1qU/FBCyKtiaWGfbDC8TMFKUCNFNU0lESxnxTAxtNomg0lm1H013OwmhsR1f0kpPpBIKmCZ59Cw+ZNSL7+BsSMPh3ba3/HirJ3YLDWIzokSXIPdfcSpL5oI0o3vQFzY7mUczVp0DWIzhrdad8xpRWln/yG+JwQ8hKzbtsqVG9YBlN1KbSxyYg97ixEDaQVWXl3erqQPvDAA5ILaUcs/YIQ+skOFmIENQIGqx5P5s1CiakIVyZPwUmxp3dovkxk/0bR8zg55kxckWK3NXWoy4BqrLSec5UQ+juIfk0ICR4PVEVrX5BD0I9GypApPglB7+8L5Qv5DPv3SQnmDftzgJAQJIy/CPHjxh+0jnBMEoWvC+ZKES2TdD0xPmMGokISfCGO6NOPEDDbTCgz7Dv0HtGwW9oLjkWj0iJZ11POjdjkbhoTktzuAbQ6538o/Xux36Wa0e/bi/yH74OhZyz+vSETeXoGbVbh2NizMTLxMujUvJ9zv1hNjSj/50PwgouXW+Epg5AyeAq0kbzLU7YorSiVnV2ro/mUEJIMFn25APU7s6U93VRUkqdF5IDhSLvwdkVIocVigcFgwIYNG3D11Vfj4osvxvz5zGLleRGE0HPsREuBgKsIfFj6Jv6oXoajI47DzenT29Wh7fVbbirFg/v/i6zQ3pjV/Yn2qgfdz5XWc4IQKriFDFX7ULD6aVj0TFI9EGkn3OX1JNUKTqfTh7Iajaj8+jNUfv8NYLUirN8ApEycCl1Gt2ayFTb+i2+LnkGjpUYKoDE27Q7oRCTRTl+/zhKA+6BYv6cpP6L8HpHWM8dCV8pUOaKp3ZoYqqHTg+wKvu1DVO36GlBpkHLcjYjJOvy9XmfNj4/xV3w7E1v75MGiA+K13XBGylSkhw/wikiN5TtRsvE1mGoPQKUJReJRExDb5xxFyIJ9AkorSq8A1/FOfEoIa7euRPG3Cx3IoIPAKjVSz7sF0Uf7PmJuWFiYRAhZrr32WixZsgRqF9OotAaxIIQd33yiB4FAWwhsq9+ElwufQqQ6Gg9kzUNsSMefFdDrZ+a+G9Foqcezfd6S3FC7UlFazwlCqPDuMjdWSKTQWL0P2qgMZIyaCW1kqsJSBP5wjTu3o2TJazAVFUIVFobES65A7JgzD4us+G/tavxcslCKUHls7FicnHStxw+cAx81MQNnCFDpVJkKm71HZACblq6m8doMpIb2RkRRPsIL9yHWEoGME+5GRPJAvwG2zJCL/5UsktJ4qCxA33WhOOPil6AN826KDpvFhIqdX6Dy368AmwWh8X2kvKuhsa5mCuoYZEoryo5J67XWbhHCfS/fBquhweXBrUa9czJ4kIWroda5lpJHHRqBnrd6FgiGlsGGhgYpqMyjjz6KK6+8EgsXLnR5Hs4qCkLYIfhEY4FAmwjUWWoxJ3cGqi2VmJI2DUOihnsNsYUFc7G1YSNmZD6OnmGHngB5bQA/7khpPScIYSdsBkYgLcp+QQrrrtHFIH3kdIQlMDOHKO0hYGloQPnH76Nm+S9S1YhjhiD5usnQJjaFzLcXHvLXV32NVeUfSJFESQQHx53TXvfi5wIBCQEmbOf7Q7sFkdZEpsJwLBqEIEUOWGO3JvKNXkfeOnkKP11j11Z8jvWVX0tElu8jj10ejZD/bZIuS+LPu8DTrttsx7yrJRsXwVC5R7KWxve/AAkD/gOVxrc3uUorSp+A536nviWELpBHEj1XSkcIoWP/S5cuxXXXXYfdu3ejT58+rgzttI4ghB5DJxoKBNpEgGetxcUvgO/9RkSfgmtTb/EqYt9VfAr+mZB0PU6NG+vVvv29M6X1nCCEnbQj+F6jbPNSVO/9ESq1FqnDbpUi+InSOgL1G9ehZOmbsFRWQB0djeQrr0PUiBMPO4AzkuhvpYvxT81vCFGFYmza7egdOVRAKxDwGAFjzQHkZD+BMpSjLiEBDcmpKDbth9Ha3AIToYk9mPKCrqYkjKFq1w7RngpX2LgTv5S8hkrTAWm/850greHm4mLk3nc31OHh6DHvRWgifCMHv2XVe35A+T8fw2YxQBvdDSlDpiI80Tsuqs5wUVpRero2Xm7nFiF0d+z8pbOhP7BLeh96eFEhLLM/Mq95xN1uO1R/69atGDRoEH7++WecccYZHvclCKHH0ImGAoE2EVhbuxJLil9CQkgS7u/+NMI13tUz2+o34uXCuTgh+hRc52Wy6e9Lq7SeE4Swk3dE1Z4fJGJIJZw48ArE9RvfKRaGToahzeHN1VUoe+8t1GU3BZ6NGnkSkq+4FpoYZjZpXph0+/ui55DXuBWRmnicnz4DKWG9/Hl6QjY/R6ChdCuK/noWVlMDorqNRMrQm6HW6KS0MpWmQiknov09Il1NbbAH5ODEVEjQdUNaaN8mohjWF4m67lCrNB2etdHaiFXlH2JzdVOQl+7hgzAmZQpitYeCvDDYUs0fvyH+gouR+J9LOzxmWx2Y6ktQsvF1NJZukeYd2/ssJB51OdRaz4LYtDWW0orSp8C53rlPCaG/vCF0hIO5CW+66SZhIXR9j4iaAgHFEKgwlWFO3kzorQ24I+MB9I/w/vOJOksNZuRMRao2Aw/1YMa7rlOU1nOCEPrB3qovXI+itQuk2/WYHmOQPHiSiEAqJ5CvXbUCZe8vhbW+DiEJiUi+7gZEHus8b2CNqQRfFz6NCmM+EnVZGJ8+A9Ha5q6kfrDcQoQAQqBm/3KJ5PCdHNPFJBw1oc3gKSarocnVVL8bxYZd0n9rzWXNZkwrXkpor6bUF1J+xL6ICkl0ehFE0rmzbhW2VC9DjakUMdpkDIo9C6GqCPxWthh15nKEqiOlnIJHRJ9yWB+mslLsn3knVFodes57AZrowy9RvLkcdB+qzf0dZVvegdVUj5DwJCQPnoyI1GNRl79KilBqaiiFNiJZIoxRmZ6lM1BaUXoTow705VNC2DzKKK2E/KNclNGxY8dKVsCBAwdCo9Hgzz//lKKLnnfeefjwww87ABsgLIQdgk80FggchgCDl71Y8AR2Nm7F6XHn4uKka3yG0oP7bkeFuRTP9FrsdQukz4T2QsdK6zlBCL2waN7oQl+5F4Vr5sGir0R48qCmCKRa75revSGnUn2YSktQ8vYbaNy6WQp7zoAxfAtF9zdnhQfvbwrnodFSjR4Rx0qRRH3tqqcUFmIc5RHg4bhi+yeo3PlFUyTRwTcgpudpHgnCpPDFhiYrYpM1cQ9MtsZmfdGabbcg8j1ialhvaFWh+KFoAfbUZ7ewOh5q2jdqBE5Nuh4RbUR0K31nCar/9xPixp2PpAlXeTQHdxuZ9VUo2/wW6g40WfVDwhNh1lcens4gYzjSjnc/nYHSitLd+fuovk8JIWU+mIdwI/MQlkEbm4TYIcrkIXzwwQfxxRdfYN++fQgJCUHv3r0xceJEyUKo1XbsTaoghD7akaLbLovAb1U/4JOyt5Guy8SszCegVet8hgVzEfKNIq2QAyKO9tk4/tax0npOEEI/2gGmhjIUMgJpTS500ZlIHzmjU3J8dSYkNqsV1b/8iPLPPoLNYIA2PQMpk25EeL/W3yPtrsvGsuKXYLYZMSjmDIxOnugVl7zOxEGM3XkIWC1GlGx4VbJoqbURSBt+FyJSBnlNIN6sVhoPNAtYU27Mha3Z2y0VojTxqLNUtDru4NhxOCX52nblMldVYv/026WLlR5Pv4CQuPh223irQl3BOhRvWAibqZVol0xnMPQWRHd3L52B0orSW3h0sB+fE8IOyue3zQUh9NulEYIFIAKFxgN4Km8WqMtmdJ+D7qE9fTqLXyq/xefl7+LCxCtwVrxvAqT5dAIedq60nhOE0MOF8lUzvlNiAvuG4r+hCY2VSGFYvOfR1Xwlpy/6NRzIQ8niRTDs3Q1oNIg/9wLEn3ch1DrnN090T9tY9S1Wlr8viXNS4tUYEjdOvMH0xeJ0kT4thhoUrnkG+op/ERKRjIyRM6GLyfT57E1WPUoMOU1vEfW7JItinbl1MkhXvvSwfrg081GXZCv78F1U/fgtYs8Yi+Srr3epjbcq5S2/vykKqdOikiIsZ452bR72LpRWlN7CooP9CELoIYCCEHoInGgmEGiBAIP2zcufjVzDXoxPuBxjEy70OUa7G7fj2QOPYHDkcExNn+bz8fxlAKX1nCCE/rLyDnLYrBaUbl6CmpxfoNLokDrsNkRleC+vi79N2WYyoeLbL1H57ZeAxYLQXn0kq2Bo99ZzmlltFiwvXYKtNb8gRKXD2am3oY8Xc9/4G0ZCHt8jYKwtQOHquTDVF0t59dJHTEdIWMeT63oq+Rs5N6LBUt1q80hNAib3ci0/m6W2Bvum3w6b2Ywec58/LE2LpzK60i7nh5slV/jWiiYsAb3OcW0eghAeHgFUkJ32d6HAqH2MRA2BgCsIfFP+MX6o/By9w/pjWreHFcnrbLDqMW3vRMSFJGBOz5ddETMo6ghC6NkyOr059awr/2glJcve/T3Kt74rCZR49FWI63tu0Fm/Gnf/i9I3X4OxIB8qnQ6JF1+G2DPPOSzBvOOqGKwN+KHoBeQ2/A2G+Wck0dSwrmFF9Y/dGXxSNJb9g8I186VAKJEZw5E69FaoQ0I7daKf5M9Gob71NADpYf1xaabraQDKP/8YlV9/jphTxiBl0lTF5pb/+2zoK9pIZ5DQH5mjXZ8HBVdaUSoGVtsDCQuhhwshCKGHwIlmAgEHBHL0uzA//yFoVVrclzUXydo0xfB5PHcGCoy5eLLnK4gNUe7Zg2ITdDKQ0npOWAg7c7VdGLuuIBvF616CzWJETK8zkHzMRKjUHQ9Z78LQPq1i1etR/tmHqP7lJ0YyJZ7IkAAAIABJREFUQPjAQUi57gZoU1LbHLfWVCZFEuWbqwRdJsanz5QiL4oiEPAUgZrcP1CyYZEUSTSu3/lS+heVSu1pd15rt6N2JX4uXug0oIwKapyZeguOiHb97Z2lvh77Z9wOa2Mjsp58FrpUZZR5bd5KFK9f6BBQxgEi8YbQnf0iCKE7aDnUFYTQQ+BEM4GAjACtdE/mzUKJqQhXJk/BSbGnK4rNO8WvYnXtctyUfg+OiRym6NidNZgghJ4hH3QWQkcY9JV7pGAzFkO1FL497fg7pGAXgVrqt/yN0rdeh7m8DOqISCRdcQ2iTxrdrvWzRL9XIoMNliop59q4tLsQ6uUkqIGKqZDbfQRoha/Y8Skqd3wGqNRIPnYSYnt5nvzafQnabsGIj4eijB5KA6CCCn0ih+OcNPejc9I1u+LTD6Vcnmk33uZtkZ32J6UzWLsA9QXZ0uVPs3QGIsqoO2sgCKE7aAlC6CFaoplA4HAEPix9E39UL8PREcfh5vTp7Z7XvI3hiupf8EHpGzgn/j84P/Eyb3fvl/0JQujZsgQ1ISQkzN1VuGoujLX50MVkNUUgjQisHHuWuloppyBzC7JEDjsByVdPREhc+++09tavw49FL8JsM2BgzBicmjwJGlWIZ7tFtOryCNgsJpRsXARar1Qh4Ugffqd02eJvpXkewjLEaJOkPIQDojzL30fL/P4Zd4BvCrs//jRCu3VXZMpSOoODeQjLpG+XyEPoNvSCELoNWVMDYSH0EDjRTCAAYFv9Jrxc+BSi1NG4P2seYttIc+QrwHL1OXgq/14cGX4M/tvtPl8N41f9CkLo2XIEPSEkLBZGIM1+Do0lW6AJi5eCXoTF9/YMMQVb0RJT99dqlL33lnQQ1cTFI/maSYgaeny7UrDt39U/4I+ydyTLwomJV+K4uPMVv51qV1BRIWAQsBhqUfjXfOjLd0j58dJHzUJojDLEyB9Aqvrpe5R9sBSRw4Yj/bbAjNimtKL0h3WT0gRKFtbmRZCd9ldHYNQ+RqKGQMAZAnWWWszJnYFqSyWmpk3D4E4K3sfopnftnQidSod5vd7oEmdApfWceEMYYN8Am9WM0k1vomb/r1BpQqWkzpHpQ/12FuaKcpS8vRgNf2+QZIwZPQaJE66CJjKyXZkZSfSPsqXYXP0TNCotzkq9Ff2iRrTbTlQQCLSGgLGuCIWrnoKpvgihcb2RPpKRRLvGA3U7JlajEftn3glLZQUyH34SYT17BdyGUVpR+glAghB6uBCCEHoInGjWpRHgBdTi4hekpPAjok/Btam3dCoe8/IeRI5hFx7Oeh4pOmXewHfmhJXWc4IQduZqezi2FIF01zco38b8eyokHXMt4vqc42FvvmnGBPM1y39B2ccfwKZvhDY1DcnXT0HEkQNdGtBobcSPRQuwr2EjwjUxOC99upR3TRSBgKcINJbtkCyDVmMtItOHSelc1CFhnnYX0O2qf/0ZpUsXI+KYIciYNjPg5qK0ovQTgAQh9HAhBCH0EDjRrEsjsLZ2JZYUv4SEkCTc3/1phHdyzIaPS9/C8uofMTH1vzg++sSgXxul9ZwghAG8peoOrEHxupdhs5oQ2/tsJA261i8ikBoLC1Cy5DXo/90BqNWIG3suEi68tNUE8y2XoNZcjm8KnkaZcT/itRkYnzELsdqUAF4pIXpnI1Cb9yeKN7wCWM1S+hamcfGHSKKdhQvzEe6/dxrMpSXodv8jCO83oLNE8WhcpRWlR0J6v5EghB5iKgihh8CJZl0WgQpTGebkzYDe2og7uj2I/uFHdToWf9WuwNvFL2NM7Dm4JPm6TpfH1wIorecEIfT1ivq4f+b3Klw9DxZjDSJShyBt+B2dZvXgIbPyh29Q+dXnsJlN0GX1lBLMu+OSVmLIkchgvaUSmeEDpUiiYZooH6Moug9WBGhNr/z3S1T885FkTU8+dqIUzEQUoGbl7yh54xWEHzkQ3WY+GFCQKK0o/QScoCeEu3fvxrx587B69Wps27YNJ598MpYvX94Mfv5OP/nkk3jllVdQVlaG448/HgsWLMDgwYNbXSZBCP1kBwsxAgIBq82KFwuewM7GrTg97lxcnHSNX8hdbCzAI7nT0CdsAO52IwevXwjvgRBK6zlBCD1YJH9rYqovRsGquTDVFUAX2xMZI2cgJDxBUTH1OXtQwgTzefuhCtEi4cJLJMugKsT1SKA59RvwY9ELMNkMODJ6NMakTBGRRBVdxeAajO9tSza+jtrc36EKCZPStUSmDQmuSXZgNnTrzr3/HpgKC5Ax4wFEHHV0B3pTtqnSilLZ2bU6WtATwq+++gq33XYbRowYga1btyI1NfUwQkgy+Oijj0rE8YgjjsCzzz6L7OxsqX5amvN3RYIQ+skOFmIEBAK/Vf2AT8reRoauO2ZmzoFWrfMLuUlUp+fcALPNjGd7L4FGFfg5udsCVmk9JwihX2zzjgthMdah6K/n0Fi2DZqwBIkUhsb17HjH7fRgNRhQ8cUnqPrpOynHWNiAI5EycQp0aRlujf131U/4o+wt2GDDyITLMCz+wi4RRcotkERllxE47Pdh1EyExvZwuX1XqVibvRrFC19AaJ9+yHzg0YD5nVNaUfrJfgh6Qmi1WqFWqyW4L7nkEskC6Ggh1Ov1Ekm8++67MXv2bKlefX09evbsiRtvvBGPP/6406UShNBPdrAQw+8RKDQewFN5s0DyNaP7HHQP9f050h1QFhyYgx2NW3Bf97nIDA1una60nhOE0J2d6Od1m1lEGIF0+J0+tYg0/LNVSjBvKimGOjxcih7KKKIqWaG7Ahc/OivL3sGm6h+kSKJnptyM/tGjXGkq6ggEnCLgaDEPje0p5exU2mIeKEtDK2HeQ/dKlv30O2cgcvBxASG60orST0DxOSFsni+yFNqI5A7li+wIbs4I4a+//orTTz8d27dvl6yD9jJp0iT8/fffWL9+vSCEHQFdtO3SCDC1w7z82cg17MUFiVfg7PgL/A6Pr8o/xE+VX+LK5Ck4KfZ0v5PPmwIprecEIfTm6vlBX0q8mbLU16P8o3dR88dv0owjhwxF8rWTERLvnpuqyarHj8UvIqd+PcLU0Tgv/R5khAdWcAs/WHIhggMC/vSmNlAWpn7jehS+MA+hPXoi86En3LrQ6aw5Kq0oO2ueLcb1KSEkGSxauwD1BdmAzSoPrQJUKkRmDJdSHCkZiMkZIVy4cCFuv/12GAwGaDSH3MXoPvrwww9L1kJnRVgI/WQHCzH8GoFvyj/GD5WfS2/07ur2ENSqJmu9P5VNdWvxWtF8nBgzBlelTPUn0bwui9J6ThBCry+hf3RYm78KJetfaYpA2mcckgZd7RVlXrcuG6XvvAlLdRU0MbFIuvp6RB0/wm1XszpzBb4pnIdSQw7itOkYnz4TcV0gr4x/7I7glKJ51N2xUjoWJQ+wgYoqL5HyH3sAhr17kHbrndLvs78XpRWln+DhU0JYm7cSxesXOpBBh1mr1Egdeguiu5+kGBTOCOGcOXOkt4NVVVXN5HjjjTcwZcoUiSjqdIe/dxKEULFlEwMFKAI5+l2Yn/8QtCot7suai2Stf+b5qzJX4L59tyBT10OSM5iL0npOEMIg3k2N5TtRuOYZr+RdM1dVovSdJahfny0hFn3iKUi64hpooqLdRrDMsB9fFz6NOnM5MsKOxHnpd4tIom6jKBrYEWjKy/k1yrd94Ld5Of19tRq2bkbBM09Am9ENWY/P83srodKK0k/Wzy1CuO/H22A1N7gsutWsd04G7T2o1C5HsFaHRKDn2JdcHttZRUEIOwSfaCwQcBkBg1WPJ/JmodRUhKuSp+LE2DEut+2Mivfm3IxaS7UUWEanDu0MERQZU2k9JwihIsvaeYMY64pQuOopmOqLEBrXG+kjpyMkLN5lgXjYrv3jN5R99B6sDfUISUpGyvU3IOLoY13uw7HivvpN+EGKJNqII6JPxpiUqQhRaT3qSzQSCPDdbOmmN1Gz/1eo+G72+NsRmT5UAOMmAvw9P/DUo9Dv3I6UKbcg5sRT3OxB2epKK0plZ9fqaL4lhKb2yaNaG+ESFL4ihMJl1CX4RSWBgFsIfFCyGCtqfsagiKG4Kf0etz2+3BrMC5UXFc7H3/VrcXe3R9AniJ8ZKa3nBCH0wub09y4shloU/vUs9OXbERKeiPSRjLiY1a7YppIilCx5HY3bt0nvSGLPHIvEiy6DOiys3bbOKmyp/gXLS9+EDVackHAJhsdf7PcfHo8mKhopgoDF1NAUWbd0CzRh8VLwmLC4XoqMHYyDNP67AweeeBghySno8eSzbqWMURoPpRWl0vNrZTy3CKG7Muf/Pht8gwvYnDRVISyhPzJHP+Jutx7XbyuozI4dOzBgwKH35pMnT8amTZtEUBmP0RYNuyoC2+o34eXCpxCljsYDWfMQExLn91D8WPEFvq74SMqPyDyJwVqU1nOCEAbrTmoxL5vFhJKNi8B3IqqQ8KYIpKnOrXw2iwVVy76X0knYjEboumU2JZjv088jtBis4M/y97Gh6luoocEZqTdJ1kFRBAKeImCqL0Hh6qdhrM2HLiZLIoPaiCRPuxPtZAQK5j+Jhi1/I/n6KYg91X8juCmtKP1kg/iUEAbCG0J72onp06fjgQcekJaloaFBSjsxdepUkXbCTzaqECMwEKiz1GJO7gxUWyoxNW0aBkcNDwjBdzRswYKCORgWNQqT0m4PCJk9EVJpPScIoSerFKBtpAikOz5DxY5PAZUaycdOQmyvM5rNxpC7HyVLFsGQsxfQaJAw/iLEn3uBx9YCk9WAZcUvY099NsLUUTg3/W50Cz8yQBEUYvsDAvrKPRIZtBiqEZF6LNKOvxNqbbg/iBbwMuhz9iD/kfsRkpCArKeeh9pJgA5/mKTSitIf5kzTHb/hLYu3AqY0jzLKcfhH2SijJHfff/+9NMX58+ejpqYGjzzSZJUcN24cIiIiwMT0jz32WLPE9H/99Re2bdsm5Sh0VryFkZ/sAyGGQKDDCPBbsrj4BWyoW4MR0aNxberNHe5TqQ4aLPW4J2cykrWpeKTHC0oNq/g4Sus5QQgVX+LOH7A2dwWKN7wK2CyI63c+EgdeAZvJjMqvP0flD98AFouUqDpl0lSEduvuscD15ip8WzgPxYY9iNWmYXz6DMTr3EtY7/HgomFQIlBXkI3idS/BZjEiptcZSD5mIlTqQ+Hng3LSCk+qcMF81G9Yi6SrrkPcmecoPLprwymtKF2Tyue1fEoIKX3zPIRlktU9tvdZiMocpUjE3n379qFXL+du3zk5OZIlkAfZJ554Aq+88grKy8sxbNgwLFiwAEOGDGl1AQQh9PneFAMEGALZtSvxVvFLSAhJwv1ZTyNc7dr7YH+Z5sP770KJqRDzer2BSE2Uv4jlVTmU1nOCEHp1+QKns8ay7ShcMx9WUx3Coo+E+ddKmAuKoAoNReIllyP29LM7FGmw3JCHrwvnotZchvSwAVIk0XBNTOAAJCT1KwSkSKK7v0P51vckuRKPvhpxfceJN6g+WCVDXi7yZs+EJjoGPea9AHWoZ2+GfSDawS6VVpQOc+kLYDqAkQAGAlgB4FQX5hoL4HkAFwJgcq9vAdDXqdyFtvYqPieEbsgSUFUFIQyo5RLC+hiBClMZ5uTNgN7aiDu6PYj+4Uf5eETvd7+k6CWsrVuJ29LvxVGRngU59L5U3u1RaT0nCKF31y+getOX7cWB3x+DTd0IVALh1Uch5ZqboU1K7tA8chu24PuiZ2G0NqJ/1CickXITQtSH54bq0CCicZdBwGa1oHTzEtTk/AKVRofUYbchKiMw3joE6iIVvboAdWtWIfHSKySXcX8rSitKh/kTDOZTWAPgaADFLhLCnwD0B3APAGZ9ZwIttnXnMbUghB5uREEIPQRONAs6BKw2K14seAI7G7dKAVkYmCUQy29VP+CTsrdxfsIEnJNwUSBOoV2ZldZzghC2uyTBWaF+03qULl0Mc10FMFIDxFkQEp6EjFGzoIvJ9HjSW6t/xfLSxbDCguHxF+GEhEuFFcdjNEVDKyOJrn0BDcV/QxMa2xRJNL6PAMbHCBiLCpB73z1Qh4ejx7wXoYnwL3cipRWlA9y07pHQsXwKgJGM2rMQ0pq4CsBoAH/IbXmj8ReAMwH84uJyCkLoIlAtqwlC6CFwolnQIfBr1ff4tGwpMnTdMTNzDrQBelm/t/FfPHNgtpQq4+YMOm0EX1FazwlCGHx7qM0ZmWuqUfbe26j7i+cTIGrEKCRcdiUqdr+PuvxVYJ6ptOF3ISJlkFvI8O3JqvKPsL7qKymS6JiUKTgqpr1zkltDiMpdDAFTQ1lTJNGaXOiiM5E+aia0ER2zXncxCDs03eLFr6J2xXLEX3AxEv9zaYf68nZjpRVlK/K7SggfBTAVQFqLfvYC+ALA3S7iIwihi0AJQughUKJZUCNQaDyAp/JmgVbCmd2fQGZoj4Cdr9FqxLS9ExGlicKTPV8NSsOD0npOEMKA/XVwT3ApwfyqFSj74B1Y62qlKILJ105G5OCmJN4kdBXbP0Hlzi8AlQYpg29ATM/TXBrEbDViWclC7K5bg1B1JMal3YXuEfSmEkUg4BkC+sq9KFwzDxZ9JcJTBkmXFBoXk2J7NqJo1RIBU2kJ9s+6CyqtDj2fWQBNVLTfgKS0ouwgIfwYQIoTS+J3cr+uJtIShNDDHSgshB4CJ5oFDQIWmxnz8mcj17AXFyRegbPj/e8pgLtgP5l3L/IMOXi8x0tI0AZf2iml9ZyrhJAvTl+UH9JXAXgDAGNBW9pZQD66fw7ASUwXBOAT+UF+XYt23JmPA2CiO96asu+P3NgcThWlG+2DuqqprBSlb78h5RdjiRlzJpIuvQLq8MPdwGr2L0fJxtelCKTx/S9AwlGXtRldrsFcjW8Ln0GRYRdiQlIwPmMmEnTdghpPMTnfIlBfuA5Fa1+EzWJATI8xSB48CSp1iG8HFb07RaD0nTdR/b9liBs3HkkTrvQblJRWlB0khD8DqJcDyjh29S6A3gBGuQisIIQuAtWymiCEHgInmgUNAt+Uf4wfKj9Hn7ABuKvbQ1Cr6P0e2OX9kjewsuYXTEm7C0OiTgjsyTiRXmk95wohjAewDcA/8kN4PuCZLxO9psywzgujqv0r/3magQEB8L/0VWSkNXshWVwOYKHsPjNOdqEZC2CZiyssCKEToGxWq3SYK//0A9gMBmjT0pEycSrCB7SdB7ChdCuK/noOVlM9orqNQMrQW6DWHB4UpsJ4AF8XzEWNuQRpof1wXvo9iAjhsosiEPAMgao9P6Bs81IpB1riwCultCjyR9GzDkWrDiFgrqzA/hl3ACoVejy9ACFxcR3qz1uNlVaUrcjtqsuop4TwYQAPOY7tyzyE3lobf+xHEEJ/XBUhk1II5Oh34Zn82dCpQnF/1lwkaZ3n61RKHm+Ns6rmN7xbsghnxY3HhUn+c2HprfkpredcIYT3ApgBgM7GNfJE+XcqK76JsP9bSwzYjn+yANCqyHI+gK8BHA9gnfxvjL6mBTDGoQNmpmWOApJFV4oghC1QMh7IlxLM63fvkhLMx59zPuLHX+RyomljzQEUrJ4Lc0MJwhL6IX3EdGhCD6WNyGvYJkUSNVjr0TdqBM5KuUVEEnVlp4o6ThFgJNGyLUtRvfcnqNRapA67VbqMEKXzESj78B1U/fgdYs8ci+SrrvdIICm/3bZVqN6wDKbqUmhjkxF73FmIGuhZfjulFWUHCSFdRvn4taUPvnAZ9Wg3ud9IEEL3MRMtggMBg1WPJ/JmodRUhKuSp+LEWMejdmDP8YAhV0qf0T98IO7s9mBgT8aJ9ErrOVcIIaOiFQC43EFekrz9AMYD+KaVVaDLJ5Wg4+5jQiu6jtIllH9CAdTK+ZhedejnWgBLACQAqHZhlQUhlEGymc2o/PZLVHzzRVOC+Z69kTLpRoRmuf942GyoRuHqZ2Co3IWQyBRkjJwJXXQ3/FOzHL+WvC5FEh0WfwFGJrTtVurC+okqXRgBq1mPomxGEt0IjS4G6SOnS5cQovgHApaaGuybcTv4bekx93loE917q0EyWPTlAtTvzAZs9gCdKsnqGDlgONIuvN3tpOdKK8oOEkIGlZkCIL1FP3sAfCmCyvh+nwtC6HuMxQj+icAHJYuxouZnKRrnTen3BJXHDYPj3L13IlRQ45nei4PCDdZxFymt51whhCWyOyctgo6FbyL4b/Na+TX4SiZ8dP20Fz4E0svhukkw+TaR7qi8OaXbqL3QgpgNgKG517rwayYIIYHdswslby4CrYMqnQ4J/7kUcWeNg0qjcQFC51WsFiNK1i9E3YE1UGkjcODY47DJsFKKJHpaymQMjAme2yaPQRINPUbA3FiBAkYSrd4HbVSGlPZEG8n4G6L4EwLln32Eym++QMzoMZLbuTuldutKFH+70IEMOrRWqZF63i2IPtpVZ5Cmtkorylbm66rLqD3tBHMOrpT7GibrNpF2wp3N5GFdQQg9BE40C2gEttVvxMuFcxGljsYDWfMQE/J/9s4DPKpi/cPvbrLpvXcIHRsiioK9gL13vahcVFDRC0pRQGwUqSoqeBUVsf/VawUVK4pSLKiAirRAQnqvm2z7P3OyaICEnN3snj2bzNyHx5tkyjfvnOTb35mZ79PHkX9PQl2Y9yDbzX9yf9YCUjtZ/Aqt/ZwaQWhxBoJ5/IBFzAPEZZ8pbSyuuGcoDvWK3UTRhyji1qdI6CvuVAwHTnQ6yIHALy366QVsA85WeY+wSwtCe6MZ8YGt6rNPRLhQQvsfTtLIWzAlHRjl3L1fQ/GGv/j31/im4SMKYsT53iDOT5tIVphrqSncG1226qwEGitzFDFoM5cTmnh4cyTRoIjOOl2/npetro7dE+/Cbm4ga/ZCgpLV/23JWz4d815xnby1YiAkozcZI8QmmvqitaNsYZmIxCXuuYsi0kWIc/T77vmJqw7iBMx2YDUwqkU7cTVCbHu3TEwvXrbKxPQtIG3fvp158+axdu1atmzZwsknn8zXX7d8VwyLFy9mxYoVrFu3jvLycr766itOO+3QKY6kIFT/uyVrdg4CtbYaZuyZSLWtkltT7uHoCLHP0vnKO6Uv80XlCm5Iup0Tok7pVBPU2s95UxD2AzY7I5KKnUQRVEYIyAHORLxi59BdQajqsn2nejLamEz95l8pXrYUa2mJEjU04Zp/EXnK6R49FtBgq2ZFwQLyzVsJbYJjcyAr+3Li+l3h0XG6wnrJOTYTqCvcSOGGx5VIopFZp5I08BYZSVTnD4c4hl7+zptEDjmJ5NFjVVu784lbsde3ddUcAiLjyB4rYoqpL1o7yhaWdQd2tWFpNpDj/CdUTMsLl+LVvIi4fSkgwvt95LwqUap+1nT6KKPvv/8+Y8eO5YQTTmDz5s0kJycfJAjFz8T6Z2dn8/rrr0tB6MIDJKt2DQIi+NTzRU/wc+06Tog8lRuSb+u0E/+x5nteKFrEqdHDuTrx351qnlr7OTWCULzFfNp5568l7PaOjIq6YnWEExRvUcXlkWedAWWEUBTOct+RUfF6T7xR3VfkkdF2HmtbbS2lry+n5jtxxRPCBw0mccRIAmNEUFjPlYqmfD4omEuVpZDk4J6caTqf6h+ew25tICLjRJKPGYMhQMQEkkUSUEegaucqSn4VV4QdSlqT2D6XyBcL6tD5tJbdbFZ2CW21NWTNmEdQesYh7XFYLZSvfY+KNe8cop7YIexDxghxpVx90dpRqrfMqzU7vSC02+0Yjc3h8K+44gpKS0sPEoT76gjBeOSRR0pB6NVHTnbujwQ21KxhWdFTxAUmMDVrLqHGg1OM+eO8WrO51FLM9N130T24J5MyZ3aWaSnz0NrPqRGEQnHsBa5tQToT2NNOUJl91UUgGXFURghL8Ta0DLgXEEFk9gWVuRP4b4v+RwDLZFCZg59t8ean9od1lL6yDFt1FQHRMSSO+DcRx4rrlp4texv+UHYGzfZaeoYfx/DksZiMwTRW51KgRCAtJSS+L6nH37NfBFLPWiF76ywExNHj0k2vULVjpRJJNGnQbURmqE3B1lko+Pc8Kj9dQenrLxN+7PGkjh3f5mTMe7dRvPK/NJXmgTEA7OJ9oOPg+v59h1DrxfS6IFSiwVZ/T1XFKiyWEkymRKJjhxMR5V402I4AaksQ7utTCsKO0JVtOyuBckupEnnTbG/gP+n30ydU7Lt03iI+E0/adSuN9gYW9lxGoKHz5CzWoyAUqSMmOtNOiIigooh7EOLSx6HSTrT2BN7oTHAvjt2UOyuIuxUi6slZLRqI4zTiiI3aSANd4g6hyAlWsvx56jb+pKCKOuV04q++noBwz9+7+rPmWz4vekaJJHpMzAWcGH/dfpEAreZKCtbNo7FiB6bwFFKHTiYo4sAgep33j5CcmWsERCTRoh+fQiSdNwZFknrCBELj+7rWiaztcwL2piYlL6GtsoLMh2YT3E2ckvyn2JvMlK1+g6ofxZ91B6HdjyDx7H9Ttvr/nFFGhSgU/zpFlFGt18OrglCJBrt3EXU1Ip5bi2iwGAiPHExKuuvRYDsCSArCjtCTbbsiARF188n8mWxt2MJZMRdwWcK/ugSGp/MfZUv9L0zOmEm3EJEqvXMUPQpCcQZRJKUXxzznAD2AhYAIMtMyMf2BF+nFMdGpgNhhtDojiYpL+CL8ttj921f2JaZ/yhmCW1zYF4JTJqZ3EhIJ5qtXf0nZ/72KvaGBwMQkJdJf2GFHePypF29bNlS8w/ryt5VQvqcljuTIaBEI7+BitzY6P+T/gNEUQeoJ9xCacOik9x43WHaoewJWcwUFa+fRWLkTU0QqqSJ9SYT6oCS6n2AXM7Dqy1WULH+BsAEDSRs/+e/Z1+38lZJPnsNaVYoxJJyEM0cQeeSpyrGXv/MQbhR5CEsxRScQPdDv8xBqvfJeFYQ1VWsoyhd3OfdWJ2OhAAAgAElEQVSJwZbTM5KcdjuR0Wrf0XYcjRSEHWcoe+haBL6sXMnbpctJC8pUxJHJGNQlAHxU9hYrK95R7hCKu4SdpehREAq2Ys9ZCDYRPlskmV/qTDlhawFeXKZveZE+HHgXEOG1Q52CUhzwFXmXDiyXADOcR0vFhX0RNOYNFxa10+4QNhXmU/zic5i3/qHk7Yo5+3wlnYQxWJy29WyxOix8UfxfttaswWQI5byUcXQLFzGA2i7ig17Z5lep3L4CjIHKncLITO0+NHiWgOzN0wQaq/Y0Hy9uKCMkvr/y0kBGEvU0ZW37E/kId987XglklT7tYYLSUin9Yjk1m79VDAnvdzyJw0YSGOG9EOdaO0ptCbc5mkuCMGfbWOx2EfRUXbHbRUao1sTgvvZGjEZxA6T9YjSG0b23+MjgfpGC0H12smXXI1DQlMejufchdgknZ84iI9j13NP+Sm1T3c8sKZjLCZGncEPy7f46jYPs1trPqblD6A9wO50gFB+6Kj9ZQfl7byOCMwRlZikJ5kOyvbMdbrbV8pESSfQPIgLjuSh1MgnBImOIulK167PmQCEOO3H9ryS272UyUIg6dJ22Vl3Rr82RRK0NRGaeTNLAW2UAok6y2tXffk3x889g6peBPbQaW301ARGxJA4fSURfz99nPhCb1o5SJ8vmZUHYvngUQk9NkYJQDSVZRxLwDAGrw8q8vPvJbdzFJfHXMjz2Ys907Ce91FirmJwzmtSgDO7Pmu8nVrdvptZ+TgrC9tdE8xrmnF3NCeb35GAINBF78WXEnnshhkDvXJattBTyQf4cKi0FJAZnc2HqRCIC41yed13RLxRueMIpAE4h6ZhbZSoBlyl2jgZVuz6n5NcXml8Q9LuC2H6XyxcEnWNplVlYKovZs2giDlOj8nXUgDOIP+N6AkLEwRDvF60dpfdnpGoElwShqh5bVMrLmY65QaT/bSX4DwZCQvuQ0d21aLCu2tCyvtwh7Ag92bYrEfiw7E0+rniXniF9GZ/+AEZDc6TerlSm5YylwlrGgh4vEGIUhxL9v2jt56Qg1NEzIwI2lL/3lrIzKKLyhfTpS9LI0QSlpnnNyvyGrXxUMB+zvYbs8EGck3wnJpXHglozqrFqt/OIYDmhCYeRcvzd8oig11ZPfx0rR4i3vE7ltg/BENB8hDjLldzb+puTtOgfAmJ9qzd+TulXr+NoaoAmMAWkkzV1vqaCX2tHqZNnwKuCUN4h1MkqSzMkARcI7DJvY37edIIMwUzNmkOCKdmF1p2n6tLCx5W8i+PS7qdP2OGdYmJa+zkpCHXy2NT/sYWSZc9hKSrEEBJKwlXXEnXaWRicOZm8YeZfNd/zWfESbA4LR0efy0kJIzzyZsnaUE7B2rk0VuVgikgjbcgkTDKIiDeWUFd9KkGGfnqauvwNMsiQrlbGM8Y0leVT/PGzmHP/bL7PfNz51H39C5bcPFLHTyZ8wEDPDKSiF60dpQqTtKjiVUG4f5TRFtFgNYwyWl9fz8qVKxWWCxYsoLq6moceat6VPO+88wgLC+PHH38kJyeH3Nxc7r77bh588EEOP/xwunfvzrHHipAFB5empiZ27NhBz549CQrqGoE2tHgg5Ri+JdBoNzMr915KLIVcn3QrJ0ad4VuDfDj6ZxUf8m7Zq1wSfx3DYy/yoSWeG1prPycFoefWzq2ebHV1SvRQEUVUlLABx5B4w78xxSe41Z+aRiKS6I8V77G2/E0MGDgl4UYGxIigrp4rIs1A4Q9PUl/4k0wz4Dmsuu3poDQkQyYRFOm9nW3dguiEhjlsVirWf0jFmv/hsFkISupG0nmjCUntQd3GHyl4Yj7B3bqT8cAsr77AaolWa0epk2X1qiAUc9w/D2EpJlOCpnkIhdDLzt4/lck+9rt27VJE30033cRLL7100JLceOONLFvWMoD5P1WkINTJEyzN8CiB14uX8m315xwZNogxqRM0PaXh0Yl4oLO/Gn7n8b0PMzD8eG5JbTtHrgeG0qwLrf2cFISaLe3BA9X+9AMlL7+g5PQKiIwi4fqbiDh+iFd/qW0OK18WP8cfNasxGYI5J+U/ZIcf4xUKzYnIX6Zqx8cyEblXCOuj06bqPPLXPoq1vpSQuL7NkUSDRdYZWfydgLlgZ3OC+eLdSkCg2JMuJ/b4CzAENN9nFi+X8h6eRuOuHaTcMZ6I447XZMpaO0pNJtX+IF4XhO2b4J81pCD0z3WTVrdNYEvdRp4umENEQBTTMucSFei9qM7+sA5mewP37Pw3sYHxzOjesQjHepmv1n5OCkIfrLy1spKSV16k7sf1yuiRQ08m4doRiij0Zmm01bGicCF5DVsID4jlorTJJAZ39+aQSt+VOz6h9DfxRtdB3GFXE9vnEq+KXq9PSA7wN4H64k0UbngMu6WeiIyhJB0zBmOAPJLl74+I3dJI+bdvU7nhI6H6CMnsR9K5txIUf/Cub/3mX8mfP5ugtAwyZ8zVZJdQa0epk/WUgtDNhZCC0E1wspkuCdTaapixZyLVtkpuTbmHoyOO06WdWhv1yJ4JKOk3uj/TKQSy1n5OCkINn1jxNr1mzWpK33gZe10dgfEJJN54M+FHHe11K6osxUok0QrLXhKCunNh2kQiA+O9Pu6+AeoKfqLwh0U4bI1EdjuNpKNvlhFINaPvnYGqc76i+Jel4LAR2/dS4vpfJYW+d1Br2mt9zmZKPn4OS2URhqBQEk6/jqiBZ2JoI3Kd+Lu299GHlVypybfeobzg8nbR2lF6ez4q+5eCUCWoA6tJQegmONlMdwTE31sRQGVj3XqGRJ7GiOQxurPRVwYtL1rCuprV3JY6kSPDB/nKDI+Nq7Wfk4LQY0t36I4sxUUUv7SUhi2blIAM0WeeTfzlV2MM9X543ALzNj4qmEeDrZruYQM5J+UugnwQlrexchf5a+diM1cQmngEKYPHExCkTZh6jZa5SwwjjgKX//4mFX+9r0QSTRp4C1HdTusSc+/Mk7SZ6yj78hWqf/1KmWZYr0Eknf1vAqPaf3HUsPUP9s5+CFNSMlmzFngtRc4+/lo7Sp2suxSEbi6EFIRugpPNdEdgfc23vFT0NPGBiUzJmkOoytygupuIFwz6pmoVb5S8wHmxl3NB/JVeGEHbLrX2c1IQenl9HXY7las+pvx//4ejqRFTWrqSYD60Vx8vj9zc/bbadawqelqJJHpU9HAlgIzREKDJ2K0NYm0oU0RhU9VuTJHppA2ZjCk8yWf2yIFdI2C3NVH802Jq967DaAon5fjxhCUe4VonsrbuCNRu3UDJpy9gq6skICyKhGE3EdHftfvMe+fPomHzbySOvJXoU70b7U5rR6mTBZOC0M2FkILQTXCyma4IlFtKmZk7CXFfblz6dHqH9teVfb42Zrd5B3PypnJY2ADGpt3na3M6PL7Wfk4Kwg4vWdsdNObuURLMi4ALBAQQe8ElxF1wCQaTyYujNnctjhX8XPkh35W9Bkok0REMiD5XF0f67JYG5fhofdFGAoKjST1hAiFxvb3ORA7QMQK2xmoK1s3DXL6NwLAkRcwHRaV3rFPZ2qcErLWVlKx6gbqtGxQ7Io84hYQzRxAQFumyXeadO8h7eCqBcfF0m/O4V//Oae0oXYbhnQZSELrJVQpCN8HJZrohYHfYeTJ/JlsbtnBWzAVclvAv3dimF0OsDit377iJYGMIc7Of08Xn3Y6w0drPSUHYkdVqo63DYqH8g/9RsfIDsNkI7tFL2RUMzsj0wmgHdykiiX5d8gJbqr8k0BDM2cl30jOi9fxMmhjUyiAOu00JNFO1a5USgTT52LFEpGsTodBXc/bncZtq9pK/dg7WumKCY3uTOmQCgcHR/jylLm27cp/5t68p/fIV7OY6AqMTSDznFsJ7DOgQF5GCQqSiEBGTY4Z5NpVNS8O0dpQdguK5xlIQuslSCkI3wclmuiHwZeVK3i5dTlpQFpMzZ2IyeH9jQTeTd8GQubnTyGnczsPdniDBlOxCS/1V1drPSUHo4WegYdtWZVfQUpCPIShYuScYPewcTSLviak02upZWfgYuQ2bCAuI4aLUSSSF9PDwLD3TnfhQKlJSiNQUIgJp/OHXE9P7Ar9/q+MZOvrppb5kC4XrF2K31BGRfgJJg26XkUT1szwuW2KpKKL4k+doyNmsnB6IPvYc4k+9GmNQiMt9HdhAnIrInT5ZiZjcbd4TGIM73mdrRmntKDsMxjMdSEHoJkcpCN0EJ5vpgoCInDk79z4lT+jkzFlkBHfThV16NOLNkhdZXfUp/06+i2Mjh+rRRNU2ae3npCBUvTSHrmhvaKDs7dep+vIzJUx76OFHknTTLZgStbsfV20p4YOCOZQ35REflKWIwUiT9xLcewgdtfk/UvTjk0oE0qjuZ5I4YKSMQOopuB3sp3r3aoo3PtscSbTPxUrakLaiTXZwKNncywTErnzlDx9T/s3/4bA2EZSQ0ZxgPt2zx7ULlyyidv33xF91HbHnXeSVWWntKL0yCdc7lYLQdWZKCykI3QQnm/mcgDgGOS/vfnIbd3FJ/LUMj73Y5zbp2YD11d/wUvFizow5n8sTRujZ1HZt09rPSUHY7pK0X6Hu142UvLQUa3kZxvBwEq69gcgTT9F0p6vIvIMPC+ZSb6siK2wA56b8h2A/ij5lrthBwdp52BorCU06ipTB4wgwhbUPX9bwCgGxe1v+59tU/PkOGIwkHn0z0d29GyjEKxORnSoEGot3U7zivzQW7gRjAHFDLyV26CV/J5j3JKamgnz2TLkHY1g43ecvwhjq+d9jrR2lJ/l0oC8pCN2EJwWhm+BkM58T+LDsTT6ueJeeIX0Zn/4AxjbS//jcUJ0YUNi0l4f33KPwuifjIZ1Y5Z4ZWvs5KQjdWyella26mpLXXqJ23XfK1xGDT1DuzgRGx3SgV9eb7qjdwKdFT2F1NHFE1FmcljjSp5FEXZ9BcwtLfSkFa+fQVJ1LUFQmqSICaZj+dzjdna9e2zlsFop+fobavO8wBoY2RxJNOkqv5kq7DkHAbm2i4rt3qVj3AdhtBKf1Jum8WwlO9O595qLnn6Hm26+Ju+QK5Z+ni9aO0tP2u9mfFIRugpOC0E1wsplPCewyb2N+3nSCDMFMzZrj93fitIApgu9M2DUK8d8FPV4gwIdR9Ts6X639nBSEbqyY2D2pXfsdJa+/hL2mhoCYWJJuHEX4QG0Dtwg7NlauZE3ZK8osToq/joEx/n0Hz26pp2DD4zQU/0ZAcAypQyYSEtvTjVWSTdwhoEQSXb8Ac9lWAsMSFFEeHOVd8eCOnbJN+wQacv+keOWzWMrzMZiCiT/1GqIHna3JfWZLSTG77x2PMSiIbvMWERDhetTSQ81Qa0fZPm1NanRqQbhs2TJGjhx5EMglS5YwZkzHkm9LQajJ8ykH8SCBRruZWbn3UmIp5PqkWzkxSp7QUYv3ib2PKNFYp2TO8ev7llr7OSkI1T5hznqWslJKXnqe+t82Kt+JOu0s5a5MQJjnj0UdyjS7w8bqkmVsqv6MQEMQw5PH0itisIuz0Wd1h91Kya8vUp3zBYaAIJKPvZOItOP0aWwnsqqptoCC7+dgqSskOLYnqSdMJDBE293uToTTZ1OxN9ZT9vUbVP28SrEhLHsAieeMwhSj3X1mMW7x8uep/vIzYs67iISrrvMoD60dpUeNd7+zLiEIv/zyS0JDQ/+m1KNHD5KSOvbsSkHo/kMnW/qGwOvFS/m2+nOODBvEmNQJml5B8s2MPTfqe6Wvs6ryfb8X0lr7OSkIVT6DIsG8CBgjAsc4zGZMySkkjbyV0H6HqezBc9Ua7fV8UvgEu+t/JTQgmgtTJ5IS0stzA+igJ7H7Wbn9I8o2v6pEQkw48l9E9zxP/lH00to0lP5BwboF2C21hKcep6QBMQYGe2k02a23CNRt/5mST5ZirSnHGBJBwlk3EHnEyT75vbFWlLN70n/AYKDb3EUExnju5YLWjtJb6+Viv11CENbU1BAREeEimkNXl4LQozhlZ14msKVuI08XzCEiIIppmXOJCvTc304vm66L7jfWbuC5woWcFHUm1yXdogub3DFCaz8nBaGKVWrK30vxi89i3rYVjEZizr2QuIsvV45DaV1qLKVK8JjSpj3EBWUokUSjTB17e6r1HFwZr3bveop+fAqH3UJ09nASjroRgzHAlS5k3XYI1OSuUe4MYrcS0+sC4o+4TkYS9bOnxlZfTclny6j9/XvF8oj+Q0gYdhOB4b7NFVn6+stUfrqC6GHnknj9jR6jqrWj9JjhHevI64JQhLUvrP2ePVWrMFtKCDElkhU9nJSIoV7/m7DvyKgUhB17SGRr/yZQa6tmxp5JVNsqGZ0ygQE6yyHtD3QrrGVMzbmDzODu3Jf5qD+Y3KqNWvs5KQgP8ag4rFYlubxIMo/VSnC3bJL+favyX1+UYvMuRQzW2SrIDD2S81LGERwQ7gtTNB3TXL6NgnXzsTVWEZY8kJTj7sJo+udIkabGdKLBxC5sxdb/Uf7HW82RRAeMJDp7WCeaYeefinKfecsaSj5fjr2hhoDIOJLOHkV470G6mLwIvJUz8U4cNhvd5jyOKd4zQaK0dpS6gCkSGjkcB5niqd0vIQZ/K1xEcd0GHNid4xgQ/0sKH8xRKXd5VRTuE4TieGhZWRk9e/bk7rvvZvTo0R3G7ylGHTZEdiAJHOozp8PB0sLH2Vi3niGRpzEiuWN3Z7sqbPF38r6c2xDiemGPZQQZtd+88QR7rf2cFIRtrJp553aKX3iWprw9GEwm4i69kpizz8cQ4JvdqZ11P/Fp4SIsjkYOizqd0xNHEWAI9MQz5xd9WOqKKVg7l6aaPIKiu5E2ZBKBofF+YbsejRT3NIt/fpaa3G8wiEiig/9DePLRejRV2tQGAUtVCSWfPE/9zl+UGlEDh5Fw+rUYg7W9z9zeApW98yYVH75L1GlnKrlZPVG0dpSesNkDfXhVEBbUrGFz0eIWYvAfiw0YOSL5dlIjT/LANFrv4tNPP+WHH35g8ODB2Gw23njjDZYvX87ChQsZP358h8aVgrBD+GRjjQisr/mWl4qeJj4wkSlZcwj1o9RhGiFSPcwzBfP4re4n7kl/iJ6hfVW301NFrf1clxaEdpuV0g9eouaPb3HYGzEYg4noMwQag6n5/NPmBPP9DiPxplsISkn12XPyS+XHfFu6HAcOhsZfy6CYi3xyJ8hnAJwD25rqKNzwGA0lmwkIiVVEYXCMb3Zr22Ih3rLXbvleCeghPrCbohOJPmY4EYd7/8iV2vWxNdVSuH4hDaW/Exga1xxJNLqb2uaynoYEWnueogaehc1cR/nqN3BYGjHFpSqpJEIz+2tomfqhbHW15NxzJ45GM0FZ3bBVVWFKSCT6jOFEnDDUrainWjtK9bP1ak2XBOE3OWOx2utVG2S1m+HvncHWmhkJNIao6i/QGMYp3Z9SVfdQla6++mo+//xzSkpKMBqNbvcnBaHb6GRDjQiUW0qZmTsJs72BcenT6R2qz7/nGuHo8DAfl/+PD8v/jysSbuCMmPM63J8vOtDaz3VZQSjE4O6Fd2GzlDevsyCx7zRODVARTOI1NxB1yulufWDxxMMj8qgIIfhr1ScEGEwMS7qdPpFDPNG13/ah7Gz9spSa3V9jCAhWjo+Gp+rjeJxy/+a9RdRt3QCOf45ciaAa4X0Hk3KJd49cqVlUS20h+WvnYqnNV8S0Ekk0NE5NU1lHYwKtP08tjTAQO/RiYk+8DGOgfo/EiIBce+6fjGVv7j/GGwxKsJnwQYNJue0ul//Gau0oNV76tobzsiBsXzwKoaemeEoQvvXWW1x11VXs2LEDEW3U3SIFobvkZDstCIjPeovyZ/JXwxbOirmQyxKu12LYTj3G7/W/8lT+bI6LOJGRKXf65Vy19nNdVhAWv/s81X981iwEDywOCMs8lsQL/uWzh0gcDf2sZjm7LVsIMYRzbuTNpJj0tRvmKzjifHj1ns+p3LVCUfKxvS4lKuMUX5nz97i1f/1I2VevtRCDLUwyGIk//Toi+mibq7IlFHPVLko2L8VuqSM0/ggSDhuBMUBGEvX5g9OGAYd8noC4U68mbuilejX/b7tqvl9D0dLFYN/3kqSFyUYjyTffTuRQ144iau0odQLZJUHoqs3r86ZTZd7W4s3o/i8fYkL6MDjjIVe77VD9t99+myuvvJKdO3eSne2+/5OCsEPLIBt7mcCXlSt5u3Q5aUFZTM6ciclg8vKInb/7OlstE3fdTJIphQe7Pe6XE9baz3VZQbhj1k04MLcuCH386JhD4MeToDoWwqvhuDUQVudjo/Q4vMh1LU7yipNEYqO3WI9G6sQmyUonC+EpMwyEZPQmY8TDnurQa/3kzZiOeftfrfdvMBDSqzcZU12bh9aO0mtwXOvYq4LQ13cIW0NxzTXX8MUXX1BUVCSPjLr2rMjafkKgoCmP2bn3IU6ETM6c5deJ1PWG/IHd4yixFDI/eylhAZ5NZaPFXLX2c11WEG6fcS0EHByx7e9FdkBo98O1WPP9xqgMNfN93900BFlJrA7nhG2ZBNl8E8hG88m7MaDNWE9T0B4w2DDaIglqSseAb3g15G0Fm7XtWQQEEpqh7eVmce/UGliK1VSsHIk2WVIItMlgPG48apo3ae95EhFFs8cu1twuVwfcNe42bJUVbTYLiI0j+zHX5qG1o3R1zl6q71VBuH+UUeEbxT/tooxefvnlSkCZo446Sgkq8+abb/LKK6+waNEi7ryzY0e+5A6hl55I2W2HCFgdVubl3U9u4y4uib+O4bEXdag/2Xh/Ai8WPskPtd9xZ9oU+ocd5Xd4tPZzXVYQHnKH0CHcYAg9pyzT9AHKqdvIx4VPYHGY6R95Kmck3dKlIom6C1sv9+Lylk/HvLftI1chGX3IGKHdkauD7luKSKIpx7iLWbbTmIDenid3p6/sEO7YpgTpOqgoO4R9yJjq2u+F1o7S3bl7uJ1XBaGwdV8ewtyqVTRYSgk1JZCpUR7CKVOm8M4775Cbmyvya3DYYYcxbtw4RowY0WGMUhB2GKHswAsEPih7k08q3qVXSD8lkIzR4H7gJC+Y5/dd7juKe2Hc1Zwbp//rFQcC19rPdVlB2N4dwqj+w0i6dJRmvxC/Va1idcmLSiTRIXFXc2zsJV0ykqi7wPUQObNm8xqKPlrc5h3C5AtuJ/II1+5Kuc/jwIiskwmO6e5ud7KdDwjo6XnqyPTlHcKO0NuvrdcFoccs1VlHUhDqbEGkOexs+IsFex8gyBDM1Ky5JJiSJBUPE9jRsFVhfFT4sYxJneDh3r3fnRSE7jFu1VEeqqtDRRkNMMXR7e5FGAO8n+dPRJf6ruxVNlauwEggw5Jvo2/kie5R6OKt9s+tF0LK4HGa5tbbPyrkP0eutI4yKnI25q+dg6Vmr8zZ6Me/E3p5njqKUEQZLVyyiLqfRPRdR/M/GWXUHaxSELpDDZCC0E1wsplXCDTazczKvVe533Z90q2cGHWGV8bp6p022Ru5e+dIIgOimZ29xO9wSEHo3pK5LAjFMK3lIYzsfzIJF92oiRi02M18WvQUO+t+JMQYyQWp95AW2s89ArKVQkAcNarY+j/K/3gLDEYSj7qJ6B7DNaPzd964jSIPYSmm6ASiB2qXh9Bcvo2CdfOxNVYRljxQScthNIVqNn85kGcJ+Pp58tRshCisXfc9VV+twlJaiikhgejTZR5CF/lKQegisH3VpSB0E5xs5hUCrxcv5dvqzzkqfBCjUybI02Beodzc6aw995LXlMOs7ouJCfSvFFtSELr3YLglCN0byjOt6qwVfFgwj+LGncSYUrgodTIxQSJkpiyeIFCTu4ain58Bu5WYXucTf8T1GDr5+fzavesp+vEpHHaLIoITjrwRg9E3AXY8sYayD0mgLQJaO0qdrIQUhG4uhBSEboKTzTxOYHPdRhYXzCEiIIr7M+cRGRjt8TFkh/8QeLX4Wb6r/pJbU+7m6IjBfoVGaz/XZe8Q+vKpKG3cwwcFc6i1lpEW0o/zU+8hNEDkBZDFkwQaSv+gYN0C7JZawlOPI/nYsRgDO1/ePbErWrntI8q2vKpEBUw4cgTRPc+Vbx09+TDJvnRFQGtHqZPJS0Ho5kJIQegmONnMowRqbdXM2DOJalulsjM4IMJ3eYk9OjEdd/Zd1Ze8WvIsZ8dezMXx1+rY0oNN09rPSUGo8eOxu+5XVhY+jsXRQN+IkzgzeTSBMgmp11ahqbaAgu/nYKkrJDi2J6knTCQwJMZr42ndsbg3WfLri1TnfIEhIJjkY+8kIk06Ga3XQY6nLQGtHaW2s2tzNCkI3VwIKQjdBCebeYyAeHG7tPBxNtatZ0jkaYxIHuOxvmVHbRPIa9zNrNzJ9A09gv+kT/MrVFr7OSkINXw8Nld9zlclL+DAzuDYyzk+7gq5i6MBf1tjDQXr52Mu20pgaAKpQycTHJWpwcjeHcJmqadww+M0FP9GQHAMqUMmERLbw7uDyt4lAR0Q0NpR6mDKwgQpCN1cCCkI3QQnm3mMwPqab3mp6GniAxOZkjWHUGOYx/qWHbVNwOawKYFlAgwBzM9+3q9Se2jt56Qg1OA3SQSG+L7sdX6q/BAjAZyZNJr+UadoMLIcYh8Bh82i3CmszfsOY2AoKcePJyzJ/xKV7puPpb6UgrVzaKrOJSgqk9QhkzGFJcgFlwS6BAGtHaVOoEpB6OZCSEHoJjjZzCMEyi2lzMidiIguOj59Or1C+3ukX9mJOgIL8h5gh3krD2QtJDkoTV0jHdTS2s+pFYSHAU8CQ4BKYCkgMgnb2mEmzq7NAvadYfsZmAqsb9FOZH+/sZV+xG/MnyrXRLdBZaz2JiWS6I66DQQbw5X7ghmhAqcsWhMQRzbK/3ybij/faY5AOmAU0dlnam1Gh8czV+ygYO08bI2VhCYdRergcRhN8m1jh8HKDvyGgGURl0MAACAASURBVNaOUidgpCB0cyGkIHQTnGzWYQIitdii/Jn81bCFs2Iu5LKE6zvcp+zANQJvlyzny6qV3Jh0O8f70WaM1n5OjSCMBbYAvwNzgJ7AAuAx4FAHcsWZvE2AEIGivigTgWOAI4Hdzu8JQXg8MPKAJf4FMKtcdl0KwnprJR8WzKeocTtRgUlcnHYvsX70dkIle7+rVr3nG4p//i84bMT0voj4w6/xmwiktfk/UPTjkzhsTUR1P4vEATdhMHo/X6bfLbI0uFMT0NpR6gSmFIRuLoQUhG6Ck806TODLypW8XbqctKAsJmfOxCRjRnSYqasd/FjzPS8ULeK06HO4KvEmV5v7rL7Wfk6NILwPmAR0A6qdZMTXDwIpLb53IDRxY/ZpQCT+qHL+UIjLUmAssC9LpBCER7TYRXQHvu4EYVlTHh/mz6HaWkJqSB/OT51AWECUO3OTbbxAoL5kC4XrF2K31BGedjzJx96BMSDICyN5pkuxu1m1YyWlm15ROow/4jpiel0g76B6Bq/sxc8IaO0odYKnSwnCvXv30rdvX+rq6qipqSEiIsLtZZCC0G10smEHCBQ05TE79z5x+5dJmTPJCBYfo2XRmkCJpZAHdo+je3AvJmXO0Hp4t8fT2s+pEYTfAPnANS1mleXc4bsI+LCN2d4JzAfCAauzjgmoBcYDi53f63SCMLd+EysKH6PJXk/viCEMS7qNQKN+xYbbT6ufN2yq2Uv+2jlY64oJju1N6pAJBAbrLyeQw26jdNNLVO1chSEgiORBY4lI9698On7+qEjzdUZAa0epk+l3KUF43XXX8eWXX1JUVCQFoU4eQGmGegJWh5V5edPIbczhkvjrGB4rPi7L4gsC4oX6pF230OhoZGGPFwk0+MepKq39nBpBWOwUb2JHsGWpc+4SzmtjgcXNTXHUdDkw01lnOnCF88hoSQtBeJVTNIokcT847xmuduHB0c0O4Zbqr/iqeCl2bBwXeyknxF3pN8cRXeDdaaraGqspWDcPc/k2AsOSSBsymaCodN3Mz25poPCHRdQXbSQgOFpJmxES10s39klDJAFfENDaUfpijq2M2WUE4TfffMMll1zClClTmDhxohSEOnkApRnqCXxQ9iafVLxLr5B+jEuf7lfRLdXP0n9qPpU/m9/rf+XejFlkhfhHNHat/ZwaQWhx3v17/IClz3OKvSmHeCSOBj4C9n3CLgDOBX5t0eY/QJPzjmIicA8wCDgJ2KDycfO5IBSRRNeWv8mPFe8rkUTPSLqFw6JOU2m+rOZLAnZbE8U/LaF271olOEvK8XcTlihOMfu2WBvKyF87l6aq3QRFZihpJUzhSb41So4uCeiAgNaOUgdTFiZ4XRAKP7a19ns2Va2i2lJClCmRI6OH0zdiqGYvNm02G8cccwwjR44kJiZG+a88MqqTJ1CaoYrAzoa/WLD3AYIMwUzNmkuCSfptVeC8WOnDsv/j44r/cU3iKE6JHubFkTzXtdZ+zpuCMBUQx01FMJp99wXvAAYCQ4E9bWAT4RLFzqIQjZe0UUfsVj7Q8mdiS9hXRUQS/ax4Cdtq1xJkDOP8lLvJDPO9oPAVD38cV3wQKv/9/6j46z0wBJA08BaiuvlO0DdW7lLEoM1cQWjiEaQMHk9AkDh9LYskIAlo7Sh1QtyrglD8Dfy4cJESEVvkym0uBuV/PcMHc27KXZqIwqeffponn3ySTZs28eqrr0pBqJOHT5qhjoDZbmZ27mRKLEX8K2k0Q6NOV9dQ1vIqgd/qfuKZgnkMiTyNEckixIn+i9Z+To0gFEdGRXAYkWaiZWnvyOhC4DKgNyB2GUURF+m2Ae8Ddx1iOcR4FwLirqKa4rMdwnpbNSsK5lNg/ouowEQuSruXuCD9HDlUA0/W+YdAdc5XFP+yVIlAGtv3UuL6a3/kt67gJ+WYqMPWSFS300k8epSMJCofUkmgBQGtHaVO4HtVEP5Zs4bPiha3EIP/zNqAkWHJt9MvUhzc8V4pKyujd+/evPLKK5x33nksW7ZMCkLv4ZY9e4HAa8VLWVP9OUeFD2J0ygQZ+M0LjN3psspayX05Y0gLymRaVls33dzp2XtttPZzagSh2OXbC1zbYtoipYTY4TtUUJmV4ogLcP4BuNr6fstqTzkFodqQTD4RhBVN+byf/yjV1mKSg3txoYgkGhjjvadD9qwJgfriTRRueAy7pZ6IjKEkHTNGswiklTs+ofS3l5RfnfjDriWmz0XSoWiy6nIQfyKgtaPUCRuXBOGLOWNptNerNt1iN7cqBvd1IEShyRiiqr9gYxgjuws37loZM2YMe/bsYeVK8TEBKQhdwydr+5jA5rqNLC6YQ0RAFPdnziMyUH9B6nyMyKfDT825g0prOQt6vECIMdSntqgZXGs/p0YQirQTIn+gEGc1zklMAB5uJ+2EOCZ6nnOHUNwRFEUEjRE7hCIyqTg+2loRqySOmW507jCq4aa5IMyr38KKwoU02uvoFX48w5PvkJFE1ayUn9Rpqs5rjkBaX0JIXF9ST7iHgGDvpQ0Rx7VKN71M1Y6PMRhNJA26nciMIX5CS5opCWhLQGtHqe3s2hzNq4JQRMVur4grEWqKO4Jwy5YtDBw4EBFQpl+/fsowr732GnfccQd5eXnExcURGurehziZdkLNqsk6HSFQa6tmxp5JVNsqlZ3BARHHdqQ72dYLBJ4rWMjGug1KkJ8+oYd5YQTPdqm1n1MjCEXuQCHQNjsT04vwPOI4qAgy0zIx/XZARAYd5UQiAsOsA1Y5o5SKsYQIPMuZc1DcERSvT0TQGZFcTbRPcKakEPcMTwR+VIlXU0H4R/U3fFH8XyWS6KCYixga7z+JzVXylNVE2FtzpRKBtLFiB6bwZFJFBNJIETzXs8VuNVP4w5PUF/6EMShSiSQaGt/Hs4PI3iSBTkRAa0epE3QuCUJXbX4rbzoFZvG+trX7+AYln+6VGQfeHHF1lLbrv/fee1x66aVtVhg1ahRLly51a0ApCN3CJhupJCBiWCwtfEwRG/50R03l9DpNtVUV7/Ne2etcGn89w2LFrTR9F639nBpBKIgJKS3Of4gti0pA/FUWgV1sLXDmAF8DN7X43pnO4C/7Iqxscn4t6okizp+8BhwHiDBMZmCts28hJtUWTQSh+KVfX/42GyreQRyfOT1xFEdEiynK0lkJ2K2NFP30NHX5GzCawkk9YQKhCf09Nl1rQ3mz6KzchSkijbShkxXxKYskIAm0TUBrR6mTtfCqIPT1HcLS0lI2bxbvnf8pn3zyCXPmzFGOkPbo0UNJVO9OkYLQHWqyjVoC66u/4aXixcQHJjIlaw6hKnfS1fYv63mGwNb6LTyR/wjHRJzAzSnjPNOpF3vR2s+pFYRenLJHuva6ILQ6LHxR9F+21q4hyBjKeSnjyQo7yiPGy070TUAc5yzb8jqV2z5sjkB6zGiisk7psNGNVbspEMdSG8oJTThMSXcREBTR4X5lB5JAZyegtaNswVO8HH3ygJejYtus5cvR1vCL82OznKdjxM9/dubbXe/CWnlVEO4fZVTsEop/2kcZbclDBpVx4emQVX1CoNxSyozciTTazYxPn06vUM+9MPbJhDrxoA32eibsHEVcYAKPdBd/xvVdtPZzUhCqeB4abDWsKFhAvvlPIgMTuCh1MvHBIq6OLF2JQNWuzyn59QVw2Intdzlx/a5wO+BLXdEvFG54HIfVTGTWKSQNvFVGEu1KD5Oca4cIaO0oncaK6xMiJZK4QjEH6AksAB474PrEgXMTzkKcjhEiUNQXRdzLPwY4EtitEoZXBaGwYf88hKVEmRI0z0MoBaHKp0FW8zkBu8POovyZ/NWwhWExF3JpwvU+t0kacGgCD+++h0LLXuZkP0tkgPfiQnhiHbT2c1IQtrNqlU0FfFAwh0pLIUnBPbgwdRLhMpKoJ551v+yjruhXp5BrIDLzJJIGjsYQYHJpLlU7V1Hy2zJFWMb1v0pJb+H8xXepH1lZEuiqBLR2lE7OIsDaJGeAtWrn98TX4vpECrDvewcui0h6JVIpxQFVLcRlKTC2RZ7e9pbT64KwPQP89efyyKi/rpy+7f6icgXvlL5MelAWkzJnYjK49llA37PrnNYtL1rMuppvuD11MkeEi3Al+i1a+zkpCA/xLOxt+EPZGTTba+kRfhxnJ9+hOuy2fh8xaVlHCTRW7XEe9SwjJL5/cwRSFUc9laOnm1+lcvsKMAaSfMwYRVTKIglIAq4R0NpROq0TKZjygWtaWCty5YodvkOlYLoTmA+Ei1hVzrbik2OtM4jaYpWzl4JQJagDq0lB6CY42axNAvmNuTyaN0VsqytiMCNYbZY0CdWXBFZXfsqbpS9yXuzlXBB/pS9NaXdsrf2cFIRtLMlWJUnvM9ixMjDmfE6Mvx6jwdjuAsoKXYOA1VxBwVoRDGYnpojU5gikEWKToPWiBKf58SnqCn5ojiR6/D2EJjSHVpdFEpAEXCOgtaN0WlfsjJgtdgRbljrnLmFb2Y5FaGJx1HQ5MNPZcDpwhfPIaInK2UtBqBLUgdWkIHQTnGzWKgGrw8q8vGnkNub4TcRKuZTNBHLMO5ibN5XDwwZyR9pkXWPR2s9JQXjA4yAiif5Q8T/Wlb+lXKc/NXEkR0UP1/VDI43zDQGRLqJZ5P3oTBcxgdD4g6Pg7Z++IoXUoUI8pvrGaDmqJNAJCGjtKJ3ILM67fyLlUsuS5xR7Uw6B9mhniqV0Z50C4FxApF9SW6QgVEvqgHpSELoJTjZrlcAHZW/yScW79Arpp+S0k5sF/vOgWBwW7t5xkxIJVtwj1PN1Ha39XJcWhPtfoC9RAsaIX+x881ZMhhDOTfkP3XV+xth/fg07p6XNCeVfoWrHSuUYaNLAMRgMIO4JWupLlGT21voy7JZaQuL7KTuDAcGRnROGnJUkoBEBrR1lBwWhePsjjpuKYDRLnH2JnLziAstQYE8b2MRO5AMtfyZeWB5YpNhp/6GTjNpnJGuoI7CjYSsL9z5IkCGYqVlzSTCJjGmy+BOBOblT2d24g0e6LSJex+untZ/rsoJw/xDb9v2e5QCDiSvTHyYpJNufnnFpqw8JVO78lNJfX3RaIH6t9v/gFhAaT7ezFmIMDPahlXJoSaBzENDaUTqpiSOjIjjMgdnZ2zsyuhC4DOgNiF1GUYIAkQX+feAulasidwhVgpKi2U1QstkhCZjtZmbnTqbEUsS/kkYzNOp0ScwPCbxR8gLfVK1iVPJ/GBQp0qvrs2jt57qsIPR1El59Pn7Sqo4QEDuFlds/ar0Lg5HkQbfLIDIdASzbSgL7XrmIbXiRJE/bInb59gLXthhWpJQQO3yHCiqz0vmG6PwDzG3r+23NSgpCN9db7hC6CU4224/Aa8VLWVP9OUeFH8volHt0fdxQLl3bBNZVr2Z58RLOjDmfyxNG6BaVFITuLY3LienfyptOgfmvNkYzkBrSmyszHnbPGtmqSxLIWz0dc3nbz1RIXG8yTpXPVJd8OOSkPUpAa0fpNF6knRD5A0U4wRrn9yYA4pf6UGknxDHR85w7hE3OduKogNgh/BAQx0fVFCkI1VBqpY4UhG6Ck83+JrC5biOLC+YQGRDNtMy5RAZGSzp+SqCgKY9H9kygV0h/7s7Y71S+rmaktZ/T+g2rt2C7LAif33UbdbaKNu0JD4hjVLbaaODempbs158I7Pr4Nmzmtp+pgJA4ss+Vz5Q/ram0VZ8EtHaUTgoiMb24B7jZmZi+ByCOg4ogM9NakNoOrAZGOb83CFgHrHJGKRV+V4jAs4BjXQgsIwWhm4+jFIRugpPNFAK1tmpm7JlIta2KMakTlB1CWfyXgN1hZ8LOUTiws6DHi7oNCqS1n+uygrB5h1C8oD34kr44iZQa0ocrMw68KuK/vwDScu8TaN4hbPuZConrQ8ap8pny/krIETo7Aa0dZQuehwFPAeLiSSWw1JlywtaiTg7wNXBTi++d6QwQc4Tze5ucX4t6aosUhGpJHVBPCkI3wclmiEBOSwsfY2PdBoZEnsaI5DGSSicg8Njeh9nW8DvTMueRFixO/uuvaO3nuqwglHcI9ffw+7tFNblrKPppMTj2D1KkzEveIfT35ZX264iA1o5SJ1OXgtDNhZCC0E1wshnrq7/hpeLFxAcmMTVrDiHGUEmlExB4t/RVPqv8UNfBgbT2c11WEO4fZVTsEop/IvOggZ7hgzk35S4MMhF9J/i1124K4pkq/GERdfkbQAkP3/xMiTwU4WmDSTlOPlParYYcqTMT0NpR6oRlpxeE27dvZ968eaxdu5YtW7Zw8skn8/XX/2yiFhQUsHDhQlatWsWOHTuIjY3ljDPOYPbs2aSlpbW5TFIQ6uQJ9jMzyi2lzMidSKPdzPj06fQK7e9nM5DmtkVgY+16nit8jJOjzuLapJt1CUprP9dlBaFY/f3zEJYSZUrgyOjh9I0YKsWgLn899G+UeKZq87535iEsxRSWQHSP4URkyGdK/6snLfQXAlo7Sp1w6fSC8P3332fs2LGccMIJbN68meTk5P0E4UcffcS4ceO4+eabOf744ykqKuLBBx/EbDYr9SMiIlpdKikIdfIE+5EZ4p7ZovyZ/NWwhWExF3JpwvV+ZL00tT0CQuxP2z2WzOBs7suc3V51n/xcaz/XpQWhT1ZYDioJSAKSgCTQIQJaO8oOGeu5xp1eENrtdoxGo0LsiiuuoLS0dD9BWFlZqYi+wMDAv6n+9ddf9O3bl2XLlnHjjTdKQei5561L9/RF5QreKX2Z9KAsJmXOxGQwdWkenW3y4m7ofTljqLXV8liPFzEZRWpYfRWt/ZwUhPpaf2mNJCAJSAKSQDsEtHaUOlkQrwtCsSvyY+33StLmMksJ8aZETokezrERQzWPxNeaIGxrHcLDw5k2bRr33Scygxxc5A6hTp5gPzEjvzGXR/OmKFc/JmfOIj04y08sl2a6QmBx/lw21//MxIxHyA7p7UpTTepq7eekINRkWeUgkoAkIAlIAp4ioLWj9JTdHezHq4JQiMEXChfxS90G7OwLjGXAiIGjwwfz75S7NBWFagXhb7/9xoABA3j77be5/PLLpSDs4EPW1ZtbHVbm5k4jrymHS+OvZ1jshV0dSaed/8ryd/io/C2uTLiJ02PO0d08tfZzUhDq7hGQBkkCkoAkIAkcioDWjlInq+FVQbihZg3Lixa3EIP/zNqIkRuSb2dw5EmaoVAjCMUR0zPPPJO9e/cqQWhMptaP9ckdQs2Wze8Her/sDT6teI9eIf0Ylz5d05cgfg/Pzyawpe4Xni54VPm7dlPyWN1Zr7Wfk4JQd4+ANEgSkAQkAUlACsKDCLgkCKfljKXBXq/6QRKRFP/ZGTy4mRCFwcYQVf2FGsOY0V2ka3S/qBGEkydP5oknnmD16tVKkJm2ihSE7q9DV2q5o2ErC/c+SLAhREkxEW9K6krT73JzrbXVMGnXLSSb0nig20LdzV8KQveWpFVH6V5XspUkIAlIApKAnglo7Sh1wsKrglCNeBRCT03RQhAuXrxYiUj6+uuvc/XVVx/SLCkI1axa165jtpuZnTuZEksRI5LGMCTqtK4NpIvMfnrOXZRai5mf/TxhAeG6mrXWfk7uEOpq+aUxkoAkIAlIAu0R0NpRtmePRj93SRC6atP8vOnsNG9z5k89sLWBHiF9mJDxkKvdul3/UDuE77zzDldddRVz5sxhwoQJ7Y4hBWG7iLp8hdeKn2NN9RccFX4so1Puwfk3pstz6ewAxL1pEUjrrrSp9As7UlfT1drPSUGoq+WXxkgCkoAkIAm0R0BrR9mePRr93KuC0F/uEIpE9eeccw5jxozh8ccfV4VeCkJVmLpspU11P7OkYC6RAdFMy5xLZGB0l2XR1Sb+RcUK3il7mYviruGcuEt0NX2t/ZwUhLpafmmMJCAJSAKSQHsEtHaU7dmj0c+9Kgj3jzLqcO4UahtltL6+npUrVyo4FyxYQHV1NQ891Lwred5557F7926GDBlC9+7dWbJkyX67OImJifTs2bPVpZCCUKMn1A+HqbVVM2PPRKptVYxJnaDsEMrSdQhsb/hTuTc6IPw4Rqfeo6uJa+3npCDU1fJLYyQBSUASkATaI6C1o2zPHo1+7lVBKObQMg9huaWUOFOCpnkIc3JyyM7ObhXnrl27lCT1I0eObPXnIim9SE7fWpGCUKMn1M+GEcnJnyt8TEm1MjTqdP6VNNrPZiDN7SgBEUzr7p0jiQ6IZVb24o5259H2Wvs5KQg9unyyM0lAEpAEJAFvE9DaUXp7Pir797ogVGmH31WTgtDvlkwTg9dVf8Py4sXEByYpUUVDjKGajCsH0ReBmXsmsbdpD7O6LyYmME43xmnt56Qg1M3SS0MkAUlAEpAE1BDQ2lGqsUmDOlIQuglZCkI3wXXiZmIHfEbuRMQO0fj06fQK7d+JZyundigCrxY/y3fVXzI6ZQIDIvRzZFhrPycFofw9kQQkAUlAEvArAlo7Sp3AkYLQzYWQgtBNcJ20mTgavSh/Jn81bGF4zEVcknBdJ52pnJYaAmuqPue1kqWcHXsJF8dfo6aJJnW09nNSEGqyrHIQSUASkAQkAU8R0NpResruDvYjBaGbAKUgdBNcJ232ReUK3il9mfSgLCZlzsRkMHXSmcppqSGQ25jD7Nx76Rd6JHelT1XTRJM6Wvs5KQg1WVY5iCQgCUgCkoCnCGjtKD1ldwf7kYLQTYBSELoJrhM2y2/M5dG8KeBwMDlzFunBWZ1wlnJKrhCwOaxKYJlAQxDzs5fqJgel1n5OCkJXnhpZVxKQBCQBScDnBLR2lD6fcLMBUhC6uRBSELoJrpM1szqszM2dRl5TDpfGX8+w2As72QzldNwlMD9vOjvNf/Fg1mMkBaW6241H22nt56Qg9Ojyyc4kAUlAEpAEvE1Aa0fp7fmo7L9VQWixWNi+fbuSgy8oKEhlV12r2j5B2KtXL0wmeTywa63+P7N9v+wNPq14j14h/RmXfj9Gg7GropDzPoDAWyUv8VXVx9yUPJbBkSfpgo/Wfk4KQl0suzRCEpAEJAFJQC0BrR2lWru8XK9VQShyqf3555+kpqYSExPjZRP8s/vKykoKCwvp27evbo6D+SdJ/7V6R8NWJQF5sCFESTERb0ry38lIyz1O4IeaNbxY9BSnRZ/DVYk3ebx/dzrU2s9JQejOKsk2koAkIAlIAj4joLWj9NlE9x+4VUEoqpSUlFBWVkZKSgphYWE6MVcfZtTX1ytiMD4+nsTERH0YJa3QlIDZbmbWnkmUWosZkTSGIVGnaTq+HEz/BIqbCnhwz3iyQ3ozMeMRXRistZ+TglAXyy6NkAQkAUlAElBLQGtHqdYuL9drUxCKXcLS0lJFFIr/L8s/BMSzIsRgQkKC3B3sog/Ga8XPsab6CwaEH8etKXfL56CLPgeHmrb4uzlh181YHU0s7PEiAYZAn1PS2s9JQejzJZcGSAKSgCQgCbhCQGtH6YptXqzbpiDcN6b4UGO1WqUodAIRz0lgYKAUAF58KPXe9aa6n1lSMJfIgGimZc0jMiBK7yZL+3xEYNHemfzZsIn7MmeTGZztIyv2f5kFaKbT1A50GPAkMASoBJYCDwG2dogdC8wCxH9F+RkQST7WH9DuYmAG0BvY6ez7TRdWo11H6UJfsqokIAlIApKAjglIQajjxZGmSQI6IVBrq2bGnolU26oYkzqRo8IH6cQyaYYeCXxQ9iafVLzLtYk3c3L0WT43UWs/p0YQxgJbgN+BOUBPYAHwGDDtEMQygU1OESjqizIROAY4Etjt/J4I5/M1sBh4FzgPuAc4B1ilckWkIFQJSlaTBCQBScDfCWjtKHXCS/o5nSyENEP/BMRu+XOFj/FL3QaGRp3Ov5JG699oaaFPCfxW9yPPFMzXzfOitZ9TIwjvAyYB3YBq52qJrx8EUlp878CFHAM8DcQBVc4fCnFZCowFlji/9ykg4kCf0aKDlYDY11cb+9UtR+lw2Kmt/p6qilVYLCWYTIlExw4nImooBhmO2Ke/mHJwSUASkATaIqC1o9TJSrjl53RiuzRDEtCUwLrqb1hevJj4wCQlqmiIMVTT8eVg6gmIz+KFtd+zp2oVZksJIaZEsqKHkxKh7WfxKmsF9+XcRlpQFtOy5qqfgJdqau3n1AjCb4B84JoWc85y7vBdBHzYBos7gflAOGB11hHCrxYY79wRDAZqgLuAZ1r0cwPw4gFi8lDIXXaUygO4dxF1NRsAu7NvgcNAeORgUtLvkqLQSw+57FYSkAQkgY4Q0NpRdsRWD7Z12c95cGzZlSTgNwTKLCXMzJ1Eo93M+PQH6BXaz29s72qGis/ivxUuorhuA44Wn8UNGEgKH8xRKdp+Fp+ScztCGIrAMsHGEJ8uh9Z+To0gLHaKN7Ej2LLUOXcJ57VBLM151HQ5MNNZZzpwhfPIaAkg7iaK46inO4+N7uvqOEAotcHADypWxGVHWVO1hqJ8cUp1nxhsOYqR5LTbiYxWu0GpwkJZRRKQBCQBScAjBLR2lB4xuuOduOznOj6k7EES8C8CdoedJ/JnsK3hd4bHXMQlCdf51wS6mLUFNWvYXLS4hRj8B4ABI0ck306qhoni/1uwgF/rfuBu5UVCf5+uhtZ+To0gtDjv/j1+AJk8QIi9KYcgdjTwEZDurFMAnAv86vz6RGANMBD4pUU/vYBtwNkq7xG67CjzcqZjbvirDdMNhIT2JqP7wz59GOTgkoAkIAlIAgcT0NpR6mQNXPZzOrFbmiEJaEbgi4oVvFP2MulBWUzKnInJIA6myaJXAuvzplNlbvuzeExIbwZnaPdZ/NOK93m/7HUujx/BmbHn+xSb1n7Om4IwFRDHTUUwmn33Be9wir+hwB7AXUEodisfaLlSruZe2rXtNmzWijYXOyAwjuzeYgdRFklAEpAEJAE9EdDaUepk7lIQa4738gAAIABJREFU6mQhpBn6JJDfmMujeVPA4WBy5izSg8XtJln0TGD1rttotLX9WTw4II5Ts7X7LP5n/SYW5c9kUMQQRqX8x6fotPZzagShODIqgsOINBMtS3tHRhcClzlTSYhdRlGCnDt/7zvvDe47MnoasLpF514/Mtq8Qyg2IVtL4it2CPuQ0f3AKfv02ZCDSwKSgCQgCYib3gbFdanxX52JlxSEnWk15Vw8SsDqsDI3dxp5TTlcGn89w2Iv9Gj/sjPPExD3B9fs/g8NVnGDrLViICakD4MztPss3mCr555d/1aCET3SfZHnJ+1Cj1r7OTUOVezy7QWubTEPkVJC7PAdKqiMiBQq1NaBe64tv78vqIwIQPPfFv2PAJZ5M6jMoe8QGkhOu0PeIXThwZVVJQFJQBLQioCLjjLJGdxsl9M+4fducd5h/+IQgdG0mo7acaQgVEtK1utyBMQxP3Hcr1dIf8al349RRorX9TNQ31TIluL/UmH+o007fXGHUBjz0O67KbLkMzf7WSICRMID3xQX/VyHjVQjCEXaCZE/UKSdEBFBRZkAiEO9h0o7IY6JipyCItl8k7OdEIBiW05EJhXHR0URaScCgJZZIMW9wxhvpp3YP8qo0K377xSmZT1AWLhvL5R2eHVlB5KAJCAJdEICLjpK8RJyu/NUiqDxCCD8mvieuK9+s/MFpN5JSUGo9xWS9vmEwI6GrSzc+yDBhhAlxUS8SbwDkkWPBMRn7z1Vn7Ct7A3sjiYiTFkEBUZT0bAFh/I5/J/P4lHBPTg+Y4bmEf+XFT3NhppvuSN1MoeHixAnviku+rkOG6lGEIrcgeIe4GZnYvoegDgOKoLMtExML5yrOPY5ymnVIGCdMyiMOAAsxhIiUAi/Y1sEltmXmP4p4D2niBSC0+uJ6ffPQ1iKyZSAKSidmqqvCAiMJTP7UQIDozsMWXYgCUgCkoAk4DkCLjrKQkDkxRX+xQgUASI6tkg0Jc4iXQyIAGh6L1IQ6n2FpH2aEzDbzczaM4lSazEjksYwJErcQJJFjwTqmgrYUvwMleatGAigR9xlZMdejNgJFHkIc6tW0WApxRQQQW3THgIMIZyYNZ8QU4Km0/mq8hPeKl3GBXFXcl7c5ZqO3XIwF/1ch+1UIwjFIOKunxBsQ4BKYKkz5YSthQU5ztQRN7X43pnO4C9HOL+3yfn11wdYfgkww7mbKI71iKAxb7gwO485ShGcprjgGWqqVhMadjhpWVMwGMQGpiySgCQgCUgCeiDgoqNsAIYD3wLifrp4USmiTYirEKcCK4AIPcyrHRs85uf8YK7SRElAFYHXip9jTfUXDAg/jltT7t53v1hVW1lJGwLNu4IfO3cFLUQGZ3NE0hgig8XBw9bLX6WvklP5IfFhAzgm9V5N13WXeRvz8u7niLCB3J42WTUkh91O7brvqfpyFZbSEkwJiUSfMZyIE4ZiMIp3ka4VF/2ca523UlutIOzwQF7uwKOO0m5vIi/nfpoadxMTfzEJSS2vT3p5JrJ7SUASkAQkgUMScNFRimsK85331MXLxiuBw50DiHvwLwDavoJ2b3096ufcM0G2kgT0Q2BT3U8sKZhHZEA007LEf31330s/VPRlSV1TPpuLl1Bl3qbsCvaMu4LusRdiNAQe0lCbvYl1ufdSZ8nn8KTRpEeJdOXaFIu9ifE7RxIeEMGj3Z9RJUaFGCxcsoi6nzaA3ZnfXAQ/MxgIHzSYlNvuclkUuujnOgxHCsI2EFqaCsndNQW7vZ6UjAlERIpTrrJIApKAJCAJ+JqAi45S3Be813l9QQQ5E18/4ZzDLOBk5z9fT6u98aUgbI+Q/HmXIVBjq2bmnolU26oYkzqRo8LFLSVZ9EJA7ArurlzB9vL/w+6wIO4DHq7sCqpPBVJp3saGvOkEGkMYKo6OBsZrNr1Hc6ewp3Enj3R7knhTYrvj1ny/hqKli/8Rgy1bGI0k33w7kUPFDTn1xUU/p77jNmpKQXgIhHU1P1GQNw+jMZSM7NkEBYkYOrJIApKAJCAJ+JKAG47yBudx0V+cO4L7Ihc8A6wFXvLlfFSOLQWhSlCyWucmIK72PFf4GL/UbeDEqDO4PunWzj1hP5tdbdNethQ9Q1Wj2BUMbLEr6Pr1K18dHX29+Hm+rf6Mm1PGcUzECe2uQN6M6Zi3/9V6PYOBkF69yZgqYnGqL274OfWdt1JTCsJ28JUVv0FF2XsEBWeR0f0RjEYRKFUWSUASkAQkAV8R0NpR+mqeB4wrBaFOFkKa4VsC66q/YXnxYiVXnIgqGmIM9a1BcnSFgN1hU3YFd5S/5dwV7KncFYwIFpnq3Cvi6Oja3HupV46OjiFdo6BBa6u/5uXiZzgr5kIuS7i+XeN3jbsNW2VFm/UCYuPIfkzE11RftPZzUhC2szZi2zs/dzYNdZuIjD6ZpNTbVZ0nVr/ksqYkIAlIApKAKwRcdJQif5AIFy2CyYgS5oyQLYKliTyET7oytg/rSkHoQ/hyaH0QKLOUMHPPJBodZsanP0Cv0H76MKyLW1HblMfmoiVUN+7AaDDRM+5KusWcj9EDQRn/OToaytCseZocHc1vzGVG7kR6hx7G+PTp7a6uskO4Yxs49k9hpzRUdgj7kDFVBLVWX1z0c+o7bqOmFIQqENqs1eTuuhertZzElFFExw5T0UpWkQQkAUlAEvAGARcd5VfA98BUpy0iYvZIZ9TRU5yRr0UaCr0XKQj1vkLSPq8SsDvsPJE/g20NvzM85mIuSZAB/7wKXEXnYlcwp+JDdpS/jQMr0cG9ODz5NiKC0lW0Vl9la+kr7K78iISwgQxMneT1jRnxrN2zU7gJAwt6vIDRcOgoocodwueebl0QyjuE6hfaAzW97ijNDdvIy3lQUfoZ3R4kJFTkM5ZFEpAEJAFJQGsCLgrCEqcA/AgwAaWAyHX7HDAOGA2IXUS9F6/7Ob0DkPZ1bQJfVKzgnbKXyQjqxqTMmQS2E6mya9Py/uxrGnPZUix2BXcqu4K94q5SdgUN7YgndyxrPjo6mXpLAYcn3UZ6lMgY5N3yWN5DbDP/wf1Z80kNyjjkYCLK6O57x2MtFmlunUVGGfXuArXRuyaOsqp8FSVFLxAYGE9m9mwCAmWIY5+sthxUEpAEujQBFwVhPXAO8I0zmqjYMUwGygCxQ/iJ8xip3plq4uf0DkHa1zUJiCN8j+ZNUXZg7s2cTVoH7qV1TYKem7XdYW2xK2gjOqS3clcw3MO7ggdaXNmwlQ17HyTQKI6OiqijcZ6bVCs9/a/0FT6v/IgRSWMY0s7dRSEIxT1Ce001wd17YK2swJSQQPTpMg+hVxeplc41cZQislVR/tPUVq8hNPxI0jLv88qbEK3hyfEkAUlAEvAnAi4Kwi3OKKJzgYVOUSgS1ItyGbDEKRD1jkATP6d3CNK+rkfA6rAyN3cqeU27uTT+eobFXtj1IOhkxjWNu5W8gjWNOc5dwWvoFnOuZp+Ft5a+rASu0eLo6M+161ha+DgnRw3j2qRRh1yBhq1/sHf2Q4T2P5z0yfd7ZLVc9HMdHlPeIXQRod1udiatzyU24TLiE69ysQdZXRKQBCQBSaAjBFx0lMKTC9H3KzDQeXz0Zef4i5zHRf3hYrgUhB15aGRbvyXwftnrfFrxPr1D+vOf9Pvbvc/ltxPVseFiV3BXxfvsLP8fDmzEhPRVEsaHB6VpanXLo6NHJN1OWpQ45OGdUmYp5v7dd5EV3IN7M0XK2rZLyasvUfXZxyTe8G+izxjuEYNc9HMdHlMKQjcQNjXmk5czFbu9gdSMSYRHHuNGL7KJJCAJSAKSgDsE3HCU4lOD2BUUeQhFZNF95UHgB2CFO3Zo3EYKQo2By+F8T2BHw1YW7n2QYEMIU7PmqkoS7nurO5cF1Y05Sl7BmiaxKxhE7/hryIo+R7NdwQNpVjRs5Qfl6GiYM+qod46OilOBk3NG02CrY2HPZZgM4gr6wUW5PzhhLNaKCro/toTAmBiPPABu+LkOjSsFoZv4aqs3ULh3IUZjOJnZszAFiSspskgCkoAkIAl4m4DWjtLb81HZvxSEKkHJap2DgNluZtaeSZRai1Xd4+ocs9bPLMSu4M7yd9lV8Z5zV7CfclcwLCjF50ZuLVnO7qqVJIQdw8DUiV6LOvp0/hy21G9kUsYMuoe0HkxSpJvIe+R+Qvr0I2OKeMfomaK1n5OCsAPrVlr0KpXlHxIc3J307g9jNAZ1oDfZVBKQBCQBSUANATccpXhlK6KJngSI18nlzrQTzwKVasbUQR0pCHWwCNIE7Qi8Wvws31V/yYDw47g15W6vfejXbkb+M1J14y4lr2Bt0x6MhmD6xF9LZvRwn+0KHkjOZm90Rh0txJtHR1eUv434d1XCTZwWI2KTHVxK33iFyk8+IuH6G4kZdq7HFtkNP9ehsaUg7AA+h8NG/p6ZNNT/TmT0aSSnjelAb7KpJCAJSAKSgBoCLjrKnsBqIBH4DhBxwcWRjqFAMXA6sEPNuD6uIwWhjxdADq8dgU11P7GkYB5RAdFMzZpHZICM6q4FfbvDotwTFPcFHdiJDen//+xdB3hb1dl+tSXLlry3HWfvRUKgYZYRRqHQ9m9pSwuUllkKBUIgIWQQQoAEKKNhz9JJKVAoI6WUEVLIIIPsZcd727Ksvf7nO1e2Jcfjal1rnJPHj2Pp3HO+854rffc938LUguuRpoo/L7gO235sqVvJXEdPKV8HjTIr6hDttmzH+oYHcVLG6biy4Mbjxie30mOLboG7pRkVj/weyuycqMkQop6LeF5OCCOE0O3uRE3lYnjcHcgvuhaGzLMiHJFfzhHgCHAEOAJDIRCiovwngNH+0hN1AeNS5eT3AFQBuCQBEOeEMAE2iYsYOQJmTxdWV9+BLo8JNxTdgen6OZEPykcYFgGT/Qj2ND+NbmcNFDINxudejjLDOXFjFRxoAftbXkG16X3kpc3BrKKFUbci0714Z+W1KFAVY/koSlId3OxVlahdsRiaseNRds+qYTEOpUOIei6UoQfsywlhxBACNusB1B27l31oSkathFY3Jgqj8iE4AhwBjgBHYCAEQlSUXQCuBPDmAGP9AMBLABLB/MAJIf84JD0CZHF5tvER7LRswSmGs3B5/rVJv+aRXiBZBY+0v4Gqjn8KVkHdVJZBNE2VP9KiDTt/kOtowa9RnHHasNeE2uGeqpvR7m7ButEvQKdIC7q87e9/Qce7byHnssuRdUF0y6GEqOdCXdZx/TkhjBhCYYDO9vfQ2vQqlKo8oWi9Ij1KI/NhOAIcAY4ARyAQgRAVpQkAlZ74+wAo/hDA8wCMCYAwJ4QJsElcxMgQ+LLrU7za/BRylflYUv4gtHJdZAPyq4dEgKyCVFfQ4qyFQqbFhNzLUWo4O66tgv0X1GHbhy1198bMdZRqEVJNwluKl2Ji2rTe6enwonrxbXA1NmDU2sehyosugQ5Rz0V8p3NCGDGEwgBC0frH0d31P6TpZ6OojLIeyaM0Oh+GI8AR4AhwBHoQCFFRvgVgot9l9FgAiqMAfABgn79AfbwDzAlhvO8Qly8iBNpcLVhdvQgOnx23lazAWB19bHmLBQJUz+9o+xuo7CSPeh+yddMxNf9a6FQUap14rdd1VD8Hswqj6zr6Uce7+Efba7g05ydYkNUXXeCorUHN0jugGTUaZSvXRB20EPVcxPNzQhgxhH0DUNH6msq74XLWITv3h8jOI28k3jgCHAGOAEcgmgiEqCgrAHwMoBTA1/6kMnSUS4FJNQDO9scRihFxCoAnAHzLn52UrIsrAXhEXPx9AIsB0BGz1V//kJSERcS11IUTQpFA8W6Jh4DX58VjdatwyL4PCzIvwaW5P0m8RSSIxJ32Q6yuoMVVB4VMh4m5P0OJ4ayox99JCYfba2dZR22uJkwvuAlFGZRQOjrtkG0fHq1biVn6ebi26LbeQdvefB0db7+B7P/7MbIvujQ6kwWMEqKei3h+TggjhjB4AKejDjVVd8PndaCo7C7o02dGeQY+HEeAI8ARSG0EwlCUVBPoan9x+iIADQC+AkDWw0kAPhOBKKWw2wNgL4AHAVD20ocBPApg6TDX/wrAkwAe8pNTGosykNF15NIqpnFCKAYl3ichEeixwpSqR2FR2WooZcqEXEc8C01WwSPtr6Oq811mFczRzcAUZhXMjWexRcvWbtuHrXUroZKnYz7LOhqdAvFUD/P2o7+AUZmF+yvW98pTffdCOOtqUf7AI1AXFouWU2zHMPSc2KEH7McJYUTwDXxxd9eXaKz7HeSKdBZPqEpQE3wMoOFDcgQ4AhyBiBGIoqIkC93fAChECEXWvUUAyNWUEtVQo7+pEjFVau55rf9Q9LRVCYCOlp8TMc9gXTghjAA8fmn8IlDvqMEDNfTxAu4qW4NiTVn8CpugknXaDmJ389OwuuqhlOswIffnKMn4dkJbBQfaiv0tL6Pa9AHy9HMxq/D2qK3vvuo7UO+swZqKpxgxdNbXoXrJ7VCXlqP8Pjrni36Lop4TJRwnhKJgCr1TS9OrMLW/B412DEpHrYRMrgp9EH4FR4AjwBHgCByHQBQVZSiEkKyI9QB+HCBQOQCKS/wugHcG2SoqXkUWRSpQ5YxgOzkhjAA8fml8IuD2ufFQzd2odR7D93N+hnOyLopPQRNUKsrCebj9bzjWSRV2fMhJm4mpeddAmyRWwf7bwlxHqxfB5m7G9ILfoCjjlKjs3B+ansb/zJ/g+qKFmKGfi/Z/von2f/wV2d/7IbIviU14WBT1nCgMOCEUBVPonXw+N+qOrYLddgCGzHOQX0QeQ7wlOwI+nxeN3ZtQbdoAu6sFWlUeyo0LUJg+nycZSvbN5+sbFAGKD9ravQmfmTaAEkfkqPJwunEB5qbPhzyM5FtRVJShEEIqYk/+QmQRDGwUA0ivrR0EgFcATAXwAoC7AVCFZ4plvBXAphBuG04IQwCLd00MBN5u+zM+7Hgb47WTcUvJPWF9HyTGSqWXkgq3U11Bq6uRZeCcmHsFijPOiJrVTPoViZux3bYXW+vujarr6Gemf+MvLS/ggqzv4eKcy1Cz/C44jlWhbPVaaEpiY9GOop4TBRwnhKJgCq+T29UuFK33mJBfdAMMmWeENxC/KiEQIDK4q/FxNFs2s1o+QpOxf/n6eZhReDMnhQmxk1zIaCJAZPDFxsexw7IZ3oDPhRwyFqR/deHNIT8ERlFRhkIIXQDuAPC7fvjUAngVwJJBcPsQwHy/Sym5mLb5XU3nAhjvT3IjBvKwCGG0ybgYQXkfjoAYBI7YDuCRuhXQyLS4u/whdlDEW+QIkFXwUNtfmOskWQVz02ZjSv410CqzIx88QUbY1/ISakwfIl9/ImYW3hYxCa62V+KB2sWYrJuB61W/xLFFt0BVVIzy+x+OeOzBII2inhO1a5wQioIp/E42y17UVd8HmUyB0or7oNFS+AlvyYhAg3kjdjetDyCDfauUQY5pBTdGNfNVMmLI15R8CGw2b8SrTesDyGDfGuWQ44qCGzEvxIxwUVSUUhDCDQDOBXCBv8wFAWDwu5pSopl7Btl1sjouD3yPyhuF0mJBxkOZn/flCAyGgN1rw/3Vd6LV3Ywr8m/AyQZ+YB6Nu4Vq8u1pfsZvFdRjUu6VKMo4LWakJRoyx2KMYNfRm1GUQWdy4TePz41bj/4CapkaS/Zfgva//QlZF38POT+4LPxBh7kyinpOlIycEIqCKbJOHW3voK35j1CpClA6+n4oFPrIBuRXxw0CLo8FXY4jMNkPo6rzX3B7B8sgL0Omdjzmld4bN7JzQTgCUiCwrnYZjtoPDjKVDGO047EwxM+FCEXZwo7Gh28aAOkik8qQy+jv/WUmAkcezmX0rwB+CCANgD3gwo/8GUbFBqCEbCGMBRkfHlLegyMwPAJ/bH4WX3R9jJn6E3FtFCw4w8+Y3D2IAB1mVkFySCCr4AmYkv+rlLIK9t/hdusebK1fBZU8A/PL10acdXRtzT2odBzCdf8sh35XNcpWPgDNKKpqFJsmQs9FdWJOCKMK58CD0aluY92jsJg3Q58+B4WllPmIF62XAPqoTuH1udHtqIHJcYgRwE77YZaxS2zTKLJxxui+lMVir+P9OAKJjMDiyhtg8nQMuoRMRTbuD/FzIUJRknVNDCHskYtqCQ7XKKlMHYDAAmkUPFI9TFKZHksfEUJbwCT/AdDuJ4vDzU3vh0wIhybjgFGRhYuyf4h8dRHyVYUwKDJTzpIgBnjeJ7oIfGPZhqca1sKgMOLu8rXIUJDBnLdwEaCYOaorSIlUlHI9JuVdhaL0U/lnGcC+lhdRY9rAwnZmFt4aESZ/a3kZn5g+wIVvAtOa8zHqocciGm+4/Rah54YbIqT3OSEMCa7wO3s9Vlaf0OVsQHbej5GdG/0iluFLx6/sjwCReLu7lRE/k+Mw+93lOAqvj8KI+ppOmQ+jdhz7qev6L7qdFE400HMoWQgnYF6pmOdOvh8cgeRBgDIIVjmODLIgshBOwMIQPxdSK0q/8JQXn2IIye/f7H9tIQAy+w9VdoJiBbcA+A4ASvVHzeh3GV0H4D6Rux0yIRyOjPefl2K5eshhvqqo9/8FqiKkKciQyhtHIDIEzJ4uUAp/s8eEG4ruwHT9nMgGTOGrySp4qO1PjPBQo1ILU/J+CY2SypzyRggEuo7OKLgFhRnfChuYr8yf45Wm3+OEr4BLVRcj90eXhz2WmAul1nOcEIrZlSj1cdhrUFu1FD6fE8XlS5Cmnx6lkfkwkSLg9lphsguunz0E0OkJrhdNWbqMmrG9BNCoHQ91wMnmUDGEJN+0/BtRbDg9UlH59RyBhEGADlYerL0b1Y6jA8ocBzGEoWBJT1lUlH63v4zEGACP+JPMBBamPwzgUwC/DBj8LQAnUZk1AK3+pDJTAEwAMLj5NFi6kAmhYCE8NKixlKyCc9NPQbOrAc2uRjQ7G2D3BRox+wRIl2cgT10IIod9ZLEIeaoCaOTaUHDkfVMUAfo+eLbxEey0bMEphrNwef61KYpE5Mtus+5msYJ2dwvLpjkp7xf+bObJ8lgfOUY9IwS7jlLBejqPC701Oeuxsvo2FNcAC0ethnbM2NAHCeEKTghDACuga8iKMrxpIr/KbNqIpvonoVAYWNF6pYpKU/EmJQJenwfdzhqB/PkJoMVJnmB9lj0ZFEjXlMOoEax/mdpxSFMVDenqG5xllMYKthSWGc7HpLwrY+piICWOfC6OwHAI/LvjHbzZ9kdoZBq4fC542WeCfijNUlxkGR1uCf3fJxJHiWDomLkTwPP+khOegI5VAD4BcFXAa2Reo7IUP/LHEn7hLzvxTQgChKznQo0hpAd2stwQQWxyNaDF2ch+098tria4+3lI9MhObr+CZbGIEcYe4pijyodSpgxhibxrMiPwZdeneLX5KeQq87Gk/CFo+UFCyNvt9tpwsPVPqO36N7uWXCEn510dcXxcyIIk2AU9rqMF+pMws4gq/oTenO2tuLP+JngUwCMT/wBljOuLc0IY+h7RFSEryvCmic5VLY0vwdTxITS68SgdtRwyrjCjA+wAo9ADjsPdhk6/26fg+lkJr88R1FurzBUsf5rx7LdBMxoKuTpkuXrqEJILh83VCp0ql7lxVLb/E25fN8Zl/whjsr8f8rj8Ao5AoiFw0LoHj9XfB6VMhYUlK9HgqmN1CNtdrchW5cZLHcJEgjVkPRecZTQyMk5jdbjbBGui029R9JNFqi3ZV1KkD1KyAFMpAWZRZD+FvcQxS5kTcrmRRNosLmswAm2uZqyuvhMOnx23lazAWN1EDlGICLRZv/FbBVtZohQiggXpJ/NDZhE4kuvopuo7mEV1RuFvUZh+soirgrt0fvQhnva+hOoxwOKyB1CmiV1CGZqZE8KQt4hdELKiDG+a6FxFRetrj62Ew3YIxqzzkFf4i+gMzEcBnZ512Y/2un0SAXT0S2ihlOtgCHT91IyL+ekaybSlbhU8Phsm5V6F8szz+W5xBJIWgU53Ox6oWYwujwlX5N+Ik6PsKi21ooyTjQpLzwXWIYwGGR8IC7fPDXrg729VJOLY6aG8Occ3Oiggd9NAotjjjpqhMPKH3Di56aIhBt2Dj9WtwiH7PpyXdQkuyQnMyxSNGZJ7DAppEayClJgYjAROyv1F2K6PyY3W4KsjN9tt9fcxMn3KqIeDQn7EYFL34Cr8u3APNp8C/DTvGpxqPFvMZWH3kVrPJYuzcViKMuxdisKFblcbqivvgtdjRkHxTcgwnhqFUVNrCLLG9bp++i2A/ZO6kGNauro8IO5vHPSq4hHJ8kp+7F83PMAS00wvuInXJEyt2zVlVkv1mn5XtwpH7AdwmuEc/CT/V1Ffu9SKMuoLCG/AhNNztEyH187cTfssi33uqN3entw8wYBoZboBktsIFsY0XrYpvLtnBK/6qONd/KPtNZSqK7CojLwGuBux2O1ote7E3uZnYXe3QaUwMKtgONYtsfMle7+9zS8wd1si1TMLfyt6uZ6uLlTech2OzkrDW9+xSBIDK7We44RQ9O0Q/Y5Wyzeor74fMpnaX7SeMpjzNhgCdne7P+5PKPtAWT89x7l+5vTG/Qmun2OgkFOpsfhozZZt2NnwMBNmZtHtyOcZ1uJjY7gUUUPg7y2v4GPT+xilGYvbSldAJVNFbeyegaRWlFFfQHgDJiQhHGqpFk93UEKbwOQ25Fo4UEtXGIQ4RXI/pZhFdc//C6GOo+/68LY4+a6qd9QwbwFqd5WtQbGGP+eI2WWXx4qDba+hrutj1r0w/VsscUxgIjsx4/A+wQiQF5ngOtoakuuo6ZP/oOXl5yC74HQ8POczlKpHYUn5gzGFV2o9xwlhTLdz+MHbW99Ce8tfoFIXoaxiNeQKKlXFG/l7E+HrTfzCXD+DXY8UMi0M2rHI9Cd+IQKYCOmW682fY3fT7yGXqXBC8WJk6yhPBW8cgcR4t6NVAAAgAElEQVRHYJv5f3ih6THo5RlYXLaGxQrGokmtKGOxhjDGTDpCOBgGFPvd5ekcILFNI0t044Z7wEspLpHFKQaUzKD/56ryoeBWqTBuucguoURSa2uWotZ5DN/P+RnOyboosgFT5OpWyw7saXkWDnc71AqjP1aQkhTzFg0EKBZzW/1qZnE9pXydKJJdt+5+2HbvQvGd92CV9kmWfOuRMS/F9BBKaj3HCWE07q4IxiC3x4bah2Ht3gZ9xjwUlkRWODMCUUbsUsH1s6634DuRQHIFDc7SKUO6uqzP9VMzHunqkhFx/YwGUNWdH2B/68tQyHQ4seQeGLSUwZ43jkDiItDgrMNDNUvg9Dnx6+K7MCVtZswWI7WijNlCQhs4ZQjhULBQPFq7u/X45DbOBrS5W+AboA6skNwm358FtS+xDZHFTGU2T24T2n0ouvdbrX/Ghs63MV43BbcUL+U4D4Ocy2PBgdY/oN5MSYrJKngKy0zOrYKibznRHfc2P89iMsnyOqPwliGv83R3M3dRRZoeFY89jWeaHsEuy1bcXrIypsmRpNZzYgkhmTCe6JdqmypsB6ba7g/oCgDLB0F5CYA1/vdeBnDlAP0mA9gvcncTWlF6PBbUVi6By9WEnPzLkZVzschlJ2Y3h7uj1/JH2T8p4QolWwlsGkV2UNwfuX4qkyxF9ZH2N3Ck/XUW4DyvdAX06pLE3FAudcojYPfa8GDN3Why1eOi7B/iwuwfxBQTqRVlTBcjfvCE1nPilxl+T7JItbqa0UJlM1gm1L76iqZ+ycV6ZiGX5h73U8Gy6HdFVRWB3FP991r4QqXolUdsB/BI3QpWo/LusodYtlneBkegxbKdxQpSEjyyCk7J+xXy00/kkMUIgUDX0ZmFt6IgfXALbNfnn6D5hadhOPNs5F91DT5ofxP/bP8rfpD7c5yd+Z0YSRifWUapGO8ef0FecpilSowUBPUogMBivP1BKQVAP4HtUgB3ApgNYEcAIaSd6J9qk94fOIjgePgTXlE67Mf8Res9KClfCp0+OdwIPV5HsOun4zALjg5sCpmGxfqxsg9aoeyDVpkdsw9ZvAxMblEHWl9Ftel9aJTZmFdyLytTwRtHIJEQoPuY3ES/7v4SU9Nm44aiO2JuCeCEMJHukPiQ1c6S2zT6S2b0EUUijRZv94BCauW6vtqKqkIWr9hTQkPHwzsG3Vg6ILq/+k60uptxRf4NONlwRnzcBHEohcvTzZ4D6s2fMemKMk5lmchVCipdylssERDrOlr/6IOw7tyO4oWLkTZtJvZZd+GJ+vsxN30+ri68OWYiSq3nxFgIKRp4EYBRALr8K6e/yQJYGPCaGFD+BYB848j619PIQjgNwFwxAwzSJ+EJIa2rq/NTNDc8BYXC6C9an1ikiFw/La76oLi/bmc1fPAGbBu5fpb6a/4JRd/16lLIZYoItj9xLyXM9jQ/zZQBFb4/sWQFTyWduNuZkpJ/3Pke/t76KnKUeSxphF6CBxmpFWWcbGxS6Lk4wTJIjG6PmZHFPqsi1VpsZBZGZ7/EZT0XUmmM4HjFIpbsJldVAHUYNWzjEZdwZfpj87P4outjzNLPwzWFqRcGIxY3SjK3r/l5v1UwE1Pyf4V8fSSPwmJn5v16ENjb/Bxqu/6DwvT5mDEAufNYrai8+VrINRqMfuwZyJRKWD3dWFj5K1Y2Z+Wox2IGptR6TgwhpGOLegA/Dlh1OYBjAL4L4B2RaOQAaABwH4B7OSEcGLXmhufR1fkRtLqJKBl1T1wXrXe4O4Pq/XU5jrA6gIFNo8gKiPsbx2LlqA4gb30IeH0e7Gx8FC2WrcjQVGBu8TKo+Okzv0USAIHDtv2sxIRcJsfCkntRrh0tidRSK0pJFjX8JJwQDo9RVHuQ9ZtcTameIpFDVmfRTxxbXU3wDBA1I4MMQnIbIftpn1WxkLlNJntym12WbXi6YS0MCiOWlq9lbre8BSNAVsH9ra+gwfw5e6Mo43RMyr2CWwVH4EahGo+bqhexrKMzC29DQfq8ICnMmzai6dknkXHamSj45fW97604div7Tlg7+vmYHYJKrefEEMJmAOv9FsFAoCz+19aK3MNrATwDYAKAQ/0I4Y8AljaM6gNsAXA3gE9FjkvdkkZRer1O1B1bAYf9KIzZFyKv4IoQYIhdV4/XiS5HpWD9cwhlH+gDFNjkMnWA6+c4lv1To8zhMRgitoXw3d7wINpte5CpnYQ5xYvjqlyGiCXwLimGgMndydLJ0wPz5fnXsrpMUjWpFaVU6xpmnqTRc3GCZ0RieHwedLhbj4tVpIdESnozcHIbBct4GmhZ7CmhkQzJbcyeLtxXfQfLwHhD0SJM158QEcbJeHFz91bsbXkeTk8n6MB8Sv41yOM4jehWt1l3YVv9/Sx2c3752qAkPg1PPAzLti0o+u0i6Gf13c8vNT6JLd0bcVPRYkzRxyaBmtR6TgwhdAG4A8Dv+u1YLYBXAVCCGDGNiqkYAczp15nS+zj9MYoUdXy7vw9Vat8sZuBkIoS0XperBTWVi+H1dKOg5BZkGL4lEobodCM3RqurAZ1E/hgBPIxuB7l+BuYQkrEkKMaAkg+UBTRVXT+jgTxZV7fW3QeytOamzcasotsh56nSowEtHyPKCNDD8ON19+GQfR/mG76Nn+VfF+UZhh5OakUp6eIGn4wTwjjZiOHEcHmdLH6OXFBZ3GJAkhsqpzFQU8nUfqIYnAU1X12EdHlG3B+skjX12cZHsNOyRZKi3cPtQby97/SYsb/lZTR2f8FEK844ExNzfw6VQh9voqakPHuan2U1HwNdR712Oyp/cw1zEx39+LOQqfpq6v6383283voKLs7+ES7I/n5MMJNaz0lFCIsAEIGkhDLrhkGOCvFREpudACgJzUDtuAym9GWUTM3SvRMNNQ9AJtew+oRqTewyUDo9XTDZBaufQACPwu0lA3Bfo5MTlvTFTwANmrHcrTEGNxwpjS11K2Fx1rIvpukFNyVsaY0YwMOHjBME/tH6R3zU+Q7KNBW4veReyWOmpFaUcQI7J4RxshGRiEEJV3riE4UsqBSvSO6ojbD107s98+jk+l6rIrmgMldUcklVF0Inj4/axf/r+gR/aH4aucp8LCl/CNokywoeyZ43dW/GvpYX4PSYQBnUBasg5VbkLV4QEFxHqWB9W6/raPfmL9G4/ndI/9apKLzupiBRj9oOYl3dMkxPm4MbislmFv0mtZ4TQwjJZfT3AKjMRGALxWWUrICUlZQS01CBueEazUe1FyhWUUxLSkXZ3vIG2ltfh0pdgrLRqyGPwhcsuSaaHVVBsX82N21xX6OC6b1ZP/0EUKvMjfsTSjE3SiL0sbvbsbl2GXPJLTWcy4rS8tTnibBzqSHj9u7NeK7xEaTJ9SyJDLnASd2kVpRSr2+Q+ZJSz8UJtiMuBh1qW7zmXqsixSv2xC42uxrh8pEj1fGNYvWEchmUAVUomSHELxZAJVFymzZXM1ZX3wmHz47bSlbEtDbbiG9UCALQYfv+lpfQ2P0/dlWJ4SxMyPkZP0wPAUMpu7ZaduLrhjV+19F1aH/mBRApLPzN7UifE1wCxOl14rajv0C6Ih1rKp6OyTOa1HpODCGkpDJ1AH4SsDFlAKpDSCpDnwZyPT1d5OY+6SeERCDFtKRUlKxofc1DsFp2IN3wLRQU3xzSTUcKhlw/e9w+6bfZcayf6yegVxUHF3zXkOunUgzuvE+MELA6G7G5bjk7URyd9T2Mz7ksRjPxYTkC4hFoctazeoN2nw03Ft2JaSN0yi21ohSPUEx7JqWeiyliSTK41+cNSG7T54JK7qgtriZ4h0luE2hVJOKYraTkNtHJ7E2yPVa3irmPn5d1CS7JCXxUTJINCGMZjd1fYl/Li3B5uqBV5mBK3rXIjVGsWRji8UsGQaDXdTRtPvQrt7Feo594DnK1+rgr1tTchRpHFe4b9SSyY1AyTGo9J4YQUtkJsocSOTP7EVnozxQqpuxEBYBKADcCeErEXUgpKPcC2A5ArGNu0ipKj6ebxRO6XS3ILbgCmdkXDgohuRv2uX0K7p/9XT9VCgNz+8xkNf/GQXD95D7sIu5LybsQed9Sdy/bQzpVrMi6SHIZ+IQcgR4EHF471tbeg3pnDS7I+j4uzqFcYCPTpFaUI7PK42ZNWj0XJ/gmpBgUz9vmakGLPwsqWRN7LIsd7rYBk9soQMltCvyWxT6rYr66EJmK7GEPnokEbu3ehM9MG1DvqGEHRNmKXCwb9Yjk7uPxtmkOtwn7W19CU/eXTLRSw9mYkHs5lHHi2htveMWbPC6PFZtqFsLhbkfB34CCzJNReONvBxTzT83PY2PXR6y0yuwhCtuHu0ap9ZwYQkiF6Ymg7QZAhempjuAj/iQzgYXpD/szg/6y3+LvArCKMusCCE5LKSSZeRfAawDoeqrKfau/cP0pALaKBDKpFaXddhR1x5aDLIYlo5ZBlzYRXp+LWfsCCaDV1RgEF7l+UhkDIe5PKPiuU+YN+2UvEnPeTQIEOm0HsLV+Nbw+J6bmX4cSw7clmJVPwREIRoC8DV5uoqxqX2CybgZ+XXxXzIvPD7UHUivKOLkfklrPxQnGSSUGubVReYyeWMXAJDddHtOAa1XLNMz1lOIUyZpIsYqCS2oh9PIMRjBfbHwcOyyb4Q2oMSyDHLP181ihbipDk2qNviOJBDKroNcMCrOZmn8tctJmpBoUCb/eVssOfN3wABTdwGzXdcg+ceDnLqq1STU3F2R+F5fm/jTq65Zaz4khhLTIKQDIjZPSXVKKrOf9JScC005WAfgEwFX9UNkBgJjK+QOgpQXwJwDknEuBKHYA5F5KSWOE4xVxLakVJX3RNLe9jYbWv8Cp0MCrKYbZWQMfq9TR16iweWDilwzNKO76Ke7+iete5Ne+veEh+ODFzMLfoiAGJ1FxDQAXbsQR+LTzQ/y19SVkK3NxV9n9I15bTGpFOeIbIAiQ1HouTjBOGTFsXitanI2stmJPYhtmXXQ1gN4bqFHccJo8Ha3upgHfl0OOKwpuxLwMShKfOo1qMhMRbLYIifFLDef4rYK85nIi3gU+lwtfvnsVzNM9KEz7FmYUUxqU41udoxqraxZhgm4qfltyT9SXKrWeE0sIo77QKA+YVIqSipaa7EeCEr/QiVNgU8nTe61+ggWQXD/TowwrHy5eEGg0/w+7mh6HDAqcULyInzrGy8akgByV9kN4pHYFqOD2baUrUaEdO+KrllpRjviCIyCE5FlyoHsTvjFtQJerBQZVHqYbF2Bi+nyewThONjaexKAD6G5PF3rIoWBVFLKg0m+Xj9JBDNZkGKMdj4Wl98bTkmImC2HV2L2JlZMQrIJ5zJMnJ21azObkA8ceAcuOr1H31EOou0kFl86FWUULka+fe9zE5K59+9GrQQch68a8EHXLuNR6jhPC2N9bQ87g9bn7XD8dQtwfJYIJbDIoQdY+o2YMPObtUDhbkZt9EfIKfjbC0vPppUSg1vQf7G15DgqZBnNKliJTO17K6flcKYgAFZpeU30XOj3t+HHeL3G68dy4QEFqRRkXiw7DQkhk8P3Gx3HEspl5GAiNqL0MY/XzcEEhJSpLPfe+ONnPhBODYgcXV93ACs8P1igG8f7R6xNubaEKLFgFX0CzZQu7tMy4AONzfgplFLLBhyoL7x9dBJpeeBrmzz+B+qaLsT/zHagVmTilfN2ARhc6LD1s3497yh9GkTq65eGk1nOcEEb3PhpyNDpNsrlb/HF/h5gFkEpAUDxgYEtTFbJkL8z9kyV+qQDFA1JzOZtQU7kEXq8FhSW3Id0wT8IV8KlGGoHKjn/iUNufoJTrcWLJcmRoxFZmGWnJ+fyJhgA9/D1Rfz8O2HbjpIzTcUX+DXETfyy1ooyTvQvZE2a/eSP+3bQ+gAz2rYRivs4tuBGTUsy9L072MmHFWFe7DEfth8iDeYA1kIVwAhaW9q9SlrDLPU5wwSr4hd8q2A2dMp9ZBbPTpibPIlN4JT63G5W3XAevw8GK0e8zv4p68ycoyjgN0wt+fRwyb7T+Af/p/BeuyL8RJxvEFlIQB7DUeo4TQnH7ElYvl8eCLscRRgA7WcH3wywFcWCjB/vAuD/6v1qRMeR8FvPXaKh9CDK5zl+0vjgs+fhFiYnAwdY/oarzn+zUal7pSqSpChJzIVzquEbg7ba/4MOOt1CiLscdpauglmviRl6pFWWcLDxkQvh67TI02A8OIr4MRdrx+GGKuPfFyR4mvBibzRvxatP6oIQyPYtK9hhCh7sDe1ueR4tFKEdQbjwf43J+zK2CCX9X9y3AunsX6tfdj7SZs1F8652g53gqWO/wtGNW0R3I188JWu1W8ya82PQ4zjAuwGV5V0cVCan1HCeEUdo+cv3sdtTA5DjUm/nT4qoPGp3iv5jrJ1n+/AXfKRFMOEXH25r/io62N6HWlKG0YlVUitZHCQo+TIwRoBPKfS3Po7brP+x08sTSFdAqs2M8Kx8+lRDYZdmKpxvWQSdPw52l97MMg/HUpFaUcbL2kAnhC5U3wOLpGFR8tTwNV1f8Hmo5T34RJ3sc92KQ50BfllGyEtIP2ZtlmJWkWUZJ5zaYP8f+1ldYGSidqkCwCuoo3yJvyYRA88vPoeuT/yD/l9fDcNqZbGktlu3Y3vAgNIoszC9fG+Q62upqxrJjN6NCMxaLylZHFQqp9RwnhGFsH3052N2tQQXfuxxHj3P9pIf1HrdPIoBUAkIhP764ZRgisBIU9TVrYLN8gwzDqcgv/nVYxDKcufk1I48A7T8lmaE01+nqMuY+ypMKjfy+JIMEVOz6gZolLNPgdYULMTP9+GD6kV6n1IpypNfrnz9kQihYCAdz7xNGJTI41XAWZhrPZwlneOMIDIeAx+vGFy2vYKP5c3T6HMiUaXBqxmk4Je9KKOTK4S5PqPft7nbsbX4OrVYqjS3zWwUv41bBhNpFccL6vF5U3XI9PFYLRj/2DBTpfYkadzc9hXrzpyjKOB3TC6isutCIDyyqvBYOrw0Pj30JKn94l7gZh+4ltZ5LaUJID9WUIaratAF2Vwu0qjyUGxegsF/2NbfXKmT99Lt90m9nv6BqKjpqDIj7o7p/aoUhGvfEoGN43F1C0Xp3G/IKroYxe0FM5+ODxxcCZJXe3rAWbdadzOJMiWZ4QHt87VGiSeP0OkAxQrXOY1iQeQkuzf1JXC5BakUZJyCETAiHiyEs1k5khNELN7PxjEufh9mZ30EhT1gVJ1sef2Kw56a6x2ExU4mFvkRFRJb0GfNQWJIciYroQZ8IwIHWV0HPgJTbYWr+9cjSTYq/TeESRQUB6749qH9wFXTTZqBk4ZKgMSn7v+A62oHZRXcgL8B19Pf1D2CPdQcWla6OahZuqfVcyhJCZmFpfJzVjemffS1LNxX5+nlC/J/jMCzOuqAAanL9TNeU97p9ZmrHQXD9lD5bm912GLVVy9mNW1qxAlodzzwZlW+GBBnE7bXj6/o16LQfQLZuOitJ0ZOAKEGWwMWMEwToAegPzU/hS/NnmKibipuKl0AhU8SJdMFiSK0o4wSEkAlhcJbRPve+wCyjVk8XvjH9G7voYNRf3qhIOwGzMi/EWP2JkMfpPRAne5JyYphNG9FUT1lEe8hgIARy5BVdC4Px9BF5HorWZtjdbX6rIJXRlmFU5oUYl/0jKOIojjpaa+Xj9CHQ8ocXYfrPBuRd9SsYzzznOGhaLF+zmtD9XUffbXsd73W8wWIIKZYwWk1qPZeyhLDBvBG7B8m+1n8ztcpcf9zfeH/Wz9FRc/2Mxo1j6tiAlsYXoVRmo2z0A1AoY2uZjIbMfIzoIUBBz1vr7oXZeYwdZMwovIU/xEUP3pQZaaPpI/yp5XlQ2vjFZWuQoTTG7dqlVpRxAkTIhJDkDq5D2AqDKnfAOoRurxNkUdze+S90uOgQFDAo8zAz83xMMXwbGnlanMDAxRgJBHw+Dxz2o2isfQxud6sIERSQyVWQyVTskJJ+9/zN/j/ka0rIZOrePux6ec9r9Jv+pvf7v9Yzbs979Lf4x1zBKviJ3ypoYwf90/KvR6Zuooj18i6JjABzF73t1/CYOlHx2NNQGgbWf8Qb6s2foTjjDEwruIEt+RvL13iq4SGcTNm4A9xJI8VDaj0n/pMS6cpie33IivKr2mUwDZp9DdAosjE5/2pmBdQoM2MrfYSj05dYc8N6mE2fQ6efjuKyxQl9OhchHCl5OdVE2lK3AlZXI4ozzmQB76EowpQEjS+6F4Eq+xE8UrscPvhwa8lyjNFNiGt0pFaUcQJGyHouHLlJnxyz7sSOzvdQbdvFhlDJdJhmpDjD82BQ5YczLL8mwRDw+dyw247Cbt0Hm3UvbLYD8HntIlYhh1KVA5/XBZ+v72fgMhUihou0i0zZR0gHI6UyFVwyL+pcx9Dt7WQz5qtGo1g7lVkF5UROGbkNJKB+0sleDyCnjLgGEGGWN0LB9fEQ+0iHVt1dm0DGDZerBSpVHoxZC5BumC/Zs6zt0AHUrV4O3eSpKLnznkGlDXYdXYQ8/Qkwu024s+o6FKpKcGvm96K2Dqn1XMoSwk8rb2C+wIM1IoRnJFBxVa/Xgdqqe+B0VCMr53vIyb8s0q9Rfn2CIWBzNWNz7XJ2X4/K/A4m5PyMK6EE28ORELfbY8YDNYvR7m7Fj3KvwpmZ54+EGCHNKbWiDEm42HWWhBAGit/mqMF203s4YN4Ij88lFLRPPwmzjReiKM4PDWK3Dck5skAAjwjkz7oXdutB+HyO3sXKZBpo0ybC5ahjeQsGbjJodRNQWhFch5AOGQDPcSSRajD7vG74fM4+8siIZM9r/t/HvUbXuVgiv0DS2Z+EsveOI6bBkpNkZhnQpgB8MkDlA/I8gHagMosRbH2QVXQgYuq3pvaQTsEq2mPxDCCi/QkokdTefn5yKu9Hgv1jxSMxjZeY1JY/vwrTh+8h92e/QOY55w25082WbdjRsNbvOkoF6/VYWnUT06E3uWRQ99boJIoVfmyt1HouZQmhYCEcvLhqpnYCq/GWSM3pbEAtK1pvQ1HpHdBnBNdLSaS1cFnDQ6DbWYsttSvh8poxLvsyjMn+XngD8atSAgFKIb++4UHste7E3PT5+EXBbxLiEEFqRRknN4PkhLBn3Va3Cd90CXGGNn8t3ULNeMzO+g6PM4yTmyNUMYgo2e2HYbMQAdwHu40IoLOPAMq10OkmQpc2BTr9FGi0o5mFbLgYwoLiG5FhPDVUcSTp30dMiYC6mUfN/rbX0MG8xWQoTT8F5emngB6MBSIp9Ov97SeXAhF1+smsWFIqjEPXwueWZL3HTyLrc9UdkpT2uPkS2exx3Q0kpYEuuWQ97ee6S27CQcS1v8twX2x6PNxPzCti4W/gbm/DqIefhDIry583xEc+98xzRrBw099e9r89Lc+hsft/KEqfj8m5V+LZujXY7arCD91A2XGHCXKE87mQWs+lLCEcKoaQsq1NK7gRRRnx+aU21DdJt3krGmvXQS5PQ9noNVCpedHyEfrmHbFpKSPu1rpV8PjsmJx3NcqiGOQ8YoviE8cEgZ5g+CJ1KRaV3geNXBuTeaI9qNSKMtryhzneiBHCHnkpzvBAN8UZvod2Zy17OUOZi5nGCzCV4gwVPM4wzL2N+WVerxN226FeF1D6P1nQeppcroM2bZJAANOIAFZANkBCoWCLTl+iokgsITFffL8JiABQHd+Dra8xPalXl7JYQSoTJkUjDAWiGUg2e6yk/V+jv/sIaDhW0UBLqXC9Gz4vkX+PFMsdYA55b0yp12MbUg5yxxWeY32sxEMfMSOa1pPYyBtM3AL6MTLn6+sn/N03Tu/7Xi/dwsKPiEbI1SoBjwwodAN7ZMDnCuA0D3DicfmWyHI+HqUV94oYua+L1HpO5NJDWsNIdA5ZUQZnGQ3OviYk5kjc1MmtzX9CZ9s/odZUsBtQHqXahyOxsXzO8BBot+7B1w0PgEpTTC/4dUIeboS3cn6VWAT2WLZjPWVMk2lxZ9lqFKiLxV464v2kVpQjvmBBgJD1XKzkpgczii8kYlht3cmmoTjDqYYzMTPzAhh5nGGsoBc9LoWREOkTXEDJAngoyDIll+sDCOBkPwEUlyk9OOarFSpVruQxX6KB6NfR6mrG3uZn0G7bw0qtVGRdjDFZP4irRIHhri3U6/qIqd8S6u2xiAZYR4Nec8HbzwV3ILfcAV/zu/16e92DBVIa6JYcqvzB/XvojByQkXN7D7uTBfztv79l8qD3vXY7fHY75Lo09gMI/ej+oLF6mKKgd3p+5Oj2WVHra4YSCvjcXryh9GG8F7h4AJ6tUGZj9HjKziu+Sa3nUpYQCtpVqENYQ24wrlboVLnMmtK/DqH47YuPnpQNrL76ftise5BhPAP5RdcnhBtYfKCXPFI0d2/FzsZH2BfYrKLbWfAzbxwBQqDV1cziBq1eC64pvBWz009KKGCkVpRxAk7cEMJAPCjOcIfpfew3f94bZzhGfyJOYPUMJ3DdI9HN4/XaWdwfkT8WA2g7HGR5IQIouH9Ohi5tMtSaUZIl7JAIgiGnoee9mq6PcKj1j/D4HEhXl7G6gkbt2HgQL2VlqK1aJhxW9MbdBUIhg0Y7FiXlSwBW1k0gY4zsMXLWc4BBrrDh0Rl2uHXnb+FqbmLuoqqc3JD24pumJ0Eeh3pk4I8+MzIAXHOcR/DAsbXDTSS1ngsPweFWIf37cakopYehb0a3u5MVrfe4O5BXeA2MWWePpDh87hFCoL7rM+xuXs+yrM0pXoIs3eQRkoRPGy8IuLxOrKtbjhpHJc7O/A5+kPvzeBFNtBxSK0rRgsW2Y1zrOSHO8CN/nKGJIVGgGcsK3Y9LP4mXwonyvUEE0GY94E8AQwTwaDABVGQw4ie4gBIBLEspAhgIt9XVhD3Nz6DDtpdZfUZnXYIx2d/nNXujfE+GM9xIxxA6jvLKiC8AACAASURBVFWhZvld0IwZi7Jlq0NeAmUd/aJ6IZyeTnzqAzpkwHUuQB80Eo8hDBnYCC6Ia0UZwboiupSURd2xe9lJSumoldDq+ElYRIAm6MXHOt9jdZWUch3mFi+DQTs6QVfCxY4GAn9sfhZfdH2McdrJuKVkadwWnx9qrZwQRuNOiM0YFGd4sHsTq2fY5qxhkwhxhudhquEsaBTBj0qxkSL5RvV6rKz0A3MBtexjNQEDi8MrFAZoewngFKg1JSlLAHt2n1kFTRtwsO3P8DKrYDmLFTRoxyTfDZKgKxrpmNS2N/6KjnfeRM6PfoqsC78bForNlq3Y0bAOOyBHFby41A2MYYlleJbRsACN8CJOCAcBsLP9fbQ2vQIlucNWrIFCSQZt3lINgcNtr+NoxxtQKQyYV7IC+gSKF0u1vYrlejd1/RevNT8DgyITi8segDHOa6wOhgUnhLG8S6IzNrli1dh2M2J4zLqDDaqSaVnyGSp2b1TxhGdDIe3xWGC37u91AXXYK4Pc6hQKI8v+KVgBp0KlLg7bbS46Ox5fo1AG0T1Nz6DDvg8yKDA661KWdVsuU8aXoFwaFr7VV4dQuphU5i66+Ha4Gusx6qHfQZVfGPZufNP4JDZ1bwRFVE+DAhN9XqhlGpRknIaKvCshl4d+30mt57jLaNjbnxgX0g3fVP8E+7Cl6WeiqOzOlD81TIydi66UdB8caH0F1aYPoFXmYF7JSmhVofnKR1ciPprUCJCL6LraZfD4PPhtyT0Yl8Duw1IrSqn3apD5Evbgs91Zxwrd7zN/FhRnODvzQhRpJ3IiQ86enm5GAK2sBiBZAKuCCaAyK8AFdApU6iKO2wAfFCIXpOcOtf0FlMAkQz0KUwuuh0HDPWPi5HssbsRw1NWg5u47oBlVgbKVD0Qkl8NtwttVN+ITeJAPYD4bTYh3DDdRpdR6jhPCiG6BxLiYYg1qK5fC6axFdu7/ITvv/xJDcC5lVBEgRbm7+Sk0mD9HmqoYJ5Ysh0ZpjOocfLD4RMDq6caamiVoczezmEGKHUzkJrWijBOsEpYQ9uBn9XRht4niDD+E1dMXZzgr80IWZ6hIIeuNx232W/+EJDBOR3UQAVQqs1n8n9ZfB1ClKuAEcJgPosXZgD3NT6PTfoBZBSlOkOIFuVUwTr7B4kyM9rffQPubryP7+5ch+7uR1WymxDI7mp7Eu/41agBQvlI6hiiDLKxs71LrOU4I4+wGjZU4Tkcdaqruhs/rYFZCffqsWE3Fx41jBKgMxc7GR9Fi2YYMTQWLKVTx2mFxvGORi0bF559uWIvd1u2YrT8Jvyr8bcI/WEqtKCPfhaiMkPCEsAcFt8+Fg+YvmNWw1UlECEhXZrN6htOSNM7Q4+7qdf+kTKACAexrSmVugAvoFChV+Qn/OY3KXS9iEMEq+L7fKuhium1a/g3I0IwScTXvMtIIMJfRPZtg+noDXKYWqIx5MJ6wAOlT58fUo6166SI4a6tRvuYRqIsiK7v0Zc09+MhxCPX9wCSSVQTgXM14nFS2KiSopdZznBCGtD2J3bm760s01v0OckU6yiruh0pNhm3eUg0Bj9fJahRSxrUs7WScULw4JWswpcq+v9/+D7zT/jcUqIpZvUGtXJfwS5daUcYJYElDCHvwZAXCWZzhe6iybmcvq2QaTPHHGWaqwo/pGek9o0zfrP6fvwyE01EbTABV+UFZQLk+Dm/HLM56v1XwILMKjs3+ASqyvsutguHBKflVLKnMW4/DcmBzQAF5oayEfuI8FF4am5rgzsZ6VN91G9QlpShfvS7idb9y5Gps9lkHKZ4BzJPpceXYF0KaR2o9xwlhSNuT+J1bm/6AzvZ/QaMdg5JRK3jR+sTf0rBW4PbasLVuFbocR5GbdgJmFd3GFWhYSMb3Rfusu/Bk/RqoZWosKluNInVpfAssUjqpFaVIsWLdLekIYSBgQpzh+9hn/pTFGVL8zRj9XFCcYbF2Utxby9yujgAL4F64nMG2ApWqkBFALasDOIUVc+ctfASISFAG7cPtf4XXR1bB0X6rYHn4g/IrJUfAvHsjmt5dH0AGA0SQyVFw0Y3ImHZq1OVqf/cttP/9L8i65AfI+d4PIx5/+ZGr0OKzDzpOvkyLFWNfDmkeqfUcJ4QhbU/id/b53Kg7dh/stv0wZJ6N/KJrEn9RfAVhIeD0dGFL7QpYXPUoTD+F+bjLWPFX3pIBgXZXK9bULIbFa8bVBTdjboYQ5p4MTWpFGSeYJTUh7MHYxuIM/4OdLM6wk72crxnDiOG49JPjJs7Q7WoXSkCwn31wORuCbhNK+tJXB5BcQLPj5DZKfDEszjrsbnoaJschyKD0WwUv5oeaCbi1ta8ug73u4CCSy6AtHY/Sn98b9ZXVrFgCR9VRlK16CJqyyA8RFh39Jbq9lkHlzJDr8eAYbiGM+kYOMGBKKMpoAUknmaxovacT+UXXw5B5ZrSG5uMkGAJ2dxs21y6H3d2KMuMCTMr9RdyfxCcYxCMirsvnwqO1K1DlOIJvGy/AD/OuHBE5YjUpJ4SxQjZ+xqU4w0Pm/7GyFa3OY0wwvSIbMzPPwzTD2dAq0iUV1uVqFYrAW4QkMC5XUz8CWOwvAi+UglCqsiSVLxUm8/o8ONb5Lxxpf51ZBQ2asayuYLqmLBWWn3RrpHjB6ucWwudyDLo2RUY2Rt+0Pqprd7U049gdN0NVWMTiB/36JKI51tbcg0rHoUHHGKOZgIVloRFbqfUctxBGdAsk7sV0oll3bBVkMgVKK1ZBo61I3MVwySNCgDKzbalbAafHhDFZ38e4nB9FNB6/eOQR+HPzC/i8698Yo52A35YsgzLJsjdKrShHfkeZBGEdfLIYne5NqDZtgN3VAq0qD+XGBShMj23ChmhhJsQZ7mXEsMr6NRtWyeIMz8As4wXIVFPKhug3l7M5KAmM29UcNIlaU9pLALVpk6BM0Jqe0UcuNiN2O2uxu+kpdDmOMKvguJwfYlTmRZDLFLGZkI8aEwTo82yr3gvT1g9gObSVvtWGmIcshBNQ+vOVUZWl4/130fbX15B10SXI+b+fRGXszeaNeLVpPbzwHjeeHHJcUXAj5mWE5voqtZ7jhDAqt0JiDtLR9i7aml9j2czKRt8PhcQnromJWnJK3eWowta6e+H2WjEx9+cYleBlCZJzl8St6quuz/BK83pkKIxYXLYGmcrkc1WTWlGKQz7mvUImhEQGdzU+jmbLZvh6H1Qiq40V81UOMUGHs743ztDtc/rjDOeAylaUaCeHfdJPD6lE+HrcP+m329XajwCW+wkgxQBOhkJpGEkoUmZusgpWdbyDI+1/hw9uGDXjWF3B9CSJh06VjfQ67TDv2QjTtg/hbKlhy1bojdAUT4D18LZBYghlKLjo11GPIaxZdQ8cRw6hdMUaaCuiU5+Ssnm/2Pg4dlg2w8tSy9CPDHLIMEs/D1cX3gx5iCE5Uus5TghT5dM4wDpJCVLWUYv5K6Sln4Ci0oU8hiyF74cO235sq7+fFfOdmn89SgzclTjRbodaxzGsrb0H5G53c/FSTEybmmhLECWv1IpSlFCx7xQyIaTaWLub1geQwT4h6VFlWsGNKArx1Dr2yxx+BpvHzOIMqZ6hxdPBLsjTjGZxhuPTvzVsnCHpPnL5tFmoCLwQB+h2twdMLINaE0gAJ3ECOPy2RL2H2VGDPc1kFTwKuUyFsdk/QkXmd/hzStSRjt2Ars5mRgK7dn0Cr12IsdMUjUXm3PORPulkQKEIyDLaQ6T88sgVKL/2Yaizopdt2N3ehqrbfg1lXj5GPfRY2IdIAyFGpHBr9yZ8ZtoAiuHPVuXidOMCzE2fHzIZpPGl1nOcEMbuc5AQI3s9VtRULWUZ0bLzLkN2bmTFORNi0VzIQRFosWzHjoZ17AFyZuGtKEifx9FKEASsHgserL0bLa5GXJrzUyzI+m6CSB66mFIrytAljMkVIRPCr2qXwWQfLGEDkK4uxwnFd0GjyEzIh2yPz41D3UKcYYujioGuV2RhhvE8TDee0xtnyAigsyEoCYzHLRBJoclY2IQ2bTLSWDH4SdxjJia3sLhBqV5un1XQA6NmPKYVXA+9ukTcALzXiCLA3EKrdsO0jdxCyc3bB8gVyJj8LRjnng9t8bgg+XrrEG6nOoStUBlzIVenwXp0BzSFY5jLqEypisqaOv/9Plr/+Aoyz78IuT/+WVTGjNUgUus5TghjtZMJNK7DUYPayqXw+ZwoLl+CNP30BJKeixptBBrNm7Cr6QlW0+mE4juRk8bvh2hjHO3xSAE/0/gwdlm2Yqb+RFxbeFtUTz6jLW+k40mtKCOVN0rXh0wIP628AQ6/BW0oGchaqFZkQqPMglaZDY0yW/ityOr7vzILyjitYUn3fx3FGZreQ6VFeABVytQYqx6DMdBCaatiSdSCCeDoXhdQgQDqo7RNfJhIEDA7jmF389MwOyqZVXBc9o8xKvOChDywiASHRLyWuYXu/gydWz+Eq62OLUGRngXj7HNgmHU2lOmZopfl87hR96dVsNcegGH2Ocg//1eirx2qY+39K2A/uB+l96yCduz4qIwZq0Gk1nOcEMZqJxNsXLPpCzTVPwG5IgNlo9fwGkkJtn/RFrfG9BH2tTwPhUyDOSVLkamN7y/OaK8/0cbb0PE23mr7M/JVhbiz9H7oFGmJtoSQ5JVaUYYkXOw6h0wIBQshZb4bOHGDUp6ONFU+HO4OOBhhGirBA6CQ6aBVCiRRII1Z0Cj85JG9Rn9Lb20kC4PTUccsgM3d27DfsR81cMJDTzg+IN8HTFAWo0x/AtL0U6HTTYQ8yT8jsbsNYzMyWQUrO97G0fZ/wAcPMrUTMTX/OujVxbGZkI8aNQSc7Y0wff0hzLs+hddhZeNqS8bDOIfcQk+CTKEMay63uR01Ly6Gx2pCwcUUS3haWOP0XOTu7ETVrTdAmZWFUeuehEwe32W2pNZznBBGdHsl18UtjS/D1PEBNNpxKB21HDJ5dEz0yYVS6qyGlPOhtj9DKdfjxJIVyOCpveNy8w9Y9+Dx+vuglKmwqPQ+lGgir6kUlwsNEEpqRRkneIRMCEOJIaTkHZRp2OFuh93dzn4TUbR7hP/biTS62+EZovgy4XS8tTGQNPZZICOxNgoEsCYoC6jXYw7YJgXk2lGoVWpw0FUDq7ebvZerrmBxhhMy5g8bZxgne54SYlBSsz1NT8PsrIJcpsb4nB+j3Hg+twrG8e7TZ9Ba+Q3LFmo9skM4eVEokTF5Poxzz4O2aGxUpLdW7Ub9X1ZDplSj9Mr7oMkLv8SI6eMNaHn1RRjPvQB5l8d/KSap9RwnhFG5ZZNjEKFo/UrYbYdgzFqAvMKrk2NhfBVhIUBuWIfa/oSqzneY69iJpSuZNYG3+EGg093Ois+bPSZcWfBrnJQR2Qlq/KxsaEmkVpRxgkvIhDA4y2hf5jvKM5qvn4cZhTeH/NBNmYgZUewhjPSbkcae1+jvcK2NgS6q5LIqWBsFAnhMIIBUB9C2D16PQPKEpoBWNy7ABXQC5HIte0eIM/zSH2dYyV4T4gwXYJrxHOgUGXGyvaknBlkFj7a/icqOt/xWwUmsrmCaOnpJRFIP1diu2OuwoeubT2HatgGu9nrh00duoScsgGHWWVDqjVEXoH3Tm2j/9K9QZRej7KrVkGt0Yc1R99B9sO3djZLFy6GbODmsMaS8SGo9xwmhlLubAHO5XW3+ovVdKCj+NTKMqfGAmQBbMyIiEinc2/Ic6ro+hk6Zj3mlK5lbGG8jj4Db58ajdStRaT/EMpn9OC91DnCkVpQjv9tMgpAJoXCRUIewxrQBNlcrdKpclMW4DmGgtbGXKHr6rIw9FkePzzYMtFRxTgmFzwOFzwslGSGoDiHk0KlLka6bAIN+NtL10yCXa4Yci77L6u37GTE8atnWG2c4OeN0VrYii7smSnqbdzkqWV3Bbmc15DINJuT8hN2XdADAW/wh4GyrZySQyKDPKXxutaUTWZKY9Aknhu0WKmal9B3W8PpaWI9sR/rkk1FwyS0hx8h7zF2ovOV6KDIMqHh0fdy7ixIuUus5TgjF3I0p1sdq2Y366tWQyVQorbgPGm3yu6Cl2BaHtFxmZWh6HE3dX7KshCeWLIOK16wMCcNYdP5by8v4xPQBKjRjcWvpCqhkqePiLbWijMX+hTFmWIQwjHkku8TttTE3VCKqFvshdNsOweasYxZHt88LtwzwsCejoUVSyLR9MY0U26jwxzP2JMdRZkOtMPYWMe90NmKH6X3s7foEbp+DDV6RNhuzM7+DUt3UkB82JQMsCSby+lwsTpBCEiibdZZ2MqYWXIc0FbcKxtv2MrfQozsFt9CjO5l4MoUK6VNPQeac86ApjE4NPzHr9ti6UfPSYrhNLcg99ypWtiKU1vXpx2h+6VkYzjoX+Vf8MpRLR6yv1HpOLCGcAuAJAN8CQL4gzwNYSd4YQyC1AsDyQd5fAmBNwHuXALgPAGWuOOof+68h7ELSKcoQ1h6Trh2tb6Ot5c9QqQtRWkFF65M7SUVMQEyiQUmJb69fizbbLpYCfE7J3VD6XbKSaJkJs5Qt5i/wUtMT0MszWPF5qneUSk1qRRkn2CaNnqPwBIe9ktUBZMXgbQfh8/ZZC2UyNbS6CawAPGUAlany4PSa++Ib+1kbKZMqkcuhm4y5oLLEN36iKJPpUe9qwBHbHti8QgxirnqUUM8wYz6Ly+Utegh02Y9idzNZBWtYwrLxOT9FmfFcbhWMHsRRGcljt8L8zSeCW2hHIxtTmZENwwkLYJx1FhRphqjME+og9oYjqP3DcnJ7QOnPVrDENWJb/cNrYP1mJ4oXLUXalGliLxvRflLrOTGEkPzD9gDYC+BBABQp+jCARwEsHQKtUgD0E9guBXAngNkAKAqV2qkAPgGwHsCbAC4EcDsAov8bRO5G0ihKkeuNeTdWtL72YVi6t0KfcSIKS5I7jX3MAU2CCdxeO7bVr2ZZC3N00zG7eBFLC86btAjUO2rwUO1SuHxO3FS8GJPTZkgrQBzMJrWiDFhyOIejgYiRP9xmAHMAXAzg3RDgTFg9RwTQbjvSmwTGbj0An98yR+uXyTTQphEBnMJ+tLqxkMlCy0woWBsD4xj7/t8T7+j0dDKrVP9G0ZVEB6k0vd3/pgpKlKqLMVo7GQZ1kT+zqpAUh0p0yGXkvMqbGAToQPFI+xuo6vinYBXUTcXU/GuRpioQcznvIxECzrY6mLZ+iK7dn8HnFD4J2rLJzBqnnzAXMvnI3/Omr/+Nlg9fYAS17OoHRJFTj6UblTdfB3laGkb/7mnIFCO/DjFbKrWeE0MIFwNYBGAUgC7/IuhvsgCSjb/nNTHr+xeAMQACozk/BEBPlWcFDPAeADqCILIopoWlKL0+L7Z2b8Jnpg1oc7UgR5XHYnHmps+HnPuxw+OxorZyCVyuRuTkX46sHHp+4W0oBJL9nnJ5urGl7l4W91GgPwkzCsmXn8d8xOpT0f9+ylJmo93dhi5PJy7OvgwXZH8vVlPH9bhSK0o/GOEejgZieS2AewHQk3DSEkKf1wW7nQggWQD3wW49GEwA5VpW+oGRP7IC6saETADDuUHJBY4S3rAyGwEZVHtiGu2uNnR4WtHic6InZQ09JFGaDNr8vijF462NfTUb/WU5FNmsbqP/Xg1H3KS4xmQ/wqyCFmctyLV3Qu7lKDWczfVGnOyuz+tlsXmdWz+AreobJhUVgc+YehqM5BZaQI/+8dPIWNH8zu9h3rMRutEzUPyju4aNB+z64jM0P7cehtPPQv7V9BWcGE1qPSeGEH4GgFIJ/TgAQgoqOwbguwDeEQltDoAGv2soKURq9P1KB3M3A3g6YJwrALwEIBuAScT4IRNCetB6sfFx7LBshrf3xJASZsswSz8PVxfezEkhAIf9GGqr7oHP50Jx+VJWw4m3gRFIlXuKHqQ2162AzdWEEsO3MSXv2pR/6InFZ2Lg+0mYyaDIxH2jnoRSHpoVJRZyjsSYUitK/xojPRwlTnEQwF3+sIukIYRerxMO2+E+CyC5gPpcvbeGTK6DTjcJOv1kRgI12tGQxbGFjbwhWmwHsavr3zhs3c4ylVLLVhiRL9dD7bXB5TENaG0M/DyQW+RQNRu1iiyolVlJaW30eJ042v4GKjv/yRL4ZOumMaugjmeqHomvzOPm9Ngt6NpFbqEfwt3ZzN5XGnJhnLMAhhnfhiItfrPvep121L6yFM7WWmSf+n/IPu3/hsS0/ndrYd2xDUW3L4Z++sy4wF+MEFLrOTGEkO4Ucucki2Bgs/hfWytmYQCIlj8DYAIAqpRLjdxvyB3123630Z6hTvS71cwDsEXE+CETws3mjXi1aX0AGeybRQ45rii4EfMyxBooRUiYwF26TJ+huX49FAojK1qvVBFP560/Aql0T1ldzdhSuxwUuzMq8yJMyLmck8IofyRS6X4KFTqpFaVfvkgPR5/0h1H8FgDVP0hYQkgEkMoTCRbAvYwMBhJAuTyNxf4JLqCTodFWxDUBHOr+c3gs2N31MXaa3ke3m5xKgRx1OWYZz8fotGlweSi2sa9eY1/9RqFuI5XpGLrJWMIbckVl8Y2K7N7/C69R+Y3EsjZ22g+xuoIWVx0UMh0m5l6OEmYVFPPIGeq3Ae8fCgJEosgaaN79OXwuIaGSbtRUZg3Uj58TF26hYtZDWU9rXl4Cn9OBosvugn7MwETPa7Oi8jfXQaZWY/Tjz0CmTJxDVKn1nJhPJx3z3QHgd/02qRbAqwAoQYyY9rHf84JiJ3raKQA29osppPfG+UnjeSLjCEMmhOtql+GonQ5rB25ZyhycYgj0YhWzxOTtYzFvg8N+FEpVDgzGMwHuJnjcZn/R9TE63G0pc0853SbUmT+Bx+dkMYVZuknJ+wEYgZUNfT/JMEY7HgtLe5wtRkDAEZxSakXpX2okh6MU6PklAPpN5qaEIoRerwN220F/Eph9sNsPA36rGWEjl+sZ8WM/+ilQa0YlnUsgWQmPdG9mZSuaHEfYLZGmMPrrGZ6LNMXAiTbI2thHEqleY3DNRrunHU73wLGNgR8xwdroT4gzQBZVwdpIsY0j98BLVsEj7a+jqpNCY31ML0zJv46VOuFt5BAgt1DL4a9h2kZuobuZIFToPWOa3y00PzEzyZv3/Q9Nbz0GuS4DZVevgcpw/H1m/vILND39BDJOOR0F19w4cpsQxsxS6zmpCGERACKQlFBmXRQI4XEZTMmvOJS2uPIGmDwdoVzC+3IEOAIcgbhBIFORjftHk/NG6jWpFaUf4UgORz8F8JU/Hr8i3gmh12sHJX7pjQG0EQHqSyouV2RA12sBJAJYlnQEcLBPFT1rNNgP+usZboEPPihkKkzKOI3VM8xR98+lN/znk2IbnZ4uIYuqRyCNdhbn6P+/JxJroz+mUZnlL8dB1sa0iKx1PbUtq00bYHe1QKvKY+SvwbwJNneD3yr4cxZSwK2Cw+9/rHpQqYaunf+F6esNrFwDNaUxj1kDDTPOhEKXHqupJRu35d8vs7IYmuJxLPOoTBF8INLw5COwbN2MolvugH52oD1KMhHDnkhqPSeGENKp6O/9pSACFxaKy+gt/qykFJ1aEzBIj8vomQBIYfa0mLuMChZC8lwdmEjmKQuwIJuqYfDWg4DH3Y2O1jdAp8WGrLNZJjje+hD4sP1ttLqbBoUkWe8pi6MOx0zvs3gaShZg1PL7Ihqfi6HvJ7IQTsDCUqr+k3pNakUZISGk+HvysKFwCUrCJpYQRnzwSXLTw3t31yaYOjbA5WqBSpUHY9YCpBvm95I4r8cGm62PADpsVP2pjwAqFAaW/EWwAhIBLE0ZAjjUp8vkasLOzg+wp+u/cPmErIyj0mayeoZluulRJ0OB1kZy1xeypxJ5FKyOVL/RwayNQ1UEAysErx3M2qjoIZADWxtZXdrGx9Fs2TxgDGW2bgam5V8LLbcKjtgXs6O5mlkDzbs3wud2Mjl0FdOROfc8pI09YdgkLCMmeBgT+zxu1L62Eo76QzDOOR95C67qHcXrsKPyN9cCcgVzF5Wr1WHMMHKXSK3nxBBCipuoA/CTAFjKAFSHkFTmfwDodPX0ftD2JJX5jT++sOftnwN4OZZJZXh8Tng3ubV7J+prHgDViSobvZo9GPAmIJDK91RT92bsbHwUlJZpVtFC5OmpsgxvkSCQyvfTcLhJrSj98oRzOEoZtIldPeJPlEZDkX8WVXkmokgZtYUCeMO3kEMjmCWn7nFYzFTpoqfcAql9GbS68dBox8Nu28dqAva9DxYv3kP+yAVUpS6JOrkZfrmJ08PhsWIPizP8AGZ3KxM8R13GLIYT00+BUi7dg+hA1kahHIdgZRQskFS3kc70h2oU22gQajb6XVQpptHmbkVd138HOUyXYVr+jSg2nJY4m5ckkvq8HlgObWPWMls1VYkDZCoNMqadzoigOjd5n9VcXa2oeXExvDYzCi65GRlT5rP1d2/5Co2/fxTpJ89H4fWUuzKxmtR6TgwhpMxqFENI1r0exbXQnzpbTNmJntNQct59aoDtoLITVBTknID3yAE9M5ZlJ4Iz+JGVkH54llExH5f21n+gveVvUKmLUVaxGnKFTsxlSd8n1e+puq5PsKf5achlaswpXowsXWB1maTf/qgvMNXvpyEfVYXkFGL0VzT3JZzDUdJjQ8UmkC8mxcyLaSETQrNpI5rqya34+Np7gRMqlFl9BDBtMvtu565+YrYkuI/X58Hh3jjDw+xNHYszPBfTDeciTUkFLOKjCdZGv1vqgNZGoTTHcNbG4NXIkKkdj3kpGts8EjvrsZrRtfNjUH0+d5dwGKHKLGDZQjPILVSrHwmxJJ/TenQn6v/6AGRqDcquWg11Tgkan3oc3V9tQuFNtyF9LuWoTKwWj4SQUmXTcQNFolJheqojSKed5AITWJievv3I7fOX/SCnFNurAFAc9k3UGQAAIABJREFUoXC3BreewvSUge0tf2F6IpwxL0wfWOOr3dWKbFUur0Mo4vNCJ5ANtWth7d6O9IyTUVBCteikfjYTIegIdEn1e+pY53s40Poqq781t2QZDJrRI7ALyTNlqt9Pg+2k1IrSL0c4h6MU0NI/XTUdpP7Zn5CNkq1RbKGYFjIhrK1axpLBDNaUqgIUl98FlaqQf4eL2YEQ+jTYhDjDI8y1UogznJhxKmYbL0SOhpys4r/1WRv7sqgebP0jPH732IFWQBlRz0jR2GYpd9TRVMWyhXbv/QI+t1DiJW30TBiZW+islHTpbv/872jf+HdmDS3+6XIcu/03gNeH0U88C7mmr4qolPsUyVxS6zmxT/EU60eE7VsAOv01lCi+IdBRvcpfOqLPgVdAYgeARj/BGwybS/31Ccf7g+1p7L+EAGTIijKEsXnXARDweLpRU7kYblcLcguuQGb2hRwnjgBD4HDb33C04x9QKQyYV7ICenUxR4YjEFUEpFaUfuEjPRztwUBsDGF/zELWc5WHboDHPbiBUqHMxujxqZmYKKo35BCDmVzNAXGGNtaznOIMjReiPG1GwhHxr2qXwTRo/gWyEE7AvBSNbY71PUXxcpaDW9G57QPYa/az6WRqLQzTz2CJYtQ5qa1rKZtq/d8egK1yF7RFk2H/ZB/0c09C0U23xnprYjK+1HpOLCGMyWKjOGjIijKKc6fsUHZbJeqOLWNJC0pG3cOyzvHGEaAsfPtbX0aN6UNolbns4UCrzOHAcASihoDUijJA8EgORyUnhIKFcLDkaRRHOAGlFamZmChqN6PIgYQ4w/+yeoY9cYbZ6lLMMl6ISRmnShpnKFLkAbs1mDdid9P6ARPKUNDNtIIbUcRrOEcC8XHXeqxdMO34D3ML9ZiFWpiqrEJ/ttAzINekRXW+RB6MsKp58S64CadGoOCnNyPjJCGmMNGa1HqOE8JEu0PiTN6uzv+iueEZUAwKK1qvpJAZ3lIdATokoIeGhu6N0KuKcWLpCpaggDeOQDQQkFpRRkPmKIwR8sHn0DGEchQU34gMY3+P1ihIyocYFAGKMzxi2cLcSRuZpY3iDA0sxpBiDdPiXIcGZxnty78ggwz5+nmYUXhzSrorxuKWtzccZdlCu/f+Dz6P3y10zCwY556PtDFkXZbHYtqEH9NWvR91r5GjIVDy02XQVdA5XuI1qfUcJ4SJd4/EncTN9c+gy/Rflpa8pHwpZDLKEcRbqiPg9bmxo+ERtFq/hkEzBnNLlrL6V7xxBCJFQGpFGam8Ubo+ZEIYnGW07+Gd8vHoM+ahsIQ/vEdpb8IahsUZmt7Dke6vWJyhHEpmLaSyFfEcZ9hTh7DGtAE2VysrPF9mXIDC9L5SJmEBwi8CuYV2H9jMsoXa64T4X5lax+oGUqIYdTal4+BtKAQsu7aj4cUHgQKqu5iLsl88kJA1F6XWc5wQ8s9VxAh4vU7UHVvOUpdnZl+E3IKfRTwmHyA5EPB4nfi6fg067PuQpZ2ME4oXQyFhCvbkQJGvoj8CUivKONmBkAkhyR1ch7AVKlXucXUI42R9KStGF8UZmj5kpSucXiHOkOoYzs68kNU15Jag5L813JZOdG3/D0zbP4KnW4j7VeUUC26h006H/P/Z+w7wNstz7VtbtiTLe9uxMwkJkA0kQAsBCuX8peNAS89poaWLnEIXtBAgJBCgUFooBbpoy+igh9NdymjCCCGEDBJIAgkZduK9Lcva67+eV59s2fGQbOnTJ+l5e/lKsd95P6/0fPf3LANnc4/1FnT++ucY2PIKDOfMgqfrKHJnLUbFFTel3edIbj3HhDDWG8b9JkTA5+0USWaCQQfKq74Nc176pfhlEScHAX/QiZ0td8HuaUBJ7lKcUfEtqFWUfJEbIzA1BORWlFPbZcJHTYkQJnwXPGHSEPAEnXhv4FW80/88BvxdYp0CXZUghqdYzk2bOMOkAZSBE7tbj4aLyL//JhDwi2o6ubMXI3/ZJaKYPGdwj0/ooUAADd/4GoIuJ2Y88GO0/u898PW2ovBDn0bhyk/EN1mKe8ut55gQpljgmbS8Y3AP2pruh0ptFPUJ9YbszniVSbKd7lm8gQHsaF4Pp69VJByg4sX81nu6qGbveLkVpUKQZkKoEEEkexsUZ3jMsUvEGba5w26DRrUlXM/QejFMCo8zTDY+6T6/cAs9uF2UjfC0hutVUmIYqhuYv/RikTCG29QQcL63D633343c0xeh8ts3w9PVhOYnb0PI70XlZ9Yit+60qU2cglFy6zkmhCkQciYv2dP1LPq6/wS9oRrVdRuhVhsz+bh8tjgQcPu6saPlDrj9Pai1XoJ5xVfz28848OOuwwjIrSgVgj0TQoUIQs5tUOKZPf3/whERZxgUcYbzLKuE1bDYMEPOrfBa00TAP9gnXELJNTTgoApuEDXzyC3UsvBcqPX8vDRNiNH55OMYeGUTSr/4VeSdd76Yzr5/Kzr+8Qg0uVbUfPFeaC2F011GlvFy6zkmhLKINXsWoXiV1qbvw+V4F+a8VSir/Do/9GeP+Cc9qcPbih0t6+ELDGBmwacwu+iKScdwB0ZgNAJyK0qFSIAJoUIEkYpt2H3deMf2AvYPbOY4w1QIYIprUhkmsgKKIvIHtwNBKt+tgmnOUlFEPmfGQn5GmiK2o4dRHcLGb16HwKAd9Q//HBqzZahL5wuPY2DPJhir56Hqs7dDpVF+2Irceo4JYYIuIk8zjEDAP4CmhrXw+7tRXHYN8gsvYXgYgSEEBjwN2NVyJ/xBF+YVfx4z8j/K6DACcSEgt6KMa3PJ6zwlQiiSyjRvg+3YS/A5u6DLLYF15sUwV3NGyOSJKnkzU9IZijPcK+IMO8VCBbpKLJLiDHVqQ/IW55ljRiDk94m4QMoW6mk/JsapjSbknXE+rEsuhi6/NOa5uGNsCLgOvY+WezcgZ8FpqLrp1hGDSB7NT98hZJG/4jIUr/5cbJOmsJfceo4JYQqFnclLu11H0Xz8Dkpxh6oZdyAnd24mH5fPFicCfa73sbv1HgRDPiwovQ5VeR+Kcwbuns0IyK0oFYJ13IRQlAfY+TAcrTuAUFA6hgpQqWCqXIHy5Vx2QiGyjXsbwVBQxBnu7X8Ore5DYjzFGZ5mvRCnizjDgrjn5AHTR4AKolMB+YG9LyPgtIkJ9SU1onagZcE5UOuYsE8f5bFn6PrtE7BtegElV38J1vMvPKmTr78TTb+5BUG3A+Wf/DbM85Sd/FBuPceEMFk3k+eFrW8Tutofh0ZbKBWttzIqjMAQAl2Ot7G37YeUGB9nlH8LpebljA4jEBMCcivKmDaV/E5xE0J701Z07H4sigxGbVKlRtnSNbDUcGH65IsuuSu0u49gb/+/cHhwuxRnqBFxhovyL0MJxxkmF3xR2iUkagaSNZBqCAq3UHrpMne5iA/MqT2V3UKTLAXhLvqdryPQ34e6h34KrTV/zBUdR95G27P3izIe1dfco+i6jnLrOSaESb6k2Tw9fUl2tv0UdtsW5OQuQGXtrZxZMpsvxBhnb7O/gX0dj0AFDZZU3oyi3IWMECMwKQJyK8pJNyRPh7gJYdNrt8PTe3jc3RnyZ6H6wxv5YVUe+SV9lXCc4YtSnKFTrFeds0AkoKnLXcz6N8ESCPq9GHxvG2y7X4SnvUHMrjaakbfoAliXXASdtSTBK/J04yHgOvIBWjaug3HefFTfcseEQPW89gz6tv0V+tJaVH/+LsVabeXWc0wI+fOVVASCQQ+aG2+H13MCBUWXo6j0qqSux5OnHwJNtpfwftevoVEZsazqNliNs9PvELxjWRGQW1HKerjxF4ubEB77+7UIBhwTbl+l0UObWyJiC0/611QCjT6PCaNCLkCs2/AG3Xif4gxtz8Pm6xDD8nUVWJR/KeZbPgSOM4wVybH7+Qd6YNvzb9j2bEbQZRed9KUzkL/sIzCfSm6h+uktwKPjRqD7mafR/8JzKP6va5B/0cR5K8ia2PrM3XAdPyBKfZRd9rW415NjgNx6jgmhHFLN8jW83nY0N6xFMOhERfWNMFmWZTkifPzRCBzr/QuO9P4ROrUZy6vXw6yvZpAYgXERkFtRKkQUcRPCo89eg5DGTUkNT24h+pUKan0ugr7xSSMTRoVIfwrboDjDBsduUc+w1X1QzGBUm7FQijM0a9Mj/f4Ujp7wIcIttPmgyBbqOLQz7IatUsM0b7koIm+sPoVfnCQc9dgmJNkcv/F6+Hu6UffgY9AWTH6v/Y5+NP36FgQG+1D60a8g74wLYltMxl5y6zkmhDIKN5uXcth3oa35AajVuaiuvwd6PRdezeb7MPrs9IX+Qc9vcbz/ORg0BVhRvQE5Os7CxndkbATkVpQKkUP8hPDxLyJU6ByfELYBucWLoC0uhzbfCnWuASFNEH53D/zObpGV1O/sYsKokAswnW10uI8OxRkGEYAaGsy1rBTZSUsN9dOZOqPHBn3kFroV/btehLfzuDirOscC6+LVyFt8IXR5xRl9/nQ4nLvxGJrXr4Vx9hxU33ZXzFt2NR1Ey+/uhEqtQfXVd8FQVhfzWDk6yq3nmBDKIVVeQyDQ0/kH9PX8DXpDLarr7oKa02PzzYhCgEjhe50/R4v9VeToyrCiagMM2rEDwxm47EZAbkWpELTjJoRNT90OT+gwECnHRRpfWAapWjOA1jFOplJDX1QpMiPqS2phKK2BtqAEIW0IAVeYJPocYaJIP/Tf8VkYi0e4pmoMVrasyHjB7P4evNsfjjP0BMOW4aqcU7E4/zLUc5zhkCR8tm7Y3n5JZAsNugfF74kwULZQ86krodayW6iM13bCpbqf/QP6n/sbij7zORRccllc2+rb8Rx6Nj8NbX4par5wLzRGU1zjk9lZbj3HhDCZ0uS5RyAQCgXQeuIeuJwHYLGeh9KK6/hBgO/IqDsSxDvtD6HTsQNmfS2WV62DTmNmlBiBEQjIrSgVAn/chNC+fys6/vkoYAkB9G5FB8AHoJ8IoQoll34VhrIZ8HaegLfrBDxdTfB2NQk3qtFNpTdCX1wjCCIRRSKMhpJaaHItCPicIwhi3IQxpxg60xgxjLklYMKYnNsXjjN8TYozbBeL5OvKhcVwvuU86NTG5Cys4FnppaTrxHsiSYzjA3ILDQFqjShPQETQWDWXn1kUJj+S2YmbvwVfRztm/OBh6Eri8yyi8e1/eRCOQztgmrMM5Z/6jmJkLLeeY0KosMud6dvx+21oargFAX8vSsq/BGvBybViMh0DPt/ECFBtwrdb70evax/yjXOxpHIttFn4cML3ZHwE5FaUCpFF3IRQ1CH8y4/Fw454uI1YCCkl/rwVKP/EN8bMPBlwDgyRQ0EUO5vg7W5CyOs+CQqNuUAihxJRLK2FvqhqRGINJowKuUFjbCMcZ/i2qGfY4n5f9DCoTViYdyHOyP8IsiHOMOjzwL7/ddh2vyRejFDT5OYhb9FqkS1Ua5k8Jk25Es7snXmajqPp9u/BUD8TNXfcM6XDBtxOND+xFr6+dhSd/18oOOv/TWmeRA+SW88xIUy0BHm+SRFwuw6juXG9qNNTPWMDjDmzJh3DHbILAX/Qjd0td8PmOYyi3DOwuOImqFXa7AKBTzsuAnIrSoWIIn5CGAyi7bEfw3l4B5AXClsI/QBsKuTOWYGKNd+ASq2O6XhELv227rAlkQhiF1kVm+DtaT25zqFKBV1BBfSlYStixP1UV1A6NgGdjoVRrQu7oLKFMSY5TtSp092APbbncNj+JiJxhnPMZwt30lJj5sUZUqFy4Rb6ziuiWLkgw+Uzw0Xk558NlZY+MNyUjEDPn/8XfX//M4quuAoFl10+5a16Oo6j+anbEAoEUPXZ25FTO3/KcyVqoNx6jglhoiTH88SFQH/vC+jueAJabTFq6u+BRpsX13junPkI+AKD2NmyAYPeJpSZz8LpZTdwHa3MF3tMJ5RbUca0qeR3ipsQ2rdtRcfjjwHB4Mm7U6tR9qU1sKycXmH6kN8nSGHY5fQEvBJZ9Nt7T1pTpTNAX1w95G5KhJHcT7Um64ToBX1O+JzdQzGLkdhF8a+DYhjDMV5jNdVowphTAq1puMwGu6SejNqgvxfviDjDTcNxhsb5otB9vWmJMDQfGtyGfbaXMODrQp6uBKdZL8Y880rFf0cLt9Dj+2Hb9SIcR3YPu4WecpbIFmqonK0Yl8Hkf6Wk/won1t4Ib2szau97CPqy6SUrHHj3VXQ+9zNoTPmo+eL3oTWnNoeB3HqOCWH6fx7S8gT0pdzR+ggGB95Ajul0VNbcrHhFkpZAp/mmPf4+7Gi+Ay5/J6ryLsCpJV9mZZ3mMk3E9uVWlInYcwLmiJsQNm9cB/eRD8ZhSqpwVr5b70zA1k6eIuAaFG6mRBAFURTxiScQ9LhO6qzJtQprYiQuMRyjWB1zweiEEMboOoxMGIWMfBRnaN8ispP2+8JxhnnaMhjURnR7mxBC5EWDCvS/WaYVuLRcmS/ugl437Pu3hN1Cu5vFWTQmq8gUal18IbTmgqR8DnjS5CHgbW3BibXfgb5mBmrvui8hC3X+6+fCYmysnY+qq24TGUhT1eTWc0wIUyVpXhfBoBvNjbfB62lGQfGnUFRyBaPCCJyEgNPXgZ3N6+EJ9KEu/2OYW/xZRinLEZBbUSoE7rgJYcM3r0Og/+QEMZHzqC0W1D/8C9lestCLQCrqHXE39YhkNuR22gIEA6NgJrfTshHZToko6grKY3ZzjUzIhHF6N5jchRucewQxbHYdGHcyFdS4qGwNTrFMz+o8vd2OHE1xYf27X4L93VcR9DjFH8kKSNZA8ylnQaXhUIRE4i3nXL1//zN6//y/KPzEFSi8/FMJWZrKjDQ/vQ7ejkbkn305ij98VULmncokcus5JoRTkRKPSRgCXk8rmhpvRSjoQkXN92AyL07Y3DxR5iBg9zRhZ8t6+IMOzCm6CvUFU48VyBxUsvckcitKhSAdNyEUFsKjh8NuceM0XWkZLCvPFT/0/1PRQgE/vL1tIttp2O00TBT9A90nbYfiuiJup6IsBpXHKK0Vbl7SvYj7COMTxnCZjaCXanSM3YZcUrPEwvj7E99FtzeceGWsZtEW4/ySa1FiqIcpRWWDhFtow7vo3/0inEf2hGutqDWwnLoS1qWXwFjJeQvi/pAocMCJ278Hb9Nx1N7zQ+grqxK2Q3qJ0PSbteIFQsV/3iiyj6aiya3nmBCmQsq85ggEBgfeQnvLg1CrTaipvxc6fXxpgxnO7ECg330Yu1s2IhDy4NSSL6Hayhlqs0PyJ59SbkWpEJzjJoQTxhBS4pfSMpGuPdKMc+fBsvI8mJefBY0p9fW4KPufcDslK6Igi5LbqZQAJFouVCxckENyN41KZqPWT798QtDngs/VDb+jUxDE4RjGWAnjyNqLugh5NEXKasSW2EcJ9/BXDdfBERjf6hy9R5OmACWGOlH4nv4lkkiEcarEfbLzkzvyALmF7noRvt5wkU3KgksuoXmLV0NrSm1M2GT757/HjoC3ox0nvvdN6CqrMOOeH8Y+MMaegx/sQvufHoDaaELNNfcIbwW5m9x6jgmh3BLm9cZEoLvjt+jv/ScMxnpUzdgAtZqLvvJVORmBHuc+vN16H0II4PSy61FuWckwZSECcitKhUAcNyEMBYNo/+nDcOyWyk6I0hMq8WNaugLl190AX2cH7G9uhX3b6/B3dYqjkhXOtHgpLKvORe7CM6DSKsetjqw/VCdxyN00Uj+R4sIClEJ1ZKOC00NEkWonltZCV1iR0NigbCKMzzavQ5v7cNjqNkbL05aiKucUdHka0eNtjoozDHc2qs2CGJZKBJGIItU/VKmmTorJuky1A+37XhuKUaWagaKI/LwV7BaqkC+wRG6j77m/oefZP6DgY59E0SevTOTUQ3N1v/w79L/1DxjK61H1uQ1Qa+V9LpVbzzEhTMo14knjRYCK1rec2Ai3833k5V+A0oqvxDsF988SBDoGd+Cd9gdB8SpUjqLYtChLTs7HjCAgt6JUCPJxE0LaN5HCwe3bYHvlJfi6u6ErLob1/IthPmvliFg8Ilruw4dgf2MLBndsR9AVjrfSWPJEX8uq82CYUZ806850MQ4FA/D1tsPTeXwogQ1ZFP39YZI7omm0olZixN00ksxGYylMyvkyiTAetG/FvzseO4noEb6jYwj9QS96vE3o9DSgS/w0CnfTQMg3Qhw6lfEkS2Khvgpq1fgJPSiu0XnsHWENdB7bG55Pox12C62YOd0rxeMVjEDThrXwNBxDzZ33wVA7Iyk7pe+Ult9vhLvpfVGTsvTSLydlnfEmlVvPMSGUVby82EQI+P39UtH6PpRWfBV5+eczYIzAmAi0DLyCA50/h1qlx9LKW1GQM4+RyiIE5FaUCoF2SoRwKnsPer1w7N0N+xuvw7lv71DZCn1ltbAaWs4+B9rCoqlMLfsYciOkrJLRJTGIKAZdJ8cFknvYcKbTcGyivrgGGmNuUvcd9LtHuqI6ukb89+QxjPK5pBIRe779YRx17EBIWAnpJ/Yso4GQH33eFokkNg4RRV/IM5Kzq3Qo1teOcDct0tdA7fNj4N3XhEWQYr0ED7QUigLy1kWrRUF5bpmNgK+7C8dvvB66snLUfv/BpLzEiSDoH+xD069vRsBhQ+l/rEHeaefJBq7ceo4JoWyi5YViQcDlPIiW43dCpdKguu5O4ULKjREYC4HGvufwQc/T0KpzsbzqDlgMyXlLyOgrDwG5FaVCEJCNEEaf1z9gw+D2NwQ59BxvCP9JpULO/AXCamheugJq4/Tj9OTEWLidOvpHxCWKOMXuZlBdxdFNm1ccznZaKiWxoTjFokrZXBGnQxih1kKXUzyi9uJQDGNuCTRGSsYTn7tmMOjHuyeexH7H63CqPcgNGrDQdC5Or70aanX87sXBUBA2X/sIkkhWRU8wXCw+0lQhFSwDgKU3BGs/UKKrRe28/0D+3JWyyULOe8prjY1A3wvPoeeZp5F/2eUoviL5WUCdxw+g9Q8bodLoUH31RuF2LkeTW88xIZRDqrxGXAj09zyH7s6nodWVhovWa8xxjefO2YPA4Z4/oqHvL9BrrFhetR4mfUX2HD6LTyq3olQI1CkhhNFn97Q0CWJIMYeBvnDheZXeAPOyFSJLac6pC+MuCaEQbMU2yL2WrE5UFsPTGU5gQ0TR19dxcsycWiNIoSCKUdlOiTwmK2nKeFjJSRjJQti+82E4WikudbgOoYhLrVyB8uWJqUNIpH3A14nm4y+jpe1N9Kg6YSsAvCe9e1ChQFcR5XIaTmBj5OcGJX20ErqXSH3V6jvuhrFenoyxfW/+DT2v/kGUvan5wj1QG5LrNSC+Wynem8zvMjXZFkryeVKuKJN8vqyaXhStb/kxBu3bkWtejIrqm+J+g5lVgGXxYemuHOz+DZpsL8GoLcaK6jth1BZmMSLZcXS5FaVCUFWMniPi5Hr/gEhEM7jrLYQ8YXc/TX4BLCvPEZlKDdU1CoFt+tugoube7pYwUZRIorezCQGn7aTJ1YYc4WYq3E0piQ1ZE0tqoMlJ3YvNcQmjyJzahYB3YHyQRlkYAz4HHC07xk4qo1KjbOkaWGqmV4cw4HaIuoFURN7XT2Qc0OYVwbrkYmgWLkGPukvEI1JcIlkS7f6Ty5NQcpvoDKeU6TQ3RWUwpn8DeYYIAv6+XjR+aw20RcWY8cBPZHv5Qi9C2v7vh3Ae2Q3TKWei/OPfTPracus5JoT8OVMkAsGAS9Qn9HlbUVhyJQqLP6nIffKmUo8AfVHv63gU7YNvwKSrwvLqO6DXcBxJ6iWTvB3IrSiTd5K4ZlYMIYzeddDtxuDuHYIcut7bP1Tz0DCjLlzC4qyV0FozM92/32ELWxE7m4aJIrmd+kbGwwmybCk8KdspJbahjK6pbkQY/c5wCY3hkhpSHONkhHHE5lUwFs5B9YfunNKRyGW3f9eLsO/fMoRhTu2pIluoac7ScTPDugJ2iSAOk8R+X9tJexhZBoMyndbDrC1K+oP9lMDgQWMi0L/pBXT/9gnkf+QyFF/1OVlRCrgG0fTEWpGoqnj155G/4qNJXV9uPceEMKni5Mmng4DX04ymhlsRCnlRWXMzcs1nTGc6HpvBCARDfuxt+yG6nXuQZ5iJZVW3Q6vOyeATZ/fR5FaUCkFbkYQwGht/bw/sFG+4dQu8rc3hP6nVyD3tDOFSalq8DGq9vKnb5ZYdvaDy9XUOuZtGktn4+tqGyPLQnlRqUQLDUCrVT5RcT7X5JYryiokmjB07f4KgP5yBdqymMeSh/qO/iBl2sjY7j76N/l0vwNW4X4xTafWwLDwH1qUfgaF0arHhnqAT3Z7jUZbERvSOWQbDIlkSw3USiSRadWWKwj9mMLOgY/O9G+A+9D6qbrsTObPnyn5id3sDWp5aB/qcV/3XOuRUJy+hndx6jgmh7NeJF4wHAfvANnS0PAy1xhIuWq8rjmc4980iBAJBD3a33ot+90EU5CzAkorvQcP1LDPyBsitKBUCouIJYQQncuX2HG8Ml7DY/gYC9rBLojonB+blZ4tMpcY589I63jDeOxH0eeHtaRGJbMKupxSj2CRqKo5uKr0R+uLqIXfTSDIbJWTQbH5tHdy949chpLMYCubAWr8a5qqzodYaxoSKrC0D774C29v/HioNorUWw7rkI8g74/ykuNiOLoNB7qbdnhMIYmT9Sp0qByWGGVKG0zBJLNBXTlgGI977wP3jR8Bv60fjN6+DxpqPuh89mrLvD9ueTeh64XFh9a/5wr3QmqzxHyaGEXLrOSaEMQiFu6QWga72J2Hrex4G4yxUz1gPlTr1LjapRYRXHw8BX8CJXa13wu5pRIlpGc4o/xYr8Qy8LnIrSoVAmDaEMBqvkN8P5/53Yd+2BY63dw9l8dQWlwirIf3oy7M3GVTAaR92N40ks+luQsjrPunaaUz54bhEKT6RktnTW3bDAAAgAElEQVQQcVTr5LO62pu2omP3Y1EJZaK3qYLGYEXA0x9+AaDLFfGEeXUXwmANZ2YkImzb9QLsB7YOu4XOWBB2C51NbqHxZTyd7mdzdBmMMElsxMRlMMIksdBQA62Kn0emK4NYx9te2YSuJx+H9cJLUPLf18Q6LOH96IVX5z9/Klybc+pOQ+Wnb0nKvZVbzzEhTPhV4QkTjUAo5EfL8bvgdh1CXv5FKK24NtFL8HwZhIDHb8POlvVw+tpQYTkPC0u/xu4/GSRfOorcilIh8KUlIYzGLuBwiCQ0ZDl0f3Bw6E/G2XMEMTSvWAmNOXXJVxQiZ+GO5rd1n5Tt1NvTejIRU6lE5sOhBDYSWdTllyXlIXVkltHhOoSRLKNly66Hp/cD2Bo2YbD1LSAYtr7pcioAmwq+BjoDoNIZYFl4btgttERZCYhGlsEIJ66hJDajy2CooUGhvnqoViKRxGLDDOjU6VWGRSn3frJ9tPzgbrgO7EPVzeuQc8qpk3VP6t+DPg+an7xNWPkLVn0SReddmfD15NZzTAgTLkKeMBkI+H294aL1ARtKK9cgzypfcdBknIfnTC4CLl83drSsg8ffi1rrJZhXfDUnDkgu5LLOLreilPVw4y+W9oQw+mi+zg5RvoKS0fg6wgXGodXCdMYSUd/QdPoiqLTx17RTiKySsg2qkUikcCjbqVQaw28PlwCJbkS4yHo4TBTDcYqJcG8jUjjYvA22Yy/B5+yGLrcY1pkXw1y9csTLN29/G7p3PQ1n7zuALhDeXlAFg2Uuihd9Bjml85OCUzImJauQ3d+FzqjsppTl1BkYnWk2XAaDyCHFJIZ/ZnAZjGkKJTBoR8MNX4XGbEHdQz9NysuOeLdIn8WmJ25FyOtCxZXfg2nW4ninmLC/3HouVkJIVPwnAM4GQL4AjwPYAED6hE94JkoPeQuAhQAoEnkngE8BiFQcfQLA1WPMQN8Uw68QJ4Y5oxRlQm9UBk3mdBxA64mNUKl0qK67Cwbj1ILNMwgSPsoECDi8LdjRvB6+oB2zCq/ArEL62uGWCQjIrSgVgllG6jl60HYfPRyON3zrTQSd4UcDtdkCy5kUb3geDPWz+IXOBJeQ4vG83U0js512nUDQ4zpplCbXCr1IYkMlMaRkNuR2qo/dqiUI4YFtsL39Eny2LuisJaIkhHlBmBB6Oo7DtltyC/X7xB709TOhLjXAYz+CUDD8O0PBbFjrL4S56iyotbGvr5DPo9iGw98nWRDJkhjOcjp5GQxyOa3jMhhxCHJgyyvo/PXPkXf+hSi9+ktxjExu18GD29H+l4egNppR88V7xWchUU1uPRcLISwAcADAewDuA0BVIH8I4EEAt01ycJLaIwDuB/AyAJrrAmlc5LUKEcIzAXxh1Fx7AZzsRD/2ghmpKBN1qTJpnr6ev6On8/fQ6cpRXX83NBpTJh2Pz5JgBAbcDdjZcicCIZewEs7IvzTBK/B0qUBAbkWZijOOsWbG67mQzwfHO3sEOXS8uwcIhN8568orpXjDc6ArTtwDl0LkmpRtENH2D/QMZzsVyWyaRGIbBEe/y1dBl186snZiaa1wRR0d0ydcRv/6MByHTi5Mb6icBag08DSH3+WTlTLvtA8Jt1B9cZX4XcBjh71pC2wNm+EbbBW/U2tzYKk5F3n1q2Gwpv+L3uEyGMPupuOXwQjHI0ZqJnIZjLE/Dq0/ug/Od/eg8qZbkbvgtKR8ZqY6ademJ2Hb+Tz05TORv/RiDOx9ecwXJfHOL7eei4UQknXvuwDoUxqpXkr/vR5AedTvRp+V0kE2APg2gF9OAAQRQrIeLosXrKj+Ga8op4FNRg0lJdfe/CM4BnfCZF6G8upvc3xYRkk48Yfpdb2Ht1vvRTDkw8LSNajMY3fjxKMs74xyK0p5Tzfualml5ygzqf2tbcKl1HPs6BAoFDtEVkPzshVQ5+QqRDTps41QwA9vb9uobKcnRMzi6EY1EiNup+RuShZFb187uv/95DhJZcIzUPyiddlHYDntw9AYx5aRsAz3HIStcTMcLW+NtBrWrYa5mjKUpqfVcKzbMFwGI2JJnKwMxjBJzPYyGBR73HDDV8TnvZ7cRRXmSk6fqebfroen9Qi9BoEIkhVNRQHvMM1bgfKP3xD3s6rcei4WQrgFAL3G+UzUJad0UccBfAzAP8b5KlwjWRSLAHiZEKaPwlD6TgMBJ5ob18LnbUdRyVUoKL5c6Vvm/aUYgS7HblGnkNoZFd9GqWk6759SfBhenpPKZNkd8La2CGJIP1TrUDxq6fUwLVkGy8rzhMVApdFkGSqJPW7Q4xwqhUGlMcJlMU4g6I5E98S2nq6oCrVf/kFcD79hq+HrIhFNploNx0KPymB0e08IN9NI4prxy2DUCTfTSK3EbCqDQZ/7jl88Csu5H0bZtV+L7SLK3Kt/5/Po3vTk2Kuq1Cj7jzWitmY8TYmEsBPAY5JFMPos9C1BVsIfjHNAQmYBgF8BuBVAGYC3AXwLwLaoMWQhpPQ8lIqKCtZQjCH1fy0O4LLqzWkcuGRsV4/7BJobb0Mo5ENl7a3INZGRmRsjMD4Cbfat2NfxKNQqLZZU3IzCXPp64paOCMitKBWCUdbrOSpi7jr0viCGgzu3I+QOR5VQXTLLWavC8Ya16e9yqJD7BrLiUZ1ET8TdtOsE7O9tG8PldHjHVJut/uv0yBh/i1gNBxo3Y3CE1XAWrHUXZpzVcCyEqAxGr7dlFEkcrwxGpFYikcXMLYPR9uMH4NizCxXf/h5Mpyc2cUv8t3TsEc1PrYO75YNxplPBWD0H1Z+7M67l5NZzsVgIKfr3JgAPjTpJM4CnAKwd54QvAlgpuZSSiym91qN/6dX8HAAd0rhvSBZEilGk4IDvAFgKgKj0jhjRy3pFGSNOGdXNbtuKjtZHoNHkoab++9DqCjPqfHyYxCNwov9FHOz+DTQqI5ZV3Q6rkUKiuaUbAnIrSoXgw3ouShBBjweOt3fC/sbrcB54FwiF3bT0NbXCamg5exW0+ZS2gFsiEQg/+I5XmJ4efOei+nOUc3B6bchq2LgZPnuLmIxiDc0154hENJkQaxgrQlQGg2IQoy2J45fBqJEsiWGSmO5lMIIuFxqu/wpUOh3qf/ILxbmLRmTY8JPrxMuT8dpUXpTIreeSSQhfAnARAMri8IIEUp7kakqJZm4fBzhyOKckNu8A+Pg4fcgyeUf03+jNErfsQ6Cr/dew9b0EY84cVM24AyoVpynPvlsQ34mP9f4ZR3r/Fzq1Bcur18OsDyc74JY+CMitKBWCDBPCcQTh7++D/c03RDIab/OJcC+VCrkLTodl1bkwLVkOtYEckLhNFwH7/q3o+Oc4hemn6Bo30Z7CVsNDGGjcNIbVkGINV2ZUrGGs8iFcBvxdI0giuZ26xiyDUTmUuCbscloHQ5ok5KM44o6fPiySSpV95X9ihUf2fsl4USK3nouFEJLL6KNSmYlokCdzGf0jgCsAEMGLzha6CQBlGJ0oBzyt9/8AUKxiLI0VZSwoZWAfSl/dfHwDPO4jsBZcgpLyazLwlHykRCJAivSD7qdx3PYvGDSFWFG9ATk6zlyYSIyTPZfcijLZ54lxftZzMQDlOXFcEEP79jcQsFGVLEBlNMK8/CzxUJkzb74iapjFcBRFdhmZZXRUYfopJs+I9aAB7yDsJ7aIRDQRq6FKZCg9B9a61TDk18U6VUb2I93mCPQJkkgWxPjKYNQjV2tVHC7tjz4k3MPLb7gR5iXKjf1PxosSufVcLISQksqQvf6qqJtSA4Bew02UVCZixSNCGF0MZzMAqqJKZHG8RhZEIoSxBgOwolTcx1i+Dfl83aJofTBgR1nl9bBYV8m3OK+UlgjQQ82Bzp+j1f4acnXlWF61HgZtflqeJRs3LbeiVAjGrOfiEEQoEIDzwD7Yt22BY/dOUEkLatqiYljOPkeQQ30lewfEAelQ16E6hHuoDmE3dNZiWBcP1yGcypzxjBm2GlKs4faoDKUUa5i9VsPxMHQFBgRBDJNEIosN6Pe1n9TdpCkcKn8RsSSmsgwGuYWTuyjUKtQ//Auo9fp4romsfZPxokRuPRcLIaSyExRDSOTMLiF8IwCKjpyo7ARReUoQcxmAf0nj6PUDZSd9AMDGcaSVI9U83AOAitrH0lhRxoJSBvdxOvah9cQ9UKn0qK7fCIOB3llwYwTGRyAYCuDd9ofQ6dgJi34GllWtgy5N3GiyXa5yK0qF4M16boqCCLqcGNy1Q1gOXQcpXUG4UcF7cim1nLkSGgtFtHBLNwTYajg1iZ1cBqMBvd5mhIZKJoTnNaoto0hiPay60riyyE5thxCf2fZHfgTzmStRft0NU51GtnGJflEit56LhRBSVDZ9g+6XykjMBPAjKclMdGF6KsBBmUGvjUL/r1LR+ZsBUJEbSipzKoC5ACj6kgjiPwH8FgCNp9qFlIWU0giRmWdXjJJkRRkjUJncrbf7L+jt+iN0+krU1G2EWsM1qjJZ3ok4WyDoxZ62+9Hr2o984zwsrVwLjZpjjRKBbTLnkFtRJvMscczNei4OsMbr6uvugn3bVmE59LW3hbtpNCJ7IVkNTYuWiAQW3NILAbYaTl9evqAHPaIMRsSS2IixymDo1Tko1ie/DEb7z36Cwe1voPx/vilcvrOtya3nYiGEJAMiceTGeTYAcsp/XCo5EYgSUCOAVwFEB3GZpbIUVFaCns7fkAjfPmkcVR39PYDlAEqlWMM3pbm3xyF8VpRxgJWpXentTFvzA3AOvg2TZQXKq74VqVeWqUfmcyUAAX/QhV0tGzHgOYri3EVYVHGjKE3BTbkIyK0oFYIE67kECoIIhKfhqBRvuA1Bx6CYXW0ywbzibFHCwjhrDuuQBGIu11QRq+FA48vw2ikhPhCONVwlxRrWy7WVtF8nXAajeYS7aZfnOPwhz4izaVV6FOlrpeQ1lLiGymBUQ6ua/OUKPbsdGtyGfbaXMODrQp6uBKeZLoD2liegCgZFdlG1gehCdjW59VyshFDpUmBFqXQJybS/QGAQTQ1r4fd1oqj0cygoIo9lbozAxAh4A3bsbNkAh7cZ5eazcVrZ9bK4xLBcpoaA3IpyartM+CjWcwmHNDxhyO+H4929ghw69u4GAuF33bqycmE1pJhDXSmVUuaWTggIq2HvBxhooFjDN0fEGubVrYYlSzOUTleGY5XBoNhEb9A5Ymo1NCjUR8pghElisaEWOvUwuSMy+Hz7wzjq2IEQgtJ4FVQhoOz9EM45vhyV11M1uuxrcus5JoTZd8cy/sQedyOaG29HKBRA1YzbkZM7P+PPzAecPgJufy92NN8Bt78L1XkXYn7JtWwdmD6sSZlBbkWZlEPEPykTwvgxi3tEYNCOwR1vwr7tdbiPUL29cDPOPUWQQ3Jd05hMcc/LA1KLgLAaNr0uyCFbDRMvi5PLYDSILKejy2CooEK+LlIGox7ugB27+/8eRQaH96YKAuc6LsaixV9M/IbTYEa59RwTwjS4FLzF+BEY6H8VnW0/g0aTj5r6e6HVcYHi+FHMvhFOXzt2NK+HN9CP+oLLMacoOrly9uGh1BPLrSgVggMTQpkF4W1vE8SQfvzdXWJ1lVYH05KlsKw8D7kLT1dsoWyZoUqb5ca1GubPRF79hWw1TKAko8tgREpgkCVx0N8T2ypBoNw4G1fWjpeDMrZp0rWX3HqOCWG63hTe96QIdLb9AgP9L8OYcwqqZtzGResnRYw7EAJ2z3HsbLkT/qADc4r+C/UFVAGHm5IQkFtRKuTsTAhTJIhQMAj34UOwv/E6Bne+iaArXEmLMpOaz1ol4g0NM+rYoyBF8pnqsmNbDY2wVJ8Daz3VNeRYw6liO9G4SBkMIodv9f4fAqFwSZixGlkUq3MWIl9fgQId/VSK/2/RFkOtUidje4qZU249x4RQMaLnjSQagWDQi5bj6+FxH0N+4WUoLvtcopfg+TIUgX7XB9jVejeCIQ9OLfkyqq2rM/Sk6XksuRWlQlBiQqgAQQS9Xjj27BLk0Ln/HSAYjnvSV1ULYkjxhtqCQgXslLcQKwITWg0jsYY6qojGLdEIPNu8Dm1ucs0OnTw1/WoclqJR6WDVlYUJIhFFiTDm6yuRo7EkepspmU9uPceEMCVi5kXlQsDn7URT41oEA4Mor/omzHnZl7pYLqwzbZ1u5zvY03q/iG04vfwbKDfz3VGKjOVWlAo5NxNChQgisg2/rR+D27eJEhae45RonR5gVcg5dWE43nDpCqiN2ZcdUWFiims7YavhVgw0bIqKNQxbDfPqV8PIVsO48Jys80H7Vvy747GxYwihxuqSr6DMOAv9vjb0+drQ76V/W9HnbYM7GCmNPnIVo9o8ZFGkeMUwWawUBFKrVm5x+9FYya3nmBBOdlv572mPgGNwL9qa7oNKbUBN3d3QG6rS/kx8AHkQaB/cjnfbfwwV1Fhc+V0U554hz8K8yoQIyK0oFSIOJoQKEcRY2/A0NwliSDUOA/1UZhlQGQwwLztTkMOc+QugUme2i5uCxRP31oasho2bMdgclaGUYg3Zahg3nuMNGJlllEyCIVCyUSInsyxn4dLyG8bN+O0ODEoksVX82+dtRb+vXZDHsd1QVcjTFksWRbIslqNAH7YwWrRFisssLreeY0KYsGvNEykZgZ6uZ9HX/Sfo9dWort8IdVTaYyXvm/eWegSabS/jva5fQK0yYFnlWuTnzEv9prJ8B3Iryii4qSbvT0bV5N0AILom72jpUJ3dNQDOBVAJoEmqv3ufVHs3VmkyIYwVqRT2o3hD13v7RQmLwd07EfKG67VpCgqFO6ll1bkwVNWkcIe8dLwIDFkNGzfDO0AfX0ouRFbDVSIRDVsN40V0ZP9gwI939z6J/d7X4TC6kdMPzHPMx/JLb4VaE39NYCKZdn+PsCSGLYrDlkX6/VjuqeSCKlxPdRWSdTEcq0j/bdRQSfXJ25j1FK0XY5555ZTIptx6jgnh5DLmHhmAgCha33QfnI53YM5bibJKqjOXKdc/AwSk8CM09v0DH/T8Dlq1Ccur1sFimKHwHWf29uRWlBKalKr4AID3ABCZmwXghwAeBHDbBIg/AGAFgKcBULDM6QDuArAJwKfikBQTwjjAUkLXoNuNwd07BDl0vX8ACIXjpAwz6gUxpIQ02jyrErbKe4gBgbDV8DAGGjeNYTW8QBBENccaxoDkcBd6gdL+04fh2L1jKB5X/FWthmnpCpRfd0NCLev+oHfIihi2KJJlMeyG6gk6xtx7jiZvTLIoXFBVOjFm3HqKUGGWacWEls7xAJNbz2XKEzEryrg+gtnZOeC3o6nxFvh93Sguuwb5hZdkJxB86ikhcLjnD2jo+xv0GitWVG1Arr58SvPwoOkjILeilHZ8C4DvAqC3AQPS7+i/1wOgyxD53egDFgPoHvXLrwD4OYA6AMdjRIT1XIxAKbGbv7cH9je3YuCNLfC1toS3qFYj97RFghyaFi2FWp8+8U1KxFjOPU1oNaxbDWPBTDm3k7ZrkYt1x+OPjSSDkdOo1Sj70hpYVp4jy/ko+2nEokgksZ9iFYV1sR1B+E/aA2VAtWhLhNspEcITrn1jWh8p5OSisjU4xRLfOeTWc0wIZblmvIhSEHC7jqL5+B3iTW3VjHXIyWX3P6XIRun7oLfD73f9Cs0Dm2DUlmBF9QYYtZxNMBVyk1tRSmfcAqAVwGeizlwrEbqPAfhHHFiQG+kOACsBvBnjOCaEMQKl5G70PeI53hB2Kd2+DQF7+D2COicX5hVniUylxjnz2INFyUKM2tuw1XAzBlveRCjgFX+lkhXhWEO2Go4nyqDHjea718N7QkrINLqjSgXj7DmovvXOlN6GoHBB7ZZiFIkktqPfGyaLsdVUVKHCOAdXVMd3Drn1HBPClF4zXjwVCNj6NqOr/ZfQaAtQU/99aLXsspMKOaTjmvQWcF/HI2gf3AaTvhrLq+6APkNSXKeTPORWlBI2nQAekyyC0XCRnxFZCX8QB4Y3SK6mFQBo3lgaE8JYUEqjPiG/X5SuoBIWjj27EfKH67FpS0pFIhr60ZexJ0K6iDTgdcDe9DoGomMNNQZYalYhr+7CrLEaEkkOOp3w9/WCLOORfwN9ffD3Rf67F0Hn2C6a0fKm2Nv6B+lrV5nNF3Sj39eBP7fcOa7LKe3cpCnEtfXxnUNuPceEUJl3jHeVRAToy6qz7Wew215DTu4CVNauhUqlSeKKPHUmIRAM+bG37QF0O/cizzALy6pug1bNNarklLHcilI6Gz2t3wTgoVFnbQbwFIC1MWJAT/jvAvgXgGtiHEPdmBDGAVa6dQ04HBjcuV1kKnV/cGho+8bZc8MlLFacDY05tuQW6Xb2TNtvJlsNKeaPrNr+vj4EiNz19kokry+K/PUOJVMaT7YqvQHawiIEbH0IulxjdxMWwrmovpXydim7TVhPEWQhnIsrquM7h9x6jgmhsu8Y7y5JCFDR+ubG2+H1HEd+0cdQXPrZJK3E02YiAoGgB7tb70G/+xAKcxZgccX3oEmj+kbpLhO5FWUCCSEFiVEymWoASwGE6xOM3cjqeEf0n+hBk1vmI+DrbBflK+zbXoevsyN8YK0WpkVLkLfqPBF3qNLGn30x85FT3gmF1bCZ6hpShtITYoOqIashxRpSbipltFAggICtf5jkRZO9IfLXCwQmSqoMqE0maAuKoC0shDa/MPxvQSE0hcO/U+fmCrdoJcUQTkcKBwe2hOspjsGqVCGEYwjzzotrCbn1HBPCuMTDnTMJAZ+3HU0NaxEMOlFe/R2YLRTWw40RiA0BX8CBXS13we5tRKlpOU4v/ybUbGmODbxp9pJbUUrbJdfORwGMfs0bq8so6ds/ALgIwCoAB+OEgS2EcQKW7t2FpenIB8KldHDHm0MudmqzBZYzV4p4Q0P9TI43TANBi9jRviOwNWxKSaxh0OsV9TFHunGSdU+y8PX2CjIYyYQ7HqSaPKuw7BHBC5O88L/Rv1MbDDFLZESWUXrhRT+UAV6lSkqW0Zg3FmfHgROv44XOR9FhlYpa0Lc9HQVAmQ24pPR/kFdLlYdib3LrOSaEscuGe2YgAg77brQ1/wBqdQ6q6++BXk8hPdwYgdgQ8Pj7sbNlPZy+dlRaPoQFpV+dUr2h2FbjXhEE5FaU0rqUVIbSQ14VJQkqKEev/WNJKvNjAJRdlAjh1ilIkwnhFEDLlCH0QO98Z49wKXW8u3fISqOrqBTEkGoc6ooooS03pSMwmdXQkB8fySeXyzCxi47Zi5C9MOEL2u0Tw6JWQ5tfIFnziqApKBi28kUIYH5BUizTRAopwZLtlZfg6+6GrrgY1vMvhvmslQktOZHMe9H82jq4ej9Aaz5wvBBw6YEcLzCjF6jsB3IK56L6Q5xUJpkyiMzNilIOlDN0jZ7OZ9DX81foDbWorrsLanXsb7cyFBI+VhwIuHxd2NF8BzyBXtRaL8W84s/zG/s48JtK1xQRQio7QTGEVHYi8nR1IwDS8hOVnaAj0tiNAK4E8KepnJljCKeIWgYOCwwMwP7WNkEOPQ3HwidUqZBzyqnheMNlZ0Kdw3HNShf9sNWQMpRuG85Qaq1DXv1qmKtWAt6gRPSGLXkR8heQXDrHjcGTAFBpdYLoUYKWsCUviuxJrp1k+VOp1UqHTJH78w2248TmmxAKhpNCjdU0xkLUX8pJZeQQIBNCOVDO0DUoc2Rr071wOfbBYj0XpRVr+IE+Q2WdrGMNeluws3k9fEE7ZhdeiZmFn0zWUjyvePYVzi1ye7hQYXoqSr9fKkxPhcZ+JCWZiS5MfwTAawCulYRFAcq/A/CEVHswWoZHAXTFKFTWczEClU3dvK0tghhSvCEl+BAfDL0epiXLheUwd8Fp/KCvwAshkrMM2IbInre3FS7bfnhCRxHUOsM7ptJ3lLKKKpX2j30IlTFnKEZPkD1h5ZNcOqWYPbXJzM80SbgDfnc/+g79GbaGzUBoorhKFYzCQshJZZIghpOmZEUpB8oZvEbAP4Cmhpvh9/eipPxaWAvIq4sbIxA7Ajb3Uexq2YhAyIVTiq9Bbf4lsQ/mnnEhkCJCSHs8FcAjAM6WHtEel0pORD8NUFGtV6MyiBIRvHqcA35BIoqxnJ/1XCwoZWkfIhiug+8JYji46y2E3G6BhCa/AJazVsGy6lwYasi4zS3ZCFA5EX8kXk+4cYate2GLnvTT3zd+chZ69USiotRTUgJ0dcACo3YOcgsWQV9UPmTpo/qV3ORFIOhzou/Ic+g//E+EAh6otDnILT0djradQCh48mZUapQtXQNLDReml0NSrCjlQDnD13C7DqO5cb1wvamesR7GnNkZfmI+XqIR6HUewNtt30cw5MNpZV9HhSU+BZDo/WTqfCkkhKmElPVcKtFPo7Wp4Ldj9y4MbNsC14F9Q4lC9DUzwvGGZ62CNj8/jU6knK0GPZ4hUheIInuC9PWHyR9Z/iZMzqJSQWPNl5KxUFKWUW6c5N6ZX4iQyo/Bpq0iEc2IDKXVq4RLabyxhspBMT13Egr4YGv4N3oP/QVBrx1Qa2GtvxiF8z4Otd6M9p0Pw9G6Q5I9ZYSWkuNUrkD58hvizi8gt56T2+UmWbeAFWWykM2yeW29L6Gr49fQaotQU38vNNq8LEOAjztdBDodu/BOG3kSAmdUfAelJqouwC2RCMitKBO592nMxXpuGuBl61CySNm3vwH7G1vgbW4Kw6BSIXfh6YIcmhYvQzxZITMVR1FMXSRn6cEw0YtK0iKRv6BjcGIINJpRbptSFs6CouGMnNb8uJKzDMUaNm7GYPObwipFTW+tg7V+NSzVq6DWsaUwWXeTworsTVvR+/6z8DvJw18FS+25KJx/BXS5JUPLUr/B5m2wHXsJPmc3dLnFsM68GObqlXGTwfDHVN7QCCaEybpBPG9aIkBfvB2tj2JwYCtyTKehsuaWKX2Q0/LwvIRzBfEAACAASURBVOmEIdBqfx37Ox6FWqXDkspbUJhDnobcEoWA3IoyUfue5jxMCKcJYDYPJ93mPXFcuJTa39watmLRQ6cxB+blZ4pkNDnz5mdkvKEge3a7VEA94sI5iuz19Q652Y53Tyg2c0SJBcmSJ2L2IrX2LHlJxTDgc45jNVyJvPoL2WqYwA853Rtnxx70HHhmyEKbW74URQs+A0MeJZhObpNbzzEhTK48efY0RCAYdEtF65tQUPQJFJV+Og1PwVtONQIn+l/Awe4noFHlYHnV7cgzUg4SbolAQG5FmYg9J2AOJoQJAJGnoPwXATgPvCvqGzre3omQL5wdUVtULIgh/egrKtMCKpGcRRRT74G/j+rs9YwqrN6LQF8fQv7xM0DSQSkWL0zqwuROZOQUWTmHf6fONSkmOUvYangUtsZNJ1sN6y6AueYcaNhqOOU77Or5AD0Hfg93T7hcrLFwHooWXIWc4lOmPGe8A+XWc0wI45UQ988KBLyeVjQ33opg0IWK6u/CZFmSFefmQyYWgaO9f8LR3mehU1uwono9TPqqxC6QpbPJrSgVAjMTQoUIIpO2EXA64dj1Fgbe2AL3ofeHjmaYOQuWlRRvuBIasyUlRyaiKpKzjEP2AhSz198PBMdI5BG1Y40lTyJ5w5a8EWSvoBBqozElZ0zEokNWw8bN8NooJSmg0hhgqV6JvLrVMBTMUgyRTcR5kzmHd6AZPe89A0fbLrGM3lItiGBu+RLZMZRbzzEhTObN4rnTGoHBgR1ob/kR1GoTaurvgU5fltbn4c3LjwC9xT3U/RRO2J6HQVuIFVV3IkfHxaOnKwm5FeV095ug8UwIEwQkTzM2Ar6uTtjffEOUsfC1t4U7aTQwnbFEWA1NZyyGSqfDUCHxl6mQeBd0xSWwXhBfIXFKfBPJvikse4L0SUlZyMrX1zfk1jquvCg5C5VZGGXJE/8dqblHxdR1uqwQecRqONC4GfZmqmsYFWvIVsMJ7wDF/PW+/3+wn6CKQSFoc4pFjCDFCqpUqanPKLeeY0KYFV8TfMipItDd+Tv09/wDBkMdquruhFqtn+pUPC5LEaBA8wOdP0OrfQtydRVYXrUeBq01S9FIzLHlVpSJ2fW0Z2FCOG0IeYJYEBDE4tgR4VJqf2sbIolU1CYTTCvOhr+9DS6yJkYsc5T8QqWCaekKlH3teoTcrpPI3oiMnH19CDodE29Fqw2TumiyF11QvbAIopi6RqrLEMvBsqjPeFZDc/XZsAqr4WzZLV5KhD/gsaPvg7+KRDBUWF6tt4isoXn1F0GtSe3zntx6jgmhEm8o70kxCIRCAbSeuBsu53uwWD+MssqvKWZvvJH0QSAYCuCd9gfR5dgFi74Oy6rWQafhrHBTlaDcinKq+0zwOCaECQaUp5scAaqp53hnj7AaOva+PX7tvMhUGi0QoIrq4zeVwTAUqzecpIUsfRGXziKoLRYmLJOLZ9Ie41sNZwhimK2xhkG/G/1Hn0f/B39H0O8SLrb5sz+K/Dn/TzGxl3LrOSaEk36cuEO2I+D396Op4RYE/H0orfgK8vIvyHZI+PxTQCAQ9OLttvvQ5zqAfOMpWFp5CzRqwxRm4iFyK0qFIM6EUCGCyNZtBAbtaNpwK/xdneNDoFZDX1k9lHVTZODML4BmKBNnEdQ5OUz2UnCJqKA6lU+wjYo1zCarYSjox8DxV9D7/p8Q8PRTsKUgxgWnfBJao7Jqc8qt55gQpuBDyUumHwIu5yG0HL9T+JJXzdgAYw5njEw/KaZ+x/6gC7taNmLAcxTFuYuxqOI7UKu0qd9Ymu1AbkWpEHiYECpEENm8jYZvXodAf9+4EFB2zvoHH8tmiBR/dmE17D+GgQaKNXxjONYwrxbW+gsz0mooagS2vIXe9/4In6NdyIjqAxbOvxJ6c7kiZSa3nmNCqMhrwJtSIgL9vf9Cd8dT0OpKwkXrNWYlbpP3pHAEvAE7drZsgMPbjHLzSpxW9vWUBa0rHKpxtye3olQITkwIFSKIbN5G88Z1cB89DIRCJ8OgUsE4ey6qb92QzRCl1dmF1bD5DdgaKENpo9g7uU+aq84WRe8zIdbQ2bkPPQf+IEgwtZzS00XmUGN+vaJlJbeeY0Ko6OvAm1MSAuGi9Q9jcOBN5JoWoaLmu/wgryQBpdFe3P5e7GheB7e/G9V5F2F+yRfZhSoO+cmtKOPYWjK7MiFMJro8d0wI2LdtRcfjj41d6kGtRtmX1sCy8pyY5uJOykFgIqthXv1qWGrOVUxsXayoufuOCSLo6tonhhjyZ6Jo4WeRW7Iw1ilS2k9uPceEMKXi5sXTDQEqWt/UcCt83hYUFl+BwpJPpdsReL8KQcDpbceOljvgDdhQX/AJzCn6tEJ2pvxtyK0oFYIIE0KFCCKbt0ElJ9p/+jAcu3eErYT0E5VltPy6G6BSpyZNfzbLJZFnD1sNt2GgYRM8Q1ZDPcxVK9PCaugdbBOuoYMt2wUsOnMFik79NEyVZ6bVi1e59RwTwkR+iniurEDA62lBU+OtCAU9qKi5GSbzGVlxbj5k4hGwe45jZ8ud8AcdmFv036gr+I/EL5KBM8qtKBUCIRNChQgi27cxVIfwFapD2A1dcTGs58dXhzDbMUyH809qNaw+Bxq9STFH8bv70HvwTxhofAUIBaAxFqDwlE8hb8aHoVKnX6y+3HqOCaFirjJvJJ0QGBzYjvaWh6DWmEU8oU5Xkk7b570qCIF+1yHsar0bwZAXp5Z+BdV5nMV2MvHIrSgn249Mf2dCKBPQvAwjwAiMREDJVkOquUjlI6iMRCjggVqXi4I5l8M66xKotembyVtuPceEkD/1jMAUEejqeAq23n/BYJyJ6hkboFLrpjgTD8t2BLod72BP2/0IIYgzyr+JMvOZ2Q7JhOeXW1EqRBhMCBUiCN4GI5DNCLj7jmKgcTPsTSMzlObVXRCONZTJahgMeGE79m/0HfoLgr5B8QxGJLBg7uXQ6NM/6Z/ceo4JYTZ/qvns00IgFPKj5fhdcLsOIS//QpRWfGla8/Hg7Eag3f4m3u14GCposKTyuyjKPT27AZng9HIrSoUIggmhQgTB22AEGAEg6HOJDKUnxxqejby61TAWzklKzB6VkLCf2ILe95+F39VDeVGFW2jh/P+ENqcoY0Qjt56LlRCeCuAnAM4G0A/gcQCUVzgQA/KfBHALAErr4wSwEwBl4nBEjb0cwEYAcwBQXlia+48xzB3pwooyDrC4a+IQ8Pt6w0XrAzaUVlyHvPwPJW5yninrEGi2bcZ7Xb+ERmXA0qrbkG+kr0RuoxGQW1EqRAKs5xQiCN4GI8AIjESAMnoONG5KqtWQYhqd7bvRc+AZeO3NYgOmiuUoOvUz0OdVZZxI5NZzsRDCAgAHALwH4D4AswD8EMCDAG6bRAJkMnkEwP0AXgZAc1GADI2zSWMpP/GrAKiS6V8AfBTAdwBcAuClGCXMijJGoLhb4hFwOd5Dy4mNUKk0qK7bCINxRuIX4RmzBoGGvr/jcM/voVWbsLzqDlgMtVlz9lgPKreijHVfSe7Hei7JAPP0jAAjMD0EhqyGjZvh6W8Qk6k0lKF0elZDV/dB9Bz4Pdy9H4g5jUXzRS3BnKK509uwgkfLrediIYRk3fsuAHrKHZCwo/9eD6A86nejYS0GQLfh2wB+OQHmL1JWWIkoRrr9C0AegFiL2bCiVPClzoat9fX8Az2dv4NWW4qC4stht70Gn69LJJuxFlwMc95KrlmYDRchQWf8oPv3aOz/O3RqKypzFqLTsRvekAd6lQFVlnNRV3I11GmUNS0Y9KOx60m02F9PyDnkVpQJEut0p2E9N10EeTwjwAjIhkDYarhZuJWG/G6xrj6vFqNjDckFdLB5G2zHXoLP2QVdbgmsMy+GuXolvAPN6HnvGTjb3x4aT0Qwt2xRUtxRZQMnhoXk1nOxEMItAFoBfCZq//TK+jiAjwH4xzjnWiNZFMmh1ztOH0r/YwdwA4CfRfX5PIDfACiMsiROBB8ryhguF3dJHgLkytDW/CM4B8kjOrrRR0wFk2UFyqtuYFKYPBFk1Mx0nw50/gKtdkqfLR2NrpL0/62aQiyvezgtSCGRwZ2NN8AW6A0fJAHnkFtRKuRysZ5TiCB4G4wAIxA7AsNWw5fh6aeosIjV8CxYZlwA29Hn4WjbCYSCw8pOpYLWWAi/q1v8TptbgqL5V8JcsyprnqPk1nOxEMJOyZ2TLILRjWIA6Xc/GOdaPAlgAYBfAbgVQBkAovjfArBNGkOxieSOer7kNhqZajmAHQBWSDGHk908VpSTIcR/TzoCtr7N6GofzxiuRlnlGlissRq9k75dXkDhCBxrfxxH7JvCBGp0CwGz8y7CzLJrFX4K4FjHr3Bk4N8JPYfcilIhILOeU4ggeBuMACMwNQTGshpONJNKk4OiBVfCWnchVJrsyuQut56LhRD6ANwE4KFRQqOIzqcArB1HmOQKulJyKSUXU0oFRP8uk5LHdABYBWArgMUA9kbNMxvAYQAfiTGOkBXl1D6bPCqBCDQ3roPbFfZvH6up1SYYc+clcEWeKpMROOLeCzeC4xIpNQXUa8izXtnNERigU4x7jhyVEefOfiKuQ8itKOPaXPI6s55LHrY8MyPACMiIQNhquA3d+54StQPHa4aC2aj5MOWczL4mt55LJiGkhDAXAbgUwAuSKOnphVxNKdHM7dMghGSZvCP6epCLFTdGIJUINBy+DgF/Xyq3wGtnEALHtUAglm/oND+zJqTC6jl/iOsUcivKuDaXvM5MCJOHLc/MCDACKUCg4fnrEHCP/9ykMRai/lLKOZl9TW49F8vjBrmMPiqVgoiWyGQuo1Q24goAuQDC0aThtkmKC6TSExGX0Q8DeC2qD7uMZt/dT/sThy2EZNge++WE3lAn3Ea5MQKxILCj+Ta4Qt5xLWsGlQ4Ly74ey1Qp7bO/4xF4Qj62EE5fCkwIp48hz8AIMAIKQqD5tXVw94733KSCsXAuqj9EleiyrymREFJSmRYAV0WJowbAiUmSykSseEQIXVFjNwOg7AJEFiNJZa4H8POoPp8DQD5EnFQm+z4DaXtiu20rOlrpTVYkMDr6KBxDmLaCTdHGkxF7l4qjJOMccivKVOA2xppMCBUiCN4GI8AIJAYBe9NWdOx+LCqhTNS8KjXKlq6BpSY7cy/IredisRBS2QmKIaSyE5QRlNqNAO6cpOwExQpSysXLAFAZCWpWyWX0AakQPf2OYg01AC6Mugb/BJDPZScS84HjWeRBgFInt7c8DIed8iGRlZB+OMuoPOhn3irJyM6ZCpSScQ65FWUqcGNCqBDUeRuMACOQNATEc9POh+Fo3QGI0C/puUmlgqlyBcqXZ292drn1XCyEkIrJU1H6/VIZiZkAfiQlmYkuTH9EcvuMTnv3VwBnArgZAOWOpaQy5CZKlSQjTsORwvQUV0j9qTA9EU4uTJ+0jyBPnCwERD2dgW2w9b0En68bOl0x1yFMFthZMG+i6/elCrJEn0NuRZkq3EatyxZChQiCt8EIMAKJQ2BkHcJu6HKLh+oQqlSUPi07m9x6LhZCSJIgEkeE7WwA/QAel0pOBKLE1CiVjrgm6ndmqSzFlVIs4RtS2Yl9o8T7ccliOEcqZk/ups/EcQVYUcYBFndlBBgBRiCdEZBbUSoEK9ZzChEEb4MRYAQYgWQjILeei5UQJvvc052fFeV0EeTxjAAjwAikCQJyK0qFwMJ6TiGC4G0wAowAI5BsBOTWc0wIky1Rnp8RYAQYAUYgoQjIrSgTuvmpT8aEcOrY8UhGgBFgBNIKAbn1HBPCtLoevFlGgBFgBBgBuRWlQhBnQqgQQfA2GAFGgBFINgJy6zkmhMmWKM/PCDACjAAjkFAE5FaUCd381CdjQjh17HgkI8AIMAJphYDceo4JYVpdD94sI8AIMAKMgNyKUiGIMyFUiCB4G4wAI8AIJBsBufUcE8JkS5TnZwQYAUaAEUgoAnIryoRufuqTMSGcOnY8khFgBBiBtEJAbj3HhDCtrgdvlhFgBBgBRkBuRakQxJkQKkQQvA1GgBFgBJKNgNx6jglhsiXK8zMCjAAjwAgkFAG5FWVCNz/1yZgQTh07HskIMAKMQFohILeeY0KYVteDN8sIMAKMACMgt6JUCOJMCBUiCN4GI8AIMALJRkBuPceEMNkS5fkZAUaAEWAEEoqA3IoyoZuf+mRMCKeOHY9kBBgBRiCtEJBbzzEhTKvrwZtlBBgBRoARkFtRKgRxJoQKEQRvgxFgBBiBZCMgt55jQphsifL8jAAjwAgwAglFQG5FmdDNT30yJoRTx45HMgKMACOQVgjIrecyhhCmlZR5s4wAI8AIMALTRSBT9FesOIRi7cj9GAFGgBFgBDICAdn0nGwLpYFYSNlmAh6ZcI5MOANd+Uw4RyacgWWhrC/gTLlTykI1tt1kAvaZcIZM+U7KlHPwnYrt+0OOXiwLOVAeY41MIECJgo4vYaKQnP48LIvpY5ioGVgWiUJy+vOwLKaPYbbPkAl3KBPOkClEKlPOwXdKOd+MLIsUyYIJ4TDwfAlTdAnHWJZlwbJINAKZcKcy4QyZ8gCZ6Psp13yZcIcy4QyZ9DnIBHlkwhky5U6xLOTSBqPWYUI4DMh6APST7i0TzpEJZ6B7lAnnyIQzsCyU9a2WKXdKWajGtptMwD4TzpAp30mZcg6+U7F9f8jRi2UhB8pjrMGEMEXA87KMACPACDACjAAjwAgwAowAI8AIpBoBJoSplgCvzwgwAowAI8AIMAKMACPACDACjECKEGBCmCLgeVlGgBFgBBgBRoARYAQYAUaAEWAEUo0AE8JUS4DXZwQYAUaAEWAEGAFGgBFgBBgBRiBFCGQzIfxPAN8GMA+ACcBxAE8DuB+AN0XySMSyVQAOSWeyABhMxKQyzHENgN+Msc51AH4mw/qJWkIL4EYA1wKoBdAF4FkA30rUAjLM8yqAD42zzkoAb8qwh0Qs8RkA3wUwF4ANwGYANwNoTcTkMs3xcQB3St9TtO+fAPiRTGtPdZnZAG4CcDaABQBeB/DhUZOR7rkFAH2+iwHsBHADgL1TXZTHjYlAJuo51nGpv+ys51Ivg8gOWM+lRhYZp+eymRB+FUANgN0A+gGskLJC/grA11NzvxKy6u8BXACgDEA6EkLauysKiWMAOhOCjDyT/FbCfwOAg9IdOxXAWnmWT8gqtN+8UTMRKVkMoAKAPyGrJHeSjwH4G4BHAfxF2vdGAH0AlgIIJnf5hMy+SiJTvwbwvwDOBLBOIlsPJWSF5ExyOYBHAGwHsBBAxxiEkMhg5Cz0OaGXc/QdTP3bk7OtrJw1E/Uc67jUX2XWc6mXAe2A9Vzq5JBxei6bCeFY1+huAP8DoAAA1UJJt3YegL8CuAfAD9KUEKYTiR19Py4B8A8AZwB4L90uzwT71UsP6X+ULDrpcLRnAMyRyF9kvxHlSYT3/TQ4xIsAcgGcG7XXHwL4AoByBXsyqKMI9/9JFsBoC6FRIol0FnrRQI28NBoB/BzAbWkgm3TeYjrrOdZxqb95rOdSL4PIDljPpU4WGafnmBCOvEz0lpqsCPRwkm6EUAPgbcntkiye5H6ZTuQq4jKaTnse/VVEVhwrgI+k7jsqKStHiBS5kW5JygqJn/RP0osdsjhHGj1Mvia5MaYDYSfLGlk4I6SJzkF36wXJ4kZnUXobixCSTMh9d75kRY+cgSyh9DKFLLjckodAuuo51nHJuxPxzMx6Lh60ktuX9Vxy8Y119ozQc0wIAVIyBgBLAJAbBH3AvhPrLVBQP7JsXg/gNAD/lcaEkNxDiwAclWKlyGKQLo3iUP8OgD5XnwdAcRb08E4uyOkUtzYab3LRIisVxUSmy4uSyyRrOcVyktWcLGqPA/ABWJ0mF4riHu8F8P2o/Z4P4GXJUpsOsbVjKco1AB6WvncDUWejuEMqSkwv5LglFoFM0HOs4xJ7J6Y6G+u5qSKX+HGs5xKP6VRmzAg9x4QQcEsPJnQJnpLcsdIhvij60hKBOgzgvwH8C0A6WtvI8rEcwA6ESToFShOporfZD07lE5qCMR7Jje8dyW2XrJ2UpIhios5KIzIVDR25LBJJJ2Kebi9K6MUIxQTTCx9q2wCQAiULejo0im8mN8pPRW32exJBvFW6Y0o/x1iKkvZO5C9/1Oa/BOCXkrzSObGXEmWS7nqOdZxybhXrOeXIgnbCei718sgIPceEMGwZpIdeSmhASQ7IGkJvsNOpkaWArDcflTadjoRwLLwpZu1CACVpkgSEHmLpZwaAHulAETdFOge5yaVb+zQAilMgsr4rjTZPljSy1j4G4HkpyRJZn4ickyyiLVNKPdaXpQy7XwNACoe+o+ilVamUoTPacqjUM2SEolQquHHsK931HOu4OISd5K6s55IMcBzTs56LA6wkds0IPceEcOQNIYvUkwAonSy5LKZDo7TuewAQ8aBsfdQ+K8UeVQPoHZW1Mx3OFNnjFVJ2xVkAKNuo0hvFfNE+Kd1+pFHgMWVNpVIUVDIg3Rpl6KTMj5SgJZ0axdNS4hh6exppVGKGPiNkcftzGhyGLOU/BkCEkP6/EwBZCOkeUWKZJ9LgDBnhSpMGOMezxXTTc6zj4pFu8vuynks+xrGuwHouVqSS2y8j9BwTwpGXhB589wG4CMCm5N6fhM1OdcrooX28Ri5z5IqVjo1qaFENv5kAGtLgAFS/jzIokntopBEhJHctqkNICULSqVGCHFL+5PZK1vN0akSe7pCy7Ubve7zfK/lslPWYXu7QZ+AUqWbf6IQsSt3/RMH2dBaqmRpp9F21iJPKJF2U6abnWMcl/UrEtQDrubjgSmpn1nNJhTfmyTNCzzEhHClvqtlErinpZCGkos6k4KMbpYUmSwK5kJLFKvqhK+YbroCO5KpICUCopmI6xHWSFZDqD5LLaLeEH6Xbf0VKyrJVAZjGs4WI63G6lGmIPhtZB6nI+VVRvyQSRdlFr5ReNMSDhVL6UiZOsnRSjcJ0aBOVnaDSOJTVmRq57VO85C+47ETSxZpueo51XNKvRFwLsJ6LC66kdmY9l1R4Y548I/RcNhNCyv5IVsADUjwRPWBR0ox/SglNYr4JCuyYjjGElN2VEsq8K7nHUewaJcm5IY1cLamY+34ALVFJZe6T3BTJ6pxujT4jlJ2TrDbp1r4hJSOihESRGEKyclJNRXqB4kiDA5Gl+RyJ2NLdInJLyZfod/Q5UWojcheJZ6bvVNo7WWupUdIreqtNhelvl5LLRArTnymVBCGrNLfEIJCpeo51XGLux1RmYT03FdSSM4b1XHJwjWXWjNNz2UwI7wLwCQB1APySJY1q95GFkFLTp3NLR2V5jxTbVSOVbSBLzkMAnk4zQZB1mVLqU80+Cr7/m+Qu2pdm56C38m3SQ3s6JC8ZDS99t1Hs3XUAKAaVMouShZaISDrEo9J5qB4ffR+RZZMs5K8DuFlya1fydaLv1PFcvOslSyDJZ60kH8ogSQmL6OUPxUNzSxwCmarnWMcl7o5MZSbWc1NBLfFjWM8lHtNYZ8w4PZfNhDBWoXM/RoARYAQYAUaAEWAEGAFGgBFgBDISASaEGSlWPhQjwAgwAowAI8AIMAKMACPACDACkyPAhHByjLgHI8AIMAKMACPACDACjAAjwAgwAhmJABPCjBQrH4oRYAQYAUaAEWAEGAFGgBFgBBiByRFgQjg5RtyDEWAEGAFGgBFgBBgBRoARYAQYgYxEgAlhRoqVD8UIMAKMACPACDACjAAjwAgwAozA5AgwIZwcI+7BCDACjAAjwAgwAowAI8AIMAKMQEYiwIQwI8XKh2IEGAFGgBFgBBgBRoARYAQYAUZgcgSYEE6OEfdgBCZDYD2ArwOgYu5zAXwWwENSMfTJxiby71cCyAXwxKhJXwXQDeA/E7kYz8UIMAKMACOQNQiwnssaUfNBsxEBJoTZKHU+c6IRiFaU/wHgHwDqATQmeqFJ5vs/iZR+eFS/UwH4AByWeT+8HCPACDACjEBmIMB6LjPkyKdgBMZEgAkhXwxGYPoIJFNR5gBwxbjF8QhhjMO5GyPACDACjAAjMCYCrOf4YjACGYwAE8IMFi4fTTYEIoqSXDJfGbXqcQB10u9qAdwP4GIARgCvA7gBwCHp79SvAcB/A/gIgI8B2AXgQgCfB/AVAGTto8/tXgA3SX+n4eQmevWotTcAoL2N5TJ6AYB7AZwBwAbgTwC+C2BQmoOsjHSW8wH8D4BLAXQCeADAY7IhywsxAowAI8AIKAEB1nNKkALvgRFIEgJMCJMELE+bVQhEFOVMAF+WSNMnAbQB8ADYA6BQInE9EhFzArgZwAwp7pCsgBFC2A7gzwD+CiAA4GUA6wDQ748C0AO4CsAVABYAOAZgFoBfAsgHsEZC//+3d/auVQRRFP+hWKYQRCHRTkELK4sEQSxsRBCxtvJvsBCDlWJQRBREbBSx8Lu1UbHzM6CNH1hYJoIgFtpYyoE7MAzuY3F5m2XnLDwCL/NmZ34zcDncmXNXAH1KQajfaEzPQtxtA84Db4CDhSD8CtwG3sY7jwPzwHJVK+zJmoAJmEDdBBzn6l5/z37kBCwIR77Anl4vBNocpTkbQm0H8DNGtTHuGS4C1zJBKCF4dMLI1wH6fATuAmeibdOR0VIQ3gf2ADtDcOrnMqR5AOwFXgMpQ6hxS4zq2QB8A26GmO0Frl9iAiZgAiaw5gQc59Z8CTwAE5geAQvC6bF1z/UQaBMoJbJ0fFTHQfPnaXyvzFvKECrLeKNotwtYCsG2OfvfnazPtoJQGUW11RHR9KwH/gASpxczQbgPeJG1exUZyXIe9ay2Z2oCJmAC9RFwnKtvzT3jighYEFa02J7q1Ai0CZRy+NzeMILncU8wCULdHZRTaXpmgM/Ad+ByCEiJN4lGHelM5STaCkIdTz0NXCrGoyOpt4BTZXrumQAAAaRJREFUmSDcHZnI1NQlLKa2jdyxCZiACQyWgOPcYJfGAzOB7gQsCLszdA8m0CZQ6g6eBJeOYJbP7zCWSYLwMPA4ayQTmieAsoRfsu9lQPPuPwShMoSPgJNZX00ZQgtC728TMAETMAHHOe8BExgxAQvCES+up9YbgTxQNok3HffUPT0JrKYyEk2C8EgYzMi0RiJQj+76vQx30JQh1H1CtVkoZl5m9e7FHUIJTJnW6JFBzcN/3CG0IOxtG/lFJmACJjBYAo5zg10aD8wEuhOwIOzO0D2YQB4o5RqqgvRXAJm3yE30QxSMfw+sAlfj7xZgf9zRk0hrEoRqp6OhyjKqbMXWKCchYxk5gyZBKPMXZf2OhbuoDGD0aXIZVdbxevR3IfovXUYtCL2/TcAETMAEHOe8B0xgxAQsCEe8uJ5abwTyQKmXnoj6gnMhzFIdwlngHHAoykOoLIUMW1QP8NMEQag+JdRUA1DlJXQfUSUrZArzIxOEm6L0hESmHEwn1SE8ECY1qkP4KzOZKesQWhD2to38IhMwARMYLAHHucEujQdmAt0J/AXFvI4kDbmcaQAAAABJRU5ErkJggg==\" 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+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM iris_multi_model_info ORDER BY validation_loss ASC LIMIT 7;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM iris_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "ax_metric.set_ylabel('Metric')\n",
+ "ax_metric.set_title('Validation metric curve')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Validation loss curve')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM iris_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " \n",
+ " ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
+ "\n",
+ "plt.legend();\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pred_prob\"></a>\n",
+ "# 2. Predict probabilities\n",
+ "\n",
+ "Predict with probabilities for each class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "90 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>class_name</th>\n",
+ " <th>class_value</th>\n",
+ " <th>prob</th>\n",
+ " <th>rank</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.9999932</td>\n",
+ " <td>1</td>\n",
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+ " <td>3</td>\n",
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+ " <td>Iris-versicolor</td>\n",
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+ " <td>3.623055e-05</td>\n",
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+ " <td>2</td>\n",
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+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.8053533</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>127</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.19464317</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>127</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>3.5179064e-06</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.7297866</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.2702134</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>2.8784607e-08</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5341273</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.4658725</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>2.3799986e-07</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>148</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.6266347</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>148</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.3733647</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>148</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>5.7692125e-07</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5517554</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.4482443</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>3.1108453e-07</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
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+ " (20, u'class_text', u'Iris-setosa', 0.9999982, 1),\n",
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+ " (148, u'class_text', u'Iris-versicolor', 0.6266347, 1),\n",
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+ " (148, u'class_text', u'Iris-setosa', 5.7692125e-07, 3),\n",
+ " (149, u'class_text', u'Iris-virginica', 0.5517554, 1),\n",
+ " (149, u'class_text', u'Iris-versicolor', 0.4482443, 2),\n",
+ " (149, u'class_text', u'Iris-setosa', 3.1108453e-07, 3)]"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('iris_multi_model', -- model\n",
+ " 'iris_test', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'attributes', -- independent var\n",
+ " 'iris_predict', -- output table\n",
+ " 'prob', -- prediction type\n",
+ " FALSE, -- use gpus\n",
+ " 3 -- mst_key to use\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_predict ORDER BY id, rank;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"warm_start\"></a>\n",
+ "# 3. Warm start\n",
+ "\n",
+ "Next, use the warm_start parameter to continue learning, using the coefficients from the run above. Note that we don't drop the model table or model summary table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit_multiple_model</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', -- source_table\n",
+ " 'iris_multi_model', -- model_output_table\n",
+ " 'mst_table', -- model_selection_table\n",
+ " 3, -- num_iterations\n",
+ " FALSE, -- use gpus\n",
+ " 'iris_test_packed', -- validation dataset\n",
+ " 1, -- metrics compute frequency\n",
+ " TRUE, -- warm start\n",
+ " 'Sophie L.', -- name\n",
+ " 'Simple MLP for iris dataset' -- description\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>model</th>\n",
+ " <th>model_info</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_selection_table</th>\n",
+ " <th>object_table</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>warm_start</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_iters</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>iris_multi_model</td>\n",
+ " <td>iris_multi_model_info</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>mst_table</td>\n",
+ " <td>None</td>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>True</td>\n",
+ " <td>Sophie L.</td>\n",
+ " <td>Simple MLP for iris dataset</td>\n",
+ " <td>2021-03-06 00:55:34.010762</td>\n",
+ " <td>2021-03-06 00:56:20.576330</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[1]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[1, 2, 3]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_test_packed', u'iris_multi_model', u'iris_multi_model_info', [u'class_text'], [u'attributes'], u'model_arch_library', u'mst_table', None, 3, 1, True, u'Sophie L.', u'Simple MLP for iris dataset', datetime.datetime(2021, 3, 6, 0, 55, 34, 10762), datetime.datetime(2021, 3, 6, 0, 56, 20, 576330), u'1.18.0-dev', [1], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], [u'character varying'], 1.0, [1, 2, 3])]"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View performance of each model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "12 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>mst_key</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[12.8246030807495, 28.3149819374084, 43.8511519432068]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.949999988079</td>\n",
+ " <td>0.125932246447</td>\n",
+ " <td>[0.983333349227905, 0.908333361148834, 0.949999988079071]</td>\n",
+ " <td>[0.0759517326951027, 0.280529856681824, 0.125932246446609]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.262804627419</td>\n",
+ " <td>[0.966666638851166, 0.933333337306976, 0.966666638851166]</td>\n",
+ " <td>[0.115140154957771, 0.282798647880554, 0.262804627418518]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[12.3267669677734, 27.5790538787842, 43.3719210624695]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.958333313465</td>\n",
+ " <td>0.646220803261</td>\n",
+ " <td>[0.916666686534882, 0.774999976158142, 0.958333313465118]</td>\n",
+ " <td>[0.760809063911438, 0.70676600933075, 0.646220803260803]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.676706075668</td>\n",
+ " <td>[0.899999976158142, 0.699999988079071, 0.966666638851166]</td>\n",
+ " <td>[0.789911270141602, 0.741125166416168, 0.676706075668335]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[13.8655989170074, 29.3921880722046, 45.186311006546]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.161019146442</td>\n",
+ " <td>[0.608333349227905, 0.975000023841858, 0.966666638851166]</td>\n",
+ " <td>[0.656926870346069, 0.154457986354828, 0.161019146442413]</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.184286847711</td>\n",
+ " <td>[0.666666686534882, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.60343611240387, 0.166501134634018, 0.184286847710609]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[12.5584180355072, 27.7957689762115, 43.5938129425049]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.925000011921</td>\n",
+ " <td>0.125614732504</td>\n",
+ " <td>[0.850000023841858, 0.908333361148834, 0.925000011920929]</td>\n",
+ " <td>[0.311796188354492, 0.228279903531075, 0.125614732503891]</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.205575048923</td>\n",
+ " <td>[0.699999988079071, 0.899999976158142, 0.933333337306976]</td>\n",
+ " <td>[0.434732705354691, 0.278642177581787, 0.205575048923492]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[14.3016650676727, 29.8289239406586, 45.6773319244385]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.916666686535</td>\n",
+ " <td>0.680241525173</td>\n",
+ " <td>[0.899999976158142, 0.899999976158142, 0.916666686534882]</td>\n",
+ " <td>[0.75947380065918, 0.717410624027252, 0.680241525173187]</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.685820519924</td>\n",
+ " <td>[0.933333337306976, 0.933333337306976, 0.933333337306976]</td>\n",
+ " <td>[0.764581918716431, 0.718774557113647, 0.685820519924164]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[13.6457929611206, 29.1624140739441, 44.9534199237823]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.891666650772</td>\n",
+ " <td>0.590237081051</td>\n",
+ " <td>[0.824999988079071, 0.783333361148834, 0.891666650772095]</td>\n",
+ " <td>[0.666068911552429, 0.633061707019806, 0.590237081050873]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.576045572758</td>\n",
+ " <td>[0.866666674613953, 0.866666674613953, 0.899999976158142]</td>\n",
+ " <td>[0.645683944225311, 0.608498632907867, 0.576045572757721]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[14.0837008953094, 29.6097829341888, 45.4142129421234]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.916666686535</td>\n",
+ " <td>0.174454689026</td>\n",
+ " <td>[0.949999988079071, 0.958333313465118, 0.916666686534882]</td>\n",
+ " <td>[0.166735425591469, 0.141851797699928, 0.174454689025879]</td>\n",
+ " <td>0.899999976158</td>\n",
+ " <td>0.219132959843</td>\n",
+ " <td>[0.966666638851166, 0.933333337306976, 0.899999976158142]</td>\n",
+ " <td>[0.186790466308594, 0.176578417420387, 0.219132959842682]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[13.1594960689545, 28.5860660076141, 44.1881170272827]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.866666674614</td>\n",
+ " <td>0.285291582346</td>\n",
+ " <td>[0.774999976158142, 0.949999988079071, 0.866666674613953]</td>\n",
+ " <td>[0.441815197467804, 0.140827313065529, 0.285291582345963]</td>\n",
+ " <td>0.866666674614</td>\n",
+ " <td>0.246576815844</td>\n",
+ " <td>[0.766666650772095, 0.966666638851166, 0.866666674613953]</td>\n",
+ " <td>[0.4128278195858, 0.146319955587387, 0.246576815843582]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[14.5546190738678, 30.0798380374908, 45.94082903862]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.850000023842</td>\n",
+ " <td>0.675731360912</td>\n",
+ " <td>[0.791666686534882, 0.841666638851166, 0.850000023841858]</td>\n",
+ " <td>[0.746130049228668, 0.706377267837524, 0.675731360912323]</td>\n",
+ " <td>0.866666674614</td>\n",
+ " <td>0.650432705879</td>\n",
+ " <td>[0.866666674613953, 0.866666674613953, 0.866666674613953]</td>\n",
+ " <td>[0.712817847728729, 0.677974581718445, 0.650432705879211]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[15.3575170040131, 30.5435180664062, 46.5635209083557]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.658333361149</td>\n",
+ " <td>0.45723798871</td>\n",
+ " <td>[0.658333361148834, 0.683333337306976, 0.658333361148834]</td>\n",
+ " <td>[0.457635939121246, 0.455960959196091, 0.457237988710403]</td>\n",
+ " <td>0.699999988079</td>\n",
+ " <td>0.48275628686</td>\n",
+ " <td>[0.699999988079071, 0.600000023841858, 0.699999988079071]</td>\n",
+ " <td>[0.48207613825798, 0.491984754800797, 0.482756286859512]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=4</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.75390625</td>\n",
+ " <td>[14.8466219902039, 30.2953569889069, 46.1656670570374]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.683333337307</td>\n",
+ " <td>0.456283688545</td>\n",
+ " <td>[0.925000011920929, 0.899999976158142, 0.683333337306976]</td>\n",
+ " <td>[0.224153310060501, 0.295417010784149, 0.456283688545227]</td>\n",
+ " <td>0.600000023842</td>\n",
+ " <td>0.494575560093</td>\n",
+ " <td>[0.966666638851166, 0.899999976158142, 0.600000023841858]</td>\n",
+ " <td>[0.227903217077255, 0.345975488424301, 0.494575560092926]</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>2</td>\n",
+ " <td>optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'</td>\n",
+ " <td>epochs=1,batch_size=8</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>1.18359375</td>\n",
+ " <td>[13.4095330238342, 28.938658952713, 44.7153990268707]</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.691666662693</td>\n",
+ " <td>0.528191685677</td>\n",
+ " <td>[0.708333313465118, 0.966666638851166, 0.691666662693024]</td>\n",
+ " <td>[0.395545929670334, 0.100506067276001, 0.528191685676575]</td>\n",
+ " <td>0.566666662693</td>\n",
+ " <td>0.720313131809</td>\n",
+ " <td>[0.633333325386047, 0.966666638851166, 0.566666662693024]</td>\n",
+ " <td>[0.508394777774811, 0.130626574158669, 0.720313131809235]</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(9, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [12.8246030807495, 28.3149819374084, 43.8511519432068], [u'accuracy'], u'categorical_crossentropy', 0.949999988079071, 0.125932246446609, [0.983333349227905, 0.908333361148834, 0.949999988079071], [0.0759517326951027, 0.280529856681824, 0.125932246446609], 0.966666638851166, 0.262804627418518, [0.966666638851166, 0.933333337306976, 0.966666638851166], [0.115140154957771, 0.282798647880554, 0.262804627418518]),\n",
+ " (7, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [12.3267669677734, 27.5790538787842, 43.3719210624695], [u'accuracy'], u'categorical_crossentropy', 0.958333313465118, 0.646220803260803, [0.916666686534882, 0.774999976158142, 0.958333313465118], [0.760809063911438, 0.70676600933075, 0.646220803260803], 0.966666638851166, 0.676706075668335, [0.899999976158142, 0.699999988079071, 0.966666638851166], [0.789911270141602, 0.741125166416168, 0.676706075668335]),\n",
+ " (6, 1, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [13.8655989170074, 29.3921880722046, 45.186311006546], [u'accuracy'], u'categorical_crossentropy', 0.966666638851166, 0.161019146442413, [0.608333349227905, 0.975000023841858, 0.966666638851166], [0.656926870346069, 0.154457986354828, 0.161019146442413], 0.966666638851166, 0.184286847710609, [0.666666686534882, 0.966666638851166, 0.966666638851166], [0.60343611240387, 0.166501134634018, 0.184286847710609]),\n",
+ " (3, 1, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [12.5584180355072, 27.7957689762115, 43.5938129425049], [u'accuracy'], u'categorical_crossentropy', 0.925000011920929, 0.125614732503891, [0.850000023841858, 0.908333361148834, 0.925000011920929], [0.311796188354492, 0.228279903531075, 0.125614732503891], 0.933333337306976, 0.205575048923492, [0.699999988079071, 0.899999976158142, 0.933333337306976], [0.434732705354691, 0.278642177581787, 0.205575048923492]),\n",
+ " (1, 1, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [14.3016650676727, 29.8289239406586, 45.6773319244385], [u'accuracy'], u'categorical_crossentropy', 0.916666686534882, 0.680241525173187, [0.899999976158142, 0.899999976158142, 0.916666686534882], [0.75947380065918, 0.717410624027252, 0.680241525173187], 0.933333337306976, 0.685820519924164, [0.933333337306976, 0.933333337306976, 0.933333337306976], [0.764581918716431, 0.718774557113647, 0.685820519924164]),\n",
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+ " (4, 1, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 0.75390625, [14.0837008953094, 29.6097829341888, 45.4142129421234], [u'accuracy'], u'categorical_crossentropy', 0.916666686534882, 0.174454689025879, [0.949999988079071, 0.958333313465118, 0.916666686534882], [0.166735425591469, 0.141851797699928, 0.174454689025879], 0.899999976158142, 0.219132959842682, [0.966666638851166, 0.933333337306976, 0.899999976158142], [0.186790466308594, 0.176578417420387, 0.219132959842682]),\n",
+ " (10, 2, u\"optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [13.1594960689545, 28.5860660076141, 44.1881170272827], [u'accuracy'], u'categorical_crossentropy', 0.866666674613953, 0.285291582345963, [0.774999976158142, 0.949999988079071, 0.866666674613953], [0.441815197467804, 0.140827313065529, 0.285291582345963], 0.866666674613953, 0.246576815843582, [0.766666650772095, 0.966666638851166, 0.866666674613953], [0.4128278195858, 0.146319955587387, 0.246576815843582]),\n",
+ " (8, 2, u\"optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [14.5546190738678, 30.0798380374908, 45.94082903862], [u'accuracy'], u'categorical_crossentropy', 0.850000023841858, 0.675731360912323, [0.791666686534882, 0.841666638851166, 0.850000023841858], [0.746130049228668, 0.706377267837524, 0.675731360912323], 0.866666674613953, 0.650432705879211, [0.866666674613953, 0.866666674613953, 0.866666674613953], [0.712817847728729, 0.677974581718445, 0.650432705879211]),\n",
+ " (11, 2, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 1.18359375, [15.3575170040131, 30.5435180664062, 46.5635209083557], [u'accuracy'], u'categorical_crossentropy', 0.658333361148834, 0.457237988710403, [0.658333361148834, 0.683333337306976, 0.658333361148834], [0.457635939121246, 0.455960959196091, 0.457237988710403], 0.699999988079071, 0.482756286859512, [0.699999988079071, 0.600000023841858, 0.699999988079071], [0.48207613825798, 0.491984754800797, 0.482756286859512]),\n",
+ " (5, 1, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=4', u'madlib_keras', 0.75390625, [14.8466219902039, 30.2953569889069, 46.1656670570374], [u'accuracy'], u'categorical_crossentropy', 0.683333337306976, 0.456283688545227, [0.925000011920929, 0.899999976158142, 0.683333337306976], [0.224153310060501, 0.295417010784149, 0.456283688545227], 0.600000023841858, 0.494575560092926, [0.966666638851166, 0.899999976158142, 0.600000023841858], [0.227903217077255, 0.345975488424301, 0.494575560092926]),\n",
+ " (12, 2, u\"optimizer='Adam(lr=0.1)',metrics=['accuracy'],loss='categorical_crossentropy'\", u'epochs=1,batch_size=8', u'madlib_keras', 1.18359375, [13.4095330238342, 28.938658952713, 44.7153990268707], [u'accuracy'], u'categorical_crossentropy', 0.691666662693024, 0.528191685676575, [0.708333313465118, 0.966666638851166, 0.691666662693024], [0.395545929670334, 0.100506067276001, 0.528191685676575], 0.566666662693024, 0.720313131809235, [0.633333325386047, 0.966666638851166, 0.566666662693024], [0.508394777774811, 0.130626574158669, 0.720313131809235])]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_multi_model_info ORDER BY validation_metrics_final DESC;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot validation results:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "7 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
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+ " this.rubberband_canvas = undefined;\n",
+ " this.rubberband_context = undefined;\n",
+ " this.format_dropdown = undefined;\n",
+ "\n",
+ " this.image_mode = 'full';\n",
+ "\n",
+ " this.root = $('<div/>');\n",
+ " this._root_extra_style(this.root)\n",
+ " this.root.attr('style', 'display: inline-block');\n",
+ "\n",
+ " $(parent_element).append(this.root);\n",
+ "\n",
+ " this._init_header(this);\n",
+ " this._init_canvas(this);\n",
+ " this._init_toolbar(this);\n",
+ "\n",
+ " var fig = this;\n",
+ "\n",
+ " this.waiting = false;\n",
+ "\n",
+ " this.ws.onopen = function () {\n",
+ " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+ " fig.send_message(\"send_image_mode\", {});\n",
+ " if (mpl.ratio != 1) {\n",
+ " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+ " }\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " }\n",
+ "\n",
+ " this.imageObj.onload = function() {\n",
+ " if (fig.image_mode == 'full') {\n",
+ " // Full images could contain transparency (where diff images\n",
+ " // almost always do), so we need to clear the canvas so that\n",
+ " // there is no ghosting.\n",
+ " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+ " }\n",
+ " fig.context.drawImage(fig.imageObj, 0, 0);\n",
+ " };\n",
+ "\n",
+ " this.imageObj.onunload = function() {\n",
+ " fig.ws.close();\n",
+ " }\n",
+ "\n",
+ " this.ws.onmessage = this._make_on_message_function(this);\n",
+ "\n",
+ " this.ondownload = ondownload;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_header = function() {\n",
+ " var titlebar = $(\n",
+ " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+ " 'ui-helper-clearfix\"/>');\n",
+ " var titletext = $(\n",
+ " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+ " 'text-align: center; padding: 3px;\"/>');\n",
+ " titlebar.append(titletext)\n",
+ " this.root.append(titlebar);\n",
+ " this.header = titletext[0];\n",
+ "}\n",
+ "\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_canvas = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var canvas_div = $('<div/>');\n",
+ "\n",
+ " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+ "\n",
+ " function canvas_keyboard_event(event) {\n",
+ " return fig.key_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+ " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+ " this.canvas_div = canvas_div\n",
+ " this._canvas_extra_style(canvas_div)\n",
+ " this.root.append(canvas_div);\n",
+ "\n",
+ " var canvas = $('<canvas/>');\n",
+ " canvas.addClass('mpl-canvas');\n",
+ " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+ "\n",
+ " this.canvas = canvas[0];\n",
+ " this.context = canvas[0].getContext(\"2d\");\n",
+ "\n",
+ " var backingStore = this.context.backingStorePixelRatio ||\n",
+ "\tthis.context.webkitBackingStorePixelRatio ||\n",
+ "\tthis.context.mozBackingStorePixelRatio ||\n",
+ "\tthis.context.msBackingStorePixelRatio ||\n",
+ "\tthis.context.oBackingStorePixelRatio ||\n",
+ "\tthis.context.backingStorePixelRatio || 1;\n",
+ "\n",
+ " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+ "\n",
+ " var rubberband = $('<canvas/>');\n",
+ " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+ "\n",
+ " var pass_mouse_events = true;\n",
+ "\n",
+ " canvas_div.resizable({\n",
+ " start: function(event, ui) {\n",
+ " pass_mouse_events = false;\n",
+ " },\n",
+ " resize: function(event, ui) {\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " stop: function(event, ui) {\n",
+ " pass_mouse_events = true;\n",
+ " fig.request_resize(ui.size.width, ui.size.height);\n",
+ " },\n",
+ " });\n",
+ "\n",
+ " function mouse_event_fn(event) {\n",
+ " if (pass_mouse_events)\n",
+ " return fig.mouse_event(event, event['data']);\n",
+ " }\n",
+ "\n",
+ " rubberband.mousedown('button_press', mouse_event_fn);\n",
+ " rubberband.mouseup('button_release', mouse_event_fn);\n",
+ " // Throttle sequential mouse events to 1 every 20ms.\n",
+ " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+ "\n",
+ " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+ " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+ "\n",
+ " canvas_div.on(\"wheel\", function (event) {\n",
+ " event = event.originalEvent;\n",
+ " event['data'] = 'scroll'\n",
+ " if (event.deltaY < 0) {\n",
+ " event.step = 1;\n",
+ " } else {\n",
+ " event.step = -1;\n",
+ " }\n",
+ " mouse_event_fn(event);\n",
+ " });\n",
+ "\n",
+ " canvas_div.append(canvas);\n",
+ " canvas_div.append(rubberband);\n",
+ "\n",
+ " this.rubberband = rubberband;\n",
+ " this.rubberband_canvas = rubberband[0];\n",
+ " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+ " this.rubberband_context.strokeStyle = \"#000000\";\n",
+ "\n",
+ " this._resize_canvas = function(width, height) {\n",
+ " // Keep the size of the canvas, canvas container, and rubber band\n",
+ " // canvas in synch.\n",
+ " canvas_div.css('width', width)\n",
+ " canvas_div.css('height', height)\n",
+ "\n",
+ " canvas.attr('width', width * mpl.ratio);\n",
+ " canvas.attr('height', height * mpl.ratio);\n",
+ " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+ "\n",
+ " rubberband.attr('width', width);\n",
+ " rubberband.attr('height', height);\n",
+ " }\n",
+ "\n",
+ " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+ " // upon first draw.\n",
+ " this._resize_canvas(600, 600);\n",
+ "\n",
+ " // Disable right mouse context menu.\n",
+ " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+ " return false;\n",
+ " });\n",
+ "\n",
+ " function set_focus () {\n",
+ " canvas.focus();\n",
+ " canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " window.setTimeout(set_focus, 100);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items) {\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) {\n",
+ " // put a spacer in here.\n",
+ " continue;\n",
+ " }\n",
+ " var button = $('<button/>');\n",
+ " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+ " 'ui-button-icon-only');\n",
+ " button.attr('role', 'button');\n",
+ " button.attr('aria-disabled', 'false');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ "\n",
+ " var icon_img = $('<span/>');\n",
+ " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+ " icon_img.addClass(image);\n",
+ " icon_img.addClass('ui-corner-all');\n",
+ "\n",
+ " var tooltip_span = $('<span/>');\n",
+ " tooltip_span.addClass('ui-button-text');\n",
+ " tooltip_span.html(tooltip);\n",
+ "\n",
+ " button.append(icon_img);\n",
+ " button.append(tooltip_span);\n",
+ "\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " var fmt_picker_span = $('<span/>');\n",
+ "\n",
+ " var fmt_picker = $('<select/>');\n",
+ " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+ " fmt_picker_span.append(fmt_picker);\n",
+ " nav_element.append(fmt_picker_span);\n",
+ " this.format_dropdown = fmt_picker[0];\n",
+ "\n",
+ " for (var ind in mpl.extensions) {\n",
+ " var fmt = mpl.extensions[ind];\n",
+ " var option = $(\n",
+ " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+ " fmt_picker.append(option)\n",
+ " }\n",
+ "\n",
+ " // Add hover states to the ui-buttons\n",
+ " $( \".ui-button\" ).hover(\n",
+ " function() { $(this).addClass(\"ui-state-hover\");},\n",
+ " function() { $(this).removeClass(\"ui-state-hover\");}\n",
+ " );\n",
+ "\n",
+ " var status_bar = $('<span class=\"mpl-message\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+ " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+ " // which will in turn request a refresh of the image.\n",
+ " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_message = function(type, properties) {\n",
+ " properties['type'] = type;\n",
+ " properties['figure_id'] = this.id;\n",
+ " this.ws.send(JSON.stringify(properties));\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.send_draw_message = function() {\n",
+ " if (!this.waiting) {\n",
+ " this.waiting = true;\n",
+ " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " var format_dropdown = fig.format_dropdown;\n",
+ " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+ " fig.ondownload(fig, format);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+ " var size = msg['size'];\n",
+ " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+ " fig._resize_canvas(size[0], size[1]);\n",
+ " fig.send_message(\"refresh\", {});\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+ " var x0 = msg['x0'] / mpl.ratio;\n",
+ " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+ " var x1 = msg['x1'] / mpl.ratio;\n",
+ " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+ " x0 = Math.floor(x0) + 0.5;\n",
+ " y0 = Math.floor(y0) + 0.5;\n",
+ " x1 = Math.floor(x1) + 0.5;\n",
+ " y1 = Math.floor(y1) + 0.5;\n",
+ " var min_x = Math.min(x0, x1);\n",
+ " var min_y = Math.min(y0, y1);\n",
+ " var width = Math.abs(x1 - x0);\n",
+ " var height = Math.abs(y1 - y0);\n",
+ "\n",
+ " fig.rubberband_context.clearRect(\n",
+ " 0, 0, fig.canvas.width, fig.canvas.height);\n",
+ "\n",
+ " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+ " // Updates the figure title.\n",
+ " fig.header.textContent = msg['label'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+ " var cursor = msg['cursor'];\n",
+ " switch(cursor)\n",
+ " {\n",
+ " case 0:\n",
+ " cursor = 'pointer';\n",
+ " break;\n",
+ " case 1:\n",
+ " cursor = 'default';\n",
+ " break;\n",
+ " case 2:\n",
+ " cursor = 'crosshair';\n",
+ " break;\n",
+ " case 3:\n",
+ " cursor = 'move';\n",
+ " break;\n",
+ " }\n",
+ " fig.rubberband_canvas.style.cursor = cursor;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+ " fig.message.textContent = msg['message'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+ " // Request the server to send over a new figure.\n",
+ " fig.send_draw_message();\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+ " fig.image_mode = msg['mode'];\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Called whenever the canvas gets updated.\n",
+ " this.send_message(\"ack\", {});\n",
+ "}\n",
+ "\n",
+ "// A function to construct a web socket function for onmessage handling.\n",
+ "// Called in the figure constructor.\n",
+ "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+ " return function socket_on_message(evt) {\n",
+ " if (evt.data instanceof Blob) {\n",
+ " /* FIXME: We get \"Resource interpreted as Image but\n",
+ " * transferred with MIME type text/plain:\" errors on\n",
+ " * Chrome. But how to set the MIME type? It doesn't seem\n",
+ " * to be part of the websocket stream */\n",
+ " evt.data.type = \"image/png\";\n",
+ "\n",
+ " /* Free the memory for the previous frames */\n",
+ " if (fig.imageObj.src) {\n",
+ " (window.URL || window.webkitURL).revokeObjectURL(\n",
+ " fig.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+ " evt.data);\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+ " fig.imageObj.src = evt.data;\n",
+ " fig.updated_canvas_event();\n",
+ " fig.waiting = false;\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var msg = JSON.parse(evt.data);\n",
+ " var msg_type = msg['type'];\n",
+ "\n",
+ " // Call the \"handle_{type}\" callback, which takes\n",
+ " // the figure and JSON message as its only arguments.\n",
+ " try {\n",
+ " var callback = fig[\"handle_\" + msg_type];\n",
+ " } catch (e) {\n",
+ " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " if (callback) {\n",
+ " try {\n",
+ " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+ " callback(fig, msg);\n",
+ " } catch (e) {\n",
+ " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+ " }\n",
+ " }\n",
+ " };\n",
+ "}\n",
+ "\n",
+ "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+ "mpl.findpos = function(e) {\n",
+ " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+ " var targ;\n",
+ " if (!e)\n",
+ " e = window.event;\n",
+ " if (e.target)\n",
+ " targ = e.target;\n",
+ " else if (e.srcElement)\n",
+ " targ = e.srcElement;\n",
+ " if (targ.nodeType == 3) // defeat Safari bug\n",
+ " targ = targ.parentNode;\n",
+ "\n",
+ " // jQuery normalizes the pageX and pageY\n",
+ " // pageX,Y are the mouse positions relative to the document\n",
+ " // offset() returns the position of the element relative to the document\n",
+ " var x = e.pageX - $(targ).offset().left;\n",
+ " var y = e.pageY - $(targ).offset().top;\n",
+ "\n",
+ " return {\"x\": x, \"y\": y};\n",
+ "};\n",
+ "\n",
+ "/*\n",
+ " * return a copy of an object with only non-object keys\n",
+ " * we need this to avoid circular references\n",
+ " * http://stackoverflow.com/a/24161582/3208463\n",
+ " */\n",
+ "function simpleKeys (original) {\n",
+ " return Object.keys(original).reduce(function (obj, key) {\n",
+ " if (typeof original[key] !== 'object')\n",
+ " obj[key] = original[key]\n",
+ " return obj;\n",
+ " }, {});\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+ " var canvas_pos = mpl.findpos(event)\n",
+ "\n",
+ " if (name === 'button_press')\n",
+ " {\n",
+ " this.canvas.focus();\n",
+ " this.canvas_div.focus();\n",
+ " }\n",
+ "\n",
+ " var x = canvas_pos.x * mpl.ratio;\n",
+ " var y = canvas_pos.y * mpl.ratio;\n",
+ "\n",
+ " this.send_message(name, {x: x, y: y, button: event.button,\n",
+ " step: event.step,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ "\n",
+ " /* This prevents the web browser from automatically changing to\n",
+ " * the text insertion cursor when the button is pressed. We want\n",
+ " * to control all of the cursor setting manually through the\n",
+ " * 'cursor' event from matplotlib */\n",
+ " event.preventDefault();\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " // Handle any extra behaviour associated with a key event\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.key_event = function(event, name) {\n",
+ "\n",
+ " // Prevent repeat events\n",
+ " if (name == 'key_press')\n",
+ " {\n",
+ " if (event.which === this._key)\n",
+ " return;\n",
+ " else\n",
+ " this._key = event.which;\n",
+ " }\n",
+ " if (name == 'key_release')\n",
+ " this._key = null;\n",
+ "\n",
+ " var value = '';\n",
+ " if (event.ctrlKey && event.which != 17)\n",
+ " value += \"ctrl+\";\n",
+ " if (event.altKey && event.which != 18)\n",
+ " value += \"alt+\";\n",
+ " if (event.shiftKey && event.which != 16)\n",
+ " value += \"shift+\";\n",
+ "\n",
+ " value += 'k';\n",
+ " value += event.which.toString();\n",
+ "\n",
+ " this._key_event_extra(event, name);\n",
+ "\n",
+ " this.send_message(name, {key: value,\n",
+ " guiEvent: simpleKeys(event)});\n",
+ " return false;\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+ " if (name == 'download') {\n",
+ " this.handle_save(this, null);\n",
+ " } else {\n",
+ " this.send_message(\"toolbar_button\", {name: name});\n",
+ " }\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+ " this.message.textContent = tooltip;\n",
+ "};\n",
+ "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+ "\n",
+ "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+ "\n",
+ "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+ " // Create a \"websocket\"-like object which calls the given IPython comm\n",
+ " // object with the appropriate methods. Currently this is a non binary\n",
+ " // socket, so there is still some room for performance tuning.\n",
+ " var ws = {};\n",
+ "\n",
+ " ws.close = function() {\n",
+ " comm.close()\n",
+ " };\n",
+ " ws.send = function(m) {\n",
+ " //console.log('sending', m);\n",
+ " comm.send(m);\n",
+ " };\n",
+ " // Register the callback with on_msg.\n",
+ " comm.on_msg(function(msg) {\n",
+ " //console.log('receiving', msg['content']['data'], msg);\n",
+ " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
+ " ws.onmessage(msg['content']['data'])\n",
+ " });\n",
+ " return ws;\n",
+ "}\n",
+ "\n",
+ "mpl.mpl_figure_comm = function(comm, msg) {\n",
+ " // This is the function which gets called when the mpl process\n",
+ " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+ "\n",
+ " var id = msg.content.data.id;\n",
+ " // Get hold of the div created by the display call when the Comm\n",
+ " // socket was opened in Python.\n",
+ " var element = $(\"#\" + id);\n",
+ " var ws_proxy = comm_websocket_adapter(comm)\n",
+ "\n",
+ " function ondownload(figure, format) {\n",
+ " window.open(figure.imageObj.src);\n",
+ " }\n",
+ "\n",
+ " var fig = new mpl.figure(id, ws_proxy,\n",
+ " ondownload,\n",
+ " element.get(0));\n",
+ "\n",
+ " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+ " // web socket which is closed, not our websocket->open comm proxy.\n",
+ " ws_proxy.onopen();\n",
+ "\n",
+ " fig.parent_element = element.get(0);\n",
+ " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+ " if (!fig.cell_info) {\n",
+ " console.error(\"Failed to find cell for figure\", id, fig);\n",
+ " return;\n",
+ " }\n",
+ "\n",
+ " var output_index = fig.cell_info[2]\n",
+ " var cell = fig.cell_info[0];\n",
+ "\n",
+ "};\n",
+ "\n",
+ "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+ " var width = fig.canvas.width/mpl.ratio\n",
+ " fig.root.unbind('remove')\n",
+ "\n",
+ " // Update the output cell to use the data from the current canvas.\n",
+ " fig.push_to_output();\n",
+ " var dataURL = fig.canvas.toDataURL();\n",
+ " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+ " // the notebook keyboard shortcuts fail.\n",
+ " IPython.keyboard_manager.enable()\n",
+ " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+ " fig.close_ws(fig, msg);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+ " fig.send_message('closing', msg);\n",
+ " // fig.ws.close()\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+ " // Turn the data on the canvas into data in the output cell.\n",
+ " var width = this.canvas.width/mpl.ratio\n",
+ " var dataURL = this.canvas.toDataURL();\n",
+ " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.updated_canvas_event = function() {\n",
+ " // Tell IPython that the notebook contents must change.\n",
+ " IPython.notebook.set_dirty(true);\n",
+ " this.send_message(\"ack\", {});\n",
+ " var fig = this;\n",
+ " // Wait a second, then push the new image to the DOM so\n",
+ " // that it is saved nicely (might be nice to debounce this).\n",
+ " setTimeout(function () { fig.push_to_output() }, 1000);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._init_toolbar = function() {\n",
+ " var fig = this;\n",
+ "\n",
+ " var nav_element = $('<div/>')\n",
+ " nav_element.attr('style', 'width: 100%');\n",
+ " this.root.append(nav_element);\n",
+ "\n",
+ " // Define a callback function for later on.\n",
+ " function toolbar_event(event) {\n",
+ " return fig.toolbar_button_onclick(event['data']);\n",
+ " }\n",
+ " function toolbar_mouse_event(event) {\n",
+ " return fig.toolbar_button_onmouseover(event['data']);\n",
+ " }\n",
+ "\n",
+ " for(var toolbar_ind in mpl.toolbar_items){\n",
+ " var name = mpl.toolbar_items[toolbar_ind][0];\n",
+ " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+ " var image = mpl.toolbar_items[toolbar_ind][2];\n",
+ " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+ "\n",
+ " if (!name) { continue; };\n",
+ "\n",
+ " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+ " button.click(method_name, toolbar_event);\n",
+ " button.mouseover(tooltip, toolbar_mouse_event);\n",
+ " nav_element.append(button);\n",
+ " }\n",
+ "\n",
+ " // Add the status bar.\n",
+ " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+ " nav_element.append(status_bar);\n",
+ " this.message = status_bar[0];\n",
+ "\n",
+ " // Add the close button to the window.\n",
+ " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+ " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+ " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+ " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+ " buttongrp.append(button);\n",
+ " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+ " titlebar.prepend(buttongrp);\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._root_extra_style = function(el){\n",
+ " var fig = this\n",
+ " el.on(\"remove\", function(){\n",
+ "\tfig.close_ws(fig, {});\n",
+ " });\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+ " // this is important to make the div 'focusable\n",
+ " el.attr('tabindex', 0)\n",
+ " // reach out to IPython and tell the keyboard manager to turn it's self\n",
+ " // off when our div gets focus\n",
+ "\n",
+ " // location in version 3\n",
+ " if (IPython.notebook.keyboard_manager) {\n",
+ " IPython.notebook.keyboard_manager.register_events(el);\n",
+ " }\n",
+ " else {\n",
+ " // location in version 2\n",
+ " IPython.keyboard_manager.register_events(el);\n",
+ " }\n",
+ "\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+ " var manager = IPython.notebook.keyboard_manager;\n",
+ " if (!manager)\n",
+ " manager = IPython.keyboard_manager;\n",
+ "\n",
+ " // Check for shift+enter\n",
+ " if (event.shiftKey && event.which == 13) {\n",
+ " this.canvas_div.blur();\n",
+ " // select the cell after this one\n",
+ " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+ " IPython.notebook.select(index + 1);\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+ " fig.ondownload(fig, null);\n",
+ "}\n",
+ "\n",
+ "\n",
+ "mpl.find_output_cell = function(html_output) {\n",
+ " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+ " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+ " // IPython event is triggered only after the cells have been serialised, which for\n",
+ " // our purposes (turning an active figure into a static one), is too late.\n",
+ " var cells = IPython.notebook.get_cells();\n",
+ " var ncells = cells.length;\n",
+ " for (var i=0; i<ncells; i++) {\n",
+ " var cell = cells[i];\n",
+ " if (cell.cell_type === 'code'){\n",
+ " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+ " var data = cell.output_area.outputs[j];\n",
+ " if (data.data) {\n",
+ " // IPython >= 3 moved mimebundle to data attribute of output\n",
+ " data = data.data;\n",
+ " }\n",
+ " if (data['text/html'] == html_output) {\n",
+ " return [cell, data, j];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "// Register the function which deals with the matplotlib target/channel.\n",
+ "// The kernel may be null if the page has been refreshed.\n",
+ "if (IPython.notebook.kernel != null) {\n",
+ " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+ "}\n"
+ ],
+ "text/plain": [
+ "<IPython.core.display.Javascript object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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width=\"720\">"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ }
+ ],
+ "source": [
+ "df_results = %sql SELECT * FROM iris_multi_model_info ORDER BY validation_loss ASC LIMIT 7;\n",
+ "df_results = df_results.DataFrame()\n",
+ "\n",
+ "df_summary = %sql SELECT * FROM iris_multi_model_summary;\n",
+ "df_summary = df_summary.DataFrame()\n",
+ "\n",
+ "#set up plots\n",
+ "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
+ "fig.legend(ncol=4)\n",
+ "fig.tight_layout()\n",
+ "\n",
+ "ax_metric = axs[0]\n",
+ "ax_loss = axs[1]\n",
+ "\n",
+ "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_metric.set_xlabel('Iteration')\n",
+ "ax_metric.set_ylabel('Metric')\n",
+ "ax_metric.set_title('Validation metric curve')\n",
+ "\n",
+ "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
+ "ax_loss.set_xlabel('Iteration')\n",
+ "ax_loss.set_ylabel('Loss')\n",
+ "ax_loss.set_title('Validation loss curve')\n",
+ "\n",
+ "iters = df_summary['metrics_iters'][0]\n",
+ "\n",
+ "for mst_key in df_results['mst_key']:\n",
+ " df_output_info = %sql SELECT validation_metrics,validation_loss FROM iris_multi_model_info WHERE mst_key = $mst_key\n",
+ " df_output_info = df_output_info.DataFrame()\n",
+ " validation_metrics = df_output_info['validation_metrics'][0]\n",
+ " validation_loss = df_output_info['validation_loss'][0]\n",
+ " \n",
+ " ax_metric.plot(iters, validation_metrics, label=mst_key, marker='o')\n",
+ " ax_loss.plot(iters, validation_loss, label=mst_key, marker='o')\n",
+ "\n",
+ "plt.legend();\n",
+ "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-MLP-v2.ipynb b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-MLP-v2.ipynb
new file mode 100644
index 0000000..28099ae
--- /dev/null
+++ b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-MLP-v2.ipynb
@@ -0,0 +1,5025 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Multilayer Perceptron Using Keras and MADlib\n",
+ "\n",
+ "E2E classification example using MADlib calling a Keras MLP.\n",
+ "\n",
+ "Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this workbook we use the well known iris data set from https://archive.ics.uci.edu/ml/datasets/iris to help get you started. It is similar to the example in user docs http://madlib.apache.org/docs/latest/index.html\n",
+ "\n",
+ "For more realistic examples with images please refer to the deep learning notebooks at\n",
+ "https://github.com/apache/madlib-site/tree/asf-site/community-artifacts\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#class\">Classification</a>\n",
+ "\n",
+ "* <a href=\"#create_input_data\">1. Create input data</a>\n",
+ "\n",
+ "* <a href=\"#pp\">2. Call preprocessor for deep learning</a>\n",
+ "\n",
+ "* <a href=\"#load\">3. Define and load model architecture</a>\n",
+ "\n",
+ "* <a href=\"#train\">4. Train</a>\n",
+ "\n",
+ "* <a href=\"#eval\">5. Evaluate</a>\n",
+ "\n",
+ "* <a href=\"#pred\">6. Predict</a>\n",
+ "\n",
+ "* <a href=\"#pred_byom\">7. Predict BYOM</a>\n",
+ "\n",
+ "<a href=\"#class2\">Classification with Other Parameters</a>\n",
+ "\n",
+ "* <a href=\"#val_dataset\">1. Validation dataset</a>\n",
+ "\n",
+ "* <a href=\"#pred_prob\">2. Predict probabilities</a>\n",
+ "\n",
+ "* <a href=\"#warm_start\">3. Warm start</a>\n",
+ "\n",
+ "<a href=\"#transfer_learn\">Transfer learning</a>\n",
+ "\n",
+ "* <a href=\"#load2\">1. Define and load model architecture with some layers frozen</a>\n",
+ "\n",
+ "* <a href=\"#train2\">2. Train transfer model</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-92-gdeae06b, cmake configuration time: Wed Mar 10 23:04:48 UTC 2021, build type: release, build system: Linux-3.10.0-1160.15.2.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-92-gdeae06b, cmake configuration time: Wed Mar 10 23:04:48 UTC 2021, build type: release, build system: Linux-3.10.0-1160.15.2.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"class\"></a>\n",
+ "# Classification\n",
+ "\n",
+ "<a id=\"create_input_data\"></a>\n",
+ "# 1. Create input data\n",
+ "\n",
+ "Load iris data set."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "Done.\n",
+ "150 rows affected.\n",
+ "150 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>attributes</th>\n",
+ " <th>class_text</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>[Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>12</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>13</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>14</td>\n",
+ " <td>[Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>[Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>[Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>18</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>[Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>20</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>22</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>23</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>24</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>25</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>26</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>[Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>29</td>\n",
+ " <td>[Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>30</td>\n",
+ " <td>[Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>31</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>33</td>\n",
+ " <td>[Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>34</td>\n",
+ " <td>[Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>36</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>37</td>\n",
+ " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>38</td>\n",
+ " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>39</td>\n",
+ " <td>[Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>40</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>41</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>42</td>\n",
+ " <td>[Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>43</td>\n",
+ " <td>[Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>44</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>47</td>\n",
+ " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>48</td>\n",
+ " <td>[Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>49</td>\n",
+ " <td>[Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>50</td>\n",
+ " <td>[Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')]</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>51</td>\n",
+ " <td>[Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>52</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>53</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>54</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>[Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>56</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>57</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>58</td>\n",
+ " <td>[Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>59</td>\n",
+ " <td>[Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>60</td>\n",
+ " <td>[Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>61</td>\n",
+ " <td>[Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>62</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>64</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>65</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>67</td>\n",
+ " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>68</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>69</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>70</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>71</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>72</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>74</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>75</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>76</td>\n",
+ " <td>[Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>77</td>\n",
+ " <td>[Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>79</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>80</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>81</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>83</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>84</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>85</td>\n",
+ " <td>[Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>86</td>\n",
+ " <td>[Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>87</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>88</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>89</td>\n",
+ " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>90</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>91</td>\n",
+ " <td>[Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>92</td>\n",
+ " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>93</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>[Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>95</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>96</td>\n",
+ " <td>[Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>97</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>98</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>[Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')]</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>101</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>103</td>\n",
+ " <td>[Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>104</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>105</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
+ " <td>[Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>107</td>\n",
+ " <td>[Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>[Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>109</td>\n",
+ " <td>[Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>110</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>111</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>112</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
+ " <td>[Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>[Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>115</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>116</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>118</td>\n",
+ " <td>[Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>119</td>\n",
+ " <td>[Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>122</td>\n",
+ " <td>[Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>[Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>124</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>125</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>127</td>\n",
+ " <td>[Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>128</td>\n",
+ " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>129</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>130</td>\n",
+ " <td>[Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>131</td>\n",
+ " <td>[Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>132</td>\n",
+ " <td>[Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>133</td>\n",
+ " <td>[Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>134</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>135</td>\n",
+ " <td>[Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>136</td>\n",
+ " <td>[Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>137</td>\n",
+ " <td>[Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>138</td>\n",
+ " <td>[Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>139</td>\n",
+ " <td>[Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>140</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>141</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>142</td>\n",
+ " <td>[Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>143</td>\n",
+ " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>144</td>\n",
+ " <td>[Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>145</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>146</td>\n",
+ " <td>[Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>147</td>\n",
+ " <td>[Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>148</td>\n",
+ " <td>[Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>[Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>150</td>\n",
+ " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')]</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (2, [Decimal('4.9'), Decimal('3.0'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (3, [Decimal('4.7'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (4, [Decimal('4.6'), Decimal('3.1'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (5, [Decimal('5.0'), Decimal('3.6'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (6, [Decimal('5.4'), Decimal('3.9'), Decimal('1.7'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (7, [Decimal('4.6'), Decimal('3.4'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (8, [Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (9, [Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (10, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (11, [Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (12, [Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (13, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (14, [Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (15, [Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (16, [Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (17, [Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (18, [Decimal('5.1'), Decimal('3.5'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (19, [Decimal('5.7'), Decimal('3.8'), Decimal('1.7'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (20, [Decimal('5.1'), Decimal('3.8'), Decimal('1.5'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (21, [Decimal('5.4'), Decimal('3.4'), Decimal('1.7'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (22, [Decimal('5.1'), Decimal('3.7'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (23, [Decimal('4.6'), Decimal('3.6'), Decimal('1.0'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (24, [Decimal('5.1'), Decimal('3.3'), Decimal('1.7'), Decimal('0.5')], u'Iris-setosa'),\n",
+ " (25, [Decimal('4.8'), Decimal('3.4'), Decimal('1.9'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (26, [Decimal('5.0'), Decimal('3.0'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (27, [Decimal('5.0'), Decimal('3.4'), Decimal('1.6'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (28, [Decimal('5.2'), Decimal('3.5'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (29, [Decimal('5.2'), Decimal('3.4'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (30, [Decimal('4.7'), Decimal('3.2'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (31, [Decimal('4.8'), Decimal('3.1'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (32, [Decimal('5.4'), Decimal('3.4'), Decimal('1.5'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (33, [Decimal('5.2'), Decimal('4.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (34, [Decimal('5.5'), Decimal('4.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (35, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (36, [Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (37, [Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (38, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris-setosa'),\n",
+ " (39, [Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (40, [Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (41, [Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (42, [Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (43, [Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (44, [Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')], u'Iris-setosa'),\n",
+ " (45, [Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')], u'Iris-setosa'),\n",
+ " (46, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')], u'Iris-setosa'),\n",
+ " (47, [Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (48, [Decimal('4.6'), Decimal('3.2'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (49, [Decimal('5.3'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (50, [Decimal('5.0'), Decimal('3.3'), Decimal('1.4'), Decimal('0.2')], u'Iris-setosa'),\n",
+ " (51, [Decimal('7.0'), Decimal('3.2'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (52, [Decimal('6.4'), Decimal('3.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (53, [Decimal('6.9'), Decimal('3.1'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (54, [Decimal('5.5'), Decimal('2.3'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (55, [Decimal('6.5'), Decimal('2.8'), Decimal('4.6'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (56, [Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (57, [Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (58, [Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (59, [Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (60, [Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (61, [Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (62, [Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (63, [Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (64, [Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (65, [Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (66, [Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (67, [Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (68, [Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (69, [Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (70, [Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (71, [Decimal('5.9'), Decimal('3.2'), Decimal('4.8'), Decimal('1.8')], u'Iris-versicolor'),\n",
+ " (72, [Decimal('6.1'), Decimal('2.8'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (73, [Decimal('6.3'), Decimal('2.5'), Decimal('4.9'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (74, [Decimal('6.1'), Decimal('2.8'), Decimal('4.7'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (75, [Decimal('6.4'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (76, [Decimal('6.6'), Decimal('3.0'), Decimal('4.4'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (77, [Decimal('6.8'), Decimal('2.8'), Decimal('4.8'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (78, [Decimal('6.7'), Decimal('3.0'), Decimal('5.0'), Decimal('1.7')], u'Iris-versicolor'),\n",
+ " (79, [Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (80, [Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (81, [Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (82, [Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (83, [Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (84, [Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (85, [Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (86, [Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')], u'Iris-versicolor'),\n",
+ " (87, [Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')], u'Iris-versicolor'),\n",
+ " (88, [Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (89, [Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (90, [Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (91, [Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (92, [Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')], u'Iris-versicolor'),\n",
+ " (93, [Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (94, [Decimal('5.0'), Decimal('2.3'), Decimal('3.3'), Decimal('1.0')], u'Iris-versicolor'),\n",
+ " (95, [Decimal('5.6'), Decimal('2.7'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (96, [Decimal('5.7'), Decimal('3.0'), Decimal('4.2'), Decimal('1.2')], u'Iris-versicolor'),\n",
+ " (97, [Decimal('5.7'), Decimal('2.9'), Decimal('4.2'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (98, [Decimal('6.2'), Decimal('2.9'), Decimal('4.3'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (99, [Decimal('5.1'), Decimal('2.5'), Decimal('3.0'), Decimal('1.1')], u'Iris-versicolor'),\n",
+ " (100, [Decimal('5.7'), Decimal('2.8'), Decimal('4.1'), Decimal('1.3')], u'Iris-versicolor'),\n",
+ " (101, [Decimal('6.3'), Decimal('3.3'), Decimal('6.0'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (102, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (103, [Decimal('7.1'), Decimal('3.0'), Decimal('5.9'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (104, [Decimal('6.3'), Decimal('2.9'), Decimal('5.6'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (105, [Decimal('6.5'), Decimal('3.0'), Decimal('5.8'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (106, [Decimal('7.6'), Decimal('3.0'), Decimal('6.6'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (107, [Decimal('4.9'), Decimal('2.5'), Decimal('4.5'), Decimal('1.7')], u'Iris-virginica'),\n",
+ " (108, [Decimal('7.3'), Decimal('2.9'), Decimal('6.3'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (109, [Decimal('6.7'), Decimal('2.5'), Decimal('5.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (110, [Decimal('7.2'), Decimal('3.6'), Decimal('6.1'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (111, [Decimal('6.5'), Decimal('3.2'), Decimal('5.1'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (112, [Decimal('6.4'), Decimal('2.7'), Decimal('5.3'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (113, [Decimal('6.8'), Decimal('3.0'), Decimal('5.5'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (114, [Decimal('5.7'), Decimal('2.5'), Decimal('5.0'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (115, [Decimal('5.8'), Decimal('2.8'), Decimal('5.1'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (116, [Decimal('6.4'), Decimal('3.2'), Decimal('5.3'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (117, [Decimal('6.5'), Decimal('3.0'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (118, [Decimal('7.7'), Decimal('3.8'), Decimal('6.7'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (119, [Decimal('7.7'), Decimal('2.6'), Decimal('6.9'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (120, [Decimal('6.0'), Decimal('2.2'), Decimal('5.0'), Decimal('1.5')], u'Iris-virginica'),\n",
+ " (121, [Decimal('6.9'), Decimal('3.2'), Decimal('5.7'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (122, [Decimal('5.6'), Decimal('2.8'), Decimal('4.9'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (123, [Decimal('7.7'), Decimal('2.8'), Decimal('6.7'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (124, [Decimal('6.3'), Decimal('2.7'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (125, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (126, [Decimal('7.2'), Decimal('3.2'), Decimal('6.0'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (127, [Decimal('6.2'), Decimal('2.8'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (128, [Decimal('6.1'), Decimal('3.0'), Decimal('4.9'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (129, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (130, [Decimal('7.2'), Decimal('3.0'), Decimal('5.8'), Decimal('1.6')], u'Iris-virginica'),\n",
+ " (131, [Decimal('7.4'), Decimal('2.8'), Decimal('6.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (132, [Decimal('7.9'), Decimal('3.8'), Decimal('6.4'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (133, [Decimal('6.4'), Decimal('2.8'), Decimal('5.6'), Decimal('2.2')], u'Iris-virginica'),\n",
+ " (134, [Decimal('6.3'), Decimal('2.8'), Decimal('5.1'), Decimal('1.5')], u'Iris-virginica'),\n",
+ " (135, [Decimal('6.1'), Decimal('2.6'), Decimal('5.6'), Decimal('1.4')], u'Iris-virginica'),\n",
+ " (136, [Decimal('7.7'), Decimal('3.0'), Decimal('6.1'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (137, [Decimal('6.3'), Decimal('3.4'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (138, [Decimal('6.4'), Decimal('3.1'), Decimal('5.5'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (139, [Decimal('6.0'), Decimal('3.0'), Decimal('4.8'), Decimal('1.8')], u'Iris-virginica'),\n",
+ " (140, [Decimal('6.9'), Decimal('3.1'), Decimal('5.4'), Decimal('2.1')], u'Iris-virginica'),\n",
+ " (141, [Decimal('6.7'), Decimal('3.1'), Decimal('5.6'), Decimal('2.4')], u'Iris-virginica'),\n",
+ " (142, [Decimal('6.9'), Decimal('3.1'), Decimal('5.1'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (143, [Decimal('5.8'), Decimal('2.7'), Decimal('5.1'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (144, [Decimal('6.8'), Decimal('3.2'), Decimal('5.9'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (145, [Decimal('6.7'), Decimal('3.3'), Decimal('5.7'), Decimal('2.5')], u'Iris-virginica'),\n",
+ " (146, [Decimal('6.7'), Decimal('3.0'), Decimal('5.2'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (147, [Decimal('6.3'), Decimal('2.5'), Decimal('5.0'), Decimal('1.9')], u'Iris-virginica'),\n",
+ " (148, [Decimal('6.5'), Decimal('3.0'), Decimal('5.2'), Decimal('2.0')], u'Iris-virginica'),\n",
+ " (149, [Decimal('6.2'), Decimal('3.4'), Decimal('5.4'), Decimal('2.3')], u'Iris-virginica'),\n",
+ " (150, [Decimal('5.9'), Decimal('3.0'), Decimal('5.1'), Decimal('1.8')], u'Iris-virginica')]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql \n",
+ "DROP TABLE IF EXISTS iris_data;\n",
+ "\n",
+ "CREATE TABLE iris_data(\n",
+ " id serial,\n",
+ " attributes numeric[],\n",
+ " class_text varchar\n",
+ ");\n",
+ "\n",
+ "INSERT INTO iris_data(id, attributes, class_text) VALUES\n",
+ "(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),\n",
+ "(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),\n",
+ "(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),\n",
+ "(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),\n",
+ "(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),\n",
+ "(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),\n",
+ "(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),\n",
+ "(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),\n",
+ "(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),\n",
+ "(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),\n",
+ "(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),\n",
+ "(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),\n",
+ "(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),\n",
+ "(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),\n",
+ "(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),\n",
+ "(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),\n",
+ "(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),\n",
+ "(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),\n",
+ "(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),\n",
+ "(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),\n",
+ "(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),\n",
+ "(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),\n",
+ "(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),\n",
+ "(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),\n",
+ "(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),\n",
+ "(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),\n",
+ "(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),\n",
+ "(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),\n",
+ "(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),\n",
+ "(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),\n",
+ "(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),\n",
+ "(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),\n",
+ "(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),\n",
+ "(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),\n",
+ "(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),\n",
+ "(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),\n",
+ "(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),\n",
+ "(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),\n",
+ "(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),\n",
+ "(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),\n",
+ "(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),\n",
+ "(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),\n",
+ "(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),\n",
+ "(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),\n",
+ "(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),\n",
+ "(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),\n",
+ "(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),\n",
+ "(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),\n",
+ "(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),\n",
+ "(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),\n",
+ "(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),\n",
+ "(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),\n",
+ "(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),\n",
+ "(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),\n",
+ "(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),\n",
+ "(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),\n",
+ "(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),\n",
+ "(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),\n",
+ "(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),\n",
+ "(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),\n",
+ "(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),\n",
+ "(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),\n",
+ "(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),\n",
+ "(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),\n",
+ "(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),\n",
+ "(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),\n",
+ "(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),\n",
+ "(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),\n",
+ "(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),\n",
+ "(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),\n",
+ "(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),\n",
+ "(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),\n",
+ "(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),\n",
+ "(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),\n",
+ "(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),\n",
+ "(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),\n",
+ "(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),\n",
+ "(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),\n",
+ "(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),\n",
+ "(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),\n",
+ "(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),\n",
+ "(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),\n",
+ "(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),\n",
+ "(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),\n",
+ "(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),\n",
+ "(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),\n",
+ "(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),\n",
+ "(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),\n",
+ "(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),\n",
+ "(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),\n",
+ "(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),\n",
+ "(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),\n",
+ "(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),\n",
+ "(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),\n",
+ "(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),\n",
+ "(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),\n",
+ "(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),\n",
+ "(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),\n",
+ "(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),\n",
+ "(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
+ "(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),\n",
+ "(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),\n",
+ "(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),\n",
+ "(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),\n",
+ "(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),\n",
+ "(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),\n",
+ "(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),\n",
+ "(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),\n",
+ "(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),\n",
+ "(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),\n",
+ "(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),\n",
+ "(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),\n",
+ "(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),\n",
+ "(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),\n",
+ "(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),\n",
+ "(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),\n",
+ "(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),\n",
+ "(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),\n",
+ "(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),\n",
+ "(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),\n",
+ "(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),\n",
+ "(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),\n",
+ "(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),\n",
+ "(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),\n",
+ "(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),\n",
+ "(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),\n",
+ "(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),\n",
+ "(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),\n",
+ "(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),\n",
+ "(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),\n",
+ "(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),\n",
+ "(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),\n",
+ "(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),\n",
+ "(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),\n",
+ "(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),\n",
+ "(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),\n",
+ "(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),\n",
+ "(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),\n",
+ "(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),\n",
+ "(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),\n",
+ "(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),\n",
+ "(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),\n",
+ "(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),\n",
+ "(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),\n",
+ "(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),\n",
+ "(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),\n",
+ "(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),\n",
+ "(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');\n",
+ "\n",
+ "SELECT * FROM iris_data ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Create a test/validation dataset from the training data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>120</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(120L,)]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_train, iris_test;\n",
+ "\n",
+ "-- Set seed so results are reproducible\n",
+ "SELECT setseed(0);\n",
+ "\n",
+ "SELECT madlib.train_test_split('iris_data', -- Source table\n",
+ " 'iris', -- Output table root name\n",
+ " 0.8, -- Train proportion\n",
+ " NULL, -- Test proportion (0.2)\n",
+ " NULL, -- Strata definition\n",
+ " NULL, -- Output all columns\n",
+ " NULL, -- Sample without replacement\n",
+ " TRUE -- Separate output tables\n",
+ " );\n",
+ "\n",
+ "SELECT COUNT(*) FROM iris_train;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pp\"></a>\n",
+ "# 2. Call preprocessor for deep learning\n",
+ "Training dataset (uses training preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train</td>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>60</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[3]</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train', u'iris_train_packed', [u'class_text'], [u'attributes'], [u'character varying'], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 60, 1.0, [3], 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;\n",
+ "\n",
+ "SELECT madlib.training_preprocessor_dl('iris_train', -- Source table\n",
+ " 'iris_train_packed', -- Output table\n",
+ " 'class_text', -- Dependent variable\n",
+ " 'attributes' -- Independent variable\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_train_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Validation dataset (uses validation preprocessor):"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>output_table</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " <th>buffer_size</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>distribution_rules</th>\n",
+ " <th>__internal_gpu_config__</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_test</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " <td>15</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[3]</td>\n",
+ " <td>all_segments</td>\n",
+ " <td>all_segments</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_test', u'iris_test_packed', [u'class_text'], [u'attributes'], [u'character varying'], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'], 15, 1.0, [3], 'all_segments', 'all_segments')]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;\n",
+ "\n",
+ "SELECT madlib.validation_preprocessor_dl('iris_test', -- Source table\n",
+ " 'iris_test_packed', -- Output table\n",
+ " 'class_text', -- Dependent variable\n",
+ " 'attributes', -- Independent variable\n",
+ " 'iris_train_packed' -- From training preprocessor step\n",
+ " ); \n",
+ "\n",
+ "SELECT * FROM iris_test_packed_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load\"></a>\n",
+ "# 3. Define and load model architecture\n",
+ "Import Keras libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow import keras\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Define model architecture"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/tensorflow/python/ops/init_ops.py:1251: calling __init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_1 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_2 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 193\n",
+ "Trainable params: 193\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_simple = Sequential()\n",
+ "model_simple.add(Dense(10, activation='relu', input_shape=(4,)))\n",
+ "model_simple.add(Dense(10, activation='relu'))\n",
+ "model_simple.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model_simple.summary();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_simple.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load into model architecture table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>model_arch</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>Sophie</td>\n",
+ " <td>A simple model</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1340 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, u'Sophie', u'A simple model')]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS model_arch_library;\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table,\n",
+ " \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Sophie', -- Name\n",
+ " 'A simple model' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT model_id, model_arch, name, description FROM model_arch_library ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"train\"></a>\n",
+ "# 4. Train\n",
+ "Train the model:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_model, iris_model_summary;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table\n",
+ " 'iris_model', -- model output table\n",
+ " 'model_arch_library', -- model arch table\n",
+ " 1, -- model arch id\n",
+ " $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params\n",
+ " $$ batch_size=5, epochs=3 $$, -- fit_params\n",
+ " 10 -- num_iterations\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>model</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>object_table</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " <th>metrics_iters</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_model</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>1</td>\n",
+ " <td> loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] </td>\n",
+ " <td> batch_size=5, epochs=3 </td>\n",
+ " <td>10</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>10</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>2021-03-10 23:41:05.753066</td>\n",
+ " <td>2021-03-10 23:41:08.647872</td>\n",
+ " <td>[2.89473080635071]</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[3]</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.816666662693</td>\n",
+ " <td>0.533572733402</td>\n",
+ " <td>[0.816666662693024]</td>\n",
+ " <td>[0.533572733402252]</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>[10]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_model', [u'class_text'], [u'attributes'], u'model_arch_library', 1, u\" loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] \", u' batch_size=5, epochs=3 ', 10, None, None, 10, None, None, u'madlib_keras', 0.7900390625, datetime.datetime(2021, 3, 10, 23, 41, 5, 753066), datetime.datetime(2021, 3, 10, 23, 41, 8, 647872), [2.89473080635071], u'1.18.0-dev', [3], [u'character varying'], 1.0, [u'accuracy'], u'categorical_crossentropy', 0.816666662693024, 0.533572733402252, [0.816666662693024], [0.533572733402252], None, None, None, None, [10], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'])]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"eval\"></a>\n",
+ "# 5. Evaluate\n",
+ "\n",
+ "Now run evaluate using model we built above:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>loss</th>\n",
+ " <th>metric</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.520632624626</td>\n",
+ " <td>0.833333313465</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(0.52063262462616, 0.833333313465118, [u'accuracy'], u'categorical_crossentropy')]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_validate;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_evaluate('iris_model', -- model\n",
+ " 'iris_test_packed', -- test table\n",
+ " 'iris_validate' -- output table\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_validate;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pred\"></a>\n",
+ "# 6. Predict\n",
+ "\n",
+ "Now predict using model we built. We will use the validation data set for prediction as well, which is not usual but serves to show the syntax. The prediction is in the estimated_class_text column:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "90 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>class_name</th>\n",
+ " <th>class_value</th>\n",
+ " <th>prob</th>\n",
+ " <th>rank</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.79537326</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.16200842</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.042618312</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.8085572</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.15361118</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.037831604</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.8436308</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.12770043</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0286688</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7641624</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.18371527</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.052122355</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.79488444</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.16299306</td>\n",
+ " <td>2</td>\n",
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+ " <tr>\n",
+ " <td>28</td>\n",
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+ " <td>Iris-virginica</td>\n",
+ " <td>0.042122513</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7354874</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.2048427</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.059669916</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.75861025</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.1874619</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.053927843</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.8050665</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.15518928</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.03974422</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7147916</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
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+ " <td>Iris-versicolor</td>\n",
+ " <td>0.21580514</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.06940326</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.52136815</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.4216067</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
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+ " <td>Iris-setosa</td>\n",
+ " <td>0.05702509</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5087182</td>\n",
+ " <td>1</td>\n",
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+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.42457458</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.06670723</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.45518628</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.45093843</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.093875304</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.579493</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>0.3848007</td>\n",
+ " <td>2</td>\n",
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+ " <tr>\n",
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+ " <td>3</td>\n",
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+ " <td>0.469466</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>Iris-versicolor</td>\n",
+ " <td>0.4419195</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>Iris-setosa</td>\n",
+ " <td>0.088614516</td>\n",
+ " <td>3</td>\n",
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+ " <tr>\n",
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+ " <td>Iris-virginica</td>\n",
+ " <td>0.541501</td>\n",
+ " <td>1</td>\n",
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+ " <tr>\n",
+ " <td>78</td>\n",
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+ " <td>Iris-versicolor</td>\n",
+ " <td>0.41141465</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
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+ " <td>Iris-setosa</td>\n",
+ " <td>0.047084346</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>Iris-versicolor</td>\n",
+ " <td>0.44548285</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.42948347</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>0.12503369</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
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+ " <td>Iris-versicolor</td>\n",
+ " <td>0.44139904</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.3979278</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>Iris-setosa</td>\n",
+ " <td>0.16067314</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.44215095</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.35698706</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.20086204</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
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+ " <td>Iris-virginica</td>\n",
+ " <td>0.58676815</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.37598613</td>\n",
+ " <td>2</td>\n",
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+ " <tr>\n",
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+ " <td>Iris-setosa</td>\n",
+ " <td>0.037245747</td>\n",
+ " <td>3</td>\n",
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+ " <tr>\n",
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+ " <td>Iris-virginica</td>\n",
+ " <td>0.677965</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
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+ " <td>Iris-versicolor</td>\n",
+ " <td>0.31183946</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
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+ " <td>0.010195577</td>\n",
+ " <td>3</td>\n",
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+ " <tr>\n",
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+ " <td>Iris-virginica</td>\n",
+ " <td>0.64965224</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>Iris-versicolor</td>\n",
+ " <td>0.33565775</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>0.014689991</td>\n",
+ " <td>3</td>\n",
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+ " <tr>\n",
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+ " <td>Iris-virginica</td>\n",
+ " <td>0.6514643</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>0.33213592</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
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+ " <td>3</td>\n",
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+ " <td>0.61708087</td>\n",
+ " <td>1</td>\n",
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+ " <tr>\n",
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+ " <tr>\n",
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+ " <td>3</td>\n",
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+ " <tr>\n",
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+ " <td>Iris-virginica</td>\n",
+ " <td>0.6166573</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
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+ " <td>Iris-versicolor</td>\n",
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+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
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+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6187312</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.3530719</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.028196828</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.57171255</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.3907827</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.037504766</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.62137836</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.35640395</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.02221771</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.68826115</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.30292466</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.008814191</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.58903533</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.3829163</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.028048303</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.55021214</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.40084097</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.04894687</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'class_text', u'Iris-setosa', 0.79537326, 1),\n",
+ " (1, u'class_text', u'Iris-versicolor', 0.16200842, 2),\n",
+ " (1, u'class_text', u'Iris-virginica', 0.042618312, 3),\n",
+ " (11, u'class_text', u'Iris-setosa', 0.8085572, 1),\n",
+ " (11, u'class_text', u'Iris-versicolor', 0.15361118, 2),\n",
+ " (11, u'class_text', u'Iris-virginica', 0.037831604, 3),\n",
+ " (16, u'class_text', u'Iris-setosa', 0.8436308, 1),\n",
+ " (16, u'class_text', u'Iris-versicolor', 0.12770043, 2),\n",
+ " (16, u'class_text', u'Iris-virginica', 0.0286688, 3),\n",
+ " (27, u'class_text', u'Iris-setosa', 0.7641624, 1),\n",
+ " (27, u'class_text', u'Iris-versicolor', 0.18371527, 2),\n",
+ " (27, u'class_text', u'Iris-virginica', 0.052122355, 3),\n",
+ " (28, u'class_text', u'Iris-setosa', 0.79488444, 1),\n",
+ " (28, u'class_text', u'Iris-versicolor', 0.16299306, 2),\n",
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+ " (32, u'class_text', u'Iris-versicolor', 0.2048427, 2),\n",
+ " (32, u'class_text', u'Iris-virginica', 0.059669916, 3),\n",
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+ " (46, u'class_text', u'Iris-setosa', 0.7147916, 1),\n",
+ " (46, u'class_text', u'Iris-versicolor', 0.21580514, 2),\n",
+ " (46, u'class_text', u'Iris-virginica', 0.06940326, 3),\n",
+ " (55, u'class_text', u'Iris-virginica', 0.52136815, 1),\n",
+ " (55, u'class_text', u'Iris-versicolor', 0.4216067, 2),\n",
+ " (55, u'class_text', u'Iris-setosa', 0.05702509, 3),\n",
+ " (63, u'class_text', u'Iris-virginica', 0.5087182, 1),\n",
+ " (63, u'class_text', u'Iris-versicolor', 0.42457458, 2),\n",
+ " (63, u'class_text', u'Iris-setosa', 0.06670723, 3),\n",
+ " (66, u'class_text', u'Iris-versicolor', 0.45518628, 1),\n",
+ " (66, u'class_text', u'Iris-virginica', 0.45093843, 2),\n",
+ " (66, u'class_text', u'Iris-setosa', 0.093875304, 3),\n",
+ " (73, u'class_text', u'Iris-virginica', 0.579493, 1),\n",
+ " (73, u'class_text', u'Iris-versicolor', 0.3848007, 2),\n",
+ " (73, u'class_text', u'Iris-setosa', 0.035706308, 3),\n",
+ " (74, u'class_text', u'Iris-virginica', 0.469466, 1),\n",
+ " (74, u'class_text', u'Iris-versicolor', 0.4419195, 2),\n",
+ " (74, u'class_text', u'Iris-setosa', 0.088614516, 3),\n",
+ " (78, u'class_text', u'Iris-virginica', 0.541501, 1),\n",
+ " (78, u'class_text', u'Iris-versicolor', 0.41141465, 2),\n",
+ " (78, u'class_text', u'Iris-setosa', 0.047084346, 3),\n",
+ " (82, u'class_text', u'Iris-versicolor', 0.44548285, 1),\n",
+ " (82, u'class_text', u'Iris-virginica', 0.42948347, 2),\n",
+ " (82, u'class_text', u'Iris-setosa', 0.12503369, 3),\n",
+ " (94, u'class_text', u'Iris-versicolor', 0.44139904, 1),\n",
+ " (94, u'class_text', u'Iris-virginica', 0.3979278, 2),\n",
+ " (94, u'class_text', u'Iris-setosa', 0.16067314, 3),\n",
+ " (99, u'class_text', u'Iris-versicolor', 0.44215095, 1),\n",
+ " (99, u'class_text', u'Iris-virginica', 0.35698706, 2),\n",
+ " (99, u'class_text', u'Iris-setosa', 0.20086204, 3),\n",
+ " (102, u'class_text', u'Iris-virginica', 0.58676815, 1),\n",
+ " (102, u'class_text', u'Iris-versicolor', 0.37598613, 2),\n",
+ " (102, u'class_text', u'Iris-setosa', 0.037245747, 3),\n",
+ " (106, u'class_text', u'Iris-virginica', 0.677965, 1),\n",
+ " (106, u'class_text', u'Iris-versicolor', 0.31183946, 2),\n",
+ " (106, u'class_text', u'Iris-setosa', 0.010195577, 3),\n",
+ " (108, u'class_text', u'Iris-virginica', 0.64965224, 1),\n",
+ " (108, u'class_text', u'Iris-versicolor', 0.33565775, 2),\n",
+ " (108, u'class_text', u'Iris-setosa', 0.014689991, 3),\n",
+ " (109, u'class_text', u'Iris-virginica', 0.6514643, 1),\n",
+ " (109, u'class_text', u'Iris-versicolor', 0.33213592, 2),\n",
+ " (109, u'class_text', u'Iris-setosa', 0.01639977, 3),\n",
+ " (112, u'class_text', u'Iris-virginica', 0.61708087, 1),\n",
+ " (112, u'class_text', u'Iris-versicolor', 0.3579519, 2),\n",
+ " (112, u'class_text', u'Iris-setosa', 0.02496719, 3),\n",
+ " (113, u'class_text', u'Iris-virginica', 0.6166573, 1),\n",
+ " (113, u'class_text', u'Iris-versicolor', 0.35984018, 2),\n",
+ " (113, u'class_text', u'Iris-setosa', 0.023502568, 3),\n",
+ " (114, u'class_text', u'Iris-virginica', 0.6187312, 1),\n",
+ " (114, u'class_text', u'Iris-versicolor', 0.3530719, 2),\n",
+ " (114, u'class_text', u'Iris-setosa', 0.028196828, 3),\n",
+ " (117, u'class_text', u'Iris-virginica', 0.57171255, 1),\n",
+ " (117, u'class_text', u'Iris-versicolor', 0.3907827, 2),\n",
+ " (117, u'class_text', u'Iris-setosa', 0.037504766, 3),\n",
+ " (121, u'class_text', u'Iris-virginica', 0.62137836, 1),\n",
+ " (121, u'class_text', u'Iris-versicolor', 0.35640395, 2),\n",
+ " (121, u'class_text', u'Iris-setosa', 0.02221771, 3),\n",
+ " (123, u'class_text', u'Iris-virginica', 0.68826115, 1),\n",
+ " (123, u'class_text', u'Iris-versicolor', 0.30292466, 2),\n",
+ " (123, u'class_text', u'Iris-setosa', 0.008814191, 3),\n",
+ " (126, u'class_text', u'Iris-virginica', 0.58903533, 1),\n",
+ " (126, u'class_text', u'Iris-versicolor', 0.3829163, 2),\n",
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+ " (149, u'class_text', u'Iris-virginica', 0.55021214, 1),\n",
+ " (149, u'class_text', u'Iris-versicolor', 0.40084097, 2),\n",
+ " (149, u'class_text', u'Iris-setosa', 0.04894687, 3)]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('iris_model', -- model\n",
+ " 'iris_test', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'attributes', -- independent var\n",
+ " 'iris_predict' -- output table\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_predict ORDER BY id, rank;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(5L,)]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM iris_predict JOIN iris_test USING (id)\n",
+ "WHERE iris_predict.class_value != iris_test.class_text AND iris_predict.rank = 1;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Percent missclassifications"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>83.33</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('83.33'),)]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from\n",
+ " (select iris_test.class_text as actual, iris_predict.class_value as estimated\n",
+ " from iris_predict inner join iris_test\n",
+ " on iris_test.id=iris_predict.id where iris_predict.rank = 1) q\n",
+ "WHERE q.actual=q.estimated;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pred_byom\"></a>\n",
+ "# 7. Predict BYOM\n",
+ "The predict BYOM function allows you to do inference on models that have not been trained on MADlib, but rather imported from elsewhere. \n",
+ "\n",
+ "We will use the validation dataset for prediction as well, which is not usual but serves to show the syntax.\n",
+ "\n",
+ "See load_keras_model()\n",
+ "http://madlib.apache.org/docs/latest/group__grp__keras__model__arch.html\n",
+ "for details on how to load the model architecture and weights. In this example we will use weights we already have:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "UPDATE model_arch_library \n",
+ "SET model_weights = iris_model.model_weights \n",
+ "FROM iris_model \n",
+ "WHERE model_arch_library.model_id = 1;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now train using a model from the model architecture table directly without referencing the model table from the MADlib training. \n",
+ "\n",
+ "Note that if you specify the class values parameter as we do below, it must reflect how the dependent variable was 1-hot encoded for training. In this example the 'training_preprocessor_dl()' in Step 2 above encoded in the order {'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'} so this is the order we pass in the parameter. If we accidently picked another order that did not match the 1-hot encoding, the predictions would be wrong."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "30 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>class_name</th>\n",
+ " <th>class_value</th>\n",
+ " <th>prob</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.79537326</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.8085572</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.8436308</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7641624</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.79488444</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7354874</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.75861025</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.8050665</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.7147916</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.52136815</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5087182</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.45518628</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.579493</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>74</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.469466</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.541501</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.44548285</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.44139904</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.44215095</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.58676815</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.677965</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.64965224</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>109</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6514643</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>112</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.61708087</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6166573</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6187312</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.57171255</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.62137836</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.68826115</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.58903533</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.55021214</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'dependent_var', u'Iris-setosa', 0.79537326),\n",
+ " (11, u'dependent_var', u'Iris-setosa', 0.8085572),\n",
+ " (16, u'dependent_var', u'Iris-setosa', 0.8436308),\n",
+ " (27, u'dependent_var', u'Iris-setosa', 0.7641624),\n",
+ " (28, u'dependent_var', u'Iris-setosa', 0.79488444),\n",
+ " (32, u'dependent_var', u'Iris-setosa', 0.7354874),\n",
+ " (35, u'dependent_var', u'Iris-setosa', 0.75861025),\n",
+ " (45, u'dependent_var', u'Iris-setosa', 0.8050665),\n",
+ " (46, u'dependent_var', u'Iris-setosa', 0.7147916),\n",
+ " (55, u'dependent_var', u'Iris-virginica', 0.52136815),\n",
+ " (63, u'dependent_var', u'Iris-virginica', 0.5087182),\n",
+ " (66, u'dependent_var', u'Iris-versicolor', 0.45518628),\n",
+ " (73, u'dependent_var', u'Iris-virginica', 0.579493),\n",
+ " (74, u'dependent_var', u'Iris-virginica', 0.469466),\n",
+ " (78, u'dependent_var', u'Iris-virginica', 0.541501),\n",
+ " (82, u'dependent_var', u'Iris-versicolor', 0.44548285),\n",
+ " (94, u'dependent_var', u'Iris-versicolor', 0.44139904),\n",
+ " (99, u'dependent_var', u'Iris-versicolor', 0.44215095),\n",
+ " (102, u'dependent_var', u'Iris-virginica', 0.58676815),\n",
+ " (106, u'dependent_var', u'Iris-virginica', 0.677965),\n",
+ " (108, u'dependent_var', u'Iris-virginica', 0.64965224),\n",
+ " (109, u'dependent_var', u'Iris-virginica', 0.6514643),\n",
+ " (112, u'dependent_var', u'Iris-virginica', 0.61708087),\n",
+ " (113, u'dependent_var', u'Iris-virginica', 0.6166573),\n",
+ " (114, u'dependent_var', u'Iris-virginica', 0.6187312),\n",
+ " (117, u'dependent_var', u'Iris-virginica', 0.57171255),\n",
+ " (121, u'dependent_var', u'Iris-virginica', 0.62137836),\n",
+ " (123, u'dependent_var', u'Iris-virginica', 0.68826115),\n",
+ " (126, u'dependent_var', u'Iris-virginica', 0.58903533),\n",
+ " (149, u'dependent_var', u'Iris-virginica', 0.55021214)]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_predict_byom;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict_byom('model_arch_library', -- model arch table\n",
+ " 1, -- model arch id\n",
+ " 'iris_test', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'attributes', -- independent var\n",
+ " 'iris_predict_byom', -- output table\n",
+ " 'response', -- prediction type\n",
+ " FALSE, -- use GPUs\n",
+ " ARRAY[ARRAY['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']], -- class values\n",
+ " 1.0 -- normalizing const\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_predict_byom ORDER BY id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count missclassifications:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(5L,)]"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM iris_predict_byom JOIN iris_test USING (id)\n",
+ "WHERE iris_predict_byom.class_value != iris_test.class_text;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Percent missclassifications:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>83.33</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('83.33'),)]"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100/(150*0.2),2) as test_accuracy_percent from\n",
+ " (select iris_test.class_text as actual, iris_predict_byom.class_value as estimated\n",
+ " from iris_predict_byom inner join iris_test\n",
+ " on iris_test.id=iris_predict_byom.id) q\n",
+ "WHERE q.actual=q.estimated;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"class2\"></a>\n",
+ "# Classification with Other Parameters\n",
+ "\n",
+ "<a id=\"val_dataset\"></a>\n",
+ "# 1. Validation dataset\n",
+ "Now use a validation dataset and compute metrics every 2nd iteration using the 'metrics_compute_frequency' parameter. This can help reduce run time if you do not need metrics computed at every iteration."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_model, iris_model_summary;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table\n",
+ " 'iris_model', -- model output table\n",
+ " 'model_arch_library', -- model arch table\n",
+ " 1, -- model arch id\n",
+ " $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params\n",
+ " $$ batch_size=5, epochs=3 $$, -- fit_params\n",
+ " 10, -- num_iterations\n",
+ " FALSE, -- use GPUs\n",
+ " 'iris_test_packed', -- validation dataset\n",
+ " 2, -- metrics compute frequency\n",
+ " FALSE, -- warm start\n",
+ " 'Sophie L.', -- name\n",
+ " 'Simple MLP for iris dataset' -- description\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "View the model summary:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>model</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>object_table</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " <th>metrics_iters</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_model</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>1</td>\n",
+ " <td> loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] </td>\n",
+ " <td> batch_size=5, epochs=3 </td>\n",
+ " <td>10</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>None</td>\n",
+ " <td>2</td>\n",
+ " <td>Sophie L.</td>\n",
+ " <td>Simple MLP for iris dataset</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>2021-03-10 23:41:19.536811</td>\n",
+ " <td>2021-03-10 23:41:20.580502</td>\n",
+ " <td>[0.593767881393433, 0.708214998245239, 0.81914496421814, 0.92844295501709, 1.04359292984009]</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[3]</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.975000023842</td>\n",
+ " <td>0.346686542034</td>\n",
+ " <td>[0.958333313465118, 0.958333313465118, 0.958333313465118, 0.975000023841858, 0.975000023841858]</td>\n",
+ " <td>[0.473859906196594, 0.429711729288101, 0.397110253572464, 0.370571583509445, 0.346686542034149]</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.352109313011</td>\n",
+ " <td>[0.866666674613953, 0.866666674613953, 0.899999976158142, 0.933333337306976, 0.933333337306976]</td>\n",
+ " <td>[0.470703303813934, 0.430528193712234, 0.400696188211441, 0.375301212072372, 0.352109313011169]</td>\n",
+ " <td>[2, 4, 6, 8, 10]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_model', [u'class_text'], [u'attributes'], u'model_arch_library', 1, u\" loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] \", u' batch_size=5, epochs=3 ', 10, u'iris_test_packed', None, 2, u'Sophie L.', u'Simple MLP for iris dataset', u'madlib_keras', 0.7900390625, datetime.datetime(2021, 3, 10, 23, 41, 19, 536811), datetime.datetime(2021, 3, 10, 23, 41, 20, 580502), [0.593767881393433, 0.708214998245239, 0.81914496421814, 0.92844295501709, 1.04359292984009], u'1.18.0-dev', [3], [u'character varying'], 1.0, [u'accuracy'], u'categorical_crossentropy', 0.975000023841858, 0.346686542034149, [0.958333313465118, 0.958333313465118, 0.958333313465118, 0.975000023841858, 0.975000023841858], [0.473859906196594, 0.429711729288101, 0.397110253572464, 0.370571583509445, 0.346686542034149], 0.933333337306976, 0.352109313011169, [0.866666674613953, 0.866666674613953, 0.899999976158142, 0.933333337306976, 0.933333337306976], [0.470703303813934, 0.430528193712234, 0.400696188211441, 0.375301212072372, 0.352109313011169], [2, 4, 6, 8, 10], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'])]"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accuracy by iteration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import os\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# get accuracy and iteration number\n",
+ "iters_proxy = %sql SELECT metrics_iters FROM iris_model_summary;\n",
+ "train_accuracy_proxy = %sql SELECT training_metrics FROM iris_model_summary;\n",
+ "test_accuracy_proxy = %sql SELECT validation_metrics FROM iris_model_summary;\n",
+ "\n",
+ "# get number of points\n",
+ "num_points_proxy = %sql SELECT array_length(metrics_iters,1) FROM iris_model_summary;\n",
+ "num_points = num_points_proxy[0]\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "iters = np.array(iters_proxy).reshape(num_points)\n",
+ "train_accuracy = np.array(train_accuracy_proxy).reshape(num_points)\n",
+ "test_accuracy = np.array(test_accuracy_proxy).reshape(num_points)\n",
+ "\n",
+ "#plot\n",
+ "plt.title('Iris validation accuracy by iteration')\n",
+ "plt.xlabel('Iteration number')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.grid(True)\n",
+ "plt.plot(iters, train_accuracy, 'g.-', label='Train')\n",
+ "plt.plot(iters, test_accuracy, 'r.-', label='Test')\n",
+ "plt.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loss by iteration"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get loss\n",
+ "train_loss_proxy = %sql SELECT training_loss FROM iris_model_summary;\n",
+ "test_loss_proxy = %sql SELECT validation_loss FROM iris_model_summary;\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "train_loss = np.array(train_loss_proxy).reshape(num_points)\n",
+ "test_loss = np.array(test_loss_proxy).reshape(num_points)\n",
+ "\n",
+ "#plot\n",
+ "plt.title('Iris validation loss by iteration')\n",
+ "plt.xlabel('Iteration number')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.grid(True)\n",
+ "plt.plot(iters, train_loss, 'g.-', label='Train')\n",
+ "plt.plot(iters, test_loss, 'r.-', label='Test')\n",
+ "plt.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Accuracy by time"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get time\n",
+ "time_proxy = %sql SELECT metrics_elapsed_time FROM iris_model_summary;\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "time = np.array(time_proxy).reshape(num_points)/60.0\n",
+ "\n",
+ "#plot\n",
+ "plt.title('Iris validation accuracy by time')\n",
+ "plt.xlabel('Time (min)')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.grid(True)\n",
+ "plt.plot(time, train_accuracy, 'g.-', label='Train')\n",
+ "plt.plot(time, test_accuracy, 'r.-', label='Test')\n",
+ "plt.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Time to achieve a given accuracy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#plot\n",
+ "plt.title('Iris time by validation accuracy')\n",
+ "plt.xlabel('Accuracy')\n",
+ "plt.ylabel('Time (min)')\n",
+ "plt.grid(True)\n",
+ "plt.plot(train_accuracy, time, 'g.-', label='Train')\n",
+ "plt.plot(test_accuracy, time, 'r.-', label='Test')\n",
+ "plt.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"pred_prob\"></a>\n",
+ "# 2. Predict probabilities\n",
+ "Predict with probabilities for each class:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "90 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>class_name</th>\n",
+ " <th>class_value</th>\n",
+ " <th>prob</th>\n",
+ " <th>rank</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.97281444</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.026144315</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0010412402</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.9769015</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.022337299</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>11</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0007612452</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.98652077</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.013145796</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>16</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0003333905</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.96028346</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.037923962</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>27</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0017925705</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.97203124</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.02692252</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>28</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0010462046</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.9519408</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.045901664</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>32</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0021574483</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.961949</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.03631745</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>35</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0017335047</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.9738652</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.025185836</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0009489085</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.941538</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.05514807</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>46</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.0033140522</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.51732117</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.47700876</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>55</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.0056700534</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.5409491</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.4484684</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>63</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.01058249</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.624784</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.3572584</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>66</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.017957589</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5829126</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.41514772</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>73</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.0019396603</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>74</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.5810816</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>74</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.40437576</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>74</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.014542614</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.51474637</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.48199108</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>78</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.0032626411</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.61229366</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.350411</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>82</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.03729541</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.61452246</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.32228386</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>94</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.0631937</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.63094443</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.2640157</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>99</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.105039924</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6317322</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.36684355</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>102</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.0014242299</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.7536085</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.24630967</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>106</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>8.183768e-05</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6934331</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.3063265</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>108</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.00024038096</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>109</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.72010636</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>109</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.27964392</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>109</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.00024974498</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>112</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.66092503</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>112</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.33838212</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>112</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.00069281814</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.65633607</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.34308842</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>113</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.000575457</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6909946</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.30828673</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>114</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.00071866764</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5825241</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.41584617</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>117</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.0016297478</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.6702286</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.3293251</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>121</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.00044631946</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.7651359</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.23479936</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>123</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>6.4739164e-05</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5913641</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.4076982</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>126</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.0009377025</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-virginica</td>\n",
+ " <td>0.5790059</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-versicolor</td>\n",
+ " <td>0.41894335</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>149</td>\n",
+ " <td>class_text</td>\n",
+ " <td>Iris-setosa</td>\n",
+ " <td>0.0020507444</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'class_text', u'Iris-setosa', 0.97281444, 1),\n",
+ " (1, u'class_text', u'Iris-versicolor', 0.026144315, 2),\n",
+ " (1, u'class_text', u'Iris-virginica', 0.0010412402, 3),\n",
+ " (11, u'class_text', u'Iris-setosa', 0.9769015, 1),\n",
+ " (11, u'class_text', u'Iris-versicolor', 0.022337299, 2),\n",
+ " (11, u'class_text', u'Iris-virginica', 0.0007612452, 3),\n",
+ " (16, u'class_text', u'Iris-setosa', 0.98652077, 1),\n",
+ " (16, u'class_text', u'Iris-versicolor', 0.013145796, 2),\n",
+ " (16, u'class_text', u'Iris-virginica', 0.0003333905, 3),\n",
+ " (27, u'class_text', u'Iris-setosa', 0.96028346, 1),\n",
+ " (27, u'class_text', u'Iris-versicolor', 0.037923962, 2),\n",
+ " (27, u'class_text', u'Iris-virginica', 0.0017925705, 3),\n",
+ " (28, u'class_text', u'Iris-setosa', 0.97203124, 1),\n",
+ " (28, u'class_text', u'Iris-versicolor', 0.02692252, 2),\n",
+ " (28, u'class_text', u'Iris-virginica', 0.0010462046, 3),\n",
+ " (32, u'class_text', u'Iris-setosa', 0.9519408, 1),\n",
+ " (32, u'class_text', u'Iris-versicolor', 0.045901664, 2),\n",
+ " (32, u'class_text', u'Iris-virginica', 0.0021574483, 3),\n",
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+ " (35, u'class_text', u'Iris-virginica', 0.0017335047, 3),\n",
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+ " (46, u'class_text', u'Iris-setosa', 0.941538, 1),\n",
+ " (46, u'class_text', u'Iris-versicolor', 0.05514807, 2),\n",
+ " (46, u'class_text', u'Iris-virginica', 0.0033140522, 3),\n",
+ " (55, u'class_text', u'Iris-versicolor', 0.51732117, 1),\n",
+ " (55, u'class_text', u'Iris-virginica', 0.47700876, 2),\n",
+ " (55, u'class_text', u'Iris-setosa', 0.0056700534, 3),\n",
+ " (63, u'class_text', u'Iris-versicolor', 0.5409491, 1),\n",
+ " (63, u'class_text', u'Iris-virginica', 0.4484684, 2),\n",
+ " (63, u'class_text', u'Iris-setosa', 0.01058249, 3),\n",
+ " (66, u'class_text', u'Iris-versicolor', 0.624784, 1),\n",
+ " (66, u'class_text', u'Iris-virginica', 0.3572584, 2),\n",
+ " (66, u'class_text', u'Iris-setosa', 0.017957589, 3),\n",
+ " (73, u'class_text', u'Iris-virginica', 0.5829126, 1),\n",
+ " (73, u'class_text', u'Iris-versicolor', 0.41514772, 2),\n",
+ " (73, u'class_text', u'Iris-setosa', 0.0019396603, 3),\n",
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+ " (94, u'class_text', u'Iris-virginica', 0.32228386, 2),\n",
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+ " (99, u'class_text', u'Iris-virginica', 0.2640157, 2),\n",
+ " (99, u'class_text', u'Iris-setosa', 0.105039924, 3),\n",
+ " (102, u'class_text', u'Iris-virginica', 0.6317322, 1),\n",
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+ " (108, u'class_text', u'Iris-virginica', 0.6934331, 1),\n",
+ " (108, u'class_text', u'Iris-versicolor', 0.3063265, 2),\n",
+ " (108, u'class_text', u'Iris-setosa', 0.00024038096, 3),\n",
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+ " (109, u'class_text', u'Iris-versicolor', 0.27964392, 2),\n",
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+ " (113, u'class_text', u'Iris-virginica', 0.65633607, 1),\n",
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+ " (117, u'class_text', u'Iris-versicolor', 0.41584617, 2),\n",
+ " (117, u'class_text', u'Iris-setosa', 0.0016297478, 3),\n",
+ " (121, u'class_text', u'Iris-virginica', 0.6702286, 1),\n",
+ " (121, u'class_text', u'Iris-versicolor', 0.3293251, 2),\n",
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+ " (126, u'class_text', u'Iris-virginica', 0.5913641, 1),\n",
+ " (126, u'class_text', u'Iris-versicolor', 0.4076982, 2),\n",
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+ " (149, u'class_text', u'Iris-virginica', 0.5790059, 1),\n",
+ " (149, u'class_text', u'Iris-versicolor', 0.41894335, 2),\n",
+ " (149, u'class_text', u'Iris-setosa', 0.0020507444, 3)]"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_predict;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict('iris_model', -- model\n",
+ " 'iris_test', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'attributes', -- independent var\n",
+ " 'iris_predict', -- output table\n",
+ " 'prob' -- response type\n",
+ " );\n",
+ "\n",
+ "SELECT * FROM iris_predict ORDER BY id, rank;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"warm_start\"></a>\n",
+ "# 3. Warm start\n",
+ "Next, use the warm_start parameter to continue learning, using the coefficients from the run above. Note that we don't drop the model table or model summary table:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table\n",
+ " 'iris_model', -- model output table\n",
+ " 'model_arch_library', -- model arch table\n",
+ " 1, -- model arch id\n",
+ " $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params\n",
+ " $$ batch_size=5, epochs=3 $$, -- fit_params\n",
+ " 10, -- num_iterations\n",
+ " FALSE, -- use GPUs\n",
+ " 'iris_test_packed', -- validation dataset\n",
+ " 2, -- metrics compute frequency\n",
+ " TRUE, -- warm start\n",
+ " 'Sophie L.', -- name \n",
+ " 'Simple MLP for iris dataset' -- description\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In the summary table and plots below note that the loss and accuracy values pick up from where the previous run left off:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>model</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>object_table</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " <th>metrics_iters</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_model</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>1</td>\n",
+ " <td> loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] </td>\n",
+ " <td> batch_size=5, epochs=3 </td>\n",
+ " <td>10</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>None</td>\n",
+ " <td>2</td>\n",
+ " <td>Sophie L.</td>\n",
+ " <td>Simple MLP for iris dataset</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>2021-03-10 23:41:27.241398</td>\n",
+ " <td>2021-03-10 23:41:28.379467</td>\n",
+ " <td>[0.687061071395874, 0.802449941635132, 0.910459995269775, 1.02436900138855, 1.13800001144409]</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[3]</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.242831289768</td>\n",
+ " <td>[0.975000023841858, 0.975000023841858, 0.975000023841858, 0.975000023841858, 0.966666638851166]</td>\n",
+ " <td>[0.321178942918777, 0.299564212560654, 0.279232144355774, 0.260169178247452, 0.242831289768219]</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.245254442096</td>\n",
+ " <td>[0.933333337306976, 0.933333337306976, 0.933333337306976, 0.933333337306976, 0.933333337306976]</td>\n",
+ " <td>[0.326333403587341, 0.303749978542328, 0.283036053180695, 0.263097137212753, 0.245254442095757]</td>\n",
+ " <td>[2, 4, 6, 8, 10]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_model', [u'class_text'], [u'attributes'], u'model_arch_library', 1, u\" loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] \", u' batch_size=5, epochs=3 ', 10, u'iris_test_packed', None, 2, u'Sophie L.', u'Simple MLP for iris dataset', u'madlib_keras', 0.7900390625, datetime.datetime(2021, 3, 10, 23, 41, 27, 241398), datetime.datetime(2021, 3, 10, 23, 41, 28, 379467), [0.687061071395874, 0.802449941635132, 0.910459995269775, 1.02436900138855, 1.13800001144409], u'1.18.0-dev', [3], [u'character varying'], 1.0, [u'accuracy'], u'categorical_crossentropy', 0.966666638851166, 0.242831289768219, [0.975000023841858, 0.975000023841858, 0.975000023841858, 0.975000023841858, 0.966666638851166], [0.321178942918777, 0.299564212560654, 0.279232144355774, 0.260169178247452, 0.242831289768219], 0.933333337306976, 0.245254442095757, [0.933333337306976, 0.933333337306976, 0.933333337306976, 0.933333337306976, 0.933333337306976], [0.326333403587341, 0.303749978542328, 0.283036053180695, 0.263097137212753, 0.245254442095757], [2, 4, 6, 8, 10], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'])]"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import os\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# get accuracy and iteration number\n",
+ "iters_proxy = %sql SELECT metrics_iters FROM iris_model_summary;\n",
+ "train_accuracy_proxy = %sql SELECT training_metrics FROM iris_model_summary;\n",
+ "test_accuracy_proxy = %sql SELECT validation_metrics FROM iris_model_summary;\n",
+ "\n",
+ "# get number of points\n",
+ "num_points_proxy = %sql SELECT array_length(metrics_iters,1) FROM iris_model_summary;\n",
+ "num_points = num_points_proxy[0]\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "iters = np.array(iters_proxy).reshape(num_points)\n",
+ "train_accuracy = np.array(train_accuracy_proxy).reshape(num_points)\n",
+ "test_accuracy = np.array(test_accuracy_proxy).reshape(num_points)\n",
+ "\n",
+ "#plot\n",
+ "plt.title('Iris validation accuracy by iteration - warm start')\n",
+ "plt.xlabel('Iteration number')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.grid(True)\n",
+ "plt.plot(iters, train_accuracy, 'g.-', label='Train')\n",
+ "plt.plot(iters, test_accuracy, 'r.-', label='Test')\n",
+ "plt.legend();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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UcjlNFtiGq6mHpzrXd9rd3bZfrF0LWbLYdo1XX4V7mKBLKaVSunSfLNYcWUONiTUY/+t4ak2q5fxgm6Ag26W2a1fbc6pSJdsYrpRSaZBLk4WI1BWR3SKyT0RuGx0iIp1FZLuIRIrIShHxdTz/hIhscry2SURquirG2Ttnc+X6FWKI4fL1yyw7uMz5wpkz28buRYvgjz/smIwhQ+y0IUoplYa4LFmIiDswHKgH+ALP30gGsUwzxvgbYwKBwcBnjudPAU8bY/yBtkDcrTtJoJlvMzK6ZwTsQJutJ7byz9V/EreT+vXtVUW9etCrFzz5JBw96oJolVLq/nDllUUlYJ8x5oAx5gowA2gUewNjzLlYDx8AO5+fMWaLMeZ3x/NRQCYRyeiKIIMLBBPRNoK2hdrSxKcJM6NmEjg6MPFzv+TODfPmwdix/80vNWuWK0JWSqlkJ8YY1+xYpClQ1xjTwfG4NVDZGNP9lu26Ab0AT6CmMWZvHPvpbIy5rXOziHQEOgLkyZMnaMaMGXcd74ULF8iSJQub/9rM4N2DOXn5JM8VeI4XCr+Ap5tnwjuIJdNvv+EzcCDZoqM5/sQT7O3Zk+tZstxTXCmNxpU4GlfiJEdc3t7eFC9ePFFlrl+/jnsSzeJw+vRpGjZsCMCJEydwd3fnwQcfBCAiIuKmEdl3MnnyZGrVqkW+fPkS3Hbfvn2cPXvzNCUhISGbjDEV4inyH2OMS25AU2BcrMetgWF32L4lMPGW5/yA/UCxhI4XFBRk7kVERMS/989eOms6Luho6IfxGeZj1h9dn/gdXr1qTN++xri7G1OokDErVtxzXCmJxpU4GlfiJEdcO3fuTHSZc+fOuSASY/r27Ws+/vjjuypbtWpVs3LlSqe2jes9AxuNE7/prqyG+g0oEOtxfsdz8ZkBNL7xQETyA/OANsaY/S6JMB7ZMmZj9NOjWdJqCeevnCf4q2DeDn+by9cuO78TDw/o1w9WrrT3H38c+vQBJxc1UUqlPG7r1sEHH7h8CeaJEydSqVIlAgMD6dq1KzExMVy7do3WrVvj7+9PmTJl+PLLL5k5cyaRkZG0a9cuUYsm3Q1XjuDeAJQQkSLYJNECe/XwLxEpYf6rdnoK2Ot4PjuwCOhtjFnlwhjv6MliT7Kjyw56LenFoJWDWLBnARMbT6R83vLO76RKFYiMtLPYfvCBXVxpyhTw8XFd4EqpxHn5Zfv/9E7OniXztm22t6Obm52h2jv+KcoJDITPEz9F+Y4dO5g3bx6rV6/Gw8ODjh07MmPGDIoVK8apU6fY7uiif+bMGbJnz87QoUP56KOPXD4NiMuuLIwx14DuwBIgGphljIkSkf4i0tCxWXcRiRKRSGy7RdsbzwPFgXcd3WojReQhV8V6J95e3nzV6CsWtVzEn//8SeVxlem3rB9Xricig2fJYhu+582DQ4egfHnb5dZF7UVKKRc4e/a/bvExMfaxC/z0009s2LCBChUqEBgYyPLly9m/fz/Fixdn9+7d9OzZkyVLluB9p0TlAi6dG8oY8z3w/S3PvRvr/kvxlBsADHBlbIlVv0R9dnTZwctLXua95e/x7e5v+brR15R9uKzzO2ncGCpXhrAwu7DSokUwfjw8/LDrAldKJcyZK4A1a6BWLVuV7OkJU6falTaTmDGGsLAw3n///dte27ZtG4sXL2b48OHMmTOHMWPGJPnx45PuR3AnRo5MOZjYeCLzm8/n2PljVBxbkQErBnD1+lXnd5I3L3z/vb2yiIiwXWznz3dd0EqppBEczMUFC+D99+2ccC5IFAC1a9dm1qxZnDp1CrC9pg4fPszJkycxxtCsWTP69+/PZseibFmzZuXChQsuiSU2TRZ3oVHpRkR1jaKpb1PeiXiH4K+CifojyvkdiNhpQjZvhoIF4Zln4MUXIRk+cKXU3YupXNnODeeiRAHg7+9P3759qV27NgEBATz55JOcOHGCI0eO8NhjjxEYGMgLL7zAoEGDAHjhhRfo3r17qm7gTtNyZc7FtCbTaOLThC6LulB+THn61+jPq4++ioebk6fVx8de2vbrBx9+aK80pkyxjeJKqXQj9hTlAC1btqRly5a3bbdly5bbnnvuueeoV68eWbNmdVV4gF5Z3LMmvk2I6hpFw1IN6R3em2rjq7Hr1C7nd+Dpaac8X74crl2DatVs8kjiJRGVUupeaLJIArkfyM2sprOY0WQGe//cS+CoQD5d/SnXY647v5Pq1WHrVmjZEt57zyaNfftcF7RSSiWCJoskIiI0L9OcqK5R1C1el9d+fI3Hvn6Mvaf3Jlz4Bm9vmDQJZsyA3bshMJC8ixZpF1ulXMSko/9b9/peNVkksYezPMy85vOY8swUdp7cSdlRZfli7RfEmERMW968uZ3FtkoVSn3yiW0AP3nSdUErlQ55eXlx+vTpdJEwjDGcPn0aLy+vu96HNnC7gIgQGhBKSJEQOi7syMtLXmburrmMbzieYjmLObeT/Plh6VL29ehB8XHjbBfbCRPsNOhKqXuWP39+jh49yslE/CF26dKle/rBdRVn4vLy8iJ//vx3fQxNFi6UL2s+Fj6/kIlbJ/LSDy8RMCqAj5/4mM4VOuMmTlzUublxtFkzinfqBK1a2XUzunaFjz+2Cy8ppe5ahgwZKFKkSKLKLFu2jHLlyrkooruXHHFpNZSLiQjtAtsR1TWK6gWr0+37bjwx+QkOnjno/E4CAmD9eruw0ogRdrqQTZtcFrNSSt1Kk0UyyZ8tP4tDFzP26bFs+G0D/iP9GbNpjPP1pV5e8Omn8NNPdvBelSp2YsLriehxpZRSd0mTRTISETqU78D2Ltup/EhlOn3XiTpT6nDk7BHnd1KrFmzbBs8+a6c8r1EDDh50VchKKQVosrgvCmUvxI+tf2RE/RGsPrKaMiPLMGHLBOevMnLmtN1rJ0+2iSMgwN5PB706lFL3hyaL+0RE6FKxC9u6bKPcw+UIWxBGg+kN+O3cndaHumkHttF761Y7b36bNrbL7Z9/ujZwpVS6pMniPiuaoyg/t/2ZL+t+ScSvEZQZWYbJWyc7f5VRuLCdU+qDD+x6Gf7+tl1DKaWSkCaLFMBN3OhRuQfbumzDL7cfbea3ofHMxhy/cNy5Hbi7Q+/esG4dZMsGTzxhe05duuTawJVS6YYmixSkeM7iLG+3nE+f/JSl+5fiN8KP8D/Cnb/KuNGltls3GDIEKla0bRpKKXWPNFmkMO5u7vQK7sWWTlsokbMEA6IH0HR2U/74+w/ndpA5MwwbZlfhO3nSJozPPvtvOUillLoLmixSqNIPlmZV2Co6FunId3u+w2+EH9/s/Mb5HdSvb+eXqlcPXn3VVk0dSUQXXaWUikWTRQrm7ubO8wWfZ3PHzRTOXphms5vR4psWnLp4yrkd5M5tG73HjbPtGQEBMHOma4NWSqVJmixSAb+H/FjTfg0Daw5kbvRc/Eb4MX+Xk+t2i0D79hAZCaVKQYsW0Lo1nD3r2qCVUmmKJotUwsPNgz7V+7Cx40byZc3HMzOfodXcVvz5j5PjKooXh5Ur7Sp806fbq4wVK1was1Iq7dBkkcoE5AlgfYf19Hu8HzOjZlJmRBm+2/Odc4U9PKBvX1i1yi7nWqOG7XLrwkXelVJpgyaLVCiDewb61ujL+g7reTDzgzw9/Wle+PYFzlw649wOKleGLVugQwf46CM7KeHOna4NWimVqmmySMXK5S3Hxo4bebv620zeOpkyI8rww74fnCucJQuMGQPz59teUkFBtsutzi+llIqDJotUztPdkwE1B7Cm/Rq8vbypN7UeHRd25Nzlc87toFEj28U2JAR69LBdbo8dc23QSqlUR5NFGlHxkYps6riJN6u+yVdbvsJ/pD8/HXByjqiHH7aD+IYPh2XL7PxS8+a5NF6lVOqiySIN8fLw4sPaH7IqbBWZPDLxxOQn6LqoKxeuXEi4sIhdsnXzZihUyK6X0b49nD/v+sCVUimeJos0qEr+KmzptIVeVXoxauMo/Ef6s+zgMucK+/jAmjXw1lswYYKd/nzNGpfGq5RK+TRZpFGZMmTi0zqfsuKFFXi4eRAyMYSei3vy95W/Ey7s6QmDBsHy5XbZ1mrVbJfbq1ddH7hSKkXSZJHGVStYjchOkfSs1JOh64dSdlRZVh5e6Vzh6tXt4kqtWkH//jZp7N3r2oCVUimSJot04AHPB/ii3hdEtI0gxsTw2ITH6LWkF/9c/Sfhwt7eMHGinVNq715bLfXmmxScOlWrp5RKRzRZpCM1CtdgW5dtdKnQhSFrhxA4OpC1R9c6V/i55+zaGL6+MHgwRcaNg5o1NWEolU5oskhnsnhmYfhTw/mx9Y9cunaJquOr8uaPb3LpmhOr6uXPD888AyII2JX4Bg3StTKUSgc0WaRTtYvWZnuX7bQv157BqwdTfnR5Nvy2IeGCISHg5YVxcwM3N/juO9uWodOFKJWmabJIx7JlzMaYp8fwQ+gPnLt8juCvgnk7/G0uX7scf6HgYAgP59ewMPjlF5g0CXbvhnLlbCO4TkqoVJqkyUJRp3gddnTdQeuyrRm0chAVx1Zk87HN8RcIDuZwaCg8+qhdGyM62lZP9e1r55haty75gldKJQtNFgqA7F7ZmdBoAgufX8ipi6eoPK4y/Zb148p1J64UHnoIZsyABQvgr7/s1ccrr8DfTozpUEqlCi5NFiJSV0R2i8g+Eekdx+udRWS7iESKyEoR8Y312luOcrtFpI4r41T/aVCyATu67qBFmRa8t/w9Ko+rzLYT25wr/PTTEBUFnTvD559DmTKwdKlrA1ZKJQuXJQsRcQeGA/UAX+D52MnAYZoxxt8YEwgMBj5zlPUFWgB+QF1ghGN/KhnkzJSTyc9MZl7zefx+/ncqjKnAwBUDuRZzLeHC3t4wYoRdhc/TE+rUgXbt4E8nV/RTSqVIrryyqATsM8YcMMZcAWYAjWJvYIyJPY/2A8CNxRQaATOMMZeNMb8C+xz7U8mocenGRHWNoolvE/4X8T+qjKtC1B9RzhW+Mfq7Tx+YMsXOOTVrlq6XoVQq5cpk8QhwJNbjo47nbiIi3URkP/bKomdiyirXezDzg0xvMp3ZzWZz6Owhyo8pT9dFXZl8aDJrjiQwIM/LCwYOhI0b7RiN5s2hcWP47bfkCV4plWTEuOgvPRFpCtQ1xnRwPG4NVDbGdI9n+5ZAHWNMWxEZBqw1xkxxvPYVsNgY880tZToCHQHy5MkTNGPGjLuO98KFC2TJkuWuy7tKSorrryt/8d7O99h6disAGSQDQ8oOwc/bL8Gycv06+b/5hsITJmA8PNjfsSPHGjSwYzWSUEo6X7FpXImjcSXOvcQVEhKyyRhTIcENjTEuuQHBwJJYj98C3rrD9m7A2bi2BZYAwXc6XlBQkLkXERER91TeVVJaXANXDDRu/dwM/TD0w1QfX91cuHzB+R3s22dMSIgxYMxjjxmze3eSxpfSztcNGlfiaFyJcy9xARuNE7/prqyG2gCUEJEiIuKJbbBeEHsDESkR6+FTwI0pTRcALUQko4gUAUoA610Yq3JSSOEQMnpkxA033MWdXw7/QpmRZViyb4lzOyhWDMLDYdw426YREAAffqjTnyuVwrksWRhjrgHdsVcF0cAsY0yUiPQXkYaOzbqLSJSIRAK9gLaOslHALGAn8APQzRhz3VWxKucFFwgmvE04YUXC+OWFX1jebjkZ3TNSd2pdQueG8sfffyS8ExG7Cl90NDz1lF1oqVIlu0qfUipFcuk4C2PM98aYksaYYsaYgY7n3jXGLHDcf8kY42eMCTTGhDiSxI2yAx3lShljFrsyTpU4wQWCCS0YSnCBYB4r9BhbO2+l7+N9mR01m9LDSjNhy4Qb1Yd3ljcvzJljb8eP24Tx5pvwjxNTpyulkpWO4Fb3LKNHRvrV6Edk50j8HvIjbEEYtSbVYs/pPc7t4Nln7USE7drB4MG2amrZMleGrJRKJE0WKsn45vZlebvljG4wms3HNhMwMoCBKwY6N2VIjhy2HSM83E55HhICHTvCmTOuD1wplSBNFipJuYkbHYM6Et0tmoalGvK/iP9RfnT5hMdk3FCzJmzfDq+9Bl99ZRdbmj/ftUErpRKkyUK5RN6seZnVbBY4p6uNAAAgAElEQVQLWizg3OVzVB1flW6LunH20tmEC2fODB9/bGevzZ3bzmjbrJlt11BK3ReaLJRLPV3qaaK6RtGzck9GbhyJ7whf5kXPc65whQp29PfAgbBwob3KmDBBpwxR6j7QZKFcLmvGrHxe93PWdVhH7sy5eXbWszSe0Zij544mXDhDBju/1Nat4OcHYWHwxBNw4IDrA1dK/UuThUo2FR+pyIYXN/BR7Y9Yun8pvsN9GbZ+GNdjnBhCU6oULF9uZ7Rdv95Of/7ZZ3Bdh98olRw0WahklcE9A29UfYMdXXdQJX8VeizuQdXxVdl+YnvChd3coEsXu2ZGrVrw6qt2oaVtTq63oZS6a5os1H1RNEdRlrRawpRnprD/r/2UH1OePuF9+OeqEwPyChSwq/JNnw4HD9qlXN95By5dcnncSqVXmizUfSMihAaEsqvbLloFtOKDlR/gP9Kf8APhzhSGFi3slCHPPw8DBkC5cnhvd+IKRSmVaJos1H2XK3MuJjSawE+tf0JEqD25Nu3mt+PUxVNOFM4FkybB4sVw8SLlevaEbt3g3LmEyyqlnKbJQqUYtYrWYlvnbfSp1oep26fiM9yHKdumODfPVN26EBXF0WefhZEjbc+pRYtcH7RS6YQmC5WiZMqQiYG1BrK542aK5ShG63mtqTOlDvv/3J9w4SxZ2NejB6xaBdmyQYMGEBoKJ0+6PnCl0jhNFipF8s/jz6qwVQyrN4y1R9fiP9KfwasGc/W6E+teBAfb6c779oXZs+3631Om6GA+pe6BJguVYrm7udOtUjd2dttJneJ1ePOnN6k4tiIbftuQcOGMGaFfP9iyBYoXh9at7doZhw65PG6l0iJNFirFy58tP/Oaz2Puc3M5efEklcdV5qXFL3H+8vmEC/v52WqpL76AFSvs46FD7cy2SimnabJQqcYzPs+ws+tOulTowtD1Q/Ed4cvC3QsTLujuDj17wo4dUK2avV+tml1DQynlFE0WKlXx9vJm+FPDWRW2Cu+M3jSc0ZBms5tx7PyxhAsXLmy72E6aBLt3Q7ly0L8/XHFivQ2l0jlNFipVCi4QzOZOmxlYcyALdy/EZ7gPozeOJsYkUL0kYtsvoqPt1Od9+9oR4OvWJU/gSqVSmixUquXp7kmf6n3Y3mU75fOWp/OizrwU+RI7TzpRvfTQQzBjhp025K+/bA+qV16Bv/92feBKpUKaLFSqVyJXCcLbhDOh0QQOXzxM4KhA+kb05dI1J+aKevpp23bRuTN8/rmdzXbpUtcHrVQqo8lCpQkiQrvAdnxd8Wue83uO/iv6EzgqkOUHlydcOFs2O/X5ihXg6Ql16kC7dvDnny6PW6nUQpOFSlNyeOZgyrNT+CH0By5fv0yNiTV4ccGL/PXPXwkXrl7dLrLUp48dxOfjA7Nm6WA+pXAyWYhIMRHJ6LhfQ0R6ikh214am1N2rU7wOO7rs4PVHX2dC5ARKDy/NzB0zE55nysvLLuO6aZOdCr15c2jcGH77LXkCVyqFcvbKYg5wXUSKA2OAAsA0l0WlVBJ4wPMBBj8xmA0vbqBAtgK0mNOCBtMbcOiME6O4y5aFtWvh44/hxx/t+t+jR+tgPpVuOZssYowx14BngKHGmNeBvK4LS6mkUy5vOdZ1WMeQOkNYfnA5viN8+WzNZ1yLuXbngh4e8NprsH277V7buTPUrAl79iRP4EqlIM4mi6si8jzQFvjO8VwG14SkVNJzd3Pn5SovE9U1ipDCIby69FUqj6vM5mObEy5crBiEh8O4cRAZCQEB8OGHcNWJSQ2VSiOcTRYvAMHAQGPMryJSBJjsurCUco1C2Qux8PmFzGw6k9/O/UalsZV4belr/H0lgfEVItC+vR3M99RT8NZbUKmSnd1WqXTAqWRhjNlpjOlpjJkuIjmArMaYj1wcm1IuISI85/cc0d2iaV+uPZ+u+RS/EX4s3rs44cJ588KcOfZ2/LhNGG++Cf84sXa4UqmYs72hlolINhHJCWwGxorIZ64NTSnXypEpB6OfHs2KdivIlCET9afVp+Wclpy4cCLhws8+awfztWsHgwfbqqlly1wdslL3jbPVUN7GmHPAs8AkY0xloLbrwlIq+VQvVJ3ITpH0e7wfc6Ln4DPch/FbxifczTZHDtuOER5ue0mFhEDHjnDmTPIErlQycjZZeIhIXuA5/mvgVirNyOiRkb41+hLZKZIyD5Wh/YL21JxUkz2nnej5VLOm7TH12mvw1Ve2m+38+a4PWqlk5Gyy6A8sAfYbYzaISFFgr+vCUur+8Mntw7J2yxjTYAxbjm0hYGQAA1YM4Mr1BKYxz5zZjslYtw5y57Yz2jZrZts1lEoDnG3gnm2MCTDGdHE8PmCMaeLa0JS6P9zEjReDXiS6WzSNSjfinYh3KDe6HKuPrE64cIUKsHGjHQW+cKG9ynj7bQpOnQpr1rg+eKVcxNkG7vwiMk9E/nDc5ohIflcHp9T9lDdrXmY2ncnC5xdy/vJ5qo6vStdFXTl76eydC2bIYOeX2roVChaEQYMoMm6cra7ShKFSKWeroSYAC4B8jttCx3NKpXkNSjZgZ7edvFz5ZUZvGo3PcB/m7JyTcAN4qVLw3HMgggBcugT/+5/9V6lUxtlkkdsYM8EYc81x+xrI7cK4lEpRsnhmYUjdIaxtv5aHHniIprOb0nhmY46cPXLngiEh4OVFjJubXQv8559tN9vw8OQJXKkk4myyOC0irUTE3XFrBZx2ZWBKpUQVH6nIhhc3MLj2YH7c/yO+I3wZum4o12Oux10gOBjCwzkYFga//AJLlthutrVrQ6tWcMKJMR1KpQDOJoswbLfZ48AxoCnQzkUxKZWiZXDPwOtVXyeqaxRVC1Sl5w89eXT8o2w7sS3uAsHBHA4NtYnjySdtN9t33rFrZZQuDWPG6Gy2KsVztjfUIWNMQ2NMbmPMQ8aYxkCCvaFEpK6I7BaRfSLSO47Xe4nIThHZJiLhIlIo1muDRSRKRKJF5EsRkUS9M6VcrEiOIiwOXczUZ6fy61+/EjQmiLd+eot/riYw9UemTNC/P2zbZqdC79QJqlWzSUSpFOpeVsrrdacXRcQdGA7UA3yB50XE95bNtgAVjDEBwDfAYEfZR4GqQABQBqgIPH4PsSrlEiJCS/+WRHeLpnVAaz5c9SH+I/356cBPCRcuXRoiIuDrr2HvXihXDt54A/5OYFJDpe6De0kWCf2lXwnY5xiTcQWYATSKvYExJsIYc9HxcC1wozuuAbwATyAjdjp0rdxVKVauzLkY32g84W3CERGemPwEbee35dTFU3cuKAJt28KuXXaeqY8/Bj8/+E4nSlApiyTY/S++giKHjTEF7/B6U6CuMaaD43FroLIxpns82w8DjhtjBjgefwJ0wCalYcaYt+Mo0xHoCJAnT56gGTNm3NV7Abhw4QJZsmS56/KuonElTkqI6/L1y0w5PIXpR6bzgPsDNMrXCK5B5Ycq4+ftd8ey3tu2UfKzz3jg0CFOVq/Ovh49uJzbdR0PU8L5iovGlTj3EldISMgmY0yFBDc0xsR7A84D5+K4nQeuJVC2KTAu1uPW2B/9uLZthb2yyOh4XBxYBGRx3NYA1e90vKCgIHMvIiIi7qm8q2hciZOS4tp+YrvxG+5n6IehH8ZrgJdZfXh1wgUvXzZm0CBjvLyMyZLFmCFDjLl61SUxpqTzFZvGlTj3Ehew0dzht/XG7Y7VUMaYrMaYbHHcshpjPBLIQ79h1+q+Ib/juZuISG3gbaChMeay4+lngLXGmAvGmAvAYuziS0qlGmUeKkNL/5Y4huRx6dolXv/x9YRHgHt62sWVoqKgenV45RW7bsaGDckQtVJxu5c2i4RsAEqISBER8QRaYEeB/0tEygGjsYnij1gvHQYeFxEPEcmAbdyOdmGsSrlESOEQvDy8cMMNd3Fn1ZFVlBpWiomRE4kxCXSXLVoUFi2yXWyPH4fKlaF7dzibQLJRygVcliyMMdeA7tjZaqOBWcaYKBHpLyINHZt9jK1mmi0ikSJyI5l8A+wHtgNbga3GmIWuilUpVwkuEEx4m3DCioTxywu/sOHFDRTOXph237aj2vhqCa8BLmJnr42Ohm7dYMQI8PGxCeQu2xuVuhuuvLLAGPO9MaakMaaYMWag47l3jTELHPdrG2PyGGMCHbeGjuevG2M6GWN8jDG+xpg7dtNVKiULLhBMaMFQggsEUyFfBVa3X82ERhPY9+c+KoypQJfvunD6YgITInh7w9ChsH69Xdq1eXOoXx8OHEieN6HSPZcmC6XU7dzEjXaB7djTYw89K/dk7OaxlBxWklEbR8U/bcgNFSrYNTM+/xxWrrTdbAcNgisJrLeh1D3SZKHUfZLdKzuf1/2cLZ224P+QP10WdaHi2IoJr5vh4QEvvWSrpurXh7fftgP6fvkleQJX6ZImC6XuM/88/kS0jWB6k+mc+PsEVcdXpd38dhy/kMAqe/nzw5w5dpGlv/+Gxx6D9u3htM7xqZKeJgulUgARoUWZFuzuvpveVXszbfs0Sg0rxZA1Q7h6/eqdCzdoYLvZvvEGTJxopxGZOFEbwFWS0mShVAqSxTMLH9T+gB1ddxCcP5heS3sRODqQiF8j7lzwgQfgo49gyxYoUcJOHRISYqcRUSoJaLJQKgUqmaski0MXM7/5fC5evUjNSTVp/k3zhBdb8ve3Dd9jxthlXQMC7HTo/yQwE65SCdBkoVQKJSI0Kt2InV138l6N91iwewGlh5dm0C+DuHztcvwF3dzgxRdh927bxXbAAJtEli5NvuBVmqPJQqkULlOGTLz7+LtEd4umTrE6vP3z25QZWYbv935/54IPPQSTJ8NPP9kEUqcOPP+8HQ2uVCJpslAqlSicvTBzm8/lh9AfcBM3npr2FA2nN2T/n/vvXLBWLbvQUt++MHeubQAfOVJX51OJoslCqVSmTvE6bO+ynY9qf8TPv/6M3wg/3o14l4tXL8ZfyMsL+vWzq/EFBUHXrvDoo2TZty/Z4lapmyYLpVIhT3dP3qj6Bru776aJbxPeX/E+PsN9mBs998a0/3ErWdJWS02eDAcOENSpE7z6Kly4kHzBq1RJk4VSqdgj2R5h6rNTWdZ2Gd4ZvWkyqwlPTnmS6JN3mKRZBFq1gl27OFavHnz2Gfj6wrffJl/gKtXRZKFUGvB44cfZ3GkzX9b9kg2/bSBgVACvL32dc5fPxV8oZ072vPYarFplJyps3NjeDh9OvsBVqqHJQqk0wsPNgx6Ve7Cnxx7alm3LJ2s+ofSw0kzdNvXOVVOPPgqbN9tBfUuX2quMTz+Fa9eSL3iV4mmyUCqNeeiBhxjXcBxr26/lkWyP0GpeKx77+jEij0fGXyhDBjtdyM6dUKMGvPbafzPcKoUmC6XSrMr5K7OuwzrGPj2WXad2ETQmiO7fd+fPf/6Mv1DhwnZiwjlz4NQpCA62PafOnEm2uFXKpMlCqTTMTdzoUL4De7rvoWuFrozcOJJSw0oxbvO4+Jd1FYFnn7VToPfsCaNH27EZ06fr5ITpmCYLpdKBHJlyMLT+UDZ33EzpB0vz4sIXqTKuCtHn7tBrKmtWu8jS+vVQoAC0bAl164KOzUiXNFkolY6UfbgsK9qtYMozUzh67ihdt3Sl/bft+ePvP+IvFBQEa9faZV3XrIEyZex8U5fvMD+VSnM0WSiVzogIoQGh7O6+m+b5mzNp2yRKDi3J0HVDuRYTTw8od3fo3t1Oed6woZ3JNjAQli1L1tjV/aPJQql0KmvGrHQu1pltnbdR8ZGK9PyhJ+VHl2fFoRXxF8qXD2bNgkWL4NIlu2ZGu3a2MVylaZoslErnfHL7sLTVUuY8N4ezl8/y+NeP03JOS34791v8herXt6vzvfUWTJ0KpUrB+PE6OWEapslCKYWI8KzPs0R3i+adx95hbvRcSg0rxeBVg7ly/UrchTJnhkGDIDLSDuRr396O0YiKStbYVfLQZKGU+lfmDJnpH9Kfnd12UqtoLd786U38R/qzZN+S+Av5+cHy5TBunE0UgYHQpw9cvMMsuCrV0WShlLpN0RxF+bbFtyxquYgYE0PdqXV5ZuYzHDxzMO4Cbm72ymLXLggNhQ8+sL2mfvghWeNWrqPJQikVr/ol6rOjyw4G1RzE0v1L8Rnuw3vL3uOfq/Gs6Z07N3z9NUREgKcn1Ktnl3b9/fdkjVslPU0WSqk7yuiRkbeqv8WubrtoWKoh/Zb3w3eEL9/u+jb+CQpr1ICtW6F/fzv1uY8PDBsG168na+wq6WiyUEo5pYB3AWY2ncnPbX4mc4bMNJ7ZmPrT6rPn9J64C2TMaMdjbN8OlSpBjx52rqnNm5M3cJUkNFkopRIlpEgIkZ0iGVJnCKuPrKbMiDL0/qk3F67Es9peiRJ26vNp0+xaGRUrwiuvwPnzyRu4uieaLJRSiZbBPQMvV3mZ3d1309K/JR+t+ojSw0ozY8eMuKumROD5520DeMeO8MUXtmpq7lydnDCV0GShlLprD2d5mK8bf83qsNXkyZKH5+c8T8jEELaf2B53gezZYeRIWL0acuWCJk3s9CGHDiVv4CrRNFkope5ZcIFg1ndYz6inRrH9j+2UG12Olxa/xJlL8ayDUaUKbNoEn3wCP/9sB/V9/DFcvZq8gSunabJQSiUJdzd3OlXoxJ7ue3ix/IsMXT+UkkNLMmHLhLjXzvDwgFdftetm1K5tV+oLCoIxYyg4daqd4ValGJoslFJJKlfmXIxsMJKNHTdSPGdxwhaE8ehXj7Lx941xFyhY0HavnTcPjh+HTp0oMm4c1KypCSMF0WShlHKJ8nnLszJsJRMbT+TgmYNUGluJjgs7cupiPDPUNm5sl3AVQcDOavu//8GFeHpZqWSlyUIp5TJu4kabsm3Y3X03L1d5mfFbxlNyaElGbBjB9Zg4BujVqQNeXhg3N7uGxs8/Q8mS8NVXOqDvPtNkoZRyOW8vbz6r8xlbO28l8OFAun3fjQpjK7Dq8KqbNwwOhvBwfg0Lg19+gVWroHBh6NDBTlC45A4TGiqX0mShlEo2fg/5Ed4mnJlNZ3Lq4imqTahGm3ltOHb+2H8bBQdzODTUJo5HH7UJY9YsO4tt3br26mPbtvv3JtIplyYLEakrIrtFZJ+I9I7j9V4islNEtolIuIgUivVaQRFZKiLRjm0KuzJWpVTyEBGe83uOXd120adaH2ZGzaTUsFJ8uvpTrl6Po+usCDRrBjt3wmefwYYN9iqjfXudoDAZuSxZiIg7MByoB/gCz4uI7y2bbQEqGGMCgG+AwbFemwR8bIzxASoBd1hRXimV2jzg+QADaw1kR5cdVCtYjdd+fI2yo8ry5bovmXp4KmuO3NITKmNGO03Ivn3238mT7VQifftqI3gycOWVRSVgnzHmgDHmCjADaBR7A2NMhDHmxgopa4H8AI6k4mGM+dGx3YVY2yml0pASuUqwqOUiFrRYwNnLZ3nph5cY9+s4ak6qeXvCAMiZEz791E4d0qCBndm2RAkYO1YbwV1I4p1i+F53LNIUqGuM6eB43BqobIzpHs/2w4DjxpgBItIY6ABcAYoAPwG9jTHXbynTEegIkCdPnqAZM2bcdbwXLlwgS5Ysd13eVTSuxNG4EielxTXp0CS+Pvg1Bvu7VCprKQb4DeDBjA/GWyZbVBTFRo7EOyqKC0WKcKBzZ/6sWNFWXyWxlHa+briXuEJCQjYZYyokuKExxiU3oCkwLtbj1sCweLZthb2yyBir7FmgKOABzAHa3+l4QUFB5l5ERETcU3lX0bgSR+NKnJQW1+rDq02mAZmMWz8349Hfw7i/524yDchk+vzUx5z550z8BWNijPnmG2OKFTMGjHniCWMiI5M8vpR2vm64l7iAjcaJ33RXVkP9BhSI9Ti/47mbiEht4G2goTHmsuPpo0CksVVY14D5QHkXxqqUSgGCCwQT3iacsCJhrGi3gt3dd9O4dGMGrRxE0S+LMmTNEC5fu3x7QRE7KeHOnTBkCGzcCOXKQVgY/Hbbz466C65MFhuAEiJSREQ8gRbAgtgbiEg5YDQ2UfxxS9nsIpLb8bgmsNOFsSqlUojgAsGEFgwluEAwxXIWY1qTaWzquIkK+SrQa2kvSg0rxeStk+Me1OfpCS+/DPv3Q69eMHWqHdSnjeD3zGXJwnFF0B1YAkQDs4wxUSLSX0QaOjb7GMgCzBaRSBFZ4Ch7HXgNCBeR7YAAY10Vq1IqZSuftzxLWi3hx9Y/kitzLtrMb0P5MeVZvHdx3Otn5MhhZ7SNjoann7aN4MWL20bwa9eS/w2kAS4dZ2GM+d4YU9IYU8wYM9Dx3LvGmBtJobYxJo8xJtBxaxir7I/GmABjjL8xpp2xPaqUUulY7aK12fDiBqY3mc6FKxeoP60+NSfVZP1v6+MuULQozJhhJyQsXtwuvBQYCIsX66JLiaQjuJVSqYqbuNGiTAuiu0UztN5Qov6IovK4yjSb3Yy9p/fGXahKFTt9yJw5cPky1K8PTz4JkZHJG3wqpslCKZUqebp70r1Sd/b33E/fx/uyeO9ifIb70HVRV45fOH57ARF49lmIioLPP4fNm6F8eXjhBW0Ed4ImC6VUqpY1Y1b61ejH/p776VyhM2M3j6XYl8V4N+Jdzl0+d3sBT0946SU7EvzVV2HaNDuo75134Pz55H8DqYQmC6VUmpAnSx6G1R9GdLdoGpRswPsr3qfYl8X4ct2XcXe3zZHDLuW6axc0agQDBtikMWaMNoLHQZOFUipNKZ6zODObzmR9h/X4P+TPSz+8hM9wH6Ztnxb38q5FisD06bB2rU0WnTpB2bLw/ffaCB6LJgulVJpU8ZGKhLcJ54fQH8iWMRuhc0MJGhPE0v1L4y5QuTKsWAFz58KVK/DUU/DEE9oI7qDJQimVZokIdYrXYXOnzUx5ZgpnLp2hzpQ61J5Um02/b4qrADzzjG0E/+IL2LLFNoK3awdHjyZ7/CmJJgulVJrnJm6EBoSyq9suPq/zOZHHI6kwtgItvmnB/j/3317A0xN69rQjwV97zVZTlSxJka++SreN4JoslFLpRkaPjLxU5SX299zP/6r/j4V7FlJ6eGl6fN+DP/6OY8mc7Nlh8GDYvRsaN6bQlCl2cN/o0emuEVyThVIq3fH28ub9mu+zr8c+OpTrwMiNIyn2ZTHeW/Ye5y/HceVQuDBMm8amESOgVCno3BkCAmDRonTTCK7JQimVbuXNmpeRDUYS1TWKusXr0m95P4oPLc7w9cO5cv32GYbO+/jA8uUwb569smjQAGrXtm0baZwmC6VUulfqwVLMbjabte3XUvrB0nRf3B3f4b7M3DHz9u62ItC4sW0EHzoUtm6FoCBo2xaOHLk/byAZaLJQSimHyvkrs6ztMha1XETmDJlpMacFlcZWIvxA+O0bZ8gA3bvbkeCvvw4zZ9rp0N9+G87FMXI8ldNkoZRSsYgI9UvUZ0unLUxsPJGTF09Se3Jt6k6py74L+24vkD07fPSRHQn+7LMwaJAd3DdqVJpqBNdkoZRScXB3c6dN2Tbs7r6bT5/8lA2/b+DFTS/Sam4rfv3r19sLFC5sF1tavx5Kl4YuXcDfH777Lk00gmuyUEqpO/Dy8KJXcC/299xPywItmRs9l1LDSvHyDy9z8u+TtxeoWBGWLYP58yEmxi6+VKuWneU2FdNkoZRSTsjulZ0Xi77I3h57aVu2LUPXD6XYl8UYsGIAf1/5++aNRezkhDt2wLBhsH27bQRv0ybVNoJrslBKqUR4JNsjjG04lh1ddlCraC3eiXiH4kOLM2rjKK5ev3rzxhkyQLduthH8zTdh1izbCN6nT6prBNdkoZRSd8Entw/zms9jVdgqiucsTpdFXSgzsgzf7Pzm9nXBvb3hww/tSPAmTeCDD+xI8BEj4OrVuA+QwmiyUEqpe/BogUdZ0W4FC1oswMPNg2azm1HlqyosP7j89o0LFYIpU2DDBvD1tVcdAQGwcGGKbwTXZKGUUvdIRHi61NNs67yN8Q3H8/v536kxsQZPTXuKbSe23V6gQgWIiIBvv7VJomFDqFkTNsUxE24KoclCKaWSiLubOy+Ue4E93fcwuPZgVh9ZTeCoQNrOb8uhM4du3ljEJont22H4cNsYXqECtG4Nhw/fnzdwB5oslFIqiWXKkInXq77OgZ4HeP3R15m5YyYlh5Xk1SWvcvri6Zs3zpABuna1jeC9e8Ps2bYR/K234OzZ+/MG4qDJQimlXCRHphx89MRH7O2xl1b+rfh83ecU/bIoH/zyARevXrx5Y29v2/C9Zw80a2YbxIsXt1cdKaARXJOFUkq5WAHvAnzV6Cu2dt7K44Uep8/PfSgxtARjN43lWswtU4IULAiTJ8PGjVCmjJ1/yt8fFiy4r43gmiyUUiqZlHmoDAueX8CKdiso5F2Ijt91xH+kP/N3zb+9u21QEPz8s00SYAf5hYTYJHIfaLJQSqlkVr1QdVaFrWJe83kAPDPzGaqOr8rKwytv3lDETheyfbsdk7Fzp51OpFUrOHQojj27jiYLpZS6D0SExqUbs73LdsY+PZZDZw9RfUJ1Gk5vSNQfUTdvnCGDnZhw3z47+nvOHLtiX+/e8OOPFJw6FdascWm8miyUUuo+8nDzoEP5DuztsZcPan3AikMrCBgVQNi3YRw5e8s8UtmywcCBdiT4c8/ZqdGffJIiX31lJyt0YcLQZKGUUilA5gyZ6V2tN/t77uflyi8zdftUSgwtwRs/vsFf//x188YFC8KkSXYEOCDGwJUrdrZbF9FkoZRSKUiuzLn4tM6n7Om+h+ZlmvPJ6k8o+mVRPl71Mf9c/efmjUNDIVMmYtzcwNMTatRwWVyaLJRSKgUqlL0QExtPJLJzJI8WeJQ3fnqDksNKMmHLBK7HXLcbBQdDeDgHw8IgPNw+dhFNFkoplYIF5AlgUctFRLSNIF/WfIQtCKPsqLKxPPQAAAn3SURBVLIs3L3QdrcNDuZwaKhLEwVoslBKqVShRuEarG2/lm+afcPVmKs0nNGQx75+jDGbxjD18FTWHNHeUEoppbDdbZv4NmFHlx2MemoUO0/upNN3nRj36zhqTarl0oShyUIppVKZDO4Z6FShEz0r9UQQAK5cv8Kyg8tcdkxNFkoplUo9WexJvDy8cMMNT3dPahSu4bJjabJQSqlUKrhAMOFtwgkrEkZ4m3CCC6TS3lAiUldEdovIPhHpHcfrvURkp4hsE5FwESl0y+vZROSoiAxzZZxKKZVaBRcIJrRgqEsTBbgwWYiIOzAcqAf4As+LiO8tm20BKhhjAoBvgMG3vP4+sMJVMSqllHKOK68sKgH7jDEHjDFXgBlAo9gbGGMijDE3VgBZC+S/8ZqIBAF5gKUujFEppZQTXJksHvl/e/cfa3Vdx3H8+eJHCeJUUBkFCVukNRcoZppKoOLUHP1iav5ImoU1f7aaC+fm6i/MaLW5mk5MNhGnCEWsIDQ0s+LHxYtA+GMJGYaCZiK6EC6v/vh8jn45HDgc7rl8D/e+H9vZ+Z4v3/P9vu6By/t8P99z3h+g2AVrY163N9cAvweQ1AuYDny/y9KFEELYb9pjwo1m7ViaBFxg+5v58VXAZ21fX2PbK4Hrgc/b3i7peqC/7R9Lmkwaqqr1vCnAFIDBgwePeeihhw4477Zt2xgwYMABP7+rRK7GRK7GRK7GdMdc48ePb7N9at0NbXfJDTgDWFR4PBWYWmO784B1wHGFdbOAl4ENwOvAVmDavo43ZswYd8aSJUs69fyuErkaE7kaE7ka0x1zASu8H/+n9zmgUrR/lgMjJY0AXgEuAy4vbiDpZOBu0hnI5sp621cUtplMOrPY49NUIYQQDo4uKxa2d+bhpEVAb+A+22sl/YhUyeYDdwIDgEckAbxse+KBHK+tre11SZ2ZZ/AY0llMq4lcjYlcjYlcjemOuY6vv0kXXrM41Eha4f0ZtzvIIldjIldjIldjenKu+AZ3CCGEuqJYhBBCqCuKxQfuKTvAXkSuxkSuxkSuxvTYXHHNIoQQQl1xZhFCCKGuKBYhhBDq6tHFQtIwSUtym/S1km4qOxOApMMkLZO0Kuf6YdmZiiT1lvSMpAVlZ6mQtEHSakntklaUnadC0lGS5kh6TtI6SV3bR3o/STohv1aV21ZJN7dAru/mf/NrJM2WdFjZmQAk3ZQzrS37dZJ0n6TNktYU1g2UtFjSi/n+6GYft0cXC2An8D3bnwJOB66r0Ua9DNuBc2yPAkYDF0g6veRMRTeRWrS0mvG2R7fY5+B/Diy0fSIwihZ53Ww/n1+r0cAY4F1gXpmZJH0UuJHUseEk0pd5LyszE4Ckk4BvkTppjwIulvTxEiPdD1xQte4HwOO2RwKP58dN1aOLhe1Ntlfm5bdJv8j76ox7UOSWLdvyw7751hKfRJA0FPgCcG/ZWVqdpCOBscAMANvv2f5vualqOhf4h+3OdEBolj5AP0l9gP7Av0vOA/BJYKntd23vBJ4EvlJWGNt/Av5TtfqLwMy8PBP4UrOP26OLRZGk4cDJwNJykyR5qKcd2Awstt0SuYCfAbcAu8oOUsXAHyS15W7ErWAEsAX4VR62u1fS4WWHquEyYHbZIWy/AvyE1ER0E/CW7VaYz2YNcLakQZL6AxcBw0rOVG2w7U15+VXSXEBNFcUCkDQAeBS42fbWsvMA2O7IQwRDgdPyqXCpJF0MbLbdVnaWGs6yfQppZsbrJI0tOxDpXfIpwC9tnwy8QxcMD3SGpA8BE4FHWiDL0aR3yCOAjwCH5+kLSmV7HXAHaSK2hUA70FFqqH3InWSbPhLR44uFpL6kQjHL9tyy81TLwxZL2HOMsgxnAhMlbSDNfHiOpAfKjZTkd6Xk7sXzSOPLZdsIbCycFc4hFY9WciGw0vZrZQchTVew3vYW2zuAucDnSs4EgO0ZtsfYHgu8CbxQdqYqr0kaApDvN9fZvmE9ulgotbqdAayz/dOy81RIOlbSUXm5HzABeK7cVGB7qu2htoeThi7+aLv0d36SDpd0RGUZOJ80dFAq268C/5J0Ql51LvD3EiPV8jVaYAgqexk4XVL//Lt5Li3ygQBJx+X7j5GuVzxYbqI9zAeuzstXA79p9gG6cj6LQ8GZwFXA6nx9AOBW278rMRPAEGCmpN6kgv6w7Zb5mGoLGgzMy23u+wAP2l5YbqT33QDMysM9LwHfKDnP+3JhnQBcW3YWANtLJc0BVpI+qfgMrdNe41FJg4AdwHVlflBB0mxgHHCMpI3A7cA04GFJ1wD/BC5p+nGj3UcIIYR6evQwVAghhP0TxSKEEEJdUSxCCCHUFcUihBBCXVEsQggh1BXFIhzyJG3L98MlXd7kfd9a9fgvzdx/s0maLOmusnOE7ieKRehOhgMNFYvcsG5fdisWtlviG8VdJX+3J4Q9RLEI3ck0UsO39jwvQm9Jd0paLulZSdcCSBon6SlJ88nfqJb069yEcG2lEaGkaaQOqO2SZuV1lbMY5X2vyfNoXFrY9xOFOSxm5W8j7yZvc4fSvCUvSDo7r9/tzEDSAknjKsfOx1wr6TFJp+X9vCRpYmH3w/L6FyXdXtjXlfl47ZLurhSGvN/pklYBLTHfRmhBtuMWt0P6BmzL9+OABYX1U4Db8vKHgRWkJnXjSE39RhS2HZjv+5FahQwq7rvGsb4KLCbNuTCY1KpiSN73W6QGkL2Av5KaHFZnfgKYnpcvAh7Ly5OBuwrbLQDG5WUDF+bleaTGdn1Jcyy0F56/CRhU+FlOJbXZ/i3QN2/3C+Drhf1eUvbfY9xa+9bT232E7u184NOSJuXHRwIjgfeAZbbXF7a9UdKX8/KwvN0b+9j3WcBs2x2kJm5PAp8BtuZ9bwTIbWSGA3+usY9K48q2vE0975G6ngKsBrbb3iFpddXzF9t+Ix9/bs66kzTJ0fJ8otOPD5rNdZCaaYawV1EsQncm4Abbi3ZbmYZ13ql6fB5whu13JT0BdGY6z+2F5Q72/nu2vcY2O9l9eLiYY4ftSn+eXZXn295Vde2luoePSa/FTNtTa+T4Xy56IexVXLMI3cnbwBGFx4uA7+Q29Ej6xF4mHzoSeDMXihNJU+xW7Kg8v8pTwKX5usixpBnxljXhZ9gAjJbUS9IwDqzV+gSlOZn7kWZMe5o01eakQvfUgZKOb0Le0EPEmUXoTp4FOvKF2vtJ818PB1bmi8xbqD3d5ELg25LWAc8Dfyv82T3As5JW2r6isH4e6WLwKtI791tsv5qLTWc8DawnXXhfR+rA2qhlpGGlocADtlcASLqNNJtgL3L3VFKH0hDqiq6zIYQQ6ophqBBCCHVFsQghhFBXFIsQQgh1RbEIIYRQVxSLEEIIdUWxCCGEUFcUixBCCHX9Hxtr8h4NiCsbAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get loss\n",
+ "train_loss_proxy = %sql SELECT training_loss FROM iris_model_summary;\n",
+ "test_loss_proxy = %sql SELECT validation_loss FROM iris_model_summary;\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "train_loss = np.array(train_loss_proxy).reshape(num_points)\n",
+ "test_loss = np.array(test_loss_proxy).reshape(num_points)\n",
+ "\n",
+ "#plot\n",
+ "plt.title('Iris validation loss by iteration - warm start')\n",
+ "plt.xlabel('Iteration number')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.grid(True)\n",
+ "plt.plot(iters, train_loss, 'g.-', label='Train')\n",
+ "plt.plot(iters, test_loss, 'r.-', label='Test')\n",
+ "plt.legend();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"transfer_learn\"></a>\n",
+ "# Transfer learning\n",
+ "\n",
+ "<a id=\"load2\"></a>\n",
+ "# 1. Define and load model architecture with some layers frozen\n",
+ "Here we want to start with initial weights from a pre-trained model rather than training from scratch. We also want to use a model architecture with the earlier feature layer(s) frozen to save on training time. The example below is somewhat contrived but gives you the idea of the steps.\n",
+ "\n",
+ "First define a model architecture with the 1st hidden layer frozen:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_1\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense_3 (Dense) (None, 10) 50 \n",
+ "_________________________________________________________________\n",
+ "dense_4 (Dense) (None, 10) 110 \n",
+ "_________________________________________________________________\n",
+ "dense_5 (Dense) (None, 3) 33 \n",
+ "=================================================================\n",
+ "Total params: 193\n",
+ "Trainable params: 143\n",
+ "Non-trainable params: 50\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_transfer = Sequential()\n",
+ "model_transfer.add(Dense(10, activation='relu', input_shape=(4,), trainable=False))\n",
+ "model_transfer.add(Dense(10, activation='relu'))\n",
+ "model_transfer.add(Dense(3, activation='softmax'))\n",
+ " \n",
+ "model_transfer.summary();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": false, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"name\": \"sequential_1\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model_transfer.to_json()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load transfer model into model architecture table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "2 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>model_arch</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_1', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>Sophie</td>\n",
+ " <td>A simple model</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>{u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_2', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'dtype': u'float32', u'activation': u'relu', u'trainable': False, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'batch_input_shape': [None, 4], u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_3', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'relu', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 10, u'use_bias': True, u'activity_regularizer': None}}, {u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u'VarianceScaling', u'config': {u'distribution': u'uniform', u'scale': 1.0, u'seed': None, u'mode': u'fan_avg'}}, u'name': u'dense_4', u'kernel_constraint': None, u'bias_regularizer': None, u'bias_constraint': None, u'activation': u'softmax', u'trainable': True, u'kernel_regularizer': None, u'bias_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}</td>\n",
+ " <td>Maria</td>\n",
+ " <td>A transfer model</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1340 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, u'Sophie', u'A simple model'),\n",
+ " (2, {u'class_name': u'Sequential', u'keras_version': u'2.1.6', u'config': [{u'class_name': u'Dense', u'config': {u'kernel_initializer': {u'class_name': u' ... (1341 characters truncated) ... s_initializer': {u'class_name': u'Zeros', u'config': {}}, u'units': 3, u'use_bias': True, u'activity_regularizer': None}}], u'backend': u'tensorflow'}, u'Maria', u'A transfer model')]"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT madlib.load_keras_model('model_arch_library', -- Output table, \n",
+ "$$\n",
+ "{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": false, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"batch_input_shape\": [null, 4], \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 3, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}\n",
+ "$$\n",
+ "::json, -- JSON blob\n",
+ " NULL, -- Weights\n",
+ " 'Maria', -- Name\n",
+ " 'A transfer model' -- Descr\n",
+ ");\n",
+ "\n",
+ "SELECT model_id, model_arch, name, description FROM model_arch_library ORDER BY model_id;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"train2\"></a>\n",
+ "# 2. Train transfer model\n",
+ "\n",
+ "Fetch the weights from a previous MADlib run. (Normally these would be downloaded from a source that trained the same model architecture on a related dataset.)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "UPDATE model_arch_library \n",
+ "SET model_weights = iris_model.model_weights \n",
+ "FROM iris_model \n",
+ "WHERE model_arch_library.model_id = 2;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now train the model using the transfer model and the pre-trained weights:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>madlib_keras_fit</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td></td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[('',)]"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS iris_model, iris_model_summary;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_fit('iris_train_packed', -- source table\n",
+ " 'iris_model', -- model output table\n",
+ " 'model_arch_library', -- model arch table\n",
+ " 2, -- model arch id\n",
+ " $$ loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] $$, -- compile_params\n",
+ " $$ batch_size=5, epochs=3 $$, -- fit_params\n",
+ " 10, -- num_iterations\n",
+ " FALSE, -- use GPUs\n",
+ " 'iris_test_packed', -- validation dataset\n",
+ " 2 -- metrics compute frequency\n",
+ " );"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>source_table</th>\n",
+ " <th>model</th>\n",
+ " <th>dependent_varname</th>\n",
+ " <th>independent_varname</th>\n",
+ " <th>model_arch_table</th>\n",
+ " <th>model_id</th>\n",
+ " <th>compile_params</th>\n",
+ " <th>fit_params</th>\n",
+ " <th>num_iterations</th>\n",
+ " <th>validation_table</th>\n",
+ " <th>object_table</th>\n",
+ " <th>metrics_compute_frequency</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " <th>model_type</th>\n",
+ " <th>model_size</th>\n",
+ " <th>start_training_time</th>\n",
+ " <th>end_training_time</th>\n",
+ " <th>metrics_elapsed_time</th>\n",
+ " <th>madlib_version</th>\n",
+ " <th>num_classes</th>\n",
+ " <th>dependent_vartype</th>\n",
+ " <th>normalizing_const</th>\n",
+ " <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
+ " <th>training_metrics_final</th>\n",
+ " <th>training_loss_final</th>\n",
+ " <th>training_metrics</th>\n",
+ " <th>training_loss</th>\n",
+ " <th>validation_metrics_final</th>\n",
+ " <th>validation_loss_final</th>\n",
+ " <th>validation_metrics</th>\n",
+ " <th>validation_loss</th>\n",
+ " <th>metrics_iters</th>\n",
+ " <th>class_text_class_values</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>iris_train_packed</td>\n",
+ " <td>iris_model</td>\n",
+ " <td>[u'class_text']</td>\n",
+ " <td>[u'attributes']</td>\n",
+ " <td>model_arch_library</td>\n",
+ " <td>2</td>\n",
+ " <td> loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] </td>\n",
+ " <td> batch_size=5, epochs=3 </td>\n",
+ " <td>10</td>\n",
+ " <td>iris_test_packed</td>\n",
+ " <td>None</td>\n",
+ " <td>2</td>\n",
+ " <td>None</td>\n",
+ " <td>None</td>\n",
+ " <td>madlib_keras</td>\n",
+ " <td>0.7900390625</td>\n",
+ " <td>2021-03-10 23:41:32.786660</td>\n",
+ " <td>2021-03-10 23:41:33.751452</td>\n",
+ " <td>[0.527504205703735, 0.640918016433716, 0.750359058380127, 0.854739189147949, 0.964727163314819]</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[3]</td>\n",
+ " <td>[u'character varying']</td>\n",
+ " <td>1.0</td>\n",
+ " <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.966666638851</td>\n",
+ " <td>0.189194485545</td>\n",
+ " <td>[0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166]</td>\n",
+ " <td>[0.228746756911278, 0.217142105102539, 0.20684190094471, 0.197493135929108, 0.189194485545158]</td>\n",
+ " <td>0.933333337307</td>\n",
+ " <td>0.192518442869</td>\n",
+ " <td>[0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166, 0.933333337306976]</td>\n",
+ " <td>[0.231074765324593, 0.21947817504406, 0.209244951605797, 0.200158298015594, 0.192518442869186]</td>\n",
+ " <td>[2, 4, 6, 8, 10]</td>\n",
+ " <td>[u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica']</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'iris_train_packed', u'iris_model', [u'class_text'], [u'attributes'], u'model_arch_library', 2, u\" loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] \", u' batch_size=5, epochs=3 ', 10, u'iris_test_packed', None, 2, None, None, u'madlib_keras', 0.7900390625, datetime.datetime(2021, 3, 10, 23, 41, 32, 786660), datetime.datetime(2021, 3, 10, 23, 41, 33, 751452), [0.527504205703735, 0.640918016433716, 0.750359058380127, 0.854739189147949, 0.964727163314819], u'1.18.0-dev', [3], [u'character varying'], 1.0, [u'accuracy'], u'categorical_crossentropy', 0.966666638851166, 0.189194485545158, [0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166], [0.228746756911278, 0.217142105102539, 0.20684190094471, 0.197493135929108, 0.189194485545158], 0.933333337306976, 0.192518442869186, [0.966666638851166, 0.966666638851166, 0.966666638851166, 0.966666638851166, 0.933333337306976], [0.231074765324593, 0.21947817504406, 0.209244951605797, 0.200158298015594, 0.192518442869186], [2, 4, 6, 8, 10], [u'Iris-setosa', u'Iris-versicolor', u'Iris-virginica'])]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT * FROM iris_model_summary;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Note loss picks up from where the last training left off:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import sys\n",
+ "import os\n",
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "# get accuracy and iteration number\n",
+ "iters_proxy = %sql SELECT metrics_iters FROM iris_model_summary;\n",
+ "train_accuracy_proxy = %sql SELECT training_metrics FROM iris_model_summary;\n",
+ "test_accuracy_proxy = %sql SELECT validation_metrics FROM iris_model_summary;\n",
+ "\n",
+ "# get number of points\n",
+ "num_points_proxy = %sql SELECT array_length(metrics_iters,1) FROM iris_model_summary;\n",
+ "num_points = num_points_proxy[0]\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "iters = np.array(iters_proxy).reshape(num_points)\n",
+ "train_accuracy = np.array(train_accuracy_proxy).reshape(num_points)\n",
+ "test_accuracy = np.array(test_accuracy_proxy).reshape(num_points)\n",
+ "\n",
+ "#plot\n",
+ "plt.title('Iris validation accuracy by iteration - transfer learn')\n",
+ "plt.xlabel('Iteration number')\n",
+ "plt.ylabel('Accuracy')\n",
+ "plt.grid(True)\n",
+ "plt.plot(iters, train_accuracy, 'g.-', label='Train')\n",
+ "plt.plot(iters, test_accuracy, 'r.-', label='Test')\n",
+ "plt.legend();"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n",
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# get loss\n",
+ "train_loss_proxy = %sql SELECT training_loss FROM iris_model_summary;\n",
+ "test_loss_proxy = %sql SELECT validation_loss FROM iris_model_summary;\n",
+ "\n",
+ "# reshape to np arrays\n",
+ "train_loss = np.array(train_loss_proxy).reshape(num_points)\n",
+ "test_loss = np.array(test_loss_proxy).reshape(num_points)\n",
+ "\n",
+ "#plot\n",
+ "plt.title('Iris validation loss by iteration - transfer learn')\n",
+ "plt.xlabel('Iteration number')\n",
+ "plt.ylabel('Loss')\n",
+ "plt.grid(True)\n",
+ "plt.plot(iters, train_loss, 'g.-', label='Train')\n",
+ "plt.plot(iters, test_loss, 'r.-', label='Test')\n",
+ "plt.legend();"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-cifar10-cnn-v3.ipynb b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-cifar10-cnn-v3.ipynb
similarity index 99%
rename from community-artifacts/Deep-learning/MADlib-Keras-cifar10-cnn-v3.ipynb
rename to community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-cifar10-cnn-v3.ipynb
index 987ff4b..f7053ef 100644
--- a/community-artifacts/Deep-learning/MADlib-Keras-cifar10-cnn-v3.ipynb
+++ b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-cifar10-cnn-v3.ipynb
@@ -59,46 +59,21 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
"# Greenplum Database 5.x on GCP - via tunnel\n",
"%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
@@ -108,7 +83,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -126,15 +101,15 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g9d9f756, cmake configuration time: Thu Mar 4 23:11:53 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-89-g9d9f756, cmake configuration time: Thu Mar 4 23:11:53 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -155,32 +130,17 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Couldn't import dot_parser, loading of dot files will not be possible.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"from __future__ import print_function\n",
- "import keras\n",
- "from keras.datasets import cifar10\n",
- "from keras.preprocessing.image import ImageDataGenerator\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense, Dropout, Activation, Flatten\n",
- "from keras.layers import Conv2D, MaxPooling2D\n",
+ "from tensorflow import keras\n",
+ "from tensorflow.keras.datasets import cifar10\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
+ "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
"import os\n",
"\n",
"batch_size = 32\n",
@@ -197,7 +157,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
diff --git a/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-cifar10-inference-v1.ipynb b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-cifar10-inference-v1.ipynb
new file mode 100644
index 0000000..86848f6
--- /dev/null
+++ b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-cifar10-inference-v1.ipynb
@@ -0,0 +1,829 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Inference for CIFAR-10 dataset using predict BYOM\n",
+ "The predict BYOM function allows you to do inference using models that have not been trained with MADlib, but rather imported or created elsewhere. It was added in MADlib 1.17.\n",
+ "\n",
+ "In this workbook we train a model in Python using\n",
+ "https://keras.io/examples/cifar10_cnn/\n",
+ "and run inference on the validation set.\n",
+ "\n",
+ "## Table of contents\n",
+ "\n",
+ "<a href=\"#setup\">1. Setup</a>\n",
+ "\n",
+ "<a href=\"#train_model\">2. Train model in Python</a>\n",
+ "\n",
+ "<a href=\"#load_model\">3. Load model into table</a>\n",
+ "\n",
+ "<a href=\"#load_images\">4. Get validation data set and load into table</a>\n",
+ "\n",
+ "<a href=\"#inference\">5. Inference</a>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"setup\"></a>\n",
+ "# 1. Setup"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load_ext sql"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
+ " \n",
+ "# PostgreSQL local\n",
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>version</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-91-g16070e5, cmake configuration time: Mon Mar 8 16:58:24 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-91-g16070e5, cmake configuration time: Mon Mar 8 16:58:24 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%sql select madlib.version();\n",
+ "#%sql select version();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"train_model\"></a>\n",
+ "# 2. Train model in Python\n",
+ "\n",
+ "Train a model in Python using https://keras.io/examples/cifar10_cnn/\n",
+ "\n",
+ "Define model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x_train shape: (50000, 32, 32, 3)\n",
+ "50000 train samples\n",
+ "10000 test samples\n",
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/tensorflow/python/ops/init_ops.py:1251: calling __init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import print_function\n",
+ "from tensorflow import keras\n",
+ "from tensorflow.keras.datasets import cifar10\n",
+ "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
+ "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+ "import os\n",
+ "\n",
+ "batch_size = 32\n",
+ "num_classes = 10\n",
+ "epochs = 25\n",
+ "data_augmentation = True\n",
+ "num_predictions = 20\n",
+ "#save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
+ "#model_name = 'keras_cifar10_trained_model.h5'\n",
+ "\n",
+ "# The data, split between train and test sets:\n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+ "print('x_train shape:', x_train.shape)\n",
+ "print(x_train.shape[0], 'train samples')\n",
+ "print(x_test.shape[0], 'test samples')\n",
+ "\n",
+ "# Convert class vectors to binary class matrices.\n",
+ "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
+ "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
+ "\n",
+ "model = Sequential()\n",
+ "model.add(Conv2D(32, (3, 3), padding='same',\n",
+ " input_shape=x_train.shape[1:]))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(Conv2D(32, (3, 3)))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model.add(Dropout(0.25))\n",
+ "\n",
+ "model.add(Conv2D(64, (3, 3), padding='same'))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(Conv2D(64, (3, 3)))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+ "model.add(Dropout(0.25))\n",
+ "\n",
+ "model.add(Flatten())\n",
+ "model.add(Dense(512))\n",
+ "model.add(Activation('relu'))\n",
+ "model.add(Dropout(0.5))\n",
+ "model.add(Dense(num_classes))\n",
+ "model.add(Activation('softmax'))\n",
+ "\n",
+ "# initiate RMSprop optimizer\n",
+ "opt = keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)\n",
+ "\n",
+ "# Let's train the model using RMSprop\n",
+ "model.compile(loss='categorical_crossentropy',\n",
+ " optimizer=opt,\n",
+ " metrics=['accuracy']);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.2.4-tf\", \"config\": {\"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"conv2d\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"conv2d_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_1\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d\", \"dtype\": \"float32\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"name\": \"dropout\", \"dtype\": \"float32\", \"trainable\": true, \"rate\": 0.25, \"seed\": null, \"noise_shape\": null}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"conv2d_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_2\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"conv2d_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_3\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"dtype\": \"float32\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"name\": \"dropout_1\", \"dtype\": \"float32\", \"trainable\": true, \"rate\": 0.25, \"seed\": null, \"noise_shape\": null}}, {\"class_name\": \"Flatten\", \"config\": {\"dtype\": \"float32\", \"trainable\": true, \"name\": \"flatten\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 512, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_4\"}}, {\"class_name\": \"Dropout\", \"config\": {\"name\": \"dropout_2\", \"dtype\": \"float32\", \"trainable\": true, \"rate\": 0.5, \"seed\": null, \"noise_shape\": null}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"dtype\": \"float32\", \"seed\": null}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {\"dtype\": \"float32\"}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"dtype\": \"float32\", \"activation\": \"softmax\", \"trainable\": true, \"name\": \"activation_5\"}}], \"name\": \"sequential\"}, \"backend\": \"tensorflow\"}'"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.to_json()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Using real-time data augmentation.\n",
+ "Epoch 1/25\n",
+ "1563/1563 [==============================] - 88s 56ms/step - loss: 1.9260 - acc: 0.2931 - val_loss: 1.7065 - val_acc: 0.3839\n",
+ "Epoch 2/25\n",
+ "1563/1563 [==============================] - 95s 61ms/step - loss: 1.6191 - acc: 0.4075 - val_loss: 1.4352 - val_acc: 0.4829\n",
+ "Epoch 3/25\n",
+ "1563/1563 [==============================] - 94s 60ms/step - loss: 1.4935 - acc: 0.4605 - val_loss: 1.3301 - val_acc: 0.5153\n",
+ "Epoch 4/25\n",
+ "1563/1563 [==============================] - 94s 60ms/step - loss: 1.4039 - acc: 0.4978 - val_loss: 1.2473 - val_acc: 0.5525\n",
+ "Epoch 5/25\n",
+ "1563/1563 [==============================] - 93s 60ms/step - loss: 1.3317 - acc: 0.5238 - val_loss: 1.1748 - val_acc: 0.5796\n",
+ "Epoch 6/25\n",
+ "1563/1563 [==============================] - 98s 63ms/step - loss: 1.2711 - acc: 0.5486 - val_loss: 1.1139 - val_acc: 0.6057\n",
+ "Epoch 7/25\n",
+ "1563/1563 [==============================] - 97s 62ms/step - loss: 1.2252 - acc: 0.5654 - val_loss: 1.0854 - val_acc: 0.6185\n",
+ "Epoch 8/25\n",
+ "1563/1563 [==============================] - 101s 64ms/step - loss: 1.1810 - acc: 0.5810 - val_loss: 1.0498 - val_acc: 0.6252\n",
+ "Epoch 9/25\n",
+ "1563/1563 [==============================] - 99s 63ms/step - loss: 1.1507 - acc: 0.5926 - val_loss: 1.0188 - val_acc: 0.6408\n",
+ "Epoch 10/25\n",
+ "1563/1563 [==============================] - 99s 63ms/step - loss: 1.1101 - acc: 0.6078 - val_loss: 1.0167 - val_acc: 0.6456\n",
+ "Epoch 11/25\n",
+ "1563/1563 [==============================] - 97s 62ms/step - loss: 1.0870 - acc: 0.6156 - val_loss: 0.9796 - val_acc: 0.6538\n",
+ "Epoch 12/25\n",
+ "1563/1563 [==============================] - 98s 63ms/step - loss: 1.0580 - acc: 0.6249 - val_loss: 0.9458 - val_acc: 0.6663\n",
+ "Epoch 13/25\n",
+ "1563/1563 [==============================] - 106s 68ms/step - loss: 1.0387 - acc: 0.6324 - val_loss: 0.8871 - val_acc: 0.6869\n",
+ "Epoch 14/25\n",
+ "1563/1563 [==============================] - 106s 68ms/step - loss: 1.0234 - acc: 0.6391 - val_loss: 0.9362 - val_acc: 0.6731\n",
+ "Epoch 15/25\n",
+ "1563/1563 [==============================] - 106s 68ms/step - loss: 1.0014 - acc: 0.6486 - val_loss: 0.9410 - val_acc: 0.6711\n",
+ "Epoch 16/25\n",
+ "1563/1563 [==============================] - 107s 69ms/step - loss: 0.9868 - acc: 0.6559 - val_loss: 0.9640 - val_acc: 0.6688\n",
+ "Epoch 17/25\n",
+ "1563/1563 [==============================] - 102s 66ms/step - loss: 0.9695 - acc: 0.6608 - val_loss: 0.9420 - val_acc: 0.6724\n",
+ "Epoch 18/25\n",
+ "1563/1563 [==============================] - 113s 73ms/step - loss: 0.9630 - acc: 0.6624 - val_loss: 0.8186 - val_acc: 0.7190\n",
+ "Epoch 19/25\n",
+ "1563/1563 [==============================] - 129s 83ms/step - loss: 0.9508 - acc: 0.6675 - val_loss: 0.8731 - val_acc: 0.6972\n",
+ "Epoch 20/25\n",
+ "1563/1563 [==============================] - 188s 121ms/step - loss: 0.9413 - acc: 0.6729 - val_loss: 0.8053 - val_acc: 0.7184\n",
+ "Epoch 21/25\n",
+ "1563/1563 [==============================] - 139s 89ms/step - loss: 0.9326 - acc: 0.6745 - val_loss: 0.8060 - val_acc: 0.7158\n",
+ "Epoch 22/25\n",
+ "1563/1563 [==============================] - 139s 89ms/step - loss: 0.9195 - acc: 0.6828 - val_loss: 0.8315 - val_acc: 0.7116\n",
+ "Epoch 23/25\n",
+ "1563/1563 [==============================] - 349s 224ms/step - loss: 0.9152 - acc: 0.6835 - val_loss: 0.8245 - val_acc: 0.7126\n",
+ "Epoch 24/25\n",
+ "1563/1563 [==============================] - 371s 237ms/step - loss: 0.9035 - acc: 0.6880 - val_loss: 0.7956 - val_acc: 0.7262\n",
+ "Epoch 25/25\n",
+ "1563/1563 [==============================] - 361s 231ms/step - loss: 0.8991 - acc: 0.6885 - val_loss: 0.7894 - val_acc: 0.7285\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<tensorflow.python.keras.callbacks.History at 0x1449559d0>"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10000/10000 [==============================] - 13s 1ms/sample - loss: 0.7894 - acc: 0.7285\n",
+ "Test loss: 0.789413549900055\n",
+ "Test accuracy: 0.7285\n"
+ ]
+ }
+ ],
+ "source": [
+ "x_train = x_train.astype('float32')\n",
+ "x_test = x_test.astype('float32')\n",
+ "x_train /= 255\n",
+ "x_test /= 255\n",
+ "\n",
+ "if not data_augmentation:\n",
+ " print('Not using data augmentation.')\n",
+ " model.fit(x_train, y_train,\n",
+ " batch_size=batch_size,\n",
+ " epochs=epochs,\n",
+ " validation_data=(x_test, y_test),\n",
+ " shuffle=True)\n",
+ "else:\n",
+ " print('Using real-time data augmentation.')\n",
+ " # This will do preprocessing and realtime data augmentation:\n",
+ " datagen = ImageDataGenerator(\n",
+ " featurewise_center=False, # set input mean to 0 over the dataset\n",
+ " samplewise_center=False, # set each sample mean to 0\n",
+ " featurewise_std_normalization=False, # divide inputs by std of the dataset\n",
+ " samplewise_std_normalization=False, # divide each input by its std\n",
+ " zca_whitening=False, # apply ZCA whitening\n",
+ " zca_epsilon=1e-06, # epsilon for ZCA whitening\n",
+ " rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)\n",
+ " # randomly shift images horizontally (fraction of total width)\n",
+ " width_shift_range=0.1,\n",
+ " # randomly shift images vertically (fraction of total height)\n",
+ " height_shift_range=0.1,\n",
+ " shear_range=0., # set range for random shear\n",
+ " zoom_range=0., # set range for random zoom\n",
+ " channel_shift_range=0., # set range for random channel shifts\n",
+ " # set mode for filling points outside the input boundaries\n",
+ " fill_mode='nearest',\n",
+ " cval=0., # value used for fill_mode = \"constant\"\n",
+ " horizontal_flip=True, # randomly flip images\n",
+ " vertical_flip=False, # randomly flip images\n",
+ " # set rescaling factor (applied before any other transformation)\n",
+ " rescale=None,\n",
+ " # set function that will be applied on each input\n",
+ " preprocessing_function=None,\n",
+ " # image data format, either \"channels_first\" or \"channels_last\"\n",
+ " data_format=None,\n",
+ " # fraction of images reserved for validation (strictly between 0 and 1)\n",
+ " validation_split=0.0)\n",
+ "\n",
+ " # Compute quantities required for feature-wise normalization\n",
+ " # (std, mean, and principal components if ZCA whitening is applied).\n",
+ " datagen.fit(x_train)\n",
+ "\n",
+ " # Fit the model on the batches generated by datagen.flow().\n",
+ " model.fit_generator(datagen.flow(x_train, y_train,\n",
+ " batch_size=batch_size),\n",
+ " epochs=epochs,\n",
+ " validation_data=(x_test, y_test),\n",
+ " workers=1)\n",
+ "\n",
+ "# Save model and weights\n",
+ "#if not os.path.isdir(save_dir):\n",
+ "# os.makedirs(save_dir)\n",
+ "#model_path = os.path.join(save_dir, model_name)\n",
+ "#model.save(model_path)\n",
+ "#print('Saved trained model at %s ' % model_path)\n",
+ "\n",
+ "# Score trained model.\n",
+ "scores = model.evaluate(x_test, y_test, verbose=1)\n",
+ "print('Test loss:', scores[0])\n",
+ "print('Test accuracy:', scores[1])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_model\"></a>\n",
+ "# 3. Load model into table\n",
+ "\n",
+ "Load the model architecture and weights into the model architecture table"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>model_id</th>\n",
+ " <th>name</th>\n",
+ " <th>description</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>CIFAR10 model</td>\n",
+ " <td>CNN model with weights trained on CIFAR10.</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'CIFAR10 model', u'CNN model with weights trained on CIFAR10.')]"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import psycopg2 as p2\n",
+ "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
+ "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
+ "cur = conn.cursor()\n",
+ "\n",
+ "from tensorflow.keras.layers import *\n",
+ "from tensorflow.keras import Sequential\n",
+ "import numpy as np\n",
+ "\n",
+ "# get weights, flatten and serialize\n",
+ "weights = model.get_weights()\n",
+ "weights_flat = [w.flatten() for w in weights]\n",
+ "weights1d = np.concatenate(weights_flat).ravel()\n",
+ "weights_bytea = p2.Binary(weights1d.tostring())\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS model_arch_library_cifar10;\n",
+ "query = \"SELECT madlib.load_keras_model('model_arch_library_cifar10', %s,%s,%s,%s)\"\n",
+ "cur.execute(query,[model.to_json(), weights_bytea, \"CIFAR10 model\", \"CNN model with weights trained on CIFAR10.\"])\n",
+ "conn.commit()\n",
+ "\n",
+ "# check weights loaded OK\n",
+ "%sql SELECT model_id, name, description FROM model_arch_library_cifar10;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"load_images\"></a>\n",
+ "# 4. Get validation data set and load into table\n",
+ "\n",
+ "First set up image loader using the script called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "import os\n",
+ "madlib_site_dir = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data'\n",
+ "sys.path.append(madlib_site_dir)\n",
+ "\n",
+ "# Import image loader module\n",
+ "from madlib_image_loader import ImageLoader, DbCredentials\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "#db_creds = DbCredentials(user='fmcquillan',\n",
+ "# host='localhost',\n",
+ "# port='5432',\n",
+ "# password='')\n",
+ "\n",
+ "# Specify database credentials, for connecting to db\n",
+ "db_creds = DbCredentials(user='gpadmin', \n",
+ " db_name='madlib',\n",
+ " host='localhost',\n",
+ " port='8000',\n",
+ " password='')\n",
+ "\n",
+ "# Initialize ImageLoader (increase num_workers to run faster)\n",
+ "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next load CIFAR-10 data from Keras consisting of 50,000 32x32 color training images, labeled over 10 categories, and 10,000 test images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MainProcess: Connected to madlib db.\n",
+ "Executing: CREATE TABLE cifar_10_test_data (id SERIAL, x REAL[], y TEXT)\n",
+ "CREATE TABLE\n",
+ "Created table cifar_10_test_data in madlib db\n",
+ "Spawning 5 workers...\n",
+ "Initializing PoolWorker-1 [pid 34877]\n",
+ "PoolWorker-1: Created temporary directory /tmp/madlib_xKS6PcacbB\n",
+ "Initializing PoolWorker-2 [pid 34878]\n",
+ "PoolWorker-2: Created temporary directory /tmp/madlib_RB5gvLkpc2\n",
+ "Initializing PoolWorker-3 [pid 34879]\n",
+ "PoolWorker-3: Created temporary directory /tmp/madlib_hU0p2y5lZq\n",
+ "Initializing PoolWorker-4 [pid 34880]\n",
+ "PoolWorker-4: Created temporary directory /tmp/madlib_K4phT5brYw\n",
+ "Initializing PoolWorker-5 [pid 34881]\n",
+ "PoolWorker-5: Created temporary directory /tmp/madlib_dcgGz3psbs\n",
+ "PoolWorker-1: Connected to madlib db.\n",
+ "PoolWorker-2: Connected to madlib db.\n",
+ "PoolWorker-3: Connected to madlib db.\n",
+ "PoolWorker-4: Connected to madlib db.\n",
+ "PoolWorker-5: Connected to madlib db.\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_xKS6PcacbB/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_hU0p2y5lZq/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_RB5gvLkpc2/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_K4phT5brYw/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_dcgGz3psbs/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_xKS6PcacbB/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_hU0p2y5lZq/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_dcgGz3psbs/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_K4phT5brYw/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_RB5gvLkpc2/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-5: Removed temporary directory /tmp/madlib_dcgGz3psbs\n",
+ "PoolWorker-4: Removed temporary directory /tmp/madlib_K4phT5brYw\n",
+ "PoolWorker-3: Removed temporary directory /tmp/madlib_hU0p2y5lZq\n",
+ "PoolWorker-2: Removed temporary directory /tmp/madlib_RB5gvLkpc2\n",
+ "PoolWorker-1: Removed temporary directory /tmp/madlib_xKS6PcacbB\n",
+ "Done! Loaded 10000 images in 120.575361967s\n",
+ "5 workers terminated.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from tensorflow.keras.datasets import cifar10\n",
+ "\n",
+ "# Load dataset into np array\n",
+ "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS cifar_10_test_data;\n",
+ "\n",
+ "# Save images to temporary directories and load into database\n",
+ "#iloader.load_dataset_from_np(x_train, y_train, 'cifar_10_train_data', append=False)\n",
+ "iloader.load_dataset_from_np(x_test, y_test, 'cifar_10_test_data', append=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<a id=\"inference\"></a>\n",
+ "# 5. Inference"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n",
+ "1 rows affected.\n",
+ "10 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>id</th>\n",
+ " <th>class_name</th>\n",
+ " <th>class_value</th>\n",
+ " <th>prob</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>3</td>\n",
+ " <td>0.41348055</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>8</td>\n",
+ " <td>0.9928235</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>1</td>\n",
+ " <td>0.52061397</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>4</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>0</td>\n",
+ " <td>0.619041</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>6</td>\n",
+ " <td>0.99125576</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>6</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>6</td>\n",
+ " <td>0.9728794</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>1</td>\n",
+ " <td>0.86263895</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>8</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>6</td>\n",
+ " <td>0.6395346</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>3</td>\n",
+ " <td>0.75210774</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>10</td>\n",
+ " <td>dependent_var</td>\n",
+ " <td>9</td>\n",
+ " <td>0.4934366</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(1, u'dependent_var', u'3', 0.41348055),\n",
+ " (2, u'dependent_var', u'8', 0.9928235),\n",
+ " (3, u'dependent_var', u'1', 0.52061397),\n",
+ " (4, u'dependent_var', u'0', 0.619041),\n",
+ " (5, u'dependent_var', u'6', 0.99125576),\n",
+ " (6, u'dependent_var', u'6', 0.9728794),\n",
+ " (7, u'dependent_var', u'1', 0.86263895),\n",
+ " (8, u'dependent_var', u'6', 0.6395346),\n",
+ " (9, u'dependent_var', u'3', 0.75210774),\n",
+ " (10, u'dependent_var', u'9', 0.4934366)]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "DROP TABLE IF EXISTS cifar10_predict_byom;\n",
+ "\n",
+ "SELECT madlib.madlib_keras_predict_byom('model_arch_library_cifar10', -- model arch table\n",
+ " 1, -- model arch id\n",
+ " 'cifar_10_test_data', -- test_table\n",
+ " 'id', -- id column\n",
+ " 'x', -- independent var\n",
+ " 'cifar10_predict_byom', -- output table\n",
+ " 'response', -- prediction type\n",
+ " FALSE, -- use gpus\n",
+ " NULL, -- class values\n",
+ " 255.0 -- normalizing const\n",
+ " );\n",
+ "SELECT * FROM cifar10_predict_byom ORDER BY id LIMIT 10;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Number of missclassifications:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2715</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(2715L,)]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT COUNT(*) FROM cifar10_predict_byom JOIN cifar_10_test_data USING (id)\n",
+ "WHERE cifar10_predict_byom.class_value != cifar_10_test_data.y;"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Predict accuracy. From https://keras.io/examples/cifar10_cnn/ accuracy claim is 75% on validation set after 25 epochs. From run above test accuracy slighly different but MADlib predict BYOM matches:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<table>\n",
+ " <tr>\n",
+ " <th>test_accuracy_percent</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>72.85</td>\n",
+ " </tr>\n",
+ "</table>"
+ ],
+ "text/plain": [
+ "[(Decimal('72.85'),)]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "%%sql\n",
+ "SELECT round(count(*)*100.0/10000.0, 2) as test_accuracy_percent from\n",
+ " (select cifar_10_test_data.y as actual, cifar10_predict_byom.class_value as estimated\n",
+ " from cifar10_predict_byom inner join cifar_10_test_data\n",
+ " on cifar_10_test_data.id=cifar10_predict_byom.id) q\n",
+ "WHERE q.actual=q.estimated;"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.16"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-imagenet-inference-v1.ipynb b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-imagenet-inference-v1.ipynb
old mode 100644
new mode 100755
similarity index 99%
rename from community-artifacts/Deep-learning/MADlib-Keras-imagenet-inference-v1.ipynb
rename to community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-imagenet-inference-v1.ipynb
index 968ea88..733e442
--- a/community-artifacts/Deep-learning/MADlib-Keras-imagenet-inference-v1.ipynb
+++ b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-imagenet-inference-v1.ipynb
@@ -5179,7 +5179,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Deep-learning/MADlib-Keras-transfer-learning-v3.ipynb b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-transfer-learning-v3.ipynb
similarity index 68%
rename from community-artifacts/Deep-learning/MADlib-Keras-transfer-learning-v3.ipynb
rename to community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-transfer-learning-v3.ipynb
index d025b81..b4dc80f 100644
--- a/community-artifacts/Deep-learning/MADlib-Keras-transfer-learning-v3.ipynb
+++ b/community-artifacts/Deep-learning/Train-single-model/MADlib-Keras-transfer-learning-v3.ipynb
@@ -26,44 +26,21 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 1,
"metadata": {
"scrolled": true
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The sql extension is already loaded. To reload it, use:\n",
- " %reload_ext sql\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
"# Greenplum Database 5.x on GCP - via tunnel\n",
"%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
@@ -73,7 +50,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -91,15 +68,15 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-91-g16070e5, cmake configuration time: Mon Mar 8 16:58:24 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-54-gec5614f, cmake configuration time: Wed Dec 18 17:08:05 UTC 2019, build type: release, build system: Linux-3.10.0-1062.4.3.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-91-g16070e5, cmake configuration time: Mon Mar 8 16:58:24 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
- "execution_count": 14,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -120,19 +97,19 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"\n",
"import datetime\n",
- "import keras\n",
- "from keras.datasets import mnist\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense, Dropout, Activation, Flatten\n",
- "from keras.layers import Conv2D, MaxPooling2D\n",
- "from keras import backend as K\n",
+ "from tensorflow import keras\n",
+ "from tensorflow.keras.datasets import mnist\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\n",
+ "from tensorflow.keras.layers import Conv2D, MaxPooling2D\n",
+ "from tensorflow.keras import backend as K\n",
"\n",
"now = datetime.datetime.now\n",
"\n",
@@ -164,7 +141,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -184,18 +161,9 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 20,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(4861, 28, 28)\n",
- "(4861, 28, 28, 1)\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# the data, split between train and test sets\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
@@ -212,11 +180,44 @@
"y_test_gte5 = y_test[y_test >= 5] - 5\n",
"\n",
"# reshape to match model architecture\n",
- "print(x_test_gte5.shape)\n",
"x_train_lt5=x_train_lt5.reshape(len(x_train_lt5), *input_shape)\n",
"x_test_lt5 = x_test_lt5.reshape(len(x_test_lt5), *input_shape)\n",
"x_train_gte5=x_train_gte5.reshape(len(x_train_gte5), *input_shape)\n",
"x_test_gte5 = x_test_gte5.reshape(len(x_test_gte5), *input_shape)\n",
+ "\n",
+ "y_train_lt5=y_train_lt5.reshape(len(y_train_lt5), 1)\n",
+ "y_test_lt5 = y_test_lt5.reshape(len(y_test_lt5), 1)\n",
+ "y_train_gte5=y_train_gte5.reshape(len(y_train_gte5), 1)\n",
+ "y_test_gte5 = y_test_gte5.reshape(len(y_test_gte5), 1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "check x shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(30596, 28, 28, 1)\n",
+ "(29404, 28, 28, 1)\n",
+ "(5139, 28, 28, 1)\n",
+ "(4861, 28, 28, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(x_train_lt5.shape)\n",
+ "print(x_train_gte5.shape)\n",
+ "print(x_test_lt5.shape)\n",
"print(x_test_gte5.shape)"
]
},
@@ -224,12 +225,42 @@
"cell_type": "markdown",
"metadata": {},
"source": [
+ "check y shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(30596, 1)\n",
+ "(29404, 1)\n",
+ "(5139, 1)\n",
+ "(4861, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(y_train_lt5.shape)\n",
+ "print(y_train_gte5.shape)\n",
+ "print(y_test_lt5.shape)\n",
+ "print(y_test_gte5.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"Load datasets into tables using image loader scripts called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning"
]
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
@@ -245,17 +276,19 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# Specify database credentials, for connecting to db\n",
- "#db_creds = DbCredentials(user='gpadmin',\n",
- "# host='35.239.240.26',\n",
+ "#db_creds = DbCredentials(user='fmcquillan',\n",
+ "# host='localhost',\n",
"# port='5432',\n",
- "# password='')\n",
+ "# password='')\n",
"\n",
- "db_creds = DbCredentials(user='gpadmin',\n",
+ "# Specify database credentials, for connecting to db\n",
+ "db_creds = DbCredentials(user='gpadmin', \n",
+ " db_name='madlib',\n",
" host='localhost',\n",
" port='8000',\n",
" password='')"
@@ -263,7 +296,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
@@ -273,271 +306,287 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Done.\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"MainProcess: Connected to madlib db.\n",
"Executing: CREATE TABLE train_lt5 (id SERIAL, x REAL[], y TEXT)\n",
"CREATE TABLE\n",
"Created table train_lt5 in madlib db\n",
"Spawning 5 workers...\n",
- "Initializing PoolWorker-1 [pid 32140]\n",
- "PoolWorker-1: Created temporary directory /tmp/madlib_bKeQrDW6UN\n",
- "Initializing PoolWorker-2 [pid 32141]\n",
- "PoolWorker-2: Created temporary directory /tmp/madlib_QnitPXnQvV\n",
- "Initializing PoolWorker-3 [pid 32142]\n",
- "PoolWorker-3: Created temporary directory /tmp/madlib_DvxZnkrs2R\n",
- "Initializing PoolWorker-4 [pid 32143]\n",
- "PoolWorker-4: Created temporary directory /tmp/madlib_qpVWvqPyAv\n",
- "Initializing PoolWorker-5 [pid 32144]\n",
- "PoolWorker-5: Created temporary directory /tmp/madlib_L4Q5odzoED\n",
- "PoolWorker-4: Connected to madlib db.\n",
- "PoolWorker-3: Connected to madlib db.\n",
- "PoolWorker-2: Connected to madlib db.\n",
+ "Initializing PoolWorker-1 [pid 45812]\n",
+ "PoolWorker-1: Created temporary directory /tmp/madlib_cUr8Y3iHQ6\n",
+ "Initializing PoolWorker-2 [pid 45813]\n",
+ "PoolWorker-2: Created temporary directory /tmp/madlib_Qg47OkNvGJ\n",
+ "Initializing PoolWorker-3 [pid 45814]\n",
+ "PoolWorker-3: Created temporary directory /tmp/madlib_znyrA6s1Nt\n",
+ "Initializing PoolWorker-4 [pid 45815]\n",
+ "PoolWorker-5: Created temporary directory /tmp/madlib_DO931mpYQ8\n",
+ "PoolWorker-4: Created temporary directory /tmp/madlib_yWXBCrh4jL\n",
+ "Initializing PoolWorker-5 [pid 45816]\n",
"PoolWorker-1: Connected to madlib db.\n",
+ "PoolWorker-3: Connected to madlib db.\n",
"PoolWorker-5: Connected to madlib db.\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_QnitPXnQvV/train_lt50000.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_DvxZnkrs2R/train_lt50000.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_qpVWvqPyAv/train_lt50000.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_L4Q5odzoED/train_lt50000.tmp\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_bKeQrDW6UN/train_lt50000.tmp\n",
+ "PoolWorker-4: Connected to madlib db.\n",
+ "PoolWorker-2: Connected to madlib db.\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_cUr8Y3iHQ6/train_lt50000.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_yWXBCrh4jL/train_lt50000.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_znyrA6s1Nt/train_lt50000.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_DO931mpYQ8/train_lt50000.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_Qg47OkNvGJ/train_lt50000.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_cUr8Y3iHQ6/train_lt50001.tmp\n",
+ "PoolWorker-5: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-4: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-3: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_yWXBCrh4jL/train_lt50001.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_znyrA6s1Nt/train_lt50001.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_DO931mpYQ8/train_lt50001.tmp\n",
"PoolWorker-2: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_Qg47OkNvGJ/train_lt50001.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_cUr8Y3iHQ6/train_lt50002.tmp\n",
+ "PoolWorker-5: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-4: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-3: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_znyrA6s1Nt/train_lt50002.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_yWXBCrh4jL/train_lt50002.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_DO931mpYQ8/train_lt50002.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_Qg47OkNvGJ/train_lt50002.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_cUr8Y3iHQ6/train_lt50003.tmp\n",
+ "PoolWorker-4: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-3: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-5: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_znyrA6s1Nt/train_lt50003.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_DO931mpYQ8/train_lt50003.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_yWXBCrh4jL/train_lt50003.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-1: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_Qg47OkNvGJ/train_lt50003.tmp\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_cUr8Y3iHQ6/train_lt50004.tmp\n",
"PoolWorker-3: Loaded 1000 images into train_lt5\n",
"PoolWorker-4: Loaded 1000 images into train_lt5\n",
"PoolWorker-5: Loaded 1000 images into train_lt5\n",
- "PoolWorker-1: Loaded 1000 images into train_lt5\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_QnitPXnQvV/train_lt50001.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_DvxZnkrs2R/train_lt50001.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_qpVWvqPyAv/train_lt50001.tmp\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_bKeQrDW6UN/train_lt50001.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_L4Q5odzoED/train_lt50001.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_yWXBCrh4jL/train_lt50004.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_DO931mpYQ8/train_lt50004.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_znyrA6s1Nt/train_lt50004.tmp\n",
"PoolWorker-2: Loaded 1000 images into train_lt5\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_QnitPXnQvV/train_lt50002.tmp\n",
"PoolWorker-1: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_cUr8Y3iHQ6/train_lt50005.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_Qg47OkNvGJ/train_lt50004.tmp\n",
+ "PoolWorker-5: Loaded 1000 images into train_lt5\n",
"PoolWorker-3: Loaded 1000 images into train_lt5\n",
"PoolWorker-4: Loaded 1000 images into train_lt5\n",
- "PoolWorker-5: Loaded 1000 images into train_lt5\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_bKeQrDW6UN/train_lt50002.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_qpVWvqPyAv/train_lt50002.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_DvxZnkrs2R/train_lt50002.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_L4Q5odzoED/train_lt50002.tmp\n",
- "PoolWorker-2: Loaded 1000 images into train_lt5\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_QnitPXnQvV/train_lt50003.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_DO931mpYQ8/train_lt50005.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_yWXBCrh4jL/train_lt50005.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_znyrA6s1Nt/train_lt50005.tmp\n",
"PoolWorker-1: Loaded 1000 images into train_lt5\n",
- "PoolWorker-3: Loaded 1000 images into train_lt5\n",
- "PoolWorker-5: Loaded 1000 images into train_lt5\n",
- "PoolWorker-4: Loaded 1000 images into train_lt5\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_bKeQrDW6UN/train_lt50003.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_qpVWvqPyAv/train_lt50003.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_DvxZnkrs2R/train_lt50003.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_L4Q5odzoED/train_lt50003.tmp\n",
"PoolWorker-2: Loaded 1000 images into train_lt5\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_QnitPXnQvV/train_lt50004.tmp\n",
- "PoolWorker-5: Loaded 1000 images into train_lt5\n",
- "PoolWorker-1: Loaded 1000 images into train_lt5\n",
- "PoolWorker-3: Loaded 1000 images into train_lt5\n",
+ "PoolWorker-1: Wrote 596 images to /tmp/madlib_cUr8Y3iHQ6/train_lt50006.tmp\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_Qg47OkNvGJ/train_lt50005.tmp\n",
+ "PoolWorker-1: Loaded 596 images into train_lt5\n",
"PoolWorker-4: Loaded 1000 images into train_lt5\n",
- "PoolWorker-2: Loaded 1000 images into train_lt5\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_L4Q5odzoED/train_lt50004.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_qpVWvqPyAv/train_lt50004.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_DvxZnkrs2R/train_lt50004.tmp\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_bKeQrDW6UN/train_lt50004.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_QnitPXnQvV/train_lt50005.tmp\n",
"PoolWorker-5: Loaded 1000 images into train_lt5\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_L4Q5odzoED/train_lt50005.tmp\n",
- "PoolWorker-1: Loaded 1000 images into train_lt5\n",
- "PoolWorker-4: Loaded 1000 images into train_lt5\n",
"PoolWorker-3: Loaded 1000 images into train_lt5\n",
"PoolWorker-2: Loaded 1000 images into train_lt5\n",
- "PoolWorker-2: Wrote 596 images to /tmp/madlib_QnitPXnQvV/train_lt50006.tmp\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_bKeQrDW6UN/train_lt50005.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_qpVWvqPyAv/train_lt50005.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_DvxZnkrs2R/train_lt50005.tmp\n",
- "PoolWorker-5: Loaded 1000 images into train_lt5\n",
- "PoolWorker-2: Loaded 596 images into train_lt5\n",
- "PoolWorker-1: Loaded 1000 images into train_lt5\n",
- "PoolWorker-3: Loaded 1000 images into train_lt5\n",
- "PoolWorker-4: Loaded 1000 images into train_lt5\n",
- "PoolWorker-2: Removed temporary directory /tmp/madlib_QnitPXnQvV\n",
- "PoolWorker-5: Removed temporary directory /tmp/madlib_L4Q5odzoED\n",
- "PoolWorker-4: Removed temporary directory /tmp/madlib_qpVWvqPyAv\n",
- "PoolWorker-1: Removed temporary directory /tmp/madlib_bKeQrDW6UN\n",
- "PoolWorker-3: Removed temporary directory /tmp/madlib_DvxZnkrs2R\n",
- "Done! Loaded 30596 images in 82.6620368958s\n",
+ "PoolWorker-5: Removed temporary directory /tmp/madlib_DO931mpYQ8\n",
+ "PoolWorker-2: Removed temporary directory /tmp/madlib_Qg47OkNvGJ\n",
+ "PoolWorker-3: Removed temporary directory /tmp/madlib_znyrA6s1Nt\n",
+ "PoolWorker-4: Removed temporary directory /tmp/madlib_yWXBCrh4jL\n",
+ "PoolWorker-1: Removed temporary directory /tmp/madlib_cUr8Y3iHQ6\n",
+ "Done! Loaded 30596 images in 87.504554987s\n",
"5 workers terminated.\n",
"MainProcess: Connected to madlib db.\n",
"Executing: CREATE TABLE test_lt5 (id SERIAL, x REAL[], y TEXT)\n",
"CREATE TABLE\n",
"Created table test_lt5 in madlib db\n",
"Spawning 5 workers...\n",
- "Initializing PoolWorker-6 [pid 32147]\n",
- "PoolWorker-6: Created temporary directory /tmp/madlib_qiEVuC6H2f\n",
- "Initializing PoolWorker-7 [pid 32148]\n",
- "PoolWorker-7: Created temporary directory /tmp/madlib_01Yrlb4kiZ\n",
- "Initializing PoolWorker-8 [pid 32149]\n",
- "PoolWorker-8: Created temporary directory /tmp/madlib_SjOxjuWfuf\n",
- "Initializing PoolWorker-9 [pid 32150]\n",
- "PoolWorker-9: Created temporary directory /tmp/madlib_Jc01ED7LdY\n",
- "Initializing PoolWorker-10 [pid 32151]\n",
- "PoolWorker-10: Created temporary directory /tmp/madlib_q26dtBioOd\n",
- "PoolWorker-7: Connected to madlib db.\n",
- "PoolWorker-8: Connected to madlib db.\n",
+ "Initializing PoolWorker-6 [pid 45832]\n",
+ "PoolWorker-6: Created temporary directory /tmp/madlib_zskbC1CxH3\n",
+ "Initializing PoolWorker-7 [pid 45833]\n",
+ "PoolWorker-7: Created temporary directory /tmp/madlib_nq4NtQVTcA\n",
+ "Initializing PoolWorker-8 [pid 45834]\n",
+ "PoolWorker-8: Created temporary directory /tmp/madlib_ottKdv45hY\n",
+ "Initializing PoolWorker-9 [pid 45835]\n",
+ "PoolWorker-9: Created temporary directory /tmp/madlib_H2i9eCgqnz\n",
+ "Initializing PoolWorker-10 [pid 45836]\n",
+ "PoolWorker-10: Created temporary directory /tmp/madlib_iLL7sa8Utg\n",
"PoolWorker-6: Connected to madlib db.\n",
+ "PoolWorker-7: Connected to madlib db.\n",
"PoolWorker-10: Connected to madlib db.\n",
"PoolWorker-9: Connected to madlib db.\n",
- "PoolWorker-10: Wrote 1000 images to /tmp/madlib_q26dtBioOd/test_lt50000.tmp\n",
- "PoolWorker-8: Wrote 1000 images to /tmp/madlib_SjOxjuWfuf/test_lt50000.tmp\n",
- "PoolWorker-9: Wrote 1000 images to /tmp/madlib_Jc01ED7LdY/test_lt50000.tmp\n",
- "PoolWorker-7: Wrote 1000 images to /tmp/madlib_01Yrlb4kiZ/test_lt50000.tmp\n",
- "PoolWorker-6: Wrote 1000 images to /tmp/madlib_qiEVuC6H2f/test_lt50000.tmp\n",
- "PoolWorker-10: Loaded 1000 images into test_lt5\n",
- "PoolWorker-10: Wrote 139 images to /tmp/madlib_q26dtBioOd/test_lt50001.tmp\n",
- "PoolWorker-8: Loaded 1000 images into test_lt5\n",
- "PoolWorker-10: Loaded 139 images into test_lt5\n",
- "PoolWorker-9: Loaded 1000 images into test_lt5\n",
- "PoolWorker-6: Loaded 1000 images into test_lt5\n",
+ "PoolWorker-8: Connected to madlib db.\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_nq4NtQVTcA/test_lt50000.tmp\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_zskbC1CxH3/test_lt50000.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_iLL7sa8Utg/test_lt50000.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_ottKdv45hY/test_lt50000.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_H2i9eCgqnz/test_lt50000.tmp\n",
"PoolWorker-7: Loaded 1000 images into test_lt5\n",
- "PoolWorker-8: Removed temporary directory /tmp/madlib_SjOxjuWfuf\n",
- "PoolWorker-10: Removed temporary directory /tmp/madlib_q26dtBioOd\n",
- "PoolWorker-7: Removed temporary directory /tmp/madlib_01Yrlb4kiZ\n",
- "PoolWorker-9: Removed temporary directory /tmp/madlib_Jc01ED7LdY\n",
- "PoolWorker-6: Removed temporary directory /tmp/madlib_qiEVuC6H2f\n",
- "Done! Loaded 5139 images in 16.0947310925s\n",
+ "PoolWorker-7: Wrote 139 images to /tmp/madlib_nq4NtQVTcA/test_lt50001.tmp\n",
+ "PoolWorker-8: Loaded 1000 images into test_lt5\n",
+ "PoolWorker-10: Loaded 1000 images into test_lt5\n",
+ "PoolWorker-6: Loaded 1000 images into test_lt5\n",
+ "PoolWorker-9: Loaded 1000 images into test_lt5\n",
+ "PoolWorker-7: Loaded 139 images into test_lt5\n",
+ "PoolWorker-8: Removed temporary directory /tmp/madlib_ottKdv45hY\n",
+ "PoolWorker-6: Removed temporary directory /tmp/madlib_zskbC1CxH3\n",
+ "PoolWorker-10: Removed temporary directory /tmp/madlib_iLL7sa8Utg\n",
+ "PoolWorker-9: Removed temporary directory /tmp/madlib_H2i9eCgqnz\n",
+ "PoolWorker-7: Removed temporary directory /tmp/madlib_nq4NtQVTcA\n",
+ "Done! Loaded 5139 images in 16.0366249084s\n",
"5 workers terminated.\n",
"MainProcess: Connected to madlib db.\n",
"Executing: CREATE TABLE train_gte5 (id SERIAL, x REAL[], y TEXT)\n",
"CREATE TABLE\n",
"Created table train_gte5 in madlib db\n",
"Spawning 5 workers...\n",
- "Initializing PoolWorker-11 [pid 32152]\n",
- "PoolWorker-11: Created temporary directory /tmp/madlib_W4MvrjG6AB\n",
- "Initializing PoolWorker-12 [pid 32153]\n",
- "PoolWorker-12: Created temporary directory /tmp/madlib_xjoS5riF15\n",
- "Initializing PoolWorker-13 [pid 32154]\n",
- "PoolWorker-13: Created temporary directory /tmp/madlib_LRF1X1Nbjw\n",
- "Initializing PoolWorker-14 [pid 32155]\n",
- "PoolWorker-14: Created temporary directory /tmp/madlib_81JT2Bqk6q\n",
- "Initializing PoolWorker-15 [pid 32156]\n",
- "PoolWorker-15: Created temporary directory /tmp/madlib_cw8IBZoiUb\n",
+ "Initializing PoolWorker-11 [pid 45842]\n",
+ "PoolWorker-11: Created temporary directory /tmp/madlib_tCjXbcR1tF\n",
+ "Initializing PoolWorker-12 [pid 45843]\n",
+ "PoolWorker-12: Created temporary directory /tmp/madlib_aXM7FynwWa\n",
+ "Initializing PoolWorker-13 [pid 45844]\n",
+ "PoolWorker-13: Created temporary directory /tmp/madlib_IUgLA1Lmzg\n",
+ "PoolWorker-15: Created temporary directory /tmp/madlib_WXvDvrTZ3D\n",
+ "Initializing PoolWorker-14 [pid 45845]\n",
+ "PoolWorker-14: Created temporary directory /tmp/madlib_qzfjEpqz4M\n",
+ "Initializing PoolWorker-15 [pid 45846]\n",
+ "PoolWorker-13: Connected to madlib db.\n",
+ "PoolWorker-15: Connected to madlib db.\n",
+ "PoolWorker-12: Connected to madlib db.\n",
"PoolWorker-11: Connected to madlib db.\n",
"PoolWorker-14: Connected to madlib db.\n",
- "PoolWorker-13: Connected to madlib db.\n",
- "PoolWorker-12: Connected to madlib db.\n",
- "PoolWorker-15: Connected to madlib db.\n",
- "PoolWorker-11: Wrote 1000 images to /tmp/madlib_W4MvrjG6AB/train_gte50000.tmp\n",
- "PoolWorker-13: Wrote 1000 images to /tmp/madlib_LRF1X1Nbjw/train_gte50000.tmp\n",
- "PoolWorker-12: Wrote 1000 images to /tmp/madlib_xjoS5riF15/train_gte50000.tmp\n"
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_IUgLA1Lmzg/train_gte50000.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_tCjXbcR1tF/train_gte50000.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_WXvDvrTZ3D/train_gte50000.tmp\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "PoolWorker-15: Wrote 1000 images to /tmp/madlib_cw8IBZoiUb/train_gte50000.tmp\n",
- "PoolWorker-14: Wrote 1000 images to /tmp/madlib_81JT2Bqk6q/train_gte50000.tmp\n",
- "PoolWorker-11: Loaded 1000 images into train_gte5\n",
- "PoolWorker-11: Wrote 1000 images to /tmp/madlib_W4MvrjG6AB/train_gte50001.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_aXM7FynwWa/train_gte50000.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_qzfjEpqz4M/train_gte50000.tmp\n",
"PoolWorker-13: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_IUgLA1Lmzg/train_gte50001.tmp\n",
+ "PoolWorker-11: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-15: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_tCjXbcR1tF/train_gte50001.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_WXvDvrTZ3D/train_gte50001.tmp\n",
"PoolWorker-14: Loaded 1000 images into train_gte5\n",
"PoolWorker-12: Loaded 1000 images into train_gte5\n",
- "PoolWorker-15: Loaded 1000 images into train_gte5\n",
- "PoolWorker-13: Wrote 1000 images to /tmp/madlib_LRF1X1Nbjw/train_gte50001.tmp\n",
- "PoolWorker-12: Wrote 1000 images to /tmp/madlib_xjoS5riF15/train_gte50001.tmp\n",
- "PoolWorker-14: Wrote 1000 images to /tmp/madlib_81JT2Bqk6q/train_gte50001.tmp\n",
- "PoolWorker-15: Wrote 1000 images to /tmp/madlib_cw8IBZoiUb/train_gte50001.tmp\n",
- "PoolWorker-11: Loaded 1000 images into train_gte5\n",
- "PoolWorker-11: Wrote 1000 images to /tmp/madlib_W4MvrjG6AB/train_gte50002.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_qzfjEpqz4M/train_gte50001.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_aXM7FynwWa/train_gte50001.tmp\n",
"PoolWorker-13: Loaded 1000 images into train_gte5\n",
- "PoolWorker-13: Wrote 1000 images to /tmp/madlib_LRF1X1Nbjw/train_gte50002.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_IUgLA1Lmzg/train_gte50002.tmp\n",
+ "PoolWorker-15: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-11: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_WXvDvrTZ3D/train_gte50002.tmp\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_tCjXbcR1tF/train_gte50002.tmp\n",
+ "PoolWorker-12: Loaded 1000 images into train_gte5\n",
"PoolWorker-14: Loaded 1000 images into train_gte5\n",
- "PoolWorker-15: Loaded 1000 images into train_gte5\n",
- "PoolWorker-12: Loaded 1000 images into train_gte5\n",
- "PoolWorker-11: Loaded 1000 images into train_gte5\n",
- "PoolWorker-15: Wrote 1000 images to /tmp/madlib_cw8IBZoiUb/train_gte50002.tmp\n",
- "PoolWorker-14: Wrote 1000 images to /tmp/madlib_81JT2Bqk6q/train_gte50002.tmp\n",
- "PoolWorker-12: Wrote 1000 images to /tmp/madlib_xjoS5riF15/train_gte50002.tmp\n",
- "PoolWorker-11: Wrote 1000 images to /tmp/madlib_W4MvrjG6AB/train_gte50003.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_qzfjEpqz4M/train_gte50002.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_aXM7FynwWa/train_gte50002.tmp\n",
"PoolWorker-13: Loaded 1000 images into train_gte5\n",
- "PoolWorker-13: Wrote 1000 images to /tmp/madlib_LRF1X1Nbjw/train_gte50003.tmp\n",
- "PoolWorker-12: Loaded 1000 images into train_gte5\n",
- "PoolWorker-12: Wrote 1000 images to /tmp/madlib_xjoS5riF15/train_gte50003.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_IUgLA1Lmzg/train_gte50003.tmp\n",
+ "PoolWorker-15: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-11: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_tCjXbcR1tF/train_gte50003.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_WXvDvrTZ3D/train_gte50003.tmp\n",
"PoolWorker-14: Loaded 1000 images into train_gte5\n",
- "PoolWorker-15: Loaded 1000 images into train_gte5\n",
- "PoolWorker-11: Loaded 1000 images into train_gte5\n",
- "PoolWorker-14: Wrote 1000 images to /tmp/madlib_81JT2Bqk6q/train_gte50003.tmp\n",
- "PoolWorker-15: Wrote 1000 images to /tmp/madlib_cw8IBZoiUb/train_gte50003.tmp\n",
- "PoolWorker-11: Wrote 1000 images to /tmp/madlib_W4MvrjG6AB/train_gte50004.tmp\n",
- "PoolWorker-13: Loaded 1000 images into train_gte5\n",
- "PoolWorker-13: Wrote 1000 images to /tmp/madlib_LRF1X1Nbjw/train_gte50004.tmp\n",
"PoolWorker-12: Loaded 1000 images into train_gte5\n",
- "PoolWorker-12: Wrote 1000 images to /tmp/madlib_xjoS5riF15/train_gte50004.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_qzfjEpqz4M/train_gte50003.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_aXM7FynwWa/train_gte50003.tmp\n",
+ "PoolWorker-13: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_IUgLA1Lmzg/train_gte50004.tmp\n",
+ "PoolWorker-11: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-15: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_tCjXbcR1tF/train_gte50004.tmp\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_WXvDvrTZ3D/train_gte50004.tmp\n",
"PoolWorker-14: Loaded 1000 images into train_gte5\n",
- "PoolWorker-15: Loaded 1000 images into train_gte5\n",
- "PoolWorker-11: Loaded 1000 images into train_gte5\n",
- "PoolWorker-11: Wrote 1000 images to /tmp/madlib_W4MvrjG6AB/train_gte50005.tmp\n",
- "PoolWorker-15: Wrote 1000 images to /tmp/madlib_cw8IBZoiUb/train_gte50004.tmp\n",
- "PoolWorker-14: Wrote 1000 images to /tmp/madlib_81JT2Bqk6q/train_gte50004.tmp\n",
- "PoolWorker-13: Loaded 1000 images into train_gte5\n",
- "PoolWorker-13: Wrote 1000 images to /tmp/madlib_LRF1X1Nbjw/train_gte50005.tmp\n",
"PoolWorker-12: Loaded 1000 images into train_gte5\n",
- "PoolWorker-12: Wrote 1000 images to /tmp/madlib_xjoS5riF15/train_gte50005.tmp\n",
- "PoolWorker-15: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-13: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_aXM7FynwWa/train_gte50004.tmp\n",
+ "PoolWorker-14: Wrote 1000 images to /tmp/madlib_qzfjEpqz4M/train_gte50004.tmp\n",
+ "PoolWorker-13: Wrote 1000 images to /tmp/madlib_IUgLA1Lmzg/train_gte50005.tmp\n",
"PoolWorker-11: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-11: Wrote 1000 images to /tmp/madlib_tCjXbcR1tF/train_gte50005.tmp\n",
+ "PoolWorker-15: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-15: Wrote 1000 images to /tmp/madlib_WXvDvrTZ3D/train_gte50005.tmp\n",
+ "PoolWorker-12: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-13: Loaded 1000 images into train_gte5\n",
"PoolWorker-14: Loaded 1000 images into train_gte5\n",
- "PoolWorker-14: Wrote 404 images to /tmp/madlib_81JT2Bqk6q/train_gte50005.tmp\n",
- "PoolWorker-15: Wrote 1000 images to /tmp/madlib_cw8IBZoiUb/train_gte50005.tmp\n",
- "PoolWorker-13: Loaded 1000 images into train_gte5\n",
- "PoolWorker-12: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-14: Wrote 404 images to /tmp/madlib_qzfjEpqz4M/train_gte50005.tmp\n",
+ "PoolWorker-12: Wrote 1000 images to /tmp/madlib_aXM7FynwWa/train_gte50005.tmp\n",
+ "PoolWorker-11: Loaded 1000 images into train_gte5\n",
"PoolWorker-14: Loaded 404 images into train_gte5\n",
"PoolWorker-15: Loaded 1000 images into train_gte5\n",
- "PoolWorker-11: Removed temporary directory /tmp/madlib_W4MvrjG6AB\n",
- "PoolWorker-14: Removed temporary directory /tmp/madlib_81JT2Bqk6q\n",
- "PoolWorker-13: Removed temporary directory /tmp/madlib_LRF1X1Nbjw\n",
- "PoolWorker-12: Removed temporary directory /tmp/madlib_xjoS5riF15\n",
- "PoolWorker-15: Removed temporary directory /tmp/madlib_cw8IBZoiUb\n",
- "Done! Loaded 29404 images in 68.5753850937s\n",
+ "PoolWorker-12: Loaded 1000 images into train_gte5\n",
+ "PoolWorker-13: Removed temporary directory /tmp/madlib_IUgLA1Lmzg\n",
+ "PoolWorker-11: Removed temporary directory /tmp/madlib_tCjXbcR1tF\n",
+ "PoolWorker-14: Removed temporary directory /tmp/madlib_qzfjEpqz4M\n",
+ "PoolWorker-15: Removed temporary directory /tmp/madlib_WXvDvrTZ3D\n",
+ "PoolWorker-12: Removed temporary directory /tmp/madlib_aXM7FynwWa\n",
+ "Done! Loaded 29404 images in 83.1629951s\n",
"5 workers terminated.\n",
"MainProcess: Connected to madlib db.\n",
"Executing: CREATE TABLE test_gte5 (id SERIAL, x REAL[], y TEXT)\n",
"CREATE TABLE\n",
"Created table test_gte5 in madlib db\n",
"Spawning 5 workers...\n",
- "Initializing PoolWorker-16 [pid 32159]\n",
- "PoolWorker-16: Created temporary directory /tmp/madlib_u7tI3bg7jU\n",
- "Initializing PoolWorker-17 [pid 32160]\n",
- "PoolWorker-17: Created temporary directory /tmp/madlib_2bBkL2sPMZ\n",
- "Initializing PoolWorker-18 [pid 32161]\n",
- "PoolWorker-18: Created temporary directory /tmp/madlib_kOLOpYin2F\n",
- "Initializing PoolWorker-19 [pid 32162]\n",
- "PoolWorker-19: Created temporary directory /tmp/madlib_Nd5O8pUMIS\n",
- "Initializing PoolWorker-20 [pid 32163]\n",
- "PoolWorker-20: Created temporary directory /tmp/madlib_TmWQybahi2\n",
+ "Initializing PoolWorker-16 [pid 45874]\n",
+ "PoolWorker-16: Created temporary directory /tmp/madlib_UOsDRcoLhX\n",
+ "Initializing PoolWorker-17 [pid 45875]\n",
+ "PoolWorker-17: Created temporary directory /tmp/madlib_GhQsnIWE2l\n",
+ "Initializing PoolWorker-18 [pid 45876]\n",
+ "PoolWorker-18: Created temporary directory /tmp/madlib_byJ6L1H4Ib\n",
+ "Initializing PoolWorker-19 [pid 45877]\n",
+ "PoolWorker-19: Created temporary directory /tmp/madlib_sKmIx32QXS\n",
+ "PoolWorker-20: Created temporary directory /tmp/madlib_dXB8oQfWZy\n",
+ "Initializing PoolWorker-20 [pid 45878]\n",
+ "PoolWorker-18: Connected to madlib db.\n",
+ "PoolWorker-17: Connected to madlib db.\n",
+ "PoolWorker-19: Connected to madlib db.\n",
"PoolWorker-16: Connected to madlib db.\n",
"PoolWorker-20: Connected to madlib db.\n",
- "PoolWorker-18: Connected to madlib db.\n",
- "PoolWorker-19: Connected to madlib db.\n",
- "PoolWorker-17: Connected to madlib db.\n",
- "PoolWorker-17: Wrote 861 images to /tmp/madlib_2bBkL2sPMZ/test_gte50000.tmp\n",
- "PoolWorker-16: Wrote 1000 images to /tmp/madlib_u7tI3bg7jU/test_gte50000.tmp\n",
- "PoolWorker-18: Wrote 1000 images to /tmp/madlib_kOLOpYin2F/test_gte50000.tmp\n",
- "PoolWorker-20: Wrote 1000 images to /tmp/madlib_TmWQybahi2/test_gte50000.tmp\n",
- "PoolWorker-19: Wrote 1000 images to /tmp/madlib_Nd5O8pUMIS/test_gte50000.tmp\n",
- "PoolWorker-17: Loaded 861 images into test_gte5\n",
- "PoolWorker-16: Loaded 1000 images into test_gte5\n",
- "PoolWorker-20: Loaded 1000 images into test_gte5\n",
- "PoolWorker-19: Loaded 1000 images into test_gte5\n",
+ "PoolWorker-20: Wrote 861 images to /tmp/madlib_dXB8oQfWZy/test_gte50000.tmp\n",
+ "PoolWorker-17: Wrote 1000 images to /tmp/madlib_GhQsnIWE2l/test_gte50000.tmp\n",
+ "PoolWorker-18: Wrote 1000 images to /tmp/madlib_byJ6L1H4Ib/test_gte50000.tmp\n",
+ "PoolWorker-19: Wrote 1000 images to /tmp/madlib_sKmIx32QXS/test_gte50000.tmp\n",
+ "PoolWorker-16: Wrote 1000 images to /tmp/madlib_UOsDRcoLhX/test_gte50000.tmp\n",
+ "PoolWorker-20: Loaded 861 images into test_gte5\n",
+ "PoolWorker-17: Loaded 1000 images into test_gte5\n",
"PoolWorker-18: Loaded 1000 images into test_gte5\n",
- "PoolWorker-20: Removed temporary directory /tmp/madlib_TmWQybahi2\n",
- "PoolWorker-16: Removed temporary directory /tmp/madlib_u7tI3bg7jU\n",
- "PoolWorker-17: Removed temporary directory /tmp/madlib_2bBkL2sPMZ\n",
- "PoolWorker-19: Removed temporary directory /tmp/madlib_Nd5O8pUMIS\n",
- "PoolWorker-18: Removed temporary directory /tmp/madlib_kOLOpYin2F\n",
- "Done! Loaded 4861 images in 11.068821907s\n",
+ "PoolWorker-19: Loaded 1000 images into test_gte5\n",
+ "PoolWorker-16: Loaded 1000 images into test_gte5\n",
+ "PoolWorker-19: Removed temporary directory /tmp/madlib_sKmIx32QXS\n",
+ "PoolWorker-20: Removed temporary directory /tmp/madlib_dXB8oQfWZy\n",
+ "PoolWorker-16: Removed temporary directory /tmp/madlib_UOsDRcoLhX\n",
+ "PoolWorker-17: Removed temporary directory /tmp/madlib_GhQsnIWE2l\n",
+ "PoolWorker-18: Removed temporary directory /tmp/madlib_byJ6L1H4Ib\n",
+ "Done! Loaded 4861 images in 15.0834438801s\n",
"5 workers terminated.\n"
]
}
@@ -567,7 +616,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 28,
"metadata": {
"scrolled": true
},
@@ -591,7 +640,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>y_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -601,23 +650,23 @@
" <tr>\n",
" <td>train_lt5</td>\n",
" <td>train_lt5_packed</td>\n",
- " <td>y</td>\n",
- " <td>x</td>\n",
- " <td>text</td>\n",
+ " <td>[u'y']</td>\n",
+ " <td>[u'x']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'0', u'1', u'2', u'3', u'4']</td>\n",
- " <td>1000</td>\n",
+ " <td>957</td>\n",
" <td>255.0</td>\n",
- " <td>5</td>\n",
+ " <td>[5]</td>\n",
" <td>all_segments</td>\n",
" <td>all_segments</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'train_lt5', u'train_lt5_packed', u'y', u'x', u'text', [u'0', u'1', u'2', u'3', u'4'], 1000, 255.0, 5, 'all_segments', 'all_segments')]"
+ "[(u'train_lt5', u'train_lt5_packed', [u'y'], [u'x'], [u'text'], [u'0', u'1', u'2', u'3', u'4'], 957, 255.0, [5], 'all_segments', 'all_segments')]"
]
},
- "execution_count": 23,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -646,7 +695,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -668,7 +717,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>y_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -678,23 +727,23 @@
" <tr>\n",
" <td>test_lt5</td>\n",
" <td>test_lt5_packed</td>\n",
- " <td>y</td>\n",
- " <td>x</td>\n",
- " <td>text</td>\n",
+ " <td>[u'y']</td>\n",
+ " <td>[u'x']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'0', u'1', u'2', u'3', u'4']</td>\n",
" <td>2570</td>\n",
" <td>255.0</td>\n",
- " <td>5</td>\n",
+ " <td>[5]</td>\n",
" <td>all_segments</td>\n",
" <td>all_segments</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'test_lt5', u'test_lt5_packed', u'y', u'x', u'text', [u'0', u'1', u'2', u'3', u'4'], 2570, 255.0, 5, 'all_segments', 'all_segments')]"
+ "[(u'test_lt5', u'test_lt5_packed', [u'y'], [u'x'], [u'text'], [u'0', u'1', u'2', u'3', u'4'], 2570, 255.0, [5], 'all_segments', 'all_segments')]"
]
},
- "execution_count": 24,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -722,7 +771,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -744,7 +793,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>y_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -754,23 +803,23 @@
" <tr>\n",
" <td>train_gte5</td>\n",
" <td>train_gte5_packed</td>\n",
- " <td>y</td>\n",
- " <td>x</td>\n",
- " <td>text</td>\n",
+ " <td>[u'y']</td>\n",
+ " <td>[u'x']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'0', u'1', u'2', u'3', u'4']</td>\n",
- " <td>1000</td>\n",
+ " <td>981</td>\n",
" <td>255.0</td>\n",
- " <td>5</td>\n",
+ " <td>[5]</td>\n",
" <td>all_segments</td>\n",
" <td>all_segments</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'train_gte5', u'train_gte5_packed', u'y', u'x', u'text', [u'0', u'1', u'2', u'3', u'4'], 1000, 255.0, 5, 'all_segments', 'all_segments')]"
+ "[(u'train_gte5', u'train_gte5_packed', [u'y'], [u'x'], [u'text'], [u'0', u'1', u'2', u'3', u'4'], 981, 255.0, [5], 'all_segments', 'all_segments')]"
]
},
- "execution_count": 25,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -799,7 +848,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -821,7 +870,7 @@
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
+ " <th>y_class_values</th>\n",
" <th>buffer_size</th>\n",
" <th>normalizing_const</th>\n",
" <th>num_classes</th>\n",
@@ -831,23 +880,23 @@
" <tr>\n",
" <td>test_gte5</td>\n",
" <td>test_gte5_packed</td>\n",
- " <td>y</td>\n",
- " <td>x</td>\n",
- " <td>text</td>\n",
+ " <td>[u'y']</td>\n",
+ " <td>[u'x']</td>\n",
+ " <td>[u'text']</td>\n",
" <td>[u'0', u'1', u'2', u'3', u'4']</td>\n",
" <td>2431</td>\n",
" <td>255.0</td>\n",
- " <td>5</td>\n",
+ " <td>[5]</td>\n",
" <td>all_segments</td>\n",
" <td>all_segments</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'test_gte5', u'test_gte5_packed', u'y', u'x', u'text', [u'0', u'1', u'2', u'3', u'4'], 2431, 255.0, 5, 'all_segments', 'all_segments')]"
+ "[(u'test_gte5', u'test_gte5_packed', [u'y'], [u'x'], [u'text'], [u'0', u'1', u'2', u'3', u'4'], 2431, 255.0, [5], 'all_segments', 'all_segments')]"
]
},
- "execution_count": 26,
+ "execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
@@ -878,39 +927,43 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "WARNING:tensorflow:From /Users/fmcquillan/Library/Python/2.7/lib/python/site-packages/tensorflow/python/ops/init_ops.py:1251: calling __init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+ "Instructions for updating:\n",
+ "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+ "Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
- "conv2d_1 (Conv2D) (None, 26, 26, 32) 320 \n",
+ "conv2d (Conv2D) (None, 26, 26, 32) 320 \n",
"_________________________________________________________________\n",
- "activation_1 (Activation) (None, 26, 26, 32) 0 \n",
+ "activation (Activation) (None, 26, 26, 32) 0 \n",
"_________________________________________________________________\n",
- "conv2d_2 (Conv2D) (None, 24, 24, 32) 9248 \n",
+ "conv2d_1 (Conv2D) (None, 24, 24, 32) 9248 \n",
"_________________________________________________________________\n",
- "activation_2 (Activation) (None, 24, 24, 32) 0 \n",
+ "activation_1 (Activation) (None, 24, 24, 32) 0 \n",
"_________________________________________________________________\n",
- "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32) 0 \n",
+ "max_pooling2d (MaxPooling2D) (None, 12, 12, 32) 0 \n",
"_________________________________________________________________\n",
- "dropout_1 (Dropout) (None, 12, 12, 32) 0 \n",
+ "dropout (Dropout) (None, 12, 12, 32) 0 \n",
"_________________________________________________________________\n",
- "flatten_1 (Flatten) (None, 4608) 0 \n",
+ "flatten (Flatten) (None, 4608) 0 \n",
"_________________________________________________________________\n",
- "dense_1 (Dense) (None, 128) 589952 \n",
+ "dense (Dense) (None, 128) 589952 \n",
"_________________________________________________________________\n",
- "activation_3 (Activation) (None, 128) 0 \n",
+ "activation_2 (Activation) (None, 128) 0 \n",
"_________________________________________________________________\n",
- "dropout_2 (Dropout) (None, 128) 0 \n",
+ "dropout_1 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
- "dense_2 (Dense) (None, 5) 645 \n",
+ "dense_1 (Dense) (None, 5) 645 \n",
"_________________________________________________________________\n",
- "activation_4 (Activation) (None, 5) 0 \n",
+ "activation_3 (Activation) (None, 5) 0 \n",
"=================================================================\n",
"Total params: 600,165\n",
"Trainable params: 600,165\n",
@@ -956,14 +1009,30 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Done.\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"1 rows affected.\n"
]
},
@@ -985,7 +1054,7 @@
"[(1, u'feature + classification layers trainable')]"
]
},
- "execution_count": 29,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -1014,39 +1083,40 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
+ "Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
- "conv2d_1 (Conv2D) (None, 26, 26, 32) 320 \n",
+ "conv2d (Conv2D) (None, 26, 26, 32) 320 \n",
"_________________________________________________________________\n",
- "activation_1 (Activation) (None, 26, 26, 32) 0 \n",
+ "activation (Activation) (None, 26, 26, 32) 0 \n",
"_________________________________________________________________\n",
- "conv2d_2 (Conv2D) (None, 24, 24, 32) 9248 \n",
+ "conv2d_1 (Conv2D) (None, 24, 24, 32) 9248 \n",
"_________________________________________________________________\n",
- "activation_2 (Activation) (None, 24, 24, 32) 0 \n",
+ "activation_1 (Activation) (None, 24, 24, 32) 0 \n",
"_________________________________________________________________\n",
- "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32) 0 \n",
+ "max_pooling2d (MaxPooling2D) (None, 12, 12, 32) 0 \n",
"_________________________________________________________________\n",
- "dropout_1 (Dropout) (None, 12, 12, 32) 0 \n",
+ "dropout (Dropout) (None, 12, 12, 32) 0 \n",
"_________________________________________________________________\n",
- "flatten_1 (Flatten) (None, 4608) 0 \n",
+ "flatten (Flatten) (None, 4608) 0 \n",
"_________________________________________________________________\n",
- "dense_1 (Dense) (None, 128) 589952 \n",
+ "dense (Dense) (None, 128) 589952 \n",
"_________________________________________________________________\n",
- "activation_3 (Activation) (None, 128) 0 \n",
+ "activation_2 (Activation) (None, 128) 0 \n",
"_________________________________________________________________\n",
- "dropout_2 (Dropout) (None, 128) 0 \n",
+ "dropout_1 (Dropout) (None, 128) 0 \n",
"_________________________________________________________________\n",
- "dense_2 (Dense) (None, 5) 645 \n",
+ "dense_1 (Dense) (None, 5) 645 \n",
"_________________________________________________________________\n",
- "activation_4 (Activation) (None, 5) 0 \n",
+ "activation_3 (Activation) (None, 5) 0 \n",
"=================================================================\n",
"Total params: 600,165\n",
"Trainable params: 590,597\n",
@@ -1072,7 +1142,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -1105,7 +1175,7 @@
" (2, u'only classification layers trainable')]"
]
},
- "execution_count": 31,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -1129,7 +1199,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
@@ -1156,7 +1226,7 @@
"[('',)]"
]
},
- "execution_count": 32,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -1184,7 +1254,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
@@ -1209,6 +1279,7 @@
" <th>fit_params</th>\n",
" <th>num_iterations</th>\n",
" <th>validation_table</th>\n",
+ " <th>object_table</th>\n",
" <th>metrics_compute_frequency</th>\n",
" <th>name</th>\n",
" <th>description</th>\n",
@@ -1219,10 +1290,10 @@
" <th>metrics_elapsed_time</th>\n",
" <th>madlib_version</th>\n",
" <th>num_classes</th>\n",
- " <th>class_values</th>\n",
" <th>dependent_vartype</th>\n",
" <th>normalizing_const</th>\n",
" <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
" <th>training_metrics_final</th>\n",
" <th>training_loss_final</th>\n",
" <th>training_metrics</th>\n",
@@ -1232,49 +1303,52 @@
" <th>validation_metrics</th>\n",
" <th>validation_loss</th>\n",
" <th>metrics_iters</th>\n",
+ " <th>y_class_values</th>\n",
" </tr>\n",
" <tr>\n",
" <td>train_lt5_packed</td>\n",
" <td>mnist_model</td>\n",
- " <td>y</td>\n",
- " <td>x</td>\n",
+ " <td>[u'y']</td>\n",
+ " <td>[u'x']</td>\n",
" <td>model_arch_library</td>\n",
" <td>1</td>\n",
" <td> loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']</td>\n",
" <td> batch_size=128, epochs=1 </td>\n",
" <td>5</td>\n",
" <td>None</td>\n",
+ " <td>None</td>\n",
" <td>5</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>madlib_keras</td>\n",
" <td>2344.43066406</td>\n",
- " <td>2019-12-18 22:08:33.212149</td>\n",
- " <td>2019-12-18 22:10:33.354468</td>\n",
- " <td>[120.142242193222]</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>5</td>\n",
- " <td>[u'0', u'1', u'2', u'3', u'4']</td>\n",
- " <td>text</td>\n",
+ " <td>2021-03-08 20:52:42.139646</td>\n",
+ " <td>2021-03-08 20:53:56.573492</td>\n",
+ " <td>[74.4337520599365]</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[5]</td>\n",
+ " <td>[u'text']</td>\n",
" <td>255.0</td>\n",
" <td>[u'accuracy']</td>\n",
- " <td>0.994607150555</td>\n",
- " <td>0.0173618607223</td>\n",
- " <td>[0.994607150554657]</td>\n",
- " <td>[0.0173618607223034]</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.579748988152</td>\n",
+ " <td>1.5176358223</td>\n",
+ " <td>[0.57974898815155]</td>\n",
+ " <td>[1.51763582229614]</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[5]</td>\n",
+ " <td>[u'0', u'1', u'2', u'3', u'4']</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'train_lt5_packed', u'mnist_model', u'y', u'x', u'model_arch_library', 1, u\" loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']\", u' batch_size=128, epochs=1 ', 5, None, 5, None, None, u'madlib_keras', 2344.43066406, datetime.datetime(2019, 12, 18, 22, 8, 33, 212149), datetime.datetime(2019, 12, 18, 22, 10, 33, 354468), [120.142242193222], u'1.17-dev', 5, [u'0', u'1', u'2', u'3', u'4'], u'text', 255.0, [u'accuracy'], 0.994607150555, 0.0173618607223, [0.994607150554657], [0.0173618607223034], None, None, None, None, [5])]"
+ "[(u'train_lt5_packed', u'mnist_model', [u'y'], [u'x'], u'model_arch_library', 1, u\" loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']\", u' batch_size=128, epochs=1 ', 5, None, None, 5, None, None, u'madlib_keras', 2344.43066406, datetime.datetime(2021, 3, 8, 20, 52, 42, 139646), datetime.datetime(2021, 3, 8, 20, 53, 56, 573492), [74.4337520599365], u'1.18.0-dev', [5], [u'text'], 255.0, [u'accuracy'], u'categorical_crossentropy', 0.57974898815155, 1.51763582229614, [0.57974898815155], [1.51763582229614], None, None, None, None, [5], [u'0', u'1', u'2', u'3', u'4'])]"
]
},
- "execution_count": 33,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -1293,7 +1367,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
@@ -1313,19 +1387,21 @@
" <th>loss</th>\n",
" <th>metric</th>\n",
" <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.0107044409961</td>\n",
- " <td>0.995719015598</td>\n",
+ " <td>1.51568281651</td>\n",
+ " <td>0.603619396687</td>\n",
" <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(0.0107044409960508, 0.995719015598297, [u'accuracy'])]"
+ "[(1.51568281650543, 0.603619396686554, [u'accuracy'], u'categorical_crossentropy')]"
]
},
- "execution_count": 34,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -1477,6 +1553,7 @@
" <th>fit_params</th>\n",
" <th>num_iterations</th>\n",
" <th>validation_table</th>\n",
+ " <th>object_table</th>\n",
" <th>metrics_compute_frequency</th>\n",
" <th>name</th>\n",
" <th>description</th>\n",
@@ -1487,10 +1564,10 @@
" <th>metrics_elapsed_time</th>\n",
" <th>madlib_version</th>\n",
" <th>num_classes</th>\n",
- " <th>class_values</th>\n",
" <th>dependent_vartype</th>\n",
" <th>normalizing_const</th>\n",
" <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
" <th>training_metrics_final</th>\n",
" <th>training_loss_final</th>\n",
" <th>training_metrics</th>\n",
@@ -1500,46 +1577,49 @@
" <th>validation_metrics</th>\n",
" <th>validation_loss</th>\n",
" <th>metrics_iters</th>\n",
+ " <th>y_class_values</th>\n",
" </tr>\n",
" <tr>\n",
" <td>train_gte5_packed</td>\n",
" <td>mnist_transfer_model</td>\n",
- " <td>y</td>\n",
- " <td>x</td>\n",
+ " <td>[u'y']</td>\n",
+ " <td>[u'x']</td>\n",
" <td>model_arch_library</td>\n",
" <td>2</td>\n",
" <td> loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']</td>\n",
" <td> batch_size=128, epochs=1 </td>\n",
" <td>5</td>\n",
" <td>None</td>\n",
+ " <td>None</td>\n",
" <td>5</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>madlib_keras</td>\n",
" <td>2344.43066406</td>\n",
- " <td>2019-12-18 22:12:24.949576</td>\n",
- " <td>2019-12-18 22:13:10.273382</td>\n",
- " <td>[45.3237240314484]</td>\n",
- " <td>1.17-dev</td>\n",
- " <td>5</td>\n",
- " <td>[u'0', u'1', u'2', u'3', u'4']</td>\n",
- " <td>text</td>\n",
+ " <td>2021-03-08 20:54:00.641208</td>\n",
+ " <td>2021-03-08 20:54:29.067638</td>\n",
+ " <td>[28.4263310432434]</td>\n",
+ " <td>1.18.0-dev</td>\n",
+ " <td>[5]</td>\n",
+ " <td>[u'text']</td>\n",
" <td>255.0</td>\n",
" <td>[u'accuracy']</td>\n",
- " <td>0.991259694099</td>\n",
- " <td>0.0289842225611</td>\n",
- " <td>[0.991259694099426]</td>\n",
- " <td>[0.028984222561121]</td>\n",
+ " <td>categorical_crossentropy</td>\n",
+ " <td>0.6377363801</td>\n",
+ " <td>1.52131533623</td>\n",
+ " <td>[0.63773638010025]</td>\n",
+ " <td>[1.52131533622742]</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>[5]</td>\n",
+ " <td>[u'0', u'1', u'2', u'3', u'4']</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'train_gte5_packed', u'mnist_transfer_model', u'y', u'x', u'model_arch_library', 2, u\" loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']\", u' batch_size=128, epochs=1 ', 5, None, 5, None, None, u'madlib_keras', 2344.43066406, datetime.datetime(2019, 12, 18, 22, 12, 24, 949576), datetime.datetime(2019, 12, 18, 22, 13, 10, 273382), [45.3237240314484], u'1.17-dev', 5, [u'0', u'1', u'2', u'3', u'4'], u'text', 255.0, [u'accuracy'], 0.991259694099, 0.0289842225611, [0.991259694099426], [0.028984222561121], None, None, None, None, [5])]"
+ "[(u'train_gte5_packed', u'mnist_transfer_model', [u'y'], [u'x'], u'model_arch_library', 2, u\" loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy']\", u' batch_size=128, epochs=1 ', 5, None, None, 5, None, None, u'madlib_keras', 2344.43066406, datetime.datetime(2021, 3, 8, 20, 54, 0, 641208), datetime.datetime(2021, 3, 8, 20, 54, 29, 67638), [28.4263310432434], u'1.18.0-dev', [5], [u'text'], 255.0, [u'accuracy'], u'categorical_crossentropy', 0.63773638010025, 1.52131533622742, [0.63773638010025], [1.52131533622742], None, None, None, None, [5], [u'0', u'1', u'2', u'3', u'4'])]"
]
},
"execution_count": 41,
@@ -1581,16 +1661,18 @@
" <th>loss</th>\n",
" <th>metric</th>\n",
" <th>metrics_type</th>\n",
+ " <th>loss_type</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.0291066691279</td>\n",
- " <td>0.990536928177</td>\n",
+ " <td>1.52041304111</td>\n",
+ " <td>0.625385701656</td>\n",
" <td>[u'accuracy']</td>\n",
+ " <td>categorical_crossentropy</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(0.0291066691279411, 0.99053692817688, [u'accuracy'])]"
+ "[(1.52041304111481, 0.625385701656342, [u'accuracy'], u'categorical_crossentropy')]"
]
},
"execution_count": 42,
@@ -1627,7 +1709,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Deep-learning/Load-images-v1.ipynb b/community-artifacts/Deep-learning/Utilities/Load-images-v1.ipynb
similarity index 83%
rename from community-artifacts/Deep-learning/Load-images-v1.ipynb
rename to community-artifacts/Deep-learning/Utilities/Load-images-v1.ipynb
index 61929e9..45d6d18 100644
--- a/community-artifacts/Deep-learning/Load-images-v1.ipynb
+++ b/community-artifacts/Deep-learning/Utilities/Load-images-v1.ipynb
@@ -25,18 +25,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
@@ -45,24 +34,13 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: fmcquillan@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.x on GCP for deep learning (PM demo machine)\n",
- "#%sql postgresql://gpadmin@35.239.240.26:5432/madlib\n",
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
- "%sql postgresql://fmcquillan@localhost:5432/madlib"
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
@@ -85,12 +63,12 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang</td>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-91-g16070e5, cmake configuration time: Mon Mar 8 16:58:24 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang',)]"
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-91-g16070e5, cmake configuration time: Mon Mar 8 16:58:24 UTC 2021, build type: release, build system: Linux-3.10.0-1160.11.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
"execution_count": 3,
@@ -117,26 +95,11 @@
"cell_type": "code",
"execution_count": 4,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Couldn't import dot_parser, loading of dot files will not be possible.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import sys\n",
"import os\n",
- "from keras.datasets import cifar10\n",
+ "from tensorflow.keras.datasets import cifar10\n",
"\n",
"madlib_site_dir = '/Users/fmcquillan/Documents/Product/MADlib/Demos/data'\n",
"sys.path.append(madlib_site_dir)\n",
@@ -145,16 +108,17 @@
"from madlib_image_loader import ImageLoader, DbCredentials\n",
"\n",
"# Specify database credentials, for connecting to db\n",
- "#db_creds = DbCredentials(user='gpadmin',\n",
- "# host='35.239.240.26',\n",
+ "#db_creds = DbCredentials(user='fmcquillan',\n",
+ "# host='localhost',\n",
"# port='5432',\n",
"# password='')\n",
"\n",
"# Specify database credentials, for connecting to db\n",
- "db_creds = DbCredentials(user='fmcquillan',\n",
- " host='localhost',\n",
- " port='5432',\n",
- " password='')\n",
+ "db_creds = DbCredentials(user='gpadmin', \n",
+ " db_name='madlib',\n",
+ " host='localhost',\n",
+ " port='8000',\n",
+ " password='')\n",
"\n",
"# Initialize ImageLoader (increase num_workers to run faster)\n",
"iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
@@ -190,6 +154,26 @@
},
{
"cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(50000, 1)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_train.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
@@ -197,186 +181,202 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Done.\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"MainProcess: Connected to madlib db.\n",
"Executing: CREATE TABLE cifar_10_train_data (id SERIAL, x REAL[], y TEXT)\n",
"CREATE TABLE\n",
"Created table cifar_10_train_data in madlib db\n",
"Spawning 5 workers...\n",
- "Initializing PoolWorker-1 [pid 82412]\n",
- "PoolWorker-1: Created temporary directory /tmp/madlib_Bt85aChbv0\n",
- "Initializing PoolWorker-2 [pid 82413]\n",
- "PoolWorker-2: Created temporary directory /tmp/madlib_cSyCSiEhHT\n",
- "Initializing PoolWorker-3 [pid 82414]\n",
- "PoolWorker-3: Created temporary directory /tmp/madlib_uvtHjGCU5S\n",
+ "Initializing PoolWorker-1 [pid 35519]\n",
+ "PoolWorker-1: Created temporary directory /tmp/madlib_WEi5uqbfca\n",
+ "Initializing PoolWorker-2 [pid 35520]\n",
+ "PoolWorker-2: Created temporary directory /tmp/madlib_n4bo04pPvd\n",
+ "Initializing PoolWorker-3 [pid 35521]\n",
+ "PoolWorker-3: Created temporary directory /tmp/madlib_OpoCiWZqpD\n",
+ "Initializing PoolWorker-4 [pid 35522]\n",
+ "PoolWorker-4: Created temporary directory /tmp/madlib_sCT7YkACNY\n",
+ "Initializing PoolWorker-5 [pid 35523]\n",
+ "PoolWorker-5: Created temporary directory /tmp/madlib_6a6arvMQdK\n",
"PoolWorker-1: Connected to madlib db.\n",
- "Initializing PoolWorker-4 [pid 82415]\n",
- "PoolWorker-4: Created temporary directory /tmp/madlib_eJmkoDZTr8\n",
"PoolWorker-2: Connected to madlib db.\n",
- "Initializing PoolWorker-5 [pid 82417]\n",
- "PoolWorker-5: Created temporary directory /tmp/madlib_websbk05x2\n",
- "PoolWorker-3: Connected to madlib db.\n",
- "PoolWorker-4: Connected to madlib db.\n",
"PoolWorker-5: Connected to madlib db.\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0000.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0000.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0000.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0000.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0000.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Connected to madlib db.\n",
+ "PoolWorker-3: Connected to madlib db.\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0000.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0000.tmp\n",
"PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0001.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0003.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0004.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0005.tmp\n",
"PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
"PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
"PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0001.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0001.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0001.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0001.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0001.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0005.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0005.tmp\n",
"PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0006.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0007.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-3: Wrote 1000 images to /tmp/madlib_OpoCiWZqpD/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-4: Wrote 1000 images to /tmp/madlib_sCT7YkACNY/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-5: Wrote 1000 images to /tmp/madlib_6a6arvMQdK/cifar_10_train_data0008.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0009.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0009.tmp\n",
"PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
"PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
"PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0002.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0002.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0002.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0002.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0002.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0010.tmp\n",
+ "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-1: Wrote 1000 images to /tmp/madlib_WEi5uqbfca/cifar_10_train_data0010.tmp\n",
+ "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
+ "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n4bo04pPvd/cifar_10_train_data0011.tmp\n",
"PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
"PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0003.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0003.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0003.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0003.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0003.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0004.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0004.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0004.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0004.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0004.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0005.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0005.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0005.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0005.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0005.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0006.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0006.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0006.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0006.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0006.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0007.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0007.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0007.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0007.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0007.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0008.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0008.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_uvtHjGCU5S/cifar_10_train_data0008.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_eJmkoDZTr8/cifar_10_train_data0008.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_websbk05x2/cifar_10_train_data0008.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-3: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-4: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-5: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0009.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0009.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0010.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_cSyCSiEhHT/cifar_10_train_data0010.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_Bt85aChbv0/cifar_10_train_data0011.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar_10_train_data\n",
- "PoolWorker-2: Removed temporary directory /tmp/madlib_cSyCSiEhHT\n",
- "PoolWorker-3: Removed temporary directory /tmp/madlib_uvtHjGCU5S\n"
+ "PoolWorker-3: Removed temporary directory /tmp/madlib_OpoCiWZqpD\n",
+ "PoolWorker-5: Removed temporary directory /tmp/madlib_6a6arvMQdK\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "PoolWorker-5: Removed temporary directory /tmp/madlib_websbk05x2\n",
- "PoolWorker-4: Removed temporary directory /tmp/madlib_eJmkoDZTr8\n",
- "PoolWorker-1: Removed temporary directory /tmp/madlib_Bt85aChbv0\n",
- "Done! Loaded 50000 images in 24.2222080231s\n",
+ "PoolWorker-4: Removed temporary directory /tmp/madlib_sCT7YkACNY\n",
+ "PoolWorker-2: Removed temporary directory /tmp/madlib_n4bo04pPvd\n",
+ "PoolWorker-1: Removed temporary directory /tmp/madlib_WEi5uqbfca\n",
+ "Done! Loaded 50000 images in 587.870312929s\n",
"5 workers terminated.\n",
"MainProcess: Connected to madlib db.\n",
"Executing: CREATE TABLE cifar_10_test_data (id SERIAL, x REAL[], y TEXT)\n",
"CREATE TABLE\n",
"Created table cifar_10_test_data in madlib db\n",
"Spawning 5 workers...\n",
- "Initializing PoolWorker-6 [pid 82423]\n",
- "PoolWorker-6: Created temporary directory /tmp/madlib_e615zVgkaE\n",
- "Initializing PoolWorker-7 [pid 82424]\n",
- "PoolWorker-7: Created temporary directory /tmp/madlib_iRi2oMNIFA\n",
- "Initializing PoolWorker-8 [pid 82425]\n",
- "PoolWorker-8: Created temporary directory /tmp/madlib_kkSktVCq3n\n",
+ "Initializing PoolWorker-6 [pid 36347]\n",
+ "PoolWorker-6: Created temporary directory /tmp/madlib_eIEvkIFuoa\n",
+ "Initializing PoolWorker-7 [pid 36348]\n",
+ "PoolWorker-7: Created temporary directory /tmp/madlib_PmdMRJu56e\n",
+ "Initializing PoolWorker-8 [pid 36349]\n",
+ "PoolWorker-8: Created temporary directory /tmp/madlib_J9JAAkDm3K\n",
+ "Initializing PoolWorker-9 [pid 36350]\n",
+ "PoolWorker-9: Created temporary directory /tmp/madlib_mevX7AscP4\n",
+ "Initializing PoolWorker-10 [pid 36351]\n",
+ "PoolWorker-10: Created temporary directory /tmp/madlib_iy5ptkp4Et\n",
"PoolWorker-6: Connected to madlib db.\n",
- "Initializing PoolWorker-9 [pid 82426]\n",
- "PoolWorker-7: Connected to madlib db.\n",
- "PoolWorker-9: Created temporary directory /tmp/madlib_0To3XX96yI\n",
- "Initializing PoolWorker-10 [pid 82428]\n",
- "PoolWorker-8: Connected to madlib db.\n",
- "PoolWorker-10: Created temporary directory /tmp/madlib_8zwK04IJsc\n",
- "PoolWorker-9: Connected to madlib db.\n",
"PoolWorker-10: Connected to madlib db.\n",
- "PoolWorker-6: Wrote 1000 images to /tmp/madlib_e615zVgkaE/cifar_10_test_data0000.tmp\n",
- "PoolWorker-7: Wrote 1000 images to /tmp/madlib_iRi2oMNIFA/cifar_10_test_data0000.tmp\n",
- "PoolWorker-8: Wrote 1000 images to /tmp/madlib_kkSktVCq3n/cifar_10_test_data0000.tmp\n",
- "PoolWorker-9: Wrote 1000 images to /tmp/madlib_0To3XX96yI/cifar_10_test_data0000.tmp\n",
- "PoolWorker-10: Wrote 1000 images to /tmp/madlib_8zwK04IJsc/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-7: Connected to madlib db.\n",
+ "PoolWorker-8: Connected to madlib db.\n",
+ "PoolWorker-9: Connected to madlib db.\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_eIEvkIFuoa/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_PmdMRJu56e/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_J9JAAkDm3K/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_iy5ptkp4Et/cifar_10_test_data0000.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_mevX7AscP4/cifar_10_test_data0000.tmp\n",
"PoolWorker-6: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-6: Wrote 1000 images to /tmp/madlib_eIEvkIFuoa/cifar_10_test_data0001.tmp\n",
"PoolWorker-7: Loaded 1000 images into cifar_10_test_data\n",
- "PoolWorker-8: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-7: Wrote 1000 images to /tmp/madlib_PmdMRJu56e/cifar_10_test_data0001.tmp\n",
"PoolWorker-10: Loaded 1000 images into cifar_10_test_data\n",
"PoolWorker-9: Loaded 1000 images into cifar_10_test_data\n",
- "PoolWorker-6: Wrote 1000 images to /tmp/madlib_e615zVgkaE/cifar_10_test_data0001.tmp\n",
- "PoolWorker-7: Wrote 1000 images to /tmp/madlib_iRi2oMNIFA/cifar_10_test_data0001.tmp\n",
- "PoolWorker-8: Wrote 1000 images to /tmp/madlib_kkSktVCq3n/cifar_10_test_data0001.tmp\n",
- "PoolWorker-9: Wrote 1000 images to /tmp/madlib_0To3XX96yI/cifar_10_test_data0001.tmp\n",
- "PoolWorker-10: Wrote 1000 images to /tmp/madlib_8zwK04IJsc/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-8: Loaded 1000 images into cifar_10_test_data\n",
+ "PoolWorker-8: Wrote 1000 images to /tmp/madlib_J9JAAkDm3K/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-9: Wrote 1000 images to /tmp/madlib_mevX7AscP4/cifar_10_test_data0001.tmp\n",
+ "PoolWorker-10: Wrote 1000 images to /tmp/madlib_iy5ptkp4Et/cifar_10_test_data0001.tmp\n",
"PoolWorker-6: Loaded 1000 images into cifar_10_test_data\n",
"PoolWorker-7: Loaded 1000 images into cifar_10_test_data\n",
"PoolWorker-8: Loaded 1000 images into cifar_10_test_data\n",
"PoolWorker-9: Loaded 1000 images into cifar_10_test_data\n",
"PoolWorker-10: Loaded 1000 images into cifar_10_test_data\n",
- "PoolWorker-10: Removed temporary directory /tmp/madlib_8zwK04IJsc\n",
- "PoolWorker-8: Removed temporary directory /tmp/madlib_kkSktVCq3n\n",
- "PoolWorker-7: Removed temporary directory /tmp/madlib_iRi2oMNIFA\n",
- "PoolWorker-6: Removed temporary directory /tmp/madlib_e615zVgkaE\n",
- "PoolWorker-9: Removed temporary directory /tmp/madlib_0To3XX96yI\n",
- "Done! Loaded 10000 images in 4.6932258606s\n",
+ "PoolWorker-10: Removed temporary directory /tmp/madlib_iy5ptkp4Et\n",
+ "PoolWorker-6: Removed temporary directory /tmp/madlib_eIEvkIFuoa\n",
+ "PoolWorker-8: Removed temporary directory /tmp/madlib_J9JAAkDm3K\n",
+ "PoolWorker-7: Removed temporary directory /tmp/madlib_PmdMRJu56e\n",
+ "PoolWorker-9: Removed temporary directory /tmp/madlib_mevX7AscP4\n",
+ "Done! Loaded 10000 images in 119.767299891s\n",
"5 workers terminated.\n"
]
}
@@ -391,7 +391,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -417,7 +417,7 @@
"[(50000L,)]"
]
},
- "execution_count": 12,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -429,7 +429,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -455,7 +455,7 @@
"[(10000L,)]"
]
},
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -642,37 +642,9 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>50000</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(50000L,)]"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%%sql\n",
"SELECT COUNT(*) FROM cifar_10_train_data_filesystem;"
@@ -701,7 +673,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -832,13 +804,7 @@
"PoolWorker-12: Wrote 1000 images to /tmp/madlib_QOyefNO7bi/cifar_10_train_data_int0009.tmp\n",
"PoolWorker-14: Loaded 1000 images into cifar_10_train_data_int\n",
"PoolWorker-11: Loaded 1000 images into cifar_10_train_data_int\n",
- "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_int\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "PoolWorker-12: Loaded 1000 images into cifar_10_train_data_int\n",
"PoolWorker-11: Wrote 1000 images to /tmp/madlib_8Pc3rPCJJL/cifar_10_train_data_int0010.tmp\n",
"PoolWorker-12: Wrote 1000 images to /tmp/madlib_QOyefNO7bi/cifar_10_train_data_int0010.tmp\n",
"PoolWorker-11: Loaded 1000 images into cifar_10_train_data_int\n",
@@ -950,7 +916,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -976,7 +942,7 @@
"[(10000L,)]"
]
},
- "execution_count": 17,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/community-artifacts/Deep-learning/madlib_image_loader.py b/community-artifacts/Deep-learning/Utilities/madlib_image_loader.py
similarity index 100%
rename from community-artifacts/Deep-learning/madlib_image_loader.py
rename to community-artifacts/Deep-learning/Utilities/madlib_image_loader.py
diff --git a/community-artifacts/Deep-learning/automl/hyperband-diag-cifar10-v1.ipynb b/community-artifacts/Deep-learning/automl/hyperband-diag-cifar10-v1.ipynb
deleted file mode 100644
index 05a7143..0000000
--- a/community-artifacts/Deep-learning/automl/hyperband-diag-cifar10-v1.ipynb
+++ /dev/null
@@ -1,5288 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Hyperband diagonal using CIFAR-10\n",
- "\n",
- "Implemention of Hyperband https://arxiv.org/pdf/1603.06560.pdf for MPP with a synchronous barrier. Uses the Hyperband schedule but runs it on a diagonal across brackets, instead of one bracket at a time, to be more efficient with cluster resources.\n",
- "\n",
- "The CIFAR-10 dataset consists of 60,000 32x32 colour images in 10 classes, with 6,000 images per class. There are 50,000 training images and 10,000 test images.\n",
- "https://www.cs.toronto.edu/~kriz/cifar.html\n",
- "\n",
- "\n",
- "## Table of contents \n",
- "\n",
- "<a href=\"#setup\">0. Setup</a>\n",
- "\n",
- "<a href=\"#load_dataset\">1. Load dataset into table</a>\n",
- "\n",
- "<a href=\"#distr\">2. Setup distribution rules and call preprocessor</a>\n",
- "\n",
- "<a href=\"#arch\">3. Define and load model architectures</a>\n",
- "\n",
- "<a href=\"#hyperband\">4. Hyperband diagonal</a>\n",
- "\n",
- "<a href=\"#plot\">5. Plot results</a>\n",
- "\n",
- "<a href=\"#print\">6. Pretty print schedules</a>\n",
- "\n",
- "<a href=\"#predict\">7. Inference</a>"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"setup\"></a>\n",
- "# 0. Setup"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "scrolled": false
- },
- "outputs": [],
- "source": [
- "%load_ext sql"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: fmcquillan@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Greenplum Database 5.x on GCP - via tunnel\n",
- "#%sql postgresql://gpadmin@localhost:8000/madlib\n",
- "#%sql postgresql://gpadmin@35.230.53.21:5432/cifar_demo\n",
- "\n",
- "# PostgreSQL local\n",
- "%sql postgresql://fmcquillan@localhost:5432/madlib"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " * postgresql://fmcquillan@localhost:5432/madlib\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>version</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'MADlib version: 1.16, git revision: rc/1.16-rc1, cmake configuration time: Mon Jul 1 17:45:09 UTC 2019, build type: Release, build system: Darwin-16.7.0, C compiler: Clang, C++ compiler: Clang',)]"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql select madlib.version();\n",
- "#%sql select version();"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Import libraries and define some params"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Using TensorFlow backend.\n"
- ]
- }
- ],
- "source": [
- "from __future__ import print_function\n",
- "import keras\n",
- "from keras.datasets import cifar10\n",
- "from keras.preprocessing.image import ImageDataGenerator\n",
- "from keras.models import Sequential\n",
- "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
- "from keras.layers import Conv2D, MaxPooling2D\n",
- "import os"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Others needed in this workbook"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "import sys\n",
- "import os\n",
- "from matplotlib import pyplot as plt"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"load_dataset\"></a>\n",
- "# 1. Load dataset into table"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "PXF can be used to load image data. \n",
- "\n",
- "For this demo, we will get the dataset from Keras and use the script called madlib_image_loader.py located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning .\n",
- "\n",
- "If the script is not in the same folder as the notebook, you can use the following lines to import it."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import sys\n",
- "sys.path.insert(1, '/Users/fmcquillan/workspace/madlib-site/community-artifacts/Deep-learning')\n",
- "from madlib_image_loader import ImageLoader, DbCredentials\n",
- "\n",
- "# Specify database credentials, for connecting to db\n",
- "db_creds = DbCredentials(user='gpadmin',\n",
- " host='localhost',\n",
- " port='8000',\n",
- " password='')\n",
- "\n",
- "#db_creds = DbCredentials(user='fmcquillan',\n",
- "# host='localhost',\n",
- "# port='5432',\n",
- "# password='')\n",
- "\n",
- "# Initialize ImageLoader (increase num_workers to run faster)\n",
- "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load the training and test data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " * postgresql://fmcquillan@localhost:5432/madlib\n",
- "Done.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "MainProcess: Connected to madlib db.\n",
- "Executing: CREATE TABLE cifar10_train (id SERIAL, x REAL[], y TEXT)\n",
- "CREATE TABLE\n",
- "Created table cifar10_train in madlib db\n",
- "Spawning 5 workers...\n",
- "Initializing PoolWorker-1 [pid 10828]\n",
- "Initializing PoolWorker-2 [pid 10829]\n",
- "PoolWorker-1: Created temporary directory /tmp/madlib_DaP40IOgzi\n",
- "Initializing PoolWorker-3 [pid 10830]\n",
- "PoolWorker-2: Created temporary directory /tmp/madlib_n5XjJvXs5s\n",
- "PoolWorker-3: Created temporary directory /tmp/madlib_99mTsCxOFF\n",
- "Initializing PoolWorker-4 [pid 10831]\n",
- "PoolWorker-5: Connected to madlib db.\n",
- "PoolWorker-4: Created temporary directory /tmp/madlib_zGujxaoQIb\n",
- "Initializing PoolWorker-5 [pid 10832]\n",
- "PoolWorker-1: Connected to madlib db.\n",
- "PoolWorker-5: Created temporary directory /tmp/madlib_D6q8olnown\n",
- "PoolWorker-2: Connected to madlib db.\n",
- "PoolWorker-3: Connected to madlib db.\n",
- "PoolWorker-4: Connected to madlib db.\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0000.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0000.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0000.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0000.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0000.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0001.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0001.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0001.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0001.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0001.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0002.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0002.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0002.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0002.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0002.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0003.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0003.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0003.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0003.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0003.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0004.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0004.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0004.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0004.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0004.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0005.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0005.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0005.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0005.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0005.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0006.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0006.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0006.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0006.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0006.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0007.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0007.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0007.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0007.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0007.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0008.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0008.tmp\n",
- "PoolWorker-2: Wrote 1000 images to /tmp/madlib_n5XjJvXs5s/cifar10_train0008.tmp\n",
- "PoolWorker-4: Wrote 1000 images to /tmp/madlib_zGujxaoQIb/cifar10_train0008.tmp\n",
- "PoolWorker-5: Wrote 1000 images to /tmp/madlib_D6q8olnown/cifar10_train0008.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-2: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-5: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0009.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0009.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0010.tmp\n",
- "PoolWorker-3: Wrote 1000 images to /tmp/madlib_99mTsCxOFF/cifar10_train0010.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-3: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-1: Wrote 1000 images to /tmp/madlib_DaP40IOgzi/cifar10_train0011.tmp\n",
- "PoolWorker-1: Loaded 1000 images into cifar10_train\n",
- "PoolWorker-4: Removed temporary directory /tmp/madlib_zGujxaoQIb\n",
- "PoolWorker-5: Removed temporary directory /tmp/madlib_D6q8olnown\n",
- "PoolWorker-2: Removed temporary directory /tmp/madlib_n5XjJvXs5s\n",
- "PoolWorker-1: Removed temporary directory /tmp/madlib_DaP40IOgzi\n",
- "PoolWorker-3: Removed temporary directory /tmp/madlib_99mTsCxOFF\n",
- "Done! Loaded 50000 images in 19.7727279663s\n",
- "5 workers terminated.\n",
- "MainProcess: Connected to madlib db.\n",
- "Executing: CREATE TABLE cifar10_val (id SERIAL, x REAL[], y TEXT)\n",
- "CREATE TABLE\n",
- "Created table cifar10_val in madlib db\n",
- "Spawning 5 workers...\n",
- "Initializing PoolWorker-6 [pid 10850]\n",
- "PoolWorker-6: Created temporary directory /tmp/madlib_OqFarH4eVS\n",
- "Initializing PoolWorker-7 [pid 10851]\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "PoolWorker-7: Created temporary directory /tmp/madlib_BHhah9z53T\n",
- "Initializing PoolWorker-8 [pid 10852]\n",
- "PoolWorker-8: Created temporary directory /tmp/madlib_G5oLCmXwQN\n",
- "Initializing PoolWorker-9 [pid 10853]\n",
- "PoolWorker-6: Connected to madlib db.\n",
- "PoolWorker-9: Created temporary directory /tmp/madlib_THDiiymnsM\n",
- "Initializing PoolWorker-10 [pid 10854]\n",
- "PoolWorker-7: Connected to madlib db.\n",
- "PoolWorker-10: Created temporary directory /tmp/madlib_DLO1TEiyo6\n",
- "PoolWorker-8: Connected to madlib db.\n",
- "PoolWorker-9: Connected to madlib db.\n",
- "PoolWorker-10: Connected to madlib db.\n",
- "PoolWorker-6: Wrote 1000 images to /tmp/madlib_OqFarH4eVS/cifar10_val0000.tmp\n",
- "PoolWorker-7: Wrote 1000 images to /tmp/madlib_BHhah9z53T/cifar10_val0000.tmp\n",
- "PoolWorker-8: Wrote 1000 images to /tmp/madlib_G5oLCmXwQN/cifar10_val0000.tmp\n",
- "PoolWorker-9: Wrote 1000 images to /tmp/madlib_THDiiymnsM/cifar10_val0000.tmp\n",
- "PoolWorker-10: Wrote 1000 images to /tmp/madlib_DLO1TEiyo6/cifar10_val0000.tmp\n",
- "PoolWorker-6: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-7: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-8: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-9: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-10: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-6: Wrote 1000 images to /tmp/madlib_OqFarH4eVS/cifar10_val0001.tmp\n",
- "PoolWorker-7: Wrote 1000 images to /tmp/madlib_BHhah9z53T/cifar10_val0001.tmp\n",
- "PoolWorker-8: Wrote 1000 images to /tmp/madlib_G5oLCmXwQN/cifar10_val0001.tmp\n",
- "PoolWorker-9: Wrote 1000 images to /tmp/madlib_THDiiymnsM/cifar10_val0001.tmp\n",
- "PoolWorker-10: Wrote 1000 images to /tmp/madlib_DLO1TEiyo6/cifar10_val0001.tmp\n",
- "PoolWorker-6: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-7: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-8: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-9: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-10: Loaded 1000 images into cifar10_val\n",
- "PoolWorker-8: Removed temporary directory /tmp/madlib_G5oLCmXwQN\n",
- "PoolWorker-7: Removed temporary directory /tmp/madlib_BHhah9z53T\n",
- "PoolWorker-10: Removed temporary directory /tmp/madlib_DLO1TEiyo6\n",
- "PoolWorker-6: Removed temporary directory /tmp/madlib_OqFarH4eVS\n",
- "PoolWorker-9: Removed temporary directory /tmp/madlib_THDiiymnsM\n",
- "Done! Loaded 10000 images in 4.03977298737s\n",
- "5 workers terminated.\n"
- ]
- }
- ],
- "source": [
- "# Load dataset into np array\n",
- "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
- "\n",
- "%sql DROP TABLE IF EXISTS cifar10_train, cifar10_val;\n",
- "\n",
- "# Save images to temporary directories and load into database\n",
- "iloader.load_dataset_from_np(x_train, y_train, 'cifar10_train', append=False)\n",
- "iloader.load_dataset_from_np(x_test, y_test, 'cifar10_val', append=False)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " * postgresql://gpadmin@localhost:8000/madlib\n",
- "(psycopg2.errors.UndefinedTable) relation \"cifar_10_train_data\" does not exist\n",
- "LINE 1: SELECT COUNT(*) FROM cifar_10_train_data;\n",
- " ^\n",
- "\n",
- "[SQL: SELECT COUNT(*) FROM cifar_10_train_data;]\n",
- "(Background on this error at: http://sqlalche.me/e/f405)\n"
- ]
- }
- ],
- "source": [
- "%sql SELECT COUNT(*) FROM cifar10_train;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10000</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(10000L,)]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql SELECT COUNT(*) FROM cifar10_val;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"distr\"></a>\n",
- "# 2. Setup distribution rules and call preprocessor\n",
- "\n",
- "Get cluster configuration\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "20 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>hostname</th>\n",
- " <th>gpu_descr</th>\n",
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- " <tr>\n",
- " <td>phoenix0</td>\n",
- " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix0</td>\n",
- " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix0</td>\n",
- " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix0</td>\n",
- " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix1</td>\n",
- " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix1</td>\n",
- " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
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- " <tr>\n",
- " <td>phoenix1</td>\n",
- " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
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- " <tr>\n",
- " <td>phoenix1</td>\n",
- " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix2</td>\n",
- " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix2</td>\n",
- " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix2</td>\n",
- " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix2</td>\n",
- " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix3</td>\n",
- " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix3</td>\n",
- " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix3</td>\n",
- " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix3</td>\n",
- " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix4</td>\n",
- " <td>device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix4</td>\n",
- " <td>device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix4</td>\n",
- " <td>device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>phoenix4</td>\n",
- " <td>device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
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- " (u'phoenix0', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
- " (u'phoenix0', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
- " (u'phoenix1', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
- " (u'phoenix1', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
- " (u'phoenix1', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
- " (u'phoenix1', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
- " (u'phoenix2', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
- " (u'phoenix2', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
- " (u'phoenix2', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
- " (u'phoenix2', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
- " (u'phoenix3', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
- " (u'phoenix3', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
- " (u'phoenix3', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
- " (u'phoenix3', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0'),\n",
- " (u'phoenix4', u'device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0'),\n",
- " (u'phoenix4', u'device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0'),\n",
- " (u'phoenix4', u'device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0'),\n",
- " (u'phoenix4', u'device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0')]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS host_gpu_mapping_tf;\n",
- "SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');\n",
- "SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Below are examples of setting up different distribution rules tables. You can customize this to your needs.\n",
- "\n",
- "Build distribution rules table for 4 VMs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS segments_to_use_4VMs;\n",
- "CREATE TABLE segments_to_use_4VMs AS\n",
- " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
- " WHERE role='p' AND content>=0 AND hostname!='phoenix4';\n",
- "SELECT * FROM segments_to_use_4VMs ORDER BY hostname, dbid;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Build distribution rules table for 2 VMs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS segments_to_use_2VMs;\n",
- "CREATE TABLE segments_to_use_2VMs AS\n",
- " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
- " WHERE role='p' AND content>=0 AND (hostname='phoenix0' OR hostname='phoenix1');\n",
- "SELECT * FROM segments_to_use_2VMs ORDER BY hostname, dbid;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Build distribution rules table for 1 VMs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS segments_to_use_1VM;\n",
- "CREATE TABLE segments_to_use_1VM AS\n",
- " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
- " WHERE role='p' AND content>=0 AND hostname='phoenix0';\n",
- "SELECT * FROM segments_to_use_1VM ORDER BY hostname, dbid;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Build distribution rules table for 1 segment"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "5 rows affected.\n"
- ]
- },
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- " <th>mode</th>\n",
- " <th>status</th>\n",
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- ]
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- "execution_count": 26,
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- "%%sql\n",
- "SELECT * FROM gp_segment_configuration WHERE role='p' AND hostname='phoenix0' ORDER BY dbid;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
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- "output_type": "stream",
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- ]
- },
- {
- "data": {
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- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS segments_to_use_1seg;\n",
- "CREATE TABLE segments_to_use_1seg AS\n",
- " SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)\n",
- " WHERE dbid=2;\n",
- "SELECT * FROM segments_to_use_1seg ORDER BY hostname, dbid;"
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- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Training dataset (uses training preprocessor):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "16 rows affected.\n"
- ]
- },
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- " <tr>\n",
- " <td>[3125, 32, 32, 3]</td>\n",
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- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS cifar10_train_packed, cifar10_train_packed_summary;\n",
- "\n",
- "SELECT madlib.training_preprocessor_dl('cifar10_train', -- Source table\n",
- " 'cifar10_train_packed', -- Output table\n",
- " 'y', -- Dependent variable\n",
- " 'x', -- Independent variable\n",
- " NULL, -- Buffer size\n",
- " 256.0, -- Normalizing constant\n",
- " NULL, -- Number of classes\n",
- " 'gpu_segments' -- Distribution rules\n",
- " );\n",
- "\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_train_packed ORDER BY buffer_id;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
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- },
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- " <th>distribution_rules</th>\n",
- " <th>__internal_gpu_config__</th>\n",
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- ]
- },
- "execution_count": 8,
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- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM cifar10_train_packed_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Validation dataset (uses validation preprocessor):"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
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- "1 rows affected.\n",
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- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS cifar10_val_packed, cifar10_val_packed_summary;\n",
- "\n",
- "SELECT madlib.validation_preprocessor_dl('cifar10_val', -- Source table\n",
- " 'cifar10_val_packed', -- Output table\n",
- " 'y', -- Dependent variable\n",
- " 'x', -- Independent variable\n",
- " 'cifar10_train_packed', -- From training preprocessor step\n",
- " NULL, -- Buffer size\n",
- " 'gpu_segments' -- Distribution rules\n",
- " ); \n",
- "\n",
- "SELECT independent_var_shape, dependent_var_shape, buffer_id FROM cifar10_val_packed ORDER BY buffer_id;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>source_table</th>\n",
- " <th>output_table</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varname</th>\n",
- " <th>dependent_vartype</th>\n",
- " <th>class_values</th>\n",
- " <th>buffer_size</th>\n",
- " <th>normalizing_const</th>\n",
- " <th>num_classes</th>\n",
- " <th>distribution_rules</th>\n",
- " <th>__internal_gpu_config__</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>cifar10_val</td>\n",
- " <td>cifar10_val_packed</td>\n",
- " <td>y</td>\n",
- " <td>x</td>\n",
- " <td>smallint</td>\n",
- " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]</td>\n",
- " <td>625</td>\n",
- " <td>256.0</td>\n",
- " <td>10</td>\n",
- " <td>[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]</td>\n",
- " <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'cifar10_val', u'cifar10_val_packed', u'y', u'x', u'smallint', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 625, 256.0, 10, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])]"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT * FROM cifar10_val_packed_summary;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"arch\"></a>\n",
- "# 3. Define and load model architectures\n",
- "\n",
- "Here we load some example model architectures from published sources.\n",
- "\n",
- "a. Model architecture from https://keras.io/examples/cifar10_cnn/"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "num_classes = 10\n",
- "\n",
- "#to be removed\n",
- "#do this just to get shape for model architecture \n",
- "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "conv2d_1 (Conv2D) (None, 32, 32, 32) 896 \n",
- "_________________________________________________________________\n",
- "activation_1 (Activation) (None, 32, 32, 32) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_2 (Conv2D) (None, 30, 30, 32) 9248 \n",
- "_________________________________________________________________\n",
- "activation_2 (Activation) (None, 30, 30, 32) 0 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32) 0 \n",
- "_________________________________________________________________\n",
- "dropout_1 (Dropout) (None, 15, 15, 32) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_3 (Conv2D) (None, 15, 15, 64) 18496 \n",
- "_________________________________________________________________\n",
- "activation_3 (Activation) (None, 15, 15, 64) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_4 (Conv2D) (None, 13, 13, 64) 36928 \n",
- "_________________________________________________________________\n",
- "activation_4 (Activation) (None, 13, 13, 64) 0 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64) 0 \n",
- "_________________________________________________________________\n",
- "dropout_2 (Dropout) (None, 6, 6, 64) 0 \n",
- "_________________________________________________________________\n",
- "flatten_1 (Flatten) (None, 2304) 0 \n",
- "_________________________________________________________________\n",
- "dense_1 (Dense) (None, 512) 1180160 \n",
- "_________________________________________________________________\n",
- "activation_5 (Activation) (None, 512) 0 \n",
- "_________________________________________________________________\n",
- "dropout_3 (Dropout) (None, 512) 0 \n",
- "_________________________________________________________________\n",
- "dense_2 (Dense) (None, 10) 5130 \n",
- "_________________________________________________________________\n",
- "activation_6 (Activation) (None, 10) 0 \n",
- "=================================================================\n",
- "Total params: 1,250,858\n",
- "Trainable params: 1,250,858\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model1 = Sequential()\n",
- "\n",
- "model1.add(Conv2D(32, (3, 3), padding='same',\n",
- " input_shape=x_train.shape[1:]))\n",
- "model1.add(Activation('relu'))\n",
- "model1.add(Conv2D(32, (3, 3)))\n",
- "model1.add(Activation('relu'))\n",
- "model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "model1.add(Dropout(0.25))\n",
- "\n",
- "model1.add(Conv2D(64, (3, 3), padding='same'))\n",
- "model1.add(Activation('relu'))\n",
- "model1.add(Conv2D(64, (3, 3)))\n",
- "model1.add(Activation('relu'))\n",
- "model1.add(MaxPooling2D(pool_size=(2, 2)))\n",
- "model1.add(Dropout(0.25))\n",
- "\n",
- "model1.add(Flatten())\n",
- "model1.add(Dense(512))\n",
- "model1.add(Activation('relu'))\n",
- "model1.add(Dropout(0.5))\n",
- "model1.add(Dense(num_classes))\n",
- "model1.add(Activation('softmax'))\n",
- "\n",
- "model1.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_2\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_2\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_3\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_3\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"conv2d_4\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"valid\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_4\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_2\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.25, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_2\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_1\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_1\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 512, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"relu\", \"trainable\": true, \"name\": \"activation_5\"}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_3\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_2\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"linear\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"Activation\", \"config\": {\"activation\": \"softmax\", \"trainable\": true, \"name\": \"activation_6\"}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model1.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "b. Model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "conv2d_5 (Conv2D) (None, 32, 32, 32) 896 \n",
- "_________________________________________________________________\n",
- "batch_normalization_1 (Batch (None, 32, 32, 32) 128 \n",
- "_________________________________________________________________\n",
- "conv2d_6 (Conv2D) (None, 32, 32, 32) 9248 \n",
- "_________________________________________________________________\n",
- "batch_normalization_2 (Batch (None, 32, 32, 32) 128 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 32) 0 \n",
- "_________________________________________________________________\n",
- "dropout_4 (Dropout) (None, 16, 16, 32) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_7 (Conv2D) (None, 16, 16, 64) 18496 \n",
- "_________________________________________________________________\n",
- "batch_normalization_3 (Batch (None, 16, 16, 64) 256 \n",
- "_________________________________________________________________\n",
- "conv2d_8 (Conv2D) (None, 16, 16, 64) 36928 \n",
- "_________________________________________________________________\n",
- "batch_normalization_4 (Batch (None, 16, 16, 64) 256 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_4 (MaxPooling2 (None, 8, 8, 64) 0 \n",
- "_________________________________________________________________\n",
- "dropout_5 (Dropout) (None, 8, 8, 64) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_9 (Conv2D) (None, 8, 8, 128) 73856 \n",
- "_________________________________________________________________\n",
- "batch_normalization_5 (Batch (None, 8, 8, 128) 512 \n",
- "_________________________________________________________________\n",
- "conv2d_10 (Conv2D) (None, 8, 8, 128) 147584 \n",
- "_________________________________________________________________\n",
- "batch_normalization_6 (Batch (None, 8, 8, 128) 512 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 128) 0 \n",
- "_________________________________________________________________\n",
- "dropout_6 (Dropout) (None, 4, 4, 128) 0 \n",
- "_________________________________________________________________\n",
- "flatten_2 (Flatten) (None, 2048) 0 \n",
- "_________________________________________________________________\n",
- "dense_3 (Dense) (None, 128) 262272 \n",
- "_________________________________________________________________\n",
- "batch_normalization_7 (Batch (None, 128) 512 \n",
- "_________________________________________________________________\n",
- "dropout_7 (Dropout) (None, 128) 0 \n",
- "_________________________________________________________________\n",
- "dense_4 (Dense) (None, 10) 1290 \n",
- "=================================================================\n",
- "Total params: 552,874\n",
- "Trainable params: 551,722\n",
- "Non-trainable params: 1,152\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model2 = Sequential()\n",
- "\n",
- "model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
- "model2.add(BatchNormalization())\n",
- "model2.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model2.add(BatchNormalization())\n",
- "model2.add(MaxPooling2D((2, 2)))\n",
- "model2.add(Dropout(0.2))\n",
- "\n",
- "model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model2.add(BatchNormalization())\n",
- "model2.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model2.add(BatchNormalization())\n",
- "model2.add(MaxPooling2D((2, 2)))\n",
- "model2.add(Dropout(0.3))\n",
- "\n",
- "model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model2.add(BatchNormalization())\n",
- "model2.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model2.add(BatchNormalization())\n",
- "model2.add(MaxPooling2D((2, 2)))\n",
- "model2.add(Dropout(0.4))\n",
- "\n",
- "model2.add(Flatten())\n",
- "model2.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
- "model2.add(BatchNormalization())\n",
- "model2.add(Dropout(0.5))\n",
- "model2.add(Dense(10, activation='softmax'))\n",
- "\n",
- "model2.summary()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'{\"class_name\": \"Sequential\", \"keras_version\": \"2.1.6\", \"config\": [{\"class_name\": \"Conv2D\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_5\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"dtype\": \"float32\", \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"filters\": 32, \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"batch_input_shape\": [null, 32, 32, 3], \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_1\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_6\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 32, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_2\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_3\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.2, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_4\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_7\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_3\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_8\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 64, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_4\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_4\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.3, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_5\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_9\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_5\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Conv2D\", \"config\": {\"kernel_constraint\": null, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"conv2d_10\", \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"data_format\": \"channels_last\", \"padding\": \"same\", \"strides\": [1, 1], \"dilation_rate\": [1, 1], \"kernel_regularizer\": null, \"filters\": 128, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"use_bias\": true, \"activity_regularizer\": null, \"kernel_size\": [3, 3]}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_6\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_5\", \"trainable\": true, \"data_format\": \"channels_last\", \"pool_size\": [2, 2], \"padding\": \"valid\", \"strides\": [2, 2]}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.4, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_6\"}}, {\"class_name\": \"Flatten\", \"config\": {\"trainable\": true, \"name\": \"flatten_2\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 2.0, \"seed\": null, \"mode\": \"fan_in\"}}, \"name\": \"dense_3\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"relu\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 128, \"use_bias\": true, \"activity_regularizer\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"beta_constraint\": null, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"name\": \"batch_normalization_7\", \"epsilon\": 0.001, \"trainable\": true, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"scale\": true, \"axis\": -1, \"gamma_constraint\": null, \"gamma_regularizer\": null, \"beta_regularizer\": null, \"momentum\": 0.99, \"center\": true}}, {\"class_name\": \"Dropout\", \"config\": {\"rate\": 0.5, \"noise_shape\": null, \"trainable\": true, \"seed\": null, \"name\": \"dropout_7\"}}, {\"class_name\": \"Dense\", \"config\": {\"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"distribution\": \"uniform\", \"scale\": 1.0, \"seed\": null, \"mode\": \"fan_avg\"}}, \"name\": \"dense_4\", \"kernel_constraint\": null, \"bias_regularizer\": null, \"bias_constraint\": null, \"activation\": \"softmax\", \"trainable\": true, \"kernel_regularizer\": null, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"units\": 10, \"use_bias\": true, \"activity_regularizer\": null}}], \"backend\": \"tensorflow\"}'"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "model2.to_json()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "c. Another model architecture from https://machinelearningmastery.com/how-to-develop-a-cnn-from-scratch-for-cifar-10-photo-classification/"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "_________________________________________________________________\n",
- "Layer (type) Output Shape Param # \n",
- "=================================================================\n",
- "conv2d_11 (Conv2D) (None, 32, 32, 32) 896 \n",
- "_________________________________________________________________\n",
- "conv2d_12 (Conv2D) (None, 32, 32, 32) 9248 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 32) 0 \n",
- "_________________________________________________________________\n",
- "dropout_8 (Dropout) (None, 16, 16, 32) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_13 (Conv2D) (None, 16, 16, 64) 18496 \n",
- "_________________________________________________________________\n",
- "conv2d_14 (Conv2D) (None, 16, 16, 64) 36928 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 64) 0 \n",
- "_________________________________________________________________\n",
- "dropout_9 (Dropout) (None, 8, 8, 64) 0 \n",
- "_________________________________________________________________\n",
- "conv2d_15 (Conv2D) (None, 8, 8, 128) 73856 \n",
- "_________________________________________________________________\n",
- "conv2d_16 (Conv2D) (None, 8, 8, 128) 147584 \n",
- "_________________________________________________________________\n",
- "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128) 0 \n",
- "_________________________________________________________________\n",
- "dropout_10 (Dropout) (None, 4, 4, 128) 0 \n",
- "_________________________________________________________________\n",
- "flatten_3 (Flatten) (None, 2048) 0 \n",
- "_________________________________________________________________\n",
- "dense_5 (Dense) (None, 128) 262272 \n",
- "_________________________________________________________________\n",
- "dropout_11 (Dropout) (None, 128) 0 \n",
- "_________________________________________________________________\n",
- "dense_6 (Dense) (None, 10) 1290 \n",
- "=================================================================\n",
- "Total params: 550,570\n",
- "Trainable params: 550,570\n",
- "Non-trainable params: 0\n",
- "_________________________________________________________________\n"
- ]
- }
- ],
- "source": [
- "model3 = Sequential()\n",
- "\n",
- "model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
- "model3.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model3.add(MaxPooling2D((2, 2)))\n",
- "model3.add(Dropout(0.2))\n",
- "model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model3.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model3.add(MaxPooling2D((2, 2)))\n",
- "model3.add(Dropout(0.3))\n",
- "model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model3.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
- "model3.add(MaxPooling2D((2, 2)))\n",
- "model3.add(Dropout(0.4))\n",
- "model3.add(Flatten())\n",
- "model3.add(Dense(128, activation='relu', kernel_initializer='he_uniform'))\n",
- "model3.add(Dropout(0.5))\n",
- "model3.add(Dense(10, activation='softmax'))\n",
- "\n",
- "model3.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load into model architecture table using psycopg2"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "3 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>model_id</th>\n",
- " <th>name</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>CNN from Keras docs for CIFAR-10</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>CNN from Jason Brownlee blog post</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>CNN from Jason Brownlee blog post - no batch normalization</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(1, u'CNN from Keras docs for CIFAR-10'),\n",
- " (2, u'CNN from Jason Brownlee blog post'),\n",
- " (3, u'CNN from Jason Brownlee blog post - no batch normalization')]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import psycopg2 as p2\n",
- "#conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')\n",
- "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
- "conn = p2.connect('postgresql://gpadmin@localhost:8000/cifar_demo')\n",
- "cur = conn.cursor()\n",
- "\n",
- "%sql DROP TABLE IF EXISTS model_arch_table_cifar10;\n",
- "query = \"SELECT madlib.load_keras_model('model_arch_table_cifar10', %s, NULL, %s)\"\n",
- "cur.execute(query,[model1.to_json(), \"CNN from Keras docs for CIFAR-10\"])\n",
- "conn.commit()\n",
- "\n",
- "query = \"SELECT madlib.load_keras_model('model_arch_table_cifar10', %s, NULL, %s)\"\n",
- "cur.execute(query,[model2.to_json(), \"CNN from Jason Brownlee blog post\"])\n",
- "conn.commit()\n",
- "\n",
- "query = \"SELECT madlib.load_keras_model('model_arch_table_cifar10', %s, NULL, %s)\"\n",
- "cur.execute(query,[model3.to_json(), \"CNN from Jason Brownlee blog post - no batch normalization\"])\n",
- "conn.commit()\n",
- "\n",
- "# check model loaded OK\n",
- "%sql SELECT model_id, name FROM model_arch_table_cifar10 ORDER BY model_id;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"hyperband\"></a>\n",
- "# 4. Hyperband diagonal\n",
- "\n",
- "Create tables for intermediate and overall results from Hyperband, which is running on top of MADlib model selection methods."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "Done.\n",
- "Done.\n",
- "Done.\n",
- "Done.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "Done.\n",
- "Done.\n",
- "Done.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "-- overall results table\n",
- "DROP TABLE IF EXISTS results_cifar10;\n",
- "CREATE TABLE results_cifar10 ( \n",
- " mst_key INTEGER, -- note not SERIAL\n",
- " model_id INTEGER, \n",
- " compile_params TEXT,\n",
- " fit_params TEXT, \n",
- " model_type TEXT, \n",
- " model_size DOUBLE PRECISION, \n",
- " metrics_elapsed_time DOUBLE PRECISION[], \n",
- " metrics_type TEXT[], \n",
- " training_metrics_final DOUBLE PRECISION, \n",
- " training_loss_final DOUBLE PRECISION, \n",
- " training_metrics DOUBLE PRECISION[], \n",
- " training_loss DOUBLE PRECISION[], \n",
- " validation_metrics_final DOUBLE PRECISION, \n",
- " validation_loss_final DOUBLE PRECISION, \n",
- " validation_metrics DOUBLE PRECISION[], \n",
- " validation_loss DOUBLE PRECISION[], \n",
- " model_arch_table TEXT, \n",
- " num_iterations INTEGER, \n",
- " start_training_time TIMESTAMP, \n",
- " end_training_time TIMESTAMP,\n",
- " s INTEGER, -- bracket number from Hyperband\n",
- " i INTEGER, -- iteration corresponding to successive having within a bracket\n",
- " run_id SERIAL -- global counter for the training runs\n",
- " );\n",
- "\n",
- "-- all model selections:\n",
- "-- model selection table containing all model configs (all brackets)\n",
- "DROP TABLE IF EXISTS mst_table_hb_cifar10;\n",
- "CREATE TABLE mst_table_hb_cifar10 (\n",
- " mst_key SERIAL, \n",
- " s INTEGER, -- bracket\n",
- " model_id INTEGER, \n",
- " compile_params VARCHAR, \n",
- " fit_params VARCHAR\n",
- " );\n",
- "\n",
- "-- model selection summary table\n",
- "DROP TABLE IF EXISTS mst_table_hb_cifar10_summary;\n",
- "CREATE TABLE mst_table_hb_cifar10_summary (model_arch_table VARCHAR);\n",
- "INSERT INTO mst_table_hb_cifar10_summary VALUES ('model_arch_table_cifar10');\n",
- "\n",
- "-- diagonal model selections:\n",
- "-- model selection table for diagonal: fit() will be called on a per diagonal basis\n",
- "DROP TABLE IF EXISTS mst_diag_table_hb_cifar10;\n",
- "CREATE TABLE mst_diag_table_hb_cifar10 (\n",
- " mst_key INTEGER, -- note not SERIAL since this table derived from main model selection table\n",
- " s INTEGER, -- bracket\n",
- " model_id INTEGER, \n",
- " compile_params VARCHAR, \n",
- " fit_params VARCHAR\n",
- " );\n",
- "\n",
- "-- model selection summary table for diagonal table\n",
- "DROP TABLE IF EXISTS mst_diag_table_hb_cifar10_summary;\n",
- "CREATE TABLE mst_diag_table_hb_cifar10_summary (model_arch_table VARCHAR);\n",
- "INSERT INTO mst_diag_table_hb_cifar10_summary VALUES ('model_arch_table_cifar10');"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generalize table names"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "results_table = 'results_cifar10'\n",
- "\n",
- "output_table = 'cifar10_multi_model'\n",
- "output_table_info = '_'.join([output_table, 'info'])\n",
- "output_table_summary = '_'.join([output_table, 'summary'])\n",
- "\n",
- "best_model = 'cifar10_best_model'\n",
- "best_model_info = '_'.join([best_model, 'info'])\n",
- "best_model_summary = '_'.join([best_model, 'summary'])\n",
- "\n",
- "\n",
- "mst_table = 'mst_table_hb_cifar10'\n",
- "mst_table_summary = '_'.join([mst_table, 'summary'])\n",
- "\n",
- "mst_diag_table = 'mst_diag_table_hb_cifar10'\n",
- "mst_diag_table_summary = '_'.join([mst_diag_table, 'summary'])\n",
- "\n",
- "model_arch_table = 'model_arch_table_cifar10'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Hyperband diagonal logic"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Define variables for Hyperband"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "max_iter = 27 # maximum iterations per configuration\n",
- "eta = 3 # defines downsampling rate (default = 3)\n",
- "skip_last = 0 # 1 means skip last run in each bracket, 0 means run full bracket"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from random import random\n",
- "from math import log, ceil\n",
- "from time import time, ctime\n",
- "\n",
- "class Hyperband_diagonal:\n",
- " \n",
- " def __init__( self, get_params_function, try_params_function ):\n",
- " self.get_params = get_params_function #\n",
- " self.try_params = try_params_function\n",
- "\n",
- " self.max_iter = max_iter \n",
- " self.eta = eta \n",
- " self.skip_last = skip_last \n",
- "\n",
- " self.logeta = lambda x: log( x ) / log( self.eta )\n",
- " self.s_max = int( self.logeta( self.max_iter ))\n",
- " self.B = ( self.s_max + 1 ) * self.max_iter\n",
- " \n",
- " #echo output\n",
- " print (\"max_iter = \" + str(self.max_iter))\n",
- " print (\"eta = \" + str(self.eta))\n",
- " print (\"B = \" + str(self.s_max+1) + \"*max_iter = \" + str(self.B))\n",
- " print (\"skip_last = \" + str(self.skip_last))\n",
- " \n",
- " self.setup_full_schedule()\n",
- " self.create_mst_superset()\n",
- " \n",
- " self.best_loss = np.inf\n",
- " self.best_accuracy = 0.0\n",
- "\n",
- " # create full Hyperband schedule for all brackets ahead of time\n",
- " def setup_full_schedule(self):\n",
- " self.n_vals = np.zeros((self.s_max+1, self.s_max+1), dtype=int)\n",
- " self.r_vals = np.zeros((self.s_max+1, self.s_max+1), dtype=int)\n",
- " \n",
- " print (\" \")\n",
- " print (\"Hyperband brackets\")\n",
- "\n",
- " # loop through each bracket in reverse order\n",
- " for s in reversed(range(self.s_max+1)):\n",
- " \n",
- " print (\" \")\n",
- " print (\"s=\" + str(s))\n",
- " print (\"n_i r_i\")\n",
- " print (\"------------\")\n",
- "\n",
- " for i in range(s+1):\n",
- " # n_i configs for r_i iterations\n",
- " n_i = n*self.eta**(-i)\n",
- " r_i = r*self.eta**(i)\n",
- "\n",
- " self.n_vals[s][i] = n_i\n",
- " self.r_vals[s][i] = r_i\n",
- "\n",
- " print (str(n_i) + \" \" + str (r_i))\n",
- " \n",
- " \n",
- " # generate model selection tuples for all brackets\n",
- " def create_mst_superset(self):\n",
- " \n",
- " print (\" \")\n",
- " print (\"Create superset of MSTs for each bracket s\")\n",
- " \n",
- " # get hyper parameter configs for each bracket s\n",
- " for s in reversed(range(self.s_max+1)):\n",
- " n = int(ceil(int(self.B/self.max_iter/(s+1))*self.eta**s)) # initial number of configurations\n",
- " r = self.max_iter*self.eta**(-s) # initial number of iterations to run configurations for\n",
- "\n",
- " print (\" \")\n",
- " print (\"s=\" + str(s))\n",
- " print (\"n=\" + str(n))\n",
- " print (\"r=\" + str(r))\n",
- " print (\" \")\n",
- " \n",
- " # n random configurations for each bracket s\n",
- " self.get_params(n, s)\n",
- " \n",
- " \n",
- " # Hyperband diagonal logic\n",
- " def run(self): \n",
- " \n",
- " print (\" \")\n",
- " print (\"Hyperband diagonal\")\n",
- " print (\"Outer loop on diagonal:\")\n",
- " \n",
- " # outer loop on diagonal\n",
- " #for i in range(self.s_max+1):\n",
- " for i in range((self.s_max+1) - int(self.skip_last)):\n",
- " print (\" \")\n",
- " print (\"i=\" + str(i))\n",
- " \n",
- " # zero out diagonal table\n",
- " %sql TRUNCATE TABLE $mst_diag_table\n",
- " \n",
- " # loop on brackets s desc to create diagonal table\n",
- " print (\"Loop on s desc to create diagonal table:\")\n",
- " for s in range(self.s_max, self.s_max-i-1, -1):\n",
- "\n",
- " # build up mst table for diagonal\n",
- " %sql INSERT INTO $mst_diag_table (SELECT * FROM $mst_table WHERE s=$s);\n",
- " \n",
- " # first pass\n",
- " if i == 0:\n",
- " first_pass = True\n",
- " else:\n",
- " first_pass = False\n",
- " \n",
- " # multi-model training\n",
- " print (\" \")\n",
- " print (\"Try params for i = \" + str(i))\n",
- " U = self.try_params(i, self.r_vals[self.s_max][i], first_pass) # r_i is the same for all diagonal elements\n",
- " \n",
- " # loop on brackets s desc to prune model selection table\n",
- " # don't need to prune if finished last diagonal\n",
- " #if i < (self.s_max):\n",
- " if i < (self.s_max - int(self.skip_last)):\n",
- " print (\"Loop on s desc to prune mst table:\")\n",
- " for s in range(self.s_max, self.s_max-i-1, -1):\n",
- " \n",
- " # compute number of configs to keep\n",
- " # remember i value is different for each bracket s on the diagonal\n",
- " k = int( self.n_vals[s][s-self.s_max+i] / self.eta)\n",
- " print (\"Pruning s = {} with k = {}\".format(s, k))\n",
- "\n",
- " # temporarily re-define table names due to weird Python scope issues\n",
- " results_table = 'results_cifar10'\n",
- "\n",
- " output_table = 'cifar10_multi_model'\n",
- " output_table_info = '_'.join([output_table, 'info'])\n",
- " output_table_summary = '_'.join([output_table, 'summary'])\n",
- "\n",
- " mst_table = 'mst_table_hb_cifar10'\n",
- " mst_table_summary = '_'.join([mst_table, 'summary'])\n",
- "\n",
- " mst_diag_table = 'mst_diag_table_hb_cifar10'\n",
- " mst_diag_table_summary = '_'.join([mst_diag_table, 'summary'])\n",
- "\n",
- " model_arch_table = 'model_arch_table_cifar10'\n",
- " \n",
- " query = \"\"\"\n",
- " DELETE FROM {mst_table} WHERE s={s} AND mst_key NOT IN (SELECT {output_table_info}.mst_key FROM {output_table_info} JOIN {mst_table} ON {output_table_info}.mst_key={mst_table}.mst_key WHERE s={s} ORDER BY validation_loss_final ASC LIMIT {k}::INT);\n",
- " \"\"\".format(**locals())\n",
- " cur.execute(query)\n",
- " conn.commit()\n",
- " \n",
- " # these were not working so used cursor instead\n",
- " #%sql DELETE FROM $mst_table WHERE s=$s AND mst_key NOT IN (SELECT $output_table_info.mst_key FROM $output_table_info JOIN $mst_table ON $output_table_info.mst_key=$mst_table.mst_key WHERE s=$s ORDER BY validation_loss_final ASC LIMIT $k::INT);\n",
- " #%sql DELETE FROM mst_table_hb_cifar10 WHERE s=1 AND mst_key NOT IN (SELECT cifar10_multi_model_info.mst_key FROM cifar10_multi_model_info JOIN mst_table_hb_cifar10 ON cifar10_multi_model_info.mst_key=mst_table_hb_cifar10.mst_key WHERE s=1 ORDER BY validation_loss_final ASC LIMIT 1);\n",
- " \n",
- " # keep track of best loss so far and save the model for inference\n",
- " # get best loss and accuracy from this diagonal run\n",
- " # (need to check if this will work OK if don't evaluate metrics every iteration)\n",
- " loss = %sql SELECT validation_loss_final FROM $output_table_info ORDER BY validation_loss_final ASC LIMIT 1;\n",
- " accuracy = %sql SELECT validation_metrics_final FROM $output_table_info ORDER BY validation_metrics_final DESC LIMIT 1;\n",
- " \n",
- " # save best model based on accuracy (could do loss if you wanted)\n",
- " if accuracy > self.best_accuracy:\n",
- " \n",
- " self.best_accuracy = accuracy\n",
- " \n",
- " # get best mst_key\n",
- " best_mst_key = %sql SELECT mst_key FROM $output_table_info ORDER BY validation_metrics_final DESC LIMIT 1; \n",
- " best_mst_key = best_mst_key.DataFrame().to_numpy()[0][0]\n",
- "\n",
- " # save model table (1 row for best model)\n",
- " %sql DROP TABLE IF EXISTS $best_model;\n",
- " %sql CREATE TABLE $best_model AS SELECT * FROM $output_table WHERE mst_key = $best_mst_key;\n",
- "\n",
- " # save info table (1 row for best model)\n",
- " %sql DROP TABLE IF EXISTS $best_model_info;\n",
- " %sql CREATE TABLE $best_model_info AS SELECT * FROM $output_table_info WHERE mst_key = $best_mst_key;\n",
- " \n",
- " # save summary table\n",
- " %sql DROP TABLE IF EXISTS $best_model_summary;\n",
- " %sql CREATE TABLE $best_model_summary AS SELECT * FROM $output_table_summary;\n",
- " \n",
- " if loss < self.best_loss:\n",
- " self.best_loss = loss\n",
- " \n",
- " print (\" \")\n",
- " print (\"Best validation loss so far = \")\n",
- " print (str(loss))\n",
- " print (\"Best validation accuracy so far = \")\n",
- " print (str(accuracy))\n",
- " \n",
- "\n",
- " \n",
- " return"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate params and insert into MST table. This version of get_params uses the same compile parameters for all optimizers, and the same compile/fit parameters for all model architectures. (This may be too restrictive in some cases.) -- Note 3/13: check SIGMOID paper runs which I think I may have addressed this to some extent"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_params(n, s):\n",
- " \n",
- " from sklearn.model_selection import ParameterSampler\n",
- " from scipy.stats.distributions import uniform\n",
- " import numpy as np\n",
- " \n",
- " # model architecture\n",
- " model_id = [1,2]\n",
- "\n",
- " # compile params\n",
- " # loss function\n",
- " loss = ['categorical_crossentropy']\n",
- " # optimizer\n",
- " optimizer = ['sgd', 'adam', 'rmsprop']\n",
- " # learning rate (sample on log scale here not in ParameterSampler)\n",
- " lr_range = [0.0001, 0.01]\n",
- " lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), n)\n",
- " # metrics\n",
- " metrics = ['accuracy']\n",
- "\n",
- " # fit params\n",
- " # batch size\n",
- " batch_size = [32, 64, 128, 256]\n",
- " # epochs\n",
- " epochs = [5]\n",
- "\n",
- " # create random param list\n",
- " param_grid = {\n",
- " 'model_id': model_id,\n",
- " 'loss': loss,\n",
- " 'optimizer': optimizer,\n",
- " 'lr': lr,\n",
- " 'metrics': metrics,\n",
- " 'batch_size': batch_size,\n",
- " 'epochs': epochs\n",
- " }\n",
- " param_list = list(ParameterSampler(param_grid, n_iter=n))\n",
- " \n",
- " for params in param_list:\n",
- "\n",
- " model_id = str(params.get(\"model_id\"))\n",
- " compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
- " fit_params = \"$$batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\")) + \"$$\" \n",
- " row_content = \"(\" + str(s) + \", \" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
- " \n",
- " %sql INSERT INTO $mst_table (s, model_id, compile_params, fit_params) VALUES $row_content\n",
- " \n",
- " return"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Generate params and insert into MST table. This version of get_params allows for more customization by optimizer and model architecture. This is sort of brute force and can be improved."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "def get_params(n, s):\n",
- " \n",
- " from sklearn.model_selection import ParameterSampler\n",
- " from scipy.stats.distributions import uniform\n",
- " import numpy as np\n",
- " \n",
- " # number of samples by optimizer\n",
- " #n_adam = int(n/3)\n",
- " n_adam = int(n/2)\n",
- " #n_rmsprop = int(n/3)\n",
- " n_rmsprop = 0\n",
- " n_sgd = int(n - n_adam - n_rmsprop)\n",
- "\n",
- " # 1) adam\n",
- " \n",
- " # model architecture\n",
- " model_id = [2,3]\n",
- "\n",
- " # compile params\n",
- " # loss function\n",
- " loss = ['categorical_crossentropy']\n",
- " # optimizer\n",
- " optimizer = ['adam']\n",
- " # learning rate (sample on log scale here not in ParameterSampler)\n",
- " lr_range = [0.0001, 0.001]\n",
- " lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), n_adam)\n",
- " # metrics\n",
- " metrics = ['accuracy']\n",
- "\n",
- " # fit params\n",
- " # batch size\n",
- " batch_size = [128, 256]\n",
- " # epochs\n",
- " epochs = [5]\n",
- "\n",
- " # create random param list\n",
- " param_grid = {\n",
- " 'model_id': model_id,\n",
- " 'loss': loss,\n",
- " 'optimizer': optimizer,\n",
- " 'lr': lr,\n",
- " 'metrics': metrics,\n",
- " 'batch_size': batch_size,\n",
- " 'epochs': epochs\n",
- " }\n",
- " param_list_adam = list(ParameterSampler(param_grid, n_iter=n_adam))\n",
- "\n",
- " # iterate over params\n",
- " for params in param_list_adam:\n",
- "\n",
- " model_id = str(params.get(\"model_id\"))\n",
- " compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
- " fit_params = \"$$batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\")) + \"$$\" \n",
- " row_content = \"(\" + str(s) + \", \" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
- " \n",
- " # populate mst table\n",
- " %sql INSERT INTO $mst_table (s, model_id, compile_params, fit_params) VALUES $row_content\n",
- " \n",
- " \n",
- " # 2) rmsprop\n",
- " \n",
- " # model architecture\n",
- " model_id = [1,2,3]\n",
- "\n",
- " # compile params\n",
- " # loss function\n",
- " loss = ['categorical_crossentropy']\n",
- " # optimizer\n",
- " optimizer = ['rmsprop']\n",
- " # learning rate (sample on log scale here not in ParameterSampler)\n",
- " lr_range = [0.0001, 0.001]\n",
- " lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), n_rmsprop)\n",
- " # decay (sample on log scale here not in ParameterSampler if want multiple values)\n",
- " decay = [1e-6]\n",
- "\n",
- " # metrics\n",
- " metrics = ['accuracy']\n",
- "\n",
- " # fit params\n",
- " # batch size\n",
- " batch_size = [32, 64, 128, 256]\n",
- " # epochs\n",
- " epochs = [5]\n",
- "\n",
- " # create random param list\n",
- " param_grid = {\n",
- " 'model_id': model_id,\n",
- " 'loss': loss,\n",
- " 'optimizer': optimizer,\n",
- " 'lr': lr,\n",
- " 'decay': decay,\n",
- " 'metrics': metrics,\n",
- " 'batch_size': batch_size,\n",
- " 'epochs': epochs\n",
- " }\n",
- " param_list_rmsprop = list(ParameterSampler(param_grid, n_iter=n_rmsprop))\n",
- "\n",
- " # iterate over params\n",
- " for params in param_list_rmsprop:\n",
- "\n",
- " model_id = str(params.get(\"model_id\"))\n",
- " compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \",decay=\" + str(params.get(\"decay\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
- " fit_params = \"$$batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\")) + \"$$\" \n",
- " row_content = \"(\" + str(s) + \", \" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
- " \n",
- " # populate mst table\n",
- " %sql INSERT INTO $mst_table (s, model_id, compile_params, fit_params) VALUES $row_content\n",
- "\n",
- "\n",
- " # 3) sgd\n",
- " \n",
- " # model architecture\n",
- " model_id = [2,3]\n",
- "\n",
- " # compile params\n",
- " # loss function\n",
- " loss = ['categorical_crossentropy']\n",
- " # optimizer\n",
- " optimizer = ['sgd']\n",
- " # learning rate (sample on log scale here not in ParameterSampler)\n",
- " lr_range = [0.001, 0.005]\n",
- " lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), n_sgd)\n",
- " # momentum (sample on log scale here not in ParameterSampler)\n",
- " # recall momentum is an exponentially weighted array\n",
- " beta_range = [0.9, 0.95]\n",
- " beta = 1.0 - 10**np.random.uniform(np.log10(1.0-beta_range[0]), np.log10(1.0-beta_range[1]), n_sgd)\n",
- " # metrics\n",
- " metrics = ['accuracy']\n",
- "\n",
- " # fit params\n",
- " # batch size\n",
- " batch_size = [128, 256]\n",
- " # epochs\n",
- " epochs = [5]\n",
- "\n",
- " # create random param list\n",
- " param_grid = {\n",
- " 'model_id': model_id,\n",
- " 'loss': loss,\n",
- " 'optimizer': optimizer,\n",
- " 'lr': lr,\n",
- " 'beta': beta,\n",
- " 'metrics': metrics,\n",
- " 'batch_size': batch_size,\n",
- " 'epochs': epochs\n",
- " }\n",
- " param_list_sgd = list(ParameterSampler(param_grid, n_iter=n_sgd))\n",
- "\n",
- " # iterate over params\n",
- " for params in param_list_sgd:\n",
- "\n",
- " model_id = str(params.get(\"model_id\"))\n",
- " compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \",momentum=\" + str(params.get(\"beta\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
- " fit_params = \"$$batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\")) + \"$$\" \n",
- " row_content = \"(\" + str(s) + \", \" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
- " \n",
- " # populate mst table\n",
- " %sql INSERT INTO $mst_table (s, model_id, compile_params, fit_params) VALUES $row_content\n",
- "\n",
- " \n",
- " #4) organize mst table\n",
- "\n",
- " #down sample\n",
- " #%sql DELETE from $mst_table WHERE mst_key NOT IN (SELECT mst_key FROM $mst_table ORDER BY random() LIMIT $n);\n",
- "\n",
- " # make mst_keys contiguous\n",
- " #%sql ALTER TABLE $mst_table DROP COLUMN mst_key;\n",
- " #%sql ALTER TABLE $mst_table ADD COLUMN mst_key SERIAL;\n",
- " \n",
- " return"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Run model hopper for candidates in MST table"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "def try_params(i, r, first_pass):\n",
- " \n",
- " # multi-model fit\n",
- " if first_pass:\n",
- " # cold start\n",
- " %sql DROP TABLE IF EXISTS $output_table, $output_table_summary, $output_table_info;\n",
- " # passing vars as madlib args does not seem to work\n",
- " #%sql SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', $output_table, $mst_diag_table, $r_i::INT, 0);\n",
- " %sql SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', 'cifar10_multi_model', 'mst_diag_table_hb_cifar10', $r::INT, True, 'cifar10_val_packed',1);\n",
- "\n",
- " else:\n",
- " # warm start to continue from previous run\n",
- " %sql SELECT madlib.madlib_keras_fit_multiple_model('cifar10_train_packed', 'cifar10_multi_model', 'mst_diag_table_hb_cifar10', $r::INT, True, 'cifar10_val_packed', 1, True);\n",
- "\n",
- " # save results via temp table\n",
- " # add everything from info table\n",
- " %sql DROP TABLE IF EXISTS temp_results;\n",
- " %sql CREATE TABLE temp_results AS (SELECT * FROM $output_table_info);\n",
- " \n",
- " # add summary table info and i value (same for each row)\n",
- " %sql ALTER TABLE temp_results ADD COLUMN model_arch_table TEXT, ADD COLUMN num_iterations INTEGER, ADD COLUMN start_training_time TIMESTAMP, ADD COLUMN end_training_time TIMESTAMP, ADD COLUMN s INTEGER, ADD COLUMN i INTEGER;\n",
- " %sql UPDATE temp_results SET model_arch_table = (SELECT model_arch_table FROM $output_table_summary), num_iterations = (SELECT num_iterations FROM $output_table_summary), start_training_time = (SELECT start_training_time FROM $output_table_summary), end_training_time = (SELECT end_training_time FROM $output_table_summary), i = $i;\n",
- " \n",
- " # get the s value for each run (not the same for each row since diagonal table crosses multiple brackets)\n",
- " %sql UPDATE temp_results SET s = m.s FROM mst_diag_table_hb_cifar10 AS m WHERE m.mst_key = temp_results.mst_key;\n",
- " \n",
- " # copy temp table into results table\n",
- " %sql INSERT INTO $results_table (SELECT * FROM temp_results);\n",
- "\n",
- " return"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Call Hyperband diagonal"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "max_iter = 27\n",
- "eta = 3\n",
- "B = 4*max_iter = 108\n",
- "skip_last = 0\n",
- " \n",
- "Hyperband brackets\n",
- " \n",
- "s=3\n",
- "n_i r_i\n",
- "------------\n",
- "27 1.0\n",
- "9.0 3.0\n",
- "3.0 9.0\n",
- "1.0 27.0\n",
- " \n",
- "s=2\n",
- "n_i r_i\n",
- "------------\n",
- "9 3.0\n",
- "3.0 9.0\n",
- "1.0 27.0\n",
- " \n",
- "s=1\n",
- "n_i r_i\n",
- "------------\n",
- "6 9.0\n",
- "2.0 27.0\n",
- " \n",
- "s=0\n",
- "n_i r_i\n",
- "------------\n",
- "4 27\n",
- " \n",
- "Create superset of MSTs for each bracket s\n",
- " \n",
- "s=3\n",
- "n=27\n",
- "r=1.0\n",
- " \n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- " \n",
- "s=2\n",
- "n=9\n",
- "r=3.0\n",
- " \n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- " \n",
- "s=1\n",
- "n=6\n",
- "r=9.0\n",
- " \n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- " \n",
- "s=0\n",
- "n=4\n",
- "r=27\n",
- " \n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- " \n",
- "Hyperband diagonal\n",
- "Outer loop on diagonal:\n",
- " \n",
- "i=0\n",
- "Done.\n",
- "Loop on s desc to create diagonal table:\n",
- "27 rows affected.\n",
- " \n",
- "Try params for i = 0\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "27 rows affected.\n",
- "Done.\n",
- "27 rows affected.\n",
- "27 rows affected.\n",
- "27 rows affected.\n",
- "Loop on s desc to prune mst table:\n",
- "Pruning s = 3 with k = 9\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- " \n",
- "Best validation loss so far = \n",
- "+-----------------------+\n",
- "| validation_loss_final |\n",
- "+-----------------------+\n",
- "| 0.782763898373 |\n",
- "+-----------------------+\n",
- "Best validation accuracy so far = \n",
- "+--------------------------+\n",
- "| validation_metrics_final |\n",
- "+--------------------------+\n",
- "| 0.72729998827 |\n",
- "+--------------------------+\n",
- " \n",
- "i=1\n",
- "Done.\n",
- "Loop on s desc to create diagonal table:\n",
- "9 rows affected.\n",
- "9 rows affected.\n",
- " \n",
- "Try params for i = 1\n",
- "1 rows affected.\n",
- "Done.\n",
- "18 rows affected.\n",
- "Done.\n",
- "18 rows affected.\n",
- "18 rows affected.\n",
- "18 rows affected.\n",
- "Loop on s desc to prune mst table:\n",
- "Pruning s = 3 with k = 3\n",
- "Pruning s = 2 with k = 3\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- " \n",
- "Best validation loss so far = \n",
- "+-----------------------+\n",
- "| validation_loss_final |\n",
- "+-----------------------+\n",
- "| 0.602479159832 |\n",
- "+-----------------------+\n",
- "Best validation accuracy so far = \n",
- "+--------------------------+\n",
- "| validation_metrics_final |\n",
- "+--------------------------+\n",
- "| 0.805599987507 |\n",
- "+--------------------------+\n",
- " \n",
- "i=2\n",
- "Done.\n",
- "Loop on s desc to create diagonal table:\n",
- "3 rows affected.\n",
- "3 rows affected.\n",
- "6 rows affected.\n",
- " \n",
- "Try params for i = 2\n",
- "1 rows affected.\n",
- "Done.\n",
- "12 rows affected.\n",
- "Done.\n",
- "12 rows affected.\n",
- "12 rows affected.\n",
- "12 rows affected.\n",
- "Loop on s desc to prune mst table:\n",
- "Pruning s = 3 with k = 1\n",
- "Pruning s = 2 with k = 1\n",
- "Pruning s = 1 with k = 2\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- " \n",
- "Best validation loss so far = \n",
- "+-----------------------+\n",
- "| validation_loss_final |\n",
- "+-----------------------+\n",
- "| 0.595765888691 |\n",
- "+-----------------------+\n",
- "Best validation accuracy so far = \n",
- "+--------------------------+\n",
- "| validation_metrics_final |\n",
- "+--------------------------+\n",
- "| 0.824999988079 |\n",
- "+--------------------------+\n",
- " \n",
- "i=3\n",
- "Done.\n",
- "Loop on s desc to create diagonal table:\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "2 rows affected.\n",
- "4 rows affected.\n",
- " \n",
- "Try params for i = 3\n",
- "1 rows affected.\n",
- "Done.\n",
- "8 rows affected.\n",
- "Done.\n",
- "8 rows affected.\n",
- "8 rows affected.\n",
- "8 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- " \n",
- "Best validation loss so far = \n",
- "+-----------------------+\n",
- "| validation_loss_final |\n",
- "+-----------------------+\n",
- "| 0.580716967583 |\n",
- "+-----------------------+\n",
- "Best validation accuracy so far = \n",
- "+--------------------------+\n",
- "| validation_metrics_final |\n",
- "+--------------------------+\n",
- "| 0.834100008011 |\n",
- "+--------------------------+\n"
- ]
- }
- ],
- "source": [
- "hp = Hyperband_diagonal(get_params, try_params )\n",
- "results = hp.run()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"plot\"></a>\n",
- "# 5. Review and plot results"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
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- "<table>\n",
- " <tr>\n",
- " <th>mst_key</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>metrics_elapsed_time</th>\n",
- " <th>metrics_type</th>\n",
- " <th>training_metrics_final</th>\n",
- " <th>training_loss_final</th>\n",
- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " <th>model_arch_table</th>\n",
- " <th>num_iterations</th>\n",
- " <th>start_training_time</th>\n",
- " <th>end_training_time</th>\n",
- " <th>s</th>\n",
- " <th>i</th>\n",
- " <th>run_id</th>\n",
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- " <tr>\n",
- " <td>45</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='sgd(lr=0.004501919010538727,momentum=0.9002808952996391)',metrics=['accuracy']</td>\n",
- " <td>batch_size=256,epochs=5</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>2159.70019531</td>\n",
- " <td>[121.955986022949, 245.619317054749, 368.365077972412, 490.415205955505, 614.768485069275, 737.048167943954, 860.508330106735, 984.307431936264, 1106.31793498993, 1229.54079914093, 1352.66811394691, 1477.57317709923, 1599.99458003044, 1723.35215711594, 1847.86346912384, 1971.57312297821, 2096.37913298607, 2221.54790210724, 2346.08665895462, 2470.83494997025, 2595.6411960125, 2722.25887513161, 2846.48335313797, 2971.13271403313, 3097.49445009232, 3222.44972395897, 3348.5662779808]</td>\n",
- " <td>[u'accuracy']</td>\n",
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- " <td>0.169452220201</td>\n",
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- " <td>[1.20952260494232, 1.00138294696808, 0.919946014881134, 0.846988558769226, 0.835236310958862, 0.748137712478638, 0.745132148265839, 0.670836567878723, 0.688502311706543, 0.673530399799347, 0.646275579929352, 0.626095473766327, 0.629233837127686, 0.623023450374603, 0.601795375347137, 0.603216171264648, 0.587353229522705, 0.635767936706543, 0.61867493391037, 0.594616591930389, 0.586753845214844, 0.60888147354126, 0.601007521152496, 0.593143999576569, 0.601291477680206, 0.583372294902802, 0.580716967582703]</td>\n",
- " <td>model_arch_table_cifar10</td>\n",
- " <td>27</td>\n",
- " <td>2020-01-23 21:12:04.749779</td>\n",
- " <td>2020-01-23 22:07:53.819497</td>\n",
- " <td>0</td>\n",
- " <td>3</td>\n",
- " <td>65</td>\n",
- " </tr>\n",
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- ]
- },
- "execution_count": 29,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql SELECT * FROM $results_table ORDER BY validation_loss_final ASC LIMIT 1;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "%matplotlib notebook\n",
- "import matplotlib.pyplot as plt\n",
- "from matplotlib.ticker import MaxNLocator\n",
- "from collections import defaultdict\n",
- "import pandas as pd\n",
- "import seaborn as sns\n",
- "sns.set_palette(sns.color_palette(\"hls\", 20))\n",
- "plt.rcParams.update({'font.size': 12})\n",
- "pd.set_option('display.max_colwidth', -1)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Training dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "65 rows affected.\n"
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- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('<span/>');\n",
- "\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
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- " }\n",
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- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
- "text/plain": [
- "<IPython.core.display.Javascript object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
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\" width=\"1000\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
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- "1 rows affected.\n",
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- "1 rows affected.\n",
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- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- }
- ],
- "source": [
- "#df_results = %sql SELECT * FROM $results_table ORDER BY run_id;\n",
- "df_results = %sql SELECT * FROM $results_table ORDER BY training_loss_final ASC LIMIT 100;\n",
- "df_results = df_results.DataFrame()\n",
- "\n",
- "#set up plots\n",
- "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
- "fig.legend(ncol=4)\n",
- "fig.tight_layout()\n",
- "\n",
- "ax_metric = axs[0]\n",
- "ax_loss = axs[1]\n",
- "\n",
- "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
- "ax_metric.set_xlabel('Iteration')\n",
- "ax_metric.set_ylabel('Metric')\n",
- "ax_metric.set_title('Training metric curve')\n",
- "\n",
- "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
- "ax_loss.set_xlabel('Iteration')\n",
- "ax_loss.set_ylabel('Loss')\n",
- "ax_loss.set_title('Training loss curve')\n",
- "\n",
- "for run_id in df_results['run_id']:\n",
- " df_output_info = %sql SELECT training_metrics,training_loss FROM $results_table WHERE run_id = $run_id\n",
- " df_output_info = df_output_info.DataFrame()\n",
- " training_metrics = df_output_info['training_metrics'][0]\n",
- " training_loss = df_output_info['training_loss'][0]\n",
- " X = range(len(training_metrics))\n",
- " \n",
- " ax_metric.plot(X, training_metrics, label=run_id, marker='o')\n",
- " ax_loss.plot(X, training_loss, label=run_id, marker='o')\n",
- "\n",
- "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Validation dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "65 rows affected.\n"
- ]
- },
- {
- "data": {
- "application/javascript": [
- "/* Put everything inside the global mpl namespace */\n",
- "window.mpl = {};\n",
- "\n",
- "\n",
- "mpl.get_websocket_type = function() {\n",
- " if (typeof(WebSocket) !== 'undefined') {\n",
- " return WebSocket;\n",
- " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
- " return MozWebSocket;\n",
- " } else {\n",
- " alert('Your browser does not have WebSocket support.' +\n",
- " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
- " 'Firefox 4 and 5 are also supported but you ' +\n",
- " 'have to enable WebSockets in about:config.');\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
- " this.id = figure_id;\n",
- "\n",
- " this.ws = websocket;\n",
- "\n",
- " this.supports_binary = (this.ws.binaryType != undefined);\n",
- "\n",
- " if (!this.supports_binary) {\n",
- " var warnings = document.getElementById(\"mpl-warnings\");\n",
- " if (warnings) {\n",
- " warnings.style.display = 'block';\n",
- " warnings.textContent = (\n",
- " \"This browser does not support binary websocket messages. \" +\n",
- " \"Performance may be slow.\");\n",
- " }\n",
- " }\n",
- "\n",
- " this.imageObj = new Image();\n",
- "\n",
- " this.context = undefined;\n",
- " this.message = undefined;\n",
- " this.canvas = undefined;\n",
- " this.rubberband_canvas = undefined;\n",
- " this.rubberband_context = undefined;\n",
- " this.format_dropdown = undefined;\n",
- "\n",
- " this.image_mode = 'full';\n",
- "\n",
- " this.root = $('<div/>');\n",
- " this._root_extra_style(this.root)\n",
- " this.root.attr('style', 'display: inline-block');\n",
- "\n",
- " $(parent_element).append(this.root);\n",
- "\n",
- " this._init_header(this);\n",
- " this._init_canvas(this);\n",
- " this._init_toolbar(this);\n",
- "\n",
- " var fig = this;\n",
- "\n",
- " this.waiting = false;\n",
- "\n",
- " this.ws.onopen = function () {\n",
- " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
- " fig.send_message(\"send_image_mode\", {});\n",
- " if (mpl.ratio != 1) {\n",
- " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
- " }\n",
- " fig.send_message(\"refresh\", {});\n",
- " }\n",
- "\n",
- " this.imageObj.onload = function() {\n",
- " if (fig.image_mode == 'full') {\n",
- " // Full images could contain transparency (where diff images\n",
- " // almost always do), so we need to clear the canvas so that\n",
- " // there is no ghosting.\n",
- " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
- " }\n",
- " fig.context.drawImage(fig.imageObj, 0, 0);\n",
- " };\n",
- "\n",
- " this.imageObj.onunload = function() {\n",
- " fig.ws.close();\n",
- " }\n",
- "\n",
- " this.ws.onmessage = this._make_on_message_function(this);\n",
- "\n",
- " this.ondownload = ondownload;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_header = function() {\n",
- " var titlebar = $(\n",
- " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
- " 'ui-helper-clearfix\"/>');\n",
- " var titletext = $(\n",
- " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
- " 'text-align: center; padding: 3px;\"/>');\n",
- " titlebar.append(titletext)\n",
- " this.root.append(titlebar);\n",
- " this.header = titletext[0];\n",
- "}\n",
- "\n",
- "\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_canvas = function() {\n",
- " var fig = this;\n",
- "\n",
- " var canvas_div = $('<div/>');\n",
- "\n",
- " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
- "\n",
- " function canvas_keyboard_event(event) {\n",
- " return fig.key_event(event, event['data']);\n",
- " }\n",
- "\n",
- " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
- " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
- " this.canvas_div = canvas_div\n",
- " this._canvas_extra_style(canvas_div)\n",
- " this.root.append(canvas_div);\n",
- "\n",
- " var canvas = $('<canvas/>');\n",
- " canvas.addClass('mpl-canvas');\n",
- " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
- "\n",
- " this.canvas = canvas[0];\n",
- " this.context = canvas[0].getContext(\"2d\");\n",
- "\n",
- " var backingStore = this.context.backingStorePixelRatio ||\n",
- "\tthis.context.webkitBackingStorePixelRatio ||\n",
- "\tthis.context.mozBackingStorePixelRatio ||\n",
- "\tthis.context.msBackingStorePixelRatio ||\n",
- "\tthis.context.oBackingStorePixelRatio ||\n",
- "\tthis.context.backingStorePixelRatio || 1;\n",
- "\n",
- " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
- "\n",
- " var rubberband = $('<canvas/>');\n",
- " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
- "\n",
- " var pass_mouse_events = true;\n",
- "\n",
- " canvas_div.resizable({\n",
- " start: function(event, ui) {\n",
- " pass_mouse_events = false;\n",
- " },\n",
- " resize: function(event, ui) {\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " stop: function(event, ui) {\n",
- " pass_mouse_events = true;\n",
- " fig.request_resize(ui.size.width, ui.size.height);\n",
- " },\n",
- " });\n",
- "\n",
- " function mouse_event_fn(event) {\n",
- " if (pass_mouse_events)\n",
- " return fig.mouse_event(event, event['data']);\n",
- " }\n",
- "\n",
- " rubberband.mousedown('button_press', mouse_event_fn);\n",
- " rubberband.mouseup('button_release', mouse_event_fn);\n",
- " // Throttle sequential mouse events to 1 every 20ms.\n",
- " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
- "\n",
- " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
- " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
- "\n",
- " canvas_div.on(\"wheel\", function (event) {\n",
- " event = event.originalEvent;\n",
- " event['data'] = 'scroll'\n",
- " if (event.deltaY < 0) {\n",
- " event.step = 1;\n",
- " } else {\n",
- " event.step = -1;\n",
- " }\n",
- " mouse_event_fn(event);\n",
- " });\n",
- "\n",
- " canvas_div.append(canvas);\n",
- " canvas_div.append(rubberband);\n",
- "\n",
- " this.rubberband = rubberband;\n",
- " this.rubberband_canvas = rubberband[0];\n",
- " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
- " this.rubberband_context.strokeStyle = \"#000000\";\n",
- "\n",
- " this._resize_canvas = function(width, height) {\n",
- " // Keep the size of the canvas, canvas container, and rubber band\n",
- " // canvas in synch.\n",
- " canvas_div.css('width', width)\n",
- " canvas_div.css('height', height)\n",
- "\n",
- " canvas.attr('width', width * mpl.ratio);\n",
- " canvas.attr('height', height * mpl.ratio);\n",
- " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
- "\n",
- " rubberband.attr('width', width);\n",
- " rubberband.attr('height', height);\n",
- " }\n",
- "\n",
- " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
- " // upon first draw.\n",
- " this._resize_canvas(600, 600);\n",
- "\n",
- " // Disable right mouse context menu.\n",
- " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
- " return false;\n",
- " });\n",
- "\n",
- " function set_focus () {\n",
- " canvas.focus();\n",
- " canvas_div.focus();\n",
- " }\n",
- "\n",
- " window.setTimeout(set_focus, 100);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items) {\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) {\n",
- " // put a spacer in here.\n",
- " continue;\n",
- " }\n",
- " var button = $('<button/>');\n",
- " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
- " 'ui-button-icon-only');\n",
- " button.attr('role', 'button');\n",
- " button.attr('aria-disabled', 'false');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- "\n",
- " var icon_img = $('<span/>');\n",
- " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
- " icon_img.addClass(image);\n",
- " icon_img.addClass('ui-corner-all');\n",
- "\n",
- " var tooltip_span = $('<span/>');\n",
- " tooltip_span.addClass('ui-button-text');\n",
- " tooltip_span.html(tooltip);\n",
- "\n",
- " button.append(icon_img);\n",
- " button.append(tooltip_span);\n",
- "\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " var fmt_picker_span = $('<span/>');\n",
- "\n",
- " var fmt_picker = $('<select/>');\n",
- " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
- " fmt_picker_span.append(fmt_picker);\n",
- " nav_element.append(fmt_picker_span);\n",
- " this.format_dropdown = fmt_picker[0];\n",
- "\n",
- " for (var ind in mpl.extensions) {\n",
- " var fmt = mpl.extensions[ind];\n",
- " var option = $(\n",
- " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
- " fmt_picker.append(option)\n",
- " }\n",
- "\n",
- " // Add hover states to the ui-buttons\n",
- " $( \".ui-button\" ).hover(\n",
- " function() { $(this).addClass(\"ui-state-hover\");},\n",
- " function() { $(this).removeClass(\"ui-state-hover\");}\n",
- " );\n",
- "\n",
- " var status_bar = $('<span class=\"mpl-message\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
- " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
- " // which will in turn request a refresh of the image.\n",
- " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_message = function(type, properties) {\n",
- " properties['type'] = type;\n",
- " properties['figure_id'] = this.id;\n",
- " this.ws.send(JSON.stringify(properties));\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.send_draw_message = function() {\n",
- " if (!this.waiting) {\n",
- " this.waiting = true;\n",
- " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
- " }\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " var format_dropdown = fig.format_dropdown;\n",
- " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
- " fig.ondownload(fig, format);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
- " var size = msg['size'];\n",
- " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
- " fig._resize_canvas(size[0], size[1]);\n",
- " fig.send_message(\"refresh\", {});\n",
- " };\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
- " var x0 = msg['x0'] / mpl.ratio;\n",
- " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
- " var x1 = msg['x1'] / mpl.ratio;\n",
- " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
- " x0 = Math.floor(x0) + 0.5;\n",
- " y0 = Math.floor(y0) + 0.5;\n",
- " x1 = Math.floor(x1) + 0.5;\n",
- " y1 = Math.floor(y1) + 0.5;\n",
- " var min_x = Math.min(x0, x1);\n",
- " var min_y = Math.min(y0, y1);\n",
- " var width = Math.abs(x1 - x0);\n",
- " var height = Math.abs(y1 - y0);\n",
- "\n",
- " fig.rubberband_context.clearRect(\n",
- " 0, 0, fig.canvas.width, fig.canvas.height);\n",
- "\n",
- " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
- " // Updates the figure title.\n",
- " fig.header.textContent = msg['label'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
- " var cursor = msg['cursor'];\n",
- " switch(cursor)\n",
- " {\n",
- " case 0:\n",
- " cursor = 'pointer';\n",
- " break;\n",
- " case 1:\n",
- " cursor = 'default';\n",
- " break;\n",
- " case 2:\n",
- " cursor = 'crosshair';\n",
- " break;\n",
- " case 3:\n",
- " cursor = 'move';\n",
- " break;\n",
- " }\n",
- " fig.rubberband_canvas.style.cursor = cursor;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
- " fig.message.textContent = msg['message'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
- " // Request the server to send over a new figure.\n",
- " fig.send_draw_message();\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
- " fig.image_mode = msg['mode'];\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Called whenever the canvas gets updated.\n",
- " this.send_message(\"ack\", {});\n",
- "}\n",
- "\n",
- "// A function to construct a web socket function for onmessage handling.\n",
- "// Called in the figure constructor.\n",
- "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
- " return function socket_on_message(evt) {\n",
- " if (evt.data instanceof Blob) {\n",
- " /* FIXME: We get \"Resource interpreted as Image but\n",
- " * transferred with MIME type text/plain:\" errors on\n",
- " * Chrome. But how to set the MIME type? It doesn't seem\n",
- " * to be part of the websocket stream */\n",
- " evt.data.type = \"image/png\";\n",
- "\n",
- " /* Free the memory for the previous frames */\n",
- " if (fig.imageObj.src) {\n",
- " (window.URL || window.webkitURL).revokeObjectURL(\n",
- " fig.imageObj.src);\n",
- " }\n",
- "\n",
- " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
- " evt.data);\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
- " fig.imageObj.src = evt.data;\n",
- " fig.updated_canvas_event();\n",
- " fig.waiting = false;\n",
- " return;\n",
- " }\n",
- "\n",
- " var msg = JSON.parse(evt.data);\n",
- " var msg_type = msg['type'];\n",
- "\n",
- " // Call the \"handle_{type}\" callback, which takes\n",
- " // the figure and JSON message as its only arguments.\n",
- " try {\n",
- " var callback = fig[\"handle_\" + msg_type];\n",
- " } catch (e) {\n",
- " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
- " return;\n",
- " }\n",
- "\n",
- " if (callback) {\n",
- " try {\n",
- " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
- " callback(fig, msg);\n",
- " } catch (e) {\n",
- " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
- " }\n",
- " }\n",
- " };\n",
- "}\n",
- "\n",
- "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
- "mpl.findpos = function(e) {\n",
- " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
- " var targ;\n",
- " if (!e)\n",
- " e = window.event;\n",
- " if (e.target)\n",
- " targ = e.target;\n",
- " else if (e.srcElement)\n",
- " targ = e.srcElement;\n",
- " if (targ.nodeType == 3) // defeat Safari bug\n",
- " targ = targ.parentNode;\n",
- "\n",
- " // jQuery normalizes the pageX and pageY\n",
- " // pageX,Y are the mouse positions relative to the document\n",
- " // offset() returns the position of the element relative to the document\n",
- " var x = e.pageX - $(targ).offset().left;\n",
- " var y = e.pageY - $(targ).offset().top;\n",
- "\n",
- " return {\"x\": x, \"y\": y};\n",
- "};\n",
- "\n",
- "/*\n",
- " * return a copy of an object with only non-object keys\n",
- " * we need this to avoid circular references\n",
- " * http://stackoverflow.com/a/24161582/3208463\n",
- " */\n",
- "function simpleKeys (original) {\n",
- " return Object.keys(original).reduce(function (obj, key) {\n",
- " if (typeof original[key] !== 'object')\n",
- " obj[key] = original[key]\n",
- " return obj;\n",
- " }, {});\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.mouse_event = function(event, name) {\n",
- " var canvas_pos = mpl.findpos(event)\n",
- "\n",
- " if (name === 'button_press')\n",
- " {\n",
- " this.canvas.focus();\n",
- " this.canvas_div.focus();\n",
- " }\n",
- "\n",
- " var x = canvas_pos.x * mpl.ratio;\n",
- " var y = canvas_pos.y * mpl.ratio;\n",
- "\n",
- " this.send_message(name, {x: x, y: y, button: event.button,\n",
- " step: event.step,\n",
- " guiEvent: simpleKeys(event)});\n",
- "\n",
- " /* This prevents the web browser from automatically changing to\n",
- " * the text insertion cursor when the button is pressed. We want\n",
- " * to control all of the cursor setting manually through the\n",
- " * 'cursor' event from matplotlib */\n",
- " event.preventDefault();\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " // Handle any extra behaviour associated with a key event\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.key_event = function(event, name) {\n",
- "\n",
- " // Prevent repeat events\n",
- " if (name == 'key_press')\n",
- " {\n",
- " if (event.which === this._key)\n",
- " return;\n",
- " else\n",
- " this._key = event.which;\n",
- " }\n",
- " if (name == 'key_release')\n",
- " this._key = null;\n",
- "\n",
- " var value = '';\n",
- " if (event.ctrlKey && event.which != 17)\n",
- " value += \"ctrl+\";\n",
- " if (event.altKey && event.which != 18)\n",
- " value += \"alt+\";\n",
- " if (event.shiftKey && event.which != 16)\n",
- " value += \"shift+\";\n",
- "\n",
- " value += 'k';\n",
- " value += event.which.toString();\n",
- "\n",
- " this._key_event_extra(event, name);\n",
- "\n",
- " this.send_message(name, {key: value,\n",
- " guiEvent: simpleKeys(event)});\n",
- " return false;\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
- " if (name == 'download') {\n",
- " this.handle_save(this, null);\n",
- " } else {\n",
- " this.send_message(\"toolbar_button\", {name: name});\n",
- " }\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
- " this.message.textContent = tooltip;\n",
- "};\n",
- "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
- "\n",
- "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
- "\n",
- "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
- " // Create a \"websocket\"-like object which calls the given IPython comm\n",
- " // object with the appropriate methods. Currently this is a non binary\n",
- " // socket, so there is still some room for performance tuning.\n",
- " var ws = {};\n",
- "\n",
- " ws.close = function() {\n",
- " comm.close()\n",
- " };\n",
- " ws.send = function(m) {\n",
- " //console.log('sending', m);\n",
- " comm.send(m);\n",
- " };\n",
- " // Register the callback with on_msg.\n",
- " comm.on_msg(function(msg) {\n",
- " //console.log('receiving', msg['content']['data'], msg);\n",
- " // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
- " ws.onmessage(msg['content']['data'])\n",
- " });\n",
- " return ws;\n",
- "}\n",
- "\n",
- "mpl.mpl_figure_comm = function(comm, msg) {\n",
- " // This is the function which gets called when the mpl process\n",
- " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
- "\n",
- " var id = msg.content.data.id;\n",
- " // Get hold of the div created by the display call when the Comm\n",
- " // socket was opened in Python.\n",
- " var element = $(\"#\" + id);\n",
- " var ws_proxy = comm_websocket_adapter(comm)\n",
- "\n",
- " function ondownload(figure, format) {\n",
- " window.open(figure.imageObj.src);\n",
- " }\n",
- "\n",
- " var fig = new mpl.figure(id, ws_proxy,\n",
- " ondownload,\n",
- " element.get(0));\n",
- "\n",
- " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
- " // web socket which is closed, not our websocket->open comm proxy.\n",
- " ws_proxy.onopen();\n",
- "\n",
- " fig.parent_element = element.get(0);\n",
- " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
- " if (!fig.cell_info) {\n",
- " console.error(\"Failed to find cell for figure\", id, fig);\n",
- " return;\n",
- " }\n",
- "\n",
- " var output_index = fig.cell_info[2]\n",
- " var cell = fig.cell_info[0];\n",
- "\n",
- "};\n",
- "\n",
- "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
- " var width = fig.canvas.width/mpl.ratio\n",
- " fig.root.unbind('remove')\n",
- "\n",
- " // Update the output cell to use the data from the current canvas.\n",
- " fig.push_to_output();\n",
- " var dataURL = fig.canvas.toDataURL();\n",
- " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
- " // the notebook keyboard shortcuts fail.\n",
- " IPython.keyboard_manager.enable()\n",
- " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
- " fig.close_ws(fig, msg);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.close_ws = function(fig, msg){\n",
- " fig.send_message('closing', msg);\n",
- " // fig.ws.close()\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
- " // Turn the data on the canvas into data in the output cell.\n",
- " var width = this.canvas.width/mpl.ratio\n",
- " var dataURL = this.canvas.toDataURL();\n",
- " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.updated_canvas_event = function() {\n",
- " // Tell IPython that the notebook contents must change.\n",
- " IPython.notebook.set_dirty(true);\n",
- " this.send_message(\"ack\", {});\n",
- " var fig = this;\n",
- " // Wait a second, then push the new image to the DOM so\n",
- " // that it is saved nicely (might be nice to debounce this).\n",
- " setTimeout(function () { fig.push_to_output() }, 1000);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._init_toolbar = function() {\n",
- " var fig = this;\n",
- "\n",
- " var nav_element = $('<div/>')\n",
- " nav_element.attr('style', 'width: 100%');\n",
- " this.root.append(nav_element);\n",
- "\n",
- " // Define a callback function for later on.\n",
- " function toolbar_event(event) {\n",
- " return fig.toolbar_button_onclick(event['data']);\n",
- " }\n",
- " function toolbar_mouse_event(event) {\n",
- " return fig.toolbar_button_onmouseover(event['data']);\n",
- " }\n",
- "\n",
- " for(var toolbar_ind in mpl.toolbar_items){\n",
- " var name = mpl.toolbar_items[toolbar_ind][0];\n",
- " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
- " var image = mpl.toolbar_items[toolbar_ind][2];\n",
- " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
- "\n",
- " if (!name) { continue; };\n",
- "\n",
- " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
- " button.click(method_name, toolbar_event);\n",
- " button.mouseover(tooltip, toolbar_mouse_event);\n",
- " nav_element.append(button);\n",
- " }\n",
- "\n",
- " // Add the status bar.\n",
- " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
- " nav_element.append(status_bar);\n",
- " this.message = status_bar[0];\n",
- "\n",
- " // Add the close button to the window.\n",
- " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
- " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
- " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
- " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
- " buttongrp.append(button);\n",
- " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
- " titlebar.prepend(buttongrp);\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._root_extra_style = function(el){\n",
- " var fig = this\n",
- " el.on(\"remove\", function(){\n",
- "\tfig.close_ws(fig, {});\n",
- " });\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._canvas_extra_style = function(el){\n",
- " // this is important to make the div 'focusable\n",
- " el.attr('tabindex', 0)\n",
- " // reach out to IPython and tell the keyboard manager to turn it's self\n",
- " // off when our div gets focus\n",
- "\n",
- " // location in version 3\n",
- " if (IPython.notebook.keyboard_manager) {\n",
- " IPython.notebook.keyboard_manager.register_events(el);\n",
- " }\n",
- " else {\n",
- " // location in version 2\n",
- " IPython.keyboard_manager.register_events(el);\n",
- " }\n",
- "\n",
- "}\n",
- "\n",
- "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
- " var manager = IPython.notebook.keyboard_manager;\n",
- " if (!manager)\n",
- " manager = IPython.keyboard_manager;\n",
- "\n",
- " // Check for shift+enter\n",
- " if (event.shiftKey && event.which == 13) {\n",
- " this.canvas_div.blur();\n",
- " event.shiftKey = false;\n",
- " // Send a \"J\" for go to next cell\n",
- " event.which = 74;\n",
- " event.keyCode = 74;\n",
- " manager.command_mode();\n",
- " manager.handle_keydown(event);\n",
- " }\n",
- "}\n",
- "\n",
- "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
- " fig.ondownload(fig, null);\n",
- "}\n",
- "\n",
- "\n",
- "mpl.find_output_cell = function(html_output) {\n",
- " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
- " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
- " // IPython event is triggered only after the cells have been serialised, which for\n",
- " // our purposes (turning an active figure into a static one), is too late.\n",
- " var cells = IPython.notebook.get_cells();\n",
- " var ncells = cells.length;\n",
- " for (var i=0; i<ncells; i++) {\n",
- " var cell = cells[i];\n",
- " if (cell.cell_type === 'code'){\n",
- " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
- " var data = cell.output_area.outputs[j];\n",
- " if (data.data) {\n",
- " // IPython >= 3 moved mimebundle to data attribute of output\n",
- " data = data.data;\n",
- " }\n",
- " if (data['text/html'] == html_output) {\n",
- " return [cell, data, j];\n",
- " }\n",
- " }\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "// Register the function which deals with the matplotlib target/channel.\n",
- "// The kernel may be null if the page has been refreshed.\n",
- "if (IPython.notebook.kernel != null) {\n",
- " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
- "}\n"
- ],
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\" width=\"1000\">"
- ],
- "text/plain": [
- "<IPython.core.display.HTML object>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
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- ]
- }
- ],
- "source": [
- "#df_results = %sql SELECT * FROM $results_table ORDER BY run_id;\n",
- "df_results = %sql SELECT * FROM $results_table ORDER BY validation_metrics_final DESC LIMIT 100;\n",
- "df_results = df_results.DataFrame()\n",
- "\n",
- "#set up plots\n",
- "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
- "fig.legend(ncol=4)\n",
- "fig.tight_layout()\n",
- "\n",
- "ax_metric = axs[0]\n",
- "ax_loss = axs[1]\n",
- "\n",
- "ax_metric.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
- "ax_metric.set_xlabel('Iteration')\n",
- "ax_metric.set_ylabel('Metric')\n",
- "ax_metric.set_title('Validation metric curve')\n",
- "\n",
- "ax_loss.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
- "ax_loss.set_xlabel('Iteration')\n",
- "ax_loss.set_ylabel('Loss')\n",
- "ax_loss.set_title('Validation loss curve')\n",
- "\n",
- "for run_id in df_results['run_id']:\n",
- " df_output_info = %sql SELECT validation_metrics,validation_loss FROM $results_table WHERE run_id = $run_id\n",
- " df_output_info = df_output_info.DataFrame()\n",
- " validation_metrics = df_output_info['validation_metrics'][0]\n",
- " validation_loss = df_output_info['validation_loss'][0]\n",
- " X = range(len(validation_metrics))\n",
- " \n",
- " ax_metric.plot(X, validation_metrics, label=run_id, marker='o')\n",
- " ax_loss.plot(X, validation_loss, label=run_id, marker='o')\n",
- "\n",
- "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"print\"></a>\n",
- "# 6. Print run schedules (display only)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Pretty print reg Hyperband run schedule"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "max_iter = 27\n",
- "eta = 3\n",
- "B = 4*max_iter = 108\n",
- "skip_last = 0\n",
- " \n",
- "s=3\n",
- "n_i r_i\n",
- "------------\n",
- "27 1.0\n",
- "9.0 3.0\n",
- "3.0 9.0\n",
- "1.0 27.0\n",
- " \n",
- "s=2\n",
- "n_i r_i\n",
- "------------\n",
- "9 3.0\n",
- "3.0 9.0\n",
- "1.0 27.0\n",
- " \n",
- "s=1\n",
- "n_i r_i\n",
- "------------\n",
- "6 9.0\n",
- "2.0 27.0\n",
- " \n",
- "s=0\n",
- "n_i r_i\n",
- "------------\n",
- "4 27\n",
- " \n",
- "sum of configurations at leaf nodes across all s = 8.0\n",
- "(if have more workers than this, they may not be 100% busy)\n"
- ]
- }
- ],
- "source": [
- "import numpy as np\n",
- "from math import log, ceil\n",
- "\n",
- "#input\n",
- "max_iter = 27 # maximum iterations/epochs per configuration\n",
- "eta = 3 # defines downsampling rate (default=3)\n",
- "skip_last = 0 # 1 means skip last run in each bracket, 0 means run full bracket\n",
- "\n",
- "logeta = lambda x: log(x)/log(eta)\n",
- "s_max = int(logeta(max_iter)) # number of unique executions of Successive Halving (minus one)\n",
- "B = (s_max+1)*max_iter # total number of iterations (without reuse) per execution of Succesive Halving (n,r)\n",
- "\n",
- "#echo output\n",
- "print (\"max_iter = \" + str(max_iter))\n",
- "print (\"eta = \" + str(eta))\n",
- "print (\"B = \" + str(s_max+1) + \"*max_iter = \" + str(B))\n",
- "print (\"skip_last = \" + str(skip_last))\n",
- "\n",
- "sum_leaf_n_i = 0 # count configurations at leaf nodes across all s\n",
- "\n",
- "#### Begin Finite Horizon Hyperband outlerloop. Repeat indefinitely.\n",
- "for s in reversed(range(s_max+1)):\n",
- " \n",
- " print (\" \")\n",
- " print (\"s=\" + str(s))\n",
- " print (\"n_i r_i\")\n",
- " print (\"------------\")\n",
- " counter = 0\n",
- " \n",
- " n = int(ceil(int(B/max_iter/(s+1))*eta**s)) # initial number of configurations\n",
- " r = max_iter*eta**(-s) # initial number of iterations to run configurations for\n",
- "\n",
- " #### Begin Finite Horizon Successive Halving with (n,r)\n",
- " #T = [ get_random_hyperparameter_configuration() for i in range(n) ] \n",
- " for i in range((s+1) - int(skip_last)):\n",
- " # Run each of the n_i configs for r_i iterations and keep best n_i/eta\n",
- " n_i = n*eta**(-i)\n",
- " r_i = r*eta**(i)\n",
- " \n",
- " print (str(n_i) + \" \" + str (r_i))\n",
- " \n",
- " # check if leaf node for this s\n",
- " if counter == (s-skip_last):\n",
- " sum_leaf_n_i += n_i\n",
- " counter += 1\n",
- " \n",
- " #val_losses = [ run_then_return_val_loss(num_iters=r_i,hyperparameters=t) for t in T ]\n",
- " #T = [ T[i] for i in argsort(val_losses)[0:int( n_i/eta )] ]\n",
- " #### End Finite Horizon Successive Halving with (n,r)\n",
- "\n",
- "print (\" \")\n",
- "print (\"sum of configurations at leaf nodes across all s = \" + str(sum_leaf_n_i))\n",
- "print (\"(if have more workers than this, they may not be 100% busy)\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Pretty print Hyperband diagonal run schedule"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "from math import log, ceil\n",
- "\n",
- "#input\n",
- "max_iter = 27 # maximum iterations/epochs per configuration\n",
- "eta = 3 # defines downsampling rate (default=3)\n",
- "skip_last = 1 # 1 means skip last run in each bracket, 0 means run full bracket\n",
- "\n",
- "logeta = lambda x: log(x)/log(eta)\n",
- "s_max = int(logeta(max_iter)) # number of unique executions of Successive Halving (minus one)\n",
- "B = (s_max+1)*max_iter # total number of iterations (without reuse) per execution of Succesive Halving (n,r)\n",
- "\n",
- "#echo output\n",
- "print (\"echo input:\")\n",
- "print (\"max_iter = \" + str(max_iter))\n",
- "print (\"eta = \" + str(eta))\n",
- "print (\"s_max = \" + str(s_max))\n",
- "print (\"B = \" + str(s_max+1) + \"*max_iter = \" + str(B))\n",
- "\n",
- "print (\" \")\n",
- "print (\"initial n, r values for each s:\")\n",
- "initial_n_vals = {}\n",
- "initial_r_vals = {}\n",
- "# get hyper parameter configs for each s\n",
- "for s in reversed(range(s_max+1)):\n",
- " \n",
- " n = int(ceil(int(B/max_iter/(s+1))*eta**s)) # initial number of configurations\n",
- " r = max_iter*eta**(-s) # initial number of iterations to run configurations for\n",
- " \n",
- " initial_n_vals[s] = n \n",
- " initial_r_vals[s] = r \n",
- " \n",
- " print (\"s=\" + str(s))\n",
- " print (\"n=\" + str(n))\n",
- " print (\"r=\" + str(r))\n",
- " print (\" \")\n",
- " \n",
- "print (\"outer loop on diagonal:\")\n",
- "# outer loop on diagonal\n",
- "for i in range((s_max+1) - int(skip_last)):\n",
- " print (\" \")\n",
- " print (\"i=\" + str(i))\n",
- " \n",
- " print (\"inner loop on s desc:\")\n",
- " # inner loop on s desc\n",
- " for s in range(s_max, s_max-i-1, -1):\n",
- " n_i = initial_n_vals[s]*eta**(-i+s_max-s)\n",
- " r_i = initial_r_vals[s]*eta**(i-s_max+s)\n",
- " \n",
- " print (\"s=\" + str(s))\n",
- " print (\"n_i=\" + str(n_i))\n",
- " print (\"r_i=\" + str(r_i))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "<a id=\"predict\"></a>\n",
- "# 7. Inference\n",
- "\n",
- "Use the best model from the last run.\n",
- "\n",
- "## 7a. Run predict on the whole validation dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 93,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>mst_key</th>\n",
- " <th>model_id</th>\n",
- " <th>compile_params</th>\n",
- " <th>fit_params</th>\n",
- " <th>model_type</th>\n",
- " <th>model_size</th>\n",
- " <th>metrics_elapsed_time</th>\n",
- " <th>metrics_type</th>\n",
- " <th>training_metrics_final</th>\n",
- " <th>training_loss_final</th>\n",
- " <th>training_metrics</th>\n",
- " <th>training_loss</th>\n",
- " <th>validation_metrics_final</th>\n",
- " <th>validation_loss_final</th>\n",
- " <th>validation_metrics</th>\n",
- " <th>validation_loss</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>2</td>\n",
- " <td>loss='categorical_crossentropy',optimizer='adam(lr=0.002826545217978097)',metrics=['accuracy']</td>\n",
- " <td>batch_size=128,epochs=5</td>\n",
- " <td>madlib_keras</td>\n",
- " <td>2159.70019531</td>\n",
- " <td>[156.498700857162, 314.38369679451, 471.076618909836]</td>\n",
- " <td>[u'accuracy']</td>\n",
- " <td>0.89631998539</td>\n",
- " <td>0.301868826151</td>\n",
- " <td>[0.817480027675629, 0.862479984760284, 0.896319985389709]</td>\n",
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- "</table>"
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- "[(6, 2, u\"loss='categorical_crossentropy',optimizer='adam(lr=0.002826545217978097)',metrics=['accuracy']\", u'batch_size=128,epochs=5', u'madlib_keras', 2159.70019531, [156.498700857162, 314.38369679451, 471.076618909836], [u'accuracy'], 0.89631998539, 0.301868826151, [0.817480027675629, 0.862479984760284, 0.896319985389709], [0.536632478237152, 0.400230169296265, 0.301868826150894], 0.805899977684, 0.613121390343, [0.764500021934509, 0.788500010967255, 0.805899977684021], [0.717438697814941, 0.662977695465088, 0.613121390342712])]"
- ]
- },
- "execution_count": 93,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql SELECT * FROM $best_model_info;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- }
- ],
- "source": [
- "best_mst_key = %sql SELECT mst_key FROM $best_model_info; \n",
- "best_mst_key = best_mst_key.DataFrame().to_numpy()[0][0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 95,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "5 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>id</th>\n",
- " <th>estimated_y</th>\n",
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- " <td>1</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>0</td>\n",
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- },
- "execution_count": 95,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql DROP TABLE IF EXISTS cifar10_val_predict;\n",
- "%sql SELECT madlib.madlib_keras_predict('cifar10_best_model', 'cifar10_val', 'id', 'x', 'cifar10_val_predict', 'response', True, $best_mst_key);\n",
- "%sql SELECT * FROM cifar10_val_predict ORDER BY id LIMIT 5;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Count missclassifications"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1941</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(1941L,)]"
- ]
- },
- "execution_count": 96,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT COUNT(*) FROM cifar10_val_predict JOIN cifar10_val USING (id) \n",
- "WHERE cifar10_val_predict.estimated_y != cifar10_val.y;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Accuracy"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 97,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>test_accuracy_percent</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>80.59</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(Decimal('80.59'),)]"
- ]
- },
- "execution_count": 97,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "SELECT round(count(*)*100.0/10000.0,2) as test_accuracy_percent from\n",
- " (select cifar10_val.y as actual, cifar10_val_predict.estimated_y as predicted\n",
- " from cifar10_val_predict inner join cifar10_val\n",
- " on cifar10_val.id=cifar10_val_predict.id) q\n",
- "WHERE q.actual=q.predicted;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## 7b. Select a random image from the validation dataset and run predict\n",
- "\n",
- "Label map"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 98,
- "metadata": {},
- "outputs": [],
- "source": [
- "label_names = {\n",
- " 0 :\"airplane\",\n",
- " 1 :\"automobile\",\n",
- " 2 :\"bird\",\n",
- " 3:\"cat\",\n",
- " 4 :\"deer\",\n",
- " 5 :\"dog\",\n",
- " 6 :\"frog\",\n",
- " 7 :\"horse\",\n",
- " 8 :\"ship\",\n",
- " 9 :\"truck\"\n",
- "}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Pick a random image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 99,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 99,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS cifar10_val_random;\n",
- "CREATE TABLE cifar10_val_random AS\n",
- " SELECT * FROM cifar10_val ORDER BY random() LIMIT 1;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Predict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 100,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>id</th>\n",
- " <th>prob_0</th>\n",
- " <th>prob_1</th>\n",
- " <th>prob_2</th>\n",
- " <th>prob_3</th>\n",
- " <th>prob_4</th>\n",
- " <th>prob_5</th>\n",
- " <th>prob_6</th>\n",
- " <th>prob_7</th>\n",
- " <th>prob_8</th>\n",
- " <th>prob_9</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9813</td>\n",
- " <td>7.9166554e-08</td>\n",
- " <td>0.00038159246</td>\n",
- " <td>8.776156e-11</td>\n",
- " <td>1.7702625e-08</td>\n",
- " <td>1.2219187e-10</td>\n",
- " <td>8.096258e-10</td>\n",
- " <td>5.192042e-10</td>\n",
- " <td>1.5758073e-09</td>\n",
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- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
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- ]
- },
- "execution_count": 100,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%sql DROP TABLE IF EXISTS cifar10_val_random_predict;\n",
- "%sql SELECT madlib.madlib_keras_predict('cifar10_best_model', 'cifar10_val_random', 'id', 'x', 'cifar10_val_random_predict', 'prob', True, $best_mst_key);\n",
- "%sql SELECT * FROM cifar10_val_random_predict ;"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Format output and display"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 101,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>feature_vector</th>\n",
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- " <tr>\n",
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- ]
- },
- "execution_count": 101,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF EXISTS cifar10_val_random_predict_array, cifar10_val_random_predict_array_summary;\n",
- "SELECT madlib.cols2vec(\n",
- " 'cifar10_val_random_predict',\n",
- " 'cifar10_val_random_predict_array',\n",
- " '*',\n",
- " 'id'\n",
- ");\n",
- "select * from cifar10_val_random_predict_array;"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 102,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n",
- "1 rows affected.\n",
- " \n",
- "truck 0.99961793\n",
- "automobile 0.00038159246\n",
- "ship 4.106987e-07\n"
- ]
- },
- {
- "data": {
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\n",
- "text/plain": [
- "<Figure size 432x288 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
- "source": [
- "x = %sql SELECT x FROM cifar10_val_random;\n",
- "x = x.DataFrame().to_numpy()\n",
- "import numpy as np\n",
- "from matplotlib.pyplot import imshow\n",
- "%matplotlib inline\n",
- "x_np = np.array(x[0][0], dtype=np.uint8)\n",
- "imshow(x_np)\n",
- "\n",
- "x = %sql SELECT * FROM cifar10_val_random_predict_array;\n",
- "x = x.DataFrame().to_numpy()\n",
- "x = np.array(x[0][0])\n",
- "top_3_prob_label_indices = x.argsort()[-3:][::-1]\n",
- "print (\" \");\n",
- "for index in top_3_prob_label_indices:\n",
- " print (label_names[index], x[index])"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 2",
- "language": "python",
- "name": "python2"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 2
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython2",
- "version": "2.7.16"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/community-artifacts/E2E-workflows/MADlib-e2e-ds-workflow-abalone.ipynb b/community-artifacts/E2E-workflows/MADlib-e2e-ds-workflow-abalone.ipynb
index f2d7a34..c5e32a6 100644
--- a/community-artifacts/E2E-workflows/MADlib-e2e-ds-workflow-abalone.ipynb
+++ b/community-artifacts/E2E-workflows/MADlib-e2e-ds-workflow-abalone.ipynb
@@ -98,7 +98,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -127,7 +127,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -279,7 +279,7 @@
" (u'F', 0.55, 0.44, 0.15, 0.8945, 0.3145, 0.151, 0.32, 19)]"
]
},
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -320,7 +320,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -486,7 +486,7 @@
" (9, u'F', 0.55, 0.44, 0.15, 0.8945, 0.3145, 0.151, 0.32, 19)]"
]
},
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -535,7 +535,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -553,7 +553,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -591,7 +591,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -613,7 +613,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -624,7 +624,7 @@
"Name: rings, dtype: int64"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -642,7 +642,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -677,7 +677,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -708,161 +708,161 @@
" <th>mature</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>1</td>\n",
+ " <td>3</td>\n",
" <td>m</td>\n",
- " <td>0.35</td>\n",
- " <td>0.265</td>\n",
- " <td>0.09</td>\n",
- " <td>0.2255</td>\n",
- " <td>0.0995</td>\n",
- " <td>0.0485</td>\n",
- " <td>0.07</td>\n",
+ " <td>0.44</td>\n",
+ " <td>0.365</td>\n",
+ " <td>0.125</td>\n",
+ " <td>0.516</td>\n",
+ " <td>0.2155</td>\n",
+ " <td>0.114</td>\n",
+ " <td>0.155</td>\n",
+ " <td>10</td>\n",
+ " <td>11.5</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
" <td>7</td>\n",
- " <td>8.5</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>i</td>\n",
+ " <td>f</td>\n",
+ " <td>0.545</td>\n",
" <td>0.425</td>\n",
- " <td>0.3</td>\n",
- " <td>0.095</td>\n",
- " <td>0.3515</td>\n",
- " <td>0.141</td>\n",
- " <td>0.0775</td>\n",
- " <td>0.12</td>\n",
- " <td>8</td>\n",
- " <td>9.5</td>\n",
- " <td>0</td>\n",
+ " <td>0.125</td>\n",
+ " <td>0.768</td>\n",
+ " <td>0.294</td>\n",
+ " <td>0.1495</td>\n",
+ " <td>0.26</td>\n",
+ " <td>16</td>\n",
+ " <td>17.5</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
" <tr>\n",
+ " <td>11</td>\n",
+ " <td>m</td>\n",
+ " <td>0.43</td>\n",
+ " <td>0.35</td>\n",
+ " <td>0.11</td>\n",
+ " <td>0.406</td>\n",
+ " <td>0.1675</td>\n",
+ " <td>0.081</td>\n",
+ " <td>0.135</td>\n",
+ " <td>10</td>\n",
+ " <td>11.5</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
+ " <td>m</td>\n",
+ " <td>0.5</td>\n",
+ " <td>0.4</td>\n",
+ " <td>0.13</td>\n",
+ " <td>0.6645</td>\n",
+ " <td>0.258</td>\n",
+ " <td>0.133</td>\n",
+ " <td>0.24</td>\n",
+ " <td>12</td>\n",
+ " <td>13.5</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>19</td>\n",
+ " <td>m</td>\n",
+ " <td>0.45</td>\n",
+ " <td>0.32</td>\n",
+ " <td>0.1</td>\n",
+ " <td>0.381</td>\n",
+ " <td>0.1705</td>\n",
+ " <td>0.075</td>\n",
+ " <td>0.115</td>\n",
" <td>9</td>\n",
+ " <td>10.5</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>23</td>\n",
" <td>f</td>\n",
" <td>0.55</td>\n",
- " <td>0.44</td>\n",
- " <td>0.15</td>\n",
- " <td>0.8945</td>\n",
- " <td>0.3145</td>\n",
- " <td>0.151</td>\n",
- " <td>0.32</td>\n",
- " <td>19</td>\n",
- " <td>20.5</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>13</td>\n",
- " <td>f</td>\n",
- " <td>0.535</td>\n",
- " <td>0.405</td>\n",
- " <td>0.145</td>\n",
- " <td>0.6845</td>\n",
- " <td>0.2725</td>\n",
- " <td>0.171</td>\n",
- " <td>0.205</td>\n",
- " <td>10</td>\n",
- " <td>11.5</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>17</td>\n",
- " <td>f</td>\n",
- " <td>0.44</td>\n",
- " <td>0.34</td>\n",
- " <td>0.1</td>\n",
- " <td>0.451</td>\n",
- " <td>0.188</td>\n",
- " <td>0.087</td>\n",
- " <td>0.13</td>\n",
- " <td>10</td>\n",
- " <td>11.5</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>21</td>\n",
- " <td>i</td>\n",
- " <td>0.38</td>\n",
- " <td>0.275</td>\n",
- " <td>0.1</td>\n",
- " <td>0.2255</td>\n",
- " <td>0.08</td>\n",
- " <td>0.049</td>\n",
- " <td>0.085</td>\n",
- " <td>10</td>\n",
- " <td>11.5</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>25</td>\n",
- " <td>f</td>\n",
- " <td>0.56</td>\n",
- " <td>0.44</td>\n",
- " <td>0.14</td>\n",
- " <td>0.9285</td>\n",
- " <td>0.3825</td>\n",
- " <td>0.188</td>\n",
- " <td>0.3</td>\n",
- " <td>11</td>\n",
- " <td>12.5</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>29</td>\n",
- " <td>m</td>\n",
- " <td>0.575</td>\n",
- " <td>0.425</td>\n",
- " <td>0.14</td>\n",
- " <td>0.8635</td>\n",
- " <td>0.393</td>\n",
- " <td>0.227</td>\n",
+ " <td>0.415</td>\n",
+ " <td>0.135</td>\n",
+ " <td>0.7635</td>\n",
+ " <td>0.318</td>\n",
+ " <td>0.21</td>\n",
" <td>0.2</td>\n",
- " <td>11</td>\n",
- " <td>12.5</td>\n",
+ " <td>9</td>\n",
+ " <td>10.5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>33</td>\n",
+ " <td>27</td>\n",
+ " <td>m</td>\n",
+ " <td>0.59</td>\n",
+ " <td>0.445</td>\n",
+ " <td>0.14</td>\n",
+ " <td>0.931</td>\n",
+ " <td>0.356</td>\n",
+ " <td>0.234</td>\n",
+ " <td>0.28</td>\n",
+ " <td>12</td>\n",
+ " <td>13.5</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>31</td>\n",
" <td>f</td>\n",
" <td>0.68</td>\n",
- " <td>0.55</td>\n",
- " <td>0.175</td>\n",
- " <td>1.798</td>\n",
- " <td>0.815</td>\n",
- " <td>0.3925</td>\n",
- " <td>0.455</td>\n",
- " <td>19</td>\n",
- " <td>20.5</td>\n",
+ " <td>0.56</td>\n",
+ " <td>0.165</td>\n",
+ " <td>1.639</td>\n",
+ " <td>0.6055</td>\n",
+ " <td>0.2805</td>\n",
+ " <td>0.46</td>\n",
+ " <td>15</td>\n",
+ " <td>16.5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>37</td>\n",
- " <td>f</td>\n",
- " <td>0.45</td>\n",
+ " <td>35</td>\n",
+ " <td>m</td>\n",
+ " <td>0.465</td>\n",
" <td>0.355</td>\n",
" <td>0.105</td>\n",
- " <td>0.5225</td>\n",
- " <td>0.237</td>\n",
- " <td>0.1165</td>\n",
- " <td>0.145</td>\n",
+ " <td>0.4795</td>\n",
+ " <td>0.227</td>\n",
+ " <td>0.124</td>\n",
+ " <td>0.125</td>\n",
" <td>8</td>\n",
" <td>9.5</td>\n",
" <td>0</td>\n",
" </tr>\n",
+ " <tr>\n",
+ " <td>39</td>\n",
+ " <td>m</td>\n",
+ " <td>0.355</td>\n",
+ " <td>0.29</td>\n",
+ " <td>0.09</td>\n",
+ " <td>0.3275</td>\n",
+ " <td>0.134</td>\n",
+ " <td>0.086</td>\n",
+ " <td>0.09</td>\n",
+ " <td>9</td>\n",
+ " <td>10.5</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
"</table>"
],
"text/plain": [
- "[(1, u'm', 0.35, 0.265, 0.09, 0.2255, 0.0995, 0.0485, 0.07, 7, Decimal('8.5'), 0),\n",
- " (5, u'i', 0.425, 0.3, 0.095, 0.3515, 0.141, 0.0775, 0.12, 8, Decimal('9.5'), 0),\n",
- " (9, u'f', 0.55, 0.44, 0.15, 0.8945, 0.3145, 0.151, 0.32, 19, Decimal('20.5'), 1),\n",
- " (13, u'f', 0.535, 0.405, 0.145, 0.6845, 0.2725, 0.171, 0.205, 10, Decimal('11.5'), 1),\n",
- " (17, u'f', 0.44, 0.34, 0.1, 0.451, 0.188, 0.087, 0.13, 10, Decimal('11.5'), 1),\n",
- " (21, u'i', 0.38, 0.275, 0.1, 0.2255, 0.08, 0.049, 0.085, 10, Decimal('11.5'), 1),\n",
- " (25, u'f', 0.56, 0.44, 0.14, 0.9285, 0.3825, 0.188, 0.3, 11, Decimal('12.5'), 1),\n",
- " (29, u'm', 0.575, 0.425, 0.14, 0.8635, 0.393, 0.227, 0.2, 11, Decimal('12.5'), 1),\n",
- " (33, u'f', 0.68, 0.55, 0.175, 1.798, 0.815, 0.3925, 0.455, 19, Decimal('20.5'), 1),\n",
- " (37, u'f', 0.45, 0.355, 0.105, 0.5225, 0.237, 0.1165, 0.145, 8, Decimal('9.5'), 0)]"
+ "[(3, u'm', 0.44, 0.365, 0.125, 0.516, 0.2155, 0.114, 0.155, 10, Decimal('11.5'), 1),\n",
+ " (7, u'f', 0.545, 0.425, 0.125, 0.768, 0.294, 0.1495, 0.26, 16, Decimal('17.5'), 1),\n",
+ " (11, u'm', 0.43, 0.35, 0.11, 0.406, 0.1675, 0.081, 0.135, 10, Decimal('11.5'), 1),\n",
+ " (15, u'm', 0.5, 0.4, 0.13, 0.6645, 0.258, 0.133, 0.24, 12, Decimal('13.5'), 1),\n",
+ " (19, u'm', 0.45, 0.32, 0.1, 0.381, 0.1705, 0.075, 0.115, 9, Decimal('10.5'), 1),\n",
+ " (23, u'f', 0.55, 0.415, 0.135, 0.7635, 0.318, 0.21, 0.2, 9, Decimal('10.5'), 1),\n",
+ " (27, u'm', 0.59, 0.445, 0.14, 0.931, 0.356, 0.234, 0.28, 12, Decimal('13.5'), 1),\n",
+ " (31, u'f', 0.68, 0.56, 0.165, 1.639, 0.6055, 0.2805, 0.46, 15, Decimal('16.5'), 1),\n",
+ " (35, u'm', 0.465, 0.355, 0.105, 0.4795, 0.227, 0.124, 0.125, 8, Decimal('9.5'), 0),\n",
+ " (39, u'm', 0.355, 0.29, 0.09, 0.3275, 0.134, 0.086, 0.09, 9, Decimal('10.5'), 1)]"
]
},
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -905,7 +905,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -938,116 +938,116 @@
" <th>sex_m</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>3</td>\n",
- " <td>0.44</td>\n",
- " <td>0.365</td>\n",
- " <td>0.125</td>\n",
- " <td>0.516</td>\n",
- " <td>0.2155</td>\n",
- " <td>0.114</td>\n",
- " <td>0.155</td>\n",
- " <td>10</td>\n",
- " <td>11.5</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>0.43</td>\n",
- " <td>0.35</td>\n",
- " <td>0.11</td>\n",
- " <td>0.406</td>\n",
- " <td>0.1675</td>\n",
- " <td>0.081</td>\n",
- " <td>0.135</td>\n",
- " <td>10</td>\n",
- " <td>11.5</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>19</td>\n",
- " <td>0.45</td>\n",
- " <td>0.32</td>\n",
- " <td>0.1</td>\n",
- " <td>0.381</td>\n",
- " <td>0.1705</td>\n",
- " <td>0.075</td>\n",
- " <td>0.115</td>\n",
- " <td>9</td>\n",
- " <td>10.5</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>27</td>\n",
- " <td>0.59</td>\n",
- " <td>0.445</td>\n",
- " <td>0.14</td>\n",
- " <td>0.931</td>\n",
- " <td>0.356</td>\n",
- " <td>0.234</td>\n",
- " <td>0.28</td>\n",
- " <td>12</td>\n",
- " <td>13.5</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>35</td>\n",
- " <td>0.465</td>\n",
- " <td>0.355</td>\n",
- " <td>0.105</td>\n",
- " <td>0.4795</td>\n",
- " <td>0.227</td>\n",
- " <td>0.124</td>\n",
- " <td>0.125</td>\n",
+ " <td>5</td>\n",
+ " <td>0.425</td>\n",
+ " <td>0.3</td>\n",
+ " <td>0.095</td>\n",
+ " <td>0.3515</td>\n",
+ " <td>0.141</td>\n",
+ " <td>0.0775</td>\n",
+ " <td>0.12</td>\n",
" <td>8</td>\n",
" <td>9.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>0</td>\n",
" <td>1</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>43</td>\n",
+ " <td>13</td>\n",
+ " <td>0.535</td>\n",
+ " <td>0.405</td>\n",
+ " <td>0.145</td>\n",
+ " <td>0.6845</td>\n",
+ " <td>0.2725</td>\n",
+ " <td>0.171</td>\n",
" <td>0.205</td>\n",
- " <td>0.15</td>\n",
- " <td>0.055</td>\n",
- " <td>0.042</td>\n",
- " <td>0.0255</td>\n",
- " <td>0.015</td>\n",
- " <td>0.012</td>\n",
- " <td>5</td>\n",
- " <td>6.5</td>\n",
+ " <td>10</td>\n",
+ " <td>11.5</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>21</td>\n",
+ " <td>0.38</td>\n",
+ " <td>0.275</td>\n",
+ " <td>0.1</td>\n",
+ " <td>0.2255</td>\n",
+ " <td>0.08</td>\n",
+ " <td>0.049</td>\n",
+ " <td>0.085</td>\n",
+ " <td>10</td>\n",
+ " <td>11.5</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>51</td>\n",
- " <td>0.4</td>\n",
- " <td>0.32</td>\n",
+ " <td>29</td>\n",
+ " <td>0.575</td>\n",
+ " <td>0.425</td>\n",
+ " <td>0.14</td>\n",
+ " <td>0.8635</td>\n",
+ " <td>0.393</td>\n",
+ " <td>0.227</td>\n",
+ " <td>0.2</td>\n",
+ " <td>11</td>\n",
+ " <td>12.5</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>37</td>\n",
+ " <td>0.45</td>\n",
+ " <td>0.355</td>\n",
+ " <td>0.105</td>\n",
+ " <td>0.5225</td>\n",
+ " <td>0.237</td>\n",
+ " <td>0.1165</td>\n",
+ " <td>0.145</td>\n",
+ " <td>8</td>\n",
+ " <td>9.5</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>45</td>\n",
+ " <td>0.39</td>\n",
+ " <td>0.295</td>\n",
" <td>0.095</td>\n",
- " <td>0.303</td>\n",
- " <td>0.1335</td>\n",
- " <td>0.06</td>\n",
- " <td>0.1</td>\n",
+ " <td>0.203</td>\n",
+ " <td>0.0875</td>\n",
+ " <td>0.045</td>\n",
+ " <td>0.075</td>\n",
" <td>7</td>\n",
" <td>8.5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
- " <td>0</td>\n",
" <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>53</td>\n",
+ " <td>0.47</td>\n",
+ " <td>0.36</td>\n",
+ " <td>0.12</td>\n",
+ " <td>0.4775</td>\n",
+ " <td>0.2105</td>\n",
+ " <td>0.1055</td>\n",
+ " <td>0.15</td>\n",
+ " <td>10</td>\n",
+ " <td>11.5</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
@@ -1100,19 +1100,19 @@
"</table>"
],
"text/plain": [
- "[(3, 0.44, 0.365, 0.125, 0.516, 0.2155, 0.114, 0.155, 10, Decimal('11.5'), 1, 0, 0, 1),\n",
- " (11, 0.43, 0.35, 0.11, 0.406, 0.1675, 0.081, 0.135, 10, Decimal('11.5'), 1, 0, 0, 1),\n",
- " (19, 0.45, 0.32, 0.1, 0.381, 0.1705, 0.075, 0.115, 9, Decimal('10.5'), 1, 0, 0, 1),\n",
- " (27, 0.59, 0.445, 0.14, 0.931, 0.356, 0.234, 0.28, 12, Decimal('13.5'), 1, 0, 0, 1),\n",
- " (35, 0.465, 0.355, 0.105, 0.4795, 0.227, 0.124, 0.125, 8, Decimal('9.5'), 0, 0, 0, 1),\n",
- " (43, 0.205, 0.15, 0.055, 0.042, 0.0255, 0.015, 0.012, 5, Decimal('6.5'), 0, 0, 1, 0),\n",
- " (51, 0.4, 0.32, 0.095, 0.303, 0.1335, 0.06, 0.1, 7, Decimal('8.5'), 0, 0, 0, 1),\n",
+ "[(5, 0.425, 0.3, 0.095, 0.3515, 0.141, 0.0775, 0.12, 8, Decimal('9.5'), 0, 0, 1, 0),\n",
+ " (13, 0.535, 0.405, 0.145, 0.6845, 0.2725, 0.171, 0.205, 10, Decimal('11.5'), 1, 1, 0, 0),\n",
+ " (21, 0.38, 0.275, 0.1, 0.2255, 0.08, 0.049, 0.085, 10, Decimal('11.5'), 1, 0, 1, 0),\n",
+ " (29, 0.575, 0.425, 0.14, 0.8635, 0.393, 0.227, 0.2, 11, Decimal('12.5'), 1, 0, 0, 1),\n",
+ " (37, 0.45, 0.355, 0.105, 0.5225, 0.237, 0.1165, 0.145, 8, Decimal('9.5'), 0, 1, 0, 0),\n",
+ " (45, 0.39, 0.295, 0.095, 0.203, 0.0875, 0.045, 0.075, 7, Decimal('8.5'), 0, 0, 1, 0),\n",
+ " (53, 0.47, 0.36, 0.12, 0.4775, 0.2105, 0.1055, 0.15, 10, Decimal('11.5'), 1, 1, 0, 0),\n",
" (59, 0.505, 0.4, 0.125, 0.583, 0.246, 0.13, 0.175, 7, Decimal('8.5'), 0, 1, 0, 0),\n",
" (67, 0.595, 0.495, 0.185, 1.285, 0.416, 0.224, 0.485, 13, Decimal('14.5'), 1, 1, 0, 0),\n",
" (75, 0.6, 0.475, 0.15, 1.0075, 0.4425, 0.221, 0.28, 15, Decimal('16.5'), 1, 1, 0, 0)]"
]
},
- "execution_count": 11,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -1153,7 +1153,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -1218,7 +1218,7 @@
" <td>1044.0</td>\n",
" <td>2088.0</td>\n",
" <td>3132.0</td>\n",
- " <td>[u'3453', u'4114', u'2806', u'2823', u'2700', u'2914', u'2970', u'3559', u'3663', u'3809']</td>\n",
+ " <td>[u'4117', u'4114', u'2700', u'3093', u'3595', u'3529', u'3114', u'3451', u'3185', u'3049']</td>\n",
" <td>[5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L]</td>\n",
" </tr>\n",
" <tr>\n",
@@ -1244,7 +1244,7 @@
" <td>0.45</td>\n",
" <td>0.545</td>\n",
" <td>0.615</td>\n",
- " <td>[u'0.55', u'0.625', u'0.575', u'0.58', u'0.62', u'0.6', u'0.5', u'0.57', u'0.63', u'0.61']</td>\n",
+ " <td>[u'0.55', u'0.625', u'0.575', u'0.58', u'0.6', u'0.62', u'0.5', u'0.57', u'0.63', u'0.61']</td>\n",
" <td>[94L, 94L, 93L, 92L, 87L, 87L, 81L, 79L, 78L, 75L]</td>\n",
" </tr>\n",
" <tr>\n",
@@ -1264,7 +1264,7 @@
" <td>0</td>\n",
" <td>0.407881254489</td>\n",
" <td>0.00984855103022</td>\n",
- " <td>[0.404871645997762, 0.410890862979976]</td>\n",
+ " <td>[0.404871645997763, 0.410890862979976]</td>\n",
" <td>0.055</td>\n",
" <td>0.65</td>\n",
" <td>0.35</td>\n",
@@ -1290,13 +1290,13 @@
" <td>2</td>\n",
" <td>0.13951639933</td>\n",
" <td>0.00174950266443</td>\n",
- " <td>[0.138247926591561, 0.140784872067761]</td>\n",
+ " <td>[0.138247926591562, 0.140784872067761]</td>\n",
" <td>0.0</td>\n",
" <td>1.13</td>\n",
" <td>0.115</td>\n",
" <td>0.14</td>\n",
" <td>0.165</td>\n",
- " <td>[u'0.15', u'0.14', u'0.155', u'0.175', u'0.16', u'0.125', u'0.165', u'0.135', u'0.145', u'0.12']</td>\n",
+ " <td>[u'0.15', u'0.14', u'0.155', u'0.175', u'0.16', u'0.125', u'0.165', u'0.135', u'0.145', u'0.13']</td>\n",
" <td>[267L, 220L, 217L, 211L, 205L, 202L, 193L, 189L, 182L, 169L]</td>\n",
" </tr>\n",
" <tr>\n",
@@ -1316,14 +1316,14 @@
" <td>0</td>\n",
" <td>0.828742159445</td>\n",
" <td>0.240481389202</td>\n",
- " <td>[0.813870324055101, 0.843613994834057]</td>\n",
+ " <td>[0.813870324055101, 0.843613994834058]</td>\n",
" <td>0.002</td>\n",
" <td>2.8255</td>\n",
" <td>0.4415</td>\n",
" <td>0.7995</td>\n",
" <td>1.153</td>\n",
- " <td>[u'1.1345', u'0.2225', u'0.196', u'0.4425', u'0.4775', u'1.0835', u'0.97', u'0.874', u'1.1155', u'0.872']</td>\n",
- " <td>[10L, 8L, 8L, 7L, 7L, 7L, 7L, 7L, 7L, 7L]</td>\n",
+ " <td>[u'1.1345', u'0.196', u'0.2225', u'0.97', u'0.858', u'1.0835', u'0.4775', u'0.872', u'1.1155', u'1.229']</td>\n",
+ " <td>[11L, 8L, 8L, 7L, 7L, 7L, 7L, 7L, 7L, 7L]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>None</td>\n",
@@ -1348,7 +1348,7 @@
" <td>0.186</td>\n",
" <td>0.336</td>\n",
" <td>0.502</td>\n",
- " <td>[u'0.175', u'0.2505', u'0.21', u'0.419', u'0.2', u'0.097', u'0.165', u'0.096', u'0.0745', u'0.2025']</td>\n",
+ " <td>[u'0.175', u'0.2505', u'0.2', u'0.302', u'0.2945', u'0.419', u'0.2025', u'0.165', u'0.096', u'0.097']</td>\n",
" <td>[11L, 10L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L]</td>\n",
" </tr>\n",
" <tr>\n",
@@ -1368,13 +1368,13 @@
" <td>0</td>\n",
" <td>0.180593607853</td>\n",
" <td>0.01201528386</td>\n",
- " <td>[0.17726937948362, 0.183917836221431]</td>\n",
+ " <td>[0.177269379483621, 0.183917836221432]</td>\n",
" <td>0.0005</td>\n",
" <td>0.76</td>\n",
" <td>0.0935</td>\n",
" <td>0.171</td>\n",
" <td>0.253</td>\n",
- " <td>[u'0.1715', u'0.196', u'0.0575', u'0.2195', u'0.061', u'0.037', u'0.096', u'0.1405', u'0.099', u'0.0265']</td>\n",
+ " <td>[u'0.1715', u'0.196', u'0.037', u'0.2195', u'0.0575', u'0.061', u'0.096', u'0.1905', u'0.207', u'0.0265']</td>\n",
" <td>[15L, 14L, 13L, 13L, 13L, 13L, 12L, 12L, 12L, 12L]</td>\n",
" </tr>\n",
" <tr>\n",
@@ -1400,7 +1400,7 @@
" <td>0.13</td>\n",
" <td>0.234</td>\n",
" <td>0.329</td>\n",
- " <td>[u'0.275', u'0.25', u'0.185', u'0.265', u'0.315', u'0.3', u'0.17', u'0.285', u'0.175', u'0.22']</td>\n",
+ " <td>[u'0.275', u'0.25', u'0.315', u'0.185', u'0.265', u'0.17', u'0.285', u'0.3', u'0.22', u'0.175']</td>\n",
" <td>[43L, 42L, 40L, 40L, 40L, 37L, 37L, 37L, 36L, 36L]</td>\n",
" </tr>\n",
" <tr>\n",
@@ -1562,14 +1562,14 @@
"</table>"
],
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" (None, None, u'rings', 9, u'int4', 4177L, 28L, 0L, None, 0.0, None, 4177L, 0L, 0L, 9.93368446253292, 10.3952659473471, [9.83590635305212, 10.0314625720137], 1.0, 29.0, 8.0, 9.0, 11.0, [u'9', u'10', u'8', u'11', u'7', u'12', u'6', u'13', u'14', u'5'], [689L, 634L, 568L, 487L, 391L, 267L, 259L, 203L, 126L, 115L]),\n",
" (None, None, u'age', 10, u'numeric', 4177L, 28L, 0L, None, 0.0, None, 4177L, 0L, 0L, 11.4336844625329, 10.3952659473471, [11.3359063530521, 11.5314625720137], 2.5, 30.5, 9.5, 10.5, 12.5, [u'10.5', u'11.5', u'9.5', u'12.5', u'8.5', u'13.5', u'7.5', u'14.5', u'15.5', u'6.5'], [689L, 634L, 568L, 487L, 391L, 267L, 259L, 203L, 126L, 115L]),\n",
" (None, None, u'mature', 11, u'int4', 4177L, 2L, 0L, None, 0.0, None, 2770L, 0L, 1407L, 0.663155374670816, 0.223433815172854, [0.648820355293342, 0.67749039404829], 0.0, 1.0, 0.0, 1.0, 1.0, [u'1', u'0'], [2770L, 1407L]),\n",
@@ -1578,7 +1578,7 @@
" (None, None, u'sex_m', 14, u'int4', 4177L, 2L, 0L, None, 0.0, None, 1528L, 0L, 2649L, 0.365812784294949, 0.232049345210086, [0.351204002328337, 0.380421566261561], 0.0, 1.0, 0.0, 0.0, 1.0, [u'0', u'1'], [2649L, 1528L])]"
]
},
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -1613,7 +1613,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -1633,15 +1633,15 @@
" <th>correlation</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>Summary for 'Correlation' function<br>Output table = abalone_correlations<br>Grouping columns: sex_f,sex_i,sex_m<br>Producing correlation for columns: length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings<br>Total run time = ('abalone_correlations', 8, 0.21109700202941895)</td>\n",
+ " <td>Summary for 'Correlation' function<br>Output table = abalone_correlations<br>Grouping columns: sex_f,sex_i,sex_m<br>Producing correlation for columns: length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings<br>Total run time = ('abalone_correlations', 8, 0.2650470733642578)</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u\"Summary for 'Correlation' function\\nOutput table = abalone_correlations\\nGrouping columns: sex_f,sex_i,sex_m\\nProducing correlation for columns: length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings\\nTotal run time = ('abalone_correlations', 8, 0.21109700202941895)\",)]"
+ "[(u\"Summary for 'Correlation' function\\nOutput table = abalone_correlations\\nGrouping columns: sex_f,sex_i,sex_m\\nProducing correlation for columns: length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,rings\\nTotal run time = ('abalone_correlations', 8, 0.2650470733642578)\",)]"
]
},
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -1663,7 +1663,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -1681,7 +1681,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -1699,7 +1699,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -1717,7 +1717,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -1752,7 +1752,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -1782,7 +1782,7 @@
"[(2924L,)]"
]
},
- "execution_count": 18,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -1824,7 +1824,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -1873,7 +1873,7 @@
"[(u'logregr', u'abalone_classif_train', u'abalone_logreg_model', u'mature', u'ARRAY[\\n 1,\\n length,\\n diameter,\\n height,\\n whole_weight,\\n shucked_weight,\\n viscera_weight,\\n shell_weight,\\n sex_f,\\n sex_m\\n ]', u'optimizer=irls, max_iter=20, tolerance=0.0001', 1, 0, 2924, 0, None)]"
]
},
- "execution_count": 19,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -1919,7 +1919,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 21,
"metadata": {
"scrolled": true
},
@@ -1949,25 +1949,25 @@
" <th>variance_covariance</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>[-4.67737274255474, -0.392212133228859, 6.15510638669522, 2.08463990575267, 4.87504773295541, -13.7623409061431, -1.36671380800461, 17.1547402958807, 1.01917554661788, 0.942108041681632]</td>\n",
- " <td>-1006.98331724</td>\n",
- " <td>[0.590840441026775, 2.97184057268197, 3.73897571658262, 2.03371620613073, 1.83990223675481, 2.03448594937021, 3.0108856938889, 2.83197606788329, 0.152140116528269, 0.139164221999888]</td>\n",
- " <td>[-7.91647358198146, -0.131976168854476, 1.64620122013548, 1.02503972750398, 2.64962324387079, -6.76452983634679, -0.453924176124849, 6.05751598342522, 6.69892708034377, 6.76975754359042]</td>\n",
- " <td>[2.44341625367833e-15, 0.895003141346682, 0.0997223381574991, 0.305344442807183, 0.00805815758922618, 1.33742797426474e-11, 0.649883402076758, 1.38239609928844e-09, 2.0995532130572e-11, 1.28998390096206e-11]</td>\n",
- " <td>[0.00930342429755957, 0.675560789867617, 471.116960371319, 8.04169519208295, 130.980405143444, 1.05460854620395e-06, 0.254943377371314, 28197398.9511836, 2.77090933640556, 2.56538365793589]</td>\n",
- " <td>121.997664048</td>\n",
+ " <td>[-4.05784038511939, -3.62328869369282, 8.55818901312571, 2.3733575038103, 5.89366234938041, -14.5809268846204, -0.83965986440594, 14.9221884676909, 0.888113766564006, 0.848744890485021]</td>\n",
+ " <td>-1046.57223389</td>\n",
+ " <td>[0.564717420593037, 3.24087632641859, 3.91946176964484, 2.15175240516033, 1.67142108880738, 1.90735829359554, 2.75043999028889, 2.60184088552192, 0.149323323315725, 0.136327386783195]</td>\n",
+ " <td>[-7.18561219673029, -1.11799659374748, 2.18351128703603, 1.10298819609474, 3.52613855888688, -7.6445662745063, -0.305282015739506, 5.73524251645295, 5.94758907612982, 6.22578419870105]</td>\n",
+ " <td>[6.69067049744631e-13, 0.263568448155912, 0.0289981786733622, 0.27003229123174, 0.000421666297028331, 2.0965080520499e-14, 0.760151371941268, 9.73729156506926e-09, 2.72120689051791e-09, 4.79153223523576e-10]</td>\n",
+ " <td>[0.0172863106068398, 0.0266947411488406, 5209.23876196897, 10.7333691819543, 362.731303482082, 4.65140240376941e-07, 0.431857388520035, 3024294.70742143, 2.4305407575436, 2.33671218095243]</td>\n",
+ " <td>133.151687225</td>\n",
" <td>2924</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
- " <td>[[0.349092426752714, -0.675574747128063, -0.397935077415633, -0.180020641088026, 0.147499936799071, -0.0153052244481757, 0.122866412950121, 0.34417106132921, -0.00868455748700556, -0.0138313591702408], [-0.675574747128063, 8.83183638943869, -9.04613626962293, -0.0601147873774726, -0.411410454286909, 0.158792353760485, -0.555224429178143, 0.279153789377445, 0.0398313893638008, 0.0352908716573887], [-0.397935077415633, -9.04613626962293, 13.9799394091945, -0.517286514331738, -0.0639955675289591, -0.0262239231108973, 0.360627238739412, -2.21869974874036, -0.0254340823712027, -0.00678581818134909], [-0.180020641088026, -0.0601147873774725, -0.517286514331738, 4.13600160707876, -0.0853550013704344, 0.0757036887508739, -0.164040489691675, -0.341456748118747, -0.0224655554447562, -0.0070919467634824], [0.147499936799071, -0.411410454286909, -0.0639955675289595, -0.0853550013704344, 3.38524024081537, -3.33519672860865, -3.49335772859094, -3.39500733031394, -0.0262681185753689, -0.0189083677062056], [-0.0153052244481757, 0.158792353760485, -0.0262239231108971, 0.0757036887508739, -3.33519672860865, 4.13913307818482, 2.35314654677864, 2.96795813139297, 0.0299367195410633, 0.00974680695082926], [0.122866412950121, -0.555224429178143, 0.360627238739412, -0.164040489691675, -3.49335772859094, 2.35314654677864, 9.06543266166485, 1.91215690195071, -0.0296918118533269, -0.0318284105816695], [0.34417106132921, 0.279153789377445, -2.21869974874036, -0.341456748118747, -3.39500733031394, 2.96795813139297, 1.91215690195071, 8.0200884490637, 0.027466190787868, 0.0262589069849619], [-0.00868455748700556, 0.0398313893638008, -0.0254340823712027, -0.0224655554447562, -0.0262681185753689, 0.0299367195410632, -0.0296918118533269, 0.027466190787868, 0.0231466150572352, 0.00991437265759586], [-0.0138313591702408, 0.0352908716573887, -0.00678581818134908, -0.0070919467634824, -0.0189083677062056, 0.00974680695082926, -0.0318284105816695, 0.0262589069849619, 0.00991437265759586, 0.019366680684834]]</td>\n",
+ " <td>[[0.318905765121253, -0.773880756388411, -0.148425130548394, -0.212962995977276, 0.147124898439516, -0.0059678292447954, 0.0793733577772294, 0.290640803033562, -0.00848213277272192, -0.0132695143230618], [-0.773880756388412, 10.5032793631405, -10.8450536351098, -0.065352511565441, -0.379260487944994, 0.115359274386607, -0.695572148171167, 0.0661722900163907, 0.0479683606457261, 0.0518480382948647], [-0.148425130548394, -10.8450536351098, 15.3621805637075, -0.532070275834464, -0.115038424926361, -0.0148346925154307, 0.739742640409678, -1.63384353907477, -0.0363792354548059, -0.030302937293815], [-0.212962995977276, -0.065352511565441, -0.532070275834464, 4.63003841311326, -0.147475204124208, 0.154261272553688, -0.205334564214196, -0.331885157675788, -0.0202805065965652, -0.00482846073312355], [0.147124898439516, -0.379260487944994, -0.115038424926361, -0.147475204124208, 2.79364845611006, -2.78954859556526, -2.71413799091265, -2.70422929299964, -0.0244641928225955, -0.0166449556760013], [-0.00596782924479522, 0.115359274386607, -0.0148346925154308, 0.154261272553688, -2.78954859556526, 3.6380156601477, 1.69176478837567, 2.32499354323252, 0.0299799840419785, 0.00932452525357895], [0.0793733577772295, -0.695572148171167, 0.739742640409678, -0.205334564214196, -2.71413799091265, 1.69176478837567, 7.56492014018035, 1.21738451933247, -0.0376135374474958, -0.0271600174605648], [0.290640803033562, 0.0661722900163907, -1.63384353907477, -0.331885157675788, -2.70422929299964, 2.32499354323252, 1.21738451933247, 6.76957599357349, 0.0255595927423807, 0.0162171662951054], [-0.00848213277272192, 0.0479683606457261, -0.0363792354548059, -0.0202805065965652, -0.0244641928225955, 0.0299799840419784, -0.0376135374474958, 0.0255595927423807, 0.0222974548860525, 0.00974956517302877], [-0.0132695143230618, 0.0518480382948647, -0.030302937293815, -0.00482846073312355, -0.0166449556760013, 0.00932452525357894, -0.0271600174605649, 0.0162171662951054, 0.00974956517302877, 0.0185851563871348]]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[([-4.67737274255474, -0.392212133228859, 6.15510638669522, 2.08463990575267, 4.87504773295541, -13.7623409061431, -1.36671380800461, 17.1547402958807, 1.01917554661788, 0.942108041681632], -1006.98331724464, [0.590840441026775, 2.97184057268197, 3.73897571658262, 2.03371620613073, 1.83990223675481, 2.03448594937021, 3.0108856938889, 2.83197606788329, 0.152140116528269, 0.139164221999888], [-7.91647358198146, -0.131976168854476, 1.64620122013548, 1.02503972750398, 2.64962324387079, -6.76452983634679, -0.453924176124849, 6.05751598342522, 6.69892708034377, 6.76975754359042], [2.44341625367833e-15, 0.895003141346682, 0.0997223381574991, 0.305344442807183, 0.00805815758922618, 1.33742797426474e-11, 0.649883402076758, 1.38239609928844e-09, 2.0995532130572e-11, 1.28998390096206e-11], [0.00930342429755957, 0.675560789867617, 471.116960371319, 8.04169519208295, 130.980405143444, 1.05460854620395e-06, 0.254943377371314, 28197398.9511836, 2.77090933640556, 2.56538365793589], 121.997664047952, 2924L, 0L, 7, [[0.349092426752714, -0.675574747128063, -0.397935077415633, -0.180020641088026, 0.147499936799071, -0.0153052244481757, 0.122866412950121, 0.34417106 ... (1703 characters truncated) ... 4908, -0.0070919467634824, -0.0189083677062056, 0.00974680695082926, -0.0318284105816695, 0.0262589069849619, 0.00991437265759586, 0.019366680684834]])]"
+ "[([-4.05784038511939, -3.62328869369282, 8.55818901312571, 2.3733575038103, 5.89366234938041, -14.5809268846204, -0.83965986440594, 14.9221884676909, 0.888113766564006, 0.848744890485021], -1046.57223388599, [0.564717420593037, 3.24087632641859, 3.91946176964484, 2.15175240516033, 1.67142108880738, 1.90735829359554, 2.75043999028889, 2.60184088552192, 0.149323323315725, 0.136327386783195], [-7.18561219673029, -1.11799659374748, 2.18351128703603, 1.10298819609474, 3.52613855888688, -7.6445662745063, -0.305282015739506, 5.73524251645295, 5.94758907612982, 6.22578419870105], [6.69067049744631e-13, 0.263568448155912, 0.0289981786733622, 0.27003229123174, 0.000421666297028331, 2.0965080520499e-14, 0.760151371941268, 9.73729156506926e-09, 2.72120689051791e-09, 4.79153223523576e-10], [0.0172863106068398, 0.0266947411488406, 5209.23876196897, 10.7333691819543, 362.731303482082, 4.65140240376941e-07, 0.431857388520035, 3024294.70742143, 2.4305407575436, 2.33671218095243], 133.151687225203, 2924L, 0L, 7, [[0.318905765121253, -0.773880756388411, -0.148425130548394, -0.212962995977276, 0.147124898439516, -0.0059678292447954, 0.0793733577772294, 0.2906408 ... (1703 characters truncated) ... 15, -0.00482846073312355, -0.0166449556760013, 0.00932452525357894, -0.0271600174605649, 0.0162171662951054, 0.00974956517302877, 0.0185851563871348]])]"
]
},
- "execution_count": 20,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -1986,7 +1986,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -2004,25 +2004,25 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(('intercept', -4.67737274255474),\n",
- " ('length', -0.392212133228859),\n",
- " ('diameter', 6.15510638669522),\n",
- " ('height', 2.08463990575267),\n",
- " ('whole_weight', 4.87504773295541),\n",
- " ('shucked_weight', -13.7623409061431),\n",
- " ('viscera_weight', -1.36671380800461),\n",
- " ('shell_weight', 17.1547402958807),\n",
- " ('sex_f', 1.01917554661788),\n",
- " ('sex_m', 0.942108041681632))"
+ "(('intercept', -4.05784038511939),\n",
+ " ('length', -3.62328869369282),\n",
+ " ('diameter', 8.55818901312571),\n",
+ " ('height', 2.3733575038103),\n",
+ " ('whole_weight', 5.89366234938041),\n",
+ " ('shucked_weight', -14.5809268846204),\n",
+ " ('viscera_weight', -0.83965986440594),\n",
+ " ('shell_weight', 14.9221884676909),\n",
+ " ('sex_f', 0.888113766564006),\n",
+ " ('sex_m', 0.848744890485021))"
]
},
- "execution_count": 22,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -2054,7 +2054,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -2075,61 +2075,61 @@
" <th>mature</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.991068065265</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0.826655523716</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0.608566963248</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0.916620487399</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0.309092601567</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0.516539096279</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0.5456252611</td>\n",
+ " <td>0.729460887544</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.318211176601</td>\n",
+ " <td>0.519030274502</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.992267215094</td>\n",
+ " <td>0.988936746274</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.831075550203</td>\n",
+ " <td>0.418889963058</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.57689078439</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.612402565835</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.997554872286</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.998395195488</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.561654937481</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.940089737771</td>\n",
" <td>1</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
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},
- "execution_count": 23,
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"metadata": {},
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}
@@ -2173,7 +2173,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -2192,61 +2192,61 @@
" <th>mature</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.999999986635</td>\n",
+ " <td>0.999999354969</td>\n",
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- " <td>0.999979937474</td>\n",
+ " <td>0.99994059283</td>\n",
" <td>1</td>\n",
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" <tr>\n",
- " <td>0.999976591803</td>\n",
+ " <td>0.999937901702</td>\n",
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" <tr>\n",
- " <td>0.999971356358</td>\n",
+ " <td>0.999913406187</td>\n",
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"</table>"
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+ "[(0.999999354969251, 1),\n",
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},
- "execution_count": 24,
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"metadata": {},
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}
@@ -2268,7 +2268,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -2288,15 +2288,15 @@
" <th>area_under_roc</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.89729162423337180618758011406069658496850</td>\n",
+ " <td>0.91611932129173508483766283524904214559400</td>\n",
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@@ -2326,7 +2326,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -2348,11 +2348,7 @@
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
+ " <td>0</td>\n",
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@@ -2360,6 +2356,18 @@
" </tr>\n",
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" <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
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@@ -2378,30 +2386,22 @@
" <td>1</td>\n",
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- " <tr>\n",
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"</table>"
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"text/plain": [
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- " (1, 1),\n",
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@@ -2424,7 +2424,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -2449,23 +2449,23 @@
" <tr>\n",
" <td>1</td>\n",
" <td>0</td>\n",
- " <td>306</td>\n",
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+ " <td>329</td>\n",
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"</table>"
],
"text/plain": [
- "[(1L, 0, Decimal('306'), Decimal('123')),\n",
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+ "[(1L, 0, Decimal('329'), Decimal('112')),\n",
+ " (2L, 1, Decimal('75'), Decimal('737'))]"
]
},
- "execution_count": 27,
+ "execution_count": 28,
"metadata": {},
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}
@@ -2500,7 +2500,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -2528,7 +2528,7 @@
"[(1253L,)]"
]
},
- "execution_count": 28,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -2550,14 +2550,14 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "19 rows affected.\n"
+ "32 rows affected.\n"
]
},
{
@@ -2581,334 +2581,555 @@
" <th>f1</th>\n",
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+ " <td>0.853152434158</td>\n",
+ " <td>0.89073634204275534442</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.516539096279</td>\n",
- " <td>727</td>\n",
- " <td>121</td>\n",
- " <td>97</td>\n",
- " <td>308</td>\n",
- " <td>0.882281553398</td>\n",
- " <td>0.717948717949</td>\n",
- " <td>0.857311320755</td>\n",
- " <td>0.76049382716</td>\n",
- " <td>0.282051282051</td>\n",
- " <td>0.142688679245</td>\n",
- " <td>0.117718446602</td>\n",
- " <td>0.826017557861</td>\n",
- " <td>0.86961722488038277512</td>\n",
+ " <td>0.480960568056</td>\n",
+ " <td>749</td>\n",
+ " <td>122</td>\n",
+ " <td>63</td>\n",
+ " <td>319</td>\n",
+ " <td>0.922413793103</td>\n",
+ " <td>0.72335600907</td>\n",
+ " <td>0.859931113662</td>\n",
+ " <td>0.835078534031</td>\n",
+ " <td>0.27664399093</td>\n",
+ " <td>0.140068886338</td>\n",
+ " <td>0.0775862068966</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.89007724301841948901</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.517108140708</td>\n",
- " <td>726</td>\n",
+ " <td>0.481340339905</td>\n",
+ " <td>749</td>\n",
" <td>121</td>\n",
- " <td>98</td>\n",
- " <td>308</td>\n",
- " <td>0.881067961165</td>\n",
- " <td>0.717948717949</td>\n",
- " <td>0.857142857143</td>\n",
- " <td>0.758620689655</td>\n",
- " <td>0.282051282051</td>\n",
- " <td>0.142857142857</td>\n",
- " <td>0.118932038835</td>\n",
- " <td>0.825219473264</td>\n",
- " <td>0.86894075403949730700</td>\n",
+ " <td>63</td>\n",
+ " <td>320</td>\n",
+ " <td>0.922413793103</td>\n",
+ " <td>0.725623582766</td>\n",
+ " <td>0.86091954023</td>\n",
+ " <td>0.835509138381</td>\n",
+ " <td>0.274376417234</td>\n",
+ " <td>0.13908045977</td>\n",
+ " <td>0.0775862068966</td>\n",
+ " <td>0.853152434158</td>\n",
+ " <td>0.89060642092746730083</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.48298187437</td>\n",
+ " <td>748</td>\n",
+ " <td>121</td>\n",
+ " <td>64</td>\n",
+ " <td>320</td>\n",
+ " <td>0.92118226601</td>\n",
+ " <td>0.725623582766</td>\n",
+ " <td>0.860759493671</td>\n",
+ " <td>0.833333333333</td>\n",
+ " <td>0.274376417234</td>\n",
+ " <td>0.139240506329</td>\n",
+ " <td>0.0788177339901</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.88994646044021415824</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.484306593506</td>\n",
+ " <td>748</td>\n",
+ " <td>120</td>\n",
+ " <td>64</td>\n",
+ " <td>321</td>\n",
+ " <td>0.92118226601</td>\n",
+ " <td>0.727891156463</td>\n",
+ " <td>0.861751152074</td>\n",
+ " <td>0.833766233766</td>\n",
+ " <td>0.272108843537</td>\n",
+ " <td>0.138248847926</td>\n",
+ " <td>0.0788177339901</td>\n",
+ " <td>0.853152434158</td>\n",
+ " <td>0.89047619047619047619</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.487331142196</td>\n",
+ " <td>747</td>\n",
+ " <td>120</td>\n",
+ " <td>65</td>\n",
+ " <td>321</td>\n",
+ " <td>0.919950738916</td>\n",
+ " <td>0.727891156463</td>\n",
+ " <td>0.861591695502</td>\n",
+ " <td>0.831606217617</td>\n",
+ " <td>0.272108843537</td>\n",
+ " <td>0.138408304498</td>\n",
+ " <td>0.0800492610837</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.88981536628945801072</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.487774653422</td>\n",
+ " <td>747</td>\n",
+ " <td>119</td>\n",
+ " <td>65</td>\n",
+ " <td>322</td>\n",
+ " <td>0.919950738916</td>\n",
+ " <td>0.730158730159</td>\n",
+ " <td>0.862586605081</td>\n",
+ " <td>0.832041343669</td>\n",
+ " <td>0.269841269841</td>\n",
+ " <td>0.137413394919</td>\n",
+ " <td>0.0800492610837</td>\n",
+ " <td>0.853152434158</td>\n",
+ " <td>0.89034564958283671037</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.488000947327</td>\n",
+ " <td>747</td>\n",
+ " <td>118</td>\n",
+ " <td>65</td>\n",
+ " <td>323</td>\n",
+ " <td>0.919950738916</td>\n",
+ " <td>0.732426303855</td>\n",
+ " <td>0.863583815029</td>\n",
+ " <td>0.832474226804</td>\n",
+ " <td>0.267573696145</td>\n",
+ " <td>0.136416184971</td>\n",
+ " <td>0.0800492610837</td>\n",
+ " <td>0.853950518755</td>\n",
+ " <td>0.89087656529516994633</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.488230774976</td>\n",
+ " <td>746</td>\n",
+ " <td>118</td>\n",
+ " <td>66</td>\n",
+ " <td>323</td>\n",
+ " <td>0.918719211823</td>\n",
+ " <td>0.732426303855</td>\n",
+ " <td>0.863425925926</td>\n",
+ " <td>0.830334190231</td>\n",
+ " <td>0.267573696145</td>\n",
+ " <td>0.136574074074</td>\n",
+ " <td>0.0812807881773</td>\n",
+ " <td>0.853152434158</td>\n",
+ " <td>0.89021479713603818616</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.490427907343</td>\n",
+ " <td>745</td>\n",
+ " <td>118</td>\n",
+ " <td>67</td>\n",
+ " <td>323</td>\n",
+ " <td>0.917487684729</td>\n",
+ " <td>0.732426303855</td>\n",
+ " <td>0.863267670915</td>\n",
+ " <td>0.828205128205</td>\n",
+ " <td>0.267573696145</td>\n",
+ " <td>0.136732329085</td>\n",
+ " <td>0.0825123152709</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.88955223880597014925</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.491019863232</td>\n",
+ " <td>744</td>\n",
+ " <td>118</td>\n",
+ " <td>68</td>\n",
+ " <td>323</td>\n",
+ " <td>0.916256157635</td>\n",
+ " <td>0.732426303855</td>\n",
+ " <td>0.863109048724</td>\n",
+ " <td>0.826086956522</td>\n",
+ " <td>0.267573696145</td>\n",
+ " <td>0.136890951276</td>\n",
+ " <td>0.0837438423645</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88888888888888888889</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.492232884267</td>\n",
+ " <td>744</td>\n",
+ " <td>117</td>\n",
+ " <td>68</td>\n",
+ " <td>324</td>\n",
+ " <td>0.916256157635</td>\n",
+ " <td>0.734693877551</td>\n",
+ " <td>0.864111498258</td>\n",
+ " <td>0.826530612245</td>\n",
+ " <td>0.265306122449</td>\n",
+ " <td>0.135888501742</td>\n",
+ " <td>0.0837438423645</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.88942020322773460849</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.493501130728</td>\n",
+ " <td>743</td>\n",
+ " <td>117</td>\n",
+ " <td>69</td>\n",
+ " <td>324</td>\n",
+ " <td>0.915024630542</td>\n",
+ " <td>0.734693877551</td>\n",
+ " <td>0.863953488372</td>\n",
+ " <td>0.824427480916</td>\n",
+ " <td>0.265306122449</td>\n",
+ " <td>0.136046511628</td>\n",
+ " <td>0.0849753694581</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88875598086124401914</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.493584643433</td>\n",
+ " <td>743</td>\n",
+ " <td>116</td>\n",
+ " <td>69</td>\n",
+ " <td>325</td>\n",
+ " <td>0.915024630542</td>\n",
+ " <td>0.736961451247</td>\n",
+ " <td>0.864959254948</td>\n",
+ " <td>0.824873096447</td>\n",
+ " <td>0.263038548753</td>\n",
+ " <td>0.135040745052</td>\n",
+ " <td>0.0849753694581</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.88928785158587672053</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.493748917388</td>\n",
+ " <td>742</td>\n",
+ " <td>116</td>\n",
+ " <td>70</td>\n",
+ " <td>325</td>\n",
+ " <td>0.913793103448</td>\n",
+ " <td>0.736961451247</td>\n",
+ " <td>0.864801864802</td>\n",
+ " <td>0.822784810127</td>\n",
+ " <td>0.263038548753</td>\n",
+ " <td>0.135198135198</td>\n",
+ " <td>0.0862068965517</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88862275449101796407</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.494077424296</td>\n",
+ " <td>742</td>\n",
+ " <td>115</td>\n",
+ " <td>70</td>\n",
+ " <td>326</td>\n",
+ " <td>0.913793103448</td>\n",
+ " <td>0.739229024943</td>\n",
+ " <td>0.865810968495</td>\n",
+ " <td>0.823232323232</td>\n",
+ " <td>0.260770975057</td>\n",
+ " <td>0.134189031505</td>\n",
+ " <td>0.0862068965517</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.88915518274415817855</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.494576932583</td>\n",
+ " <td>741</td>\n",
+ " <td>115</td>\n",
+ " <td>71</td>\n",
+ " <td>326</td>\n",
+ " <td>0.912561576355</td>\n",
+ " <td>0.739229024943</td>\n",
+ " <td>0.865654205607</td>\n",
+ " <td>0.821158690176</td>\n",
+ " <td>0.260770975057</td>\n",
+ " <td>0.134345794393</td>\n",
+ " <td>0.0874384236453</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88848920863309352518</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.495493689667</td>\n",
+ " <td>740</td>\n",
+ " <td>115</td>\n",
+ " <td>72</td>\n",
+ " <td>326</td>\n",
+ " <td>0.911330049261</td>\n",
+ " <td>0.739229024943</td>\n",
+ " <td>0.865497076023</td>\n",
+ " <td>0.819095477387</td>\n",
+ " <td>0.260770975057</td>\n",
+ " <td>0.134502923977</td>\n",
+ " <td>0.0886699507389</td>\n",
+ " <td>0.850758180367</td>\n",
+ " <td>0.88782243551289742052</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.496196268759</td>\n",
+ " <td>740</td>\n",
+ " <td>114</td>\n",
+ " <td>72</td>\n",
+ " <td>327</td>\n",
+ " <td>0.911330049261</td>\n",
+ " <td>0.741496598639</td>\n",
+ " <td>0.866510538642</td>\n",
+ " <td>0.81954887218</td>\n",
+ " <td>0.258503401361</td>\n",
+ " <td>0.133489461358</td>\n",
+ " <td>0.0886699507389</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88835534213685474190</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.497619293744</td>\n",
+ " <td>740</td>\n",
+ " <td>113</td>\n",
+ " <td>72</td>\n",
+ " <td>328</td>\n",
+ " <td>0.911330049261</td>\n",
+ " <td>0.743764172336</td>\n",
+ " <td>0.867526377491</td>\n",
+ " <td>0.82</td>\n",
+ " <td>0.256235827664</td>\n",
+ " <td>0.132473622509</td>\n",
+ " <td>0.0886699507389</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.88888888888888888889</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.497949186519</td>\n",
+ " <td>739</td>\n",
+ " <td>113</td>\n",
+ " <td>73</td>\n",
+ " <td>328</td>\n",
+ " <td>0.910098522167</td>\n",
+ " <td>0.743764172336</td>\n",
+ " <td>0.867370892019</td>\n",
+ " <td>0.817955112219</td>\n",
+ " <td>0.256235827664</td>\n",
+ " <td>0.132629107981</td>\n",
+ " <td>0.0899014778325</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88822115384615384615</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.49884003613</td>\n",
+ " <td>738</td>\n",
+ " <td>113</td>\n",
+ " <td>74</td>\n",
+ " <td>328</td>\n",
+ " <td>0.908866995074</td>\n",
+ " <td>0.743764172336</td>\n",
+ " <td>0.867215041128</td>\n",
+ " <td>0.81592039801</td>\n",
+ " <td>0.256235827664</td>\n",
+ " <td>0.132784958872</td>\n",
+ " <td>0.0911330049261</td>\n",
+ " <td>0.850758180367</td>\n",
+ " <td>0.88755261575466025256</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.499712871697</td>\n",
+ " <td>738</td>\n",
+ " <td>112</td>\n",
+ " <td>74</td>\n",
+ " <td>329</td>\n",
+ " <td>0.908866995074</td>\n",
+ " <td>0.746031746032</td>\n",
+ " <td>0.868235294118</td>\n",
+ " <td>0.816377171216</td>\n",
+ " <td>0.253968253968</td>\n",
+ " <td>0.131764705882</td>\n",
+ " <td>0.0911330049261</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88808664259927797834</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.50087770307</td>\n",
+ " <td>737</td>\n",
+ " <td>112</td>\n",
+ " <td>75</td>\n",
+ " <td>329</td>\n",
+ " <td>0.90763546798</td>\n",
+ " <td>0.746031746032</td>\n",
+ " <td>0.868080094229</td>\n",
+ " <td>0.814356435644</td>\n",
+ " <td>0.253968253968</td>\n",
+ " <td>0.131919905771</td>\n",
+ " <td>0.0923645320197</td>\n",
+ " <td>0.850758180367</td>\n",
+ " <td>0.88741721854304635762</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.506796588394</td>\n",
+ " <td>737</td>\n",
+ " <td>111</td>\n",
+ " <td>75</td>\n",
+ " <td>330</td>\n",
+ " <td>0.90763546798</td>\n",
+ " <td>0.748299319728</td>\n",
+ " <td>0.869103773585</td>\n",
+ " <td>0.814814814815</td>\n",
+ " <td>0.251700680272</td>\n",
+ " <td>0.130896226415</td>\n",
+ " <td>0.0923645320197</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88795180722891566265</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.508934372804</td>\n",
+ " <td>737</td>\n",
+ " <td>110</td>\n",
+ " <td>75</td>\n",
+ " <td>331</td>\n",
+ " <td>0.90763546798</td>\n",
+ " <td>0.750566893424</td>\n",
+ " <td>0.87012987013</td>\n",
+ " <td>0.815270935961</td>\n",
+ " <td>0.249433106576</td>\n",
+ " <td>0.12987012987</td>\n",
+ " <td>0.0923645320197</td>\n",
+ " <td>0.852354349561</td>\n",
+ " <td>0.88848704038577456299</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.509579182757</td>\n",
+ " <td>736</td>\n",
+ " <td>110</td>\n",
+ " <td>76</td>\n",
+ " <td>331</td>\n",
+ " <td>0.906403940887</td>\n",
+ " <td>0.750566893424</td>\n",
+ " <td>0.869976359338</td>\n",
+ " <td>0.813267813268</td>\n",
+ " <td>0.249433106576</td>\n",
+ " <td>0.130023640662</td>\n",
+ " <td>0.0935960591133</td>\n",
+ " <td>0.851556264964</td>\n",
+ " <td>0.88781664656212303981</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.510595338082</td>\n",
+ " <td>735</td>\n",
+ " <td>110</td>\n",
+ " <td>77</td>\n",
+ " <td>331</td>\n",
+ " <td>0.905172413793</td>\n",
+ " <td>0.750566893424</td>\n",
+ " <td>0.869822485207</td>\n",
+ " <td>0.811274509804</td>\n",
+ " <td>0.249433106576</td>\n",
+ " <td>0.130177514793</td>\n",
+ " <td>0.0948275862069</td>\n",
+ " <td>0.850758180367</td>\n",
+ " <td>0.88714544357272178636</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.513324120805</td>\n",
+ " <td>734</td>\n",
+ " <td>110</td>\n",
+ " <td>78</td>\n",
+ " <td>331</td>\n",
+ " <td>0.9039408867</td>\n",
+ " <td>0.750566893424</td>\n",
+ " <td>0.869668246445</td>\n",
+ " <td>0.809290953545</td>\n",
+ " <td>0.249433106576</td>\n",
+ " <td>0.130331753555</td>\n",
+ " <td>0.0960591133005</td>\n",
+ " <td>0.84996009577</td>\n",
+ " <td>0.88647342995169082126</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.513653874352</td>\n",
+ " <td>734</td>\n",
+ " <td>109</td>\n",
+ " <td>78</td>\n",
+ " <td>332</td>\n",
+ " <td>0.9039408867</td>\n",
+ " <td>0.75283446712</td>\n",
+ " <td>0.870699881376</td>\n",
+ " <td>0.809756097561</td>\n",
+ " <td>0.24716553288</td>\n",
+ " <td>0.129300118624</td>\n",
+ " <td>0.0960591133005</td>\n",
+ " <td>0.850758180367</td>\n",
+ " <td>0.88700906344410876133</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.518047576593</td>\n",
+ " <td>733</td>\n",
+ " <td>109</td>\n",
+ " <td>79</td>\n",
+ " <td>332</td>\n",
+ " <td>0.902709359606</td>\n",
+ " <td>0.75283446712</td>\n",
+ " <td>0.87054631829</td>\n",
+ " <td>0.807785888078</td>\n",
+ " <td>0.24716553288</td>\n",
+ " <td>0.12945368171</td>\n",
+ " <td>0.0972906403941</td>\n",
+ " <td>0.84996009577</td>\n",
+ " <td>0.88633615477629987908</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>0.519030274502</td>\n",
+ " <td>732</td>\n",
+ " <td>109</td>\n",
+ " <td>80</td>\n",
+ " <td>332</td>\n",
+ " <td>0.901477832512</td>\n",
+ " <td>0.75283446712</td>\n",
+ " <td>0.870392390012</td>\n",
+ " <td>0.805825242718</td>\n",
+ " <td>0.24716553288</td>\n",
+ " <td>0.129607609988</td>\n",
+ " <td>0.0985221674877</td>\n",
+ " <td>0.849162011173</td>\n",
+ " <td>0.88566243194192377495</td>\n",
" </tr>\n",
"</table>"
],
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+ " (0.497619293744468, Decimal('740'), Decimal('113'), Decimal('72'), Decimal('328'), 0.911330049261084, 0.743764172335601, 0.867526377491208, 0.82, 0.256235827664399, 0.132473622508792, 0.0886699507389163, 0.852354349561053, Decimal('0.88888888888888888889')),\n",
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+ " (0.498840036130481, Decimal('738'), Decimal('113'), Decimal('74'), Decimal('328'), 0.908866995073892, 0.743764172335601, 0.867215041128085, 0.81592039800995, 0.256235827664399, 0.132784958871915, 0.0911330049261084, 0.850758180367119, Decimal('0.88755261575466025256')),\n",
+ " (0.499712871696899, Decimal('738'), Decimal('112'), Decimal('74'), Decimal('329'), 0.908866995073892, 0.746031746031746, 0.868235294117647, 0.816377171215881, 0.253968253968254, 0.131764705882353, 0.0911330049261084, 0.851556264964086, Decimal('0.88808664259927797834')),\n",
+ " (0.500877703069986, Decimal('737'), Decimal('112'), Decimal('75'), Decimal('329'), 0.907635467980296, 0.746031746031746, 0.868080094228504, 0.814356435643564, 0.253968253968254, 0.131919905771496, 0.0923645320197044, 0.850758180367119, Decimal('0.88741721854304635762')),\n",
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+ " (0.508934372803905, Decimal('737'), Decimal('110'), Decimal('75'), Decimal('331'), 0.907635467980296, 0.750566893424036, 0.87012987012987, 0.815270935960591, 0.249433106575964, 0.12987012987013, 0.0923645320197044, 0.852354349561053, Decimal('0.88848704038577456299')),\n",
+ " (0.509579182757293, Decimal('736'), Decimal('110'), Decimal('76'), Decimal('331'), 0.9064039408867, 0.750566893424036, 0.869976359338062, 0.813267813267813, 0.249433106575964, 0.130023640661939, 0.0935960591133005, 0.851556264964086, Decimal('0.88781664656212303981')),\n",
+ " (0.510595338082324, Decimal('735'), Decimal('110'), Decimal('77'), Decimal('331'), 0.905172413793103, 0.750566893424036, 0.869822485207101, 0.811274509803922, 0.249433106575964, 0.130177514792899, 0.0948275862068965, 0.850758180367119, Decimal('0.88714544357272178636')),\n",
+ " (0.513324120805451, Decimal('734'), Decimal('110'), Decimal('78'), Decimal('331'), 0.903940886699507, 0.750566893424036, 0.869668246445498, 0.809290953545232, 0.249433106575964, 0.130331753554502, 0.0960591133004926, 0.849960095770152, Decimal('0.88647342995169082126')),\n",
+ " (0.513653874352143, Decimal('734'), Decimal('109'), Decimal('78'), Decimal('332'), 0.903940886699507, 0.752834467120181, 0.870699881376038, 0.809756097560976, 0.247165532879819, 0.129300118623962, 0.0960591133004926, 0.850758180367119, Decimal('0.88700906344410876133')),\n",
+ " (0.518047576592981, Decimal('733'), Decimal('109'), Decimal('79'), Decimal('332'), 0.902709359605911, 0.752834467120181, 0.870546318289786, 0.807785888077859, 0.247165532879819, 0.129453681710214, 0.0972906403940887, 0.849960095770152, Decimal('0.88633615477629987908')),\n",
+ " (0.51903027450154, Decimal('732'), Decimal('109'), Decimal('80'), Decimal('332'), 0.901477832512315, 0.752834467120181, 0.870392390011891, 0.805825242718447, 0.247165532879819, 0.129607609988109, 0.0985221674876847, 0.849162011173184, Decimal('0.88566243194192377495'))]"
]
},
- "execution_count": 29,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -2926,7 +3147,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 31,
"metadata": {},
"outputs": [
{
@@ -2944,12 +3165,12 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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YxM9il19OLdVNVSIikIaBv2bHYX62bCv5uVncUVbCTRdrIjQREUjDwD/5VMKf/fVlTCkdFGgtIiK9SVpdh/+LN7bx2Z+uAEA30YqInCqtAv+ZFR8AMOOiYUwsLgi4GhGR3iWtDukYxoyLhvHUX3006FJERHqdtBrhi4hI59Ii8CMRZ9G7u9m492jQpYiI9FpxBb6ZzTCzjWZWaWaPdNLmDjNbb2brzGxBYsvsnLsz59m3+PwzbwEwqqhfsj5aRCSldHkM38wygbnAXwBVwCozK3f39W3ajAX+GZjm7ofMbEhPFdzeturjLHx3DwD/9eAnuPS8c5L10SIiKSWeEf4UoNLdt7h7A/AcMLNdm/uBue5+CMDd9yW2zI69UXmAqx9fAsBjt1+ssBcROY14An8EsKPNclVsXVvnA+eb2etmttzMZnS0IzObbWYVZlaxf//+7lVM9DDOy2t2c1fsmvsRA/ty1fmDu70/EZEwSNRlmVnAWGA6UAwsNbOJ7n64bSN3nw/MBygrK/P2O4nXqm2H+LsF0WP2s6acx7c+PbG7uxIRCY14An8nUNJmuTi2rq0qYIW7NwJbzWwT0T8AqxJSZTvHGpoA+Pc7JzFzUvsvGyIi0pF4DumsAsaaWamZ5QB3AuXt2rxEdHSPmRURPcSzJYF1dui8QXk9/REiImmjy8B39yZgDrAY2AC84O7rzOxRM7sl1mwxUG1m64HXgIfdvbqnihYRkTMX1zF8d18ILGy37mttXjvwpdg/ERHphdLiTlsREelayk2etmDFdl56u/05YxER6UrKBf5P/7SFvUfquKRkICMLNY2CiEi8Ui7wAa4ZN5Qfzro06DJERFKKjuGLiISEAl9EJCQU+CIiIaHAFxEJCQW+iEhIpMxVOpGIs2HPEeoam4MuRUQkJaXMCP+3a3Zx0w+WsaumjrzszKDLERFJOSkzwj9aF50S+d/+chLTL9DDTkREzlTKjPBP+sSYQgbm5QRdhohIykm5wBcRke5R4IuIhERKBL6787s1u4IuQ0QkpaVE4Ffuq2X5loMA9O+TMueZRUR6lZQI/IbmCBC9QicvR4EvItIdKRH4J/XN0fX3IiLdlRKBf/BYQ9AliIikvJQI/HueXglAru6wFRHptl4f+D989X3cYcKIAUz7SGHQ5YiIpKxeewa0OeI88YdNPPlaJQDfue1isjJ7/d8nEZFeq1cGfnPEufrxJWw/eByIXp1z0fCCgKsSEUltvTLwDx5raAn7Pz50FaMH9w+4IhGR1Ncrj5E88p9rAPg//2uCwl5EJEF6ZeC/t+coANeOGxJwJSIi6aPXBf7anTXsPHyCT08ewbkFfYMuR0QkbfS6wP/Jn7YAMG7YgIArERFJL70u8CMOo4v6cf+Vo4MuRUQkrfS6wBcRkZ6hwBcRCQkFvohISCjwRURCQoEvIhIScQW+mc0ws41mVmlmj5ym3W1m5mZWlrgSRUQkEboMfDPLBOYCNwDjgVlmNr6DdvnA3wMrulOIu1O+ehe/Xb2LiHt3diEiIqcRzwh/ClDp7lvcvQF4DpjZQbtvAt8B6rpTyKa9tXzx2bcBmDBCM2OKiCRaPIE/AtjRZrkqtq6FmU0GStz95dPtyMxmm1mFmVXs37//lG0nGpuB6IRpT941OY6yRETkTJz1SVszywC+DzzUVVt3n+/uZe5eNnjw4A7bjBio+XNERHpCPPPh7wRK2iwXx9adlA9MAJaYGcAwoNzMbnH3iniK+J91e3jxz1XxVSwiIt0ST+CvAsaaWSnRoL8TuOvkRnevAYpOLpvZEuDL8YY9wI+XbuHdqhpGFeZRWtQv3reJiMgZ6DLw3b3JzOYAi4FM4Gl3X2dmjwIV7l7e3Q+vrW/i4RdX8+cPDnHF2CJ+dd/U7u5KRES6ENcjDt19IbCw3bqvddJ2erwfvuz9/SxauweAmy8ZHu/bRESkGwJ9pm1zJPrfxf9wJRcMyw+yFBGRtNcrplaInusVEZGe1CsCX0REep4CX0QkJBT4IiIhocAXEQkJBb6ISEgo8EVEQkKBLyISEgp8EZGQUOCLiISEAl9EJCQU+CIiIaHAFxEJCQW+iEhIBBr4K7ZWB/nxIiKhEljgN0WcX775AQADcrODKkNEJDQCC/yt+48B8PD1FzCsIDeoMkREQiPAEX70cVd3Tx0ZVAkiIqES6DH8Oy8roSBPh3NERJJBV+mIiISEAl9EJCQU+CIiIaHAFxEJCQW+iEhIKPBFREJCgS8iEhIKfBGRkFDgi4iEhAJfRCQkFPgiIiGhwBcRCQkFvohISCjwRURCIq7AN7MZZrbRzCrN7JEOtn/JzNab2Roze9XMNMm9iEgv02Xgm1kmMBe4ARgPzDKz8e2avQ2UufvFwK+B7ya6UBEROTvxjPCnAJXuvsXdG4DngJltG7j7a+5+PLa4HChObJkiInK24gn8EcCONstVsXWduQ9Y1NEGM5ttZhVmVhGJPeJQRESSI6Enbc3sbqAMeKyj7e4+393L3L0sI0Pni0VEkikrjjY7gZI2y8Wxdacws08CXwGucvf6xJQnIiKJEs8wexUw1sxKzSwHuBMob9vAzC4Ffgzc4u77El+miIicrS4D392bgDnAYmAD8IK7rzOzR83sllizx4D+wItm9o6ZlXeyOxERCUg8h3Rw94XAwnbrvtbm9ScTXJeIiCSYzpyKiISEAl9EJCQU+CIiIaHAFxEJCQW+iEhIKPBFREJCgS8iEhIKfBGRkFDgi4iEhAJfRCQkFPgiIiGhwBcRCYnAAr8p4kF9tIhIKAU6wr/uoqFBfryISKgEFvi5WZlcc6ECX0QkWXQMX0QkJBT4IiIhocAXEQkJBb6ISEgo8EVEQkKBLyISEgp8EZGQUOCLiISEAl9EJCQU+CIiIaHAFxEJCQW+iEhIKPBFREJCgS8iEhIKfBGRkFDgi4iEhAJfRCQkFPgiIiGhwBcRCQkFvohISMQV+GY2w8w2mlmlmT3SwfY+ZvZ8bPsKMxuV6EJFROTsdBn4ZpYJzAVuAMYDs8xsfLtm9wGH3H0M8ATwnUQXKiIiZyeeEf4UoNLdt7h7A/AcMLNdm5nAL2Kvfw1ca2aWuDJFRORsZcXRZgSwo81yFTC1szbu3mRmNUAhcKBtIzObDcyOLdab2druFJ2GimjXVyGmvmilvmilvmh1QXffGE/gJ4y7zwfmA5hZhbuXJfPzeyv1RSv1RSv1RSv1RSszq+jue+M5pLMTKGmzXBxb12EbM8sCCoDq7hYlIiKJF0/grwLGmlmpmeUAdwLl7dqUA38de3078Ed398SVKSIiZ6vLQzqxY/JzgMVAJvC0u68zs0eBCncvB34G/MrMKoGDRP8odGX+WdSdbtQXrdQXrdQXrdQXrbrdF6aBuIhIOOhOWxGRkFDgi4iERI8HvqZlaBVHX3zJzNab2Roze9XMRgZRZzJ01Rdt2t1mZm5maXtJXjx9YWZ3xH421pnZgmTXmCxx/I6cZ2avmdnbsd+TG4Oos6eZ2dNmtq+ze5Us6gexflpjZpPj2rG799g/oid5NwOjgRxgNTC+XZsHgadir+8Enu/JmoL6F2dfXA3kxV5/Psx9EWuXDywFlgNlQdcd4M/FWOBt4JzY8pCg6w6wL+YDn4+9Hg9sC7ruHuqLK4HJwNpOtt8ILAIM+BiwIp799vQIX9MytOqyL9z9NXc/HltcTvSeh3QUz88FwDeJzstUl8zikiyevrgfmOvuhwDcfV+Sa0yWePrCgQGx1wXAriTWlzTuvpToFY+dmQn80qOWAwPN7Nyu9tvTgd/RtAwjOmvj7k3AyWkZ0k08fdHWfUT/gqejLvsi9hW1xN1fTmZhAYjn5+J84Hwze93MlpvZjKRVl1zx9MU3gLvNrApYCHwhOaX1OmeaJ0CSp1aQ+JjZ3UAZcFXQtQTBzDKA7wOfC7iU3iKL6GGd6US/9S01s4nufjjQqoIxC/i5u3/PzD5O9P6fCe4eCbqwVNDTI3xNy9Aqnr7AzD4JfAW4xd3rk1RbsnXVF/nABGCJmW0jeoyyPE1P3Mbzc1EFlLt7o7tvBTYR/QOQbuLpi/uAFwDc/U0gl+jEamETV56019OBr2kZWnXZF2Z2KfBjomGfrsdpoYu+cPcady9y91HuPoro+Yxb3L3bk0b1YvH8jrxEdHSPmRURPcSzJZlFJkk8fbEduBbAzMYRDfz9Sa2ydygH7oldrfMxoMbdd3f1ph49pOM9Ny1DyomzLx4D+gMvxs5bb3f3WwIruofE2RehEGdfLAauM7P1QDPwsLun3bfgOPviIeAnZvaPRE/gfi4dB4hm9izRP/JFsfMVXweyAdz9KaLnL24EKoHjwL1x7TcN+0pERDqgO21FREJCgS8iEhIKfBGRkFDgi4iEhAJfRCQkFPgSamb2RTPbYGbPBF2LSE/TZZkSamb2HvBJd6+Ko21WbL4nkZSkEb6Elpk9RXQq3kVmVmNmvzKzN83sfTO7P9Zmupn9yczKgfWBFixyljTCl1CLzdVTBswBbiU6b08/ovPPTyU6jcHLwITYPDYiKUsjfJFW/+3uJ9z9APAa0fnZAVYq7CUdKPBFWrX/unty+ViyCxHpCQp8kVYzzSzXzAqJTly1KuB6RBJKgS/Sag3RQznLgW+6e1o+Pk/CSydtRQAz+wZQ6+6PB12LSE/RCF9EJCQ0whcRCQmN8EVEQkKBLyISEgp8EZGQUOCLiISEAl9EJCT+P4X9NilZxBTaAAAAAElFTkSuQmCC\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2974,7 +3195,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
@@ -3003,7 +3224,7 @@
"[('',)]"
]
},
- "execution_count": 32,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
@@ -3035,7 +3256,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 34,
"metadata": {},
"outputs": [
{
@@ -3053,15 +3274,15 @@
" <th>get_tree</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>digraph \"Classification tree for abalone_classif_train\" {<br>\"0\" [label=\"length <= 0.495\\n impurity = 0.453016\\n samples = 3011\\n value = [1044 1967]\\n class = 1\", shape=ellipse];<br>\"0\" -> \"1\"[label=\"yes\"];<br>\"0\" -> \"2\"[label=\"no\"];<br>\"1\" [label=\"diameter <= 0.345\\n impurity = 0.430643\\n samples = 1125\\n value = [772 353]\\n class = 0\", shape=ellipse];<br>\"1\" -> \"3\"[label=\"yes\"];<br>\"1\" -> \"4\"[label=\"no\"];<br>\"2\" [label=\"shell_weight <= 0.2335\\n impurity = 0.246842\\n samples = 1886\\n value = [ 272 1614]\\n class = 1\", shape=ellipse];<br>\"2\" -> \"5\"[label=\"yes\"];<br>\"2\" -> \"6\"[label=\"no\"];<br>\"3\" [label=\"sex_m <= 0\\n impurity = 0.293184\\n samples = 751\\n value = [617 134]\\n class = 0\", shape=ellipse];<br>\"3\" -> \"7\"[label=\"yes\"];<br>\"3\" -> \"8\"[label=\"no\"];<br>\"4\" [label=\"shell_weight <= 0.1655\\n impurity = 0.485358\\n samples = 374\\n value = [155 219]\\n class = 1\", shape=ellipse];<br>\"4\" -> \"9\"[label=\"yes\"];<br>\"4\" -> \"10\"[label=\"no\"];<br>\"5\" [label=\"sex_f <= 0\\n impurity = 0.482952\\n samples = 417\\n value = [170 247]\\n class = 1\", shape=ellipse];<br>\"5\" -> \"11\"[label=\"yes\"];<br>\"5\" -> \"12\"[label=\"no\"];<br>\"6\" [label=\"shell_weight <= 0.2725\\n impurity = 0.129228\\n samples = 1469\\n value = [ 102 1367]\\n class = 1\", shape=ellipse];<br>\"6\" -> \"13\"[label=\"yes\"];<br>\"6\" -> \"14\"[label=\"no\"];<br>\"7\" [label=\"sex_f <= 0\\n impurity = 0.209691\\n samples = 605\\n value = [533 72]\\n class = 0\", shape=ellipse];<br>\"7\" -> \"15\"[label=\"yes\"];<br>\"15\" [label=\"0\\n impurity = 0.122069\\n samples = 536\\n value = [501 35]\",shape=box];<br>\"7\" -> \"16\"[label=\"no\"];<br>\"8\" [label=\"length <= 0.29\\n impurity = 0.488647\\n samples = 146\\n value = [84 62]\\n class = 0\", shape=ellipse];<br>\"8\" -> \"17\"[label=\"yes\"];<br>\"17\" [label=\"0\\n impurity = 0\\n samples = 28\\n value = [28 0]\",shape=box];<br>\"8\" -> \"18\"[label=\"no\"];<br>\"9\" [label=\"shucked_weight <= 0.222\\n impurity = 0.498485\\n samples = 218\\n value = [115 103]\\n class = 0\", shape=ellipse];<br>\"9\" -> \"19\"[label=\"yes\"];<br>\"9\" -> \"20\"[label=\"no\"];<br>\"10\" [label=\"height <= 0.15\\n impurity = 0.381328\\n samples = 156\\n value = [ 40 116]\\n class = 1\", shape=ellipse];<br>\"10\" -> \"21\"[label=\"yes\"];<br>\"10\" -> \"22\"[label=\"no\"];<br>\"22\" [label=\"0\\n impurity = 0.444444\\n samples = 18\\n value = [12 6]\",shape=box];<br>\"11\" [label=\"height <= 0.125\\n impurity = 0.495403\\n samples = 292\\n value = [132 160]\\n class = 1\", shape=ellipse];<br>\"11\" -> \"23\"[label=\"yes\"];<br>\"11\" -> \"24\"[label=\"no\"];<br>\"12\" [label=\"length <= 0.53\\n impurity = 0.423168\\n samples = 125\\n value = [38 87]\\n class = 1\", shape=ellipse];<br>\"12\" -> \"25\"[label=\"yes\"];<br>\"12\" -> \"26\"[label=\"no\"];<br>\"13\" [label=\"length <= 0.565\\n impurity = 0.289085\\n samples = 291\\n value = [ 51 240]\\n class = 1\", shape=ellipse];<br>\"13\" -> \"27\"[label=\"yes\"];<br>\"13\" -> \"28\"[label=\"no\"];<br>\"14\" [label=\"shell_weight <= 0.3645\\n impurity = 0.0828387\\n samples = 1178\\n value = [ 51 1127]\\n class = 1\", shape=ellipse];<br>\"14\" -> \"29\"[label=\"yes\"];<br>\"14\" -> \"30\"[label=\"no\"];<br>\"16\" [label=\"diameter <= 0.29\\n impurity = 0.497375\\n samples = 69\\n value = [32 37]\\n class = 1\", shape=ellipse];<br>\"16\" -> \"33\"[label=\"yes\"];<br>\"33\" [label=\"0\\n impurity = 0.32\\n samples = 20\\n value = [16 4]\",shape=box];<br>\"16\" -> \"34\"[label=\"no\"];<br>\"18\" [label=\"diameter <= 0.34\\n impurity = 0.498707\\n samples = 118\\n value = [56 62]\\n class = 1\", shape=ellipse];<br>\"18\" -> \"37\"[label=\"yes\"];<br>\"18\" -> \"38\"[label=\"no\"];<br>\"38\" [label=\"0\\n impurity = 0.21875\\n samples = 8\\n value = [7 1]\",shape=box];<br>\"19\" [label=\"shell_weight <= 0.125\\n impurity = 0.472126\\n samples = 144\\n value = [55 89]\\n class = 1\", shape=ellipse];<br>\"19\" -> \"39\"[label=\"yes\"];<br>\"39\" [label=\"0\\n impurity = 0.444444\\n samples = 27\\n value = [18 9]\",shape=box];<br>\"19\" -> \"40\"[label=\"no\"];<br>\"20\" [label=\"height <= 0.13\\n impurity = 0.306793\\n samples = 74\\n value = [60 14]\\n class = 0\", shape=ellipse];<br>\"20\" -> \"41\"[label=\"yes\"];<br>\"20\" -> \"42\"[label=\"no\"];<br>\"42\" [label=\"0\\n impurity = 0\\n samples = 13\\n value = [13 0]\",shape=box];<br>\"21\" [label=\"shucked_weight <= 0.3015\\n impurity = 0.323461\\n samples = 138\\n value = [ 28 110]\\n class = 1\", shape=ellipse];<br>\"21\" -> \"43\"[label=\"yes\"];<br>\"21\" -> \"44\"[label=\"no\"];<br>\"44\" [label=\"0\\n impurity = 0.197531\\n samples = 9\\n value = [8 1]\",shape=box];<br>\"23\" [label=\"shucked_weight <= 0.289\\n impurity = 0.444444\\n samples = 72\\n value = [48 24]\\n class = 0\", shape=ellipse];<br>\"23\" -> \"47\"[label=\"yes\"];<br>\"47\" [label=\"0\\n impurity = 0.277778\\n samples = 42\\n value = [35 7]\",shape=box];<br>\"23\" -> \"48\"[label=\"no\"];<br>\"48\" [label=\"1\\n impurity = 0.491111\\n samples = 30\\n value = [13 17]\",shape=box];<br>\"24\" [label=\"diameter <= 0.46\\n impurity = 0.472066\\n samples = 220\\n value = [ 84 136]\\n class = 1\", shape=ellipse];<br>\"24\" -> \"49\"[label=\"yes\"];<br>\"24\" -> \"50\"[label=\"no\"];<br>\"50\" [label=\"0\\n impurity = 0.244898\\n samples = 7\\n value = [6 1]\",shape=box];<br>\"25\" [label=\"shell_weight <= 0.1765\\n impurity = 0.32\\n samples = 65\\n value = [13 52]\\n class = 1\", shape=ellipse];<br>\"25\" -> \"51\"[label=\"yes\"];<br>\"51\" [label=\"0\\n impurity = 0.345679\\n samples = 9\\n value = [7 2]\",shape=box];<br>\"25\" -> \"52\"[label=\"no\"];<br>\"52\" [label=\"1\\n impurity = 0.191327\\n samples = 56\\n value = [ 6 50]\",shape=box];<br>\"26\" [label=\"shucked_weight <= 0.3875\\n impurity = 0.486111\\n samples = 60\\n value = [25 35]\\n class = 1\", shape=ellipse];<br>\"26\" -> \"53\"[label=\"yes\"];<br>\"26\" -> \"54\"[label=\"no\"];<br>\"54\" [label=\"0\\n impurity = 0.4352\\n samples = 25\\n value = [17 8]\",shape=box];<br>\"27\" [label=\"shucked_weight <= 0.435\\n impurity = 0.183494\\n samples = 137\\n value = [ 14 123]\\n class = 1\", shape=ellipse];<br>\"27\" -> \"55\"[label=\"yes\"];<br>\"27\" -> \"56\"[label=\"no\"];<br>\"56\" [label=\"1\\n impurity = 0.459184\\n samples = 14\\n value = [5 9]\",shape=box];<br>\"28\" [label=\"shell_weight <= 0.265\\n impurity = 0.36507\\n samples = 154\\n value = [ 37 117]\\n class = 1\", shape=ellipse];<br>\"28\" -> \"57\"[label=\"yes\"];<br>\"28\" -> \"58\"[label=\"no\"];<br>\"29\" [label=\"viscera_weight <= 0.289\\n impurity = 0.117514\\n samples = 670\\n value = [ 42 628]\\n class = 1\", shape=ellipse];<br>\"29\" -> \"59\"[label=\"yes\"];<br>\"29\" -> \"60\"[label=\"no\"];<br>\"30\" [label=\"diameter <= 0.48\\n impurity = 0.0348053\\n samples = 508\\n value = [ 9 499]\\n class = 1\", shape=ellipse];<br>\"30\" -> \"61\"[label=\"yes\"];<br>\"30\" -> \"62\"[label=\"no\"];<br>\"34\" [label=\"whole_weight <= 0.3855\\n impurity = 0.439817\\n samples = 49\\n value = [16 33]\\n class = 1\", shape=ellipse];<br>\"34\" -> \"69\"[label=\"yes\"];<br>\"69\" [label=\"1\\n impurity = 0.349636\\n samples = 31\\n value = [ 7 24]\",shape=box];<br>\"34\" -> \"70\"[label=\"no\"];<br>\"70\" [label=\"0\\n impurity = 0.5\\n samples = 18\\n value = [9 9]\",shape=box];<br>\"37\" [label=\"shucked_weight <= 0.194\\n impurity = 0.49405\\n samples = 110\\n value = [49 61]\\n class = 1\", shape=ellipse];<br>\"37\" -> \"75\"[label=\"yes\"];<br>\"37\" -> \"76\"[label=\"no\"];<br>\"76\" [label=\"0\\n impurity = 0.18\\n samples = 10\\n value = [9 1]\",shape=box];<br>\"40\" [label=\"length <= 0.485\\n impurity = 0.432464\\n samples = 117\\n value = [37 80]\\n class = 1\", shape=ellipse];<br>\"40\" -> \"81\"[label=\"yes\"];<br>\"40\" -> \"82\"[label=\"no\"];<br>\"82\" [label=\"0\\n impurity = 0\\n samples = 6\\n value = [6 0]\",shape=box];<br>\"41\" [label=\"shell_weight <= 0.1525\\n impurity = 0.353668\\n samples = 61\\n value = [47 14]\\n class = 0\", shape=ellipse];<br>\"41\" -> \"83\"[label=\"yes\"];<br>\"83\" [label=\"0\\n impurity = 0.264514\\n samples = 51\\n value = [43 8]\",shape=box];<br>\"41\" -> \"84\"[label=\"no\"];<br>\"84\" [label=\"1\\n impurity = 0.48\\n samples = 10\\n value = [4 6]\",shape=box];<br>\"43\" [label=\"shell_weight <= 0.203\\n impurity = 0.262003\\n samples = 129\\n value = [ 20 109]\\n class = 1\", shape=ellipse];<br>\"43\" -> \"87\"[label=\"yes\"];<br>\"43\" -> \"88\"[label=\"no\"];<br>\"88\" [label=\"1\\n impurity = 0\\n samples = 26\\n value = [ 0 26]\",shape=box];<br>\"49\" [label=\"shell_weight <= 0.1765\\n impurity = 0.464194\\n samples = 213\\n value = [ 78 135]\\n class = 1\", shape=ellipse];<br>\"49\" -> \"99\"[label=\"yes\"];<br>\"49\" -> \"100\"[label=\"no\"];<br>\"53\" [label=\"shell_weight <= 0.206\\n impurity = 0.352653\\n samples = 35\\n value = [ 8 27]\\n class = 1\", shape=ellipse];<br>\"53\" -> \"107\"[label=\"yes\"];<br>\"107\" [label=\"1\\n impurity = 0.492188\\n samples = 16\\n value = [7 9]\",shape=box];<br>\"53\" -> \"108\"[label=\"no\"];<br>\"108\" [label=\"1\\n impurity = 0.099723\\n samples = 19\\n value = [ 1 18]\",shape=box];<br>\"55\" [label=\"sex_f <= 0\\n impurity = 0.135634\\n samples = 123\\n value = [ 9 114]\\n class = 1\", shape=ellipse];<br>\"55\" -> \"111\"[label=\"yes\"];<br>\"55\" -> \"112\"[label=\"no\"];<br>\"112\" [label=\"1\\n impurity = 0\\n samples = 42\\n value = [ 0 42]\",shape=box];<br>\"57\" [label=\"viscera_weight <= 0.2225\\n impurity = 0.388198\\n samples = 129\\n value = [34 95]\\n class = 1\", shape=ellipse];<br>\"57\" -> \"115\"[label=\"yes\"];<br>\"57\" -> \"116\"[label=\"no\"];<br>\"58\" [label=\"viscera_weight <= 0.187\\n impurity = 0.2112\\n samples = 25\\n value = [ 3 22]\\n class = 1\", shape=ellipse];<br>\"58\" -> \"117\"[label=\"yes\"];<br>\"117\" [label=\"1\\n impurity = 0\\n samples = 8\\n value = [0 8]\",shape=box];<br>\"58\" -> \"118\"[label=\"no\"];<br>\"118\" [label=\"1\\n impurity = 0.290657\\n samples = 17\\n value = [ 3 14]\",shape=box];<br>\"59\" [label=\"shucked_weight <= 0.4015\\n impurity = 0.0881506\\n samples = 541\\n value = [ 25 516]\\n class = 1\", shape=ellipse];<br>\"59\" -> \"119\"[label=\"yes\"];<br>\"119\" [label=\"1\\n impurity = 0\\n samples = 166\\n value = [ 0 166]\",shape=box];<br>\"59\" -> \"120\"[label=\"no\"];<br>\"120\" [label=\"1\\n impurity = 0.124444\\n samples = 375\\n value = [ 25 350]\",shape=box];<br>\"60\" [label=\"height <= 0.175\\n impurity = 0.228832\\n samples = 129\\n value = [ 17 112]\\n class = 1\", shape=ellipse];<br>\"60\" -> \"121\"[label=\"yes\"];<br>\"60\" -> \"122\"[label=\"no\"];<br>\"61\" [label=\"viscera_weight <= 0.24\\n impurity = 0.114952\\n samples = 49\\n value = [ 3 46]\\n class = 1\", shape=ellipse];<br>\"61\" -> \"123\"[label=\"yes\"];<br>\"123\" [label=\"1\\n impurity = 0\\n samples = 25\\n value = [ 0 25]\",shape=box];<br>\"61\" -> \"124\"[label=\"no\"];<br>\"62\" [label=\"shell_weight <= 0.44\\n impurity = 0.025802\\n samples = 459\\n value = [ 6 453]\\n class = 1\", shape=ellipse];<br>\"62\" -> \"125\"[label=\"yes\"];<br>\"62\" -> \"126\"[label=\"no\"];<br>\"126\" [label=\"1\\n impurity = 0\\n samples = 195\\n value = [ 0 195]\",shape=box];<br>\"75\" [label=\"height <= 0.115\\n impurity = 0.48\\n samples = 100\\n value = [40 60]\\n class = 1\", shape=ellipse];<br>\"75\" -> \"151\"[label=\"yes\"];<br>\"75\" -> \"152\"[label=\"no\"];<br>\"152\" [label=\"1\\n impurity = 0\\n samples = 20\\n value = [ 0 20]\",shape=box];<br>\"81\" [label=\"viscera_weight <= 0.11\\n impurity = 0.402565\\n samples = 111\\n value = [31 80]\\n class = 1\", shape=ellipse];<br>\"81\" -> \"163\"[label=\"yes\"];<br>\"81\" -> \"164\"[label=\"no\"];<br>\"87\" [label=\"whole_weight <= 0.5465\\n impurity = 0.312942\\n samples = 103\\n value = [20 83]\\n class = 1\", shape=ellipse];<br>\"87\" -> \"175\"[label=\"yes\"];<br>\"87\" -> \"176\"[label=\"no\"];<br>\"99\" [label=\"length <= 0.5\\n impurity = 0.488522\\n samples = 33\\n value = [19 14]\\n class = 0\", shape=ellipse];<br>\"99\" -> \"199\"[label=\"yes\"];<br>\"199\" [label=\"1\\n impurity = 0.277778\\n samples = 6\\n value = [1 5]\",shape=box];<br>\"99\" -> \"200\"[label=\"no\"];<br>\"100\" [label=\"shucked_weight <= 0.3325\\n impurity = 0.440679\\n samples = 180\\n value = [ 59 121]\\n class = 1\", shape=ellipse];<br>\"100\" -> \"201\"[label=\"yes\"];<br>\"100\" -> \"202\"[label=\"no\"];<br>\"111\" [label=\"shucked_weight <= 0.3445\\n impurity = 0.197531\\n samples = 81\\n value = [ 9 72]\\n class = 1\", shape=ellipse];<br>\"111\" -> \"223\"[label=\"yes\"];<br>\"111\" -> \"224\"[label=\"no\"];<br>\"115\" [label=\"shell_weight <= 0.26\\n impurity = 0.280654\\n samples = 77\\n value = [13 64]\\n class = 1\", shape=ellipse];<br>\"115\" -> \"231\"[label=\"yes\"];<br>\"115\" -> \"232\"[label=\"no\"];<br>\"232\" [label=\"1\\n impurity = 0.493827\\n samples = 9\\n value = [4 5]\",shape=box];<br>\"116\" [label=\"height <= 0.145\\n impurity = 0.481509\\n samples = 52\\n value = [21 31]\\n class = 1\", shape=ellipse];<br>\"116\" -> \"233\"[label=\"yes\"];<br>\"233\" [label=\"0\\n impurity = 0.375\\n samples = 16\\n value = [12 4]\",shape=box];<br>\"116\" -> \"234\"[label=\"no\"];<br>\"234\" [label=\"1\\n impurity = 0.375\\n samples = 36\\n value = [ 9 27]\",shape=box];<br>\"121\" [label=\"whole_weight <= 1.2085\\n impurity = 0.165289\\n samples = 99\\n value = [ 9 90]\\n class = 1\", shape=ellipse];<br>\"121\" -> \"243\"[label=\"yes\"];<br>\"121\" -> \"244\"[label=\"no\"];<br>\"122\" [label=\"diameter <= 0.5\\n impurity = 0.391111\\n samples = 30\\n value = [ 8 22]\\n class = 1\", shape=ellipse];<br>\"122\" -> \"245\"[label=\"yes\"];<br>\"122\" -> \"246\"[label=\"no\"];<br>\"246\" [label=\"1\\n impurity = 0\\n samples = 8\\n value = [0 8]\",shape=box];<br>\"124\" [label=\"sex_m <= 0\\n impurity = 0.21875\\n samples = 24\\n value = [ 3 21]\\n class = 1\", shape=ellipse];<br>\"124\" -> \"249\"[label=\"yes\"];<br>\"249\" [label=\"1\\n impurity = 0.336735\\n samples = 14\\n value = [ 3 11]\",shape=box];<br>\"124\" -> \"250\"[label=\"no\"];<br>\"250\" [label=\"1\\n impurity = 0\\n samples = 10\\n value = [ 0 10]\",shape=box];<br>\"125\" [label=\"shell_weight <= 0.43\\n impurity = 0.0444215\\n samples = 264\\n value = [ 6 258]\\n class = 1\", shape=ellipse];<br>\"125\" -> \"251\"[label=\"yes\"];<br>\"125\" -> \"252\"[label=\"no\"];<br>\"151\" [label=\"height <= 0.075\\n impurity = 0.5\\n samples = 80\\n value = [40 40]\\n class = 0\", shape=ellipse];<br>\"151\" -> \"303\"[label=\"yes\"];<br>\"303\" [label=\"1\\n impurity = 0.345679\\n samples = 9\\n value = [2 7]\",shape=box];<br>\"151\" -> \"304\"[label=\"no\"];<br>\"304\" [label=\"0\\n impurity = 0.49752\\n samples = 71\\n value = [38 33]\",shape=box];<br>\"163\" [label=\"length <= 0.455\\n impurity = 0.469649\\n samples = 69\\n value = [26 43]\\n class = 1\", shape=ellipse];<br>\"163\" -> \"327\"[label=\"yes\"];<br>\"163\" -> \"328\"[label=\"no\"];<br>\"164\" [label=\"viscera_weight <= 0.1205\\n impurity = 0.209751\\n samples = 42\\n value = [ 5 37]\\n class = 1\", shape=ellipse];<br>\"164\" -> \"329\"[label=\"yes\"];<br>\"329\" [label=\"1\\n impurity = 0.33241\\n samples = 19\\n value = [ 4 15]\",shape=box];<br>\"164\" -> \"330\"[label=\"no\"];<br>\"175\" [label=\"viscera_weight <= 0.1015\\n impurity = 0.19438\\n samples = 55\\n value = [ 6 49]\\n class = 1\", shape=ellipse];<br>\"175\" -> \"351\"[label=\"yes\"];<br>\"175\" -> \"352\"[label=\"no\"];<br>\"352\" [label=\"1\\n impurity = 0\\n samples = 33\\n value = [ 0 33]\",shape=box];<br>\"176\" [label=\"whole_weight <= 0.587\\n impurity = 0.413194\\n samples = 48\\n value = [14 34]\\n class = 1\", shape=ellipse];<br>\"176\" -> \"353\"[label=\"yes\"];<br>\"353\" [label=\"1\\n impurity = 0.499055\\n samples = 23\\n value = [11 12]\",shape=box];<br>\"176\" -> \"354\"[label=\"no\"];<br>\"200\" [label=\"diameter <= 0.405\\n impurity = 0.444444\\n samples = 27\\n value = [18 9]\\n class = 0\", shape=ellipse];<br>\"200\" -> \"401\"[label=\"yes\"];<br>\"401\" [label=\"0\\n impurity = 0.375\\n samples = 20\\n value = [15 5]\",shape=box];<br>\"200\" -> \"402\"[label=\"no\"];<br>\"402\" [label=\"1\\n impurity = 0.489796\\n samples = 7\\n value = [3 4]\",shape=box];<br>\"201\" [label=\"shucked_weight <= 0.264\\n impurity = 0.21643\\n samples = 81\\n value = [10 71]\\n class = 1\", shape=ellipse];<br>\"201\" -> \"403\"[label=\"yes\"];<br>\"403\" [label=\"1\\n impurity = 0\\n samples = 35\\n value = [ 0 35]\",shape=box];<br>\"201\" -> \"404\"[label=\"no\"];<br>\"202\" [label=\"whole_weight <= 0.7775\\n impurity = 0.499949\\n samples = 99\\n value = [49 50]\\n class = 1\", shape=ellipse];<br>\"202\" -> \"405\"[label=\"yes\"];<br>\"405\" [label=\"0\\n impurity = 0.424383\\n samples = 36\\n value = [25 11]\",shape=box];<br>\"202\" -> \"406\"[label=\"no\"];<br>\"406\" [label=\"1\\n impurity = 0.471655\\n samples = 63\\n value = [24 39]\",shape=box];<br>\"223\" [label=\"diameter <= 0.435\\n impurity = 0.282481\\n samples = 47\\n value = [ 8 39]\\n class = 1\", shape=ellipse];<br>\"223\" -> \"447\"[label=\"yes\"];<br>\"447\" [label=\"1\\n impurity = 0.192841\\n samples = 37\\n value = [ 4 33]\",shape=box];<br>\"223\" -> \"448\"[label=\"no\"];<br>\"448\" [label=\"1\\n impurity = 0.48\\n samples = 10\\n value = [4 6]\",shape=box];<br>\"224\" [label=\"whole_weight <= 0.9\\n impurity = 0.0570934\\n samples = 34\\n value = [ 1 33]\\n class = 1\", shape=ellipse];<br>\"224\" -> \"449\"[label=\"yes\"];<br>\"449\" [label=\"1\\n impurity = 0\\n samples = 26\\n value = [ 0 26]\",shape=box];<br>\"224\" -> \"450\"[label=\"no\"];<br>\"450\" [label=\"1\\n impurity = 0.21875\\n samples = 8\\n value = [1 7]\",shape=box];<br>\"231\" [label=\"viscera_weight <= 0.166\\n impurity = 0.229671\\n samples = 68\\n value = [ 9 59]\\n class = 1\", shape=ellipse];<br>\"231\" -> \"463\"[label=\"yes\"];<br>\"231\" -> \"464\"[label=\"no\"];<br>\"243\" [label=\"sex_m <= 0\\n impurity = 0.352653\\n samples = 35\\n value = [ 8 27]\\n class = 1\", shape=ellipse];<br>\"243\" -> \"487\"[label=\"yes\"];<br>\"487\" [label=\"1\\n impurity = 0.18\\n samples = 20\\n value = [ 2 18]\",shape=box];<br>\"243\" -> \"488\"[label=\"no\"];<br>\"488\" [label=\"1\\n impurity = 0.48\\n samples = 15\\n value = [6 9]\",shape=box];<br>\"244\" [label=\"shell_weight <= 0.339\\n impurity = 0.0307617\\n samples = 64\\n value = [ 1 63]\\n class = 1\", shape=ellipse];<br>\"244\" -> \"489\"[label=\"yes\"];<br>\"489\" [label=\"1\\n impurity = 0.0644444\\n samples = 30\\n value = [ 1 29]\",shape=box];<br>\"244\" -> \"490\"[label=\"no\"];<br>\"490\" [label=\"1\\n impurity = 0\\n samples = 34\\n value = [ 0 34]\",shape=box];<br>\"245\" [label=\"shucked_weight <= 0.572\\n impurity = 0.46281\\n samples = 22\\n value = [ 8 14]\\n class = 1\", shape=ellipse];<br>\"245\" -> \"491\"[label=\"yes\"];<br>\"491\" [label=\"1\\n impurity = 0\\n samples = 9\\n value = [0 9]\",shape=box];<br>\"245\" -> \"492\"[label=\"no\"];<br>\"492\" [label=\"0\\n impurity = 0.473373\\n samples = 13\\n value = [8 5]\",shape=box];<br>\"251\" [label=\"whole_weight <= 1.2085\\n impurity = 0.0331856\\n samples = 237\\n value = [ 4 233]\\n class = 1\", shape=ellipse];<br>\"251\" -> \"503\"[label=\"yes\"];<br>\"503\" [label=\"1\\n impurity = 0.15879\\n samples = 23\\n value = [ 2 21]\",shape=box];<br>\"251\" -> \"504\"[label=\"no\"];<br>\"252\" [label=\"viscera_weight <= 0.3175\\n impurity = 0.137174\\n samples = 27\\n value = [ 2 25]\\n class = 1\", shape=ellipse];<br>\"252\" -> \"505\"[label=\"yes\"];<br>\"505\" [label=\"1\\n impurity = 0\\n samples = 11\\n value = [ 0 11]\",shape=box];<br>\"252\" -> \"506\"[label=\"no\"];<br>\"506\" [label=\"1\\n impurity = 0.21875\\n samples = 16\\n value = [ 2 14]\",shape=box];<br>\"327\" [label=\"whole_weight <= 0.444\\n impurity = 0.389273\\n samples = 34\\n value = [ 9 25]\\n class = 1\", shape=ellipse];<br>\"327\" -> \"655\"[label=\"yes\"];<br>\"327\" -> \"656\"[label=\"no\"];<br>\"656\" [label=\"0\\n impurity = 0.473373\\n samples = 13\\n value = [8 5]\",shape=box];<br>\"328\" [label=\"shucked_weight <= 0.2\\n impurity = 0.499592\\n samples = 35\\n value = [17 18]\\n class = 1\", shape=ellipse];<br>\"328\" -> \"657\"[label=\"yes\"];<br>\"328\" -> \"658\"[label=\"no\"];<br>\"658\" [label=\"0\\n impurity = 0.391111\\n samples = 15\\n value = [11 4]\",shape=box];<br>\"330\" [label=\"whole_weight <= 0.514\\n impurity = 0.0831758\\n samples = 23\\n value = [ 1 22]\\n class = 1\", shape=ellipse];<br>\"330\" -> \"661\"[label=\"yes\"];<br>\"661\" [label=\"1\\n impurity = 0\\n samples = 17\\n value = [ 0 17]\",shape=box];<br>\"330\" -> \"662\"[label=\"no\"];<br>\"662\" [label=\"1\\n impurity = 0.277778\\n samples = 6\\n value = [1 5]\",shape=box];<br>\"351\" [label=\"viscera_weight <= 0.087\\n impurity = 0.396694\\n samples = 22\\n value = [ 6 16]\\n class = 1\", shape=ellipse];<br>\"351\" -> \"703\"[label=\"yes\"];<br>\"703\" [label=\"1\\n impurity = 0.142012\\n samples = 13\\n value = [ 1 12]\",shape=box];<br>\"351\" -> \"704\"[label=\"no\"];<br>\"704\" [label=\"0\\n impurity = 0.493827\\n samples = 9\\n value = [5 4]\",shape=box];<br>\"354\" [label=\"diameter <= 0.385\\n impurity = 0.2112\\n samples = 25\\n value = [ 3 22]\\n class = 1\", shape=ellipse];<br>\"354\" -> \"709\"[label=\"yes\"];<br>\"709\" [label=\"1\\n impurity = 0\\n samples = 17\\n value = [ 0 17]\",shape=box];<br>\"354\" -> \"710\"[label=\"no\"];<br>\"710\" [label=\"1\\n impurity = 0.46875\\n samples = 8\\n value = [3 5]\",shape=box];<br>\"404\" [label=\"length <= 0.54\\n impurity = 0.340265\\n samples = 46\\n value = [10 36]\\n class = 1\", shape=ellipse];<br>\"404\" -> \"809\"[label=\"yes\"];<br>\"404\" -> \"810\"[label=\"no\"];<br>\"810\" [label=\"0\\n impurity = 0.46281\\n samples = 11\\n value = [7 4]\",shape=box];<br>\"463\" [label=\"whole_weight <= 0.833\\n impurity = 0.375\\n samples = 20\\n value = [ 5 15]\\n class = 1\", shape=ellipse];<br>\"463\" -> \"927\"[label=\"yes\"];<br>\"927\" [label=\"1\\n impurity = 0\\n samples = 14\\n value = [ 0 14]\",shape=box];<br>\"463\" -> \"928\"[label=\"no\"];<br>\"928\" [label=\"0\\n impurity = 0.277778\\n samples = 6\\n value = [5 1]\",shape=box];<br>\"464\" [label=\"height <= 0.145\\n impurity = 0.152778\\n samples = 48\\n value = [ 4 44]\\n class = 1\", shape=ellipse];<br>\"464\" -> \"929\"[label=\"yes\"];<br>\"929\" [label=\"1\\n impurity = 0\\n samples = 23\\n value = [ 0 23]\",shape=box];<br>\"464\" -> \"930\"[label=\"no\"];<br>\"930\" [label=\"1\\n impurity = 0.2688\\n samples = 25\\n value = [ 4 21]\",shape=box];<br>\"504\" [label=\"whole_weight <= 1.4095\\n impurity = 0.0185169\\n samples = 214\\n value = [ 2 212]\\n class = 1\", shape=ellipse];<br>\"504\" -> \"1009\"[label=\"yes\"];<br>\"1009\" [label=\"1\\n impurity = 0.0399833\\n samples = 98\\n value = [ 2 96]\",shape=box];<br>\"504\" -> \"1010\"[label=\"no\"];<br>\"1010\" [label=\"1\\n impurity = 0\\n samples = 116\\n value = [ 0 116]\",shape=box];<br>\"655\" [label=\"length <= 0.435\\n impurity = 0.0907029\\n samples = 21\\n value = [ 1 20]\\n class = 1\", shape=ellipse];<br>\"655\" -> \"1311\"[label=\"yes\"];<br>\"1311\" [label=\"1\\n impurity = 0.277778\\n samples = 6\\n value = [1 5]\",shape=box];<br>\"655\" -> \"1312\"[label=\"no\"];<br>\"1312\" [label=\"1\\n impurity = 0\\n samples = 15\\n value = [ 0 15]\",shape=box];<br>\"657\" [label=\"shell_weight <= 0.14\\n impurity = 0.42\\n samples = 20\\n value = [ 6 14]\\n class = 1\", shape=ellipse];<br>\"657\" -> \"1315\"[label=\"yes\"];<br>\"1315\" [label=\"0\\n impurity = 0.444444\\n samples = 6\\n value = [4 2]\",shape=box];<br>\"657\" -> \"1316\"[label=\"no\"];<br>\"1316\" [label=\"1\\n impurity = 0.244898\\n samples = 14\\n value = [ 2 12]\",shape=box];<br>\"809\" [label=\"height <= 0.13\\n impurity = 0.156735\\n samples = 35\\n value = [ 3 32]\\n class = 1\", shape=ellipse];<br>\"809\" -> \"1619\"[label=\"yes\"];<br>\"1619\" [label=\"1\\n impurity = 0.375\\n samples = 8\\n value = [2 6]\",shape=box];<br>\"809\" -> \"1620\"[label=\"no\"];<br>\"1620\" [label=\"1\\n impurity = 0.0713306\\n samples = 27\\n value = [ 1 26]\",shape=box];<br><br>} //---end of digraph--------- </td>\n",
+ " <td>digraph \"Classification tree for abalone_classif_train\" {<br>\"0\" [label=\"diameter <= 0.365\\n impurity = 0.445362\\n samples = 2895\\n value = [ 969 1926]\\n class = 1\", shape=ellipse];<br>\"0\" -> \"1\"[label=\"yes\"];<br>\"0\" -> \"2\"[label=\"no\"];<br>\"1\" [label=\"height <= 0.095\\n impurity = 0.378198\\n samples = 932\\n value = [696 236]\\n class = 0\", shape=ellipse];<br>\"1\" -> \"3\"[label=\"yes\"];<br>\"1\" -> \"4\"[label=\"no\"];<br>\"2\" [label=\"shell_weight <= 0.24\\n impurity = 0.239463\\n samples = 1963\\n value = [ 273 1690]\\n class = 1\", shape=ellipse];<br>\"2\" -> \"5\"[label=\"yes\"];<br>\"2\" -> \"6\"[label=\"no\"];<br>\"3\" [label=\"sex_m <= 0\\n impurity = 0.139694\\n samples = 450\\n value = [416 34]\\n class = 0\", shape=ellipse];<br>\"3\" -> \"7\"[label=\"yes\"];<br>\"3\" -> \"8\"[label=\"no\"];<br>\"4\" [label=\"shell_weight <= 0.135\\n impurity = 0.486906\\n samples = 482\\n value = [280 202]\\n class = 0\", shape=ellipse];<br>\"4\" -> \"9\"[label=\"yes\"];<br>\"4\" -> \"10\"[label=\"no\"];<br>\"5\" [label=\"length <= 0.495\\n impurity = 0.438989\\n samples = 584\\n value = [190 394]\\n class = 1\", shape=ellipse];<br>\"5\" -> \"11\"[label=\"yes\"];<br>\"5\" -> \"12\"[label=\"no\"];<br>\"6\" [label=\"height <= 0.16\\n impurity = 0.113132\\n samples = 1379\\n value = [ 83 1296]\\n class = 1\", shape=ellipse];<br>\"6\" -> \"13\"[label=\"yes\"];<br>\"6\" -> \"14\"[label=\"no\"];<br>\"7\" [label=\"length <= 0.43\\n impurity = 0.0610081\\n samples = 381\\n value = [369 12]\\n class = 0\", shape=ellipse];<br>\"7\" -> \"15\"[label=\"yes\"];<br>\"7\" -> \"16\"[label=\"no\"];<br>\"16\" [label=\"0\\n impurity = 0.444444\\n samples = 9\\n value = [6 3]\",shape=box];<br>\"8\" [label=\"shucked_weight <= 0.0545\\n impurity = 0.434363\\n samples = 69\\n value = [47 22]\\n class = 0\", shape=ellipse];<br>\"8\" -> \"17\"[label=\"yes\"];<br>\"17\" [label=\"0\\n impurity = 0.0739645\\n samples = 26\\n value = [25 1]\",shape=box];<br>\"8\" -> \"18\"[label=\"no\"];<br>\"9\" [label=\"shucked_weight <= 0.1255\\n impurity = 0.440547\\n samples = 348\\n value = [234 114]\\n class = 0\", shape=ellipse];<br>\"9\" -> \"19\"[label=\"yes\"];<br>\"9\" -> \"20\"[label=\"no\"];<br>\"10\" [label=\"length <= 0.475\\n impurity = 0.45088\\n samples = 134\\n value = [46 88]\\n class = 1\", shape=ellipse];<br>\"10\" -> \"21\"[label=\"yes\"];<br>\"21\" [label=\"1\\n impurity = 0.340265\\n samples = 92\\n value = [20 72]\",shape=box];<br>\"10\" -> \"22\"[label=\"no\"];<br>\"11\" [label=\"height <= 0.135\\n impurity = 0.481135\\n samples = 139\\n value = [56 83]\\n class = 1\", shape=ellipse];<br>\"11\" -> \"23\"[label=\"yes\"];<br>\"11\" -> \"24\"[label=\"no\"];<br>\"24\" [label=\"1\\n impurity = 0.244898\\n samples = 35\\n value = [ 5 30]\",shape=box];<br>\"12\" [label=\"viscera_weight <= 0.1255\\n impurity = 0.420896\\n samples = 445\\n value = [134 311]\\n class = 1\", shape=ellipse];<br>\"12\" -> \"25\"[label=\"yes\"];<br>\"12\" -> \"26\"[label=\"no\"];<br>\"13\" [label=\"shucked_weight <= 0.396\\n impurity = 0.19002\\n samples = 602\\n value = [ 64 538]\\n class = 1\", shape=ellipse];<br>\"13\" -> \"27\"[label=\"yes\"];<br>\"27\" [label=\"1\\n impurity = 0.0774598\\n samples = 223\\n value = [ 9 214]\",shape=box];<br>\"13\" -> \"28\"[label=\"no\"];<br>\"14\" [label=\"diameter <= 0.465\\n impurity = 0.0477101\\n samples = 777\\n value = [ 19 758]\\n class = 1\", shape=ellipse];<br>\"14\" -> \"29\"[label=\"yes\"];<br>\"29\" [label=\"1\\n impurity = 0.152778\\n samples = 120\\n value = [ 10 110]\",shape=box];<br>\"14\" -> \"30\"[label=\"no\"];<br>\"15\" [label=\"viscera_weight <= 0.032\\n impurity = 0.0472164\\n samples = 372\\n value = [363 9]\\n class = 0\", shape=ellipse];<br>\"15\" -> \"31\"[label=\"yes\"];<br>\"31\" [label=\"0\\n impurity = 0\\n samples = 168\\n value = [168 0]\",shape=box];<br>\"15\" -> \"32\"[label=\"no\"];<br>\"18\" [label=\"shucked_weight <= 0.1125\\n impurity = 0.49973\\n samples = 43\\n value = [22 21]\\n class = 0\", shape=ellipse];<br>\"18\" -> \"37\"[label=\"yes\"];<br>\"18\" -> \"38\"[label=\"no\"];<br>\"38\" [label=\"0\\n impurity = 0.244898\\n samples = 7\\n value = [6 1]\",shape=box];<br>\"19\" [label=\"sex_f <= 0\\n impurity = 0.498555\\n samples = 93\\n value = [44 49]\\n class = 1\", shape=ellipse];<br>\"19\" -> \"39\"[label=\"yes\"];<br>\"19\" -> \"40\"[label=\"no\"];<br>\"40\" [label=\"1\\n impurity = 0.124444\\n samples = 15\\n value = [ 1 14]\",shape=box];<br>\"20\" [label=\"viscera_weight <= 0.1405\\n impurity = 0.379854\\n samples = 255\\n value = [190 65]\\n class = 0\", shape=ellipse];<br>\"20\" -> \"41\"[label=\"yes\"];<br>\"20\" -> \"42\"[label=\"no\"];<br>\"42\" [label=\"1\\n impurity = 0.277778\\n samples = 6\\n value = [1 5]\",shape=box];<br>\"22\" [label=\"viscera_weight <= 0.108\\n impurity = 0.471655\\n samples = 42\\n value = [26 16]\\n class = 0\", shape=ellipse];<br>\"22\" -> \"45\"[label=\"yes\"];<br>\"45\" [label=\"0\\n impurity = 0.188366\\n samples = 19\\n value = [17 2]\",shape=box];<br>\"22\" -> \"46\"[label=\"no\"];<br>\"23\" [label=\"shell_weight <= 0.1535\\n impurity = 0.499815\\n samples = 104\\n value = [51 53]\\n class = 1\", shape=ellipse];<br>\"23\" -> \"47\"[label=\"yes\"];<br>\"23\" -> \"48\"[label=\"no\"];<br>\"25\" [label=\"diameter <= 0.375\\n impurity = 0.491493\\n samples = 46\\n value = [26 20]\\n class = 0\", shape=ellipse];<br>\"25\" -> \"51\"[label=\"yes\"];<br>\"51\" [label=\"0\\n impurity = 0\\n samples = 7\\n value = [7 0]\",shape=box];<br>\"25\" -> \"52\"[label=\"no\"];<br>\"52\" [label=\"1\\n impurity = 0.499671\\n samples = 39\\n value = [19 20]\",shape=box];<br>\"26\" [label=\"length <= 0.52\\n impurity = 0.394822\\n samples = 399\\n value = [108 291]\\n class = 1\", shape=ellipse];<br>\"26\" -> \"53\"[label=\"yes\"];<br>\"26\" -> \"54\"[label=\"no\"];<br>\"54\" [label=\"1\\n impurity = 0.447971\\n samples = 248\\n value = [ 84 164]\",shape=box];<br>\"28\" [label=\"viscera_weight <= 0.2105\\n impurity = 0.248119\\n samples = 379\\n value = [ 55 324]\\n class = 1\", shape=ellipse];<br>\"28\" -> \"57\"[label=\"yes\"];<br>\"57\" [label=\"1\\n impurity = 0.408163\\n samples = 84\\n value = [24 60]\",shape=box];<br>\"28\" -> \"58\"[label=\"no\"];<br>\"30\" [label=\"whole_weight <= 1.415\\n impurity = 0.027022\\n samples = 657\\n value = [ 9 648]\\n class = 1\", shape=ellipse];<br>\"30\" -> \"61\"[label=\"yes\"];<br>\"30\" -> \"62\"[label=\"no\"];<br>\"62\" [label=\"1\\n impurity = 0\\n samples = 320\\n value = [ 0 320]\",shape=box];<br>\"32\" [label=\"shell_weight <= 0.081\\n impurity = 0.0843426\\n samples = 204\\n value = [195 9]\\n class = 0\", shape=ellipse];<br>\"32\" -> \"65\"[label=\"yes\"];<br>\"32\" -> \"66\"[label=\"no\"];<br>\"66\" [label=\"0\\n impurity = 0\\n samples = 32\\n value = [32 0]\",shape=box];<br>\"37\" [label=\"shucked_weight <= 0.086\\n impurity = 0.493827\\n samples = 36\\n value = [16 20]\\n class = 1\", shape=ellipse];<br>\"37\" -> \"75\"[label=\"yes\"];<br>\"37\" -> \"76\"[label=\"no\"];<br>\"76\" [label=\"1\\n impurity = 0.18\\n samples = 10\\n value = [1 9]\",shape=box];<br>\"39\" [label=\"diameter <= 0.255\\n impurity = 0.49474\\n samples = 78\\n value = [43 35]\\n class = 0\", shape=ellipse];<br>\"39\" -> \"79\"[label=\"yes\"];<br>\"79\" [label=\"0\\n impurity = 0\\n samples = 8\\n value = [8 0]\",shape=box];<br>\"39\" -> \"80\"[label=\"no\"];<br>\"41\" [label=\"shell_weight <= 0.11\\n impurity = 0.365801\\n samples = 249\\n value = [189 60]\\n class = 0\", shape=ellipse];<br>\"41\" -> \"83\"[label=\"yes\"];<br>\"41\" -> \"84\"[label=\"no\"];<br>\"46\" [label=\"whole_weight <= 0.875\\n impurity = 0.476371\\n samples = 23\\n value = [ 9 14]\\n class = 1\", shape=ellipse];<br>\"46\" -> \"93\"[label=\"yes\"];<br>\"93\" [label=\"1\\n impurity = 0.152778\\n samples = 12\\n value = [ 1 11]\",shape=box];<br>\"46\" -> \"94\"[label=\"no\"];<br>\"94\" [label=\"0\\n impurity = 0.396694\\n samples = 11\\n value = [8 3]\",shape=box];<br>\"47\" [label=\"sex_f <= 0\\n impurity = 0.32699\\n samples = 34\\n value = [27 7]\\n class = 0\", shape=ellipse];<br>\"47\" -> \"95\"[label=\"yes\"];<br>\"95\" [label=\"0\\n impurity = 0.204142\\n samples = 26\\n value = [23 3]\",shape=box];<br>\"47\" -> \"96\"[label=\"no\"];<br>\"96\" [label=\"0\\n impurity = 0.5\\n samples = 8\\n value = [4 4]\",shape=box];<br>\"48\" [label=\"whole_weight <= 0.599\\n impurity = 0.450612\\n samples = 70\\n value = [24 46]\\n class = 1\", shape=ellipse];<br>\"48\" -> \"97\"[label=\"yes\"];<br>\"48\" -> \"98\"[label=\"no\"];<br>\"98\" [label=\"0\\n impurity = 0.429688\\n samples = 16\\n value = [11 5]\",shape=box];<br>\"53\" [label=\"height <= 0.125\\n impurity = 0.267357\\n samples = 151\\n value = [ 24 127]\\n class = 1\", shape=ellipse];<br>\"53\" -> \"107\"[label=\"yes\"];<br>\"53\" -> \"108\"[label=\"no\"];<br>\"58\" [label=\"shell_weight <= 0.32\\n impurity = 0.188084\\n samples = 295\\n value = [ 31 264]\\n class = 1\", shape=ellipse];<br>\"58\" -> \"117\"[label=\"yes\"];<br>\"58\" -> \"118\"[label=\"no\"];<br>\"61\" [label=\"shucked_weight <= 0.585\\n impurity = 0.051986\\n samples = 337\\n value = [ 9 328]\\n class = 1\", shape=ellipse];<br>\"61\" -> \"123\"[label=\"yes\"];<br>\"123\" [label=\"1\\n impurity = 0.0239965\\n samples = 247\\n value = [ 3 244]\",shape=box];<br>\"61\" -> \"124\"[label=\"no\"];<br>\"65\" [label=\"shucked_weight <= 0.08\\n impurity = 0.0991752\\n samples = 172\\n value = [163 9]\\n class = 0\", shape=ellipse];<br>\"65\" -> \"131\"[label=\"yes\"];<br>\"131\" [label=\"0\\n impurity = 0.21875\\n samples = 64\\n value = [56 8]\",shape=box];<br>\"65\" -> \"132\"[label=\"no\"];<br>\"75\" [label=\"shucked_weight <= 0.0675\\n impurity = 0.488166\\n samples = 26\\n value = [15 11]\\n class = 0\", shape=ellipse];<br>\"75\" -> \"151\"[label=\"yes\"];<br>\"151\" [label=\"1\\n impurity = 0.396694\\n samples = 11\\n value = [3 8]\",shape=box];<br>\"75\" -> \"152\"[label=\"no\"];<br>\"152\" [label=\"0\\n impurity = 0.32\\n samples = 15\\n value = [12 3]\",shape=box];<br>\"80\" [label=\"height <= 0.105\\n impurity = 0.5\\n samples = 70\\n value = [35 35]\\n class = 0\", shape=ellipse];<br>\"80\" -> \"161\"[label=\"yes\"];<br>\"80\" -> \"162\"[label=\"no\"];<br>\"83\" [label=\"sex_f <= 0\\n impurity = 0.21491\\n samples = 98\\n value = [86 12]\\n class = 0\", shape=ellipse];<br>\"83\" -> \"167\"[label=\"yes\"];<br>\"167\" [label=\"0\\n impurity = 0.1298\\n samples = 86\\n value = [80 6]\",shape=box];<br>\"83\" -> \"168\"[label=\"no\"];<br>\"168\" [label=\"0\\n impurity = 0.5\\n samples = 12\\n value = [6 6]\",shape=box];<br>\"84\" [label=\"viscera_weight <= 0.092\\n impurity = 0.433665\\n samples = 151\\n value = [103 48]\\n class = 0\", shape=ellipse];<br>\"84\" -> \"169\"[label=\"yes\"];<br>\"84\" -> \"170\"[label=\"no\"];<br>\"97\" [label=\"whole_weight <= 0.5305\\n impurity = 0.365569\\n samples = 54\\n value = [13 41]\\n class = 1\", shape=ellipse];<br>\"97\" -> \"195\"[label=\"yes\"];<br>\"97\" -> \"196\"[label=\"no\"];<br>\"107\" [label=\"whole_weight <= 0.599\\n impurity = 0.389838\\n samples = 49\\n value = [13 36]\\n class = 1\", shape=ellipse];<br>\"107\" -> \"215\"[label=\"yes\"];<br>\"215\" [label=\"0\\n impurity = 0.489796\\n samples = 14\\n value = [8 6]\",shape=box];<br>\"107\" -> \"216\"[label=\"no\"];<br>\"216\" [label=\"1\\n impurity = 0.244898\\n samples = 35\\n value = [ 5 30]\",shape=box];<br>\"108\" [label=\"shucked_weight <= 0.31\\n impurity = 0.192426\\n samples = 102\\n value = [11 91]\\n class = 1\", shape=ellipse];<br>\"108\" -> \"217\"[label=\"yes\"];<br>\"108\" -> \"218\"[label=\"no\"];<br>\"218\" [label=\"1\\n impurity = 0.46875\\n samples = 24\\n value = [ 9 15]\",shape=box];<br>\"117\" [label=\"viscera_weight <= 0.227\\n impurity = 0.263672\\n samples = 192\\n value = [ 30 162]\\n class = 1\", shape=ellipse];<br>\"117\" -> \"235\"[label=\"yes\"];<br>\"117\" -> \"236\"[label=\"no\"];<br>\"118\" [label=\"diameter <= 0.48\\n impurity = 0.019229\\n samples = 103\\n value = [ 1 102]\\n class = 1\", shape=ellipse];<br>\"118\" -> \"237\"[label=\"yes\"];<br>\"118\" -> \"238\"[label=\"no\"];<br>\"238\" [label=\"1\\n impurity = 0\\n samples = 79\\n value = [ 0 79]\",shape=box];<br>\"124\" [label=\"length <= 0.66\\n impurity = 0.124444\\n samples = 90\\n value = [ 6 84]\\n class = 1\", shape=ellipse];<br>\"124\" -> \"249\"[label=\"yes\"];<br>\"124\" -> \"250\"[label=\"no\"];<br>\"250\" [label=\"1\\n impurity = 0.304688\\n samples = 16\\n value = [ 3 13]\",shape=box];<br>\"132\" [label=\"sex_f <= 0\\n impurity = 0.0183471\\n samples = 108\\n value = [107 1]\\n class = 0\", shape=ellipse];<br>\"132\" -> \"265\"[label=\"yes\"];<br>\"265\" [label=\"0\\n impurity = 0\\n samples = 97\\n value = [97 0]\",shape=box];<br>\"132\" -> \"266\"[label=\"no\"];<br>\"266\" [label=\"0\\n impurity = 0.165289\\n samples = 11\\n value = [10 1]\",shape=box];<br>\"161\" [label=\"diameter <= 0.3\\n impurity = 0.491493\\n samples = 46\\n value = [26 20]\\n class = 0\", shape=ellipse];<br>\"161\" -> \"323\"[label=\"yes\"];<br>\"323\" [label=\"1\\n impurity = 0.497449\\n samples = 28\\n value = [13 15]\",shape=box];<br>\"161\" -> \"324\"[label=\"no\"];<br>\"324\" [label=\"0\\n impurity = 0.401235\\n samples = 18\\n value = [13 5]\",shape=box];<br>\"162\" [label=\"shell_weight <= 0.098\\n impurity = 0.46875\\n samples = 24\\n value = [ 9 15]\\n class = 1\", shape=ellipse];<br>\"162\" -> \"325\"[label=\"yes\"];<br>\"325\" [label=\"0\\n impurity = 0.49827\\n samples = 17\\n value = [9 8]\",shape=box];<br>\"162\" -> \"326\"[label=\"no\"];<br>\"326\" [label=\"1\\n impurity = 0\\n samples = 7\\n value = [0 7]\",shape=box];<br>\"169\" [label=\"height <= 0.105\\n impurity = 0.491966\\n samples = 71\\n value = [40 31]\\n class = 0\", shape=ellipse];<br>\"169\" -> \"339\"[label=\"yes\"];<br>\"339\" [label=\"1\\n impurity = 0.396694\\n samples = 22\\n value = [ 6 16]\",shape=box];<br>\"169\" -> \"340\"[label=\"no\"];<br>\"170\" [label=\"shucked_weight <= 0.1595\\n impurity = 0.334688\\n samples = 80\\n value = [63 17]\\n class = 0\", shape=ellipse];<br>\"170\" -> \"341\"[label=\"yes\"];<br>\"341\" [label=\"1\\n impurity = 0.497778\\n samples = 15\\n value = [7 8]\",shape=box];<br>\"170\" -> \"342\"[label=\"no\"];<br>\"195\" [label=\"height <= 0.12\\n impurity = 0.483471\\n samples = 22\\n value = [ 9 13]\\n class = 1\", shape=ellipse];<br>\"195\" -> \"391\"[label=\"yes\"];<br>\"391\" [label=\"1\\n impurity = 0.197531\\n samples = 9\\n value = [1 8]\",shape=box];<br>\"195\" -> \"392\"[label=\"no\"];<br>\"392\" [label=\"0\\n impurity = 0.473373\\n samples = 13\\n value = [8 5]\",shape=box];<br>\"196\" [label=\"height <= 0.125\\n impurity = 0.21875\\n samples = 32\\n value = [ 4 28]\\n class = 1\", shape=ellipse];<br>\"196\" -> \"393\"[label=\"yes\"];<br>\"196\" -> \"394\"[label=\"no\"];<br>\"394\" [label=\"1\\n impurity = 0\\n samples = 12\\n value = [ 0 12]\",shape=box];<br>\"217\" [label=\"length <= 0.515\\n impurity = 0.0499671\\n samples = 78\\n value = [ 2 76]\\n class = 1\", shape=ellipse];<br>\"217\" -> \"435\"[label=\"yes\"];<br>\"217\" -> \"436\"[label=\"no\"];<br>\"436\" [label=\"1\\n impurity = 0.197531\\n samples = 9\\n value = [1 8]\",shape=box];<br>\"235\" [label=\"shell_weight <= 0.265\\n impurity = 0.0407986\\n samples = 48\\n value = [ 1 47]\\n class = 1\", shape=ellipse];<br>\"235\" -> \"471\"[label=\"yes\"];<br>\"471\" [label=\"1\\n impurity = 0.104938\\n samples = 18\\n value = [ 1 17]\",shape=box];<br>\"235\" -> \"472\"[label=\"no\"];<br>\"472\" [label=\"1\\n impurity = 0\\n samples = 30\\n value = [ 0 30]\",shape=box];<br>\"236\" [label=\"viscera_weight <= 0.231\\n impurity = 0.321663\\n samples = 144\\n value = [ 29 115]\\n class = 1\", shape=ellipse];<br>\"236\" -> \"473\"[label=\"yes\"];<br>\"473\" [label=\"0\\n impurity = 0.408163\\n samples = 7\\n value = [5 2]\",shape=box];<br>\"236\" -> \"474\"[label=\"no\"];<br>\"474\" [label=\"1\\n impurity = 0.288987\\n samples = 137\\n value = [ 24 113]\",shape=box];<br>\"237\" [label=\"viscera_weight <= 0.2475\\n impurity = 0.0798611\\n samples = 24\\n value = [ 1 23]\\n class = 1\", shape=ellipse];<br>\"237\" -> \"475\"[label=\"yes\"];<br>\"475\" [label=\"1\\n impurity = 0\\n samples = 15\\n value = [ 0 15]\",shape=box];<br>\"237\" -> \"476\"[label=\"no\"];<br>\"476\" [label=\"1\\n impurity = 0.197531\\n samples = 9\\n value = [1 8]\",shape=box];<br>\"249\" [label=\"shucked_weight <= 0.5935\\n impurity = 0.077794\\n samples = 74\\n value = [ 3 71]\\n class = 1\", shape=ellipse];<br>\"249\" -> \"499\"[label=\"yes\"];<br>\"499\" [label=\"1\\n impurity = 0.277778\\n samples = 12\\n value = [ 2 10]\",shape=box];<br>\"249\" -> \"500\"[label=\"no\"];<br>\"340\" [label=\"sex_m <= 0\\n impurity = 0.424823\\n samples = 49\\n value = [34 15]\\n class = 0\", shape=ellipse];<br>\"340\" -> \"681\"[label=\"yes\"];<br>\"681\" [label=\"0\\n impurity = 0.460224\\n samples = 39\\n value = [25 14]\",shape=box];<br>\"340\" -> \"682\"[label=\"no\"];<br>\"682\" [label=\"0\\n impurity = 0.18\\n samples = 10\\n value = [9 1]\",shape=box];<br>\"342\" [label=\"viscera_weight <= 0.102\\n impurity = 0.23858\\n samples = 65\\n value = [56 9]\\n class = 0\", shape=ellipse];<br>\"342\" -> \"685\"[label=\"yes\"];<br>\"685\" [label=\"0\\n impurity = 0\\n samples = 31\\n value = [31 0]\",shape=box];<br>\"342\" -> \"686\"[label=\"no\"];<br>\"686\" [label=\"0\\n impurity = 0.389273\\n samples = 34\\n value = [25 9]\",shape=box];<br>\"393\" [label=\"sex_f <= 0\\n impurity = 0.32\\n samples = 20\\n value = [ 4 16]\\n class = 1\", shape=ellipse];<br>\"393\" -> \"787\"[label=\"yes\"];<br>\"787\" [label=\"1\\n impurity = 0.408163\\n samples = 14\\n value = [ 4 10]\",shape=box];<br>\"393\" -> \"788\"[label=\"no\"];<br>\"788\" [label=\"1\\n impurity = 0\\n samples = 6\\n value = [0 6]\",shape=box];<br>\"435\" [label=\"shucked_weight <= 0.2815\\n impurity = 0.0285654\\n samples = 69\\n value = [ 1 68]\\n class = 1\", shape=ellipse];<br>\"435\" -> \"871\"[label=\"yes\"];<br>\"871\" [label=\"1\\n impurity = 0\\n samples = 52\\n value = [ 0 52]\",shape=box];<br>\"435\" -> \"872\"[label=\"no\"];<br>\"872\" [label=\"1\\n impurity = 0.110727\\n samples = 17\\n value = [ 1 16]\",shape=box];<br>\"500\" [label=\"shucked_weight <= 0.665\\n impurity = 0.0317378\\n samples = 62\\n value = [ 1 61]\\n class = 1\", shape=ellipse];<br>\"500\" -> \"1001\"[label=\"yes\"];<br>\"1001\" [label=\"1\\n impurity = 0\\n samples = 51\\n value = [ 0 51]\",shape=box];<br>\"500\" -> \"1002\"[label=\"no\"];<br>\"1002\" [label=\"1\\n impurity = 0.165289\\n samples = 11\\n value = [ 1 10]\",shape=box];<br><br>} //---end of digraph--------- </td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'digraph \"Classification tree for abalone_classif_train\" {\\n\"0\" [label=\"length <= 0.495\\\\n impurity = 0.453016\\\\n samples = 3011\\\\n value = [1044 196 ... (21658 characters truncated) ... \" -> \"1620\"[label=\"no\"];\\n\"1620\" [label=\"1\\\\n impurity = 0.0713306\\\\n samples = 27\\\\n value = [ 1 26]\",shape=box];\\n\\n} //---end of digraph--------- ',)]"
+ "[(u'digraph \"Classification tree for abalone_classif_train\" {\\n\"0\" [label=\"diameter <= 0.365\\\\n impurity = 0.445362\\\\n samples = 2895\\\\n value = [ 969 1 ... (16625 characters truncated) ... 0\" -> \"1002\"[label=\"no\"];\\n\"1002\" [label=\"1\\\\n impurity = 0.165289\\\\n samples = 11\\\\n value = [ 1 10]\",shape=box];\\n\\n} //---end of digraph--------- ',)]"
]
},
- "execution_count": 33,
+ "execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
@@ -3080,7 +3301,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
@@ -3102,65 +3323,65 @@
" <th>impurity_var_importance</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>shucked_weight</td>\n",
- " <td>12.0025318256</td>\n",
- " <td>20.4821989904</td>\n",
- " </tr>\n",
- " <tr>\n",
" <td>shell_weight</td>\n",
- " <td>31.2254609526</td>\n",
- " <td>19.8024587933</td>\n",
+ " <td>24.0017517253</td>\n",
+ " <td>17.642923945</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>length</td>\n",
- " <td>23.0067732101</td>\n",
- " <td>11.2012793065</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>viscera_weight</td>\n",
- " <td>3.60975085939</td>\n",
- " <td>10.8568589594</td>\n",
+ " <td>shucked_weight</td>\n",
+ " <td>11.2097740558</td>\n",
+ " <td>15.1628653464</td>\n",
" </tr>\n",
" <tr>\n",
" <td>whole_weight</td>\n",
- " <td>9.63781565072</td>\n",
- " <td>10.4829872168</td>\n",
+ " <td>8.46230650442</td>\n",
+ " <td>14.5878704374</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>diameter</td>\n",
- " <td>8.83966394397</td>\n",
- " <td>9.84865534216</td>\n",
+ " <td>length</td>\n",
+ " <td>26.5982102015</td>\n",
+ " <td>13.2047421128</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>viscera_weight</td>\n",
+ " <td>6.12782403239</td>\n",
+ " <td>12.834102126</td>\n",
" </tr>\n",
" <tr>\n",
" <td>height</td>\n",
- " <td>0.0</td>\n",
- " <td>9.54744115005</td>\n",
+ " <td>2.41261514408</td>\n",
+ " <td>12.5441452907</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>sex_m</td>\n",
- " <td>5.96519495962</td>\n",
- " <td>4.53823943951</td>\n",
+ " <td>diameter</td>\n",
+ " <td>20.1648004008</td>\n",
+ " <td>8.54486297237</td>\n",
" </tr>\n",
" <tr>\n",
" <td>sex_f</td>\n",
- " <td>5.712808598</td>\n",
- " <td>3.23988080193</td>\n",
+ " <td>0.0</td>\n",
+ " <td>3.08539525197</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>sex_m</td>\n",
+ " <td>1.02271793567</td>\n",
+ " <td>2.39309251727</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'shucked_weight', 12.0025318256043, 20.4821989904084),\n",
- " (u'shell_weight', 31.2254609525938, 19.8024587932943),\n",
- " (u'length', 23.0067732101024, 11.2012793064942),\n",
- " (u'viscera_weight', 3.60975085939451, 10.8568589593852),\n",
- " (u'whole_weight', 9.63781565071796, 10.4829872167693),\n",
- " (u'diameter', 8.83966394396736, 9.84865534216095),\n",
- " (u'height', 0.0, 9.54744115004596),\n",
- " (u'sex_m', 5.96519495962168, 4.53823943950833),\n",
- " (u'sex_f', 5.71280859799804, 3.23988080193335)]"
+ "[(u'shell_weight', 24.0017517253058, 17.6429239449908),\n",
+ " (u'shucked_weight', 11.2097740558388, 15.1628653463997),\n",
+ " (u'whole_weight', 8.4623065044228, 14.5878704374295),\n",
+ " (u'length', 26.5982102014748, 13.2047421128126),\n",
+ " (u'viscera_weight', 6.12782403238892, 12.8341021260091),\n",
+ " (u'height', 2.41261514407632, 12.5441452907451),\n",
+ " (u'diameter', 20.164800400827, 8.54486297236967),\n",
+ " (u'sex_f', 0.0, 3.0853952519707),\n",
+ " (u'sex_m', 1.02271793566559, 2.39309251727284)]"
]
},
- "execution_count": 34,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
@@ -3188,7 +3409,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 36,
"metadata": {},
"outputs": [
{
@@ -3215,7 +3436,7 @@
"[('',)]"
]
},
- "execution_count": 35,
+ "execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
@@ -3235,7 +3456,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 37,
"metadata": {},
"outputs": [
{
@@ -3255,41 +3476,37 @@
" <th>estimated_prob_1</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>10</td>\n",
- " <td>0.0</td>\n",
- " <td>1.0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>16</td>\n",
+ " <td>5</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>20</td>\n",
- " <td>0.8</td>\n",
- " <td>0.2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>24</td>\n",
+ " <td>7</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>26</td>\n",
+ " <td>13</td>\n",
+ " <td>0.1</td>\n",
+ " <td>0.9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>15</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
+ " <tr>\n",
+ " <td>17</td>\n",
+ " <td>0.6</td>\n",
+ " <td>0.4</td>\n",
+ " </tr>\n",
"</table>"
],
"text/plain": [
- "[(10, 0.0, 1.0),\n",
- " (16, 1.0, 0.0),\n",
- " (20, 0.8, 0.2),\n",
- " (24, 0.0, 1.0),\n",
- " (26, 0.0, 1.0)]"
+ "[(5, 1.0, 0.0), (7, 0.0, 1.0), (13, 0.1, 0.9), (15, 0.0, 1.0), (17, 0.6, 0.4)]"
]
},
- "execution_count": 36,
+ "execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
@@ -3303,7 +3520,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 38,
"metadata": {},
"outputs": [
{
@@ -3320,7 +3537,7 @@
"[]"
]
},
- "execution_count": 37,
+ "execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
@@ -3345,7 +3562,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 39,
"metadata": {},
"outputs": [
{
@@ -3379,197 +3596,197 @@
" </tr>\n",
" <tr>\n",
" <td>0.0</td>\n",
- " <td>824</td>\n",
- " <td>429</td>\n",
+ " <td>812</td>\n",
+ " <td>441</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
- " <td>0.657621707901</td>\n",
+ " <td>0.648044692737</td>\n",
" <td>None</td>\n",
" <td>1.0</td>\n",
- " <td>0.342378292099</td>\n",
+ " <td>0.351955307263</td>\n",
" <td>0.0</td>\n",
- " <td>0.657621707901</td>\n",
- " <td>0.79345209436687530091</td>\n",
+ " <td>0.648044692737</td>\n",
+ " <td>0.78644067796610169492</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.1</td>\n",
" <td>807</td>\n",
- " <td>263</td>\n",
- " <td>17</td>\n",
- " <td>166</td>\n",
- " <td>0.979368932039</td>\n",
- " <td>0.386946386946</td>\n",
- " <td>0.754205607477</td>\n",
- " <td>0.907103825137</td>\n",
- " <td>0.613053613054</td>\n",
- " <td>0.245794392523</td>\n",
- " <td>0.0206310679612</td>\n",
- " <td>0.776536312849</td>\n",
- " <td>0.85216473072861668427</td>\n",
+ " <td>254</td>\n",
+ " <td>5</td>\n",
+ " <td>187</td>\n",
+ " <td>0.993842364532</td>\n",
+ " <td>0.424036281179</td>\n",
+ " <td>0.760603204524</td>\n",
+ " <td>0.973958333333</td>\n",
+ " <td>0.575963718821</td>\n",
+ " <td>0.239396795476</td>\n",
+ " <td>0.00615763546798</td>\n",
+ " <td>0.793296089385</td>\n",
+ " <td>0.86171916711158569140</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.2</td>\n",
- " <td>800</td>\n",
- " <td>226</td>\n",
- " <td>24</td>\n",
- " <td>203</td>\n",
- " <td>0.970873786408</td>\n",
- " <td>0.473193473193</td>\n",
- " <td>0.779727095517</td>\n",
- " <td>0.894273127753</td>\n",
- " <td>0.526806526807</td>\n",
- " <td>0.220272904483</td>\n",
- " <td>0.0291262135922</td>\n",
- " <td>0.800478850758</td>\n",
- " <td>0.86486486486486486486</td>\n",
+ " <td>794</td>\n",
+ " <td>221</td>\n",
+ " <td>18</td>\n",
+ " <td>220</td>\n",
+ " <td>0.977832512315</td>\n",
+ " <td>0.498866213152</td>\n",
+ " <td>0.782266009852</td>\n",
+ " <td>0.924369747899</td>\n",
+ " <td>0.501133786848</td>\n",
+ " <td>0.217733990148</td>\n",
+ " <td>0.0221674876847</td>\n",
+ " <td>0.809257781325</td>\n",
+ " <td>0.86918445539135194308</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.3</td>\n",
- " <td>787</td>\n",
- " <td>198</td>\n",
- " <td>37</td>\n",
- " <td>231</td>\n",
- " <td>0.955097087379</td>\n",
- " <td>0.538461538462</td>\n",
- " <td>0.798984771574</td>\n",
- " <td>0.861940298507</td>\n",
- " <td>0.461538461538</td>\n",
- " <td>0.201015228426</td>\n",
- " <td>0.0449029126214</td>\n",
- " <td>0.812450119713</td>\n",
- " <td>0.87009397457158651189</td>\n",
+ " <td>783</td>\n",
+ " <td>190</td>\n",
+ " <td>29</td>\n",
+ " <td>251</td>\n",
+ " <td>0.964285714286</td>\n",
+ " <td>0.569160997732</td>\n",
+ " <td>0.804727646454</td>\n",
+ " <td>0.896428571429</td>\n",
+ " <td>0.430839002268</td>\n",
+ " <td>0.195272353546</td>\n",
+ " <td>0.0357142857143</td>\n",
+ " <td>0.825219473264</td>\n",
+ " <td>0.87731092436974789916</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.4</td>\n",
- " <td>769</td>\n",
- " <td>167</td>\n",
- " <td>55</td>\n",
- " <td>262</td>\n",
- " <td>0.933252427184</td>\n",
- " <td>0.610722610723</td>\n",
- " <td>0.821581196581</td>\n",
- " <td>0.826498422713</td>\n",
- " <td>0.389277389277</td>\n",
- " <td>0.178418803419</td>\n",
- " <td>0.0667475728155</td>\n",
- " <td>0.822825219473</td>\n",
- " <td>0.87386363636363636364</td>\n",
+ " <td>774</td>\n",
+ " <td>159</td>\n",
+ " <td>38</td>\n",
+ " <td>282</td>\n",
+ " <td>0.953201970443</td>\n",
+ " <td>0.639455782313</td>\n",
+ " <td>0.829581993569</td>\n",
+ " <td>0.88125</td>\n",
+ " <td>0.360544217687</td>\n",
+ " <td>0.170418006431</td>\n",
+ " <td>0.0467980295567</td>\n",
+ " <td>0.842777334397</td>\n",
+ " <td>0.88710601719197707736</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.5</td>\n",
- " <td>743</td>\n",
- " <td>148</td>\n",
- " <td>81</td>\n",
- " <td>281</td>\n",
- " <td>0.901699029126</td>\n",
- " <td>0.655011655012</td>\n",
- " <td>0.833894500561</td>\n",
- " <td>0.776243093923</td>\n",
- " <td>0.344988344988</td>\n",
- " <td>0.166105499439</td>\n",
- " <td>0.0983009708738</td>\n",
- " <td>0.817238627294</td>\n",
- " <td>0.86647230320699708455</td>\n",
+ " <td>756</td>\n",
+ " <td>136</td>\n",
+ " <td>56</td>\n",
+ " <td>305</td>\n",
+ " <td>0.931034482759</td>\n",
+ " <td>0.691609977324</td>\n",
+ " <td>0.847533632287</td>\n",
+ " <td>0.84487534626</td>\n",
+ " <td>0.308390022676</td>\n",
+ " <td>0.152466367713</td>\n",
+ " <td>0.0689655172414</td>\n",
+ " <td>0.846767757382</td>\n",
+ " <td>0.88732394366197183099</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.6</td>\n",
- " <td>719</td>\n",
- " <td>119</td>\n",
- " <td>105</td>\n",
- " <td>310</td>\n",
- " <td>0.872572815534</td>\n",
- " <td>0.722610722611</td>\n",
- " <td>0.85799522673</td>\n",
- " <td>0.746987951807</td>\n",
- " <td>0.277389277389</td>\n",
- " <td>0.14200477327</td>\n",
- " <td>0.127427184466</td>\n",
- " <td>0.821229050279</td>\n",
- " <td>0.86522262334536702768</td>\n",
+ " <td>744</td>\n",
+ " <td>122</td>\n",
+ " <td>68</td>\n",
+ " <td>319</td>\n",
+ " <td>0.916256157635</td>\n",
+ " <td>0.72335600907</td>\n",
+ " <td>0.859122401848</td>\n",
+ " <td>0.824289405685</td>\n",
+ " <td>0.27664399093</td>\n",
+ " <td>0.140877598152</td>\n",
+ " <td>0.0837438423645</td>\n",
+ " <td>0.848363926576</td>\n",
+ " <td>0.88676996424314660310</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.7</td>\n",
- " <td>690</td>\n",
- " <td>97</td>\n",
- " <td>134</td>\n",
- " <td>332</td>\n",
- " <td>0.837378640777</td>\n",
- " <td>0.773892773893</td>\n",
- " <td>0.876747141042</td>\n",
- " <td>0.712446351931</td>\n",
- " <td>0.226107226107</td>\n",
- " <td>0.123252858958</td>\n",
- " <td>0.162621359223</td>\n",
- " <td>0.815642458101</td>\n",
- " <td>0.85661080074487895717</td>\n",
+ " <td>726</td>\n",
+ " <td>110</td>\n",
+ " <td>86</td>\n",
+ " <td>331</td>\n",
+ " <td>0.894088669951</td>\n",
+ " <td>0.750566893424</td>\n",
+ " <td>0.868421052632</td>\n",
+ " <td>0.79376498801</td>\n",
+ " <td>0.249433106576</td>\n",
+ " <td>0.131578947368</td>\n",
+ " <td>0.105911330049</td>\n",
+ " <td>0.843575418994</td>\n",
+ " <td>0.88106796116504854369</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.8</td>\n",
- " <td>658</td>\n",
- " <td>79</td>\n",
- " <td>166</td>\n",
+ " <td>697</td>\n",
+ " <td>91</td>\n",
+ " <td>115</td>\n",
" <td>350</td>\n",
- " <td>0.79854368932</td>\n",
- " <td>0.815850815851</td>\n",
- " <td>0.892808683853</td>\n",
- " <td>0.678294573643</td>\n",
- " <td>0.184149184149</td>\n",
- " <td>0.107191316147</td>\n",
- " <td>0.20145631068</td>\n",
- " <td>0.804469273743</td>\n",
- " <td>0.84304932735426008969</td>\n",
+ " <td>0.858374384236</td>\n",
+ " <td>0.793650793651</td>\n",
+ " <td>0.884517766497</td>\n",
+ " <td>0.752688172043</td>\n",
+ " <td>0.206349206349</td>\n",
+ " <td>0.115482233503</td>\n",
+ " <td>0.141625615764</td>\n",
+ " <td>0.835594573025</td>\n",
+ " <td>0.87125000000000000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.9</td>\n",
- " <td>619</td>\n",
- " <td>60</td>\n",
- " <td>205</td>\n",
- " <td>369</td>\n",
- " <td>0.751213592233</td>\n",
- " <td>0.86013986014</td>\n",
- " <td>0.911634756996</td>\n",
- " <td>0.642857142857</td>\n",
- " <td>0.13986013986</td>\n",
- " <td>0.0883652430044</td>\n",
- " <td>0.248786407767</td>\n",
- " <td>0.788507581804</td>\n",
- " <td>0.82368596141051230872</td>\n",
+ " <td>650</td>\n",
+ " <td>62</td>\n",
+ " <td>162</td>\n",
+ " <td>379</td>\n",
+ " <td>0.800492610837</td>\n",
+ " <td>0.859410430839</td>\n",
+ " <td>0.912921348315</td>\n",
+ " <td>0.700554528651</td>\n",
+ " <td>0.140589569161</td>\n",
+ " <td>0.0870786516854</td>\n",
+ " <td>0.199507389163</td>\n",
+ " <td>0.821229050279</td>\n",
+ " <td>0.85301837270341207349</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1.0</td>\n",
- " <td>538</td>\n",
- " <td>38</td>\n",
- " <td>286</td>\n",
- " <td>391</td>\n",
- " <td>0.652912621359</td>\n",
- " <td>0.911421911422</td>\n",
- " <td>0.934027777778</td>\n",
- " <td>0.577548005908</td>\n",
- " <td>0.0885780885781</td>\n",
- " <td>0.0659722222222</td>\n",
- " <td>0.347087378641</td>\n",
- " <td>0.741420590583</td>\n",
- " <td>0.76857142857142857143</td>\n",
+ " <td>529</td>\n",
+ " <td>40</td>\n",
+ " <td>283</td>\n",
+ " <td>401</td>\n",
+ " <td>0.651477832512</td>\n",
+ " <td>0.909297052154</td>\n",
+ " <td>0.929701230228</td>\n",
+ " <td>0.586257309942</td>\n",
+ " <td>0.0907029478458</td>\n",
+ " <td>0.0702987697715</td>\n",
+ " <td>0.348522167488</td>\n",
+ " <td>0.74221867518</td>\n",
+ " <td>0.76611151339608979001</td>\n",
" </tr>\n",
"</table>"
],
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+ " (1.0, Decimal('529'), Decimal('40'), Decimal('283'), Decimal('401'), 0.651477832512315, 0.909297052154195, 0.929701230228471, 0.58625730994152, 0.090702947845805, 0.070298769771529, 0.348522167487685, 0.742218675179569, Decimal('0.76611151339608979001'))]"
]
},
- "execution_count": 38,
+ "execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
@@ -3594,7 +3811,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
@@ -3613,22 +3830,22 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.axes._subplots.AxesSubplot at 0x133b46650>"
+ "<matplotlib.axes._subplots.AxesSubplot at 0x12665c490>"
]
},
- "execution_count": 40,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3645,7 +3862,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
@@ -3665,15 +3882,15 @@
" <th>area_under_roc</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>0.87614287007490890986052886029827777400595</td>\n",
+ " <td>0.89330116282966388525837478916032751359985</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(Decimal('0.87614287007490890986052886029827777400595'),)]"
+ "[(Decimal('0.89330116282966388525837478916032751359985'),)]"
]
},
- "execution_count": 41,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
@@ -3713,7 +3930,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
@@ -3760,29 +3977,29 @@
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>8</td>\n",
- " <td>0.475</td>\n",
- " <td>0.37</td>\n",
- " <td>0.125</td>\n",
- " <td>0.5095</td>\n",
- " <td>0.2165</td>\n",
- " <td>0.1125</td>\n",
- " <td>0.165</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>1</td>\n",
" <td>9</td>\n",
- " <td>10.5</td>\n",
+ " <td>0.55</td>\n",
+ " <td>0.44</td>\n",
+ " <td>0.15</td>\n",
+ " <td>0.8945</td>\n",
+ " <td>0.3145</td>\n",
+ " <td>0.151</td>\n",
+ " <td>0.32</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>19</td>\n",
+ " <td>20.5</td>\n",
" <td>1</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, 0.35, 0.265, 0.09, 0.2255, 0.0995, 0.0485, 0.07, 0, 0, 1, 7, Decimal('8.5'), 0),\n",
- " (8, 0.475, 0.37, 0.125, 0.5095, 0.2165, 0.1125, 0.165, 0, 0, 1, 9, Decimal('10.5'), 1)]"
+ " (9, 0.55, 0.44, 0.15, 0.8945, 0.3145, 0.151, 0.32, 1, 0, 0, 19, Decimal('20.5'), 1)]"
]
},
- "execution_count": 42,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
@@ -3796,7 +4013,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
@@ -3827,25 +4044,25 @@
" <th>variance_covariance</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>[4.410357699688, 1.27175722855722, 9.64141451253988, 9.33882415610471, 9.5183803866192, -20.1419380713264, -12.1045936650436, 8.74328089164249, 0.817371808454084, 0.876398154242832]</td>\n",
- " <td>0.545016598</td>\n",
- " <td>[0.317420812747666, 2.14523563920031, 2.63744840317991, 1.71067733937981, 0.892068852840643, 1.00753029831926, 1.58728493291856, 1.39432077797183, 0.123340170109636, 0.11520852987613]</td>\n",
- " <td>[13.894355765493, 0.592828687589443, 3.65558412476067, 5.45913828465771, 10.6700064196945, -19.991396888934, -7.62597402268961, 6.27063802660992, 6.6269716324173, 7.60705960908552]</td>\n",
- " <td>[1.51375990657579e-42, 0.55334180633369, 0.000261129452464093, 5.18665415881891e-08, 4.23697868795871e-26, 2.00173103596502e-83, 3.25682639467242e-14, 4.12820640447016e-10, 4.06638451488027e-11, 3.7597744950155e-14]</td>\n",
- " <td>136.28036679</td>\n",
- " <td>345.394268412</td>\n",
- " <td>5.91944435666e-69</td>\n",
+ " <td>[4.68130687790161, -1.86032692402494, 13.1337133646984, 9.36556248253984, 9.15490233796625, -19.8355744128492, -9.45078918839029, 7.28175512507301, 0.82552533889907, 0.851845802967165]</td>\n",
+ " <td>0.524226908127</td>\n",
+ " <td>[0.325501686813575, 2.21450278665342, 2.69481453648041, 1.67685708658124, 0.846912057199183, 0.975099282125397, 1.53333189633054, 1.32122333145544, 0.123253358534541, 0.115179060509793]</td>\n",
+ " <td>[14.3818206404034, -0.840065289254517, 4.87369842595988, 5.58518824143461, 10.809743774628, -20.3421074924947, -6.16356394268405, 5.51137340047691, 6.69779183881402, 7.39583913253694]</td>\n",
+ " <td>[2.31896345659929e-45, 0.400940696068839, 1.15390387709793e-06, 2.54960021405737e-08, 9.88008208055196e-27, 3.97564395495313e-86, 8.09318445456783e-10, 3.87139941463432e-08, 2.52935540568213e-11, 1.82743538026922e-13]</td>\n",
+ " <td>139.647947137</td>\n",
+ " <td>303.441716007</td>\n",
+ " <td>4.8590409809e-60</td>\n",
" <td>2924</td>\n",
" <td>0</td>\n",
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+ " <td>[[0.105951348118483, -0.292329098226724, 0.0194603449925006, -0.0871355644604966, 0.0146423904770704, 0.0390601332305124, 0.0589660004544375, 0.0656130962684031, 0.00222975556481855, 0.000152924021689542], [-0.292329098226724, 4.90402259209574, -5.36636267929686, -0.0786225408257424, 0.0220697166917959, -0.169874156804451, -0.337971118818921, 0.0657973228862898, 0.0167393243815521, 0.0136965714655914], [0.0194603449925005, -5.36636267929686, 7.2620253860261, -0.437146854612175, -0.053808296838563, 0.0181406050801768, 0.227114374073945, -0.375033629840268, -0.0374784317066128, -0.0274059586165004], [-0.0871355644604966, -0.0786225408257424, -0.437146854612175, 2.81184968881772, -0.0299439811129126, 0.0445862732437427, -0.128425515936003, -0.190501468163422, -0.0149539942784054, -0.0107792271561558], [0.0146423904770705, 0.0220697166917958, -0.0538082968385629, -0.0299439811129126, 0.717260032629352, -0.671693721463654, -0.771338497709632, -0.883172823008288, -0.00421870870335176, -0.00363929394666502], [0.0390601332305124, -0.169874156804451, 0.0181406050801768, 0.0445862732437427, -0.671693721463655, 0.950818610001464, 0.331613424031253, 0.782013976696468, 0.0102411355006135, 0.00420242508277869], [0.0589660004544376, -0.337971118818921, 0.227114374073946, -0.128425515936003, -0.771338497709632, 0.331613424031252, 2.35110670430461, 0.61385954457365, -0.0150418231892282, -0.00922276580850259], [0.0656130962684031, 0.0657973228862898, -0.375033629840268, -0.190501468163422, -0.883172823008288, 0.782013976696468, 0.61385954457365, 1.74563109158222, 0.00308257049568346, 0.00310841801699227], [0.00222975556481855, 0.0167393243815521, -0.0374784317066128, -0.0149539942784054, -0.00421870870335176, 0.0102411355006135, -0.0150418231892282, 0.00308257049568346, 0.0151913903900442, 0.00919494968673803], [0.000152924021689539, 0.0136965714655914, -0.0274059586165004, -0.0107792271561558, -0.00363929394666502, 0.00420242508277869, -0.00922276580850259, 0.00310841801699227, 0.00919494968673804, 0.0132662159799185]]</td>\n",
" </tr>\n",
"</table>"
],
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+ "[([4.68130687790161, -1.86032692402494, 13.1337133646984, 9.36556248253984, 9.15490233796625, -19.8355744128492, -9.45078918839029, 7.28175512507301, 0.82552533889907, 0.851845802967165], 0.524226908126896, [0.325501686813575, 2.21450278665342, 2.69481453648041, 1.67685708658124, 0.846912057199183, 0.975099282125397, 1.53333189633054, 1.32122333145544, 0.123253358534541, 0.115179060509793], [14.3818206404034, -0.840065289254517, 4.87369842595988, 5.58518824143461, 10.809743774628, -20.3421074924947, -6.16356394268405, 5.51137340047691, 6.69779183881402, 7.39583913253694], [2.31896345659929e-45, 0.400940696068839, 1.15390387709793e-06, 2.54960021405737e-08, 9.88008208055196e-27, 3.97564395495313e-86, 8.09318445456783e-10, 3.87139941463432e-08, 2.52935540568213e-11, 1.82743538026922e-13], 139.647947137047, 303.441716007005, 4.8590409808993e-60, 2924L, 0L, [[0.105951348118483, -0.292329098226724, 0.0194603449925006, -0.0871355644604966, 0.0146423904770704, 0.0390601332305124, 0.0589660004544375, 0.065613 ... (1737 characters truncated) ... , -0.0107792271561558, -0.00363929394666502, 0.00420242508277869, -0.00922276580850259, 0.00310841801699227, 0.00919494968673804, 0.0132662159799185]])]"
]
},
- "execution_count": 43,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@@ -3888,7 +4105,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
@@ -3910,41 +4127,41 @@
" <th>actual_age</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>3918</td>\n",
- " <td>14.0356025263</td>\n",
- " <td>19.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>684</td>\n",
- " <td>11.9578243725</td>\n",
- " <td>11.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1664</td>\n",
- " <td>10.2519022088</td>\n",
+ " <td>1132</td>\n",
+ " <td>12.0617258993</td>\n",
" <td>10.5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>3936</td>\n",
- " <td>12.7607562191</td>\n",
- " <td>14.5</td>\n",
+ " <td>4053</td>\n",
+ " <td>13.2360737267</td>\n",
+ " <td>11.5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>654</td>\n",
- " <td>9.80860239621</td>\n",
- " <td>11.5</td>\n",
+ " <td>2487</td>\n",
+ " <td>12.8595897242</td>\n",
+ " <td>12.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2547</td>\n",
+ " <td>7.26327656336</td>\n",
+ " <td>6.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1723</td>\n",
+ " <td>13.7737768871</td>\n",
+ " <td>13.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(3918, 14.0356025263472, Decimal('19.5')),\n",
- " (684, 11.9578243724615, Decimal('11.5')),\n",
- " (1664, 10.2519022087522, Decimal('10.5')),\n",
- " (3936, 12.760756219147, Decimal('14.5')),\n",
- " (654, 9.80860239621498, Decimal('11.5'))]"
+ "[(1132, 12.0617258992578, Decimal('10.5')),\n",
+ " (4053, 13.2360737267023, Decimal('11.5')),\n",
+ " (2487, 12.8595897242336, Decimal('12.5')),\n",
+ " (2547, 7.26327656336034, Decimal('6.5')),\n",
+ " (1723, 13.773776887084, Decimal('13.5'))]"
]
},
- "execution_count": 44,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@@ -3981,7 +4198,7 @@
},
{
"cell_type": "code",
- "execution_count": 45,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
@@ -4001,15 +4218,15 @@
" <th>mean_squared_error</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>4.70025372012</td>\n",
+ " <td>4.58151398735</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(4.70025372011683,)]"
+ "[(4.58151398734849,)]"
]
},
- "execution_count": 45,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -4041,7 +4258,7 @@
},
{
"cell_type": "code",
- "execution_count": 46,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
@@ -4069,7 +4286,7 @@
"[('',)]"
]
},
- "execution_count": 46,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -4108,7 +4325,7 @@
},
{
"cell_type": "code",
- "execution_count": 47,
+ "execution_count": 48,
"metadata": {},
"outputs": [
{
@@ -4137,20 +4354,20 @@
" <td>gaussian</td>\n",
" <td>[u'[1]', u'[2]', u'[3]', u'[4]', u'[5]', u'[6]', u'[7]', u'[8]', u'[9]']</td>\n",
" <td>[u'[1]', u'[2]', u'[3]', u'[7]', u'[8]']</td>\n",
- " <td>[1.3220343857, 3.14984818966, 8.52839367593, 6.31126111497, 0.120956346376]</td>\n",
- " <td>[1.3220343857, 3.14984818966, 8.52839367593, 0.0, 0.0, 0.0, 6.31126111497, 0.120956346376, 0.0]</td>\n",
- " <td>6.74458430257</td>\n",
- " <td>-3.83846837185</td>\n",
+ " <td>[1.21459288235, 3.33557620239, 8.12688807296, 5.89517432659, 0.10448485871]</td>\n",
+ " <td>[1.21459288235, 3.33557620239, 8.12688807296, 0.0, 0.0, 0.0, 5.89517432659, 0.10448485871, 0.0]</td>\n",
+ " <td>6.87900865574</td>\n",
+ " <td>-3.78542947405</td>\n",
" <td>True</td>\n",
- " <td>193</td>\n",
+ " <td>107</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'gaussian', [u'[1]', u'[2]', u'[3]', u'[4]', u'[5]', u'[6]', u'[7]', u'[8]', u'[9]'], [u'[1]', u'[2]', u'[3]', u'[7]', u'[8]'], [1.3220343857, 3.14984818966, 8.52839367593, 6.31126111497, 0.120956346376], [1.3220343857, 3.14984818966, 8.52839367593, 0.0, 0.0, 0.0, 6.31126111497, 0.120956346376, 0.0], 6.74458430257, -3.83846837185, True, 193)]"
+ "[(u'gaussian', [u'[1]', u'[2]', u'[3]', u'[4]', u'[5]', u'[6]', u'[7]', u'[8]', u'[9]'], [u'[1]', u'[2]', u'[3]', u'[7]', u'[8]'], [1.21459288235, 3.33557620239, 8.12688807296, 5.89517432659, 0.10448485871], [1.21459288235, 3.33557620239, 8.12688807296, 0.0, 0.0, 0.0, 5.89517432659, 0.10448485871, 0.0], 6.87900865574, -3.78542947405, True, 107)]"
]
},
- "execution_count": 47,
+ "execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
@@ -4162,7 +4379,7 @@
},
{
"cell_type": "code",
- "execution_count": 48,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
@@ -4208,7 +4425,7 @@
"[(u'elastic_net', u'abalone_classif_train', u'abalone_elasticnet_model', u'age', u'ARRAY[\\n length,\\n diameter,\\n height,\\n whole_weight,\\n shucked_weight,\\n viscera_weight,\\n shell_weight,\\n sex_f,\\n sex_m\\n ]', u'gaussian', 0.5, 0.5, u'NULL', 1, 0)]"
]
},
- "execution_count": 48,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -4220,7 +4437,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 50,
"metadata": {},
"outputs": [
{
@@ -4237,7 +4454,7 @@
"[]"
]
},
- "execution_count": 49,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
@@ -4271,7 +4488,7 @@
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 51,
"metadata": {},
"outputs": [
{
@@ -4291,71 +4508,71 @@
" <th>actual_age</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>3941</td>\n",
- " <td>12.5704131046</td>\n",
- " <td>16.5</td>\n",
+ " <td>2979</td>\n",
+ " <td>11.3946975401</td>\n",
+ " <td>9.5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>3441</td>\n",
- " <td>10.3744669188</td>\n",
- " <td>8.5</td>\n",
+ " <td>1148</td>\n",
+ " <td>11.6158230971</td>\n",
+ " <td>9.5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>92</td>\n",
- " <td>12.9497348984</td>\n",
- " <td>14.5</td>\n",
+ " <td>84</td>\n",
+ " <td>12.3445089064</td>\n",
+ " <td>15.5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>849</td>\n",
- " <td>11.982881522</td>\n",
- " <td>11.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>224</td>\n",
- " <td>10.029998676</td>\n",
- " <td>11.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>651</td>\n",
- " <td>9.10540972598</td>\n",
- " <td>7.5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1907</td>\n",
- " <td>11.6905236073</td>\n",
+ " <td>1121</td>\n",
+ " <td>11.0038577104</td>\n",
" <td>10.5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>2603</td>\n",
- " <td>12.8194751477</td>\n",
+ " <td>1129</td>\n",
+ " <td>11.3303543014</td>\n",
+ " <td>9.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>2384</td>\n",
+ " <td>10.3001744313</td>\n",
+ " <td>12.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1709</td>\n",
+ " <td>13.7373573387</td>\n",
" <td>10.5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>592</td>\n",
- " <td>11.5163675347</td>\n",
- " <td>19.5</td>\n",
+ " <td>3061</td>\n",
+ " <td>13.5259365913</td>\n",
+ " <td>11.5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>261</td>\n",
- " <td>11.7751231628</td>\n",
+ " <td>2761</td>\n",
+ " <td>11.5343145387</td>\n",
+ " <td>11.5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1351</td>\n",
+ " <td>12.3149150345</td>\n",
" <td>13.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(3941, 12.5704131046376, Decimal('16.5')),\n",
- " (3441, 10.374466918844, Decimal('8.5')),\n",
- " (92, 12.9497348983795, Decimal('14.5')),\n",
- " (849, 11.9828815219954, Decimal('11.5')),\n",
- " (224, 10.0299986760329, Decimal('11.5')),\n",
- " (651, 9.1054097259781, Decimal('7.5')),\n",
- " (1907, 11.6905236073399, Decimal('10.5')),\n",
- " (2603, 12.8194751476722, Decimal('10.5')),\n",
- " (592, 11.5163675346797, Decimal('19.5')),\n",
- " (261, 11.7751231627786, Decimal('13.5'))]"
+ "[(2979, 11.3946975400595, Decimal('9.5')),\n",
+ " (1148, 11.6158230971101, Decimal('9.5')),\n",
+ " (84, 12.3445089063653, Decimal('15.5')),\n",
+ " (1121, 11.0038577104079, Decimal('10.5')),\n",
+ " (1129, 11.330354301378, Decimal('9.5')),\n",
+ " (2384, 10.3001744312792, Decimal('12.5')),\n",
+ " (1709, 13.7373573387002, Decimal('10.5')),\n",
+ " (3061, 13.5259365912704, Decimal('11.5')),\n",
+ " (2761, 11.5343145387027, Decimal('11.5')),\n",
+ " (1351, 12.3149150344784, Decimal('13.5'))]"
]
},
- "execution_count": 50,
+ "execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
@@ -4367,7 +4584,7 @@
},
{
"cell_type": "code",
- "execution_count": 51,
+ "execution_count": 53,
"metadata": {},
"outputs": [
{
@@ -4387,15 +4604,15 @@
" <th>mean_squared_error</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>6.28606344646</td>\n",
+ " <td>6.59702758346</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(6.28606344645616,)]"
+ "[(6.59702758345526,)]"
]
},
- "execution_count": 51,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/community-artifacts/Graph/PageRank-v2.ipynb b/community-artifacts/Graph/PageRank-v2.ipynb
index eea174f..becfc4c 100644
--- a/community-artifacts/Graph/PageRank-v2.ipynb
+++ b/community-artifacts/Graph/PageRank-v2.ipynb
@@ -12,54 +12,29 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.4.0 on GCP (demo machine)\n",
- "%sql postgresql://gpadmin@35.184.253.255:5432/madlib\n",
+ "# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib\n",
- "\n",
- "# Greenplum Database 4.3.10.0\n",
- "#%sql postgresql://gpdbchina@10.194.10.68:61000/madlib"
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -77,15 +52,15 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.14-dev, git revision: rc/1.13-rc1-66-g4cced1b, cmake configuration time: Mon Apr 23 16:26:17 UTC 2018, build type: release, build system: Linux-2.6.32-696.20.1.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-100-g4987e8f, cmake configuration time: Wed Mar 24 23:51:47 UTC 2021, build type: release, build system: Linux-3.10.0-1160.21.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.14-dev, git revision: rc/1.13-rc1-66-g4cced1b, cmake configuration time: Mon Apr 23 16:26:17 UTC 2018, build type: release, build system: Linux-2.6.32-696.20.1.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]"
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-100-g4987e8f, cmake configuration time: Wed Mar 24 23:51:47 UTC 2021, build type: release, build system: Linux-3.10.0-1160.21.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -104,7 +79,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -265,7 +240,7 @@
" (6, 3, 2)]"
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -329,7 +304,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -389,7 +364,7 @@
" (5, 0.0525777229481976)]"
]
},
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -469,7 +444,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -529,7 +504,7 @@
" (5, 0.0884570570850217)]"
]
},
- "execution_count": 9,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -557,7 +532,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -651,23 +626,23 @@
"</table>"
],
"text/plain": [
- "[(1, 0, 0.278254883885528),\n",
- " (1, 3, 0.201881146670752),\n",
- " (1, 2, 0.142881123460599),\n",
- " (1, 6, 0.114536378321472),\n",
- " (1, 4, 0.10026745615438),\n",
- " (1, 1, 0.10026745615438),\n",
- " (1, 5, 0.0619115553528898),\n",
- " (2, 0, 0.318546250041731),\n",
- " (2, 3, 0.237866867733431),\n",
- " (2, 2, 0.159148764893974),\n",
- " (2, 1, 0.111683344379718),\n",
- " (2, 4, 0.111683344379718),\n",
- " (2, 6, 0.0396428571428571),\n",
- " (2, 5, 0.0214285714285714)]"
+ "[(1, 0L, 0.278254883885528),\n",
+ " (1, 3L, 0.201881146670752),\n",
+ " (1, 2L, 0.142881123460599),\n",
+ " (1, 6L, 0.114536378321472),\n",
+ " (1, 4L, 0.10026745615438),\n",
+ " (1, 1L, 0.10026745615438),\n",
+ " (1, 5L, 0.0619115553528898),\n",
+ " (2, 0L, 0.318546250041731),\n",
+ " (2, 3L, 0.237866867733431),\n",
+ " (2, 2L, 0.159148764893974),\n",
+ " (2, 1L, 0.111683344379718),\n",
+ " (2, 4L, 0.111683344379718),\n",
+ " (2, 6L, 0.0396428571428571),\n",
+ " (2, 5L, 0.0214285714285714)]"
]
},
- "execution_count": 10,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -881,7 +856,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Supervised-learning/Decision-trees-v2.ipynb b/community-artifacts/Supervised-learning/Decision-trees-v2.ipynb
index 5b55b03..f8e4c0b 100644
--- a/community-artifacts/Supervised-learning/Decision-trees-v2.ipynb
+++ b/community-artifacts/Supervised-learning/Decision-trees-v2.ipynb
@@ -15,44 +15,22 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 17,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.4.0 on GCP (demo machine)\n",
- "%sql postgresql://gpadmin@35.184.253.255:5432/madlib\n",
+ "# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
+ "#%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib"
+ "%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
@@ -110,7 +88,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -311,7 +289,7 @@
" (14, u'rain', 71.0, 80.0, [71.0, 80.0], [u'low', u'unhealthy'], True, u\"Don't Play\", 1.0)]"
]
},
- "execution_count": 23,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -361,7 +339,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -386,7 +364,7 @@
" </tr>\n",
" <tr>\n",
" <td>0</td>\n",
- " <td>[u'overcast', u'rain', u'sunny', u'False', u'True']</td>\n",
+ " <td>[u'overcast', u'rain', u'sunny', u'false', u'true']</td>\n",
" <td>[3, 2]</td>\n",
" <td>[0.102040816326531, 0.0, 0.85905612244898]</td>\n",
" <td>5</td>\n",
@@ -394,10 +372,10 @@
"</table>"
],
"text/plain": [
- "[(0, [u'overcast', u'rain', u'sunny', u'False', u'True'], [3, 2], [0.102040816326531, 0.0, 0.85905612244898], 5)]"
+ "[(0, [u'overcast', u'rain', u'sunny', u'false', u'true'], [3, 2], [0.102040816326531, 0.0, 0.85905612244898], 5)]"
]
},
- "execution_count": 24,
+ "execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
@@ -433,7 +411,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -466,8 +444,8 @@
" <th>total_rows_skipped</th>\n",
" <th>dependent_var_levels</th>\n",
" <th>dependent_var_type</th>\n",
- " <th>input_cp</th>\n",
" <th>independent_var_types</th>\n",
+ " <th>input_cp</th>\n",
" <th>n_folds</th>\n",
" <th>null_proxy</th>\n",
" </tr>\n",
@@ -488,20 +466,20 @@
" <td>0</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
- " <td>\"Don't Play\",\"Play\"</td>\n",
+ " <td>Don't Play,Play</td>\n",
" <td>text</td>\n",
- " <td>0.0</td>\n",
" <td>text, boolean, double precision</td>\n",
+ " <td>0.0</td>\n",
" <td>0</td>\n",
" <td>None</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'tree_train', True, u'dt_golf', u'train_output', u'id', u'\"OUTLOOK\", temperature, windy', u'None', u'class', u'\"OUTLOOK\",windy,temperature', u'\"OUTLOOK\",windy', u'temperature', None, 1, 0, 14, 0, u'\"Don\\'t Play\",\"Play\"', u'text', 0.0, u'text, boolean, double precision', 0, None)]"
+ "[(u'tree_train', True, u'dt_golf', u'train_output', u'id', u'\"OUTLOOK\", temperature, windy', u'None', u'class', u'\"OUTLOOK\",windy,temperature', u'\"OUTLOOK\",windy', u'temperature', None, 1, 0, 14, 0, u\"Don't Play,Play\", u'text', u'text, boolean, double precision', 0.0, 0, None)]"
]
},
- "execution_count": 25,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -520,7 +498,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -560,7 +538,7 @@
" (u'windy', 0.0)]"
]
},
- "execution_count": 26,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -585,7 +563,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -695,7 +673,7 @@
" (14, u\"Don't Play\", u\"Don't Play\")]"
]
},
- "execution_count": 27,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -722,7 +700,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -847,7 +825,7 @@
" (14, u\"Don't Play\", 1.0, 0.0)]"
]
},
- "execution_count": 28,
+ "execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
@@ -874,7 +852,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -892,15 +870,15 @@
" <th>tree_display</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>-------------------------------------<br> - Each node represented by 'id' inside ().<br> - Each internal nodes has the split condition at the end, while each<br> leaf node has a * at the end.<br> - For each internal node (i), its child nodes are indented by 1 level<br> with ids (2i+1) for True node and (2i+2) for False node.<br> - Number of (weighted) rows for each response variable inside [].'<br> The response label order is given as ['\"Don\\'t Play\"', '\"Play\"'].<br> For each leaf, the prediction is given after the '-->'<br> <br>-------------------------------------<br>(0)[5 9] \"OUTLOOK\" in {overcast}<br> (1)[0 4] * --> \"Play\"<br> (2)[5 5] temperature <= 75<br> (5)[3 5] temperature <= 65<br> (11)[1 0] * --> \"Don't Play\"<br> (12)[2 5] temperature <= 70<br> (25)[0 3] * --> \"Play\"<br> (26)[2 2] temperature <= 72<br> (53)[2 0] * --> \"Don't Play\"<br> (54)[0 2] * --> \"Play\"<br> (6)[2 0] * --> \"Don't Play\"<br><br>-------------------------------------</td>\n",
+ " <td>-------------------------------------<br> - Each node represented by 'id' inside ().<br> - Each internal nodes has the split condition at the end, while each<br> leaf node has a * at the end.<br> - For each internal node (i), its child nodes are indented by 1 level<br> with ids (2i+1) for True node and (2i+2) for False node.<br> - Number of (weighted) rows for each response variable inside [].'<br> The response label order is given as [\"Don't Play\", 'Play'].<br> For each leaf, the prediction is given after the '-->'<br> <br>-------------------------------------<br>(0)[5 9] \"OUTLOOK\" in {overcast}<br> (1)[0 4] * --> Play<br> (2)[5 5] temperature <= 75<br> (5)[3 5] temperature <= 65<br> (11)[1 0] * --> Don't Play<br> (12)[2 5] temperature <= 70<br> (25)[0 3] * --> Play<br> (26)[2 2] temperature <= 72<br> (53)[2 0] * --> Don't Play<br> (54)[0 2] * --> Play<br> (6)[2 0] * --> Don't Play<br><br>-------------------------------------</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'-------------------------------------\\n - Each node represented by \\'id\\' inside ().\\n - Each internal nodes has the split condition at the end, while each\\n leaf node has a * at the end.\\n - For each internal node (i), its child nodes are indented by 1 level\\n with ids (2i+1) for True node and (2i+2) for False node.\\n - Number of (weighted) rows for each response variable inside [].\\'\\n The response label order is given as [\\'\"Don\\\\\\'t Play\"\\', \\'\"Play\"\\'].\\n For each leaf, the prediction is given after the \\'-->\\'\\n \\n-------------------------------------\\n(0)[5 9] \"OUTLOOK\" in {overcast}\\n (1)[0 4] * --> \"Play\"\\n (2)[5 5] temperature <= 75\\n (5)[3 5] temperature <= 65\\n (11)[1 0] * --> \"Don\\'t Play\"\\n (12)[2 5] temperature <= 70\\n (25)[0 3] * --> \"Play\"\\n (26)[2 2] temperature <= 72\\n (53)[2 0] * --> \"Don\\'t Play\"\\n (54)[0 2] * --> \"Play\"\\n (6)[2 0] * --> \"Don\\'t Play\"\\n\\n-------------------------------------',)]"
+ "[(u'-------------------------------------\\n - Each node represented by \\'id\\' inside ().\\n - Each internal nodes has the split condition at the en ... (746 characters truncated) ... (53)[2 0] * --> Don\\'t Play\\n (54)[0 2] * --> Play\\n (6)[2 0] * --> Don\\'t Play\\n\\n-------------------------------------',)]"
]
},
- "execution_count": 29,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -934,7 +912,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 25,
"metadata": {},
"outputs": [
{
@@ -952,15 +930,15 @@
" <th>tree_display</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>digraph \"Classification tree for dt_golf\" {<br>\t subgraph \"cluster0\"{<br>\t label=\"\"<br>\"g0_0\" [label=\"\\\"OUTLOOK\\\" <= overcast\", shape=ellipse];<br>\"g0_0\" -> \"g0_1\"[label=\"yes\"];<br>\"g0_1\" [label=\"\\\"Play\\\"\",shape=box];<br>\"g0_0\" -> \"g0_2\"[label=\"no\"];<br>\"g0_2\" [label=\"temperature <= 75\", shape=ellipse];<br>\"g0_2\" -> \"g0_5\"[label=\"yes\"];<br>\"g0_2\" -> \"g0_6\"[label=\"no\"];<br>\"g0_6\" [label=\"\\\"Don't Play\\\"\",shape=box];<br>\"g0_5\" [label=\"temperature <= 65\", shape=ellipse];<br>\"g0_5\" -> \"g0_11\"[label=\"yes\"];<br>\"g0_11\" [label=\"\\\"Don't Play\\\"\",shape=box];<br>\"g0_5\" -> \"g0_12\"[label=\"no\"];<br>\"g0_12\" [label=\"temperature <= 70\", shape=ellipse];<br>\"g0_12\" -> \"g0_25\"[label=\"yes\"];<br>\"g0_25\" [label=\"\\\"Play\\\"\",shape=box];<br>\"g0_12\" -> \"g0_26\"[label=\"no\"];<br>\"g0_26\" [label=\"temperature <= 72\", shape=ellipse];<br>\"g0_26\" -> \"g0_53\"[label=\"yes\"];<br>\"g0_53\" [label=\"\\\"Don't Play\\\"\",shape=box];<br>\"g0_26\" -> \"g0_54\"[label=\"no\"];<br>\"g0_54\" [label=\"\\\"Play\\\"\",shape=box];<br><br>\t } //--- end of subgraph------------<br>} //---end of digraph--------- </td>\n",
+ " <td>digraph \"Classification tree for dt_golf\" {<br>\t subgraph \"cluster0\"{<br>\t label=\"\"<br>\"g0_0\" [label=\"\\\"OUTLOOK\\\" <= overcast\", shape=ellipse];<br>\"g0_0\" -> \"g0_1\"[label=\"yes\"];<br>\"g0_1\" [label=\"Play\",shape=box];<br>\"g0_0\" -> \"g0_2\"[label=\"no\"];<br>\"g0_2\" [label=\"temperature <= 75\", shape=ellipse];<br>\"g0_2\" -> \"g0_5\"[label=\"yes\"];<br>\"g0_2\" -> \"g0_6\"[label=\"no\"];<br>\"g0_6\" [label=\"Don't Play\",shape=box];<br>\"g0_5\" [label=\"temperature <= 65\", shape=ellipse];<br>\"g0_5\" -> \"g0_11\"[label=\"yes\"];<br>\"g0_11\" [label=\"Don't Play\",shape=box];<br>\"g0_5\" -> \"g0_12\"[label=\"no\"];<br>\"g0_12\" [label=\"temperature <= 70\", shape=ellipse];<br>\"g0_12\" -> \"g0_25\"[label=\"yes\"];<br>\"g0_25\" [label=\"Play\",shape=box];<br>\"g0_12\" -> \"g0_26\"[label=\"no\"];<br>\"g0_26\" [label=\"temperature <= 72\", shape=ellipse];<br>\"g0_26\" -> \"g0_53\"[label=\"yes\"];<br>\"g0_53\" [label=\"Don't Play\",shape=box];<br>\"g0_26\" -> \"g0_54\"[label=\"no\"];<br>\"g0_54\" [label=\"Play\",shape=box];<br><br>\t } //--- end of subgraph------------<br>} //---end of digraph--------- </td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'digraph \"Classification tree for dt_golf\" {\\n\\t subgraph \"cluster0\"{\\n\\t label=\"\"\\n\"g0_0\" [label=\"\\\\\"OUTLOOK\\\\\" <= overcast\", shape=ellipse];\\n\"g0_0\" -> \"g0_1\"[label=\"yes\"];\\n\"g0_1\" [label=\"\\\\\"Play\\\\\"\",shape=box];\\n\"g0_0\" -> \"g0_2\"[label=\"no\"];\\n\"g0_2\" [label=\"temperature <= 75\", shape=ellipse];\\n\"g0_2\" -> \"g0_5\"[label=\"yes\"];\\n\"g0_2\" -> \"g0_6\"[label=\"no\"];\\n\"g0_6\" [label=\"\\\\\"Don\\'t Play\\\\\"\",shape=box];\\n\"g0_5\" [label=\"temperature <= 65\", shape=ellipse];\\n\"g0_5\" -> \"g0_11\"[label=\"yes\"];\\n\"g0_11\" [label=\"\\\\\"Don\\'t Play\\\\\"\",shape=box];\\n\"g0_5\" -> \"g0_12\"[label=\"no\"];\\n\"g0_12\" [label=\"temperature <= 70\", shape=ellipse];\\n\"g0_12\" -> \"g0_25\"[label=\"yes\"];\\n\"g0_25\" [label=\"\\\\\"Play\\\\\"\",shape=box];\\n\"g0_12\" -> \"g0_26\"[label=\"no\"];\\n\"g0_26\" [label=\"temperature <= 72\", shape=ellipse];\\n\"g0_26\" -> \"g0_53\"[label=\"yes\"];\\n\"g0_53\" [label=\"\\\\\"Don\\'t Play\\\\\"\",shape=box];\\n\"g0_26\" -> \"g0_54\"[label=\"no\"];\\n\"g0_54\" [label=\"\\\\\"Play\\\\\"\",shape=box];\\n\\n\\t } //--- end of subgraph------------\\n} //---end of digraph--------- ',)]"
+ "[(u'digraph \"Classification tree for dt_golf\" {\\n\\t subgraph \"cluster0\"{\\n\\t label=\"\"\\n\"g0_0\" [label=\"\\\\\"OUTLOOK\\\\\" <= overcast\", shape=ellipse];\\n\"g0_0 ... (683 characters truncated) ... =box];\\n\"g0_26\" -> \"g0_54\"[label=\"no\"];\\n\"g0_54\" [label=\"Play\",shape=box];\\n\\n\\t } //--- end of subgraph------------\\n} //---end of digraph--------- ',)]"
]
},
- "execution_count": 11,
+ "execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
@@ -979,7 +957,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 26,
"metadata": {},
"outputs": [
{
@@ -997,15 +975,15 @@
" <th>tree_display</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>digraph \"Classification tree for dt_golf\" {<br>\t subgraph \"cluster0\"{<br>\t label=\"\"<br>\"g0_0\" [label=\"\\\"OUTLOOK\\\" <= overcast\\n impurity = 0.459184\\n samples = 14\\n value = [5 9]\\n class = \\\"Play\\\"\", shape=ellipse];<br>\"g0_0\" -> \"g0_1\"[label=\"yes\"];<br>\"g0_1\" [label=\"\\\"Play\\\"\\n impurity = 0\\n samples = 4\\n value = [0 4]\",shape=box];<br>\"g0_0\" -> \"g0_2\"[label=\"no\"];<br>\"g0_2\" [label=\"temperature <= 75\\n impurity = 0.5\\n samples = 10\\n value = [5 5]\\n class = \\\"Don't Play\\\"\", shape=ellipse];<br>\"g0_2\" -> \"g0_5\"[label=\"yes\"];<br>\"g0_2\" -> \"g0_6\"[label=\"no\"];<br>\"g0_6\" [label=\"\\\"Don't Play\\\"\\n impurity = 0\\n samples = 2\\n value = [2 0]\",shape=box];<br>\"g0_5\" [label=\"temperature <= 65\\n impurity = 0.46875\\n samples = 8\\n value = [3 5]\\n class = \\\"Play\\\"\", shape=ellipse];<br>\"g0_5\" -> \"g0_11\"[label=\"yes\"];<br>\"g0_11\" [label=\"\\\"Don't Play\\\"\\n impurity = 0\\n samples = 1\\n value = [1 0]\",shape=box];<br>\"g0_5\" -> \"g0_12\"[label=\"no\"];<br>\"g0_12\" [label=\"temperature <= 70\\n impurity = 0.408163\\n samples = 7\\n value = [2 5]\\n class = \\\"Play\\\"\", shape=ellipse];<br>\"g0_12\" -> \"g0_25\"[label=\"yes\"];<br>\"g0_25\" [label=\"\\\"Play\\\"\\n impurity = 0\\n samples = 3\\n value = [0 3]\",shape=box];<br>\"g0_12\" -> \"g0_26\"[label=\"no\"];<br>\"g0_26\" [label=\"temperature <= 72\\n impurity = 0.5\\n samples = 4\\n value = [2 2]\\n class = \\\"Don't Play\\\"\", shape=ellipse];<br>\"g0_26\" -> \"g0_53\"[label=\"yes\"];<br>\"g0_53\" [label=\"\\\"Don't Play\\\"\\n impurity = 0\\n samples = 2\\n value = [2 0]\",shape=box];<br>\"g0_26\" -> \"g0_54\"[label=\"no\"];<br>\"g0_54\" [label=\"\\\"Play\\\"\\n impurity = 0\\n samples = 2\\n value = [0 2]\",shape=box];<br><br>\t } //--- end of subgraph------------<br>} //---end of digraph--------- </td>\n",
+ " <td>digraph \"Classification tree for dt_golf\" {<br>\t subgraph \"cluster0\"{<br>\t label=\"\"<br>\"g0_0\" [label=\"\\\"OUTLOOK\\\" <= overcast\\n impurity = 0.459184\\n samples = 14\\n value = [5 9]\\n class = Play\", shape=ellipse];<br>\"g0_0\" -> \"g0_1\"[label=\"yes\"];<br>\"g0_1\" [label=\"Play\\n impurity = 0\\n samples = 4\\n value = [0 4]\",shape=box];<br>\"g0_0\" -> \"g0_2\"[label=\"no\"];<br>\"g0_2\" [label=\"temperature <= 75\\n impurity = 0.5\\n samples = 10\\n value = [5 5]\\n class = Don't Play\", shape=ellipse];<br>\"g0_2\" -> \"g0_5\"[label=\"yes\"];<br>\"g0_2\" -> \"g0_6\"[label=\"no\"];<br>\"g0_6\" [label=\"Don't Play\\n impurity = 0\\n samples = 2\\n value = [2 0]\",shape=box];<br>\"g0_5\" [label=\"temperature <= 65\\n impurity = 0.46875\\n samples = 8\\n value = [3 5]\\n class = Play\", shape=ellipse];<br>\"g0_5\" -> \"g0_11\"[label=\"yes\"];<br>\"g0_11\" [label=\"Don't Play\\n impurity = 0\\n samples = 1\\n value = [1 0]\",shape=box];<br>\"g0_5\" -> \"g0_12\"[label=\"no\"];<br>\"g0_12\" [label=\"temperature <= 70\\n impurity = 0.408163\\n samples = 7\\n value = [2 5]\\n class = Play\", shape=ellipse];<br>\"g0_12\" -> \"g0_25\"[label=\"yes\"];<br>\"g0_25\" [label=\"Play\\n impurity = 0\\n samples = 3\\n value = [0 3]\",shape=box];<br>\"g0_12\" -> \"g0_26\"[label=\"no\"];<br>\"g0_26\" [label=\"temperature <= 72\\n impurity = 0.5\\n samples = 4\\n value = [2 2]\\n class = Don't Play\", shape=ellipse];<br>\"g0_26\" -> \"g0_53\"[label=\"yes\"];<br>\"g0_53\" [label=\"Don't Play\\n impurity = 0\\n samples = 2\\n value = [2 0]\",shape=box];<br>\"g0_26\" -> \"g0_54\"[label=\"no\"];<br>\"g0_54\" [label=\"Play\\n impurity = 0\\n samples = 2\\n value = [0 2]\",shape=box];<br><br>\t } //--- end of subgraph------------<br>} //---end of digraph--------- </td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'digraph \"Classification tree for dt_golf\" {\\n\\t subgraph \"cluster0\"{\\n\\t label=\"\"\\n\"g0_0\" [label=\"\\\\\"OUTLOOK\\\\\" <= overcast\\\\n impurity = 0.459184\\\\n samples = 14\\\\n value = [5 9]\\\\n class = \\\\\"Play\\\\\"\", shape=ellipse];\\n\"g0_0\" -> \"g0_1\"[label=\"yes\"];\\n\"g0_1\" [label=\"\\\\\"Play\\\\\"\\\\n impurity = 0\\\\n samples = 4\\\\n value = [0 4]\",shape=box];\\n\"g0_0\" -> \"g0_2\"[label=\"no\"];\\n\"g0_2\" [label=\"temperature <= 75\\\\n impurity = 0.5\\\\n samples = 10\\\\n value = [5 5]\\\\n class = \\\\\"Don\\'t Play\\\\\"\", shape=ellipse];\\n\"g0_2\" -> \"g0_5\"[label=\"yes\"];\\n\"g0_2\" -> \"g0_6\"[label=\"no\"];\\n\"g0_6\" [label=\"\\\\\"Don\\'t Play\\\\\"\\\\n impurity = 0\\\\n samples = 2\\\\n value = [2 0]\",shape=box];\\n\"g0_5\" [label=\"temperature <= 65\\\\n impurity = 0.46875\\\\n samples = 8\\\\n value = [3 5]\\\\n class = \\\\\"Play\\\\\"\", shape=ellipse];\\n\"g0_5\" -> \"g0_11\"[label=\"yes\"];\\n\"g0_11\" [label=\"\\\\\"Don\\'t Play\\\\\"\\\\n impurity = 0\\\\n samples = 1\\\\n value = [1 0]\",shape=box];\\n\"g0_5\" -> \"g0_12\"[label=\"no\"];\\n\"g0_12\" [label=\"temperature <= 70\\\\n impurity = 0.408163\\\\n samples = 7\\\\n value = [2 5]\\\\n class = \\\\\"Play\\\\\"\", shape=ellipse];\\n\"g0_12\" -> \"g0_25\"[label=\"yes\"];\\n\"g0_25\" [label=\"\\\\\"Play\\\\\"\\\\n impurity = 0\\\\n samples = 3\\\\n value = [0 3]\",shape=box];\\n\"g0_12\" -> \"g0_26\"[label=\"no\"];\\n\"g0_26\" [label=\"temperature <= 72\\\\n impurity = 0.5\\\\n samples = 4\\\\n value = [2 2]\\\\n class = \\\\\"Don\\'t Play\\\\\"\", shape=ellipse];\\n\"g0_26\" -> \"g0_53\"[label=\"yes\"];\\n\"g0_53\" [label=\"\\\\\"Don\\'t Play\\\\\"\\\\n impurity = 0\\\\n samples = 2\\\\n value = [2 0]\",shape=box];\\n\"g0_26\" -> \"g0_54\"[label=\"no\"];\\n\"g0_54\" [label=\"\\\\\"Play\\\\\"\\\\n impurity = 0\\\\n samples = 2\\\\n value = [0 2]\",shape=box];\\n\\n\\t } //--- end of subgraph------------\\n} //---end of digraph--------- ',)]"
+ "[(u'digraph \"Classification tree for dt_golf\" {\\n\\t subgraph \"cluster0\"{\\n\\t label=\"\"\\n\"g0_0\" [label=\"\\\\\"OUTLOOK\\\\\" <= overcast\\\\n impurity = 0.459184\\\\ ... (1331 characters truncated) ... [label=\"Play\\\\n impurity = 0\\\\n samples = 2\\\\n value = [0 2]\",shape=box];\\n\\n\\t } //--- end of subgraph------------\\n} //---end of digraph--------- ',)]"
]
},
- "execution_count": 12,
+ "execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
@@ -1024,7 +1002,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -1037,12 +1015,12 @@
},
{
"data": {
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MXJg4eBJHbC0jqmd/KOd3uf2QXH96D5hcJomUHyyyyCI6duFzP//8s2IiEVUCGKKnH3/8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\nopl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ybTwkITxna7k+/IUQjHFiP4Lx2GOPDdjNTCBia4tTvL/x/Vn/064pqumA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COcZnBQKvch+XoUke4MCfvBBx/ssBsVKarmWhkdzkxxe9R26FjK/tlf8q1iWiL1YzsoM8wR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\nYAc+ORuP+u1vf+u+9rWvZaKxtnPMxDClr5G4N7vpppvS1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JBGbRczi0DstTRN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+ "image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
- "execution_count": 13,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
@@ -1087,7 +1065,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -1114,16 +1092,16 @@
" <td>0</td>\n",
" <td>[u'medium', u'none', u'high', u'low', u'unhealthy', u'good', u'moderate']</td>\n",
" <td>[4, 3]</td>\n",
- " <td>[0.0, 0.330612244897959, 0.0466666666666666, 0.444444444444445]</td>\n",
+ " <td>[0.0, 0.330612244897959, 0.0466666666666666, 0.444444444444444]</td>\n",
" <td>3</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(0, [u'medium', u'none', u'high', u'low', u'unhealthy', u'good', u'moderate'], [4, 3], [0.0, 0.330612244897959, 0.0466666666666666, 0.444444444444445], 3)]"
+ "[(0, [u'medium', u'none', u'high', u'low', u'unhealthy', u'good', u'moderate'], [4, 3], [0.0, 0.330612244897959, 0.0466666666666666, 0.444444444444444], 3)]"
]
},
- "execution_count": 30,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -1159,7 +1137,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -1192,8 +1170,8 @@
" <th>total_rows_skipped</th>\n",
" <th>dependent_var_levels</th>\n",
" <th>dependent_var_type</th>\n",
- " <th>input_cp</th>\n",
" <th>independent_var_types</th>\n",
+ " <th>input_cp</th>\n",
" <th>n_folds</th>\n",
" <th>null_proxy</th>\n",
" </tr>\n",
@@ -1214,20 +1192,20 @@
" <td>0</td>\n",
" <td>14</td>\n",
" <td>0</td>\n",
- " <td>\"Don't Play\",\"Play\"</td>\n",
+ " <td>Don't Play,Play</td>\n",
" <td>text</td>\n",
- " <td>0.0</td>\n",
" <td>text, text, double precision, double precision</td>\n",
+ " <td>0.0</td>\n",
" <td>0</td>\n",
" <td>None</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'tree_train', True, u'dt_golf', u'train_output', u'id', u'\"Temp_Humidity\", clouds_airquality', u'None', u'class', u'(clouds_airquality)[1],(clouds_airquality)[2],(\"Temp_Humidity\")[1],(\"Temp_Humidity\")[2]', u'(clouds_airquality)[1],(clouds_airquality)[2]', u'(\"Temp_Humidity\")[1],(\"Temp_Humidity\")[2]', None, 1, 0, 14, 0, u'\"Don\\'t Play\",\"Play\"', u'text', 0.0, u'text, text, double precision, double precision', 0, None)]"
+ "[(u'tree_train', True, u'dt_golf', u'train_output', u'id', u'\"Temp_Humidity\", clouds_airquality', u'None', u'class', u'(clouds_airquality)[1],(clouds_airquality)[2],(\"Temp_Humidity\")[1],(\"Temp_Humidity\")[2]', u'(clouds_airquality)[1],(clouds_airquality)[2]', u'(\"Temp_Humidity\")[1],(\"Temp_Humidity\")[2]', None, 1, 0, 14, 0, u\"Don't Play,Play\", u'text', u'text, text, double precision, double precision', 0.0, 0, None)]"
]
},
- "execution_count": 31,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -1247,7 +1225,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -1267,15 +1245,15 @@
" <th>tree_display</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>-------------------------------------<br> - Each node represented by 'id' inside ().<br> - Each internal nodes has the split condition at the end, while each<br> leaf node has a * at the end.<br> - For each internal node (i), its child nodes are indented by 1 level<br> with ids (2i+1) for True node and (2i+2) for False node.<br> - Number of (weighted) rows for each response variable inside [].'<br> The response label order is given as ['\"Don\\'t Play\"', '\"Play\"'].<br> For each leaf, the prediction is given after the '-->'<br> <br>-------------------------------------<br>(0)[17 19] temperature <= 75<br> (1)[ 7 16] temperature <= 72<br> (3)[ 7 10] temperature <= 70<br> (7)[ 1 8.5] * --> \"Play\"<br> (8)[ 6 1.5] \"OUTLOOK\" in {overcast}<br> (17)[ 0 1.5] * --> \"Play\"<br> (18)[6 0] * --> \"Don't Play\"<br> (4)[0 6] * --> \"Play\"<br> (2)[10 3] \"OUTLOOK\" in {overcast}<br> (5)[0 3] * --> \"Play\"<br> (6)[10 0] * --> \"Don't Play\"<br><br>-------------------------------------</td>\n",
+ " <td>-------------------------------------<br> - Each node represented by 'id' inside ().<br> - Each internal nodes has the split condition at the end, while each<br> leaf node has a * at the end.<br> - For each internal node (i), its child nodes are indented by 1 level<br> with ids (2i+1) for True node and (2i+2) for False node.<br> - Number of (weighted) rows for each response variable inside [].'<br> The response label order is given as [\"Don't Play\", 'Play'].<br> For each leaf, the prediction is given after the '-->'<br> <br>-------------------------------------<br>(0)[17 19] temperature <= 75<br> (1)[ 7 16] temperature <= 72<br> (3)[ 7 10] temperature <= 70<br> (7)[ 1 8.5] * --> Play<br> (8)[ 6 1.5] \"OUTLOOK\" in {overcast}<br> (17)[ 0 1.5] * --> Play<br> (18)[6 0] * --> Don't Play<br> (4)[0 6] * --> Play<br> (2)[10 3] \"OUTLOOK\" in {overcast}<br> (5)[0 3] * --> Play<br> (6)[10 0] * --> Don't Play<br><br>-------------------------------------</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'-------------------------------------\\n - Each node represented by \\'id\\' inside ().\\n - Each internal nodes has the split condition at the end, while each\\n leaf node has a * at the end.\\n - For each internal node (i), its child nodes are indented by 1 level\\n with ids (2i+1) for True node and (2i+2) for False node.\\n - Number of (weighted) rows for each response variable inside [].\\'\\n The response label order is given as [\\'\"Don\\\\\\'t Play\"\\', \\'\"Play\"\\'].\\n For each leaf, the prediction is given after the \\'-->\\'\\n \\n-------------------------------------\\n(0)[17 19] temperature <= 75\\n (1)[ 7 16] temperature <= 72\\n (3)[ 7 10] temperature <= 70\\n (7)[ 1 8.5] * --> \"Play\"\\n (8)[ 6 1.5] \"OUTLOOK\" in {overcast}\\n (17)[ 0 1.5] * --> \"Play\"\\n (18)[6 0] * --> \"Don\\'t Play\"\\n (4)[0 6] * --> \"Play\"\\n (2)[10 3] \"OUTLOOK\" in {overcast}\\n (5)[0 3] * --> \"Play\"\\n (6)[10 0] * --> \"Don\\'t Play\"\\n\\n-------------------------------------',)]"
+ "[(u'-------------------------------------\\n - Each node represented by \\'id\\' inside ().\\n - Each internal nodes has the split condition at the en ... (745 characters truncated) ... lay\\n (2)[10 3] \"OUTLOOK\" in {overcast}\\n (5)[0 3] * --> Play\\n (6)[10 0] * --> Don\\'t Play\\n\\n-------------------------------------',)]"
]
},
- "execution_count": 32,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -1304,7 +1282,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -1317,12 +1295,12 @@
},
{
"data": {
- "image/png": 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r9DqCNE2ePDmQF/LfeuutgrImTJggqGPp0qXy+uuv6wG/6LCTR0IZOA8lNNbp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\nOQfoVCz57D6Zgh/mUH3HpuTXx7/cP43ig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QfKE0a+djj0x+RViBl2mAgOmJKf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\noCn5he16azgI3HLLLe6HH35wm222mQGSYwSmmGIKt9NOO7kzzzzT/fjjj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ItYg2+55RZH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GcEYz8yenBYaefee+81KTwS3Dj+s88+M7UKom+ocYQfZH5UcfSQkUM2jzb48aUy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ag8EPgr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\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+ "image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
- "execution_count": 18,
+ "execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
@@ -1975,13 +1953,13 @@
" <td>0</td>\n",
" <td>[u'0', u'1', u'4', u'6', u'8']</td>\n",
" <td>[2, 3]</td>\n",
- " <td>[0.0, 22.6309172500675, 4.79024943310651, 2.32115000000003, 13.8967382920111]</td>\n",
+ " <td>[0.0, 22.6309172500677, 4.79024943310653, 2.32115, 13.8967382920109]</td>\n",
" <td>4</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(0, [u'0', u'1', u'4', u'6', u'8'], [2, 3], [0.0, 22.6309172500675, 4.79024943310651, 2.32115000000003, 13.8967382920111], 4)]"
+ "[(0, [u'0', u'1', u'4', u'6', u'8'], [2, 3], [0.0, 22.6309172500677, 4.79024943310653, 2.32115, 13.8967382920109], 4)]"
]
},
"execution_count": 34,
@@ -2055,8 +2033,8 @@
" <th>total_rows_skipped</th>\n",
" <th>dependent_var_levels</th>\n",
" <th>dependent_var_type</th>\n",
- " <th>input_cp</th>\n",
" <th>independent_var_types</th>\n",
+ " <th>input_cp</th>\n",
" <th>n_folds</th>\n",
" <th>null_proxy</th>\n",
" </tr>\n",
@@ -2079,15 +2057,15 @@
" <td>0</td>\n",
" <td>None</td>\n",
" <td>double precision</td>\n",
- " <td>0.0</td>\n",
" <td>integer, integer, double precision, double precision, double precision</td>\n",
+ " <td>0.0</td>\n",
" <td>0</td>\n",
" <td>None</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'tree_train', False, u'mt_cars', u'train_output', u'id', u'*', u'id, hp, drat, am, gear, carb', u'mpg', u'vs,cyl,disp,qsec,wt', u'vs,cyl', u'disp,qsec,wt', None, 1, 0, 32, 0, None, u'double precision', 0.0, u'integer, integer, double precision, double precision, double precision', 0, None)]"
+ "[(u'tree_train', False, u'mt_cars', u'train_output', u'id', u'*', u'id, hp, drat, am, gear, carb', u'mpg', u'vs,cyl,disp,qsec,wt', u'vs,cyl', u'disp,qsec,wt', None, 1, 0, 32, 0, None, u'double precision', u'integer, integer, double precision, double precision, double precision', 0.0, 0, None)]"
]
},
"execution_count": 35,
@@ -2152,10 +2130,10 @@
"</table>"
],
"text/plain": [
- "[(u'cyl', 51.8593201959491),\n",
- " (u'wt', 31.8447278473682),\n",
- " (u'disp', 10.9769779291289),\n",
- " (u'qsec', 5.3189740275538),\n",
+ "[(u'cyl', 51.8593201959496),\n",
+ " (u'wt', 31.8447278473677),\n",
+ " (u'disp', 10.976977929129),\n",
+ " (u'qsec', 5.31897402755374),\n",
" (u'vs', 0.0)]"
]
},
@@ -2405,25 +2383,25 @@
" (3, 24.4, 22.58, 1.82),\n",
" (4, 21.0, 19.7428571428571, 1.25714285714286),\n",
" (5, 17.8, 19.7428571428571, -1.94285714285714),\n",
- " (6, 16.4, 16.84, -0.439999999999998),\n",
+ " (6, 16.4, 16.84, -0.440000000000001),\n",
" (7, 22.8, 22.58, 0.219999999999999),\n",
" (8, 17.3, 13.325, 3.975),\n",
" (9, 21.4, 19.7428571428571, 1.65714285714286),\n",
" (10, 15.2, 13.325, 1.875),\n",
" (11, 18.1, 19.7428571428571, -1.64285714285714),\n",
- " (12, 32.4, 30.0666666666667, 2.33333333333334),\n",
+ " (12, 32.4, 30.0666666666667, 2.33333333333333),\n",
" (13, 14.3, 14.78, -0.48),\n",
" (14, 22.8, 22.58, 0.219999999999999),\n",
- " (15, 30.4, 30.0666666666667, 0.333333333333336),\n",
+ " (15, 30.4, 30.0666666666667, 0.333333333333332),\n",
" (16, 19.2, 19.7428571428571, -0.542857142857141),\n",
- " (17, 33.9, 30.0666666666667, 3.83333333333334),\n",
+ " (17, 33.9, 30.0666666666667, 3.83333333333333),\n",
" (18, 15.2, 16.84, -1.64),\n",
" (19, 10.4, 13.325, -2.925),\n",
- " (20, 27.3, 30.0666666666667, -2.76666666666666),\n",
+ " (20, 27.3, 30.0666666666667, -2.76666666666667),\n",
" (21, 10.4, 13.325, -2.925),\n",
- " (22, 26.0, 30.0666666666667, -4.06666666666666),\n",
+ " (22, 26.0, 30.0666666666667, -4.06666666666667),\n",
" (23, 14.7, 16.84, -2.14),\n",
- " (24, 30.4, 30.0666666666667, 0.333333333333336),\n",
+ " (24, 30.4, 30.0666666666667, 0.333333333333332),\n",
" (25, 21.5, 22.58, -1.08),\n",
" (26, 15.8, 14.78, 1.02),\n",
" (27, 15.5, 14.78, 0.719999999999999),\n",
@@ -2484,7 +2462,7 @@
"</table>"
],
"text/plain": [
- "[(u\"-------------------------------------\\n - Each node represented by 'id' inside ().\\n - Each internal nodes has the split condition at the end, while each\\n leaf node has a * at the end.\\n - For each internal node (i), its child nodes are indented by 1 level\\n with ids (2i+1) for True node and (2i+2) for False node.\\n - Number of rows and average response value inside []. For a leaf node, this is the prediction.\\n\\n-------------------------------------\\n(0)[32, 20.0906] cyl in {4}\\n (1)[11, 26.6636] wt <= 2.2\\n (3)[6, 30.0667] *\\n (4)[5, 22.58] *\\n (2)[21, 16.6476] disp <= 258\\n (5)[7, 19.7429] *\\n (6)[14, 15.1] qsec <= 17.42\\n (13)[10, 15.81] qsec <= 16.9\\n (27)[5, 14.78] *\\n (28)[5, 16.84] *\\n (14)[4, 13.325] *\\n\\n-------------------------------------\",)]"
+ "[(u\"-------------------------------------\\n - Each node represented by 'id' inside ().\\n - Each internal nodes has the split condition at the end, ... (564 characters truncated) ... 81] qsec <= 16.9\\n (27)[5, 14.78] *\\n (28)[5, 16.84] *\\n (14)[4, 13.325] *\\n\\n-------------------------------------\",)]"
]
},
"execution_count": 38,
@@ -2499,7 +2477,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 40,
"metadata": {},
"outputs": [
{
@@ -2512,12 +2490,12 @@
},
{
"data": {
- "image/png": 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BirK42fAMCZCAAwnMmjVLmjZtKs2bN5c5c+ZonicHdtPxXbrmmms0l1v16tUd2df3339f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UwULh0z8LMqNK7hx7X22msHc801l44R/MaXkudQNwlVED6LA9npDkSByjnnf3z66aclxwfGD/pS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c+17ucQw0HmZQzkJ9SmAqNKphpqN7771XaV8lZkcnwBCR8GA79dRTlc0od9LIF1lNLTujfORQ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6asqBxIj64IMPwrVr18KOHTvqD1n73Y7+duoeqh3UBQn0EQE9ZX10sRyqBCQggV4h8Oab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\nJCABCUhAAhIoT+B//rs0AHT2EagAAAAASUVORK5CYII=\n",
+ "image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
- "execution_count": 31,
+ "execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
@@ -2549,7 +2527,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 41,
"metadata": {},
"outputs": [
{
@@ -2567,15 +2545,15 @@
" <th>tree_surr_display</th>\n",
" </tr>\n",
" <tr>\n",
- " <td><br> -------------------------------------<br> Surrogates for internal nodes<br> -------------------------------------<br> <br>(0) cyl in {4}<br> 1: disp <= 146.7 [common rows = 29]<br> 2: vs in {1} [common rows = 26]<br> [Majority branch = 11 ]<br><br>(1) wt <= 2.2<br> [Majority branch = 19 ]<br><br>(2) disp <= 258<br> 1: cyl in {4,6} [common rows = 19]<br> 2: vs in {1} [common rows = 18]<br> [Majority branch = 7 ]<br><br>(6) qsec <= 17.42<br> 1: disp > 275.8 [common rows = 11]<br> 2: vs in {0} [common rows = 10]<br> [Majority branch = 10 ]<br><br>(13) qsec <= 16.9<br> 1: wt <= 3.84 [common rows = 8]<br> 2: disp <= 360 [common rows = 7]<br> [Majority branch = 5 ]<br><br><br>-------------------------------------</td>\n",
+ " <td><br> -------------------------------------<br> Surrogates for internal nodes<br> -------------------------------------<br> <br>(0) cyl in {4}<br> 1: disp <= 146.7 [common rows = 29]<br> 2: vs in {1} [common rows = 26]<br> [Majority branch = 19 ]<br><br>(1) wt <= 2.2<br> 1: disp <= 108 [common rows = 9]<br> 2: qsec <= 18.52 [common rows = 8]<br> [Majority branch = 6 ]<br><br>(2) disp <= 258<br> 1: cyl in {4,6} [common rows = 19]<br> 2: vs in {1} [common rows = 18]<br> [Majority branch = 14 ]<br><br>(6) qsec <= 17.42<br> 1: disp > 275.8 [common rows = 11]<br> 2: vs in {0} [common rows = 10]<br> [Majority branch = 10 ]<br><br>(13) qsec <= 16.9<br> 1: wt <= 3.84 [common rows = 8]<br> 2: disp <= 360 [common rows = 7]<br> [Majority branch = 5 ]<br><br><br>-------------------------------------</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'\\n -------------------------------------\\n Surrogates for internal nodes\\n -------------------------------------\\n \\n(0) cyl in {4}\\n 1: disp <= 146.7 [common rows = 29]\\n 2: vs in {1} [common rows = 26]\\n [Majority branch = 11 ]\\n\\n(1) wt <= 2.2\\n [Majority branch = 19 ]\\n\\n(2) disp <= 258\\n 1: cyl in {4,6} [common rows = 19]\\n 2: vs in {1} [common rows = 18]\\n [Majority branch = 7 ]\\n\\n(6) qsec <= 17.42\\n 1: disp > 275.8 [common rows = 11]\\n 2: vs in {0} [common rows = 10]\\n [Majority branch = 10 ]\\n\\n(13) qsec <= 16.9\\n 1: wt <= 3.84 [common rows = 8]\\n 2: disp <= 360 [common rows = 7]\\n [Majority branch = 5 ]\\n\\n\\n-------------------------------------',)]"
+ "[(u'\\n -------------------------------------\\n Surrogates for internal nodes\\n -------------------------------------\\n \\n(0) cyl in {4}\\n ... (542 characters truncated) ... 1: wt <= 3.84 [common rows = 8]\\n 2: disp <= 360 [common rows = 7]\\n [Majority branch = 5 ]\\n\\n\\n-------------------------------------',)]"
]
},
- "execution_count": 32,
+ "execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
@@ -2595,7 +2573,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 42,
"metadata": {},
"outputs": [
{
@@ -2622,16 +2600,16 @@
" <td>0</td>\n",
" <td>[u'0', u'1', u'4', u'6', u'8']</td>\n",
" <td>[2, 3]</td>\n",
- " <td>[0.0, 22.6309172500675, 4.79024943310651, 2.32115000000003, 13.8967382920111]</td>\n",
+ " <td>[0.0, 22.6309172500677, 4.79024943310653, 2.32115, 13.8967382920109]</td>\n",
" <td>4</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(0, [u'0', u'1', u'4', u'6', u'8'], [2, 3], [0.0, 22.6309172500675, 4.79024943310651, 2.32115000000003, 13.8967382920111], 4)]"
+ "[(0, [u'0', u'1', u'4', u'6', u'8'], [2, 3], [0.0, 22.6309172500677, 4.79024943310653, 2.32115, 13.8967382920109], 4)]"
]
},
- "execution_count": 39,
+ "execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
@@ -2668,7 +2646,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 43,
"metadata": {},
"outputs": [
{
@@ -2689,46 +2667,46 @@
" </tr>\n",
" <tr>\n",
" <td>0.0</td>\n",
- " <td>4.68400917828</td>\n",
- " <td>1.39530594243</td>\n",
+ " <td>4.01812944124</td>\n",
+ " <td>0.870675032425</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.00942145242026</td>\n",
- " <td>4.75161498297</td>\n",
- " <td>1.42104757555</td>\n",
+ " <td>4.10308280532</td>\n",
+ " <td>0.892617855912</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.0156685263245</td>\n",
- " <td>4.83947690361</td>\n",
- " <td>1.46418069607</td>\n",
+ " <td>4.21061708252</td>\n",
+ " <td>0.924444218562</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.0893348335771</td>\n",
- " <td>4.95961627984</td>\n",
- " <td>1.54465774642</td>\n",
+ " <td>4.35132530835</td>\n",
+ " <td>0.977602088947</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.135752855572</td>\n",
- " <td>5.12719717712</td>\n",
- " <td>1.72850238039</td>\n",
+ " <td>4.54090006418</td>\n",
+ " <td>1.09111750021</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.643125226048</td>\n",
- " <td>5.36368238062</td>\n",
- " <td>2.25277170578</td>\n",
+ " <td>4.875624198</td>\n",
+ " <td>1.36328936017</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(0.0, 4.68400917827875, 1.3953059424276),\n",
- " (0.00942145242026098, 4.75161498296557, 1.42104757555057),\n",
- " (0.0156685263245236, 4.83947690361089, 1.46418069607494),\n",
- " (0.0893348335770666, 4.95961627983649, 1.54465774641974),\n",
- " (0.135752855572154, 5.1271971771168, 1.72850238038959),\n",
- " (0.643125226048458, 5.36368238062471, 2.25277170577689)]"
+ "[(0.0, 4.01812944124071, 0.870675032425261),\n",
+ " (0.00942145242026152, 4.10308280531985, 0.892617855912068),\n",
+ " (0.0156685263245235, 4.21061708251747, 0.924444218561895),\n",
+ " (0.08933483357707, 4.35132530834854, 0.977602088947029),\n",
+ " (0.13575285557216, 4.54090006418156, 1.09111750020962),\n",
+ " (0.643125226048452, 4.87562419799747, 1.36328936017285)]"
]
},
- "execution_count": 40,
+ "execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
@@ -2755,7 +2733,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 44,
"metadata": {},
"outputs": [
{
@@ -2787,13 +2765,6 @@
" <td>a</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>3</td>\n",
- " <td>US</td>\n",
- " <td>NY</td>\n",
- " <td>None</td>\n",
- " <td>c</td>\n",
- " </tr>\n",
- " <tr>\n",
" <td>2</td>\n",
" <td>US</td>\n",
" <td>None</td>\n",
@@ -2801,6 +2772,13 @@
" <td>b</td>\n",
" </tr>\n",
" <tr>\n",
+ " <td>3</td>\n",
+ " <td>US</td>\n",
+ " <td>NY</td>\n",
+ " <td>None</td>\n",
+ " <td>c</td>\n",
+ " </tr>\n",
+ " <tr>\n",
" <td>4</td>\n",
" <td>US</td>\n",
" <td>NY</td>\n",
@@ -2811,12 +2789,12 @@
],
"text/plain": [
"[(1, None, None, None, u'a'),\n",
- " (3, u'US', u'NY', None, u'c'),\n",
" (2, u'US', None, None, u'b'),\n",
+ " (3, u'US', u'NY', None, u'c'),\n",
" (4, u'US', u'NY', u'rainy', u'd')]"
]
},
- "execution_count": 41,
+ "execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
@@ -2852,7 +2830,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
@@ -2882,7 +2860,7 @@
"[([u'US', u'__NULL__', u'rainy', u'__NULL__', u'NY', u'__NULL__'], [2, 2, 2])]"
]
},
- "execution_count": 42,
+ "execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
@@ -2913,79 +2891,9 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>method</th>\n",
- " <th>is_classification</th>\n",
- " <th>source_table</th>\n",
- " <th>model_table</th>\n",
- " <th>id_col_name</th>\n",
- " <th>list_of_features</th>\n",
- " <th>list_of_features_to_exclude</th>\n",
- " <th>dependent_varname</th>\n",
- " <th>independent_varnames</th>\n",
- " <th>cat_features</th>\n",
- " <th>con_features</th>\n",
- " <th>grouping_cols</th>\n",
- " <th>num_all_groups</th>\n",
- " <th>num_failed_groups</th>\n",
- " <th>total_rows_processed</th>\n",
- " <th>total_rows_skipped</th>\n",
- " <th>dependent_var_levels</th>\n",
- " <th>dependent_var_type</th>\n",
- " <th>input_cp</th>\n",
- " <th>independent_var_types</th>\n",
- " <th>n_folds</th>\n",
- " <th>null_proxy</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>tree_train</td>\n",
- " <td>True</td>\n",
- " <td>null_handling_example</td>\n",
- " <td>train_output</td>\n",
- " <td>id</td>\n",
- " <td>country, weather, city</td>\n",
- " <td>None</td>\n",
- " <td>response</td>\n",
- " <td>country,weather,city</td>\n",
- " <td>country,weather,city</td>\n",
- " <td></td>\n",
- " <td>None</td>\n",
- " <td>1</td>\n",
- " <td>0</td>\n",
- " <td>4</td>\n",
- " <td>0</td>\n",
- " <td>\"a\",\"b\",\"c\",\"d\"</td>\n",
- " <td>text</td>\n",
- " <td>0.0</td>\n",
- " <td>text, text, text</td>\n",
- " <td>0</td>\n",
- " <td>__NULL__</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(u'tree_train', True, u'null_handling_example', u'train_output', u'id', u'country, weather, city', u'None', u'response', u'country,weather,city', u'country,weather,city', u'', None, 1, 0, 4, 0, u'\"a\",\"b\",\"c\",\"d\"', u'text', 0.0, u'text, text, text', 0, u'__NULL__')]"
- ]
- },
- "execution_count": 43,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"%%sql\n",
"SELECT * FROM train_output_summary;"
@@ -3002,7 +2910,7 @@
},
{
"cell_type": "code",
- "execution_count": 44,
+ "execution_count": 46,
"metadata": {},
"outputs": [
{
@@ -3052,7 +2960,7 @@
"[(1, u'a', u'a'), (2, u'b', u'b'), (3, u'c', u'c'), (4, u'd', u'd')]"
]
},
- "execution_count": 44,
+ "execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
@@ -3105,7 +3013,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 47,
"metadata": {},
"outputs": [
{
@@ -3123,15 +3031,15 @@
" <th>tree_display</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>-------------------------------------<br> - Each node represented by 'id' inside ().<br> - Each internal nodes has the split condition at the end, while each<br> leaf node has a * at the end.<br> - For each internal node (i), its child nodes are indented by 1 level<br> with ids (2i+1) for True node and (2i+2) for False node.<br> - Number of (weighted) rows for each response variable inside [].'<br> The response label order is given as ['\"a\"', '\"b\"', '\"c\"', '\"d\"'].<br> For each leaf, the prediction is given after the '-->'<br> <br>-------------------------------------<br>(0)[1 1 1 1] city in {NY}<br> (1)[0 0 1 1] weather in {rainy}<br> (3)[0 0 0 1] * --> \"d\"<br> (4)[0 0 1 0] * --> \"c\"<br> (2)[1 1 0 0] country in {US}<br> (5)[0 1 0 0] * --> \"b\"<br> (6)[1 0 0 0] * --> \"a\"<br><br>-------------------------------------</td>\n",
+ " <td>-------------------------------------<br> - Each node represented by 'id' inside ().<br> - Each internal nodes has the split condition at the end, while each<br> leaf node has a * at the end.<br> - For each internal node (i), its child nodes are indented by 1 level<br> with ids (2i+1) for True node and (2i+2) for False node.<br> - Number of (weighted) rows for each response variable inside [].'<br> The response label order is given as ['a', 'b', 'c', 'd'].<br> For each leaf, the prediction is given after the '-->'<br> <br>-------------------------------------<br>(0)[1 1 1 1] country in {US}<br> (1)[0 1 1 1] weather in {rainy}<br> (3)[0 0 0 1] * --> d<br> (4)[0 1 1 0] city in {NY}<br> (9)[0 0 1 0] * --> c<br> (10)[0 1 0 0] * --> b<br> (2)[1 0 0 0] * --> a<br><br>-------------------------------------</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'-------------------------------------\\n - Each node represented by \\'id\\' inside ().\\n - Each internal nodes has the split condition at the end, while each\\n leaf node has a * at the end.\\n - For each internal node (i), its child nodes are indented by 1 level\\n with ids (2i+1) for True node and (2i+2) for False node.\\n - Number of (weighted) rows for each response variable inside [].\\'\\n The response label order is given as [\\'\"a\"\\', \\'\"b\"\\', \\'\"c\"\\', \\'\"d\"\\'].\\n For each leaf, the prediction is given after the \\'-->\\'\\n \\n-------------------------------------\\n(0)[1 1 1 1] city in {NY}\\n (1)[0 0 1 1] weather in {rainy}\\n (3)[0 0 0 1] * --> \"d\"\\n (4)[0 0 1 0] * --> \"c\"\\n (2)[1 1 0 0] country in {US}\\n (5)[0 1 0 0] * --> \"b\"\\n (6)[1 0 0 0] * --> \"a\"\\n\\n-------------------------------------',)]"
+ "[(u\"-------------------------------------\\n - Each node represented by 'id' inside ().\\n - Each internal nodes has the split condition at the end, ... (558 characters truncated) ... 0] city in {NY}\\n (9)[0 0 1 0] * --> c\\n (10)[0 1 0 0] * --> b\\n (2)[1 0 0 0] * --> a\\n\\n-------------------------------------\",)]"
]
},
- "execution_count": 40,
+ "execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
@@ -3143,7 +3051,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 49,
"metadata": {},
"outputs": [
{
@@ -3156,12 +3064,12 @@
},
{
"data": {
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Qg127dkmXLl00bh2e80AJYuJHjBih/WjRooUcOXIkRtOxPdPYmS5dOuWDfX379pVDhw7p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\np06dBCVHKSQQF4EyZcpoxU0YePhlA8VokNKPEnwCvF+DzzjSrsD7NdJ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IAhv5\n9ttvbYt/rjlIvKAM23SXNPqGQBs8cuRI95S0bt1a0J9G/rA2sP3mm28EYfu+++47K+e2HT0utwGM\nC3aUCP3nEsL4IVY02kCoP7c+riPEFOzI0RY016kImm4Iw6n+EDIvEcG2uGfPnibAuzbewAbhBydP\nnhzz1QMva4ceeqilR4dAjxcApOl2SSO52BwGDhzonvLtFl97MFeETCwGkV/Jr9H8S37NjOuKza+Z\njY6lA4tALt6bDMWXC3qlq4vQc61atSrdAArU88svvxwJxaexmB2NHe1obGlHHeoswoMmP3FUWLXw\ndvCsV6a2VN4I4Ydy++23n53r0KGDowKis2bNGjtu2bKlRVW5/PLLHdVYx0QswFRUeI/UVVMKZ/bs\n2RYBpE2bNhbqD2Hy1HHU2qpRo4ajiUAcjXdtWRnHjBnjVK9e3a6p9thBunGQCm4W8q9q1aoOysRT\n9+7dHTVNiD+d92ONG27ZGNWZ0sYBDDSOd5l+pk+fbnNAuDgVBBwV/u1YhVCrry8jCbNQlmnIJycm\nTJjgqMOrrWOhh0x+fc6i75Bfy7/TyK+JMSomvyYeAc/6CQE8axiKz08r5pGxauQKZ/jw4R4ZjXeH\n4QrXwEo1xyaY48crGSG8nkv5SPON+OOImZ2IjjjiCEe14okuFeQcwgciDGEqQnpzl1TDbS8oGlXF\nPRWoLV6+8ABWk5mCz4v8mh7E5NcNOJFfN2CBvWLya2zPPPIjAvkQrmkWoiiGifQHyELNaQzmME07\n57ki2gUyDaZynoJdn0v5SPMN0w9NMOM2Gdm+9dZbAmdK1WpHzhV6B06PCEOYihCtwCVEY0FWy1q1\narmnArXF3PSLg5kIFXJi5Nfs0CW/kl+j75xi8Wt0n9wPNwJ0aAzZ+kMwA0XHdw4ZBGlPF3FlQapB\nTrtOoQrC5hJOh4jVrRkSZc6cOYXqiu2miQB4SJMWpVk6u2Lk1/RxI7+mj1UYS4JfXX4K4/w55+Ii\nQOG6uHiXvDc8XJDMY+utty75WLw8AERoGDx4sA1Rszha8hY4ByJaRylIzVFMSwohWzM0Whi+UoyD\nfW5AAD/WL7744oYTBdgjv6YHKvk1PZzCXKoY/BpmfDn3WAQoXMfiEfgjRHVo1qxZ4OeZ6wSRUVGd\n8+zPbctNWuEeF3O79957W2QTeL6HNTZ5MfFOpy/w0fjx4y1bZipzoXTaSlaG/JoMmdjz5NdYPHhU\nFoFi8GvZXnkmrAhQuA7ZyiOhQnQa8JBNP+3pQkNdCi21OgXKo48+aqHzJk2aFDPeVMliYgoW4QDh\nDZF6HSng69evL6eddprAzjVMhCQ+CE2ojqzl2qNniwv5NT3kyK+pcSK/in3tKzS/pl4FXg0TAhSu\nw7TaOleN3pAyXXbI4PDUdBF7Gwlbhg0bltJxstSD1tCCcsghh5izJYQ/DSso1157rSxYsCCSWKfU\nYyxG/zCvAoGnynP2zHY85NdskSt8PfJr4THOZw/F4Nd8jpdt+RsBRgvx9/plNHoNiWORQtyHTEaV\nWbjgCFSuXFlOPfVU2XfffQveVy4dXHTRRfLkk08KksVA+OvatasluhkwYEAuzfqurstHwKAQRH4t\nBKr5a5P8mj8si9FSofm1GHNgH/5BgMK1f9Yq55Ei6gWyASL7H8m7CMD8o1A2vLnOGg6VcOx07fYR\nfnDo0KFmB66JeHJt3lf1EW4R4RA18U9Bxk1+LQiseW+U/Jp3SAvSYKH5tSCDZqO+RYBmIb5duswH\n7oaqCpttbDxS0Ai+8MILsnjxYkH85t133100KUukmCaAsXB3b7zxhl0/88wzY+I1v/vuu5aaXDM3\nyuOPPy4wk9BspYI4z4jqAdOOl156SQ4++GDRrI+RdqHhnDt3rvTo0cP6h/YXcaC7dOli6egjBZPs\nzJs3T5D6uFq1atKxY0eLs+wWLW9Obrlct7Az1uyWMc3gZU0zV4qXbMJjBljAA83SKC5f5bsbt13y\nK/k123uL/BqLXCH5NbYnHoUdAWquQ3QHQGgEhf3HeuDAgbJy5Urp06ePIJkOjl2CHSUSDlSqVEku\nu+wy0UxncuCBBwqwg1NQ3759pVGjRnLrrbdKr169TJBGzGkkmHnsscfkjDPOkIceesiijBx00EEm\nDKPte+65x7S9qH/++efLXXfdZTGS0Qbsl9etW+cOocwWNs3nnnuufP3116JpyEVTo9sLwbJlyyJl\nU80pUujfndWrV5t9NGykk/3hBSERIXFKIq36Z599FkpbftwnrhCcCK9czpFf/0Ev1b1Nfv2Hh8mv\n6XFaIfk1vRGwVGgQUI1X1qTaOgd/JH8goDFzLWXze++9548BF2CUSF++zTbbOCqgRlpXB8LI/t13\n3+1oqLtIqm/Vbhtmr776aqSMZk50NDSeo0KVnUOKcg3T56itdOQc0qVrBAMnum0VvB0VTB0NrxZp\na9CgQdb+hAkTIufAU2ofGDm+/vrrHY25HTlWQdbqtGnTxs6VN6dIxX93brzxRquvD7mkW8wnXdKv\nADZefflIt0pgymnkHUeFv4LMh/zqOOXd2+TXf3iY/JoeCxaSX9MbAUv5AQH8Ns6YMSOXoc6g5jo0\nr1FiJguYbpjjJEPruttuu5lZBTTMIGiTXYJDIWILI/oDwjbBfAS0YsUKt4hstdVWUq9evYgpB1KU\nI86uq/FGQXwdgJkIQtW5hE+SMJ1o3Lixe8q04ziXKhmJCsPy5ptvygUXXGB/I0aMsDl8++231k55\nc4p09u8OtOXQtqb6++GHH+KrJTxev369XHnllWbuAgevsBF4CaZAhSC3XfIr+TUVr+Ia+TU9Diwk\nv6Y3ApYKCwK0uQ7LSus8XXMQ93NziKYeM1WYdMBG+rjjjpNWrVqZyYYbSg0PX+xDYIQDDJK3gFxB\nJ6ahqIOKFStGHf2zi6QzqsEucz76BNYEXuxfffVV9OnIPpzaYMaBiBzt27ePnI/fSTWn+LIQ5vNl\nH40Xk4svvlj++9//xncTimPwEj41F4LIr/+gmureJr9mdueRXwvHr5mtBEsHHQEK10Ff4aj5uUJA\n2IXrFi1aCJwVYVN92223mYPekiVLLCU8NM2wgR47dqzZNyPcXDqUyA4Z9ZKdd9tE9Ja1a9eKmni4\np2K2rtYS40slXKeaU0yDerBo0SKBc2QqgqNnv379UhWR22+/3YTqY445JmW5IF8EL7lCcL7nSX79\nB9FU9zb59R+MyK/pcV8h+TW9EbBUWBCgWUhYVlrn6f5Y4zNiWAnCLJwJYcoBARrZENesWSOzZ882\nSK666ipzLoTjIKg8jbUVyuEfoorA/MTtL74pmKDAWRJptuNfitTeVJDRsbw5xbeJF4ZZs2al/Hvg\ngQfiq8UcP/jgg5b2u1OnTjHnXTOamJMBPgAvuXyV72m67ZJfya/k1/xwVyH5NT8jZCtBQYCa66Cs\nZBrzQExeaEKTmSCk0YTvi6iHgqjzoEX1gFa5devWog6O9ofJwYwDwjYif+yzzz4ybtw4mzNMM2Ci\noc6MVgYCbTQhaoFrA+2eR1sQnKMJ0UcQyq9hw4Z2GkIsQvpFC9ewn0RdjBVjvPTSSy3CyGGHHSaw\nt8YYEKGkRo0asvPOO1sfqeYU3T/2Eacaf9kStN4jR440DPHJHgTba0QvadKkic0n27b9VA9zxj2B\nCCqFIPKrehWRX8mveWKuQvNrnobJZoKCQC7ukIwWkgt6pamrjnfODTfcUJrOPdCran8djcvsnHLK\nKc7MmTOdUaNGOWpfHRmZJkJxateu7agNtXP88cc7qhl2NIazo7GlHRW0nauvvtoibGjyFGf69OkO\nImSgvj4PHNWGO7fccotFDNF04HZOBSRn6tSp1n63bt0c/Xzr9OzZ01GB2cagph4Ooo2AMLbRo0c7\nqrG0umhXE5RYxITLL7/cUTtpO4+tmrQ4+mMRqZdqTlYoT/80iYyjjpk2Dsw5+k9t1J1vvvkmTz15\nvxk3aouGQSvYYMmv5Ndcbi7y6wb0isGvG3rjnp8RwO9artFCKgAAbSgr6tChg9XTQWRVn5WKjwBS\nayNuMyJQhJWgPYa5B2ydofmNJ1yDCQaie4DAIohDraH14otmdNy9e3eZMmWKIG414kJDAw2zj3QJ\nY/rwww/NTCTezre8OaXbB8uljwBMeg444AD55JNPEt5H6beUvCT5VSzWPPk1+T3CK+khUAx+TW8k\nLOV1BPDFGHItAh9kSTNpFpIlcn6thvBwsNMNM7mRMhIJ1sAFpjOuYI1jMFqugjXaiSasQ6YEG9zo\nMH7R9cubU3RZ7ucHAbwg4V5BGMZCEflVIpFtyK+FusvC0W4x+DUcSHKW6SBAh8Z0UApQGdj6Ll26\nNEAz8s9U4EwDDTPss0n+RwDx0BHb3H2xKcSMyK+FQDW9Nsmv6eHkl1LF4Fe/YMFxFh4BCteFx9hT\nPTRv3lwQLQImBqTiIYD050899ZSZmPTv318082PxOmdPBUFAMygK+KmQRH4tJLrJ2ya/JsfGr1eK\nwa9+xYbjzj8CNAvJP6aebhE/1rBfxFu8myDF0wMOyOAQDaRdu3aR2SRKOhO5yB1fIIAf63PPPbeg\nYyW/FhTepI2TX5NC49sLxeBX34LDgecdAQrXeYfU2w0ibTdiPCORCIXr4q0VnBdJwUEA4SzhyLjH\nHnsUdFLk14LCm7Rx8mtSaHx5oVj86ktwOOiCIEDhuiCwerdROGAdfPDB8txzz1nsZO+OtHAjg0Mn\nksdomCqZNGlS4TrKQ8sff/yxwMvdpQYNGoiGBrRDxNpG0haYmPzf//2f7LfffuZg55ZNts22ntse\noqy89957csghh7in0tomqzd37tyYNPEnnXSSIHW8lwn8g6x4iLxTSCK/ijlgB4Ff3fskGR+415Nt\nM62XLZ+XV4/8mmyFeJ4IbECANtcbsAjN3qGHHmrCdQ5RGH2LFZwJNS6xDBs2TJ544gnPzwNjPe20\n0yxiCdYNDnSgL7/80hLR4EWhc+fOllQGachh8pOKsq2HNqH96du3r9StW1eQoTFdKq8eXhaQsAeJ\nezBXP/gDPPvss7LXXntlFEoxXbziy5Ff/c+vWNPy+CB+3d3jbOply+fp1CO/uivDLRFIjgCF6+TY\nBPYKMv1psg+BDVrYqHLlynLqqacK4gf7idq2bSs1a9Y0YQ4C9IknnihNmzaVrl27WnZJZG6EHf0V\nV1yRdFrZ1nMbhBYd6c4zFX7Lq1erVi2B+cPhhx/uduX5LYRr8FExiPzqb35175Hy+MAtF7/NtF62\nfJ5uPfJr/ArxmAiURYDCdVlMAn8GTlKa0U/weS+shPBpiF/tR3rxxRdlwYIFMc50MFE466yzBOnI\nkTo9EWVbz20LNvq77767e5j2Ntt6aXdQ5IJIX79ixQo58sgji9Iz+fWfWNd+5Vf3JsmWDzKtly2f\nZ1vPnZ9Xt8XmV6/iwHEVFwHaXBcXb0/0BjvOE044QTT9t2iKbU+MqbxBwJxj4sSJlt0Q44cmt0mT\nJqKpw0XTiwti0mJOrtkEwg2+/PLL8vbbb5tdrKYyT9rFmjVrZPbs2ZaF8YgjjrBELbCpdTX7aDc6\ngcXq1avNpGTVqlXWdqtWrZK2XYgLrkkGNNfRBDwgWMO8IlFmqWzrRffBfTG+wVcE2LkXg8ivsSj7\njV9jR1/4o2z5PNt6hZ9Rbj3gd66Y/JrbaFk7KAhQuA7KSmY4DwhfY8eOFbzVI1GF1wnmHBBm9t9/\nfzMfuPTSS23ISB+O7Invv/9+RLC+6aab5KGHHhJ8ukdEB9iswhmoR48eCacJLX6NGjWkQ4cO5uCI\nLIioM3/+fBk8eLA0atQoIlxD6L7vvvusLURdOe6448xUAlgmIgjiSFmeiqCRy8QxDlpTEMYdTZgD\nCC8WiSjbeonaCvM5/FjDLAdCb7GI/LoBab/x64aRF2cvWz7Ptl5xZpV9L6Xg1+xHy5pBQYDCdVBW\nMsN5HHTQQfY2D0Fx6NChGdYuTXF8Hj3jjDNMc/jDDz+IGy7rtddek4EDB0YGBUG3TZs2ZvZRp04d\nadGihTzyyCNJhWtUhAAdT//9739jTkF7DhtnaMORHh3Xn3zySRk3bpyceeaZFq0jpoIe3H///XLx\nxRfHn445RmSMP//8M+ZcqoMvvvjCIlXEp2TffPPNrRo0e4ko23qJ2grruSVLlphtO8xviknk11i0\n/cSvsSMv/FG2fJ5tvcLPKPseSsWv2Y+YNYOCQPFUL0FBLCDzgNbtnHPOkcmTJ1tKbr9M64ILLjAT\nkLvvvtuG/NNPPwn+ateuHZnC888/b9FAcGLZsmXy2WefmY1spECWO3gRgTNfv379BOPAHzTicMZb\nuXJlwlZ79epl44XZSrI/vChkQtDiJ6L169fbaXwCTUTZ1kvUVljP3XbbbfaFBOEsi0nk18zR9gq/\nZj7y3Gpky+fZ1stttIWtXSp+Leys2LofEKDm2g+rVKAxnnfeeTJy5EgzocBnbj8QtNf4w0MTwu30\n6dPl9NNPjxk6vNmRahza6pYtW5rwi5jWudLSpUvNFCOZCUii9uE4ib980k477SQQpBGPNjrTI14y\nQIm0ejifbT3UJYngy8Vdd91lfgqlcK4jv2Z2F3qFXzMbde6ls+XzbOvlPuLCtFBqfi3MrNiqXxDI\n76++X2bNcRoCMJmA+cT48ePNhtQvsECoPvvssy25yuOPP25mItFjHzRokCVXgclGpUqV5IEHHoi+\nnPU+InLAtnvdunVpJzlBJsx58+al7BPtQhueLrk28tDI169fP1Lt66+/tv1kwnW29SIdhHznnnvu\nsRca3HulIPJrZqh7hV8zG3XupbPl82zr5T7iwrRQan4tzKzYql8QoFmIX1aqQOPs3bu3PPPMM5at\nsEBd5L3Zjh07SvXq1eWiiy6SZs2amf2x28lHH31kJiGwzYZgDUL81vLI1S7//vvvSYsiJBqicUyY\nMCGmzPfff2921zEn/z2Ac+GsWbNS/mUq/Hfp0sU01kgwE03QzsO+HFkcE1G29RK1FbZz+FJwww03\n2FcS3HulIvLrP8j7iV+Lfa9ky+fZ1iv2/NLpzyv8ms5YWSagCGiWvqxJPdgd/JH8jYBm3HI06oWv\nJqGaXkc1U45qb2PGrc6GjrKqo9E+HLVldjR2q6PRBZytt97aUbMJR0P3WfnWrVs71apVc1TwtmNs\nVTPoaEQSR5M2OBpFxVEnRWvr2muvdfRh7ajg7einU0cdCZ3rrrvOUXtuRx0WjQfcdmMGk4cDtS23\nMagAH9PaJZdc4mhUk8j41RbcUaHaUQE7plz8Qbb13HbUxtzGoyYK7qm0tunUu/POO61trJvXSLVg\ndr/py1LJh0Z+dey+9xO/ujdNOnzglo3eZlovWz7PpB75NXqFuB8kBCBDzJgxI5cpzZBcalO4zgU9\n79TV+KaO2pA6EEz9QqqhTvpCoOnAHdVsOWoy4aiW2VHNsQnEmunO+fzzz53Ro0c7qtU2QU7jfDvq\nJW/TnjRpklO1alVHHXsczeLovPDCC86OO+7o9OnTx1FzECsDgRpCLJgPfxpb2nnjjTcKBlsy4Rov\nA/3793eOPvpoZ8yYMc7ll1/uTJs2rdxxZFsPDWv8bEe/Gti8Neyfo3HHHY1MUm6f6dbz6o81MFNT\nG0dTs5c712IUIL/6j19xX6TLB/H3UDb1suXzTOqRX+NXisdBQYDCdVBWssTzwANVE5IkFVZLPLyk\n3auJRtJr8ZpkaJ3TIWiA3boaHs801onqQbutMbQTXcrruWTCtdvJX3/95UCrlSllWy/TfjIp79Uf\na2itNVqHo+nlM5lOwcqSXzdA6zd+3TDy4uxly+fp1CO/FmcN2UvxEciHcE2ba0Ux7ITIB6NGjZI5\nc+YIkqT4hdy4zonGiwQv0RQdVSP6fPz+ZpttJm5dxJ9OligEof+iszbGt5PvY0QGSURw2tpuu+0S\nXUp5Ltt6KRvN8SLsJL1GCL142WWXCZwYkVzIC0R+3bAKfuPXDSMvzl62fJ5OPfJrcdaQvfgTAUYL\n8ee65X3UiBqClOJwElQzh6RCZd47ZoMpEYCAjyyUSF6D7JR77bWXIEV7kOj222+X7777zqK+YK6l\nCHOXDM/rr7/exjZ8+PBkRUpynvxaEtjL7ZT8Wi5EBS3gVX4t6KTZuCcRqACFe7YjQ7pokBp+Z9sE\n63kIASRcQUQMxHFGTF0SEQgzAqtWrZLdd99d1J5dBgwY4DkoyK+eWxIOqIQIeJ1fSwgNu84QASh4\nINeqX2GGNSPFZ9IsJIIFdxAf+cILL7SYy6tXryYgRCDUCHTr1k2QkEgjKHgSB/KrJ5eFgyoRAl7n\n1xLBwm5LhACF6xIB79Vuhw4dKttss4306NHDq0PkuIhAwRFQR1JBgiKNICOw6/UqkV+9ujIcVzER\n8Au/FhMT9lVaBChclxZ/z/UOJ0ENsSYPP/yw3HfffZ4bXykGhIyMSLQDe3QNi1WKIeTcJxLdaNhB\nyy752muv2Rpn0miu9TPpq9RlNTSjaPhFe8E86KCDSj2clP2TX8vCQ34VIb+WvS94hggUEwEK18VE\n2yd9aQIW6d69u5x//vmiIed8MurCDXPJkiVmf3XTTTeJX81l4KR69dVXi4YPtLloYpyMAMu1fkad\nlbAwXFDOOusscyLNFKNSDZv8Gos8+VXMKT0Xfo9F1LtHfuRX76LJkeUTAQrX+UQzQG3deOONotkI\nRZOpmLYzQFPLeCp77LGHXHDBBRnX81KFHXbYwYaDsH2asVJq1qyZ0fByrZ9RZyUsrJk35dlnn5Xp\n06dHQjKWcDhpd01+3QAV+VWE/LrhfuAeESgFAhSuS4G6D/qEnamm9hbN2ujJSAnFhlAzPlqXXgoT\nlwkGmipaNN27CYzYx4tTJpRr/Uz6KlXZl156SQYOHCjXXHON7LPPPqUaRlb9kl9jYSO/5sbvsWh6\n88jP/OpNRDmqfCLAONf5RDNgbTVs2FBuvfVW6dKli+y99965hKXxBTI///yzJdLRVOeiGSsFsYSr\nVKmScuxIMvL888/bZ1gkXjjzzDMtwoRb6csvv5RHH31UsK1Xr55Aq1a3bl27jE+ammJdFi9eLKiL\nsG+FimEN4csVGCEoJ0qAA1vsF198UTSbpRx11FHSokULdxrm1Fde/UhhH+7A3Oekk06S1q1bezY6\nSHmwkl/Dw6/lPXfS4ffy7icvXw8Cv3oZX44tdwSouc4dw0C3cM4550jPnj3NDvX1118P7Fzfe+89\n6dixozRr1kwGDx5sQjaE4Q8//DDpnCGM77rrrlKpUiXL4qcpg+XAAw8U/PCB4FQEIRWxMvv27Suz\nZ882IdxtEFrSlStXmvMcEsTgOBnhx2TBggUp//73v/8lq27n3bj0EO7xshRNgwYNspcARIlp166d\nXYcDZzSlqh9dzm/7WK/jjjvOtPr33nuvp5LYZIol+TX4/Frec8e9Z8ivLhLcEoESIKDas6xJhQYH\nf6RgI6BCo3P44Yc7GvPXUSEvcJPF/FRL62imwMjc9EXC2XTTTR2NmmLnli5dimRLjoZmi5TR8E+O\npkd31q5da+dUA21lXn31VTu+5ZZbnJYtW0bKq6DuqPBmx3///bejIQ8dTTcfuT5s2LDIfvyO2tRa\n2xhDsj/NDhdfLa3jBx54wNY2uvAJJ5zgaDbI6FOB3T/llFMcNZlxli9fHog5kl8dJ8j8Wt5zJxA3\ncYpJBI1fU0yVl0qEAH5jNYlMLr3PkFxqU7jOBT1/1f3222+dBg0amBCqGll/Db6c0c6dO9cE1s8/\n/zym5B9//BE5TvRjvX79ekez5FkZ1X46N998s7Vzzz332Lmnn37ajk8//XRHzULsnJpcRNpULbdT\no0YNZ86cOWWuRQr9u6PhxZxff/213L/4eukcq6mKoxk5Y4pC+EefQad+/fo5ap/rYK2CROTXsi/D\nQeHX8uYRpPs4fi5B5df4efK4tAjkQ7imWYiiSCofATjDPfnkk2Y73L59+4jpQ/k1vV/irbfeki22\n2EK23XbbmMGq5jrmOP5AtdaC6BuIH41oDbB5BalgatvDDjvMzEFgagATkzvuuEMqVqxo1/AP9uxb\nbbWVmSTolwEzI4lcjNuBgxbMT8r7i6tW7qH+UIu+OAjssKMJjpuuU1j0+SDtjxo1SvCHdQH+QSLy\na9nVDAK/YlblzaPszINxJsj8GowV4iyiEaBDYzQa3E+JAASwp556Sg4++GCBPd+DDz4YCAEMwvAv\nv/wiaqJhDm0pQYi6+NFHH8khhxwiY8eOlaOPPlrUrCDq6j8/gvhBgJMc7NY7d+5sLyf9+/e3cnAY\nRPzoyy67TG677TZzdkSM3q233jqmHRwsWrRI5s2bV+Z89Ak4RapmJ/pUufuqH7CXASQNuvzyy8st\nH5QCkydPNqwQu/yMM84IyrRi5kF+jYFDgsCvmFF584iddTCOwsCvwVgpziKCQC7Kd5qF5IKef+tq\nCCRHNb0O7HL//PNP/07k35Gro6GZb2jykJi5fP311w6ugRKZhXTq1MnRmNGROu+++661c9ddd9k5\n2GfjEy5IhXenVatWEdtmmIdMmzbNruHfE0884ai22NHsmJFz0Tuws4T5Rqo/jeYRXSXtfXXitL4/\n+OCDmDroE6YoQSPVVJutvDpxBm1qCedDfv0HlqDwa3nzSHgT+Phk2PjVx0sVmKGrgEyb68Csps8m\nMn/+fGfLLbd01ETEibYj9tk0bLhwAPvvf/9rgnG3bt0c1RA7cCA85phjInNbuHChXVdNZ2SKJ554\nop3TUHvOV1995fTq1cuOR44c6Xz33XeOaqhNaHYrwMGxefPmdggb7QMOOMCBbTMIWzVLcfRrgB0X\n8x8cGvEw0fB8ztSpUx1N8e7gRcN9SSjmWArdl34hsBeJAQMGFLorT7VPfnWcoPBrefPw1I2X42DC\nyq85wsbqOSJA4TpHAFk9NwSgEdM40M6RRx7pew3nqlWrHI0xbYIXNMhq7uHgHOiVV15xNOa1CaAQ\nwiF8giBw165d21E7auf44493Pv30U2fPPfe0yBPQtqgttqOxqx0I1YgS0rt3b0fNQKwuhGtoveH5\nPnPmTEfNR6y8XSzBP2jMq1atanNUO3BnwoQJJRhFYbvEOmBthwwZUtiOPNo6+TUY/Frec8ejt1/G\nwwo7v2YMGCvkDYF8CNcVMBptKCty42hqyJKs6rOS/xGALbAK17LbbrsJ7HarV6/u60khNjVssBPZ\nPSeaGMoiTjIcIkFgJ42yIXCGVI242aQjgQwcGeMT0uA66msov4RJXRL1V8hzGIu+UMiOO+5oTlOF\n7KvYbSOG+PDhw2XEiBFm417s/r3SH/k1GPya6rnjlXstl3GQX3NBj3VzRQAO/ZBr1fQ526ZmUrjO\nFjrWiyCAjIYQsCFQPv7445EMhJEC3CECJUIALzpdu3YVRGzROOaCJCthJ/Jr2O8A786f/OrdtQnT\nyPIhXDMUX5jumALNFVpr/eQslStXFmQaVDOKAvXEZolA+gi4GTKRGRNfVShY/4Md+TX9e4gli4cA\n+bV4WLOnwiNA4brwGIeih5o1a8oLL7xgabM1K6EgdBKJCJQKAcTuRop3jeBi9yW+rJA2IEB+3YAF\n90qPAPm19GvAEeQXAQrX+cUz1K1Bc63ZDi1xyrnnnivnn3++2R+HGhROvugIaPQT2W+//QQCpKax\nt/jhRR+EDzokv/pgkUIwRPJrCBY5hFOkcB3CRS/klJE9bNiwYaIRMERDuVmSlU8++aSQXbJtImAI\nwF7z0ksvNScUJIZ59tlnLYMm4UmOAPk1OTa8UlgEyK+FxZetlxYBCtelxT+wvWssVrO9/vHHHwWZ\nCGfNmhXYuXJipUdAE+CIxg2X8ePHWzpzbDfZZJPSD8wnIyC/+mShAjJM8mtAFpLTSIoAheuk0PBC\nrgg0atTI0nZrLGfTJp533nmWZjzXdlmfCEQjgC8kGn9cNBumpZPXBDjRl7mfJgLk1zSBYrGcECC/\n5gQfK/sEAQrXPlkovw5zs802M20i7Orwp6m25fnnn/frdDhuDyGA+ODHHXecQJju0qWLvPzyy9Kg\nQQMPjdB/QyG/+m/N/DJi8qtfVorjzAcCFK7zgSLbKBeBE044QeARDuH6sMMOM2fHn3/+udx6LEAE\nEiEA7Rc0rUuWLDHb6tGjR1uc9URleS5zBMivmWPGGskRIL8mx4ZXgokAhetgrqsnZ4XoDQ8++KDc\nc889lv2ocePGMmfOHE+OlYPyJgIrV66Utm3bmrYaTotvv/22Oc16c7T+HhX51d/r54XRk1+9sAoc\nQykQoHBdCtRD3uepp54qy5Ytk4MPPliOP/54OeqoowQPYRIRSIYAUswPGjRImjRpIp9//rnMnz9f\nxowZE0k7n6wez+eOAPk1dwzD1gL5NWwrzvnGI0DhOh4RHhcFgRo1alioPiSe+eyzz0xoGjhwoPz0\n009F6Z+d+AcBRJpp2LChCdPXXnutOS0eeOCB/plAAEZKfg3AIhZpCuTXIgHNbjyNAIVrTy9P8AcH\n7fWbb74pI0aMkHHjxkm9evXk1ltvZfKZ4C99uTN88cUXLRlMhw4d7CvHe++9J3369JGNN9643Los\nUBgEyK+FwTUIrZJfg7CKnEO+EKBwnS8k2U7WCEBYuuiiiwSxTzt16mQZHuGsNmPGDHEcJ+t2WdGf\nCMBJ8ZhjjpGWLVvKlltuaVkWp02bJttvv70/JxSwUZNfA7agOU6H/JojgKweSAQoXAdyWf05qWrV\nqsn1118v77//vuy7774CW0/Y2E6fPl3+/vtvf06Ko04bgbfeektOOukkad68uZkKPfHEE/L0009b\nDOu0G2HBoiFAfi0a1J7siPzqyWXhoDyCAIVrjywEh7EBgdq1a8vdd99tofuQ3RFRIaDJRjgnpMwl\nBQuBRYsWWbxqJIL58MMPZfbs2WZX3aZNm2BNNKCzIb8GdGGTTIv8mgQYniYCUQhQuI4Cg7veQmD3\n3Xe3sH2ILLLPPvtI586dpW7dujJy5Ej57rvvvDVYjiYjBPAlAmEZYcOLtUUEkIceesiEaiSGqVCh\nQkbtsXDpESC/ln4NCjUCl18POugg8muhQGa7gUKAwnWgljOYk0HWPdjcwia7Y8eO5vy40047Sa9e\nveTdd98N5qQDOqvvv//eon5gTU888USpWrWqZeyENqx9+/YBnXW4pkV+Dc56x/Pr1ltvTX4NzvJy\nJgVEgMJ1AcFl0/lFYOeddzabbITuGzZsmDz22GNmLgLtJ8xIfv/99/x2yNbyhsDChQvl7LPPlh12\n2EGuuOIKad26tSD6x9y5c81xMW8dsSHPIEB+9cxSZDyQZPyKr0twNCYRASKQGgEK16nx4VUPIoAI\nEgjJhsQzTz75pCAGL0xGILj17t1bXnnlFQ+OOnxDgqnHDTfcYE6piEuNbIo33nijrFmzxsIuQsNJ\nCj4C5Fd/rDH51R/rxFH6A4EKGuos61hniD8LQsg0EhEoJQJffPGF3HHHHTJ16lTTiNavX19OO+00\nOf3004VCXPFW5ocffhAkkUCKeyQIqlKlipx88snStWtX2XvvvYs3EPbkaQTIr95YHvKrN9aBo/AW\nAvD5gVyL364saSaF6yyRYzXvIvDGG2+YcIcQfqtXr5ZmzZpZNAo4yiEiBSm/CKxdu9acEefMmSPP\nPvus/Oc//5Gjjz7aXmyQ2n7TTTfNb4dsLVAIkF+Lu5zR/PrMM8/IRhttRH4t7hKwN48jQOHa4wvE\n4ZUWAXi4Q3uKqBQQ/GCrjbBhSFBy5JFHmu3gFltsUdpB+rB3fOyCQPTUU0/Jww8/LC+//LJUqlTJ\nMMULDPCFxppEBDJBgPyaCVrpl03Gr/vtt589H/FsxMswiQgQgX8QoHDNO4EIZIDA66+/boL2I488\nYva/yDS35557yrHHHiuHH364abWhxSGVReCTTz4xrTQE6nnz5snXX38tNWvWlLZt29pXgSOOOMIE\n7LI1eYYIZIdAPL9usskmAtt9OMOSX1Njmi6/Hn/88bJ48WJ7HsI2nkQEiIBYKFiahfBOIAJZIACb\nz27duplD5FZbbSVffvmlVK5cWaDN+b//+z/7Q5ZInAsbrV+/Xt555x1ZsGBB5G/VqlWy2WabCeLc\nQrjBH8xtSESgGAiAX5GtEw7M2OKY/PoP8rnwK557jRs3FgjZt99+ezGWkn0QAc8jQM2155eIA/Qq\nAvCMh6PjkCFD5JJLLrF42dHC5EcffWS2wyizxx57mIYbW2SMRGzmoNCff/5pc4eZBzSF2CKt8a+/\n/mqmHQcccEDkZQPJXiBgk4hAKRGAmQPi25Nf88OvDzzwgJx00kkW2hRfokhEIOwIULgO+x3A+WeN\nwDnnnGPJEBBruWLFimXagfD96quvRgROCJ7Q8oC23357QTa6hg0b2h/269SpI4jr60XnPQgjcGL6\n+OOPZcWKFSaYQDjBH9KN//XXX2bS0bx588hLxF577WUh9OCcSCICXkeA/JobvyKyEvxT8MWqWrVq\nXl9ujo8IFBQBCtcFhZeNBxUBaGahhUbimVNPPTXtaeIHHLGaXcHU3X777bfWBhgSgjcEbThOwiZ5\nu+22szjc7hY/XDBDgX1jLlpgCMQ//fST/SGcFgR//OFzubuFAycE6k8//VT++OMPGyNeJKCNd18M\nGjVqZJ+F8YJAe/O0bwUW9AEC5Nf0FwnPsCZNmshhhx1mz8X0a7IkEQgeAhSug7emnFEREIC9MNL6\nItkMmChX+uabb0yIhSALRyJsIdBCW+wKujCziCc4aEHIRsQS7MPB0t1iH9ET1q1bZ5pld4sslD/+\n+KP89ttv8c1Z/c0339zaxA9lrVq1TNCHsO/+4Ry10WWg44kQIeAlft12223tBRwv36XmV2S8bdeu\nncBM5IQTTgjRHcGpEoFYBPIhXG8c2ySPiECwEXjiiSfMIQqfQPMhWAOt6tWr2x8ijySjX375xbTK\nEOqhcYaA7G4heLvCs7uFZhpCcLTADcEb2m4I5K72G1v84Ud66623tgyVixYtEsyTRASIQFkEcuFX\nfA264oorzBkawnCu/JqvZ1DZWWZ+BjHpu3TpIt27dzfHZTxTSESACGSHAJPIZIcba/kQAWiCYVdc\nr149i3vtwymUO2RkqbzgggtMcKeZR7lwsQARyAiBZcuWmRkVbJMRZSNohBf+pk2bmtnc7NmzgzY9\nzocIpIVAPjTX9FZKC2oWCgICU6ZMsdTo1113XRCmk3AOyEAJkxE4apKIABHILwKuf0VQnf7wVQwv\n6EgsA58UEhEgAtkhQOE6O9xYy2cIwCzjyiuvtM+5cOgLKkGbBqdFhNQjEQEikF8EvvvuO2sQJlhB\npUMPPVR69uwpvXr1EjiFkogAEcgcAQrXmWPGGj5E4PrrrxcI2IMHD/bh6NMfMuyy4cxI4Tp9zFiS\nCKSLADTX8HvIJdJPun2Vsty1115rfhxdu3Yt5TDYNxHwLQIUrn27dBx4ugisWbNGRo0aJZdffrn9\nYKRbz6/lEGbwzTff9OvwOW4i4FkEoLkOstbaBR5Rh6ZOnSpPPfUUMze6oHBLBDJAgMJ1BmCxqD8R\ngDkIfhD79OnjzwlkOGrYXUO4RvIYEhEgAvlDAJrroNpbx6O0//77y6WXXmoZbBFelEQEiED6CFC4\nTh8rlvQhAkuXLjUHneHDhwf+U667PNBcI9TfBx984J7ilggQgTwgEBbNtQvVkCFDLEb+2WefzZd1\nFxRuiUAaCFC4TgMkFvEvAtC8IPzeGWec4d9JZDjyZs2aWbZFmoZkCByLE4FyEIDmOgxmIS4McI6e\nNm2aLFy4UMaMGeOe5pYIEIFyEKBwXQ5AvOxfBObNmyePP/64wJnRS8kaCo1opUqVBOnM6dRYaKTZ\nftgQgOY6LGYh7trCzGzgwIHms/L++++7p7klAkQgBQIUrlOAw0v+RQAJY6C1RjpfhJYKG8E0hMJ1\n2Fad8y00AmHTXLt4Iitlo0aN5KyzzpL169e7p7klAkQgCQIUrpMAw9P+RgCfMpcsWSJBThiTaoUY\nMSQVOrxGBLJDIIyaayC18cYbm3nI4sWLQ/tMze6OYa2wIkDhOqwrH+B5I0PhoEGDBDFaoW0JI+FT\n7ldffSWrVq0K4/Q5ZyJQEATCqrkGmHiWXn311XLVVVfJ22+/XRB82SgRCAoCFK6DspKcRwSBG2+8\nUb7//nuBp3tYCcI17MxpGhLWO4DzLgQCeK6EzeY6GsdLLrlE9t57bzMPWbduXfQl7hMBIhCFAIXr\nKDC4638EvvzySxk5cqT0799ftttuO/9PKMsZbLXVVlKvXj0K11nix2pEIB4BhLf866+/QhUtJB6D\n//znP3LnnXfK8uXLZejQofGXeUwEiMC/CFC45q0QKASQ3hyC5cUXXxyoeWUzGTeZTDZ1WYcIEIFY\nBGASAgqz5hrzr1+/vtldI0X6okWLcIpEBIhAHAIUruMA4aF/EXjvvfdk0qRJZheI9L1hJ0YMCfsd\nwPnnEwE4M4LCFOc6GX7nn3++tGzZ0sxDfv/992TFeJ4IhBYBCtehXfrgTbxfv36RcFHBm13mM4Jw\nDYdGODaSiAARyA0Baq434Ad/jilTpsjnn38uAwYM2HCBe0SACBgCFK55IwQCgeeff14efvhhSxgD\nu0CSCMxCQMzUyLuBCOSOADTXECqrVq2ae2MBaGHnnXeWm266yf7mz58fgBlxCkQgfwhQCskflmyp\nRAg4jiN9+/aVNm3ayBFHHFGiUXiv22233VZ23HFHOjV6b2k4Ih8iAM01/Dk22mgjH46+MEM+55xz\n5KijjpKzzz5bfvnll8J0wlaJgA8RoHDtw0XjkGMRuPfee007O2rUqNgLPBLaXfMmIAL5QQCaa9pb\nl8Vy4sSJFvoUGXFJRIAI/IMAhWveCb5GAM40SM0LDUrTpk19PZdCDJ6ZGguBKtsMIwJhTiCTar1r\n1qwp48aNk/Hjx8vTTz+dqiivEYHQIEDhOjRLHcyJ3nzzzfLNN99YhJBgzjC3WcHu+oMPPhDE6CUR\nASKQPQJhTX2eDmIdO3aUDh06SOfOneWHH35IpwrLEIFAI0DhOtDLG+zJff311zJixAizt95+++2D\nPdksZwfNNWzS6dSYJYCsRgT+RYCa69S3ArTXyNp44YUXpi7Iq0QgBAhQuA7BIgd1isgQVqlSJaGt\nX/IVhkMjHBspXCfHiFeIQDoIUHOdGqXq1avL7bffLlOnTpW5c+emLsyrRCDgCFC4DvgCB3V6K1as\nkAkTJlgK3i222CKo08zLvGAa8sYbb+SlLTZCBMKKADXX5a/8McccY4llzjvvPMGXRRIRCCsCFK7D\nuvI+n3f//v2lQYMGZuPn86kUfPiMGFJwiNlBCBCg5jq9RYYfzKabbirI4kgiAmFFgMJ1WFfex/Ne\nsGCBPPjgg4LQe4w5W/5CQrhGavjffvut/MIsQQSIQEIEqLlOCEuZk1WqVJHJkyfLzJkzZfr06WWu\n8wQRCAMCFK7DsMoBmiOc8y655BJp1aqVtG3bNkAzK9xUIFyvX79e3n777cJ1wpaJQIAR+Ouvv+Sn\nn36SatWqBXiW+Zsaknn16NFDLrjgAlm7dm3+GmZLRMAnCFC49slCcZj/IDBjxgx57bXXLM05MUkP\ngbp161pmOdpdp4cXSxGBeARgEgJiEpl4ZJIf48siXkbOPffc5IV4hQgEFAEK1wFd2CBO688//5TL\nL79czjzzTGnRokUQp1iQOVWoUEHo1FgQaNloSBCASQiImuv0FxyO5nfeeac89thjMmXKlPQrsiQR\nCAACFK4DsIhhmcItt9xinxiHDRsWlinnbZ4wDWE4vrzByYZChgA119kt+P/93//JRRddZH+ffvpp\ndo2wFhHwIQIUrn24aGEcMjRHw4cPt4c0YjeTMkMAmuslS5ZYkofMarI0ESACruaaZiGZ3wtQhtSq\nVcsiO8FnhkQEwoAAheswrHIA5ogH9CabbCKXXXZZAGZT/ClAcw2zmqVLlxa/c/ZIBHyOADTXeP4w\npn7mC7nZZpvJtGnT5IUXXpCxY8dm3gBrEAEfIkDh2oeLFrYhf/jhh/ZQvuqqq2TLLbcM2/TzMt/d\nd9/dslnSNCQvcLKRkCHAMHy5Lfhee+1l/jLIT7By5crcGmNtIuADBChc+2CRwj5EaKsR8YJe59nf\nCYgH3qxZM2ZqzB5C1gwxAkwgk/viDxo0yBJ/nXXWWfL333/n3iBbIAIeRoDCtYcXh0MTeemllywZ\nwXXXXScbb7wxIckBAZiGMBxfDgCyamgRoOY696WHWQ3MQxBK9YYbbsi9QbZABDyMAIVrDy8OhybS\nt29fadmypbRv355w5IgAhOu33nqLWqMccWT18CFAzXV+1rxp06YC8z5osen/kR9M2Yo3EaBw7c11\n4agUgQceeMA019Ry5Od2QMSQX375RZYvX56fBtkKEQgJAtRc52+h+/XrZ3H3YR6CzJckIhBEBChc\nB3FVAzCndevWWWSQ0047Tfbcc88AzKj0U4DWCJ9maRpS+rXgCPyFADXX+Vsv+H9MnTpVli1bZuFV\n89cyWyIC3kGAwrV31oIjiUJg3LhxsmrVKj58ozDJdXfTTTeVxo0bU7jOFUjWDx0C1Fznd8kbNGgg\nI0aMEIRY5ct+frFla95AgMK1N9aBo4hC4Pvvv5err75aevfuLbVr1466wt1cEYBpCMPx5Yoi64cN\nAQjXTH2e31XH8x0ZHDt16iR//PFHfhtna0SgxAhQuC7xArD7sghcc801UqFCBbniiivKXuSZnBBg\nGvSc4GPlkCIAsxBmZ8zv4uMZf8cddwjSol955ZX5bZytEYESI0DhusQLwO5jEfj4449lzJgx9rCt\nUqVK7EUe5YwAhGsICh999FHObbEBIhAGBOAEjOym1Fznf7Xr1KljYfmuv/56WbhwYf47YItEoEQI\nULguEfDsNjEC0FbvvPPO0r1798QFeDYnBJo3by7/+c9/aBqSE4qsHCYE8DIKoua6MKuO5GBt2rSR\ns88+W3799dfCdMJWiUCREaBwXWTA2V1yBBYtWiTTp0+Xa6+91qJaJC/JK9kisMUWW1iWNDoRZYsg\n64UNAdhbg6i5LtzKT5o0Sb7++mtBenQSEQgCAhSug7CKAZkDEsYceOCBcsIJJwRkRt6cBjM1enNd\nOCpvIkDNdeHXZYcddpBbbrlFxo4dK88++2zhO2QPRKDACFC4LjDAbD49BB566CF58cUXBbZ3pMIi\nQKfGwuLL1oOFADXXxVnP008/XY4//njp3Lmz/Pjjj8XplL0QgQIhQOG6QMCy2fQRQJYufA7s2LGj\n7LvvvulXZMmsEEA4vrVr18qaNWuyqs9KRCBMCEBzXblyZZqqFWHRJ0yYYHbXF198cRF6YxdEoHAI\nULguHLZsOU0EbrvtNotegaQCpMIjAOEaRLvrwmPNHvyPABPIFG8Nt912W4GAPXnyZHn00UeL1zF7\nIgJ5RoDCdZ4BZXOZIYDPf0OGDJGePXvKLrvsklllls4KAThmAWsmk8kKPlYKGQLQXNOZsXiLDp+b\nM844QxBFxDXJKV7v7IkI5AcBCtf5wZGtZIkAIoOsX79eBg4cmGULrJYNAtBeU3OdDXKsEzYEqLku\n/ooj1wFChkLpQiICfkSAwrUfVy0gY/7ss89k9OjRJlhTM1TcRWXEkOLizd78iwA118VfO/weIDzf\nfffdJ7NmzSr+ANgjEcgRAQrXOQLI6tkjMGDAAEEIpgsuuCD7RlgzKwQgXH/yySf87JoVeqwUJgSo\nuS7Nah955JFmGtKjRw/58ssvSzMI9koEskSAwnWWwLFabgjA3vfuu+8WODFuuummuTXG2hkjAOEa\nRLvrjKFjhZAhAOGaX9ZKs+g33nijRWo577zzSjMA9koEskRg4yzrsRoRyAkBJIxB2L0OHTrIM888\nIzARAVWsWNGSyGD76quvyrJly+yH7dhjj430N2/ePHnllVfsPML3Va9ePXINGg54mWNbr149gRBZ\nt27dyHXu/IPAdtttJ9tvv73ZXbdq1crwnz17tvTq1cswR9xxpKFH7FnYPkYTbLXnz59vIbOAb+vW\nraVChQrRRbhPBAKDwOrVqy2a0Z133snnU5FXFSEQ77jjDjnssMNk2rRp0qlTp4QjwO8Hn18JoeHJ\nUiHg5EAnn3yygz8SEcgEgUceecTR+91ZsGCBVfvll1+cxo0b27kPPvggpqndd9/def/99+3cH3/8\n4XTt2tVROzxn8eLFzkknneRss802ztKlS+262kY6e+65p/PTTz85GjvbOfXUU52ZM2fGtMeDDQi0\na9fOOeWUU5y5c+c6GgLL8FcbeOecc85xjj76aDu+5pprNlTQvYsuusjRFyIH66RCttOsWTPnkEMO\ncTR1cUw5HhCBoCBQpUoVR19E+Xwq4YJeeOGFTtWqVR0VosuMgs+vMpDwRI4IQD6ZMWNGLq3MkFxq\nU7jOBb1w1oXQ26hRI+fEE0+MAQAPSNzQEydOjJxXjZEJ0O4Jzd7oDB482D20By3qtGnTxs5p+lyn\nZcuWkesffvihc++990aOuROLwKBBg5zddtvNTl522WWGv34ViBRSrbS9rLgnpk6d6my11VbO999/\n756yFx+sgYbOipzjDhEICgIaycjRrzJOv379+Hwq4aL++uuv9qzSr2QJR8HnV0JYeDJLBPIhXMd+\n79UWSUSgkAjAA3zFihWCEHzRpJpSadiwocDGTvnBLqlgHPMZENdgIwwHSPzBXluFw4hTnmq55YUX\nXrAYqV999ZXFckbMVFJiBBCOD2vx888/S6VKlawQMHRJX4Lk008/dQ/lpptuElxXTV7kXIMGDQxn\n2M8zZXEEFu4EBAF9kbTn0eGHH87nUwnXFM8nfbk3E0IkmYknPr/iEeFxqRGgcF3qFQhR/xDiVPMs\n8P6uX79+zMxhs3vppZfKu+++K4899phdg21127ZtbR8/crB9VLMQGTt2bOTvvffeM9tsFIJdHmy5\nIZTD3hq2erDdJiVGAPbSf//9t6iJTcICG220UeRFBy88WBvYQMbTQQcdZKewFiQiECQEEIYPBL8O\nPp9Ku7Lw0dEvCLYO+lWy3MHw+VUuRCxQQAQoXBcQXDYdi8B1110najctV155ZeyFf4/gPFerVi25\n4YYbRO2oRe2wZeON//G5dZ3qlixZkrAuTqLMqFGj5MknnzRnvc6dO8vIkSOTlg/7hdq1a8vWW2+d\nVsQQvPwgYsKiRYss6U80drvuuqsdMqJCNCrcDwICboZA8AmfT6Vf0auuusq+lKlfiCkG0h0Rn1/p\nIsVy+UKAwnW+kGQ7KRGA1hlC8xVXXBET3SO6EkLy9enTR5577jnTTuAB6pLa+tpDdfz48fLbb7+5\np20LkwSYL0yePNkeuEcccYQJjIiCoXbYMWV5EItAJpkaoTlSZ9Eywjiih9SoUYNRWWKh5VEAEHA1\n13hx5POp9AuKNUDUkJdeekluvvnmjAbE51dGcLFwjghQuM4RQFZPDwGkN9eIFNK7d++UFbp162Y2\nvRp9wjTX0YXxWXbVqlVm/vH888+bkAczkx9++MHCxsF++Omnn7Yqm2++uRx33HGi0USim+B+HAIw\nDYFw7NpL//nnn5ESWAN8aXBt4GEnDzObu+66K1IGZiX4ocM1fIYlEYEgIQDNNe5rvNyD+Hwq/eq2\naNHCvn5CUeOaovH5Vfp14QjiEMjSmdKqMVpILuiFp+5bb73lqMmGc88996Q16e7duztqV12mrApy\nzuWXX+6oqYh57mMLL3F49IPU3MRB6D5EDUGUEBXkLVxcmYZ4IoIAwhqq8ODssssuhilCHa5Zs8bC\nHSIyiD4uHP0U66xbt87qaHxrp06dOo5+YXA0FrajcWcTrlWkA+4QG2G03wAAQABJREFUAR8jMG7c\nOEftrWNmwOdTDBwlOcDzaO+993b22WcfR/MkOJrLgM+vkqxEMDvF716uofgqAJo4eTvtQyQAAekg\n0q7DguFDQEPlWUQPJIWB7Vt5hKQkuKc0rmnCojALgUOLCoQCDbVLGubPbLSRQAYa1uioFm4ZbmMR\n0BjiFgEEttR77bVX7MUkR3hkLF++3ExEmjZtSqfRJDjxtP8RGD58uEWpwP3uEp9PLhKl3cLBGl/e\n8FV0wIABaQ+Gz6+0oQptQcgpkEFUgZwtBjOZoTFb6FgvLQTgXPjUU09ZiLx0BGvVcpvtbjLBGp0i\n7BKcHePJdX6E/S8pPQTgjIgIIDANSVe4xjoiBCKJCAQdgfjU53w+eWfFEbp12LBhol8zBaFcmzdv\nntbg+PxKCyYWyhEBCtc5AsjqyRGAPS7spJG6/OCDD05a8PXXX7cQS9CCwpZ6zpw5ScvyQn4RQIQV\n2DBCuCYRASIQiwAcGmFzDedoPp9isfHCkWaMFTVPs3wI+PoGh0cSEfACAhSuvbAKAR0D4kzj052m\nIE85QwjheDBCyNYMjVKnTp2U5Xkxvwjg0+rLL7+c30bZGhEIAAIQrrfccktz2uXzyXsLCuXAnXfe\nKc2aNZMhQ4YIzHhIRMALCFC49sIqBHAMmq5WNL22nHfeeeWaEKhjitlk40GJP1JxEUA4vttvv11c\nm/Xi9s7eiIB3EYBZCDTWjz76qD2b+Hzy3lqpM6PlN+jVq5d9JVUnR+8NkiMKHQKUZEK35MWZ8PXX\nXy+//PKLIOh/OgR7af5wpYNU/stAc/3777/bV4b8t84WiYB/EXBtrvl88vYaagQXC9Gq0YvK5EHw\n9sg5uqAiQOE6qCtbwnmtXbvWNAkaJs9iW5dwKOw6DQQaNWpkET/efPPNNEqzCBEIDwIwC0F2RpK3\nEYCT4pQpUwS/PYh/TSICpUaAwnWpVyCA/SO9OTKaIdsiyfsIQCuHT990avT+WnGExUUAmmsK18XF\nPNvedtxxR8vaiMyNL7zwQrbNsB4RyAsCFK7zAiMbcRFYunSpaRAQIgkh80j+QMDN1OiP0XKURKDw\nCCA7KWLqQ1FA8gcCZ511lrRv317OOecc+fnnn/0xaI4ykAhQuA7kspZuUv369TPP7TPPPLN0g2DP\nGSMA4Xrx4sWRVOcZN8AKRCBgCEBrDaLm2l8LC+dspEO/5JJL/DVwjjZQCFC4DtRylnYymoZWHnvs\nMYEzYzoJY0o7WvYejQAihvz000+ycuXK6NPcJwKhRQD21iBqrv11C2y33XYyfvx4i4CEJGYkIlAK\nBChclwL1APaJWNV9+/aVdu3amdd2AKcY6CkhTixsr2l3Hehl5uQyQICa6wzA8lhRpK0+5ZRTpEuX\nLvL99997bHQcThgQoHAdhlUuwhzvuusuWbJkiVx33XVF6I1d5BuBzTbbTJBOmBFD8o0s2/MrAtRc\n+3Xl/hn32LFjZf369dK7d29/T4Sj9yUCFK59uWzeGjScfgYOHGhaAoR1I/kTAZiGUHPtz7XjqPOP\nADTXcMrGiyfJfwjAVh4Zf6H4mTNnjv8mwBH7GgEK175ePm8MfvTo0fbpbejQod4YEEeRFQKMGJIV\nbKwUUASguaa9tb8X9+ijj7bIId26dZOvvvrK35Ph6H2FAIVrXy2X9wb75ZdfyrXXXiuIEgJHEpJ/\nEYBw/c0338inn37q30lw5EQgTwgwxnWegCxxMzfddJN9fejRo0eJR8Luw4QAheswrXYB5or05ltu\nuSXDHhUA22I32aJFC4vyQrvrYiPP/ryIAIRraq69uDKZjWmrrbay3AuzZ8+We++9N7PKLE0EskSA\nwnWWwLGayHvvvWc2bVdffbVsvvnmhMTnCOAlqX79+rS79vk6cvj5QYCpz/ODoxdaadWqlZx//vnS\ns2dPWb16tReGxDEEHAEK1wFf4EJOr3///gIHxrPPPruQ3bDtIiJAu+sigs2uPI0ANdeeXp6MB4dI\nVtWrV5dzzz0347qsQAQyRYDCdaaIsbwh8MILL8jcuXNl1KhR8p//8DYKym0B4ZpmIUFZTc4jFwSo\nuc4FPe/VxdfVO++8U5544gmZNGmS9wbIEQUKAUpFgVrO4kzGcRxLGNOmTRtp3bp1cTplL0VBAOH4\nPv/8c4GjKokIhBkBaq6Dt/oHHnig+QddfPHF8sknnwRvgpyRZxCgcO2ZpfDPQOAUgnjI0FqTgoUA\nNNcgxrsO1rpyNpkjQM115pj5oQZ8hHbeeWcL0QdFEYkIFAIBCteFQDXAbf7+++8yYMAAs7Nu2rRp\ngGcazqnBJhE/PBSuw7n+nPUGBChcb8AiSHsVK1aUqVOnyvz58+WWW24J0tQ4Fw8hQOHaQ4vhh6GM\nGTPGgvHj7Z8UTARgGkK762CuLWeVHgI//vijpc5mKL708PJbqT333NOURJdddpksX77cb8PneH2A\nAIVrHyySV4aIBCPXXHON2VvvsMMOXhkWx5FnBBgxJM+AsjnfIQB7axBSaJOCicDAgQOlYcOG9hV2\n/fr1wZwkZ1UyBChclwx6/3U8ZMgQqVSpklx66aX+GzxHnDYCEK4//PBDS2mfdiUWJAIBQgAmISBq\nrgO0qHFT2Xjjjc08BCZw119/fdxVHhKB3BCgcJ0bfqGpvWLFCpkwYYJAwK5cuXJo5h3GicIsBLR4\n8eIwTp9zJgJCzXU4boImTZrI0KFD5corr5R33nknHJPmLIuCAIXrosDs/05gm7brrrtKly5d/D8Z\nziAlArVq1ZIaNWrQqTElSrwYZAQgXFeoUEGqVq0a5GlybopA3759Za+99pJOnTrJunXriAkRyAsC\nFK7zAmOwG/nf//4ns2fPttB7G220UbAny9kZArS75o0QZgRgFlKlShUmyArBTYAkaEgu8/7778uw\nYcNCMGNOsRgIULguBso+7+OSSy6RVq1ayVFHHeXzmXD46SIA4ZoRQ9JFi+WChgATyARtRVPPB19l\nr732WnPYf+2111IX5lUikAYCFK7TACnMRe6//3559dVXmTAmZDcB7K6hyfn1119DNnNOlwiIMMZ1\n+O6Cnj17ysEHHyxnnXWW/PHHH+EDgDPOKwIUrvMKZ7Aa+/PPP+Xyyy+XM888U1wnt2DNkLNJhgA0\n1whP9dZbbyUrwvNEILAIUHMd2KVNOjHY2E+ZMkU+++wzQZg+EhHIBQEK17mgF/C6t956q6xdu1aG\nDx8e8JlyevEI1K1b15y5aBoSjwyPw4AANddhWOWyc6xdu7aMHj1abrzxRoGvEYkIZIsAhetskQt4\nPfy4wLnjoosukh133DHgs+X0EiHQokULRgxJBAzPBR4Baq4Dv8RJJ4iIWG3btjXzkF9++SVpOV4g\nAqkQoHCdCp0QX0N6cwTZ79+/f4hRCPfUGTEk3Osf5tlTcx3m1ReZOHGi2d3369cv3EBw9lkjQOE6\na+iCWxHZ+caOHStXXXWVbLXVVsGdKGeWEgEI10uXLhXY3pOIQJgQgOaaqc/DtOKxc91+++0FZpHj\nx4+XefPmxV7kERFIAwEK12mAFLYicGLcZZdd5Lzzzgvb1DnfKATgxArBGgI2iQiECQForpn6PEwr\nXnaup556qpx44onSuXNn+fHHH8sW4BkikAIBCtcpwAn6pa+++qrMFF9++WWZMWOGXHfddWYWUqYA\nT4QGgd13310233xz2l2HZsU5USDw119/yU8//UTNNW8H01wjLN+FF15INIhARghQuM4IruAURrih\nevXqyYgRI+S3336LTAypYFu2bCnHHHNM5Bx3wokAMpc1b97chOsvvvhCHn/8cUuyAG3O5MmTwwkK\nZx04BEaNGmVCFGL6P/XUU/Lcc8/ZHOFz8vfffwduvpxQ+ghss802cvvtt1sGx4cffjim4ooVKywu\nNrXaMbDw4F8ENiYS4UQAn/qhnRk0aJCMGTPGNNXQUi5cuNCSxoQTFc4aCHzyySeWnfGNN94wp56p\nU6fKuHHjDJxNNtlE1q1bJx07diRYRCAQCDz00EMJw665CobKlStbKnSEaXv22WelYsWKgZg3J5Ee\nAscee6x06tTJzCTfeecd+6IBe2woomA2h5cxlCERgWgEKFxHoxGifTwkXEEJWklkpULIvRNOOEH2\n2muvECHBqboIIO3vEUccId9//72d2nTTTU2QdhzHLWLHOGjQoEHkHHeIgJ8ROOqoo0yhgJfGRPTz\nzz8LQrKBNyhYJ0Io+Oduvvlmadq0qSBMH5xdFyxYIHgu4jcUDo8UroN/D2Q6Q5qFZIpYQMpDuHY/\neeIhgb/Vq1fLAw88IEceeaQsW7YsIDPlNNJFAC9VzZo1E5iDgKCViRaso9upX79+9CH3iYBvEWjT\npk3kpTHZJMAHffr0SXaZ5wOOQNWqVeW0006TJ554Ql566aXIcxEvZI899ljAZ8/pZYMAhetsUAtA\nHXzyR3rraHKPn3nmGWnSpIl9BoNWmxQeBBCCMZlA7aJQvXp1wadyEhEIAgKIilOlSpWkU9loo41k\n//33N/+DpIV4IbAIrFmzRvACBid/ODfC4TWaELr2888/jz7FfSIgFK5DeBNAY718+fKkM8fDAwLW\npEmT5MEHH0xajheChwBeqs4///yUkWJ222234E2cMwotAvhSA+EJQnQigtLhkksuSXSJ5wKOwL33\n3it43sHWPhnh/mEs7GTohPc8hesQrv1HH31kb+DJpo6HBTzl4T3fvXv3ZMV4PqAIIDsnnFsTEe6L\nRo0aJbrEc0TAtwgg3XWyLzbbbbedHHfccb6dGweeHQIwkxw8eLA5/sdrq6NbxO8losyQiEA0AhSu\no9EIyX6qpCDQ3kCwwpv4ySefHBJEOM1oBJA8Y+TIkVKhQoXo07aPH5Jdd921zHmeIAJ+RgDOiq4P\nSvQ88DIJW+tkWu3ostwPFgI77LCDRU2CrXUqguANW2wSEYhGgMJ1NBoh2YczIyJBxBN+SBDXEw4b\niHVNCi8CyM4JDXW8UAEnRwrX4b0vgjrzWrVqJbyv8YJ57rnnBnXanFc5CMC35J577pEpU6ZYpBj8\nRiYiRBBJpbRKVIfngo0Ahetgr2/C2S1ZsqSMUwYeGnXr1hWEY4PdLSncCEBDPX78+DJOr0CFYfjC\nfW8Edfbt27e30Gru/BBm7YwzzhA48JLCjcA555xjWmxESYpXOAAZ/H4+/fTT4QaJs49BgMJ1DBzh\nOHjzzTdjPoHiYbHPPvvIK6+8YrGuw4ECZ1keAgcddJCZBsVra5DZk0QEgoZA69atY0LyIcwa014H\nbZWzn0/Dhg0tWy0E7XiC0+uTTz4Zf5rHIUaAwnXIFh/2YR988EFk1tBQIhMZvKERy5NEBKIRuOGG\nG2I0NXDu2myzzaKLcJ8IBAKBgw8+OKK5hsKB4fcCsax5nUSlSpVk4sSJMn36dPNNchUPcIZ9/vnn\nY17O8toxG/MdAhSufbdkuQ14xYoVMSYhCLs2a9YsZh7LDdbA1t5pp51kwIABEQEb2hsSEQgiAhCc\nDjzwQJsaw+8FcYXzN6eOHTvK22+/LY0bN448G3///Xf7+pu/XtiSnxGgcO3n1cti7HBmdAkRIW65\n5ZZIRj73PLdEIBqBSy+9VGrWrGmnKFxHI8P9oCHQrl07mxLD7wVtZfM/H5jHvfrqq9KzZ89I44x3\nHYEi9DuJXV/zDAve6Dp16pTnVtlcNgjAoxke8Hvvvbc5L3bo0CGbZjxTp0WLFnLFFVcUbDzPPfec\nOfYVrAOfNLzzzjtbFrKFCxeK3+8Zr0Deo0cPOfTQQws2nGuuuUYWL15csPaD2PD3339v06pRo4ac\neuqpQZxiXuY0bdq0gpmH+VFewBcP+CzdeuutsmzZsrxgzEbyj0Ch5YXoERdFcw3HkJkzZ8ratWuj\n++Z+CRD49ddfBY5qtWvXLkHv+e1y0aJFMn/+/Pw2GtcaEu4wS6WYoysEji233DIOIR5mgwDuKdxb\nhSTwBniElD4C8DvZYostZJdddkm/UohK4jccv+X4TS8U+VFeQExsZPmED1MhsSkU5mFotxjyQjSO\nRdFcux1Cw3jkkUe6h9yWAIEvvvhC8MkzCNSlSxdBFq1CExz4ZsyYUehuPN8+vnrALhUhG0m5IVCs\nl5TDDjtMJk+enNtgQ1YbjmmHHHJIyGad3nSRLAXZLItBfpQXEDAAmnfExyZ5C4FiyQvurIsqXLud\ncls6BIIiWJcOwfD2DOcdEhEIOgIUrIO+woWbH6KHULAuHL5+arkoZiF+AoRjJQJEgAgQASJABIgA\nESAC2SJA4Tpb5FiPCBABIkAEiAARIAJEgAjEIUDhOg4QHhIBIkAEiAARIAJEgAgQgWwRoHCdLXKs\nRwSIABEgAkSACBABIkAE4hCgcB0HCA+JABEgAkSACBABIkAEiEC2CFC4zha5DOshOcGVV15pMTBf\ne+01mThxYsoW3n33Xbn++uvl6aefjpTLtI1IRe4QgRwQyPW+y7V+DkNn1RAjkOq++/nnn+Whhx6S\nIUOGpEQoVRspK/IiEcgRgVmzZskzzzxjrQwfPlw+/fTTtFvkfZs2VAUrSOG6YNDGNvzGG2/I1Vdf\nLZ988onFTL722mtjC0QdffDBB3LbbbcJ0k6vWrUqciWTNiKVuEMEckQg1/su1/o5Dp/VQ4pAqvsO\ngkvXrl3lvvvuS4lOqjZSVuRFIpAjAmPGjJE77rhDkPht4MCB8sILL6TdIu/btKEqWEEK1wWDNrZh\nZHACIc709ttvLzVr1owtEHVUr1496datm51B3EyXMmnDrcMtEcgVgVzvu1zr5zp+1g8nAqnuu7PP\nPlv22muvcoFJ1Ua5lVmACOSAQK1atUxe2HzzzWWrrbZKKTPEd8P7Nh6R4h9vkNyK33eoeqxTp45U\nq1bN0kdjf6eddko5f6RRBblb7GfaBuqQiECuCOR63+VaP9fxs344ESjvvttoo42kQoUKKcEpr42U\nlXmRCOSAAO49VwmH/fJkhuiueN9Go1GafQrXRcIdKbT32Wcf6w03/s4771ym5xdffFGQerdixYqy\nxx572PXoh386bZRplCeIQI4IpHPfwY8A9y9S/x511FHSokWLSK/p1I8U5g4RyBMCmdx3CxculCef\nfFKaNWsmJ554YmQEmbQRqcQdIpAHBHbZZRfZdtttrSXsx8sMy5cvl5dfflnefvttOfDAA+X444+P\n9Mr7NgJFyXYoXBcR+g4dOlhvdevWlb333jum5wEDBsiXX34pN910k3z99ddyxhln2PVo4RonUrUR\n0yAPiEAeEUh13w0aNEhgvtSvXz/BAx+f23v27CmjR4+OjCBV/Ugh7hCBPCNQ3n33xx9/SPv27cVx\nHPnwww9l6NCh9uy96667IiMpr41IQe4QgTwigBc9mIOA2rVrJzAPcQlyAhxyn332WfPjOvTQQ2Xt\n2rXSo0cPtwhlhQgSpdmhcF1E3Dt37my9ValSRU4++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KBDOvmS4ZXZ/Y4Hh6WX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+ "image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
- "execution_count": 42,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -3200,7 +3108,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Supervised-learning/Linear-regression-v1.ipynb b/community-artifacts/Supervised-learning/Linear-regression-v1.ipynb
index 4ae89cf..ca4c481 100644
--- a/community-artifacts/Supervised-learning/Linear-regression-v1.ipynb
+++ b/community-artifacts/Supervised-learning/Linear-regression-v1.ipynb
@@ -11,18 +11,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
@@ -31,21 +20,10 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpdbchina@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum 4.3.10.0\n",
- "%sql postgresql://gpdbchina@10.194.10.68:61000/madlib\n",
+ "# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
"#%sql postgresql://fmcquillan@localhost:5432/madlib"
@@ -71,12 +49,12 @@
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>MADlib version: 1.13-dev, git revision: rel/v1.12-31-g4b7d9cc, cmake configuration time: Tue Nov 21 22:31:28 UTC 2017, build type: Release, build system: Linux-2.6.18-238.27.1.el5.hotfix.bz516490, C compiler: gcc 4.4.0, C++ compiler: g++ 4.4.0</td>\n",
+ " <td>MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-100-g4987e8f, cmake configuration time: Wed Mar 24 23:51:47 UTC 2021, build type: release, build system: Linux-3.10.0-1160.21.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(u'MADlib version: 1.13-dev, git revision: rel/v1.12-31-g4b7d9cc, cmake configuration time: Tue Nov 21 22:31:28 UTC 2017, build type: Release, build system: Linux-2.6.18-238.27.1.el5.hotfix.bz516490, C compiler: gcc 4.4.0, C++ compiler: g++ 4.4.0',)]"
+ "[(u'MADlib version: 1.18.0-dev, git revision: rel/v1.17.0-100-g4987e8f, cmake configuration time: Wed Mar 24 23:51:47 UTC 2021, build type: release, build system: Linux-3.10.0-1160.21.1.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
]
},
"execution_count": 3,
@@ -97,7 +75,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -278,7 +256,7 @@
" (15, 650, 3, 1.5, 65000, 1450, 12000)]"
]
},
- "execution_count": 17,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -320,7 +298,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -359,10 +337,10 @@
"</table>"
],
"text/plain": [
- "[(u'linregr', u'houses', u'houses_linregr', u'price', u'ARRAY[1, tax, bath, size]', 15, 0, None)]"
+ "[(u'linregr', u'houses', u'houses_linregr', u'price', u'ARRAY[1, tax, bath, size]', 15L, 0, None)]"
]
},
- "execution_count": 18,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -390,7 +368,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -429,10 +407,10 @@
"</table>"
],
"text/plain": [
- "[(u'linregr', u'houses', u'houses_linregr_bedroom', u'price', u'ARRAY[1, tax, bath, size]', 15, 0, u'bedroom')]"
+ "[(u'linregr', u'houses', u'houses_linregr_bedroom', u'price', u'ARRAY[1, tax, bath, size]', 15L, 0, u'bedroom')]"
]
},
- "execution_count": 19,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -460,7 +438,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -489,20 +467,20 @@
" <td>[-12849.4168959872, 28.9613922651775, 10181.6290712649, 50.516894915353]</td>\n",
" <td>0.768577580597</td>\n",
" <td>[33453.0344331377, 15.8992104963991, 19437.7710925915, 32.9280231740856]</td>\n",
- " <td>[-0.384103179688204, 1.82156166004197, 0.523806408809164, 1.53416118083608]</td>\n",
- " <td>[0.708223134615411, 0.0958005827189554, 0.610804093526515, 0.153235085548177]</td>\n",
+ " <td>[-0.384103179688204, 1.82156166004197, 0.523806408809163, 1.53416118083608]</td>\n",
+ " <td>[0.708223134615411, 0.0958005827189556, 0.610804093526516, 0.153235085548177]</td>\n",
" <td>9002.5045707</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
- " <td>[[1119105512.7847, 217782.067878005, -283344228.394538, -616679.693190829], [217782.067878005, 252.784894408806, -46373.1796964037, -369.864520095145], [-283344228.394538, -46373.1796964038, 377826945.047986, -209088.217319699], [-616679.693190829, -369.864520095145, -209088.217319699, 1084.25471015312]]</td>\n",
+ " <td>[[1119105512.7847, 217782.067878005, -283344228.394538, -616679.693190829], [217782.067878005, 252.784894408806, -46373.1796964038, -369.864520095145], [-283344228.394538, -46373.1796964038, 377826945.047986, -209088.217319699], [-616679.693190829, -369.864520095145, -209088.217319699, 1084.25471015312]]</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[([-12849.4168959872, 28.9613922651775, 10181.6290712649, 50.516894915353], 0.768577580597462, [33453.0344331377, 15.8992104963991, 19437.7710925915, 32.9280231740856], [-0.384103179688204, 1.82156166004197, 0.523806408809164, 1.53416118083608], [0.708223134615411, 0.0958005827189554, 0.610804093526515, 0.153235085548177], 9002.50457069859, 15L, 0L, [[1119105512.7847, 217782.067878005, -283344228.394538, -616679.693190829], [217782.067878005, 252.784894408806, -46373.1796964037, -369.864520095145], [-283344228.394538, -46373.1796964038, 377826945.047986, -209088.217319699], [-616679.693190829, -369.864520095145, -209088.217319699, 1084.25471015312]])]"
+ "[([-12849.4168959872, 28.9613922651775, 10181.6290712649, 50.516894915353], 0.768577580597462, [33453.0344331377, 15.8992104963991, 19437.7710925915, 32.9280231740856], [-0.384103179688204, 1.82156166004197, 0.523806408809163, 1.53416118083608], [0.708223134615411, 0.0958005827189556, 0.610804093526516, 0.153235085548177], 9002.50457069859, 15L, 0L, [[1119105512.7847, 217782.067878005, -283344228.394538, -616679.693190829], [217782.067878005, 252.784894408806, -46373.1796964038, -369.864520095145] ... (5 characters truncated) ... 83344228.394538, -46373.1796964038, 377826945.047986, -209088.217319699], [-616679.693190829, -369.864520095145, -209088.217319699, 1084.25471015312]])]"
]
},
- "execution_count": 20,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -521,7 +499,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -574,12 +552,12 @@
],
"text/plain": [
"[(u'intercept', -12849.4168959872, 33453.0344331377, -0.384103179688204, 0.708223134615411),\n",
- " (u'tax', 28.9613922651775, 15.8992104963991, 1.82156166004197, 0.0958005827189554),\n",
- " (u'bath', 10181.6290712649, 19437.7710925915, 0.523806408809164, 0.610804093526515),\n",
+ " (u'tax', 28.9613922651775, 15.8992104963991, 1.82156166004197, 0.0958005827189556),\n",
+ " (u'bath', 10181.6290712649, 19437.7710925915, 0.523806408809163, 0.610804093526516),\n",
" (u'size', 50.516894915353, 32.9280231740856, 1.53416118083608, 0.153235085548177)]"
]
},
- "execution_count": 25,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -605,7 +583,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -635,29 +613,29 @@
" <td>2</td>\n",
" <td>[-84242.0345406619, 55.4430144648689, -78966.9753675336, 225.611910021196]</td>\n",
" <td>0.968809546465</td>\n",
- " <td>[35018.9991666351, 19.5731125321026, 23036.8071292953, 49.0448678149637]</td>\n",
- " <td>[-2.40560942760823, 2.83261103076655, -3.42786111479457, 4.60011251069905]</td>\n",
+ " <td>[35018.999166635, 19.5731125321026, 23036.8071292953, 49.0448678149636]</td>\n",
+ " <td>[-2.40560942760823, 2.83261103076655, -3.42786111479457, 4.60011251069906]</td>\n",
" <td>[0.250804617665626, 0.21605133377637, 0.180704400437667, 0.136272031474349]</td>\n",
- " <td>10086.1048725</td>\n",
+ " <td>10086.104872</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
- " <td>[[1226330302.63279, -300921.595597853, 551696673.399772, -1544160.63236657], [-300921.595597853, 383.106734194352, -304863.397298569, 323.251642470093], [551696673.399772, -304863.397298569, 530694482.712349, -946345.586402425], [-1544160.63236657, 323.251642470093, -946345.586402425, 2405.39905898726]]</td>\n",
+ " <td>[[1226330302.63279, -300921.595597853, 551696673.399771, -1544160.63236657], [-300921.595597853, 383.106734194352, -304863.397298569, 323.251642470093], [551696673.399771, -304863.397298569, 530694482.712349, -946345.586402424], [-1544160.63236657, 323.251642470093, -946345.586402424, 2405.39905898726]]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>[-88155.8292501588, 27.1966436294421, 41404.0293363614, 62.6375210753234]</td>\n",
" <td>0.841699901311</td>\n",
" <td>[57867.9999702679, 17.8272309154706, 43643.1321511158, 70.8506824864022]</td>\n",
- " <td>[-1.52339512848988, 1.52556747362489, 0.948695185143874, 0.884077878675974]</td>\n",
+ " <td>[-1.52339512848988, 1.52556747362489, 0.948695185143874, 0.884077878675973]</td>\n",
" <td>[0.188161432894911, 0.187636685729916, 0.38634003237497, 0.417132778705835]</td>\n",
" <td>11722.6225642</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
- " <td>[[3348705420.55893, 433697.545104307, -70253017.4577515, -2593488.13800241], [433697.545104307, 317.810162113512, -90019.0797451144, -529.274668274391], [-70253017.4577515, -90019.0797451146, 1904722983.95976, -2183233.19448568], [-2593488.13800241, -529.27466827439, -2183233.19448568, 5019.81920878897]]</td>\n",
+ " <td>[[3348705420.55893, 433697.545104307, -70253017.4577515, -2593488.13800241], [433697.545104307, 317.810162113512, -90019.0797451145, -529.274668274391], [-70253017.4577515, -90019.0797451147, 1904722983.95976, -2183233.19448568], [-2593488.13800241, -529.27466827439, -2183233.19448568, 5019.81920878898]]</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
- " <td>[0.0112536020318379, 41.4132554771633, 0.0225072040636757, 31.3975496688276]</td>\n",
+ " <td>[0.0112536020318378, 41.4132554771633, 0.0225072040636757, 31.3975496688276]</td>\n",
" <td>1.0</td>\n",
" <td>[0.0, 0.0, 0.0, 0.0]</td>\n",
" <td>[inf, inf, inf, inf]</td>\n",
@@ -670,12 +648,12 @@
"</table>"
],
"text/plain": [
- "[(2, [-84242.0345406619, 55.4430144648689, -78966.9753675336, 225.611910021196], 0.968809546465205, [35018.9991666351, 19.5731125321026, 23036.8071292953, 49.0448678149637], [-2.40560942760823, 2.83261103076655, -3.42786111479457, 4.60011251069905], [0.250804617665626, 0.21605133377637, 0.180704400437667, 0.136272031474349], 10086.1048725104, 5L, 0L, [[1226330302.63279, -300921.595597853, 551696673.399772, -1544160.63236657], [-300921.595597853, 383.106734194352, -304863.397298569, 323.251642470093], [551696673.399772, -304863.397298569, 530694482.712349, -946345.586402425], [-1544160.63236657, 323.251642470093, -946345.586402425, 2405.39905898726]]),\n",
- " (3, [-88155.8292501588, 27.1966436294421, 41404.0293363614, 62.6375210753234], 0.841699901311221, [57867.9999702679, 17.8272309154706, 43643.1321511158, 70.8506824864022], [-1.52339512848988, 1.52556747362489, 0.948695185143874, 0.884077878675974], [0.188161432894911, 0.187636685729916, 0.38634003237497, 0.417132778705835], 11722.6225642065, 9L, 0L, [[3348705420.55893, 433697.545104307, -70253017.4577515, -2593488.13800241], [433697.545104307, 317.810162113512, -90019.0797451144, -529.274668274391], [-70253017.4577515, -90019.0797451146, 1904722983.95976, -2183233.19448568], [-2593488.13800241, -529.27466827439, -2183233.19448568, 5019.81920878897]]),\n",
- " (4, [0.0112536020318379, 41.4132554771633, 0.0225072040636757, 31.3975496688276], 1.0, [0.0, 0.0, 0.0, 0.0], [inf, inf, inf, inf], None, inf, 1L, 0L, [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]])]"
+ "[(2, [-84242.0345406619, 55.4430144648689, -78966.9753675336, 225.611910021196], 0.968809546465205, [35018.999166635, 19.5731125321026, 23036.8071292953, 49.0448678149636], [-2.40560942760823, 2.83261103076655, -3.42786111479457, 4.60011251069906], [0.250804617665626, 0.21605133377637, 0.180704400437667, 0.136272031474349], 10086.1048720296, 5L, 0L, [[1226330302.63279, -300921.595597853, 551696673.399771, -1544160.63236657], [-300921.595597853, 383.106734194352, -304863.397298569, 323.251642470093 ... (4 characters truncated) ... 551696673.399771, -304863.397298569, 530694482.712349, -946345.586402424], [-1544160.63236657, 323.251642470093, -946345.586402424, 2405.39905898726]]),\n",
+ " (3, [-88155.8292501588, 27.1966436294421, 41404.0293363614, 62.6375210753234], 0.841699901311221, [57867.9999702679, 17.8272309154706, 43643.1321511158, 70.8506824864022], [-1.52339512848988, 1.52556747362489, 0.948695185143874, 0.884077878675973], [0.188161432894911, 0.187636685729916, 0.38634003237497, 0.417132778705835], 11722.6225642065, 9L, 0L, [[3348705420.55893, 433697.545104307, -70253017.4577515, -2593488.13800241], [433697.545104307, 317.810162113512, -90019.0797451145, -529.274668274391 ... (5 characters truncated) ... 70253017.4577515, -90019.0797451147, 1904722983.95976, -2183233.19448568], [-2593488.13800241, -529.27466827439, -2183233.19448568, 5019.81920878898]]),\n",
+ " (4, [0.0112536020318378, 41.4132554771633, 0.0225072040636757, 31.3975496688276], 1.0, [0.0, 0.0, 0.0, 0.0], [inf, inf, inf, inf], None, inf, 1L, 0L, [[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]])]"
]
},
- "execution_count": 22,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -933,7 +911,7 @@
},
{
"cell_type": "code",
- "execution_count": 49,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -1143,7 +1121,7 @@
" (15, 650, 3, 1.5, 65000, 1450, 12000, 82452.4386727394, -17452.4386727394)]"
]
},
- "execution_count": 49,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -1180,7 +1158,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Supervised-learning/MLP-mnist-v3.ipynb b/community-artifacts/Supervised-learning/MLP-mnist-v3.ipynb
index 1fa6210..2f153a2 100644
--- a/community-artifacts/Supervised-learning/MLP-mnist-v3.ipynb
+++ b/community-artifacts/Supervised-learning/MLP-mnist-v3.ipynb
@@ -112,78 +112,36 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.4.0 on GCP (demo machine)\n",
- "%sql postgresql://gpadmin@35.184.253.255:5432/madlib\n",
+ "# Greenplum Database 5.x on GCP - via tunnel\n",
+ "%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib\n",
- "\n",
- "# Greenplum Database 4.3.10.0\n",
- "#%sql postgresql://gpdbchina@10.194.10.68:61000/madlib"
+ "#%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "DROP TABLE\n",
- "CREATE TABLE\n",
- "COPY 60000\n",
- "DROP TABLE\n",
- "CREATE TABLE\n",
- "COPY 10000\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "psql:../data/mnist_train.sql:2: NOTICE: CREATE TABLE will create implicit sequence \"mnist_train_id_seq\" for serial column \"mnist_train.id\"\n",
- "psql:../data/mnist_train.sql:2: NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'y' as the Greenplum Database data distribution key for this table.\n",
- "HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.\n",
- "psql:../data/mnist_test.sql:2: NOTICE: CREATE TABLE will create implicit sequence \"mnist_test_id_seq\" for serial column \"mnist_test.id\"\n",
- "psql:../data/mnist_test.sql:2: NOTICE: Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'y' as the Greenplum Database data distribution key for this table.\n",
- "HINT: The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.\n"
+ "Process is interrupted.\n"
]
}
],
@@ -195,8 +153,8 @@
"gunzip -c ../data/mnist_test.sql.gz > ../data/mnist_test.sql\n",
"\n",
"# Greenplum load\n",
- "psql postgresql://gpadmin@35.184.253.255:5432/madlib -f ../data/mnist_train.sql\n",
- "psql postgresql://gpadmin@35.184.253.255:5432/madlib -f ../data/mnist_test.sql\n",
+ "psql postgresql://gpadmin@localhost:8000/madlib -f ../data/mnist_train.sql\n",
+ "psql postgresql://gpadmin@localhost:8000/madlib -f ../data/mnist_test.sql\n",
"\n",
"# PostgreSQL load\n",
"#psql postgresql://fmcquillan@localhost:5432/madlib -f ../data/mnist_train.sql\n",
@@ -205,7 +163,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -225,29 +183,29 @@
" <th>id</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>5</td>\n",
- " <td>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 18, 18, 18, 126, 136, 175, 26, 166, 255, 247, 127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 30, 36, 94, 154, 170, 253, 253, 253, 253, 253, 225, 172, 253, 242, 195, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 49, 238, 253, 253, 253, 253, 253, 253, 253, 253, 251, 93, 82, 82, 56, 39, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 219, 253, 253, 253, 253, 253, 198, 182, 247, 241, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 80, 156, 107, 253, 253, 205, 11, 0, 43, 154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 14, 1, 154, 253, 90, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 139, 253, 190, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 190, 253, 70, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 35, 241, 225, 160, 108, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 81, 240, 253, 253, 119, 25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 45, 186, 253, 253, 150, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 93, 252, 253, 187, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 249, 253, 249, 64, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 46, 130, 183, 253, 253, 207, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 148, 229, 253, 253, 253, 250, 182, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 24, 114, 221, 253, 253, 253, 253, 201, 78, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 23, 66, 213, 253, 253, 253, 253, 198, 81, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 171, 219, 253, 253, 253, 253, 195, 80, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 172, 226, 253, 253, 253, 253, 244, 133, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 136, 253, 253, 253, 212, 135, 132, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
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- " <td>4</td>\n",
+ " <td>6</td>\n",
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+ " <td>3230</td>\n",
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- " <td>2</td>\n",
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+ " <td>3232</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
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+ " <td>3178</td>\n",
" </tr>\n",
"</table>"
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+ "[(6, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... (2223 characters truncated) ... 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 3230),\n",
+ " (0, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... (2380 characters truncated) ... 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 3232),\n",
+ " (1, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... (2191 characters truncated) ... 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 3178)]"
]
},
- "execution_count": 8,
+ "execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
@@ -267,7 +225,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -294,7 +252,7 @@
"[('',)]"
]
},
- "execution_count": 22,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -323,7 +281,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -343,30 +301,29 @@
" <th>num_iterations</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>[0.015136274397893, 0.00342899109428272, 0.0193016817464848, 0.0280858284258549, -0.00466378052683358, -0.0084171335345117, -0.0255419678315392, -0.0317172381159957, 0.015363143562056, -0.00912699551322654]</td>\n",
- " <td>3.29578743494</td>\n",
+ " <td>[-0.0518191613009853, -0.00525955941306539, -0.0184535767032735, 0.00415361301847328, 0.00258512088963953, -0.0156133637151305, -0.00520830709898702, 0.0117474570512119, -0.0268259505560811, 0.00109697660252863]</td>\n",
+ " <td>3.27146719275</td>\n",
" <td>1</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[([0.015136274397893, 0.00342899109428272, 0.0193016817464848, 0.0280858284258549, -0.00466378052683358, -0.0084171335345117, -0.0255419678315392, -0.0317172381159957, 0.015363143562056, -0.00912699551322654], 3.29578743493968, 1)]"
+ "[([-0.0518191613009853, -0.00525955941306539, -0.0184535767032735, 0.00415361301847328, 0.00258512088963953, -0.0156133637151305, -0.00520830709898702, 0.0117474570512119, -0.0268259505560811, 0.00109697660252863], 3.27146719275099, 1)]"
]
},
- "execution_count": 25,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
- "-- Select first 10 coefficients\n",
"SELECT coeff[1:10], loss, num_iterations FROM mnist_result;"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -424,14 +381,13 @@
"[(u'mnist_train', u'x', u'y', u'integer', 0.0, 0.001, u'constant', 0.9, True, 1, 1, [784, 100, 10], u'tanh', True, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], u'1', u'NULL')]"
]
},
- "execution_count": 27,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
- "-- Summary table\n",
"SELECT * FROM mnist_result_summary;"
]
},
@@ -444,7 +400,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -467,44 +423,32 @@
" <th>estimated</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>5</td>\n",
- " <td>5</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
" <td>5</td>\n",
- " <td>5</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
+ " <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
@@ -512,7 +456,11 @@
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
- " <td>9</td>\n",
+ " <td>4</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
@@ -523,55 +471,63 @@
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>1</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>3</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>7</td>\n",
- " </tr>\n",
- " <tr>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
- " <td>7</td>\n",
+ " <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
+ " <td>2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
" <td>5</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(5, 5),\n",
- " (1, 1),\n",
+ "[(1, 1),\n",
+ " (3, 3),\n",
+ " (5, 8),\n",
+ " (3, 3),\n",
+ " (5, 8),\n",
" (9, 9),\n",
- " (1, 1),\n",
- " (3, 3),\n",
- " (1, 1),\n",
- " (3, 3),\n",
- " (5, 5),\n",
- " (3, 3),\n",
- " (1, 1),\n",
+ " (5, 8),\n",
" (7, 7),\n",
+ " (9, 4),\n",
+ " (3, 3),\n",
" (9, 9),\n",
- " (9, 9),\n",
- " (1, 1),\n",
" (1, 1),\n",
" (3, 3),\n",
+ " (9, 4),\n",
+ " (5, 2),\n",
+ " (5, 3),\n",
" (7, 7),\n",
- " (3, 3),\n",
- " (9, 7),\n",
- " (5, 5)]"
+ " (7, 7),\n",
+ " (7, 7),\n",
+ " (1, 1)]"
]
},
- "execution_count": 11,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -579,7 +535,6 @@
"source": [
"%%sql\n",
"\n",
- "-- Training data\n",
"DROP TABLE IF EXISTS mnist_train_prediction;\n",
"SELECT madlib.mlp_predict(\n",
" 'mnist_result',\n",
@@ -588,7 +543,6 @@
" 'mnist_train_prediction',\n",
" 'response');\n",
"\n",
- "-- Test data\n",
"DROP TABLE IF EXISTS mnist_test_prediction;\n",
"SELECT madlib.mlp_predict(\n",
" 'mnist_result',\n",
@@ -611,7 +565,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -629,15 +583,15 @@
" <th>train_accuracy_percent</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>89.23</td>\n",
+ " <td>82.96</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(Decimal('89.23'),)]"
+ "[(Decimal('82.96'),)]"
]
},
- "execution_count": 12,
+ "execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
@@ -660,7 +614,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -678,15 +632,15 @@
" <th>test_accuracy_percent</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>88.94</td>\n",
+ " <td>82.54</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(Decimal('88.94'),)]"
+ "[(Decimal('82.54'),)]"
]
},
- "execution_count": 13,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@@ -726,7 +680,7 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -753,7 +707,7 @@
"[('',)]"
]
},
- "execution_count": 28,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -781,7 +735,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -825,7 +779,7 @@
"[(u'mnist_train', u'mnist_train_packed', u'y', u'x', u'integer', 30000, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], 60000, 0, None)]"
]
},
- "execution_count": 29,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -844,7 +798,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -871,7 +825,7 @@
"[('',)]"
]
},
- "execution_count": 30,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -900,7 +854,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -920,30 +874,29 @@
" <th>num_iterations</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>[0.00447226412119156, -0.0141756656794687, 0.0241908868410943, -0.0117552624907149, -0.00567577206950439, 1.12456825954975e-05, -0.0177735982451553, -0.00318466065493621, -0.00440214494076337, 0.0152249560301159]</td>\n",
- " <td>0.554609580152</td>\n",
+ " <td>[-0.043463625417071, -0.0037202939970283, 0.0279118746442649, 0.0133757623641995, 0.0173655737109133, 0.00850723784625859, -0.0285255139709349, 0.0306238280834051, 0.0339285071113394, 0.0210575130190751]</td>\n",
+ " <td>0.555252298552</td>\n",
" <td>1</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[([0.00447226412119156, -0.0141756656794687, 0.0241908868410943, -0.0117552624907149, -0.00567577206950439, 1.12456825954975e-05, -0.0177735982451553, -0.00318466065493621, -0.00440214494076337, 0.0152249560301159], 0.554609580151537, 1)]"
+ "[([-0.043463625417071, -0.0037202939970283, 0.0279118746442649, 0.0133757623641995, 0.0173655737109133, 0.00850723784625859, -0.0285255139709349, 0.0306238280834051, 0.0339285071113394, 0.0210575130190751], 0.555252298552008, 1)]"
]
},
- "execution_count": 31,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
- "-- Select first 10 coefficients\n",
"SELECT coeff[1:10], loss, num_iterations FROM mnist_result;"
]
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -1011,14 +964,13 @@
"[(u'mnist_train_packed', u'independent_varname', u'dependent_varname', u'mnist_train', u'x', u'y', 200, 1, u'integer', 0.0, 0.1, u'constant', 0.9, True, 1, 1, [784, 100, 10], u'tanh', True, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], u'1', u'NULL')]"
]
},
- "execution_count": 32,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
- "-- Summary table\n",
"SELECT * FROM mnist_result_summary;"
]
},
@@ -1031,7 +983,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -1054,111 +1006,111 @@
" <th>estimated</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>6</td>\n",
- " <td>6</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>4</td>\n",
- " <td>4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>4</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>2</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>6</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>8</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
+ " <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
+ " <td>7</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>4</td>\n",
- " <td>4</td>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>7</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>6</td>\n",
- " <td>8</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
" <tr>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>3</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>7</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>5</td>\n",
+ " <td>5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(6, 6),\n",
- " (0, 0),\n",
- " (4, 4),\n",
- " (4, 4),\n",
- " (4, 4),\n",
- " (0, 0),\n",
- " (2, 2),\n",
- " (8, 8),\n",
- " (2, 2),\n",
- " (6, 6),\n",
- " (0, 0),\n",
- " (0, 0),\n",
- " (8, 8),\n",
- " (8, 3),\n",
- " (0, 0),\n",
- " (4, 7),\n",
- " (4, 4),\n",
- " (0, 0),\n",
- " (6, 8),\n",
- " (0, 0)]"
+ "[(5, 5),\n",
+ " (9, 9),\n",
+ " (3, 3),\n",
+ " (7, 7),\n",
+ " (5, 5),\n",
+ " (3, 7),\n",
+ " (1, 1),\n",
+ " (1, 1),\n",
+ " (1, 1),\n",
+ " (9, 9),\n",
+ " (9, 9),\n",
+ " (1, 1),\n",
+ " (7, 7),\n",
+ " (1, 1),\n",
+ " (3, 3),\n",
+ " (7, 7),\n",
+ " (7, 9),\n",
+ " (5, 5),\n",
+ " (9, 9),\n",
+ " (1, 1)]"
]
},
- "execution_count": 33,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -1166,7 +1118,6 @@
"source": [
"%%sql\n",
"\n",
- "-- Training data\n",
"DROP TABLE IF EXISTS mnist_train_prediction;\n",
"SELECT madlib.mlp_predict(\n",
" 'mnist_result',\n",
@@ -1175,7 +1126,6 @@
" 'mnist_train_prediction',\n",
" 'response');\n",
"\n",
- "-- Test data\n",
"DROP TABLE IF EXISTS mnist_test_prediction;\n",
"SELECT madlib.mlp_predict(\n",
" 'mnist_result',\n",
@@ -1198,7 +1148,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -1216,15 +1166,15 @@
" <th>train_accuracy_percent</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>89.47</td>\n",
+ " <td>90.20</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(Decimal('89.47'),)]"
+ "[(Decimal('90.20'),)]"
]
},
- "execution_count": 34,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -1247,7 +1197,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -1265,15 +1215,15 @@
" <th>test_accuracy_percent</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>89.27</td>\n",
+ " <td>89.72</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(Decimal('89.27'),)]"
+ "[(Decimal('89.72'),)]"
]
},
- "execution_count": 35,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -1321,7 +1271,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Supervised-learning/SVM-binary-classification-v1.ipynb b/community-artifacts/Supervised-learning/SVM-binary-classification-v1.ipynb
index b04f221..859cde2 100644
--- a/community-artifacts/Supervised-learning/SVM-binary-classification-v1.ipynb
+++ b/community-artifacts/Supervised-learning/SVM-binary-classification-v1.ipynb
@@ -14,42 +14,17 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated since IPython 4.0. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: fmcquillan@madlib'"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.x on GCP (PM demo machine) - direct external IP access\n",
- "#%sql postgresql://gpadmin@34.67.65.96:5432/madlib\n",
- "\n",
"# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
"#%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
@@ -59,7 +34,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -83,7 +58,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {
"scrolled": true
},
@@ -1513,7 +1488,7 @@
" (200, 1.340973515, -0.527066068, 1)]"
]
},
- "execution_count": 5,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -1738,7 +1713,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -2466,7 +2441,7 @@
" (100, -1.06826377671416, -0.266085820059313, 1)]"
]
},
- "execution_count": 6,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -2593,7 +2568,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 6,
"metadata": {
"scrolled": true
},
@@ -2613,22 +2588,12 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x11423f350>"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2640,7 +2605,7 @@
}
],
"source": [
- "plt.scatter(training_dataset[:, 1], training_dataset[:, 2], c=training_dataset[:, 3])"
+ "plt.scatter(training_dataset[:, 1], training_dataset[:, 2], c=training_dataset[:, 3]);"
]
},
{
@@ -2652,7 +2617,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -2670,22 +2635,12 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x116339b90>"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -2697,7 +2652,7 @@
}
],
"source": [
- "plt.scatter(test_dataset[:, 1], test_dataset[:, 2], c=test_dataset[:, 3])"
+ "plt.scatter(test_dataset[:, 1], test_dataset[:, 2], c=test_dataset[:, 3]);"
]
},
{
@@ -2717,7 +2672,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -2747,7 +2702,7 @@
"[('',)]"
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -2773,7 +2728,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -2808,10 +2763,10 @@
"</table>"
],
"text/plain": [
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]
},
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -2823,7 +2778,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
@@ -2856,7 +2811,7 @@
" </tr>\n",
" <tr>\n",
" <td>SVC</td>\n",
- " <td>1.16</td>\n",
+ " <td>1.18.0</td>\n",
" <td>svm_train</td>\n",
" <td>svm_training_model</td>\n",
" <td>y</td>\n",
@@ -2874,10 +2829,10 @@
"</table>"
],
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- "[(u'SVC', u'1.16', u'svm_train', u'svm_training_model', u'y', u'ARRAY[1, x1_norm, x2_norm]', u'gaussian', u'gamma=0.05, n_components=200,random_state=1, fit_intercept=False, fit_in_memory=True', u'NULL', u' init_stepsize=0.01,\\n decay_factor=0.9,\\n max_iter=200,\\n tolerance=1e-10,\\n epsilon=0.01,\\n eps_table=,\\n class_weight=\\n ', u'lambda=0.01, norm=l2, n_folds=4', 1, 0, 200L, 0L)]"
+ "[(u'SVC', u'1.18.0', u'svm_train', u'svm_training_model', u'y', u'ARRAY[1, x1_norm, x2_norm]', u'gaussian', u'gamma=0.05, n_components=200,random_state=1, fit_intercept=False, fit_in_memory=True', u'NULL', u' init_stepsize=0.01,\\n decay_factor=0.9,\\n max_iter=200,\\n tolerance=1e-10,\\n epsilon=0.01,\\n eps_table=,\\n class_weight=\\n ', u'lambda=0.01, norm=l2, n_folds=4', 1, 0, 200L, 0L)]"
]
},
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
@@ -2889,7 +2844,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -2921,11 +2876,11 @@
"</table>"
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+ "[(1, [0.000282463148844145, 1.06854926775554, 1.27363439881687, 4.92113244385683, 5.88050478186476, 2.38371197270601, 0.188191753905778, 5.00152091298268, ... (3324 characters truncated) ... 6681, 0.862422957415364, 3.91603917597464, 1.49715016768542, 6.02163404564795, 3.86152789612169, 2.83780532030526, 3.97781206811536, 5.10658970250879], u'offsets'),\n",
+ " (1, [0.132244135344213, 0.0188290441940648, 0.0759280169061063, -0.0138424226185057, -0.127275168329622, -0.0834052175304927, 0.140194970184786, 0.5555475 ... (11509 characters truncated) ... 01960647753032, -0.188438993287052, -0.143863293064765, 0.434553161915615, -0.274577644487135, 0.64880218007095, 0.347559247517978, 0.146438887476848], u'weights')]"
]
},
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -2937,7 +2892,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -2985,7 +2940,7 @@
" (Decimal('10.0'), Decimal('0.75'), Decimal('0.0503322295685'))]"
]
},
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -3006,7 +2961,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -3033,7 +2988,7 @@
"[('',)]"
]
},
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -3058,7 +3013,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -3084,7 +3039,7 @@
"[(189L,)]"
]
},
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -3107,7 +3062,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -3133,7 +3088,7 @@
"[(Decimal('94.00'),)]"
]
},
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
@@ -3156,15 +3111,57 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Done.\n",
- "Done.\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'Persisted grid_points'"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"10000 rows affected.\n"
]
},
@@ -3174,7 +3171,7 @@
"[]"
]
},
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -3186,7 +3183,7 @@
"grid_points = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()], columns=['x1_norm', 'x2_norm'])\n",
"\n",
"%sql DROP TABLE IF EXISTS grid_points CASCADE\n",
- "%sql PERSIST grid_points\n",
+ "%sql --persist grid_points\n",
"\n",
"%sql ALTER TABLE grid_points add column X float[]\n",
"%sql update grid_points set X = array[x1_norm, x2_norm]::float[]"
@@ -3194,7 +3191,7 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -3208,7 +3205,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3235,7 +3232,7 @@
"plt.axis('tight')\n",
"plt.xlim((-5, 5))\n",
"plt.ylim((-5, 5))\n",
- "plt.show()"
+ "plt.show();"
]
},
{
@@ -3249,7 +3246,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
@@ -3276,7 +3273,7 @@
"[('',)]"
]
},
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
@@ -3301,7 +3298,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"outputs": [
{
@@ -3327,7 +3324,7 @@
"[(94L,)]"
]
},
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
@@ -3350,7 +3347,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
@@ -3376,7 +3373,7 @@
"[(Decimal('94.00'),)]"
]
},
- "execution_count": 23,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
@@ -3399,12 +3396,12 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -3424,7 +3421,7 @@
"plt.axis('tight')\n",
"plt.xlim((-5, 5))\n",
"plt.ylim((-5, 5))\n",
- "plt.show()"
+ "plt.show();"
]
},
{
@@ -3436,7 +3433,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
@@ -3476,7 +3473,7 @@
" (2L, True, [Decimal('4'), Decimal('21')])]"
]
},
- "execution_count": 25,
+ "execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
@@ -3517,7 +3514,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Supervised-learning/SVM-novelty-detection-v2.ipynb b/community-artifacts/Supervised-learning/SVM-novelty-detection-v2.ipynb
index 678d7c9..9d4c525 100755
--- a/community-artifacts/Supervised-learning/SVM-novelty-detection-v2.ipynb
+++ b/community-artifacts/Supervised-learning/SVM-novelty-detection-v2.ipynb
@@ -13,18 +13,7 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead.\n",
- " \"You should import from traitlets.config instead.\", ShimWarning)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n",
- " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"%load_ext sql"
]
@@ -33,35 +22,19 @@
"cell_type": "code",
"execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "u'Connected: gpadmin@madlib'"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Greenplum Database 5.4.0 on GCP (demo machine)\n",
- "%sql postgresql://gpadmin@35.184.253.255:5432/madlib\n",
+ "# Greenplum Database 5.x on GCP (PM demo machine) - via tunnel\n",
+ "#%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
- "#%sql postgresql://fmcquillan@localhost:5432/madlib\n",
- "\n",
- "# Greenplum Database 4.3.10.0\n",
- "#%sql postgresql://gpdbchina@10.194.10.68:61000/madlib"
+ "%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
"cell_type": "code",
"execution_count": 3,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"# Setup\n",
@@ -80,12 +53,14 @@
"outputs": [
{
"data": {
- "image/png": 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fttaeBh4HPhJym0JhrX3NWvt0cH8KeA64KNxWhSfo4N0EfCfstpTScOFtjOkC\nfmmtfTbsttSYTwH/FHYjquwi4Jd5j1+hgQMryxjzQeBq4F/DbUmosh28mp2OV5eLdIwxY8DG/Kdw\n/xHuBf4cVzLJf61uneW72G+t/YfgmP3AaWvtYyE0UWqIMWY18EPgtqAH3nCMMX8IvG6tfdoY00mN\nZkRdhre1Nl7seWPMVcAHgWeMMQZXJnjKGHOttfbXVWxi1ZT6LrKMMZ/A/Ty8vioNqi2/Ai7Oe7wp\neK4hGWOaccH9iLX278NuT4h2Al3GmJuAGLDGGPN9a+2fhNyuORp6kY4x5iVgu7X2zbDbEgZjzI1A\nCvhv1to3wm5PtRljVgH/DnwYeBX4v8BHrbXPhdqwkBhjvg9MWmvvDLsttcIYswvwrLVdYbelUMPV\nvAtYavQnUZV8C1gNjAVTov467AZVk7X2XeBzuFk3x4HHGzi4dwIfA643xhwL/jzcGHa7pLSG7nmL\niERVo/e8RUQiSeEtIhJBCm8RkQhSeIuIRJDCW0QkghTeIiIRpPAWEYkghbeISAT9fyOwCfJ3eAFP\nAAAAAElFTkSuQmCC\n",
+ "image/png": "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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x109749490>"
+ "<Figure size 432x288 with 1 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
@@ -104,7 +79,7 @@
"plt.axis('tight')\n",
"plt.xlim((-5, 5))\n",
"plt.ylim((-5, 5))\n",
- "plt.show()"
+ "plt.show();"
]
},
{
@@ -116,11 +91,111 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Done.\n",
- "Done.\n",
- "200 rows affected.\n",
- "Done.\n",
- "Done.\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'Persisted x_train_d'"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "200 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'Persisted x_outliers_d'"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"40 rows affected.\n"
]
},
@@ -137,15 +212,15 @@
],
"source": [
"# Build tables\n",
- "%sql DROP TABLE IF EXISTS X_train_D CASCADE\n",
- "%sql PERSIST X_train_D\n",
- "%sql ALTER TABLE X_train_D add column X float[]\n",
- "%sql update X_train_D set X = array[x1, x2]::float[]\n",
+ "%sql DROP TABLE IF EXISTS X_train_D CASCADE;\n",
+ "%sql --persist X_train_D;\n",
+ "%sql ALTER TABLE X_train_D add column X float[];\n",
+ "%sql update X_train_D set X = array[x1, x2]::float[];\n",
"\n",
- "%sql DROP TABLE IF EXISTS X_outliers_D CASCADE\n",
- "%sql PERSIST X_outliers_D\n",
- "%sql ALTER TABLE X_outliers_D add column X float[]\n",
- "%sql update X_outliers_D set X = array[x1, x2]::float[]"
+ "%sql DROP TABLE IF EXISTS X_outliers_D CASCADE;\n",
+ "%sql --persist X_outliers_D;\n",
+ "%sql ALTER TABLE X_outliers_D add column X float[];\n",
+ "%sql update X_outliers_D set X = array[x1, x2]::float[];"
]
},
{
@@ -176,9 +251,9 @@
" <th>dep_var_mapping</th>\n",
" </tr>\n",
" <tr>\n",
- " <td>[-0.0432454845758051, -0.159444816692118, -0.737806938475955, 0.0534370865459891, -0.302783984437678, 0.216663708966247, 0.413951840077392, -0.624686692573685, 0.383245902283947, -0.93194741477619, 0.398359325873191, -0.106725591800195, -0.149623841552614, -0.0586264154241909, -0.0712714894313125, -0.752820609462399, -0.0387893518368972, -0.902369710478552, -0.559534808391375, -0.525734075427885, -0.125932502169152, -0.159125403795632, -0.554052542709238, -0.295316988845407, -0.343813368149046, -0.144503832215855, 0.372471877108083, 0.482314750223444, 0.43941447611859, -0.251685614368991, -0.167096578461358, 0.33496817207315, -0.0951761758107272, 0.617859995418938, 0.481179708913367, 0.425753691404979, 0.142601387350667, -0.0702737675277522, -0.625499347350069, -0.10838092278605, 0.659909999982923, 0.189460860381574, 0.560772005661166, 0.637457997296956, 0.142566086425306, 0.307329400451873, -0.0570729145059836, 0.0923408623395759, 0.673831714495205, 0.745802707398761, -0.12887594773649, -0.226376229070672, 0.358019840034591, -0.285312281448689, -0.4181210434903, -1.00005186868902]</td>\n",
- " <td>0.115298833215</td>\n",
- " <td>114.886308162</td>\n",
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" <th>decision_function</th>\n",
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}
@@ -381,7 +540,86 @@
"y_pred_outliers = %sql SELECT * from y_pred_outliers; \n",
"\n",
"#%sql SELECT * FROM y_pred_outliers limit 20; -- Show the outliers\n",
- "%sql SELECT * FROM y_pred_train limit 20; -- Show the training data"
+ "%sql SELECT * FROM y_pred_train limit 20; "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "'Persisted grid_points'"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "10000 rows affected.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Predict over the decision grid for plotting\n",
+ "# xx, yy = np.meshgrid(np.linspace(-7, 7, 500), np.linspace(-7, 7, 500))\n",
+ "xx, yy = np.meshgrid(np.linspace(-7, 7, 100), np.linspace(-7, 7, 100))\n",
+ "grid_points = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()], columns=['x1', 'x2'])\n",
+ "\n",
+ "%sql DROP TABLE IF EXISTS grid_points CASCADE;\n",
+ "%sql --persist grid_points\n",
+ "%sql ALTER TABLE grid_points add column X float[]\n",
+ "%sql update grid_points set X = array[x1, x2]::float[]"
]
},
{
@@ -394,65 +632,20 @@
"output_type": "stream",
"text": [
"Done.\n",
- "Done.\n",
- "10000 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Predict over the decision grid for plotting\n",
- "# xx, yy = np.meshgrid(np.linspace(-7, 7, 500), np.linspace(-7, 7, 500))\n",
- "xx, yy = np.meshgrid(np.linspace(-7, 7, 100), np.linspace(-7, 7, 100))\n",
- "grid_points = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()], columns=['x1', 'x2'])\n",
- "\n",
- "%sql DROP TABLE IF EXISTS grid_points CASCADE\n",
- "%sql PERSIST grid_points\n",
- "%sql ALTER TABLE grid_points add column X float[]\n",
- "%sql update grid_points set X = array[x1, x2]::float[]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
"1 rows affected.\n",
"10000 rows affected.\n"
]
},
{
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:6442: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
- " return self.reduce(a)\n",
- "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/numpy/ma/core.py:6442: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
- " return self.reduce(a)\n"
- ]
- },
- {
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H1tfBWA14iqST62wTXZGwn6TO8d53IM6GnRN7eZX38vaT8WH9PeB0/KDjCjpP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\nMQAAAABJRU5ErkJggg==\n",
+ "image/png": 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\n",
"text/plain": [
- "<matplotlib.figure.Figure at 0x109871950>"
+ "<Figure size 432x288 with 1 Axes>"
]
},
- "metadata": {},
+ "metadata": {
+ "needs_background": "light"
+ },
"output_type": "display_data"
}
],
@@ -474,17 +667,8 @@
"plt.axis('tight')\n",
"plt.xlim((-5, 5))\n",
"plt.ylim((-5, 5))\n",
- "plt.show()"
+ "plt.show();"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -503,7 +687,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.12"
+ "version": "2.7.16"
}
},
"nbformat": 4,
diff --git a/community-artifacts/Unsupervised-learning/Kmeans-auto-k-selection-v1.ipynb b/community-artifacts/Unsupervised-learning/Kmeans-auto-k-selection-v1.ipynb
index 567f2b9..196b5b7 100644
--- a/community-artifacts/Unsupervised-learning/Kmeans-auto-k-selection-v1.ipynb
+++ b/community-artifacts/Unsupervised-learning/Kmeans-auto-k-selection-v1.ipynb
@@ -77,7 +77,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
@@ -89,17 +89,7 @@
},
{
"data": {
- "text/plain": [
- "<matplotlib.collections.PathCollection at 0x123c4d2d0>"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1728x1728 with 1 Axes>"
]
@@ -128,8 +118,8 @@
" shuffle=True,\n",
" random_state=3) # For reproducibility\n",
"\n",
- "plt.subplot(221)\n",
- "plt.scatter(X[:, 0], X[:, 1])"
+ "plt.subplot(221);\n",
+ "plt.scatter(X[:, 0], X[:, 1]);"
]
},
{
@@ -141,15 +131,47 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Done.\n",
- "Done.\n",
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Done.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
"10 rows affected.\n"
]
},
@@ -216,7 +238,7 @@
" (9, [7.13956902442378, 7.62758573800053])]"
]
},
- "execution_count": 35,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -256,7 +278,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
@@ -283,7 +305,7 @@
"[('',)]"
]
},
- "execution_count": 36,
+ "execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
@@ -343,7 +365,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -370,22 +392,22 @@
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
- " <td>[[7.75388350107222, 8.08676436352274], [0.951631227556576, 4.27134045806723], [-7.67133723903141, -5.67479830709146], [-4.09993373227142, 0.133772932149813]]</td>\n",
- " <td>[252.579945002578, 250.668120368364, 238.381668272382, 266.841814287837]</td>\n",
+ " <td>[[7.75388350107222, 8.08676436352274], [-7.67133723903141, -5.67479830709146], [0.951631227556576, 4.27134045806723], [-4.09993373227142, 0.133772932149813]]</td>\n",
+ " <td>[252.579945002578, 238.381668272382, 250.668120368364, 266.841814287837]</td>\n",
" <td>1008.47154793</td>\n",
" <td>0.0</td>\n",
- " <td>2</td>\n",
+ " <td>3</td>\n",
" <td>0.947183389806</td>\n",
- " <td>1632.81887659</td>\n",
+ " <td>1634.75657555</td>\n",
" <td>silhouette</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(4, [[7.75388350107222, 8.08676436352274], [0.951631227556576, 4.27134045806723], [-7.67133723903141, -5.67479830709146], [-4.09993373227142, 0.133772932149813]], [252.579945002578, 250.668120368364, 238.381668272382, 266.841814287837], 1008.47154793116, 0.0, 2, 0.947183389806136, 1632.81887659201, u'silhouette')]"
+ "[(4, [[7.75388350107222, 8.08676436352274], [-7.67133723903141, -5.67479830709146], [0.951631227556576, 4.27134045806723], [-4.09993373227142, 0.133772932149813]], [252.579945002578, 238.381668272382, 250.668120368364, 266.841814287837], 1008.47154793116, 0.0, 3, 0.947183389806136, 1634.75657555445, u'silhouette')]"
]
},
- "execution_count": 37,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -397,7 +419,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -427,61 +449,61 @@
" <td>[3411.12491065012, 4305.00403855033]</td>\n",
" <td>7716.1289492</td>\n",
" <td>0.0</td>\n",
- " <td>4</td>\n",
+ " <td>6</td>\n",
" <td>0.888731292527</td>\n",
- " <td>765.710080896</td>\n",
+ " <td>642.506959942</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
- " <td>[[-1.3733146744389, 2.42634391044183], [7.72272439730575, 8.05762299221737], [-7.39233693268625, -5.22064315165525]]</td>\n",
- " <td>[2731.60697792782, 281.246665990702, 583.736523751119]</td>\n",
- " <td>3596.59016767</td>\n",
- " <td>0.0</td>\n",
- " <td>8</td>\n",
- " <td>0.839743765935</td>\n",
- " <td>1390.20452721</td>\n",
+ " <td>[[-0.445923987752407, 3.22002683618452], [-6.58703006319867, -3.85883678132065], [7.75388350107222, 8.08676436352274]]</td>\n",
+ " <td>[1545.67797004569, 1921.53537357514, 252.579945002578]</td>\n",
+ " <td>3719.79328862</td>\n",
+ " <td>0.008</td>\n",
+ " <td>20</td>\n",
+ " <td>0.822013601895</td>\n",
+ " <td>1297.29633564</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
- " <td>[[7.75388350107222, 8.08676436352274], [0.951631227556576, 4.27134045806723], [-7.67133723903141, -5.67479830709146], [-4.09993373227142, 0.133772932149813]]</td>\n",
- " <td>[252.579945002578, 250.668120368364, 238.381668272382, 266.841814287837]</td>\n",
+ " <td>[[7.75388350107222, 8.08676436352274], [-7.67133723903141, -5.67479830709146], [0.951631227556576, 4.27134045806723], [-4.09993373227142, 0.133772932149813]]</td>\n",
+ " <td>[252.579945002578, 238.381668272382, 250.668120368364, 266.841814287837]</td>\n",
" <td>1008.47154793</td>\n",
" <td>0.0</td>\n",
- " <td>2</td>\n",
+ " <td>3</td>\n",
" <td>0.947183389806</td>\n",
- " <td>1632.81887659</td>\n",
+ " <td>1634.75657555</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
- " <td>[[7.75388350107222, 8.08676436352274], [-7.67133723903141, -5.67479830709146], [-4.09993373227142, 0.133772932149813], [1.41264613541449, 5.16566487628978], [0.623237046616695, 3.6342874478265]]</td>\n",
- " <td>[252.579945002578, 238.381668272382, 266.841814287837, 68.8229674093348, 91.7043184805875]</td>\n",
- " <td>918.330713453</td>\n",
+ " <td>[[7.6739770922683, 8.83133734212243], [-4.09993373227142, 0.133772932149813], [0.951631227556576, 4.27134045806723], [-7.67133723903141, -5.67479830709146], [7.85894563116626, 7.10778878054908]]</td>\n",
+ " <td>[104.630219073373, 266.841814287837, 250.668120368364, 238.381668272382, 55.7854880156828]</td>\n",
+ " <td>916.307310018</td>\n",
" <td>0.0</td>\n",
- " <td>9</td>\n",
- " <td>0.875351698489</td>\n",
- " <td>626.444333343</td>\n",
+ " <td>4</td>\n",
+ " <td>0.872465052521</td>\n",
+ " <td>662.638064082</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
- " <td>[[0.974626476974784, 4.28005194603721], [-7.46930148336113, -6.39575229009392], [7.75388350107222, 8.08676436352274], [-4.49954638714482, -0.524834322019787], [-7.88330917940678, -4.91838757082658], [-3.52637326592224, 1.0685334009989]]</td>\n",
- " <td>[241.295711832285, 75.0616661021815, 252.579945002578, 102.096803159267, 89.7995966388727, 71.2559302947208]</td>\n",
- " <td>832.08965303</td>\n",
+ " <td>[[0.623237046616695, 3.6342874478265], [-4.09993373227142, 0.133772932149813], [-8.47470451067351, -6.06693939111612], [7.75388350107222, 8.08676436352274], [-7.04012009702691, -5.36668745535779], [1.41264613541449, 5.16566487628978]]</td>\n",
+ " <td>[91.7043184805875, 266.841814287837, 84.3430527543194, 252.579945002578, 75.5483509450023, 68.8229674093348]</td>\n",
+ " <td>839.84044888</td>\n",
" <td>0.0</td>\n",
- " <td>7</td>\n",
- " <td>0.801837714461</td>\n",
- " <td>1.94988702781</td>\n",
+ " <td>10</td>\n",
+ " <td>0.798618198672</td>\n",
+ " <td>7.84868838777</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
- "[(2, [[-5.88563548565142, -2.77051268747082], [4.3527573643144, 6.17905241079498]], [3411.12491065012, 4305.00403855033], 7716.12894920045, 0.0, 4, 0.888731292527151, 765.710080896163),\n",
- " (3, [[-1.3733146744389, 2.42634391044183], [7.72272439730575, 8.05762299221737], [-7.39233693268625, -5.22064315165525]], [2731.60697792782, 281.246665990702, 583.736523751119], 3596.59016766964, 0.0, 8, 0.839743765934837, 1390.20452721117),\n",
- " (4, [[7.75388350107222, 8.08676436352274], [0.951631227556576, 4.27134045806723], [-7.67133723903141, -5.67479830709146], [-4.09993373227142, 0.133772932149813]], [252.579945002578, 250.668120368364, 238.381668272382, 266.841814287837], 1008.47154793116, 0.0, 2, 0.947183389806136, 1632.81887659201),\n",
- " (5, [[7.75388350107222, 8.08676436352274], [-7.67133723903141, -5.67479830709146], [-4.09993373227142, 0.133772932149813], [1.41264613541449, 5.16566487628978], [0.623237046616695, 3.6342874478265]], [252.579945002578, 238.381668272382, 266.841814287837, 68.8229674093348, 91.7043184805875], 918.330713452719, 0.0, 9, 0.875351698489193, 626.444333342823),\n",
- " (6, [[0.974626476974784, 4.28005194603721], [-7.46930148336113, -6.39575229009392], [7.75388350107222, 8.08676436352274], [-4.49954638714482, -0.524834322019787], [-7.88330917940678, -4.91838757082658], [-3.52637326592224, 1.0685334009989]], [241.295711832285, 75.0616661021815, 252.579945002578, 102.096803159267, 89.7995966388727, 71.2559302947208], 832.089653029905, 0.0, 7, 0.801837714460516, 1.94988702781365)]"
+ "[(2, [[-5.88563548565142, -2.77051268747082], [4.3527573643144, 6.17905241079498]], [3411.12491065012, 4305.00403855033], 7716.12894920045, 0.0, 6, 0.888731292527151, 642.506959942394),\n",
+ " (3, [[-0.445923987752407, 3.22002683618452], [-6.58703006319867, -3.85883678132065], [7.75388350107222, 8.08676436352274]], [1545.67797004569, 1921.53537357514, 252.579945002578], 3719.79328862341, 0.008, 20, 0.822013601895243, 1297.29633563708),\n",
+ " (4, [[7.75388350107222, 8.08676436352274], [-7.67133723903141, -5.67479830709146], [0.951631227556576, 4.27134045806723], [-4.09993373227142, 0.133772932149813]], [252.579945002578, 238.381668272382, 250.668120368364, 266.841814287837], 1008.47154793116, 0.0, 3, 0.947183389806136, 1634.75657555445),\n",
+ " (5, [[7.6739770922683, 8.83133734212243], [-4.09993373227142, 0.133772932149813], [0.951631227556576, 4.27134045806723], [-7.67133723903141, -5.67479830709146], [7.85894563116626, 7.10778878054908]], [104.630219073373, 266.841814287837, 250.668120368364, 238.381668272382, 55.7854880156828], 916.307310017639, 0.0, 4, 0.872465052520762, 662.638064082453),\n",
+ " (6, [[0.623237046616695, 3.6342874478265], [-4.09993373227142, 0.133772932149813], [-8.47470451067351, -6.06693939111612], [7.75388350107222, 8.08676436352274], [-7.04012009702691, -5.36668745535779], [1.41264613541449, 5.16566487628978]], [91.7043184805875, 266.841814287837, 84.3430527543194, 252.579945002578, 75.5483509450023, 68.8229674093348], 839.840448879659, 0.0, 10, 0.798618198671787, 7.84868838777101)]"
]
},
- "execution_count": 38,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -501,7 +523,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 8,
"metadata": {
"scrolled": false
},
@@ -535,18 +557,18 @@
"500 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
- "('For n_clusters =', 3, 'The average silhouette_score is :', [(0.839743765934837,)])\n",
+ "('For n_clusters =', 3, 'The average silhouette_score is :', [(0.822013601895243,)])\n",
"Done.\n",
"1 rows affected.\n",
- "234 rows affected.\n",
+ "186 rows affected.\n",
"1 rows affected.\n",
"Done.\n",
"1 rows affected.\n",
- "126 rows affected.\n",
+ "189 rows affected.\n",
"1 rows affected.\n",
"Done.\n",
"1 rows affected.\n",
- "140 rows affected.\n",
+ "125 rows affected.\n",
"1 rows affected.\n",
"500 rows affected.\n",
"Done.\n",
@@ -581,7 +603,11 @@
"500 rows affected.\n",
"1 rows affected.\n",
"1 rows affected.\n",
- "('For n_clusters =', 5, 'The average silhouette_score is :', [(0.875351698489193,)])\n",
+ "('For n_clusters =', 5, 'The average silhouette_score is :', [(0.872465052520762,)])\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "71 rows affected.\n",
+ "1 rows affected.\n",
"Done.\n",
"1 rows affected.\n",
"125 rows affected.\n",
@@ -596,51 +622,47 @@
"1 rows affected.\n",
"Done.\n",
"1 rows affected.\n",
- "52 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "73 rows affected.\n",
- "1 rows affected.\n",
- "500 rows affected.\n",
- "Done.\n",
- "500 rows affected.\n",
- "1 rows affected.\n",
- "500 rows affected.\n",
- "500 rows affected.\n",
- "1 rows affected.\n",
- "1 rows affected.\n",
- "('For n_clusters =', 6, 'The average silhouette_score is :', [(0.801837714460516,)])\n",
- "Done.\n",
- "1 rows affected.\n",
- "124 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "64 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "125 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "72 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "61 rows affected.\n",
- "1 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
"54 rows affected.\n",
"1 rows affected.\n",
+ "500 rows affected.\n",
+ "Done.\n",
+ "500 rows affected.\n",
+ "1 rows affected.\n",
+ "500 rows affected.\n",
+ "500 rows affected.\n",
+ "1 rows affected.\n",
+ "1 rows affected.\n",
+ "('For n_clusters =', 6, 'The average silhouette_score is :', [(0.798618198671787,)])\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "73 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "125 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "55 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "125 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "70 rows affected.\n",
+ "1 rows affected.\n",
+ "Done.\n",
+ "1 rows affected.\n",
+ "52 rows affected.\n",
+ "1 rows affected.\n",
"500 rows affected.\n"
]
},
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1296x504 with 2 Axes>"
]
@@ -652,7 +674,7 @@
},
{
"data": {
- "image/png": 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JfUj9tLdxRaR/qIaBSJFiJ0SHQqgb6e6PE6qgAmzexsKC3vwn/t3PzGbFOE4gVJGG8Lo96Lz5H22hg64U3W5g3X0NnU/MniVUqz/K3Y8i9ET8XXdfGX9viH9rusXTkPg/+dtjdF4M3Z58qkDoOf8HBZYx+8TjOnc/EDiJcLGzLWbTebF3kLsfSZG1P+JTph/HrxcS2oKuAJKvDnwtcKe7f8zdX0doMgLwhgKTfg3wD3c/z93fAHwkDj9kG2p0ZLeHcuAMADM7kPB0Hzq3h63ichXN3T9HeJoFofnLH82susAo2RoMSwiFKblk484An05sG8cQ8vDWmBfZmiVfdfdDgXewjTepZrZ/WBz/hrufTug0axPhQv31BeLMzu/T2RoWFjqYm1Jgdtn9b9+Yfnj3eZjZYMJN6lXu/m53P5zObSu7/ezI/WtH3dwvoLOA4gwLaoGTixg3228FxE5j45PC7LhP9bHWSw8WOt97zt0/5+5vTkx7PGGdb/MxYRucZGbVsUA5W0OpidAvQC7Z/cKBdyfW6X8R1un/5hkveSN4FmytQXZst3TZwjaAN7v7LOB7OaaXb7ubRrjBB/iAu7+KzjcFFSNbyyD75qB/Af8kXHt+OA57oJdpbI3NzLqfj0op++rHKWb29uzA7DVCHslC+mytk9O7J7LwGsE17v4Vdz+ZUAgGoaAr29lkNl+y21tWX7epLseJIo9TuQwm7GuFPhPzjWxmE83sbEt0JkrXY8yutO5F9jgqMBAp3oeAJy30Mv24mS0hlLwD3N5P87yacNNqwD/N7Fk6b8oWEDqWAvhb/FtOeIL7FLlvir4U/54CrIjVDZcTChI+k0iXLQh5W1zW7HyyHYoB3GxmD5vZmbG69rVx+MWxV+Y5ZraecLNzfIFlzFan/5CZPRfnUVkgfSFPJ/5/xsyeBz7fh/F/TrgQy16c3NrthuYWYENsjvAkof+K7vPt7mJgnYUerx8HfhaHv0Koct8XtxMKewB+GfPrUcJN8FqKf0pdkLtfQGgLC+Ei8c/5qoS6+wPAKMLNWL6bv9sJ26QB/7bQY/3zhPbN9xCePq4n5AnA1y30nP4EhZ94F/J6YEFs6vIEoVbN4PhbzvXl7ovpbJpyGqFK79OEffDYAvPK7n9Hm9kjcfrdnxyOJrS1XmfhXePzCLVOkvH0x/61Xdy9gdCmGkIh1UuEvJxQxLgL6dzeP2lmC+K4+xOashTszb5I5wErLfTk/jjhiTiE9vIL2f5jQl9MIhxLXyI0X4Nw49WQJ/1VhM4HBwFz43Yxn9Dk7Dd03qx3EfM1e+P3GTN7gVA7qvtN1XOE/ikg7MPP0Lkuk9YQal9A2L4eMbNPxeXIvsruhrgv5HpjSj4PxL9DYhwP01k9vjb+vZ/C5iX+fy7uE/v0IYb+cjWdzVN+ZWbzzSzbWWs+8wkdCQJcZmb301lInfR2YKmFJjaP09nMK9tkADrzZRQwL+ZLFdu4TSUUc5zqwd0fcHfr5XNRgfmOBH4JbDSzZ8zsZToLrjcR+gQRkRJRgYFI8S4g9F+wmfDkaizhAuA7hN7dd7jYLvUowo1qHeGJzxpCz+OviW2Jcfd7Y3zLCdUG55HjYtzdf0WoDv93QuHCdMITsN/Q9SnlBYSLuxZCk4uD4vjrCBfoSwm1HGYT8gHgk/G3OYSLjr0JN1o/Jv+TMggdtd0f46gmPMEqdAOel7vfB3yRkA9VhHz4eB/GryO2tY5u6pbkZ4SLtxGEapJrCJ3LvaPAZH9FuKkfRMjHzYTt6MRtePrfRLgRzhYkTSVc0P8KOMrdXykwep+4+zcJbcEh1Ky4N1f12Zh2rYe26fmm1Uy44b6McEO1P+FC9zngW8CzMS/OIDwhaycUgpxD7+3783mScJHZTNjOB8VhH4zbST7nEZ6IPknYHvch9Ng9v8A43yU0rakjFH7cRrj4TVpHKJBaEdNMJtzQfo9YU6Wf9q8d4QLCjcjGGNdv6CwIyNlcJeGjhBv0uYQb6grC/n68u/9pB8T2R0Ib+ArC/tUK/JWwf9Vt7zGhj64gFI4NIdzk/JgChSIe2twfRTieryZsp4MJ+8CXKdys5EOEbayB0F79x8T+DBLTn0doXrWIcLxfS+frgZPpnPDEfwEhj44EJrv7BkINhrmE68UWOpsSFCNZGDDH3evdfRWd+5ID/9fLNO4m9DmxjrDPzCbslyUV99WjgJ8Q9tcphHV3T4Fx2gjNM54kHN+GE2uKdfOPOB0jNGPLEG7iz4jrFML+97+EfXIqIV/S27lNQRHHqX6yinBcWU2ofTGWULhyCzDb3ZcUGFdE+pn18XpVRGS3ZuH97ZcC/4nVl0X2aGY2Bmhy943xexXhBmQm8LC7H13K+ErNQsdyAF/v5SmqiIjIgKNOD0VEADN7G+Hp24lx0PdLGI7IruRoQj8T/yHUkJlFeALYRujvQ2S3YmZvIf+2/cdYA0tEZI+gAgMRkeBgQkdla4HvufsdJY5HZFexiNCB4cGE6vbrCX2pXOzuD5UyMJF+MorOt0F0Ny/PcBGR3ZKaJIiIiIiIiIhID+r0UERERERERER6UIGBiIiIiIiIiPSgAgMRERERERER6UEFBiIiIiIiIiLSgwoMRERERERERKQHFRiIiIiIiIiISA8qMBARERERERGRHlRgICIiIiIiIiI9qMBARERERERERHpQgYGIiIiIiIiI9KACAxERERERERHpQQUGIiIiIiIiItKDCgxEREREREREpAcVGIiIiIiIiIhIDyowEBEREREREZEeVGAgIiIiIiIiIj2owEBEREREREREelCBgYiIiIiIiIj0oAIDEREREREREelBBQYiIiIiIiIi0oMKDKRPzOwcM7s38d3NbL/4/41m9q3SRbdzmNm5ZvavHTi9081sqZnVm9lhO2q63eaxdT3tSszsy2Z2fanjADCzi8zs1lLHISIi/cPMrjWzC/t5Hg+Y2Yfi/12umXbgPEpy7uzr9YqZHWtmr+yEuBab2Rv7ez47WnJbKSLtTslLkVxUYCA9mNkxZvZvM9toZuvN7EEzmwXg7re5+/GljjEp1wF3V7lBLvJk8APgk+5e6+5P7qR57hLc/TvuPiBiLYaZTYnbXqbUsYiI7EnM7M9m9o0cw99qZivNLOPuH3P3b+6smHbENVOuG8USnjsLXq/sKtde22J3emiwox9siajAQLows8HA3cCVwHBgAvB1oLmUce3mJgPPbcuIZpbewbHsNLqp7kl5IiKyzW4C3m1m1m34e4Db3L2tBDHtbrb5emV3p/O37M5UYCDdTQVw99vdvd3dG939Xnd/GooqtRxmZn80s81m9oiZ7Zv9wcxebWb/iTUX/mNmr0781qU6WfeSXjM7KtZ6qDOzOWZ2bBz+beC1wFWxitxVZvZ/cbQ5cdjZMe3JZvZUnMa/zezgfAsRS8nPM7OXzGytmV1iZjn3l3zLlSu2buNVmFk9kI6xLozDp8daAnVm9pyZnZoY50Yz+7GZ3WNmW4D/6jbNQvN8o5nNj9O9OnlRZWYfMLPnzWyDmf3FzCbnWdY/mdknuw2bY2Zvi///KFZX3GRmj5vZaxPpLjKzO8zsVjPbBJybYz2fGpe5LubB9G7rZL/E961NYMxspJndHcdbb2b/LLC+ZprZfTHdKjP7co40PZ7oJLdRMzvSzB6Ly7nKzC6LybLbXl3M/6N7y9+4XJ8ws/nAfAt+aGar4/SfMbMDcy2LiIhs9TtgBOEcCICZDQNOBm6O34s6b/RyvhkWx1sTj+l3m9nEXAFZ4prJzL4QzwvZT6uZ3Rh/e388R2y2cN3x0Ti8BvgTMD4x3vg+njsXm9nnzOxpC9cpvzKzyjzxpszsAjNbEs9BN5vZEMtzvdJt3JzXXvG38+P0VpjZ+xPDK8zsB2b2cjyXXmtmVblii+k/nMinuWZ2eI40XZrHdj+fm9kXzWxZnMYLZnacmZ0AfBk4O8Y+J6YdYmY3xLiXmdm3LD6oiev2wXi+XgdcFIcXOt+/yczmxfVwFdC9cCu5HFVxWTaY2VxgVrffv2RmCxN5cXocPh24Fjg6LktdHP4WM3syXlcsNbOL8s1bpAd310efrR9gMLCOUFJ/IjCs2+/nAv9KfHdgv/j/jXHcI4EMcBvwy/jbcGADoaQ/A7wzfh8Rf18MvDEx3YuAW+P/E+J0TyIUcr0pfh8Vf38A+FC3OLfGFb8fBqwGZhNOeO+L86zIkw8O3B/j3gt4MTuPZB4UsVw9Ysszr2welgELCCeucuANwGZgWiKPNwKviXlRmWN6+fLjbmBoXJ41wAnxt7fGeU6Py3AB8O88sb4XeDDxfQZQl81H4N2EC7YMcD6wMhtjXKetwGkx9qpu63kqsCWu3zLgCzGu8jzr9EbgW/H/7xJOkGXx81rAcsQ/CFgRY6uM32fn2OaOBV7pNu5i4jYKPAS8J/5fCxwV/58S48wkxiuYvzH9fYRtqQp4M/B4XFcWxxtX6mODPvroo8+u/gGuA65PfP8o8FTie1HnjV7ONyOAM4DqeA75DfC7RNoHyHG90C3OScBy4MT4/S3AvvGY/3qgATg8/pbrfNSXc+di4FFgfDzPPA98LE/+fSCOu088t/0WuCXxe5d8yTF+93w7FmgDvhFjOyku27D4+w+Bu2Jcg4A/AN/NM+2zgGWEG2cD9gMmJ5Yxe37euq665x8wDVgKjI/fpwD7ds/TxLh3Aj8BaoDRMR8/mli3bcCnCOf2Kgqc74GRhOu5M2NefCaOn/MaEbgY+GfMm0nAs8ntIObHeML11NlxGxiXb7uL+XBQTH8wsAo4rdT7rD4D46MaBtKFu28CjiEc9K8D1pjZXWY2pshJ3Onuj3qo+ncbcGgc/hZgvrvf4u5t7n47MA84pYhpvhu4x93vcfcOd78PeIxw4inWR4CfuPsjHmpO3ERoZnFUgXG+5+7r3f1l4HJCYUB327NcuRxFOElf7O4t7v53wo1+ct6/d/cHY1409WHaF7t7XVye++lcNx8jnKCfj+vtO8ChlruWwZ3dfjsH+K27NwO4+63uvi7mxaVABeEEnfWQu/8uxt7YbdpnA3909/vcvZXQVrIKeDW9awXGES4eWt39n+7uOdKdDKx090vdvcndN7v7I0VMP9f89jOzke5e7+4PF0hbTP5+N25rjXHag4ADCBevz7v7im2IUURkT3MTcGbiCfp747Bcij1vdBHPcf/r7g3uvhn4NuEmvyjxCfrvgB+5+5/iNP/o7gs9+AdwL4maEr0o5tx5hbsvd/f1hJvyQ3NMB8I5/TJ3f8nd64H/Ad5h21fdvhX4Rszje4B6YJqZGeHa7DPx/LeZcH58R57pfAj4vrv/J+bTAndf0sdY2gnXJTPMrMzdF7t7j9oSAPG69yTg0+6+xd1XEwo4kvEtd/cr4zVPI4XP9ycBz7n7HXE9XU54qJLP24Fvx7xZClyR/NHdfxPXaYe7/wqYT3hgl5O7P+Duz8T0TwO304ftVvZsKjCQHuKB7lx3nwgcSCjBvLzI0ZMHvwbCzS9xGt0P7EsItQd6Mxk4K1a1q4vVq44hnOiLNRk4v9s0JsW48lnaLdZcabdnuXIZDyx1944C01vKtsm3biYDP0rky3pC6X2PZYgn9D/SecJ8J6FgCIBY7fH5WN2uDhhCKFUvJvYueRnzYGmuOHK4hFCqf2+szvmlPOkmATkvDvrog4SnOvMsNEM5uUDaYvJ3a77EQqKrgKuB1Wb2Uwt9i4iISAHu/i9gLXCahSaRRwK/yJO82PNGF2ZWbWY/idX2NxGaog214vsUugF4wd2/l5jmiWb2sIWmEXWEm8uReafQVTHnznzn/4LTiv9ngGIfGuWyzrv2H5Gd/yhCLY3HE+fHP8fhuWz3+dvdFwCfJtQmWG1mvzSzfNeBkwk1AVYk4vsJoaZBVvdrmkLn+/F0Pdd7jvGTxtPzOnQrM3uvdTazrSNcr+fdZsxstpndb6EpzUZC4Uax25js4VRgIAW5+zxC9a7tbUO9nHAgTdqLUL0MQlWq6sRvYxP/LyVUiRua+NS4+8XZMIuY/1JCSW1yGtWxRkA+k7rFujxHmt6Wq5jYuk9vknVtf5+cXjHT7Os8lxKq2CXzpsrd/50n/e3AOy20z6+mMOBcAAAgAElEQVQk1FbAQn8FXyCUig9z96GE5hPJNnqFYuuSl/HpwyQ6l72BPNtIrClwvrvvA5wKfNbMjsuzrPsUiCGry/YYLwS3XsS4+3x3fyfhwuF7wB0W2prmWr5i8rfLeO5+hbu/itDkYyrw+SJiFhGR0F/Bewm1E//i7qtyJerlvJH3fENo0jaN0JxtMPC6ODxve/SsWCgxlVDonB1WAfwvoWbAmHjuvCcxvd7O6b2dO/ui+zXNXoRq8znzcDutBRqBmYlz4xB3z1eYsZTQbKM3ha4ncfdfuPsxhOV0wjkceubzUkJN1JGJ+Aa7+8zk5HKMk+98v4LEdWViPeWzgp7XodlxJxNqAX+S0AR2KKHJQqFt5heE5h+T3H0IoTlOr9usCKjAQLoxswMsdE4zMX6fRHiKXKjKdTHuAaaa2bvMLGOhM5wZhOr2AE8Rqr2VmdkRhDZeWbcCp5jZm80sbWaVFjqxyXYytIqeN4Hdh10HfCyWsJqZ1cQOYAYViPnzFjo3mgT8P+BX27BcuWIr5BHChcoXYl4cS2je8Ms+TKOv87wW+B8zmwlbO/k5q0D6ewgn2m8Av0rUhhhEuLBYA2TM7KuEPjGK9WvgLRY6ICojXJQ1A9kb66eAd8Vt4AQSVeksdGi5XzwBbyRUO+ygp7uBcWb2aQudLQ0ys9k50r0IVMZtpIzQDrEiMb93m9mouOx1cXBHXPYOuuZ/n/LXzGbF7bSMcOHTlGdZRESkp5uBNwIfJn9zhN7OG3nPN4RzXSOhc9vhwNeKCcrMTgTOA073rk3yygnnlzVAW0yXfBXjKmCEmQ3JM+nezp19cTvwGTPb28xqCVXqf+XFv2Gi6OuPeP68DvihmY0GMLMJZvbmPKNcD3zOzF4Vr+P2s9xNJ58CTjKz4WY2llCjgDj9aWb2hlhI00RYj9l1vgqYkn1gE5sC3gtcamaDLXQIua+ZFarGX+h8/0dgppm9zUITj/PoVpjRza/jtIbF691PJX7LPqBYE+fzfro+2FsFTDSz8sSwQcB6d28ysyOBdxWYt0gXKjCQ7jYTOgZ8xEIv/A8TSi3P356Juvs6Qvvx8wkdFn4BONnd18YkFxJKjjcQXuP4i8S4SwkdyXyZcHBcSnjimt1+f0Ros7jBzLJtvC4CbopVtd7u7o8RLh6uivNYQOgUppDfEzqfe4pwoL9hG5YrV2x5uXsLoYDgRELp+zXAe2NNj2L1dZ53EkrYf2mheuWzcf750jcTOkJ6I12rev6FUJ3wRULVuSb60HzC3V8gPBG6krDspwCnxDyBUGhzCuEG/RxCG9Cs/YG/EtpGPgRc4+7355jHZkLHUKcQqmjOp9ubJmK6jcB/Ey5QlhFu3JNvTTgBeM5Cr9E/At7h4Y0iDYT2rA/Gbe+ovuYvoZDlOsJ2uoSwXV0CYGZfNrM/FRhXRGSP5u6LCTfLNYQnqvkUOm8UOt9cTugjYC3hGunPRYZ2NqGm2vPW+caDa+N56TzCDeIGwo3c1rjj+f924KV4XulShb6Ic2df/Ay4hdDMYhHhPP6pgmN0dRGJa68i0n+RcD32cDw//pWu/R5t5e6/IZxff0G4Vv0doUPA7m4B5hA6QryXrg97KgidCa4lXAOMJvTTAKHzSoB1ZvZE/P+9hAKduYR1cwcFmsMWOt/H68Kz4vzXEba/B/NNi3AtvISwHu6Ny5Wdz1zgUsJ2u4rQmWFyWn8nvP5ypZllr0f/G/iGmW0GvkrY3raK22Ox/WbIHibbG6yIJJiZA/vH9m4iIiIiIiJ7HNUwEBEREREREZEeVGAgIiIiIiIiIj2oSYKIiIiIiIiI9KAaBiIiIrJTmNnPzGy1mT2bGHaJmc0zs6fN7E4zG5pn3MVm9oyFd48/tvOiFhER2XP1Sw2DkSNH+pQpU3b4dEVERAa6xx9/fK27jyp1HKVgZq8j9Ep/s7sfGIcdD/zd3dvM7HsA7v7FHOMuBo5IvIWmV7oeERERya3Y65FMf8x8ypQpPPaYCv9FRAR44YXwd1rOt2XtccxsSaljKBV3/z8zm9Jt2L2Jrw8DZ+6o+el6REREJLdir0fUJEFERPrXRz8aPiK9+wDwpzy/OXCvmT1uZh/JNwEz+4iZPWZmj61Zs6ZfghQREdlTqMBARERESs7MvgK0AbflSXKMux8OnAh8IjZv6MHdf+ruR7j7EaNG7ZEtP0RERHYYFRiIiIhISZnZucDJwDmep3Mld18W/64G7gSO3GkBioiI7KFUYCAiIiIlY2YnAF8ATnX3hjxpasxsUPZ/4Hjg2VxpRUREZMdRgYGIiIjsFGZ2O/AQMM3MXjGzDwJXAYOA++IrE6+Naceb2T1x1DHAv8xsDvAo8Ed3/3MJFkFERGSP0i9vSRAREdnqggtKHYHsItz9nTkG35An7XLgpPj/S8Ah/RiaiIiI5LDHFhg0+TwWt70fp6PUoYiI7N5eH/+2fqWkYewa2hk1nrGljkJERESkGHtsgUG7b8a9gwwjSx2KiMhurXzOJgBaDhlc4khKr401mGGljkNERESkGHtsgQGAWQpTNw4iIv1q9OfmArDsr68ucSS7AO+gvY22UochIiIiUgzdLYuIiOwkjtPergIDERERGRhUYCAiIrKTmKVpb1fnOSIiIjIwqMBARERkpzG8QwUGIiIiMjCowEBERGRn8RQd7bSXOgwRERGRYuzRnR6KiEj/W/fNA0odwq7DnLZW9WEgIiIiA4MKDEREpF81HT281CHsEtwdrI02vSVBRERkQGnvcG5YuIym9g4+sv8EKtPpUoe006jAQERE+lXlQ+sBFRw4raSopUOdHoqIiAwoc+o284dlawGYObSW48buOdc06sNARET61YgL5zHiwnmlDqPkOmig0maUOgwRERHpo31rq5lQVcGIijJmDKkpdTg7lWoYiIiI7AROCxVMLnUYIiIi0kdDyjNcO3t6qcMoCdUwEBER2SnaSdvgUgchIiIiUjQVGIiIiOwMZpTb3qWOQkRERKRoKjAQERHZGTxDORNLHYWIiIhI0dSHgYiI9Ks1P5hZ6hB2DdZB2oaVOgoRERHZSTa2tPH9uYupSKf4/PTJVGUG3usYVWAgIiL9quXQIaUOoeTcHWinjHGlDkVERER2ksfWb+LpunoAnt1Yz6wRA++aSAUGIiLSr6r+tgaAxuNGlTiSUmojZdWYqSWgiIjInuKI4YOZOaSG8lSKmUNqSx3ONlGBgYiI9Kvh350PwLI9uMDAaSHDmFKHISIiIjvRkPIMFx+2f6nD2C561CEiItLPHCdFZanDEBEREekTFRiIiIj0M6dVHR6KiIjIgKMCAxERkX7mNFNh+5U6DBEREZE+UYGBiIhIP3PayTC81GGIiIiI9Ik6PRQRkX61+uqDSx1CyZllyNjIUochIiIi0icqMBARkX7VOm1gvkZoh/I0Gfbct0SIiIjIwKQmCSIi0q+q715F9d2rSh1GSRmQsqpShyEiIiLSJ6phICIi/WrY5QsBaDh5TIkjKR23DjLoLQkiIiIysKiGgYiISD9yb8NIkWZEqUMRERER6RMVGIiIiPSjDhqpYBopKy91KCIiIiJ9ogIDERGRfuWkUGGBiIiIDDwqMBAREelHHTRQblNKHYaIiIhIn6nTQxER6Verfn5YqUMoLTOGpk8vdRQiIiIifaYCAxER6Vdtk/b01wk65YwvdRAiIiIifaYCAxER6Ve1v14OQP3b97ybZvcOAFIMLnEkIiIiIn2nAgMREelXQ366GNhDCwxoJW1DMLNShyIiIiLSZ+r0UEREpJ84zZQxsdRh7DLM7GdmttrMnk0MG25m95nZ/Ph3WJ5x3xfTzDez9+28qEVEZHexuqmFcx96jvc/9Bxrm1pKHc6AoAIDERGRftJBI5W2f6nD2JXcCJzQbdiXgL+5+/7A3+L3LsxsOPA1YDZwJPC1fAULIiIi+by4qYF1za2sbW5l/uaGUoczIKjAQEREpN+kGZI6sdRB7DLc/f+A9d0GvxW4Kf5/E3BajlHfDNzn7uvdfQNwHz0LHkRERAqaPXIwbxk/klMmjGTWCPUvVAz1YSAiItIP3NvB2im3yaUOZVc3xt1XxP9XAmNypJkALE18fyUO68HMPgJ8BGCvvfbagWGKiEiprWhs5q8r1vOaUUPYZ1B1n8cvS6X42FQ1FewLFRiIiEi/WvHLI0odQkm0s4Faey0ZG1HqUAYMd3cz8+2cxk+BnwIcccQR2zUtERHZtVz5wlKeqavnwTV1XDt7eqnD2SOoSYKIiPSrjpHldIwsL3UYO10HLVTboaUOYyBYZWbjAOLf1TnSLAMmJb5PjMNERGQPsld1ZfhbU1niSPYcA6rAYOnSpfzXf/0XM2bMYObMmfzoRz8qdUgiItKLQTcvZdDNS3tPuJsxS5OxkaUOYyC4C8i+9eB9wO9zpPkLcLyZDYudHR4fh4mIyG5qbVMLn3n8Bb7y1AIa29oB+Oj+E/jJkdP54owppQ1uDzKgCgwymQyXXnopc+fO5eGHH+bqq69m7ty5pQ5LREQKGHzzUgbvgQUGkCLNoFIHsUsxs9uBh4BpZvaKmX0QuBh4k5nNB94Yv2NmR5jZ9QDuvh74JvCf+PlGHCYiIrupx9ZvYsHmRp6uq+eFTeGNBmbG+OoK0ikrcXR7jgHVh8G4ceMYN24cAIMGDWL69OksW7aMGTNmlDgyERGRTu4O1kGZ5eyXb4/l7u/M89NxOdI+Bnwo8f1nwM/6KTQREdkOi+obGV5expDyHXd7efTIofx7zUZqMmlmDKkBoKW9g0fXbeKAwdWMrNzzmjuWwoAqMEhavHgxTz75JLNnzy51KCIiIl04TaQZTjlTSh2KiIhIv7pvxTqueGEpQ8syXHfUdCrT6aLGe2TtRn69ZBUnjB/Bm8b17CB4SHmGbxyyb5dhP12wjL+sWMeYynKuP6p/HhpvaWvnxoXLGVVZztsn53pxT7C8oZnvPLeI4eVlXHDg3pSnB1Tl/aINyAKD+vp6zjjjDC6//HIGD9b7M0VEZNfh7rTbBkamPo6ZqkyKiMjubV1zKwD1be00tzuVxZUXcPWLS3mubgurm1q2Fhh0uHP3srXUZtK8YezwHuNYt7/byt27nKNb2ju4d8U6JtdUsaC+gT+vWAfAEcMH5X194yPrNrJkSxNLtjSxaEsj0waHWhDLGprY0tbO1Ph9oBtwBQatra2cccYZnHPOObztbW8rdTgiIiJdhNoFIxiRem+pQxEREel3Z+w1mtpMmsk1VUU3SWhqb2fh5kZWN7cwK9P5APj+VRu4bkF4Cc6E6oqtN+FZH95vAocNH8S0wblv4otx80vLuePl1bx98hjevXdo7v7bpau5bfFKMmZccOAUKtMpRpSXMaaqIu90Xjd6KE+s38yIijL2qw3xrGxs5lOPvUBrh3PBgXsze+SQbY5zVzGgCgzcnQ9+8INMnz6dz372s6UOR0REirD8rj2r6VgHjdTYkaRMbStFRGT3V5ZKcfLEUX0bx1IcOLSW4RVlnJIYd1xVOWkzKlIphpWX9RivPJ3i1aOGble8D6/dhBOaRGQLDEZUhHnVZtLMHFrLL19zECmjYE3BERXlfLNbk4nWDqetwwFoiG92GOgGVIHBgw8+yC233MJBBx3EoYeGd1t/5zvf4aSTTipxZCIiko9XF1k3cTfRQROVdmCpwxAREdllpVPG5a+ayvqWViZUV24dPmNILdfPnk5ZKrVDO1BM+uj+E7hn2VreMqHz1cdvGjeCfWurGFFRXnQfDLlMqqnk24fsx8bWNl4zqmvtgl8sWsmSLY18eL8JA6rDxgFVYHDMMceEnqdFRGTAGHztYgA2fWxKSePYWczKqLT9Sx2GiIjILq0qk2ZCpufNeX/fTB8ybBCHDOv52uN8fRX01UHDansMW9nYzO1LVgIwvrqC9+0zfofMa2fYPbtyFBGRXcagO5Yz6I7lpQ5jpzE30qhDXhERkYHi8XWb+OfqDUWn/9nCZXzkked5Yv2motKPrCjj4KG1DMqkOXLEwOrXYEDVMBAREdnVuTkp2z16RhYRESmF5zdu4dF1Gzlp/EhG9XONg/mbGrjomZcASGG8ZnTuPhLcnXmbGphUXcHvlq7BgXtXrOfw4b0/JMikUnz70P12ZNg7jQoMREREdpAOb8EsQxkDp6qhiIjIrubbzy5iY2sbS7c0ccFB+/TrvKozKTJmtLtTk6OJRNbNi1Zwx8urmVRdybumjOXRdZs4JdEPwu5KBQYiIiI7SDsbGGqnk1YNAxERkW02paaSOXX17F1bBcCaphaqM+kuN/QvbW5gTFVF3pv8NU0tPFtXz1Ejh1BVoCBgQnUlV82aRkuHb51fLnUtbQBsbG3jHVPG8o4pY7dl0QYcFRiIiIjsAO4O5oxIv6fUoYiIiJTUMxvqWdvcwrFjhhV8NWE+Fx28D+uaWxlTVcF/1m3km88sYkhZhmuOPIBBZRnueHkVN720gvFVFVx75AE553HhnIUsa2zm9aOH8bkZk9nS1s4/V2/gkGGDGFdV0SVt8k0N+XxovwnsW1vFgUN7dmq4O1OBgYiI9Ktlf311qUPYKZwW0gyl3CaWOhQREZGSWdHYzFfmLMCB5o4OThjf92r7mVSKMfGm/pWGZlo7Oljf0kp9WzuDyjKsaWoFYH1LK+3uZHIUGJSlwrDy+Pdbz7zEg2s2MnVwNdcfNSPnfN2d1g6nPN3z3QA1mTQnTxzV52UZ6FRgICIisgM4req7QERE9ngVqRQV6RRN7R3UZrb/drMmnWbB5kaGlmcYWham9959xjG2qpwZQ2rIpHK/+O9bh+zHi5u3cOiwQaxqbOZPy9exoqmZUZVlWzswnFhdwaA4zbaODj7/5HwW1TfxPzOnMHvkwHqbQX/RaxVFRKRfDb1sIUMvW1jqMPqd00il7V/qMEREREpqeEUZVx0xjcsOn8oxed44kE+He49hj67byKqmFp6pq+frT4friZpMmtMnjWba4Px9Bg0pzzBrxBDKUikcmFJbyQGDa/jgvuP5xeKVfOHJ+Zz/xHwg1CzY0NLKgs2NrG1u4WtPL+SBVev7FPvuSgUGIiLSr2ruWUXNPatKHUa/c2unyg4pdRgiIiIlN6aqgv0HV/dpnHkbt3D2v57hE4/Oo6m9fevw9+0znoq0UZNJs7aldZviGVtVwaWHT+Wyw6dy0oRRWzsw3NTaRmt7O+c/MZ+PPDKPN40dTlls3nDropXbNK/djZokiIiI7AieodwmlToKERGRAWVjSxvt7jxTV09TewcvNzSxsrGFKfGNBZNqKrnp6Jn8c00d79l73DbPZ99B1bR1dPC3let53eihTK6pZMaQGhranfmbGwCozqT5/Iwp/GrJKk4YP2KHLN9ApwIDERGRHcAAo6zUYYiIiAwYKxqbOe+xF2jrcL560D68aexwxlZVbC0sgPDGhesWLGd4RRmTa/K/9rC1o4OrX3yFhrZ2zps2idqynre6v126hlsWraA8Zdx49Myt/Rd8YupEXtzUwNsmjWZ4RRknTeh7R427KxUYiIiI7ABuTsr6Vv1SRERkT7auuZWm9g4A6tvaOO+AvXqkWVDfQH1bG1va2lnb1EJNbe5Cg2fr6vnbytDvwBEjBnP8uJ41BKrj2w8qUqkub1Y4YfxITkj0W7y5tY1F9Y0cOLSW1Da8FnJ3ogIDERHpVx1V6VKH0O/cWzHLUM7kUociIiJSMn9Zvo7bFq/g5AmjePvkMb2mP3BoLZ+aNonm9g6OGZW7g8QpNZWsbW6lOp0mk8p/8z51cA3TBlfT0NbBocMG5Uxz8sRR7F1bxZjKcqoy+a9PPv/EfJY1NnP6pFF8YN8JvS7H7kwFBiIi0q9W/GF2qUPoV+7ttLKa4fZuUlZe6nBERERK5t4V69jQ0saflq8tqsAA6FETwN2xxFP9hrYOJlZXArCptZ18t+81mTQ/OHxql2F/W7meJVsaOXvyWGoyaRra2lmypWlrU4SkDc2t1JalKUulqG8LnS7Wt7b3SLezdc+PnU0FBiIiItuhnc3UpI5gTPozpQ5FRESkpN4xZQy/WbJ6mzoMbO9wLpizkBc3b+HLM/fmVSMGA/DqUUP49AF7UZlKMX1I/tcodre6qYXL570MQGUqzbv2Hst1C5bx15XrGVKW4dbXHLg17f0r1/PDeS8zsbqSK46YyncP3Y/nNtbz+tHD+rwcO9LTGzbzzWcXsXdNFd85dF8yqZ3/kkMVGIiISL8a9u0XAdjwlam9pByYnFbKmFjS0n8REZFdwawRQ5g1Ysg2jVvf1s6zG+sBeGLD5q0FBmbGcWOHFz2dvyxfx/2r1nPGXqMZU5ZmRd1G9qkJNQAHlaW3/r3rlTXcvnglb504ivq2dhxY1thEU3sHk2oqmVQTajV0uLOlrT1nrYT+9vj6zTS1d/D8pi3UtbQxsnLn12RUgYGIiPSr6vvXArtvgQHWRpUd2Hs6ERGRPUCHO5fPe5mXY9X/oeUZPjl1EhXpwk/Hh5Rn+PB+E3hh0xZOnzhqm+f/s3mLefFvf+Y3f7qTJU8/RXV1Ncc0NHDkkUfy8Y9/nK8ffxL7Dx/ChXMWUt/Wzt9XbeCig/Zm/uYtvGHM8B5vV/jKUwt5dmM9H99/4k5/e8KpE0eytrmFfWurS1JYACowEBER2Wahs8NyBqfeVOpQREREdgkvb2ni/lUbWNfcSnN7B+OrKzh65BBenadTw6RTJ44C+l5YsLqphb+tXE/V4hf5+7vfwfQZM/naVy/g5JNPJpPJ0NbWxt13383V11zD8//zP9x55528e59p3Ll0NSeOH8lPFyxj7sYG2jrgzeM7CwU63Jm3aQsAz2/asrXAoL3DWd7YzMTqin6tYTiiopzPz5jSb9MvhgoMREREtkGHN9HOekak3k/acvfGLCIisqeZWF3BMaOG8lJ9A80dzuCyDDP60PfAtrhi3sv84+FHePor/49bfv4zTj311C6/ZzIZTjvtNE477TTuuusuTjrpJO655x6+PWsWAP+3egMA5d36CEiZ8aWZU3hy/WbO2Gv01uHfn7uYf6/dyPHjhvOpaT1fBbk7UYGBiIjINmi3OsakvsDw9NmlDkVERGSXkUml+OLMKds07gOr1tPu9KnPAoDRGePpiz7XpbBgw4YN3H///cx58glSqRQXfPVrpNNpTj31VG644QZOP/10Fi5cSEVFBZ+dvhfP1NUzZ0M933zmJT66/0RGxyYAs0cOYfbIrv0yvNLQDMCy+LcYDW3tXPnCUlIG503bq9cmGrsKFRiIiEi/ah++u75qMEN16vBSByEiIrJbeGr9Zi59PrzVYHBZuk+dJw578t/MOujALjULvvHVC7j8qmu2fv/kef+PESPC2xtOPfVUrrjySu644w7OOeccKtNp9qqu5EtPLqC5vYNJ1RVMqqniX6vreNeUsew/uBoITR/WNbfyxZlT+OfqDbyhDwUb/1m3iX+tqQPgtaOHcdTIbesccmdTgYGIiPSrlb8+otQh7HDuDrRSxthShyIiIrJTtbR3cMPC5ZSljPfvM550ase04R9aniET+wMYVl62dXhbRwe3vLSCzW3tfGrapB59BqxobOZrl/2Iy752QZfhx77hONraWrn1ttuo29zQY36f+O//5pJLLuGcc84BIG2wvLGZza3tNLR3cO38V2hq72BVUzOHDhvEsWOGccGchTS2d/DpA/binL3H9Wn5Dhpay5SaSjIp6/cmGjuSCgxERET6qIMG0jZcfReIiMge58E1ddyzPLwB6dBhgzgivv5we02preIns6fj7oypqtg6/P9W13HRM4to6mhnY0sbFx68T5fxHli+liXPPMXJJ5/cZfhbT38bbz39bdx91+9yFhiccsopnHXWWbS3t5NOpylLpTlwaC1N7R3sXVNFx2j4x+oNzNvUwNKGZl6qb6S1wwHY0tbe5+UbXlHGlbMO6PN4paYCAxER6VcjvvI8AOu+Pb3Ekew4HbaZcamvlzoMERGRne6AITUMK89QlkqxT21Vr+n/tnI9DW3tnDxhZK9vFBid49WBk2sqcZyMWc7aDIdWZ6ioqiKT6dutbSaTobKqioVr1jF17GiGlGe4/FVTWd3UwmHDQyHIJ6dN4utPv8Rj6zdx2LBBvG+f8axsbOb1Y4b1aV4DmQoMRESkX1U+sqHUIexQ7g4GtamjSx2KiIjITjeuqoKbjp5Z1OsE526s5/J52X4JMtt0o73voGruO+5w5m3cwvHjRnT5ra2jg0xVNc2NjbS1tfWp0KCtrY3GxkYufGE5N48aSUU6xYTqSiZUV3ZJd+FBe7OmqYX7V21gRWNzn/ot2B2owEBERKRIbV6HWxO1dgxpen+ftIiIyO6omMICgOHlZVSmU7R1OGOrylnV2Mym1vatnQgWa9rgGqYN7tru/6XNDXzpqQW0dDjDph/I3XffzWmnnbb1940bN/Loo4/S2NwCwAMPPMDs2bOZOHEiAH/4wx8YPv1ALJ3G8bzzTpnx8NqN3LZ4JQD7D6pmUk1l3vS7m4HxLgcREZESc2/HrYm90j9mUuaHRV8siYiI7KnGVlVw/ewZXDd7OiMryvjkYy/w2Sde5MHVdXnH2dLWTntH/hv4rBc2NdDY3sHSLU0MPfF0fnDFlV1+v/nmmzn++ONZtW4jAGeeeSZf+cJntv7+gyuuZPY73sMlh+3H0i3NBfsl2KumkrQZw8ozDC3PsLyhmZ8tXMaLm7b0SFvX0soDq9ZT39rW6zIMBKphICIi0gt3p5VVjLBzqEntfm99EBER6S9DysMt54rGZprbOwDY3Jb7Zvqfqzdwydwl7F1bxWWHTy34BoY3jB3OK43NtLS38+Sg07jzlp9w1113bX214nHHHccXzv9sl3He+KbjAbjrrrt4Yd48fvzTm/jec0tYUN/APrXVXHNk7k4JDxs+mJ8fNYPKdIqqTJqLn1vM03X1PHvtAAIAACAASURBVLRmI9cdNaNL2m89u4gXNjVwxPDBfK1bB40DkQoMRESkX7VN6L1DpF1dB1sot3GMTn+61KGIiIgMSOOqKvjWIfuyrrmVY/P0ZfD8xi04sKi+kcb2dmpT+W9XK9IpPrzfhPBl2l589K7fc9JJJ3HDDTdw6qmnMmPGDL73g0t7jHfXXXfx/g98kBnfupzzn11MRSoVaw+U4e55axAOq+h81eM+tVU8XVefs9PH7Ksh07tJRUQVGIiISL9addNhpQ5hu3XYFkanzsNMLfn6g5lNA36VGLQP8FV3vzyR5ljg98CiOOi37v6NnRakiIhst4OHFX4d8dsnj8GBAwbXUFvW9VZ1fXMr33z2JcosxVcP2rvH77NmzeKee+7h9NNP56qrruLjH/84p5xyCplMhra2Nv7whz9wzTXX8Pzzz3P1r+/gF6khrNq0hXFVFRw0pJbPTt+r6OaGH9xvAqdMHMXIRCFC1gUH7s1zG7dwyLDaoqa1q1OBgYiISAFtXodZObWpo0odym7L3V8ADgUwszSwDLgzR9J/uvvJOYaLiMhuYGh5GR/df2LO357csJkFmxsBeH7TFmaNGNIjzaxZs1i4cCF33HEHl1xyCWeddRZVVVU0NjZy5JFH8olPfIIzzzyTiooKjtjcQEeHU55OMbmmss99E+V6BSRAbVmG2SN7xjZQqcBARET61cjznwNg7aUzSxxJ37i302arKbNxTEh/lzIbV+qQ9hTHAQvdfUmpAxERkf7zr9V1VKZTHDFicFHpZ48YzKwRg6lIpTh4aP6aChUVFZxzzjmcc845tLe3U19fT21tLel0emuaXy5eyfLGZj6474StfSwU64fPL+E3L6/msGGD+OYh+zI8Ry2D3YkKDEREpF9VzNlY6hC2SRtrGGRvYnz6q6Rs4PfDMIC8A7g9z29Hm9kcYDnwOXd/bueFJSIiO8q/19TxvbmLAbj08P2Z2u2ViQCbW9tIm1GdCTf6tWUZvnpQ3zoRTKfTDBnS9Wn/y1uatr4icUJVBWdPGVv09JrbO7h72VoW1jewsbWNv65cz9snj+lTTAONGmOKiIh04x5e5zQsdboKC3YiMysHTgV+k+PnJ4DJ7n4IcCXwuzzT+IiZPWZmj61Zs6b/ghURkW1Wm0ljhA4CqxJP/rNe3LSFcx96jg89PJe1TS3bPT9356XNDf+fvfsOj7JKHz7+PVPSK6RACiUQQlFDSxRFigiKIIKiKDZWXdcF27qXiOUnqKy66gqii4jyIiqooCCIWNZOV1DAREAggPRAII3UmTnvHzMJ6ZmUyWSS+3NdcZ45z3nOuSeGZJ57TqHQaiPSx4u4AF/8jEZ6t6l5TYWKvI0G7uwSRRd/P3oE+dG/jtd7IhlhIIQQQlRgIxtvFYef6uvuUFqbkcAvWusTFU9orbPLHK9RSs1VSoVprU9VqDcfmA/Qv3//2jfyFkII0eQuCA1kTv8EzAZFtJ9PpfOH8grJKbZyzFLI4bxCwqpZL6AmxTYbRqUwKMXCtKOsOHSS7kH+vNg3nlf6J1TaEUFrzb8cWyJO7dmJ86tZtHBi5/ZM7Nx6pinKCAMhhBAC+xsFmy6gWKdjJZsQw3UoJXn1JnYT1UxHUEq1U453dkqpZOzvYTKaMDYhhBBOKBmlV5tOAb5VJgsABkeEUKw1Fg0/pJ+pdH5vTh7fHD+NxWarsv892XlMXJ/CXzfvJKfYwol8+yiF9DKjFSoucphVbGFzRjaZxRY2nMoE4EBuPjvO5Dj1eloqeSckhBDCpYrim8+2QvY3McXYKEJTiI1ClDKhtEIrK0baEKxGEWAYQKAa4u5wWxWllD8wHPhbmbJ7ALTW84DxwN+VUhYgH7hRO/uuVAghhMvZtOaJ7fvYlX2WaT07kdyAnQJMBgPdAv04ml+IqcKN/VmLlUd+3UORTZNeUMRNZdYg+ODAcd4/eJwuAb4UWG0UWIs4ml/I37vFEB/kR98atnUM8TJzc6d27Mo+y9XR4ZzIL+QfW//AojUP9+jIoMjQer8eZ1lsNv48W0CnAF8Mddy1wVUkYSCEEC5W/p6mbseaivdDulKdmo61E3Vqal/XWKe6eMr3fXSu4w95pVHm1bdTfdwKUKhyZ+3PHJ89O86oMvVBaSNaacCCQQViJgpv1QFv1Q0v1QGzisKLaAwE1nlbJdE4tNZngbYVyuaVOX4NeK2p4xJCCOGcPIuV3zJzAdh6OqdBCQOA53t3ZU9OHn0rrBNgVOBjNFJks+BnLD9gfuOpLGwasousjIkJJ8RsIsGxoOL4DrUvTnhjmeTDsfzC0nch1ibKTz+XeoCfMrK5vF0bHujeoUn6rI0kDIRoIew3pfYbIo0VsFVxs1fx5u7cY/3rVrymYr2KdZTjVq6qm7yS47I3e+Vv/M7VVmh06a2j1ufKz9U/F2npDaai3M2mKhOnqhB15djLtuZ4rbrsGV2h14qv2vGlFCUzwlTpzDBD+TqlN8YldZWjjqHa82XbOldflR6Xr2Ms07/hXFuq7HVV9Wso16/9saQtYxV9lb9eYTzXv1Kl/Zfvz1jNtWVjAKXKv96qv38GwIiJMMwqAhPhGFTVwx+FEEIIUX8BZhP3xMfwe1Yu13WIaHB7od5mkr0rJx18jEbm9O/GsfwizgspP4rxzi5RrD5yiiuj2tK3jXPbNZa15sgplv15grEx4VwTG8ELfbqSWWRpcPLDWSccUyZONMJCj41FEgZCuJH9Jt8GWNHYsN/kW6t5tKGUkdKbI63QCuw3pvY2UEYM+GMkAIVPmZtBQ+mN07kbM4PjhstQoZ6xXFnJTeC5m0FjuToKk71cGR3PS2Is6ctUoQ2D4xPc6m5EDdUcl4m7mvpVX69Alb/RPvdaqqlf6XnZm/OyN+A4V6e1f2J99932x/nz3RuHEEIIIVxqVHQYo6LDXN7P4bxC3t1/jCERoYyOCS8tvyA0kAtqmHZQm8+PnuJUYTFrjmZwTWxElds9utK0Xp3YcDKLIU0w/cFZkjAQwgXsiQArNgqwkQfKhtImUDgSAxq0/VEpbxTeGPBzfPliUP6lN/4GAjCqQAwEYnSU2+v5Y1B+jnr2MqXMbn7lQlThjz/cHYEQQgghmol16ZnM/eMQF4eHcG9CbL3a+OjPdHZn53HobGG5hIEzsoos7MvNIzEkEKOh/Ic6N3Zsx4rD6YyKcn3Soyoxfj7c0LF5jYSUhIEQFWhtRVPk+Cp2fMJvtQ83L/sJv/3u336NKp3hhNY2++fLygcvYvFVifiq8/BWnTCoAAz4oPB1PHrLp89CCCGEEKLV+DH9DDkWK18fP11rwuBIXgE/nMhkcGRIuR0VrmjflsN5BVwW2abK6zafysLPaKxya8SHf93DsfxCxsSE89eu0QBYbZr1JzOJ9fPmpb7dsNpqX7Ng+5kcvj1+mtHR4cQH+dVa31NJwkA0S+fm49uH2+syx/aZ4tU9r1i//HOFsv9XlR02XjLzXaGxgDJiJhKzisdMO4wEYlBB9k/ylS8KHwz4YMBxrHzKlNmTAQqzJAKEEEIIIYSo4PoOkeRbbQyoZl2A9IIi3t53lO7B/vxw4gx/5OTx8+ksZvVLKK0zMCKEgREhVV6/Pj2T538/gAJe6Z9A5wDfcucLrfatGAus1tKydw8cY8aONKxac3PHdvyefZY7u0RxTWz1azHM2X2I9IIijuUX8ULf+Ern39t/jLTcfO6JjyHCx6vadpo7SRgIl9Paio18x6f1FuyL8tkc+5sbHHPxHTf/2uq4uVf2rc4o+TI7vnzKPPdy3JifOzbg7Th2fCnvCmUl9U0VvnwwqXDMRMgq6UIIIYQQQrhIfJAfzyR2qfb8ikPprD2ZydqTmVwUFgQ5EOXr7XT7Po6dE4xKYTZUfk//XO+u/J6VWy7h8M2x0xzNK8TfZGT9qUxCvMz8nJFdY8Kgb2ggXxzLoHcVayakFxTx4UH77lCxfqf4S5cop+NvbiRhIFzCpvOwqmwURjQab9UZs2qPkbaYVThGQjGqIIwEOeboB9k/yccfhZlzC+MJITxe797ujkAIIYQQHqJ/myA+PHiCsxYr5wcHcGvnKKLrkDDo1zaIl/rG42s0EuNXeT2AKD9vovzKt7c1Iwsvo8LLoJjWqxMfHDjBsHZVT3coMSUhlru6RuNdYWtHgLZeZvq2CSQtJ5+LmmiHBVeRhIFwitY2x5x+C/b5/CVb91ns4wGUfaV8pR03+cpIpGEqfobeeNNJFuMTojWbPdvdEQghhBDCQ/RrG0RymyBSs8/y4Z/pjKnhU/7qJNRxd4PLo8JYcSid6ztE8HvWWTKLLSzYd5ShjqTBkbwCAs0mgszlb5+rShYAGA2Kpy6ofhSFJ5GEgQeyz+8Hys3TL5nHX1JuKz3WFepVXV9XUdcKyuSY22/FpCIwEYFRBWEixDFKoC0mFYyBgHOr9asAzERiVJ6dTRNCCCGEEM1PQUEBZrMZo9Ho7lBEDV7/4zBbTmczOT6Gfm2D6nTtyOgwThQWcUX7ti6Krrw5/ROY09++RsKitKMAhPvYP/DccDKT51IPEGw28XpydwLNresWunW92gq01o4t7hrKVmYlfWu5Y/ujrXR1ffvK+ucW2gPAscJ+yX9VmePSZfixnVuRH5u9HWV0zL8vadsMGFEYHXP9jY7nJig7X1+VLas4n99c+mhQAXirLnirTpiJxaA8d7EOIYQb3XKL/fG999wbhxBCCI+3detWZs6cSfv27Zk1axbe3s4PVRdNx2rTrDl6CoBvT5yuc8JgcGQogyNDXRFarW7r3J6B4SFEO6YtHM8vAiC72EK+1UagY+B0nsWKr7HlT6NutQkDgwpAKbDo9Aa2pDAoXwz4YSDYsZJ+IEbs8/ONKgQDQRiVn6OO77lV9DE4hvIby9zclz+utqyF/2AKIVqQw4fdHYEQQogWYteuXVgsFg4dOkRWVhYREXUfri5cz2hQ3Nq5PT9nZDMmJtzd4dSJUoougee2Sbw6Jgyjsq99ULLbwdfHMpiz+xA9gv35d5/KOyS0JK02YeCretDdvMndYQghhBAeRSk1EIjXWi9USoUDAVrr/e6OSwjROowZM4bc3Fw6duwoyYJm7oaOkdzQMdLdYTjNatMYq9hVwWwwVNotITXrLBrYnZ1Hsc2G2VD1WgYtQatNGAghhBCibpRS04H+QAKwEDAD7wGXuDMuIUTrERgYyN/+9jd3hyFamC0Z2Tybup+4AF+e790VUy0JgJs7tcOkFImhgS06WQDQsl+dEEIIIRrTOGAMcBZAa30UqLwBtRBCCFFPWzOyWZR2lKwiCzatOZCbj8XWGOvOVW/7mRyKbZrd2XnkFFvLnSu22fjtTC4F1nPlYT5eTEmIZWBEiEvjag5khIEQQgjXGjDA3RGIxlOktdZK2VfrVUrVbd8qIYSop8OHDzNv3jwSEhK49dZb3R2OcJEiq42ZKfuxaE1usRWL1nx9/DQXhQXz+HmdXdbvuNgIcixWugb4Eupdfjv4WTv/ZO3JTHqHBvJMYsvYKrEuJGEghBDCtZ57zt0RiMazVCn1BhCilPorcAfwpptjEkK0Ap999hnbt29n+/btXH311YSE1P+T3X379hEREUFgoAyQam7MBkWsvw/7c/PpHODL2vRMAI7nF7q03zbeZh7s3qHKczkW+8iC7GKLS2NoriRhIIQQQginaK1fUkoNB7Kxr2PwpNb6f24OSwjRCgwcOJBNmzbRvXt3goOD693OypUreeuttwgPD2f+/PmYTHI71JwopfhP33iyiiyE+XjRr00g3584wyVuHPr/UPcObDiVRVIdt4ZsKVrtv5Bim43fs86itbsjEUKIlq3T7bcAcGDRe26OpJkwGI3uDqE+lFJG4Gut9VBAkgRCCJfbuHEjS5cuZeTIkYwYMYKFCxc2uM2MjAwAsrKyyM7O5vPPP6dz585cfPHFDW5bNA6zwUCYY/vCSF9vJnRq59Z4Qr3NjIoOc2sM7tRqEwbbz+QyaWMqQZJVFEIIl3rt4GEA7v1pl5sjcb/0wiIICPTIFZK01lallE0pFay1znJ3PEKIlmvr1q0YDAaWLVvG3r17+eCDDxgxYkTp+YyMDFJSUkhOTsbX17fW9tatW8fChQsZOnQoEydOJCwsjPj4eD777DOWLl2KUopFixYRGhrqypclhEdqtXfLGk2g2UjbCotaCCGEaFxmx57GbX3k922hzQo2m7X2ms1WLvCbUup/OHZKANBa3+++kIQQLcm2bduYMWMGAGPHjiUrK4vRo0eXq/Pkk0/y559/cumllzJ16tRa2/zyyy9JT09n9erV3HLLLYwZMwaAkydPopQiMjISf//ya7hmZ2czc+ZMAJ544gmCglrncHRPcLKgiP/bvg+TQfGvxK4Ee7XaW1yXkO+mEEII0UQMSoG2efJkuOWOLyGEcAlvb2+UsieaBw0axJ133lmpTsm6A86uP3DdddeRl5fHkCFDypUPGjSIXr16ERAQgJeXV7lzO3bsYOfOnaXHAwcOrOtLEU0kJTOXI45FEXdnnyU5rPY1LradzmHr6WyuiQkvnf4gqiYJAyGEEKKJ2BMG2rWbSbuQ1nqRu2MQQrRsPXr0YNasWSiliIuLq3R+165dpKSkkJaWxrBhw5xqs3fv3vTu3bvKc23btq2yvG/fviQlJZUei+ZrQHgwQ86EYlaKPm2c2/niX6n7KbDaOF1UzMM9O7k2QA8nCQMhhBAutb1vsrtDaDYUgM3msQkDpdR+oNIICa115Xf1QghRT126VL/X/datW0lJSSEzM5MFCxZw7733NspOB99//z0ff/wxV199NSNGjMDPz48nn3yywe0K1/MxGvlnj451uqZrgB8pWbl0DfRzUVQthyQMhBBCuNSHt93t7hCaDQUePcIA6F/m2Ae4HmjjpliEEC1MYWEhX3/9NfHx8XTr1q3KOiNHjuTZZ58FoGvXro22LeLy5cs5cOAAH330UbkFFoXnO11YTHaxhU4B5xbInJnYhaxiC21kPbtaScJACCGEaEraczf01VpnVCiarZTaCsjHcEKIBlu8eDErVqzA29ubxYsX4+3tXalOmzZtWLx4MT/++GPp4oWNYcyYMXz00UeN2qZwv6wiC5N/3sVZi5VHenZiYIR9oyKjQUmywEmSMBBCCOFSMx6ZYn/893/dHIn7WdFgtVjcHUd9KaXKTuQ1YB9xIO8lhBCNomRbw8DAQIxGY7X1alqToL4uv/xyLr/88kZtU7jGqYIiQrxMmAyGWusW2mzkWeybE50uKnZ1aC2S/JEXQgjhUl6Fhe4OoVnQWmOxAVarxyYMgP+UObYA+4Eb3BSLEKKFGTduHD179qR9+/aNNtVAtCwrD6Xz1r6jxAf68XK/qqetlBXh48XTF3TheEEhw9tVvcClqJn8SxRCCCGaQJFNE+75wx/v1FqnlS1QSnV2VzBCiObDarXy2WefERoaSmxsLOHh4fj7+9e5nYSEBBdEJ1qK/WcLADh4Nh+b1vbdh2rRu00g4NzuCaIySRgIIYQQTcCqNUFmj/+z+xFQcX+xj4B+bohFCNGMfP3117z55pscP36coKAg4uLimDdvXo1TCwB27NiB1WqlT58+TRRpeR9++CGpqan89a9/JTY2trQ8PT0dX19fAgPlRrM5ub1ze0LMJnqHBjqVLBAN5/HvXIQQQghPUGi10THEx91h1ItSqjvQCwhWSl1b5lQQ9t0ShBCtXGRkJAbHnHKTycTp06exWCw1Jgx27tzJ448/DsDMmTNJTExsklhL5OTk8N577wGwatUqpkyxr7nzyy+/MGPGDAICAvjvf/9buraCcL9QbzOTukS5O4xWRRIGQgghXOrnAYPcHUKzUGSz0cHfY++tE4DRQAhwdZnyHOCvbolICNGs9O7dmzfffBOr1crGjRtJSEiocpeDskwmE0optNaNsmZBamoqW7ZsYdSoUYSFhdVaPyAggIEDB5KamsrAgQNLy48fP47WmpycHLKzsyVhIFo1SRgIIYRwqRUTbnN3CM2CRWsifbzcHUa9aK1XAiuVUgO01hvdHY8QonmKiIgA4Nprr62lpl18fDwvv/wyNpuNbt1qX8CuNjNnzuT48eOkpaXx1FNP1VpfKcUjjzxSqXzEiBEUFRXRtm1bOnbs2KCYTp48yZtvvklcXBw33nhjg9oSwh0kYSCEEEI0AZNShHtowqCMX5VSU7BPTygdLqG1vsN9IQkhPFnXrl0bra2CggJ+//13tNZYrdZa10+ojslkYuzYsaSmprJ582YuvPDCese0evVqNm7cyMaNG7nssstKkypCeIraN68UQgghGuDZB+/i2QfvcncYbmc0GIjxq3l4rgd4F2gHXAH8AMRgn5YghBB1lpaWxqJFizh69GijtHfllVeSmJhIeHg4Vqu1QW0dPHiQRx99lJkzZ7Ju3bp6t5OcnExgYCCJiYm0adOmQTEJ4Q4ywkAIIYRoAlprAjx/X/GuWuvrlVLXaK0XKaWWAGvdHZQQwjO98MILHDlyhF27dvHcc8/V6dpjx45hNBrLfWJ/++23ExMTQ7du3fDyatiILpPJhNFoxGKxNKitXr16sWTJkgbFIoQ7efw7FyGEEKK501pj0Zp2nj8lodjxmKmUOg84DjTK+Fql1AHsoxWsgEVr3b/CeQW8AlwF5AGTtNa/NEbfQgj36NixI0eOHKnzOgG7d+/mkUcewWAw8Morr5Ruh+jj48OoUaMaJbbo6Ghmz55NXl4ePXr0aJQ2hfBEkjAQQgghXKzIpgnzNuNl9PiZgPOVUqHA/wGrgADgyUZsf6jW+lQ150YC8Y6vC4HXHY9CiGYoPz+fQ4cOER8fjz3fZ7dlyxaOHDnCVVddxdSpU0lPT6ddu3Z1avvMmTNYrVasVitZWVmlCYPG1tAFD4VoCSRhIIQQQriYVWsCzZ7/J1dr/Zbj8Acgrom7vwZ4R2utgU1KqRClVHut9bEmjkMI4YRp06aRlpbGuHHjuOMO+7qoJ0+e5Omnn0ZrTXFxMePHj6d9+/bVtrF9+3YWL17M0KFDGTlyZGn5RRddxIMPPojZbOa8885r9NiXL1/O4cOHmTRpEkFBQY3evhCexPPfvQghhGjW1g0Z4e4Q3C7XYuXSiBB3h9FgSqlI4FkgSms9UinVExigtV7QCM1r4CullAbe0FrPr3A+GjhU5vlhR5kkDIRohk6fPl3uEcDPz4+goCCysrKIjIystY1ly5axc+dODh06VC5hADBs2LDGDdjhyJEjLFy4EIDw8HBuuukml/QjhKeQhIEQQgiXWjP2BneH4HZWrUkMCXR3GI3hbWAh8Ljj+R/Ah0BjJAwGaq2PKKUigP8ppXZprX+sayNKqbuBuwE6dOjQCGEJIerjmWeeYdu2beVu7P39/Xn99dfJzs4mOjq6XP09e/bw008/ceWVV9K2bVsAhg8fzp49e+jQoQO5ubkEBAS4PO7w8HA6d+7M0aNHOf/8813enxDNncdPphRCCNG8eRfk412Q7+4w3MqsFN2C/NwdRmMI01ovBWwAWmsL9kUKG0xrfcTxmA6sAJIrVDkClJ2oHOMoq9jOfK11f611//Dw8MYITQhRC/tMofI6derE2LFjCQwsnywNDAyslCwAmDlzJh988AGvv/56adngwYMJCwvj999/Z86cOQ2K8fDhwyxatIj9+/fXWM/Ly4s5c+awdOnSOk93WL9+Pffccw/Lly9vSKhCNCuSMBBCCOFS06fdx/Rp97k7DLeyASFeLWJQ31mlVFvs0wdQSl0EZDW0UaWUv1IqsOQYGAGkVKi2CrhN2V0EZMn6BUK4V0FBAffeey8TJkxg9+7dDWorJiYGoNIChr6+voB9B4SGeOWVV/joo4/4z3/+U2vdffv2MXDgQKKjo7n//vuxWp3Li65evZojR45IwkC0KC3i3YsQQgjRnNm0JqgFLHoIPIT9xr2LUmo9EA6Mb4R2I4EVjpXUTcASrfUXSql7ALTW84A12LdU3It9W8W/NEK/QogGOH78OAcPHgRgx44dJCQk1LutGTNmcOLEidLEQdny3bt3c8EFF1S65scff+TgwYOMHz++NLFQnU6dOrFr1y6ndj744IMPSE1NpaCggG+++Ybjx49XOSqiomuuuYbc3Fwuv/zyWusK4SlaxLsXIYQQornSWqMBY5ltxTyNUup6rfUy4AwwGEgAFLBba13c0Pa11mlAYhXl88oca2BKQ/sSQjSeTp06ccstt3DixAmuvPLKBrVlNpvx9vYmKyuL4ODg0vKAgAD69etXqf7Jkyd56aWXsNls5OXl8be//a3G9idPnsy1115b5WKLFouFtWvXEh8fT0xMDCNGjGDlypWcPHmSm2++maioKKdew0UXXcRFF13kVF0hPIUkDIQQQggXKtaaIJMJP5PR3aE0xKPAMuBjrXVfINXN8QghmokJEyY0SjspKSk8+uijmM1m3njjDapag+TMmTMcOXKEXr16ERgYSEREBOvWrWPRokWYzebS7RuropSqdgvHGTNm8NZbbxEUFMS6detISkrip59+apTXJYSnkzUMhBBCCBcqstpo7+vl7jAaKkMp9RXQWSm1quKXu4MTQniGoqIiTp06RVFREampqRQVFZWeO3DgANu2bWPDhg2sX7++0rUWi4UHH3yQRx99lI8//hgfHx/mzp1LXFwcaWlpvPrqq+Tl5dUrrpMnT6K1pqCggDNnztT79QnREskIAyGEEC71zZVj3B2CWxVrTYSPxycMRgF9gXeB2lcME0KICqxWKw8++CCHDh2ibdu2ZGRkcPHFF/Poo48CkJiYSPv27TGZTFUuMmi1WksTArm5uYB9R4MrrriCI0eOEBYWxunTp/Hzc25HmqysLPz8/DCbzbz00ktERUXRq1evBq3DIERLJAkDIYQQLtXaEwaFVhux/g1b3dvdtNZFwCal1MVa65PujkcI4XkKVGqvJAAAIABJREFUCws5evQoBQUFpKamEhkZSVbWuU1WYmNjee655zh27BiXXHIJv/32G+eddx6OxVBRSvH888+TlpbG4MGDAfj999+JiYlhypQpxMbGli6Y+OOPP7JlyxYmTJhQ5WKF69at44UXXiA6Opo5c+YQGBjI9OnTm+C7UH8Wmw2TQQaHi6YnP3VCCCFcKijrDEFZrXuIZ9eAmlfv9hSSLBBCOGPBggVMmzaNQ4cOlZb5+fnx8MMPk52dTVBQEN26dePhhx8ud92wYcOYOHEijz76KI899hhLliwB4Oeff2bChAm89tprDB06FKPRyLRp07jiiiuYM2cOfn5+jBw5ErAvNDtr1iy+++47Fi9eXGV8X331FVu2bOHbb7/l7Nmz9XqN+/bt4/HHH2+SLRT/lbKfa3/cwZdHM1zelxAVScJACCGES02b/jDTpj9ce8UWyqgUwV5md4chhBBNIiMjg08++YTU1FS++OKLcueSkpLo0qULfn5+JCUl0bZt23LnMzMz+eOPP8jPzwconYLw22+/UVBQwK+//kpWVhaZmZns2LGj9Jo2bdqUtqGUok+fPqWPVQkJCSEiIoKwsDDM5qp/P6ekpPD++++Tk5OD1pp9+/ZRWFhYen7FihXs2LGDhQsXYrFY6vhdqputp7PRwC+ns13ajxBVkSkJQgghhIuFeMmfWyFE69CmTRsGDRrEnj17GDRoULlzXl5ezJo1iz///JN+/fqRnp5OWFgYBoOBgoIC7rvvPjIzMxk/fjzt27dnyJAhAFx77bX8v//3/yguLubzzz/nlltu4fbbb+e3335jzJgxJCcnl+vnySefxGKxYDJV/bv3pptuoqCggF69euHv71/pvNVqZfr06aWLNPr6+rJy5Up69OjBCy+8AMDgwYPZsWMH/fr1q7afxvJQ945szshiQsfKW0IK4WryDkYIIYRwEYtNo4HOHj4lQSn1KqCrO6+1vr8JwxFCNGNKqXJTDb7++mv++OMPJk6cSEhICJGRkURGRvLWW2/xySefEBcXh9aa6OhocnJyAHtiYcSIEaVtBAcHExMTw9mzZ0vXPZgwYUKNWzrWdBPfvn17HnvssWrPGwwGoqKiOHDgADExMezcuROw76ZQIikpiXfeeaeW70bjGBgRwsCIkCbpS4iKJGEghBBCuEiuxUqf0ECCzB7/53aL4/ESoCfwoeP59cDvbolICFEne/bs4cUXXyQuLo6pU6diaIIF9LKyspgzZw5aa7y8vLjrrrtKz+3du5eUlBQ2btxIjx49OHDgAA8//DC5ubkMHz68XDtKKZ577jlSU1MZNmyYy+NWSnHdddfx73//m99++43777+fbt260b9/f5f3LURzI2sYCCGEEC5i0Tbaenv++gVa60Va60XABcAQrfWrWutXgWFAb/dGJ4Rwxtq1azl27Bjr16/n1KlTTdKnv78/cXFxGI1GevXqVe7crbfeSkhICN26dcPPzw+LxcIbb7xB586dq1xXoHPnzowePRpf36YZsbV9+3ZMJhNbtmzB19eX8ePH06lTpybpG8BisfDzzz+X20lCCHfwqI887rjjDlavXk1ERAQpKSnuDkcIIYQT1oy53t0huE2hVRMf6NnTESoIBYKA047nAY4yIUQzd8UVV7Bnzx66dOlCREREk/RpMpl47rnnKC4uJigoqNy5Xr16MWvWLNLS0hg6dCgPPPAA2dnZbN68mcOHD7N9+3bi4uIYMmRIuUUNm8r1119PRkYGBw4cYM2aNYwbN65J+3/jjTf44osviI2NZe7cuU3atxBleVTCYNKkSdx7773cdttt7g5FCCGEk9ZddoW7Q3Abg6Kl7ZDwPPCrUuo7QAGDgBlujUgI4ZTo6Giee+65Ju1z//79TJ06FW9vb1555ZVyuyJ8+eWXfPHFF9xwww3ExcVx4403sn//fgYOHMg//vEPdu7cSUFBAQkJCXz88ceEhNR/Dv+8efNYu3Yt99xzD5deein/+9//WL9+PRMnTqRbt25VXhMVFcX27dvZtGkT27ZtIzExkbi4uHrHUFfFxcUAFBUVNVmfQlTFoxIGgwYN4sCBA+4OQwghRB2EpR8H4FREOzdH0vSMKEI8f/2CUlrrhUqpz4ELHUWPaK2PuzMmIUTzlZaWRnZ2Nrt37+aBBx7g9ddfJzAwEIAPPviAU6dOlSYDzGYzDzzwAL6+vnTt2pV169Zx9uxZiouL+fjjj7nzzjvrFYPWmqVLl+Ll5cX333/PpZdeyhtvvEFhYSEGg4Enn3yy2mtNJhMGgwFvb+8mG5VR4p577iExMbHSVA4hmpqsYSCEEMKlHnr2CR569gl3h+EWBqVaxBoGFRiBk8AZoJtSalAt9YUQHmLjxo3cd999rFq1qlHaGzx4MH369CEsLIysrCx27dpVem7MmDFERERw5ZVXMn36dN59910WLFiAyWTi5Zdf5qqrriI8PJw2bdpw/vnn1zuGJUuWkJuby9GjR7nuuuvYtWsXcXFx+Pn5cemll9Z47ezZs3n11Vf58ssvCQgIqHcM9eHj48PQoUObPFEhREUt52MPIYQQotnRhLSgKQlKqX8DE4BUwOYo1sCPbgtKCNFoVq5cyYEDB1i6dCljxoxpcHsmk4n/+7//IzQ0FC8vLxITE0vPjRs3rnRdgJJ+o6KigHO7ImzZsoVLL72U8PDwesdQUFBAZGQk/v7++Pn5cf/996O15uGHH2bQoJrznbGxscTGxta7byFaAkkYCCGEEC5i1eBnbFGD+cYCCVrrQncHIoRofKNHjyYzM5MRI0Y0Wpv+/v488sgjNdZ58cUXSU9Pp0OHDqVlHTp0KPe8JlarlR9//JGoqCgSEhLKnbvwwgtZu3YtI0eOxMvLC6PRiMViwWSS2yAhnCH/UoQQQggXsNhsmA2KNi1rSkIaYAYkYSBECzRw4EAGDhzY5P36+Pg4nRyoKCcnh9WrV7NkyRLMZjMLFy4kODiYEydO8Oqrr/Lrr79iMBhYsWIFEyZM4Pnnnyc7O5ukpKRGfhVCtEwe9bHHTTfdxIABA9i9ezcxMTEsWLDA3SEJIYQQVSq0aaL9vDEo5e5QGlMesE0p9YZSak7Jl7uDEkK0HFprvv/+e3755ZdyZVX55ptvuPHGG5k/fz7FxcWlIwgAvvrqK7Zv305GRgY2m43Bgwdz4sQJZsyYwQsvvMDBgwdL20lLS2PDhg3V9iNEa+ZRIwzef/99d4cghBCijlbccKu7Q3ALq9b4m4zuDqOxrXJ8CSGES3z//fc8//zzmM1mXnzxRV577TUyMjJ45plniI+PL623efNmHn/8cf7880/OP/987rvvPpKSkkoXJ7zkkktYu3YtycnJPProo5hMJn799Vdyc3OxWCzMmjWLIUOGMGTIEB5++GGKioq4++67ufrqq9310oVoljwqYSCEEMLz/HzxYHeH4Ba5xVYGhAW7O4xGpbVe5O4YhBCeo7CwkM8//5zOnTuXW/CwJvv27WP79u34+Phw8uRJ/vzzTwBSUlLKJQyysrIwmUz4+/tz4403MmrUqHLtxMXFMX/+/HJlvXv35i9/+Qvfffcde/fuZd++fSQkJGAwGLDZbFgslga+YiFaHo+akiCEEMLzRP95gOg/D7g7jCZnUNDe19vdYTQKpVSQUuo5pdS7SqmJFc7NdVdcQojmbdmyZSxYsIAZM2aQnZ3t1DW+vr706tWLbt26cd5553HTTTdx+eWXM3z48HL1evbsidVqpV27dkRGRjrVtlKKa6+9lrvvvhtvb286duxIXFwczz77LGazmXfeeYft27fX+XUK0ZJVO8JAKZWDfaskgJIJmNpxrLXWQS6OTQghRAsw5eWZADw2+y03R9J0SubBRrWQhAGwENgDfAzcoZS6Dpjo2C3hIrdGJoRotkpu5IODg/H29sZisfDUU0+xf/9+Hn/8cXr06FGufl5eHjabjWHDhqG1ZunSpdx22234+flVatvf35+OHTuSl5dHWFhYneI6//zz+fDDDzEajSil8PHxwWq1ArB3716nR0MI0RpUmzDQWgc2ZSBCCCFES3HWYqWDvw9927SYP6VdtNbXOY4/UUo9DnyrlGr4Ru1CiBZr+PDhJCQk0KZNG7y9vUlPT2fbtm2AfQ2CigmDDz/8kOXLl1NQUIDJZMJkMtGuXTvGjh1bqe3Q0FBef/11zp49S0xMTI1xnDhxgrfeeouEhATGjx8PUG5bxdjYWKZMmcLx48crTW0QorVzag0DpdRAIF5rvVApFQYEaq33uzY0IYQQwjMVWG30DA5AtZwdEryVUgattQ1Aa/0vpdQR4EcgwL2hCSGas7LbJUZERDBhwgT279/PVVddValuyY1/TEwMRqORrKysSkmFskJDQwkNDa01hpUrV7Jp0yY2bdrEsGHDWL58Ofv27eP8889n5MiRhISEcOWVV9bj1QnR8tWaMFBKTQf6AwnYhyR6Ae8Bl7g2NCGEEMIzWTRc2LZFzdz7FLgM+LqkQGv9tlLqOPCq26ISQnicW265pdpzw4cP57zzziMkJAQfHx8sFgtms7nG9t5++222bt3K3/72N84777wq61x44YV8++23dOvWjdOnT/PJJ5+QkpLCl19+ya5du3jqqaca9JqEaMmcGWEwDugD/AKgtT6qlGoxYyyFEEKIxmZS0NHf191hNBqt9dRqyr8A4qs6J4Rwrz179pCRkcGFF15Y79FOVquV5cuX4+XlxTXXXNPIEVatffv2pce1JQssFgsff/wxAF988UW1CYPExEQ++OADAIqKiujVqxcHDx4kODiYwEC5rRGiJs4kDIq01loppQGUUv4ujkkIIUQL8uGtd7k7hCZn1ZooPy93hyGEaKVOnTrF1KlTsVgs3HvvvVxxxRX1amf9+vW88847AHTq1KlRFwNMTU3l4MGDDB8+vNbEQHVMJhPXXnstW7dudXpKgZeXF88//zwzZsxg7969dO/evV59C9FaOJMwWKqUegMIUUr9FbgDeNO1YQkhhGgptvdrXYvoa62xaggwObVMkBBCNDqlFAaDffd0UwN+F8XExODt7V26+GBjycrK4oknnsBisZCdnc2NN95Y77b+8pe/8Je//KW03blz5xIeHs6dd95Z48gKHx+fakck5OTkEBDQotahEaLeav0NorV+SSk1HMgGugFPaq3/5/LIhBBCtAid9+4GYH/XBDdH0jQKbDbCfMx4Gw3uDkUI0Uq1bduW2bNnc+bMGS644IJ6txMXF8fbb7+NwWCocmvD+vLy8sLf35+srCynFi101jfffMOGDRsAGDx4MPHxdZ8x9c4777Bs2TKGDh3KQw89VG29tLQ03nzzTRITExuU8BCiuXM25fgb4Atox7EQQgjhlL++9iIAj81+y82RNI2sIguT4qLcHYZLKKX8gH8CHbTWf1VKxQMJWuvVbg5NCFFBbGwssbGxDW4nIKDxN0Lx9fXltdde49SpU3Tt2rXR2u3bty+ffvopYWFh9X7tqampAPz+++811vv0009JSUkhJSWFsWPH4uPjU6/+hGjunNkl4S7gSeBbQAGvKqWe1lr/P1cHJ4QQQngSrTVKKS4OD3Z3KK6yENgKDHA8PwIsAyRhIISok5CQEEJCQhq1zU6dOrFw4cIGtfH3v/+dzz77jMGDB9dYb/DgwWzbto3ExERJFogWzZkRBg8DfbTWGQBKqbbABkASBkIIIUQZeVYb4d5mLghpsatud9FaT1BK3QSgtc5TMslXCNFEDhw4wOeff86QIUPo0aNHpfMbNmzgs88+Y+zYsSQlJdWp7XXr1rF+/XpuuOEGpkyZUmv93r17Nzg5IYQncCZhkAHklHme4ygTQgghRBnZxRbu6tIRo6HF3kMXKaVKpiiilOoCFLo3JCFEazF37lx27tzJtm3beOONNyqdX7RoEUePHiUnJ6fOCYM5c+aQn59PQUEB06dPb6yQhfB41a7IpJR6SCn1ELAX2KyUmqGUmg5sAv5oqgCFEEIIT6GBcbHh7g7DlaYDXwCxSqnFwDfAVPeGJIRoKIvFQlZWFlar1d2h1KhkC8SqRhcADBs2jMDAQIYNG1bnti+66CKUUiQnJzcoRiFaGqW1rvqEPTlQLa31U9Wd69+/v96yZUsDQ3OtnzOyuH/LbsK8ZZ9sIYRwpe4p2wDYdV5vN0fiWoVWGxat+fbyfjXWU0pt1Vr3b6KwGp1jauJF2Nc12qS1PuXmkKrlCe9HhHCXwsJCli1bxty5c/npp5/w8/MjLy+P5ORkJk+ezPXXX4+3t7e7w6wkKyuLoKAgl2x5WLIOjRCtgbPvR6qdklBTQkAIIYRwVktPFJQ4a7HSv22Qu8NoCj7AGezvIXoqpdBa/+jmmIQQdfDTTz9x7bXX0qtXL6ZOncro0aMxmUxYLBZWr17N3LlzmTZtGitWrKjz0P7GkJ6eTnp6Oh06dCAoqPzv1eBg1y0qK8kCISpzZpeEcOzDDXthf5MAgNb6MhfGJYQQooVoLSMMCqw2BoU37orfzY1S6t/ABCAVsDmKNdCghIFSKhZ4B4h0tDdfa/1KhTpDgJXAfkfRcq310w3pV4jW6Oeff2bUqFEsWLCAMWPGlDtnMpkYO3YsY8eOZdWqVVx11VWsWbOmSZMGu3fv5q677mLv3r0MGDCAd955xyVbOwohnOPMooeLgQ+B0cA9wO3ASVcGJYQQouW47a3XAHhs9ltujsR1zhQV08Hfhyui2ro7FFcbCyRorRt7oUML8E+t9S9KqUBgq1Lqf1rrihuhr9Vaj27kvoXwOB9//DF//PEHd9xxB5GRkU5fV1hYyLhx4yolC3bu3Mn7H3xAZEQEkyZNwt/fnzFjxrBgwQLGjRvHvn37mmx6wqlTp8jPz8dms3H69Gny8vIqJQxsNhuzZ8/m4MGDPPTQQ3Ts2LFJYhOiNap20cMy2mqtFwDFWusftNZ3ADK6QAghhHAotNr4Z4+OBJidycN7tDTA3NiNaq2Paa1/cRznADuB6MbuR4iWIDMzk7fffpsNGzawcuXKOl27bNkyevbsWS5Z8M0339CzZ0+eefpp7r33XhJ6nk9BQQEAY8aMoUePHnz00UeN+hpqcvHFFzNjxgwSExMJDAwkI6Py5mwnTpzgu+++Iy0tje+//77JYhOiNXImYVDseDymlBqllOoDtHFhTEIIIYTHKLTaCPYycXG46+bVuptS6lWl1BwgD9imlHpDKTWn5KuR++oE9AE2V3F6gFJqu1Lqc6VUr2quv1sptUUpteXkSRkQKVqeoKAg+vTpg5+fX51X9J87dy6TJ08uV/bYdPvMnlmzZnHpkMs48uf+cgmCyZMn89///rfhgddg//79/Otf/+Kbb75BKcXFF19cOqJh3bp17Nmzh6effppvvvkGgHbt2jF8+HDi4+O57DL5HFMIV3Lmo5CZSqlg4J/Aq0AQ8A+XRiWEEEJ4iEKbjbgAXwwte7Gskq0GtgKrKpyrerulelBKBQAfAw9qrbMrnP4F6Ki1zlVKXQV8AsRXbENrPR+YD/ZdEhorNiGaC4PBwNNP1335DqvVyk8//cTo0eVn9fy03r4EyX333UdERARrv/+WdRs3c8sttwBw9dVXc/3112O1WjEajQ1/AVVYsmQJmzZtYuvWrQwbNow2bdowfvx4/vjjD6666ioWLFjAzz//zI4dOxg2bBhKKe6//36XxCKEKK/WhIHWerXjMAsY6tpwhBBCCM+SZ7HSLdDP3WG4lNZ6EYBS6oEqFiN8oDH6UEqZsScLFmutl1cRQ3aZ4zVKqblKqbDmvK2jEM1Jbm4ufn5+mEzn3v4XFtqXIwkJi8BoNNK2rX0dluPp50bnmEwmfH19yc3NddkOBQMGDGDr1q0MHDiwtOz2228vPb7kkkv47bffGDRokEv6z8nJ4d1336Vjx46MGjXKJX0I4amqTRgopV6lhk8NtNaS1hNCCFGrN+992N0huIzW9j+TN3R0ftExD3c78EqFsklVlNWJsu9ltgDYqbV+uZo67YATWmutlErGPq2y8uRmIUSVAgICyMvLw2KxlCYNSob9Z55Kx2q1lq4X0C4ivPQ6i8VCfn6+S3cquOyyy0qnFhw6dIjFixeTlJTEsGHDABg2bFjpsSusXLmSzz//HID+/fvXaSFJIVq6mkYYbKnhnBBCCOGU/V0T3B2Cy+RZbcT6+dClhY8wUErdBEwEOiulyk5JCARON0IXlwC3Ar8ppbY5yh4DOgBorecB44G/K6UsQD5woy7J2AjhBl988QXFxcWMHj0a5QFTkoxGI8nJyaxevZqxY8eWlidfMoif1v/InDlzWLHKPrB44IALS89/+umnJCcnu2w6QkXvv/8+69evZ+PGjVx22WVN8r3t1asXJpOJ6OhoQkNDXd6fEJ6k2oRByfBDIYQQoiESt24CYHu/i9wcSeMrtmna+zXNVmNutgE4BoQB/ylTngPsaGjjWut1QI13BVrr14DXGtqXEI1hx44dpQsBhoaGlhtK35xNnjyZuXPnlksYPPvUk1x++eU89NBDAER36Mz48eNLz8+dO5cpU6Y0WYzJycls3LiR5OTkJkvE9OnThw8//BCz2ewRyR8hmlKL3/9JCCGEe0149y2gZSYMLNpGaMvfShGt9UHgIDDA3bEI0RiWLl3KqVOnmDRpEn5+dR8hFBYWho+PD1arlfbt27sgQte4/vrrmTZtGqtWrSrdWnHYsGHs3LmTJe+/T7vISCZNmoSPjw8Aq1atYufOneUSCK42ZMgQBg8e3OQ37l5eXk3anxCeouW/yxFCCCFcQGvNWYuVWH8fd4cihKiDvXv38u677wIQHR3NNddcU+c2oqKiWLBgAVar1aOGsHt7e7NixYrSnQdKkgbdu3fn6aeeKld31apV3HnnnaxZs6Z0rYOmIp/yC9F8GGo6qZQyKqVkC0UhhBCigpOFxZwfEsi42Ah3hyKEqIP27dsTHR2Nr68vPXv2rHc7QUFBHpUsKJGUlMSaNWuYPHkyI0aMYMWKFVgsFsC+wOGKFSsYPnw4kydPZs2aNSQlJbk5YiGEO9U4wkBrbXUsdDSrieIRQgghPILWmju6RBHh0zqGsSqljMA7Wuub3R2LEA3h7+/P66+/js1ma7KF/JqbpKQk9u3bx0cffcSLL77I9ddfj6+vL/n5+SQnJzNlyhTGjx/f5CMLhBDNjzNTEtYrpV4DPgTOlhRqrX9xWVRCCCFEM1ZgteFrNJIY4rptxpobx4cIHZVSXlrrInfHI0R9bN++HYvFQr9+/RolWZCSkkJKSgqjRo0iMDCwESJsOt7e3tx8883cfPPNWK1WcnNzCQgIaLVJFCFE1ZxJGPR2PD5dpkwDlzV+OEIIIVqa/z70hLtDaFQWm+Z0YTGT4toT1kpGF5SRhv2DhFWU/xDhZfeFJIRzdu3axRNP2H8fzZgxg379+jWoPYvFwvTp0ykqKiIjI6PGnQTWrFnD//73P2666SaSk5Mb1K8rGI1GgoOD3R2Gx1uxYgU//PADkyZNonfv3rVfIIQHqDVhoLUe2hSBCCGEaJmOdOjk7hAaVWZxMUltg7g3IdbdobjDPseXAfCsj1NFq1eyZZ7Wuk5D7Y8ePUpOTg4JCQnlyo1GI5GRkRw6dIjo6Oga21iyZAlZWVksX768WSYM3Gnnzp289957DBw4kJEjR7o7nAZZsmQJBQUFfPLJJ5IwEC1GrQkDpVQk8CwQpbUeqZTqCQzQWi9weXRCCCE8XtKGHwD4+eLBbo6k4WxaU2yzr13QGlfx1lo/BaCUCnA8z3VvREI4r0uXLsyePRur1Up8fLxT15w8eZL77ruPoqIipk2bxiWXXFJ6TinFyy+/TEZGRq0JgzFjxvDVV195/A2xKyxbtowdO3awa9cuj//+jB07lh9++IErr7zS3aEI0WicmZLwNrAQeNzx/A/s6xlIwkAIIUStxi21b1/myQkDrTWniywU2WxcEh5C3zat88N1pdR5wLtAG8fzU8BtWutUtwYmhIPWusZkXufOnVmzZg1HjhxhyJAhtbZXXFxMcXExAPn5+ZXO+/j41JosALjhhhu44YYbaq3XGg0ZMoTdu3czaNAgd4fSYCVrQgjRkjiTMAjTWi9VSj0KoLW2KKWsLo5LCCGEaDbSC4roEujH4+d15rxWtNBhFeYDD2mtvwNQSg0B3gQudmdQQgB88cUXzJs3j8GDB/OPf1S9K/gPP/zAvHnzAGjXrh3du3evsc2oqCieffZZMjMzy40uEI1n0KBBLSJZIERLZXCizlmlVFvsCx2ilLoIyHJpVEIIIUQzkW+xolG8lpTQ2pMFAP4lyQIArfX3gL/7whHinM2bN2O1Wtm4cWO1dSIiIjAajfj4+BAaGgrYdzrYs2dPpbqFhYU8+OCDPPPMM4SFhVUauVBYWEhBQUG9461qxIIQQjQ3zowweAhYBXRRSq0HwoHrXRqVEEII4UZaa7KKrRRYrQSZTTzSsyNtvVvdjghVSVNK/R/2aQkAt2DfOUEIt7v11lvx8vKqcSRAz549mT9/Pl5eXoSEhLB161ZmzJiBUoo5c+bQqVOn0rqHDx9m9+7dmEwmtm3bVm40Qnp6Og8++CAWi4UXX3yRjh07OhXj6dOn2bhxI2lpaXz11VdcddVV/P3vf6/3axZCCFdzJmGQCgwGEgAF7Ma5kQlCCCGEx7FpzfH8IjoF+PC3rp0YEtkGL6P82XO4A3gKWI595OFaR5kQbhcXF8ejjz5aa72IiIjSY5vNVuVxQUEB//73vzl58mSl1ftPnTrF1KlT+fXXX4mLi+PgwYNVJgyOHj3KrFmziImJ4b777sNgMPDiiy+yceNG9u/fT7du3di+fXt9X64QQjQJZxIGG7XWfbEnDgBQSv0C9HVZVEIIIVqMlx+b6e4QnGLTmozCYqxac3F4ME9f0IVQb7O7w2oWlFLvaq1vxb7A4f3ujkeIxpKCzmnbAAAgAElEQVSUlMQzzzyDt7c3cXFxpeXZ2dkcO3aM9u3bc+GFFxIcHExmZiZGo5GNGzdy6tQp/Pz8GDJkSLUjGr777jt27drFrl27uPbaa4mNjSUoKIh9+/ZhMpkwGo3cf7/8cxJCNG/VJgyUUu2AaMBXKdUH++gCgCDArwliE0II0QKcimjn7hCqZdOa7GIL+VYbBiCpbTD3JsTSI1im5VfQTykVBdyhlHqHc+8JANBan3ZPWEI0XO/evSuVRURE8OCDD3Lo0CGuueYa9u7dy9SpUzGZTDz55JN0796dCy+8kAceeACj0Vhlu4MGDWLz5s2Eh4ezatUqIiIi+Oc//8mRI0dIS0tj4sSJ9OzZ09UvTwghGqSmEQZXAJOAGOA/nHtzkAM85tqwhBBCtBQDv/0SgHWXXeHmSM4ptNo4U2RBAReEBjAyqi0Xh4fQ3tfb3aE1V/OAb4A4YCvlEwbaUS5EizJs2LDS42PHjpVusWiz2XjppZdqvT42NpY5c+bwySefsGCBfTfyvn37MmfOHDIzM2nTpo3LYhdCiMZSbcJAa70IWKSUuk5r/XETxtRk8ixW8ozu3yFS1/W8rvZM3duqrX4NFzjbe619aifrOVFH11ZD1/i0Tv1Ve66a/z+u7cvZtnSl86rcGftzhUJT4SPEMpUVZX82NKrsiSooR/vV74xdtm7lWhpdWqpUSVuq3M9nVdtul+2zJIbq4qvqmupjrLotpezfl0rtVfgml+uj7EFV/+AqNuYosMegK9U79/+mwqtQusL/t7JxqzI/tpXbLBt7mV7Kna/4usteMvyTpQCsuHho9d+7asortlddH3Vl1Zp7u8VyXYcIAs3OzM5r3bTWc4A5SqnXtdayQpvwKF9++SWbNm3illtuoUuXLvVq45JLLuGuu+7ixIkTlXZLqE2PHj1Kd2Vo164dBoNBkgVCCI/hzLukGKVUEPaRBW9iX7tgmtb6K5dG5mLh3l7EB/pVeXPiDqrMm/1y5SU3BxXOn7t5OnfzUPGezVChtWrbqKaPc9fZSwwV7z8q9l9DmyVKlg2rXF4xhvIVDFWUG6qJ16Bqfj0V+6q1vEzflb//5fukwnlDhe9PpRidaK/i/8/q4zsXZ7m2Kn0va/n/DZXeDFWMr1J5NbFVfH2l5dV8X6r6mao9htpfT9k+a4qt4s9f5bjLn689tvJ9qyrPVbymtr6ricHJ3yFV9V3n1+Pk75YS7YP9UcAHA8+vog/nXu+565z7WVNV/Bsqe4nJoAjxkvUJ6kqSBcJTWCwWfvnlF7p27cq8efOwWCyYzWYee6zqQbLHjh3jk08+oUePHhQXF5OcnExwcHDpeYPBQPfu3VmwYAGffvopM2fOJDExsfT8xo0bsVqtDBw4sFLbCQkJTJw4kUWLFrF48WLuvvtup17DmjVrmD17NqNHj5a1DoQQbuNMwuAOrfUrSqkrgLbArdi3U/LohEGnAF8+GpRYe0UhhBAN49hhoFOAr5sDEUK0FgsXLmTVqlWEh4czePBgNmzYUON2iwsWLGDz5s288sorxMfH07t3b5555plydcomInNzc8nKyiI4OJiUlBSeffZZALy9vUlKSqrU/k8//YTVamXdunVOJwxeeuklUlNT2bNnDxMnTiQsLMyp64QQojE5kzAo+e14FfD/2bvz6Kir+//jz/uZyb6wh4QkrGFHAWWTItQNlSqCCqItLriDtFSr4q6t4ta6gwjIz6VarVq+ImKtVHGrCoK7iDsEiAkJIXsymfnc3x+BCJKVZDJZXo9zembms9z7Gs4xnXnPXZ6w1n5h6jsWS0RERESkgay1fPbZZ0RERNClS5dqh/b7/X4AAoEA8+bNY968eTW2O3DgQD744AO6du0KVHzx/2W/Tz75JB06dGDGjBnce++9WGu55557iI2NxePx4Lou8fHxVbZ/9tln88ILL3D00UfX+b2eeOKJfPvttwwcOLDadkVEgs3YmiapA8aY/0fFbgm9gKGAB1hrrT28untGjBhhP/zww8bMKSIiLdWvf13xuHZtKFM0G8aYDdbaEaHOcbCMMT2AvtbaNcaYKMBrrS0Ida6q6PNI6/PUU0+xZMkSfvjhB8aMGcPChQvp0qXLAdf5fD7ef/99+vfvX1kEqE1BQQGO4/DVV18xePBgIiMjK8/93//9HxdffDEAM2bM4PvvvwfghhtuYNSoUWzfvh3XdVm7di2rV69m5syZTJo0qcHvt6CggKioKLxerbUiIo2rrp9H6vLX53xgGPC9tbbYGNMJOK+hAUVEpI14/vlQJ5BGYoy5ELgI6Aj0oWInpcXAMTXdJ9JYioqK8Pl8BAIBiouLyc/Pr7JgEB4ezvjx4+vVdlxcHACHH37gb2L9+vUjKiqK8vJyTj75ZMrKyrDWVk4/SE5OBuC///0vhYWFvPHGG41SMNib6ZfKy8tZtWoVXbt2ZezYsXzzzTe8//77nHDCCVX+e4iIHKy6FAz2rt5yqGYiiIhIvWnebWsyBxgFfABgrf3GGJMQ2kjSlpxzzjl0796dn376ibS0tIPe9aC+Bg0axPr16ykvL6dbt26Vx7/44gu6dOlCQkLFfwbnnXcer732GqeffnpQ86xevZrly5djjGHx4sUsWLCA7OxsfvjhB2688cY6tZGens59991Hr169mDNnTr13fxCRtqEuBYMr93keScUHhQ1A3SdhiYhI2/XYYxWP554byhTSOMqstb7K3S2M8XLwu1uK1FtERAQnnHBCSPr+5S/3//73v1m4cCExMTEsW7aM2NhYJkyYwIQJE4KeJSkpCWMMcXFxxMbGkpqaSnZ2NqmpqXVuY82aNXz99dd8/fXXTJs2rc5TN0Skbam1YGCtPXnf18aYVOC+oCVqInuHi9W2hoOIiDTM2L/9DYD/tdF9x30+H0OGDGHAgAGhjtIY3jTGXAtEGWOOA2YDL4U4k0iD+P1+br31Vnbs2MG1115Lz54963RfSUkJUPHf+N5FFoMhIyODBx54gNTUVC699FKMMYwaNYqlS5cSHR1NXFwcN954I5mZmZVTI+riqKOOYuPGjfTq1atyhISIyC8dzAoq24CBjR2kqW3atInLL7+c9u3bhzqKiEirtvjHHwEO2KKsLSgrK6O4uJjrrruutRQM5lOxttFnwMXAamBZSBOJNND27dvZsGEDAO+9916tBYNAIMCSJUvYvXs3l156KWlpaUH9PLlmzRo+//xzPv/8c0455ZTKosC+IwK8Xm+9igUAPXv25MEHH2zUrCLS+tRaMDDGPMjPww0dKhZA3BjMUE2lc+fO2tNWRCTIwvesJp6YmBjiJMGzdwG24uJiXNfF6/VirSU6Opo//vGPnH322aGO2FiigOXW2qUAxhjPnmPFIU0lcpA2bNhAVlYWqamp+P1+jjlm//U7MzIySE9PZ+TIkZVz/Ddt2sTq1asBOPTQQ+nXr19QMx555JG89957pKamtuq/oyLSPNVlhMG++xH5gX9Ya98NUh4REZEWIycnB9d1sdaSlpbGIYccQr9+/UhOTiYlJYWuXbu2tu3Q/gscCxTueR0F/AcYG7JEIvuw1rJ06VK+/fZb5s6dW+Oc/m3btnHLLbewY8cOvF4vXbt2xXGcyvNlZWVcfvnlFBYWMnPmTKZPnw5Ar169SEtLIz8/n2HDhgX9PfXs2ZNFixZVe97v9/PKK6/QqVMnxo7Vf4oi0rjqsobB400RREREpKXx+Xw89thj9O/fn7CwsFDHaQqR1tq9xQKstYXGmOhQBhLZV05ODv/617/w+/28+uqrXHDBBdVe+8knn7Bx40Y8Hg/du3cnJiaGiIiIyvPW2sq1rlzXrTweExPDvffee1D5rLV8+eWXdO/evdotE+vazldffUVKSgpvv/02S5YsAWDRokX1WvhQRKQ21RYMjDGfUfXKxwaw1tpDg5ZKRERajVtGjw51hKAIBAI4jsPgwYPb0nZkRcaYw6y1GwGMMYcDJSHOJFIpPDyczMxMdu3aRXR0zbWsL7/8ksGDBxMIBHjkkUfo2LHjfl/iIyMj+etf/8rWrVsZM2YMP/zwA3fffTc9evTgyiuv3G80Ql098cQTPP/88yQnJ7N48eJ637/X008/zTPPPENSUhIXXXQRxhhiYmIaVIQQEalKTSMMTmqyFCIi0mqVeTyhjtDofD4fO3fuZOrUqW2pWAAwD3jOGLODih8QEoEzQhtJ2gq/309mZibdunWr9r+70tJSUlNTSUlJqbVgMH36dMrKyhgxYgR9+vSp8poNGzbw3nvv0aFDB9avX096ejrp6emcffbZJCUl1fs95OXlAVBQUIC19qD/fuzevRuA/Px8DjvsMB555BGio6Np167dQbUnIlKdmgoGYUDXX65XYIz5FfBTUFOJiEirMWnPLgmr67hVWXPnui47d+5kzpw5zJw5M9RxmpS1dr0xZgDQf8+hzdba8lBmktbj+++/Z/PmzRx99NH7TQ3Y6y9/+QsbN25k6tSpzJo1q8o2EhISuOWWW8jIyGDixIk19tejRw+uv/76Gq957LHH8Pv9vPDCC8yaNYsvvviCHj161GnxwbKyMgoKCvZbYPvCCy8kLS2twSOTZs2aRY8ePRg0aBCO4xxU8UJEpC5qKhjcB1xTxfH8PedODkoiERFpVcbt2AG0joKB67rs2LGDSZMmcf7554c6Tqj0BwYBkcBhxhistU+EOJO0cH6/n/nz51NSUkJ6ejoXXXTRAdekp6cDsHXr1hrbGj58OMOHD69z3xkZGbz11luMGzfugK0JTzzxRN59912OPvpounXrxp133lmnNsvLy5k7dy4ZGRlcfvnlHHXUUQBERUUxadKkOmerTlRUFCedpMHAIhJ8NRUMulprP/vlQWvtZ8aYnkFLJCIi0gyVlpaya9cuJk2axNVXXx3qOCFhjLkJ+DUVBYPVwInAO4AKBtIgjuMQGxtLSUkJ8fHxVV5zzTXX8P7773P88ccfcM51XYwxlb/a5+Xlcdttt+E4Dtddd13l3P6CggLuuusuPB4PV111FdHR0dxzzz189dVXvP/++9x777289957FBcXc/TRR3PRRRdVWbyoTWlpKZmZmQBs2bKl2uvS09Pp0qULubm5REVF0b59+3r3JSISTDUVDGr6ixXV2EFERESaq0AgQGZmJpdddhkXXnhhW1u3YF+nA0OBj6y15xljugJ/D3EmaQUcx+Hee+9lx44dDBgwoMpr+vbtS9++fXFdlxUrVhATE8PEiRP58ccfueaaayq//Ldr146PP/6YTz/9lC+++IKPPvqI5cuXk5yczIcffsjHH38MwGeffcbo0aNJTEzkq6++IjExkc2bN7NgwQIAIiIiGDduXJVZfvrpJ8477zxiYmJ48skniYra/6NxXFwc1157Ld9++y1Tpkypso0VK1awfPlyIiMjKSsrIzo6mkWLFtGxY8eD/WcUEWl0NRUMPjTGXGitXbrvQWPMBcCG4MYSEREJPWst2dnZWGuZOnUq06ZNa8vFAoASa61rjPEbY+KBLKBR9nAzxpwA3A94gGXW2jt+cT6CipEMhwM5wBnW2h8bo29pHtq1a1enRfvefPNNli9fDkBqaipbtmyhsLCQwsJC0tPTadeuHYcffjjdu3fnhx9+wOPxsHnzZpKTkznssMMYMmQIHo+HIUOGADBv3jxOPfVUUlNTyczMJDw8nPLy8hp/7X/88cfZuHEjAC+99BLTp08/4JrRo0czuoZdYnbsma61Y8cOOnToQFFREfn5+SoYiEizUlPBYB6wwhjzW34uEIwAwoGpwQ4mIiISarm5uSQmJvLggw9qb/MKHxpj2gNLqfhsUAi819BGjTEeYCFwHLANWG+MWWmt/XKfy84Hcq21acaYGcCdaIeGNqlbt254vV4iIiLo1KkTvXr14ocffiA2NpbBgwcDEBsby+LFi3n88cfx+XyVIwXatWvH7bffvl97Ho+HXr16AZCcnMzDDz9MeXn5AesZ7OuEE07g8ccfrxyFEAgEWLp0KUVFRVxyySXExMTU+j7OOeccunTpwoABA9i6dSsdO3akZytY60VEWpdqCwbW2kxgrDHmKGDInsMvW2tfb5JkIiLSKlw7dmyoIxyUwsJCAG6//XYVC/aw1s7e83SxMebfQLy19tNGaHoU8K219nsAY8wzwCnAvgWDU4Cb9zx/HnjIGGOstbYR+pdmYvPmzSQmJtY40qB///4sX74cr9dbuTbBpZdeesB1Pp+P9u3b07dvX8LDw+ucISEhodZrfD4f5513HlOnTqVbt2588sknvPzyywAMHjyYE044odY2YmNjK0cmHHrooXXOJyLSlGoaYQCAtfYN4I0myCIiItIs+Hw+8vPzuf/++xk4cGCo4zQrxphTgXGApWLBw8YoGCQD6fu83gb8cix35TXWWr8xJg/oBGT/It9FwEUA3bt3b4Ro0lRefPFFli1bRseOHVm2bBlhYWHVXtuhQ4da23vmmWd44YUXCAsL48knn6zTr/6/9NRTT7FmzRrOO+88xo8fX3n86quvZtu2baSnp/PAAw/Qu3dvevbsSWFhYaN8+S8tLSU8PBzHcRrclohIQ9RaMBAREWmIqd99B8CKPn1CnKTudu3axSWXXFLtgmdtlTFmEZAG/GPPoYuNMcdaa+eEMNZ+rLVLgCUAI0aM0OiDFmT37t1AxeieQCBQWTB45513SE9PZ+rUqURGRta5vb0jBdq3b1+vEQb7evnllykoKODVV1/dr2CQk5NDXl4eGRkZQMUihw8++OBB9fFLH3/8MbfccgtJSUnce++9REREVJ4rLS3l+eefZ+PGjSQkJDBnzpzKURYiIsGggoGIiATVyD1bi7WEgkFxcTF5eXmEh4fzm9/8JtRxmqOjgYF7pwEYYx4HvmiEdrez/+KJKXuOVXXNNmOMF2hHxeKH0kxYawmQi9cc3KJ9Z555Jl26dKFPnz6VhYHs7GzuuusurLUYY5gxY0ad25s0aRKDBg2ic+fONY5WqMnvfvc7Xn/9dU4//fT9jl9yySUsWbKE5ORk/H4/Xm/FR+qSkhIiIyMbtDjql19+id/vJz09vXIdlb1WrFjB0qVL2bx5M0OHDuXQQw9l0qRJB92XiEhtVDAQERGholhQXFzM3LlzOfroo+nWrVuoIzVH3wLdgb0by6fuOdZQ64G+xpheVBQGZgBn/eKalcA5VCyyeDrwutYvaF5+cm+nyH2PDs4ZdPL8rt73h4eHH/DlNzY2ls6dO5OdnX1QU0yqWkRwb/GhKjk5OaxZs4ZRo0bRq1cvJk2aVOUX8qFDh5KSksJ3333HunXrGDt2LKtWreKRRx5hxIgR3HTTTfXOutfkyZMpKCggNTV1v2KB3++nW7duxMTE0LlzZ5KTkxk2bNhB9yMiUhcqGIiISJtnrSUvL4877riDY489NtRxmrM4YJMxZt2e1yOp2DlhJYC1dvLBNLpnTYLLgFep2FZxubX2C2PMn4EPrbUrgUeBJ40x3wK7qCgqSDNSajcDUGa/brQ2IyMjWbRoEUVFRXTq1KnB7a1Zs4YHH3yQI444gvnz5x9wfuHChaxfv57//Oc/PProo9W2k5aWVrmOQlpaGgCfffbZfo8HKzY2losvvni/Y7t27WLevHmUlpZy2223MXDgwHpNzxAROVgqGIiISJtXVFREjx49OOaYY0Idpbm7MVgNW2tXA6t/cezGfZ6XAtOC1b80XKJzFYX2Xdo5jTudJzIystG+HK9fvx7Xdfnggw+qPL/3F/2kpKQa20lISODxxx8HqByt0LNnTz766KMDvuw3hm3btpGbmwtUFA9ULBCRpqKCgYiIBJXP4wl1hFr5fD769OnToHnHbcSHQIm11jXG9AMGAK9Ya8tDnEuagShnMFEMDknfOTk5/PnPfyY8PJybbrqJ2NjYKq8788wzKSkp2a84+N5777Fr1y5Gjx7NhRdeyMSJEw+YkpSXl8ezzz5L//79mTBhAvDzNKYuXbqwa9cunn76aQDS09NpbIcccggzZ86kuLh4v8UXRUSCTQUDEREJqptH/3J3vObH5/PVaZs24S3gSGNMB+A/VKw9cAbw25Cmkjbv448/5vvvvwdg06ZNjBw5ssrrHnvsMT766CNiYmLYvn07vXr14sYbb+SLL76gb9++PPLIIwwYMOCA+5577jleeuklVq1axWGHHYbjOMyePZvc3FyuvfZaDj/8cHr27El6ejqDBg1q9PdnjGH69OmN3q6ISG1UMBARkTatrKwMj8fDzJkzQx2lJTDW2mJjzPnAImvtXcaYT0IdSmT06NGMHj2a8PBwhg4dWu11P/zwAwB///vf6dmzJ4cccgjWWlzXxXEcsrKyqiwYDBgwgJUrV9KzZ0+ioqLIyckhNzcXay3btm1jzJgx3H///fh8Pk0XEJFWRQUDEREJqjO+rlgA7dl+/UKc5EDWWnbt2sXVV19NSkpKqOO0BMYYcwQVIwrO33PMCWEeEaBiocDrr7/+gOMbN25k+fLljBs3jhkzZjB//nw++OAD1q1bR3p6OkOGDGHu3Lm8/fbbxMTEcOSRR1bZ/rhx4xg+fDiRkZF4PB66du3K/Pnz2bZtG5MnV6z16ThOkxULvvjiCxITExtlIUgRkZqoYCAiIkE1NDsbaJ4Fg507dzJkyJAD9liXav0BuAZYsWcXg97AGyHOJFKtVatWsWXLFnbs2MGMGTMYOHAgAwcO5KyzziIrK6uyUFiX4f4xMTH7vR47dmyV161bt47FixczduxYLrjggiqvsdby6aef0qNHD9q3bw9U/D1q3749YWFhNeZ48cUXWbZsGfHx8Tz66KMa0SAiQaVfBUREpE3KyckhJSWF+++/X4sd1pG19i1r7WRr7Z17Xn9vrf19qHOJ/FJGRgZXXHEFOTk5JCcnM23a/htshIeHN9qoovfff5/f/e53LFy4EIDXXnuNnTt3smrVqmrvefLJJ7n++uu54oorsNaycuVKZs2axZVXXllrf0VFRQCUlpYSCAQa5T2IiFRHIwxERKTNKSwsJBAIcMstt9CuXbtQxxGRRvbmm2/y9Z7pUA888AC9evVqUHuFhYU88cQTJCUlMXXq1P3OrV27lry8PF599VVmz57N1KlT2b17N0cccUS17RUXFwNQUlIC/Ly2wtatW/H7/Xi91X9Enz59OgkJCfTs2fOAUQ8iIo1NBQMREWlTfD4fRUVF3H///QwZMiTUcUSkEZXbLArctYw7qi/r1/cjMTGR1NTUBre7evVqXnnlFQBGjBixX5unnXYaBQUFjBo1CmMMgwYN4u67766xvfPOO48+ffowYMAAjDGcc845tG/fnqFDh9ZYLADwer0ce+yxDX5PIiJ1oYKBiIgEVUF4eKgj7Cc3N5ejjjqqxl//RKRlygrcR4n9jPBOPfnb3x484Hx6ejp/+ctf6NSpEzfffDMRERF1anfQoEGEh4eTkJBAly5d9jvXt29fbrvttnrljIiI4LjjjgPgpZde4quvvuLcc889oG0RkVBTwUBERILq9hEjQh0BqFhkbOfOnSQmJnLFFVeEOk6LZIzpBzwMdLXWDjHGHApMttbeGuJo0sIFbB4uxYSZpDrfU2a/p9huJN4ch8dUTC3ymq5gPyPMJFR5z/r168nIyCAjI4MtW7bQr46LsQ4ZMoRnn32W7777jvLy8sqFBgOBAD/++CPdu3evdbHCquTn57NkyRIA4uPjufjii+vdhohIMGnRQxERaROys7MZMGAATz75JAkJVX+ZkFotpWKXhHIAa+2nwIyQJpIWz29z2RK4hC3+iylyP6zzfTsCN5MTeJyd7uLKYwnOXFK995PoXFPlPUcddRQjR47kxBNPJC0trV45V6xYwZ/+9CfmzZuH67oALFy4kHnz5rFgwYJ6tbVXbGwshxxyCOHh4Rx22GH1unfnzp28/vrrlesgiIgEg0YYiIhIUJ29aRMATwwcGNIcruty6623apHDhom21q77xa4S/lCFkdbBpRDXFgLgJ6vO93npQoBcvPw8jN8Yhwh6V3tPhw4duPHGGw8qZ25uLlAxKiAQCOA4DpmZmQCVj/XlOA4LFizAWlvv3VpuuOEGtm/fzoYNG+q0u4KIyMFQwUBERIJqwJ4P2aGydypC586d6datW0iztALZxpg+gAUwxpwOZIQ2krR04SaVRM+1+Mkm3kys833JntsoZzvh9MZvcyiz3xJtDseY4Hy8Pfvss0lKSmLAgAGV0w/++Mc/8sYbbzR4TZSD2do1fM/6MOHNbJ0YEWldVDAQEZFWLSsri/79+3PLLbfg8XhCHaelmwMsAQYYY7YDPwC/DW0kaQ1infp/4XZMJBH0AWCb/0r8diftnMl08VzY2PEAiIyM5OSTT97vWOfOnZk2bVpQ+qvNrbfeyubNmxk2bFhI+heRtkFrGIiISKtVWlqK67rceeed9O5d/TBlqZ0xxgFGWGuPBboAA6y146y1W0IcTdo4ay2WwJ5XrWOGjLWWm266iTPOOIONGzdWeU18fDwjR448qMUWRUTqSgUDERFplfLz8ykuLubKK68kKanuK69L1ay1LnDVnudF1tqCEEcSASqG86d47qSr50o6Oefvd67cZlBiK9ZRsdZWHs9zV7PNfxXF7ic1tv3TTz/xwAMP8M477zRK1i1btrBpz7ouNSkoKGDjxo0UFxezbt26RulbRORgaEqCiIgEVXZUVEj6LSoq4pprruG0004LSf+t1BpjzJ+AZ4GivQettbtCF0kEwkwiYSZxv2N+m8tW/++xlNLBOYs8uxIPcaR4/kaO+wSuLSLXfZ5oZ2i17T755JO89dZbvPHGG/zqV786qLUG9tq+fTvz5s3D7/dz/fXXM3r06P3OL1u2jHfffZeLL76YMWPGMGvWLDZt2sSUKVMOuk8RkYZSwUBERILqnuHDm7zPvSuO13fbNKnVGXse5+xzzEINy/zESwwAACAASURBVNKLhEwAW7EDKGV2M64txKUQH+m0M7+hgP8S7xxXYwtDhw7l7bffZsiQIQ0qFgAEAgECgYqpE+Xl5Qecf/nll/H7/axZs4YxY8YwdepUpk6d2qA+RUQaSgUDERFpdXJychg8eDCHHHJIqKO0KtbaXqHOIFKVgM1np7uEMLrQ0TkbYwxe05kU7x2U2yyiGUGO/X94iCeSgUR5BtGJmbW2O3HiRCZMmNAoOxF0796dO+64g4KCggNGFwCcc845/O9//+PUU09tcF8iIo1FBQMREQmqC774AoBlgwc3SX8+nw/XdbnppptwHC3V05iMMWdXddxa+0RTZxHZV759jUL3TQBinXGVuydEmgFEmgEAJOw3MKbuIiIiGickMGjQoGrPTZkyRdMPRKTZUcFARESCqndeXpP2l5OTw5w5c+jVSz+GB8HIfZ5HAscAGwEVDCSkos0wdpt4vHQhjORQxxERaTVUMBARkVbD5/NhrWXGjBmhjtIqWWvn7vvaGNMeeCZEcUQqRZg+9PI+ddD357kvU2o308k5G6/pXPv1eXlce+21lJeXc+utt5KQkHDQfYuINGcaqykiIi2e3+8nMzOT3bt38/vf/56oEO3M0AYVARrKIS1awOaxM7CYAvcNct1/1emer7/+mq1bt5KRkcHnn39ep3vef/99br31Vr788suGxBURaVIaYSAiIi1ednY2xxxzDHPnziU5WcORg8UY8xIVuyJAxY8Og4DnQpdIpOEcYok0Aymz3xBt6rary7Bhw5g4cSLl5eWMHTu2TvcsXLiQXbt2kZ+fz1133dWQyCIiTUYFAxERCartsbFBadd1XYqLiyksLMRay/Tp01UsCL6/7vPcD2yx1m4LVRiRxmCMhxTvXVjrYkzdBt+GhYUxd+7c2i/c4/vvv+fLL78kKyuLM844o/YbRESaCRUMREQkqBYeemhQ2s3OziY5OZkpU6YwYsQIhg+v2y+D0iCTrLVX73vAGHPnL4+JNGc+u5WswENEmDS6eC6qPF7XYsHB2LJlC926daNbt24MGzYsaP2IiDQ2FQxERKRFsdaSl5eHMYaFCxeSlJQU6khtyXHAL4sDJ1ZxTKTZynf/Q6ndRKndRAfnNLymU9D7PPLII8nMzCQyMpJDg1REFREJBhUMREQkqOZ8+inQ8JEGgUCAXbt2EQgE6NevH3PnzlWxoIkYYy4FZgO9jTGf7nMqDng3NKlEDk6sM4Eiu4FI0xcPHZukT6/Xq91bRKRFUsFARESCKrmwsEH3+3w+du3aheM4/OpXv+Lss89m6NChGGMaKaHUwdPAK8DtwPx9jhdYa3eFJpLIwYk0fenhfbje9xW67+JSRJw5Tn9/RKTNUMFARESardLSUrKysjjrrLO45JJLaNeuXagjtUnW2jwgDzgTwBiTAEQCscaYWGvt1lDmk7bHtT6K7YdEmgF4TeOPEihw3yJAHu3MJIzxUGq/4afAHQAYTzhx5teN3qeISHOkgoGIiDRLJSUlFBQUcPXVV3PaaacRERER6khtnjHmZOAeoBuQBfQANgGDQ5lL2p6d7iIK3P8SblLp7l3UqG2X2e/JDNwNgOOJIN5MxEMchggsPrwEf80DEZHmQgUDERFpNvZulVhUVITP5+Paa69l+vTpoY4lP7sVGAOssdYON8YcBfwuxJmkDbO4jd6mh3Y4JgbXluClKwBhJpEe3sW4+Ag33Rq9TxGR5koFAxERCarv6ziNoLy8nMzMTPr168fIkSMZPnw4EyZMCHI6qadya22OMcYxxjjW2jeMMfeFOpS0PV2cS4k2hxNlBjV6217TiR6eR3ApI8wk7HO8c6P3JSLS3KlgICIiQbVscN1Gq+fn53PMMcfw17/+NciJpAF2G2NigbeBp4wxWUBRiDNJG+SYCOLMkUFr32Pa4Qla6yIiLYcKBiIiElKFhYUUFBQAFXuVS7N2ClACzAN+C7QD/hzSRCIiIhI0KhiIiEhQXf7RRwDcM3z4AeeKiooIBALMmTOH0aNHM3DgwKaOJ/VgrS0yxvQA+lprHzfGREPDfog1xtwNnAz4gO+A86y1u6u47kegAAgAfmvtiIb0KyIiIrVzQh1ARERat84lJXQuKanyXFFREePGjeO8885j0KBB2tu8mTPGXAg8Dzyy51Ay8H8NbPY1YIi19lDga+CaGq49ylo7TMUCERGRpqGCgYiIhITruvj9fs4666xQR5G6mwP8CsgHsNZ+AyTUeEctrLX/sdb697x8H0hpUEIRERFpNCoYiIhIkwsEAmRkZHDccccxZMiQUMeRuiuz1vr2vjDGeAHbiO3PAl6p5pwF/mOM2WCMuagR+xQREZFqaA0DERFpctnZ2Rx//PEsWLAg1FGkft40xlwLRBljjgNmAy/VdpMxZg2QWMWp66y1L+655jrADzxVTTPjrLXbjTEJwGvGmK+stW9V0ddFwEUA3bt3r8t7EhERkWqoYCAiIkH1VYcOBxwzxjBz5kytWdDyzAfOBz4DLgZWA8tqu8lae2xN540x5wInAcdYa6scsWCt3b7nMcsYswIYBRxQMLDWLgGWAIwYMaIxRz+IiIi0OSoYiIhIUD3xi50PcnNziYiIIC0tLUSJpL6MMd2ttVuttS6wdM//GqvtE4CrgAnW2uJqrokBHGttwZ7nE9F2jiIiIkGnNQxERKTJZGdnExcXx+LFiwkPDw91HKm7yp0QjDEvNHLbDwFxVEwz+NgYs3hPP92MMav3XNMVeMcY8wmwDnjZWvvvRs4hUi9F7np+9J/HzsDDoY4iIhI0GmEgIiJBdc2HHwJwdVoaYWFhLFu2jOTk5BCnknrad+5I78Zs2Fpb5VATa+0OYNKe598DQxuzX5GGKrCv47fZ5NlX6OxcoilWItIqqWAgIiJBFevzUe7zER8fz913361iQctkq3ku0ma1d6bgt7uIcUapWCAirZYKBiIiElR7iwXPPfcc0dHRoY4jB2eoMSafipEGUXues+e1tdbGhy6aSGhEmv6keO8MdQwRkaBSwUBERIKmrKwM4zikpKQQpWJBi2Wt9YQ6g4iIiDQ9LXooIiJBU1RURGxsLFFRUaGOIiIiIiL1pBEGIiISFCUlJZSWlhJ2wgnQs2eo44iIiIhIPalgICIija60tJTc3FxuuOEGep5yCmhBMBEREZEWR1MSRESk0VhryczMxOfzceWVVzJlyhStHi4iIiLSQmmEgYiINJrdu3fTqVMnXnzxRcLDwysOnnhixeMrr4QumIiIiIjUmwoGIiLSKKy1lJWVsWjRop+LBQAlJaELJSIUuu+Qb1+ng3M6UWZQqOOIiEgLoikJIiLSKHbv3k3fvn055JBDQh1FRPax011CsbueXYEnQx1FRERaGBUMRESkwVzXpaSkhIsvvlhrFog0M3FmPIZwYp1xoY4iIiItTIsrGPz73/+mf//+pKWlcccdd4Q6joiIAFlZWUyaNInx48eHOoqI/EJnzwX0CXuBds5vKo9Z61LsfkLA5ocwmYiINHctag2DQCDAnDlzeO2110hJSWHkyJFMnjyZQYM0H09EJBSstWRlZZGcnMzVV19d9eiCk05q+mAiUqMcdzm73RcJM8n08C4OdRwREWmmWlTBYN26daSlpdG7d28AZsyYwYsvvqiCgYhICPh8PrKzsznssMO4/fbbiY2NrfrCP/2paYOJSK1civc8alFSERGpXosqGGzfvp3U1NTK1ykpKXzwwQchTCQi0nbl5ORw+umnM3/+fK1bINLCdHYuJML0J8oMCXUUERFpxlpUwUBERJoH13UxxnDJJZfUXiz49a8rHteuDXYsEakjx0TRzhwf6hgiItLMtahFD5OTk0lPT698vW3bNpKTk0OYSESkbdq9ezeDBw+mQ4cOoY4iIi2A3+4mO/AYRe6GUEcREZF6aFEFg5EjR/LNN9/www8/4PP5eOaZZ5g8eXKoY4mItCnWWsrKyrjiiitCHUVEWohd7pPsdl/gp8CtWFse6jgiIlJHLWpKgtfr5aGHHuL4448nEAgwa9YsBg8eHOpYIiJtRllZGdnZ2fTp04chQzT3WSRYym0m2e5SIkijo2dGqOM0WITpA0C46UEL+/gpItKmtbi/2JMmTWLSpEmhjiEi0uZkZWURERHBueeey7Rp07TQoUgQ5bkvUeR+QBEfEO8ch9d0CnWkBmnnTCLGjMZDO/3tEBFpQVpcwUBERJqOtZbi4mJyc3Pp27cvDz/8MJ061fOLy/TpwQkn0orFmDEUmDeIoC8e2jeorSJ3A4X2Ldo7U4gwvRopYf219KKHiEhbpIKBiIgcIBAIkJ2djbWWpKQkzjzzTM444wzi4+Pr39js2Y0fUKSVi3KG0Mt5qlHaynLvJ2Bz8dsckr23NkqbIiLSNqhgICIilQoLCykqKsIYw8SJE/ntb3/LgAEDGjaEuLi44jE6unFCiki9RJthFNg3iHaGBa2PgM0j375GtBleuV6BiIi0fCoYiIgIADk5OXi9Xm666SbGjx9PXFxc4zS8d92ZtWsbpz0RqZeunsvp4lyGY8KD1sdOdwmF7lvsNv9HL+/fg9aPiIg0LRUMRESEsrIyHMfhySefpEePHqGOIyKNbG+xIGAL8NktRJrBjbr4YBgJAHj3PIqISOuggoGIiJCTk8OJJ56oYoFIK7c9cDU+m057ZyqdPbMard2OztnEOkcSRrdGa1NEREJPBQMRkTaqoKCAwsJCPB4Pffr0Yd68eaGOJCJBFiB/v8fGYowhgt6N2mZtCtw3yQrcR4wzmkTP/CbtW0SkrVDBQESkjXFdl127dhEWFsbcuXM5/PDDGTBgAF6v/i9BJFiK3A/Jcu8lygwj0XNlve/fGXiYAvsmXZyLiXOOOugcyZ7bKbGfEWd+fdBt1JVriym2nxJlDsFjYvY7Z22Ane4iAuwmwfk9HtOu3u0X2fex+Cl038M6tlGnWIiISAV9OhQRaeVc16WoqKhy9wNrLYceeiiXXXYZw4cPD36Ac88Nfh8izVyhfYeAzafQvoXrzMUxkfW6P999HUspBe6bxJpf4ycTL11r/JLs2lIK7FoizQAiTE8Awk0q4Sa1Ae+kagXuW5TZr+ngTKv88v9T4C6K7QaizNADtnMsZTP57n8I2HysdUnyXIcx9ftY2tE5E4tLjBmhYoGISJCoYCAi0soEAgF8Ph9lZWWUlJRgjCEtLY1Ro0YxdOhQBg4cSFJSUtMFUsFAhPbOVAI2lyhnWL2LBQBdPBdR6L5NR8+Z7HQXku++SpxzFF09l1d7T477BHnuSxii6OFZjNfp2JC3UK2ALSAz8FfAAtDZcwEAlsCeR/8B90TQm3B6k8dKCu077LYv0cFMrVe/4aY7SZ5rGhZeRERqpIKBiEgLVl5eTn5+PuXl5Xi9XgKBAI7j0LVrV/r3709aWhonnXQSffv2DV3I7OyKx86dQ5dBJMQiTA+6eW856PvjneOId44DINsuBaDM/lDjPQ4xWOtSymf8GDiXrvyJOGd8rX0VuG9h8RFnjqnTL/cOUYSbHvjsFiJM/8rjiZ6rKLYbiDYVI5mK3PWU2E9p70zFazqS7L0VfyAL1xbiof5TEkREJPhUMBARaaFycnIAOOKII5gwYQJ9+/ala9eudOjQoXkNzz399IrHtWtDGkOkObPWT559hTASiHFG13htgucP5Lv/Jc75dY3XdXTOIoI0MtybAUuZ/YY4ai4YlLifkxm4GwDHE02sGVtrdmO8pHruxaUEj4mrPO4xcZVrJVjr56fAAix+AhTS1fMHPCaO7p6H8JNLpEmrtR8REWl6KhiIiLQwZWVl5OTkkJCQwEMPPUSfPn1CHUlEGqDEfklW4CHK7Nc4RNPdPEy4Sa72+nCTSmfPubW2a4wh1jOaJHMdpXvWF6iNx7TDEIbFxSGKIncDUeYQHBNeS19ePMTVeD7c9KLMfkOk+XnEk9d0wkunWnOJiEhoqGAgItKC5Ofn4/P5uOyyy5gxYwZRUVGhjiQiDZQZ+Bul7mb8ZBLtjMBDbKO2H+uMI5Zxdbo23KTS3fsIECAzcC+l9ktinfEHtbPDL6V47iJAAV7TocFtiYhI01DBQESkGbPWkpOTg+u6AERFRbFgwQKOOurgt1UTkeYl0gzAb7LoYE6ns+e8GrcYtLacMr4jgj4YE1bFeUuAXDzUPjVpV+CflPEdnZ0LCDNdKo/vfW4pBcCl5GDe1gGM8eJFxQIRkZZEBQMRkWYqPz+fwsJCRowYwfTp0xkwYADdunVrXusTiDSQMeZm4EJg555D11prV1dx3QnA/YAHWGatvaPJQgZZV+dPdHEuqrFQsFem+zcK3XeJccaQ5LnugPPZ7mLy3NXEOxNJ8Mytth2fu42MwJ8xePC7WUQ7h9HBmY5jIiqvSfLcQLH9iHD/MHIKfqR9XCoej6fK9lxbQpZ7P2BIcP5wUDtBiIhI86OCgYhIM1NeXk52djaJiYksWLCAsWPHtuwiwaWXhjqBNH/3Wmv/Wt1JY4wHWAgcB2wD1htjVlprv2yqgMFkjKl1lwDXlmIJ4Le7AAjY3MpzAVtEOTuIII1S+zVA5WN1SuxnWMrwk0+BfZsy91s8Jo72ZgpQsVbKc8/9m0WLFrFu3QdER0dQXOxj1KhRzJ49m2nTphER8XNxoch+SKH7LgCx5sg6LZYoIiLNnwoGIiLNhOu6ZGdnY4zhggsu4NxzzyUyshX8SnfGGaFOIC3fKOBba+33AMaYZ4BTgFZRMKiN32azNfB7rPWR6LkKH9uJNb+qPL8tcAXldjsdnTNJ8Mwl311DnHP0Ae2U2yw8xOOYSKKcQ4mxR4D1Aj5ciginNwDr1q3j1FNPZfDgwVx11VWcdNJJeL1e/H4/q1atYtGihcyfP58VK1YwcuRIAKLMIUSYPoBDlBncaO+9zH5PwOYR7QxvtDZFRKTuVDAQEWkG/H4/mZmZjB8/nj/96U+kpKSEOlLjSU+veExNDW0Oac4uM8acDXwIXGHtPj+fV0gG0vd5vQ2oee/BVqTcZuDaAgACFNLBmVp5rmLNgt0A+MkhwvSmi+eiA9rId/9LVuA+wkwS3T0LCTfJ9PI+AYBrfVjK8Jg41q9fz29+8xseffRRJk+evF8bXq+XKVOmMGXKFFauXMmkSZNYvXo1I0eOxGvak+q9r5Hfdybb/Fdg8dOVK2rdRlJERBqfCgYiIs1Abm4uRx99NHfffXfLnn5QlZkzKx7Xrg1pDAkdY8waILGKU9cBDwN/Aeyex78BsxrQ10XARQDdu3c/2GaalSjnEDpzAa4tJs6MP/C8GY6P7+lgzqq2DZ/dClSMMnApxcPPCyZWbJkYTllZGVOnTt2vWJCbm8uqVavY9NUmUpJTOPPMM+nQoQOTJ0/m0UcfZerUqWz45kGKwp6lnfkNnTwzK9sN2EIyA3cDhq6eK/GYmANyldhN5AaeJc6ZQJxT02KutuZ/JBERCQoVDEREQsxaS1lZGX369Gl9xQIRwFp7bF2uM8YsBVZVcWo7sO8QlZQ9x6rqawmwBGDEiBGt5ltme+eUKo+X2M8oct+peM4GwphY5XUdnTNwiCLC9MFj4g44n+v+i8efWczAQf33G1kw9LChpP/48+COa66/hs1fbiYxMZHJkyfz4IMP8s/nlnHimQ4FvE4nfi4YFLnrKLIbMBhK7CdVrmuwK/A0JfZjSt2vDigYhJmupHj/ht/mEuMcXsO/joiIBIsT6gAiIm2NtZaSkhJ27txJZmYmmZmZjBkzhrPOqv7XQZHWyhiTtM/LqcDnVVy2HuhrjOlljAkHZgArmyJfcxdhehBmEvGYeCLNwMrjue4Kfgrcjd/mAOCYaDp6ZhDjjKyynVz3Wf6++AvmzN5/Z4XDRxzOOeedw6pVq5hwzATyc/P5xz/+UXl+9uzZPLU4gygzjE7OzwND8tyXSQ/8gVL7DZGmP1Hm0Cr7jXPGY4gkzlQ9uiDC9FaxQEQkhDTCQESkCWVlZeG6LomJiYwbN46xY8dy+OGH06lTp1BHEwmVu4wxw6gYc/4jcDGAMaYbFdsnTrLW+o0xlwGvUrGt4nJr7RehCtyceEw7eniXYq2tHKHktznkBJYD4KUTnT21z/CIc0/h4/XPc9JJJ+13fMVzKyqfP/3Ppw+47+STT2batGkkmvV4nJ+3XMwKPISfLBxi6eSZhcfEVtlvvHMc8c5xtb9REREJCRUMRESaQElJCXl5eXTu3JlnnnmGdu1q329dpC2w1s6s5vgOYNI+r1cDq5sqV2Oy1o/F3bNWQHDsO53JQzsizUDK7PdEm8NqvTczcB+ZBRuJjo7G6636o+HSZUt5+omnCQsP43e/+13lca/XS1RUFIWFhfv9XYs3J1BuM4g1RxJlBlXbd7nNYJf7LNHmMOKcA9dnEBGR0FLBQEQkiKy1FBYWUlBQwBVXXMEpp5xCTMyBC3+1aldcEeoEIiFT4n7BjsBNGOMlxXM34Sb4u4UY4yXFe9d+ow6qE7BFFLj/JSLGUlxcgt/vP6Bo8MQTT3DRhRU7L7y59k26dOlSec7v91NSUkJs7P4jCLp6/0AXewmOiaix/13u0xS4aylgLbHmVxjjqfF6ERFpWlrDQEQkSKy1ZGRk0LFjR2644QbOPPPMtlcsADj55Ir/ibQxpfZbtvrnUGjfwW93Uma/a9L+67KIqsfE0MGZTox3MCNHDWPVqv3XnPzHM//gnHPOAeDmm2+mtLSUnTt3Vp5/6aWXGDVqFB7PgV/09xYLXFvKNv9V/Og/v3K3hr2izDDAEGUOUbFARKQZ0ggDEZEgKSwspHfv3jz77LM4Thuuz27eXPHYv39oc4g0sYDdjUMsYXQj1kwg1oxr8gyu9VFgXyPc9Kp2asDerRDnzP47ixYtYsqUKZXnrrruqsrnN998MwB/uPwP3Pe3+wBYtGgRc+bM2a+9X45s8LGVUrsJgCK7gXDz83aX8c4xxJojgzpdQ0REDp4KBiIiQWCtpaCggPHjx7ftYgHAxRdXPK5dG9IYIk0txhlBovcKwCHOmRCSDLnuP8l1n8UQRk/vE9UuPggwbdo05s+fz8qVKyu3Vrxh/g1s/nrzftedOuVUAFauXMmmTZuYeGonviufSowzljASyHVfoIMznU6eirUOIuhLO+ck/OQQb44+oF8VC0REmi8VDEREGlkgECAzM5N+/fod8MubiLQtcU7V2wVWx2fTKXDfJM75NeEmpcH9e01HABwTiyGsxmsjIiJYsWIFkyZN4tFHH2Xy5MmVaxf80sqVKzn//PNZvXo1/vDXsK6fIvd/eOhIqd3MTncpHZ0zMcaDMYYunosb/F5ERKTptfGfvUREGpfruvz000+ceuqpPP7443Tt2jXUkUSkBckM3EOu+yyZgXsapb12ziRSvPfS3fNgrQsQAowcOZLVq1cze/ZsJk48jhUrVuD3+4GKBQ5XrFjBMceN49LZF7F05Uh6HPYeHZ2ziHGOoItnbsVaBDhAgBz3KQK2qFHeh4iIhIYKBiIijWRvseDkk0/m6quvJjxcw2xFpH72jiqobnSB32azM/Awhe57tbZlrR/X+og0aXhM1Vu5BmwhmYH7yQk8VXls5MiRfPfdd5x19iRuu+syIiMjiIuLIjIyglvvupTJM8P53+Y/MGSEh3z3NcJJIclzLfHO0XTwTCfW+TXW+skNPEOm+7f6/yOIiEizoSkJIiKNYOfOnbiuy8SJE7nhhhuq3ctcRKQmCc4f6eCcQRjdALDWpcR+RoTphcfEk+P+nQL3v+TzH2LMcxhT9d+aQvd//OifhcfE09v7TyJMzyqvy7evkR94DUsJMWYEkU7F4qQRERGc9dvTGHfGWgKBAHEll9Mx9lDKPJ8Qa8bg5yeyA4VEO4ftlyHcdKOH92G2+a+m1H6JofadGkREpPnSJ1oRkQbKzs6mW7duPPjggyQnJ4c6TvNz/fWhTiDSYhjjEM7Powty3OXsdl8kzCTTw7uYSDOQAl4nwvSrtlgAkOu+QIBcAjaXUvcrIjw9q7wuygyh3OwgYHPZ5T5NN+eWynPhpjup3vux3lIiIwewxX8pvsBW/M4MOnlmkuy9rdr+kzzXU2I/I9oMr/8/goiINBsqGIiINEB+fj7GGO6//34VC6pz7LGhTiASMn6bTandTLQZeVC7AbgUY3Epct9ji/8SkjzX0cv7Dxyiaryvk3MuJfZzwkkmzjlwZwKf3U5W4H7CTQ/iOZ5S8zll/EhG4FaizQjaOScAVI5MsNZSbrdTYj8my80k3jmeMJOwX5u57r8ocN+gk3M2Mc5IYs3Yer9fERFpXrSGgYhIPVhrKSwsJCMjg8zMTOLi4rjrrrtITU0NdbTm6+OPK/4n0gZtC1zNT4E7yHaXHtT9nZ0L6eBMw0sXyu12Ct338JgYjKn5I1y0cwj9wl6lZ9jyKgsVBe4aSu0m8t1/08lzFu2dU/DYdhS5H7AzsAhrA5XXWmspthuJNycQRhIe25EC93WsLd+vzVz3n/jsj+x2Vx7UexURkeZHIwxEROrIWktGRga9evVi2rRpjBo1iuHDh+PxeEIdrXmbN6/ice3akMYQaYkcE0Un52xcivDbTOKrGC1wMGKd8RTZdYSbHmAjyQzcQ4BCIk0a8c7xGPPz37Xd9l/kBB7DIZrOzqXk21XkuH+n1H5NjDOKGDMSDx2xrsXHdro4lzVKRhERCT0VDERE6qiwsJCePXvy7LPPqkggInWS4rmz4ou1GXnQbRjjkOCZ06Ac1rqAW7nuQYTpRXfvQgByAk/hJweA9ua0A/raO9rA4tLJcyalgU8ps9+T775KsV1PgRlIpBlAIWsBDz67pUFZRUSk+VDBQESkFgUFBRQWFgIwe/ZsFQtEpEoldhMl7me0c07EY+IA8JrOxJrOTZbBWpef3Dsps9+S6LkKL51wbTnb3flYykjx3E242X8KVXvnJPLcVwA/HZ1zDmizxUK7OAAAIABJREFUg3M64aYb4aY7jokm0XMdRXYdRe57lNhPcYjGQzwOcVjKiDbDmujdiohIsKlgICJSBb/fT2FhIUVFRSQlJTFr1izGjBlD3759Qx1NRJoha112+G/EUoqfn0jw/D4kOQLsosj9HwA7A0sos19jiMClFIOh1H5zQMHAUk47M4lYzxi8JvaANo1xiDXjKl+HmQTam5OIN8dRar8i0vTHEEG46UGY6Ua40QKwIiKthQoGItLmlZeXU1RURHFxMV5vxZ/FsLAwBg8ezJgxYzj99NOJi4sLcUoRac6McQgzXfHZLYSZpJDl8JrOtHemUma/w0N7yvga15bT3jMVv80E68FaizEGqJhu8K3/FMrsd0TbUfQNq/uChY6JINoMrXzdkGkXIiLSPKlgICJtkuu67N69m/LycsLCwhgyZAjDhw+nf//+9O7dm9TUVBxHG8k0igULQp1ApEmkeP5KOZlEmB4hzdHZMwuAgC0kzE0i0vQnwvT+/+3de5xdZX3v8c937T2TZGYyk7nkMplMEgKEECAG5FpDIIpa0OINBBSVlpaeo9ZTWjlq7TlQ4bSgtVWxUEAt1lZF7aGiIIgohYOCoCJyFV4aBUQC5sJkkrnsvX/nj7VmmMx1J8zMnsv3/Xqt11577XX5rWfNnvXs33rWs/hV4Y/pjB8Q2kWDTgagRCfF2AkEUKhc0GZmNiU5YWBms0apVOKFF15g9+7dJEnC4Ycfzumnn86GDRuYO3dupcObuX7Pz2K32SHRXOZQ2WTBQDnV0Zw7G4BCbAVyQAFl1b+IEttLX2e+XkXCPBbnKvt0g2J08kzxbyjRRXPyDiChNnl5RWMyM5vtnDAws1nhhRdeoLOzk3Xr1vH617+e448/noULF1Y6rNnh++n91E4cmE2cztKPyKuZOVo57Od5NdGe/wSF+B01SXobwa64l22lr1Cim7lazW4epoqJvZ2iEM/zXOka5rCSptxZe3zWFY/SFY9QopsnC39OXk0s5i+Yn2ya0JjMzGxkThiY2YwTEXR3d9Pd3U1XVxeFQoGmpiauvvpqjjjiiEqHN/v81V+lr7ffXtEwzGaqF0rfZkvxckQ1K/LXkFfTsPNVaxnVWjbg/X7k1EBvPEJX6TG2xCep0wYSzZmwWHeUbqSz9H06+B7bS18np/m05S4jrybm6TDqko0U4nm64qFsCd8aZmZWSU4YmNm0VygU2LFjB729vSRJQqlUoqWlhVWrVvUPJ5xwAosWLap0qGZm4y6I/jH6x8dWpUWszF1LR+lOtpQ+yTwdgqge//gi6Iy7KNHL9tJN9PAr5nIYxdhOiU6643HyOoZE1SzJXQBAd/ySYuygJvEjGs3MKskJAzObliKC3bt309HRAcCGDRvYuHEjhx12GMuXL6eqqqrCEZqZTY6G5LXkaSKvheTVDEBvPEOehUijV/WkPPW5TcxPjh9z3oF2ln7Azvg+jTqNOckKtha/RIHnaE7OJafaPebdETfyfPEqeuMZcjRTrRUszX2EF+JbiBw1GtpPwRztByo7HDMzmyBOGJjZlBQR9Pb20tPTQ09PD729vRSLRfL5PJIoFossWbKETZs2cdppp7FmzZpKh2xmVjG1yVFEFOks3ctvCx+ji0doSF7PsvzflbX83iQLSrGbp4sfoCt+zjZ9meVcxdbSFwGoUjuNetMe8yfMyT5rZQ6rmZPszxytYlFS2U4WzcxsbE4YmFnFlUoltm7d2v+Iw4igWCyyYMECli5dypIlS2hra6OtrY2Wlhaam5tZsWIFjY2NlQ7dzGxCPFe8mq54lEW591LFMoIucqofdZnnS1fzfOnzdPNzRBW744F92nYpuvhd6V9IqKcpeRtSeql/V+l+tpa+SJ1OJKEBAEWeHPVUq50Cv2OeDh2yvvrk1eRZTF4tVGvpPsVkZmaV4YSBmVVUsVjkt7/9La94xSvYsGEDy5Yto62tjdbWVubMmbiOt2wSfeITlY7AbFopxHZ2lL4BwI7ijezmZ/TGs7Tm/ora5JgRlyvyAjnmk6OROVrF0tzF+7T9jvguO0o3AVCTHME8DgZgW+mrdMUj9PAU++e+yra4nrlaxdzkAJYnVxAR/cmFwWqSdfsUi5mZVZYTBmZWMRHBli1bOPXUU7nwwgtHrGjaNLfenZaZ7Y0cDdQnr6YrHqMmOYoXit8GoCueoJaREwYLk3czT4cyT+upVtuY24kIoISU22P6XB1MoloS6qgmfapCbzxHdzxOD0+xSO8jSebSzJ6PRfT/cDOzmccJAzObVBFBV1cXHR0dFItFDjjgAM4//3xXNGey73wnfT3ppMrGYTZNSGJR7n397xfzF/TEr1mQvHHU5XKaT4NeV9Y2irGDJ4vnU2IXbblLmaOV/Z/N0X7sl/t3IOn/39wZd1NiN9Uso1ZHj7n+nngSkadKrWXFY2ZmU5MTBmY2aTo7O9m+fTutra1s3Lixf0gSP2d7RrvkkvTVCQOzfTI/2TTu6+yJX1OI5wDoisf2SBgAQ1od1Ok4duoucmpgjg4cdd2742GeLnwQkaM9/0mqtXw8Qzczs0nkhIGZTYpt27bR1dXFBRdcwFlnnTX2AmZmNmHm6lAakzMp0cl8nTjm/Hm1sCx/aVnrLkUHEAQFinS+tEDNzKyinDAwswlRLBbp7Oyks7MTSbS2tnLJJZewbp07vjIzqzRJNOfePiHrrk2OYTEXkDCHrniE54pX0py8i9rk5ROyPTMzmzhOGJjZSxYRdHR0sHPnTvL59N9KkiSsWbOGI444gkMPPZSjjjqKurq6CkdqZjazdcXjiBxztGrU+dIODyeuo8L5yUYAni28lVLsZkfp6/uUMCjEVopsH3N/zMxsYjhhYGb7pFQqsWvXLjo7O4kIli9fzjve8Q5Wr17NqlWrWLp0qfsmMDObRLtLD/F08UMALMt/nLkj9DXQG1t4qnhBOl/u76nSwgmLaYHeTAe3U5+cstfLFqODXxffSyk6WJz7S+YnJ457fGZmNjonDMxsD7t27aKnp4disUihUKBQKJDL5cjlcv1XoiKCYrHIihUreN3rXsexxx7LcccdRy6XG2PtNitddVWlIzCbFYJeIAaMD687HqcYW7PxJyY0YdCUO5MmztynZYNuSpH2gVBg23iGZWZmZXLCwGyWiwi6u7vZuXMnvb29tLS0cNBBB9HU1ERzczMtLS3U19dTX19PXV1d/2tjYyM1NTWVDt+mg4MOqnQEZrNCTbKeVi5C5JintSPPGAnVWsE8HUatjpq8APdSXi0szV1ML7+hXn7KiplZJThhYDZLRQRbt26lt7eX5uZmNm3axIknnsgrX/nK/n4IzMbFN76Rvv7BH1Q2DrNZYKx+AnrjGX5b+jsgqE9egzS1/9/XJOsAd5ZrZlYpU/ssYWYvSV/Lge7ubpIk6b+toFQqUSgUaGlp4fLLL2f16tWVDtVmso9/PH11wsCs4hJqSFRHKXaSZ+JuRTAzs5nBCQOzGaBYLNLd3c3u3bvp7u4mn88TEdTV1bFhwwZWr17NokWL+m8zaGpqorGxkerq6kqHbmaznKTrgL77VhYA2yNi/TDzbQY6gCJQiIgjJy3IGSSnBlbkrqRIB9VaVulwzMxsinPCwGwaKhaLdHZ20tnZ2d/R4LJlyzjyyCNZs2YNK1euZPXq1SxdunTCHpllZjYeIuKMvnFJHwd2jDL7poh4fuKjmtlyaiBHw0teT0Qvvyt9ATGHpuRtPt+Ymc1AThiYTQMRwY4dO+ju7kYSSZKwZs0aNm7cyNFHH81BBx3kfgfMbFpT+mvzrcArKx2Llacj7mR76XoA5ukQajSkYYiZmU1z/oVhNsVEBLt27WL37t309PRQVVVFqVRi1apVvPOd72Tt2rW0t7f7EYZmNtMcDzwbEY+P8HkA35YUwFURcfVwM0k6DzgPYPny5RMS6Gz2fPEz7CjdTEvuj5inw0hUi5hDtdorHZqZmU0AJwzMppCtW7fS09NDW1sbxx13HGvXrmXFihW0t7ezfPlyN/e06ekLX6h0BFZhkr4DLBnmow9HxNez8bOAL42ymg0R8bSkRcCtkh6NiDsGz5QlEq4GOPLII+Mlhm6DdMR/EXSzs3QnDflT2C/3b4CQnMQ2M5uJnDAwq5C+jgq7urro6uoil8tRVVXFNddcw8te9jInB2zmaPeVx9kuIk4a7XOlz/Z7MzDiMwEj4unsdYuk64GjgSEJg+nmhdJt7Ip7aUreRrWmfouIluQ8OuK7NCanA+zxWMZidPBs8WOgPEuS95OoplJhmpnZOHHCwGyCRQRdXV3s3LmTQqHQ/wQDgLa2Ng4//HBWr17NypUrOfDAA9lvv/0qHLHZOLvuuvT1jDNGn89ms5OARyPiqeE+lFQLJBHRkY2/BvjIZAY4ESKCLcXLSR/8IJbkPlD2ss8XP8PueIiFufcwVwdMWIyDzU+OZz7HD/vZrvgRu+InELBLD1CnYyctLjMzmxhOGJi9BKVSiUKhQG9vL8VisX+8UCiQJAn5fJ5isciiRYvYuHEja9euZfny5bS3t7N48WKSJKn0LphNvCuvTF+dMLCRncmg2xEkLQU+ExGnAIuB67OWV3ngixFx82QGWIoutpWuI6+FNCSnjMs6JVGXHEtn6R5qdXTZyxWjg+2l9E6OF0q3MDc3eQmD0dTocOZqLSLPPB1W6XDMzGwcOGFgVoaI6H+MIUCSJPT29pLP56mvr6e5uZnGxkaamppobm5m4cKFNDY20tDQwMEHH8zixYsrvAdmZlNXRJwzzLTfAKdk478AXjbJYe3hhbiZbaWvATBXa5mjleOy3iW5DxJJ7NVtaDnNpz75fbriIeqTUe/2mFQ5NbAsf1mlwzAzs3HkhIHZIH19C/T09NDT09PfWqC9vZ2TTz6Zww47jP3335/29nbmzp3rvgbMzGaBORyAyJPTAvK0jOu69+U8sij3nj3eF2MHvyn+DRC05i4krwXjFJ2Zmc1mThiYkSYJtmzZgiSqqqpYvHgxS5cuZfny5axatYqjjz6aFStWVDpMMzOrkHnJoazUvyGqSFRd6XCG2B0P0Z09kXJ3/Iz5Gr6fATMzs73hhIHNaBFBb29vf2uBnp4eSqUS+XyeJEmICIrFIpI4/vjjufjii6mtrXWrATMzGyKn2kqHMKIaHUFd8gqCoFZHVjocMzObIZwwsGmrWCz2JwP6XiOCqqoqJFEqlSgWi9TX17NkyRLa2tpob2+ntbWV5uZmmpqaaGxspLm5mbq6OicJzCbK175W6QjMZrxEc1mS+2ClwzAzsxnGCQPbK4VCgc7OTkqlEhHR/zrcUCqVSJIESf0DMGR8JH2PHhy8XkifTlBdXU1LS0t/EqCtrY0lS5bQ3NxMc3MzLS0tNDc3U1VVNcGlYmajahnf+73NzMzMbHI4YWB76LtqP3AoFAr9P7qrqqpYs2YN9fX1VFdXU11dTVVV1R6vA4d8Pj9kyOVyo07L5XJUVVWNOc2PJDSbJq69Nn0955xKRmFmZmZme8kJg1mq7979vhYDhUKh/97+pqYmli5dyqJFi2htbaW1tZWFCxeyevVqli1b5h/qZrZ3nDAwMzMzm5acMJhBSqUSXV1d/QNALpfrvy2gb56+oaamhrq6Oo444gjOPvts1q1bx7x583wvv5mZmZmZmTlhMN2USiV6e3v7kwKlUomqqqr+vgTa2to4/PDDOfDAA2lpaaG2tpa6urohQ01NjVsKmJmZmZmZ2YicMJhiCoUCW7dupVAokCQJuVwOSf23EAA0NjZy8MEHs3r1avbff3/a29tpa2tj4cKF5HK5Cu+BmZmZmZmZzQROGEyCvsf7jTYAJElCoVDgpJNO4pBDDqGpqYkFCxb0Dw0NDcyfP98tA8zMzMzMzGzCzeqEQUdHB9XV1WXPXywWh/z47+sjYODjAwc+VrBUKiGJmpoaamtraWxsZP78+dTX11NfX09DQ0P/UFNTQ319Pcceeyz5/Kw+NGY2k9x0U6UjMDMzM7N9MGt/lS5ZsoRjjjmm7Pn7fvTX19fv8YO/pqaGefPm9b8OHPqmVVdXuyNBM5u9amoqHYGZmZmZ7YNZmzBob2/ns5/9bKXDMDOb+a64In1997srG4eZmZmZ7RXfDG9mZhPrK19JBzMzMzObVpwwMDMzMzMzM7MhnDAwMzMzMzMzsyGcMDAzMzMzMzOzIZwwMDMzMzMzM7MhFBHjv1KpA3hs3Fc8/lqA5ysdxAzhshw/Lsvx47IcPy7L8XNQRMyvdBCzgaTngF9VYNP+vkw9PiZTi4/H1ONjMrVMxvFYERELx5ppoh6r+FhEHDlB6x43ku6bDnFOBy7L8eOyHD8uy/Hjshw/ku6rdAyzRTkVoYng78vU42Mytfh4TD0+JlPLVDoeviXBzMzMzMzMzIZwwsDMzMzMzMzMhpiohMHVE7Te8TZd4pwOXJbjx2U5flyW48dlOX5cljOfj/HU42Mytfh4TD0+JlPLlDkeE9LpoZmZmZmZmZlNb74lwczMzMzMzMyGGJeEgaQmSbdKejx7bRxhvpslbZf0zfHYbpmx/b6kxyQ9IemDw3w+R9J12ef3SFo5WbFNN2WU5V9IeljSA5Juk7SiEnFOB2OV5YD53iIpJE2JXlKnonLKUtJbs7/NhyR9cbJjnC7K+I4vl/Q9ST/JvuenVCLO6UDS5yRtkfTgCJ9L0qeysn5A0hGTHaONP0mnZ/9nSoP/b0v6UHa8H5P02krFOJtJukjS05Luzwb/D6uAcutANnkkbZb0s+x74Sf5TLLh6gzl/r6eDOPVwuCDwG0RcSBwW/Z+OB8D3jFO2xyTpBzwT8DJwFrgLElrB812LrAtIg4A/hG4bLLim07KLMufAEdGxDrga8BHJzfK6aHMskTSfOB/APdMboTTRzllKelA4EPAKyLiEODPJz3QaaDMv8u/Br4SEYcDZwJXTG6U08q1wO+P8vnJwIHZcB5w5STEZBPvQeDNwB0DJ2bfpTOBQ0j/Lq7IvnM2+f4xItZnw02VDma2KbcOZBWxKfte+CLV5LuWoXWGcn9fT7jxShi8Afh8Nv554I3DzRQRtwEd47TNchwNPBERv4iIHuDLpLEONDD2rwGvkqRJjHG6GLMsI+J7EbEre3s3sGySY5wuyvm7BLiYNIHVNZnBTTPllOWfAP8UEdsAImLLJMc4XZRTlgHUZ+MNwG8mMb5pJSLuALaOMssbgH+N1N3AAkmtkxOdTZSIeCQiHhvmozcAX46I7oj4JfAE6XfObLYptw5kNmuMUGco6/f1ZBivhMHiiHgmG/8tsHic1vtStQFPDnj/VDZt2HkiogDsAJonJbrppZyyHOhc4FsTGtH0NWZZZs2T2yPixskMbBoq5+9yNbBa0l2S7pY02lXf2aycsrwIOFvSU8BNwJ9NTmgz0t7+T7Xpzcd76nhvdhvQ5yrZxHcW83dhagrg25J+JOm8SgdjwBT6fZ0vd0ZJ3wGWDPPRhwe+iYiQ5EcvzGKSzgaOBE6odCzTkaQE+AfgnAqHMlPkSZt9n0ja6uUOSYdFxPaKRjU9nQVcGxEfl3Qc8AVJh0ZEqdKBmU2W0epDEfH1yY7H9jRGffVK0tZ7kb1+HPijyYvObMraEBFPS1oE3Crp0eyqt00Blf59XXbCICJOGukzSc9Kao2IZ7ImlVOlye/TQPuA98uyacPN85SkPGkz299NTnjTSjlliaSTSE/KJ0RE9yTFNt2MVZbzgUOB27O7Y5YAN0g6NSLcEc2eyvm7fAq4JyJ6gV9K+jlpAuHeyQlx2iinLM8lu8cuIn4gaS7QwtT5nz+dlPU/1aae0epDo/DxniTlHh9J1wCT1gm39fN3YQqKiKez1y2Srie9dcQJg8qaMr+vx+uWhBuAd2Xj7wKmSob9XuBASftJqibtcOiGQfMMjP004LsR4RYSQ41ZlpIOB64CTvV94qMatSwjYkdEtETEyohYSdofhJMFwyvnO/6fpK0LkNRCeovCLyYzyGminLL8NfAqAEkHA3OB5yY1ypnjBuCd2dMSjgV2DGh6aDPPDcCZSp/MtB9p0vKHFY5p1hnUT8ibSDuptMlVzrnGJpGk2qyjbSTVAq/B342pYMr8vi67hcEYLgW+Iulc4FfAWwGyRwr9t4j44+z9ncAaoC67B/bciLhlnGIYIiIKkt4L3ALkgM9FxEOSPgLcFxE3AJ8lbVb7BGlnE2dOVDzTWZll+TGgDvhqdmX81xFxasWCnqLKLEsrQ5lleQvwGkkPA0XggohwK6JByizLvwSukXQ+aZPec5xgHZ6kL5Emqlqy892FQBVARPwzaR8Qp5B2frcL+MPKRGrjSdKbgMuBhcCNku6PiNdm36WvAA8DBeA9EVGsZKyz1EclrSf9/7UZ+NPKhjP7jHSuqXBYs91i4Pqs7p4HvhgRN1c2pNllhDrDsL+vKxKf63pmZmZmZmZmNth43ZJgZmZmZmZmZjOIEwZmZmZmZmZmNoQTBmZmZmZmZmY2hBMGZmZmZmZmZjaEEwZmZmZmZmZmNoQTBjZjSPqwpIckPSDpfknHZNM/I2ltNr5ZUouklZIm9Bmz2TbeNuD9ekmnTOQ2R4lloaR7JP1E0vGSTpf0iKTvSTpS0qfGWP4mSQv2cdtv7Cv/l0rSRZLePx7rMjMzK5ekYla36BtW7sM6Fkh69/hHN3VIel9Wv/j3fVx+cN3pHEmffgnxnJ7VDUvZ495Hm3fU4/NS9m3wfplNJ04Y2Iwg6Tjg9cAREbEOOAl4EiAi/jgiHq5AWCuBgSeH9aTPXa+EVwE/i4jDI+JO4FzgTyJiU0TcFxHvG23hiDglIrbv47bfCIxLwsDMzKxCdkfE+gHD5n1YxwJgrxMGknL7sK1KeTfw6oh4ezkzS8oPmrSSPetOL9WDwJuBO8qYd6zjs1f7NshK9mG/ptmxtxnKCQObKVqB5yOiGyAino+I3wBIun2ErHJO0jVZ5vnbkuZl86+XdHfWUuF6SY2D15O1UticjeckfUzSvdkyf5qt/1Lg+OxKxAeAjwBnZO/PkFQr6XOSfphd+X/DcDsm6QOSfibpp5IuHSPG/SXdLOlHku6UtEbSeuCjwBuybV8IbAA+m8V9oqRvZsvXSfqXbHsPSHpLNn2zpJZs/Ows5vslXdV3MpO0U9L/yeK8W9JiSb8HnAp8LJt//wH71SDpV5KS7H2tpCclVUn6k6w8fyrpPyTVDFMue3U8JLVKuiOL40FJx4/852RmZja6Uc43dZJuk/Tj7Hzad36/FNg/Ow/tcf7Nlvu0pHOy8c2SLpP0Y+D04c7vw8Rzgl5sAfETSfOzbdwh6UZJj0n65wHn3Ssl3ae0HvQ3A9ZzlKTvZ+fgH2brGamuM3D7/wysAr4l6XxJTZL+M5v/bknrsvkukvQFSXcBXxi0moF1p/OzaUuzfX9c0kcHbO81kn6QlfNXJdUNjikiHomIx4aJ9ZABdZkHJB04+PiMsW/D1uGUtiS4M4vpx1k9aMh+aVDLCUnflHRiNr5T0scl/RQ4TtLLJf1XduxvkdQ6eH/MJlREePAw7QegDrgf+DlwBXDCgM9uB47MxjcDLaSZ3gKwPpv+FeDsbPyBvuVJf+R/Ypj1tACbs/HzgL/OxucA9wH7AScC3xwQxznApwe8/9sB21yQxV47aL9OBr4P1GTvm8aI8TbgwGz8GOC7I2x74L70xwlc1reu7H3joHI7GPgGUJVNvwJ4ZzYewB9k4x8dUCbXAqeNcNy+DmzKxs8APpONNw+Y5xLgz7Lxi4D37+Px+Evgw9n0HDC/0n+3Hjx48OBhegxAkbSecT9wfTZtpPNNHqjPprcATwAirXs8OGCdg+sJnwbOycY3A/9zwGfDnt8HxfgN4BXZeF0Wx4lAF+mP3Rxwa985mRfrFLnsnLoOqAZ+ARyVfVafrWfYfR0mhs1ASzZ+OXBhNv5K4P5s/CLgR8C8YZYfXCbnZPE0AHOBXwHtWbneQVZvAj4A/O9Rjl9/nWFAbG/PxquBeYOPzxj7NmwdDqgB5mbTDwTuG2W/BtbLvgmcmI0H8NZsvIq0Hrgwe38G8LlKfx88zK5hcDMgs2kpInZKejlwPLAJuE7SByPi2lEW+2VE3J+N/whYKakBWBAR/5VN/zzw1TE2/xpgnaTTsvcNpCeJnjKWO1Uv3pM/F1gOPDJgnpOAf4mIXdl+bh0pxiyz/nvZeN/yc8aIYbCTgDP73kTEtkGfvwp4OXBvto15wJbssx7SEx6k5fnqMrZ3HenJ73vZdq/Iph8q6RLSk3AdcMte7MNIx+Ne4HOSqoD/HHDszczMxrI7ItYPmjbS+eYp4G8lbQRKQBuweB+2eR2kLRYo7/x+F/APSu+x/78R8VQ2/w8j4hfZur5E2srwa8BbJZ1HmhBoJb19MIBnIuJegIh4IVtupH395SjxbwDekq3nu5KaJdVnn90QEbvLLIfbImJHFsfDwArS+sFa4K5sH6uBH5S5PrJ5PyxpGWlZPT6gbMsxUh3uN8CnlbbuLAKr92almSLwH9n4QcChwK1ZfDngmX1Yp9k+c8LAZoyIKJJmkG+X9DPgXaRXt0fSPWC8SPrjdzQFXryNZ+6A6SK9Ar7Hj9q+pmWjEPCWGKap3D5KgO3DVGjGk4DPR8SHhvmsNyIiGy9S3v+XG0grVU2kiYjvZtOvBd4YET/NmmeeOMyye3U8ALLK2+uAayX9Q0T8axkxmpmZDWek8/85wELg5RHRq/SWublDF9/jPMYw83Rmr2Wd3yPiUkk3kvaXdJek1/Z9NHhWSfsB7ydtSbBN0rUjxNi/W4xwbt1HnWPP0m9wfS2fxXNrRJy1LxuPiC9Kuoe0TnBTdovFL/ZiFcPW4SRdBDwLvIz0uHWNsPxox74rq9P2beehiDhuL2IzG1fuw8BmBEkHZfef9VlP2mxtr2QZ7G168f72dwB9V/I3k/6oBThtwGK3AP+4zZ0kAAADGElEQVQ9u3KNpNWSaoEOYP6A+Qa/vwX4M2UpY0mHDxPSrcAfKruHX1LTSDFmVwF+Ken0bF5JetleFUC6vff0vVHWN8IAtwGnSVrUF4+kFWOsc/B+94uInaRX/j9J2lSv7wQ5H3gmK9OROhfazF4cjyzOZyPiGuAzwBFjxG1mZjaakc7/DcCWLFmwifSKOAw9H/4KWCtpjtInEb1quI2Ue36XtH9E/CwiLiM9t/b1c3C0pP2U9l1wBvD/SG816AR2SFpMegskwGNAq6SjsnXOV9ox4Uj7Opo7yc7h2UWU5/taLIxixDrDIHcDr5B0QLb+WkllX82XtAr4RUR8ivT2yHV7sW0YuQ7XQNpCo0RaP+vrtHDwujcD6yUlktqBo0fYzmPAQqWde6O0n6dDyozRbFw4YWAzRR3weUkPS3qAtJnaRfu4rneRdtL3AGni4SPZ9L8nPVn+hPTeuT6fAR4Gfqz0UY1XkWa/HwCKSjsNOp+02f1aZZ0eAheT3pv2gKSHsvd7iIibSa/C3yfpftKrAaPF+HbgXKUd5TwEDNuR4iguARqVdgr4U9LbOwbG8zDw18C3s23fStqMcTRfBi5Q2inQ/sN8fh1wdvba538B95A2r3x0hPXu7fE4EfhpNv8ZpEkKMzOzfTXS+ebfgSOz1o7vJDuPRcTvSK/8PyjpYxHxJGkfSg9mrz8ZZVvlnN//PFv3A0Av8K1s+r2k/SM8QnoLwfUR8dNse48CXyQ93xIRPaTnyMuzbd1KevV7pH0dzUXAy7N4LiWtu4xlcN1pWBHxHGk/AF/K1v8DXkyQ9JP0JklPAccBN0rqayHxVuDBrG51KPCvg4/PGHGOVIe7AnhXVnZreLElxeD9uov0WDwMfAr48Qj72UN6UeSybJ33k96eYjZp9GILYjMzMzMzmymyK/vvj4jXVzoWM5ue3MLAzMzMzMzMzIZwCwMzMzMzMzMzG8ItDMzMzMzMzMxsCCcMzMzMzMzMzGwIJwzMzMzMzMzMbAgnDMzMzMzMzMxsCCcMzMzMzMzMzGwIJwzMzMzMzMzMbIj/DzWSNV4hbXMpAAAAAElFTkSuQmCC\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1296x504 with 2 Axes>"
]
@@ -664,7 +686,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1296x504 with 2 Axes>"
]
@@ -676,7 +698,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1296x504 with 2 Axes>"
]
@@ -688,7 +710,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 1296x504 with 2 Axes>"
]
@@ -809,7 +831,7 @@
" \"with n_clusters = %d\" % kval),\n",
" fontsize=14, fontweight='bold')\n",
"\n",
- "plt.show()"
+ "plt.show();"
]
},
{
@@ -821,7 +843,7 @@
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -834,17 +856,7 @@
},
{
"data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x112f200d0>"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -866,7 +878,7 @@
"plt.ylabel('Silhouette score')\n",
"plt.grid(True,)\n",
"plt.plot(k, silhouette, 'g.-')\n",
- "plt.legend()"
+ "plt.legend();"
]
},
{
@@ -878,7 +890,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -890,17 +902,7 @@
},
{
"data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x128a64c90>"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -921,7 +923,7 @@
"plt.ylabel('Sum of squared errors')\n",
"plt.grid(True,)\n",
"plt.plot(k, sse, 'g.-')\n",
- "plt.legend()"
+ "plt.legend();"
]
},
{
@@ -933,7 +935,7 @@
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
@@ -945,17 +947,7 @@
},
{
"data": {
- "text/plain": [
- "<matplotlib.legend.Legend at 0x123bc9f10>"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
@@ -976,8 +968,15 @@
"plt.ylabel('Score')\n",
"plt.grid(True,)\n",
"plt.plot(k, elbow, 'g.-')\n",
- "plt.legend()"
+ "plt.legend();"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -996,7 +995,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
- "version": "2.7.10"
+ "version": "2.7.16"
}
},
"nbformat": 4,
