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apache / madlib-site / refs/heads/automl^! / .
commitb51d9c9d050494c67a0736677566ae947c00842c[log] [tgz]
authorFrank McQuillan <fmcquillan@pivotal.io>Fri Dec 20 17:00:05 2019 -0800
committerFrank McQuillan <fmcquillan@pivotal.io>Fri Dec 20 17:00:05 2019 -0800
treefe216f2fa7071e0d784f4d7f0a79dea7e381b60c
parent94a7f7e81077ccd67710648850b696e2344e39d9 [diff]
add warm start to hyperband diag

diff --git a/community-artifacts/Deep-learning/automl/.ipynb_checkpoints/hyperband_diag_v2_mnist-checkpoint.ipynb b/community-artifacts/Deep-learning/automl/.ipynb_checkpoints/hyperband_diag_v2_mnist-checkpoint.ipynb
index b62f8d5..fa92b05 100644
--- a/community-artifacts/Deep-learning/automl/.ipynb_checkpoints/hyperband_diag_v2_mnist-checkpoint.ipynb
+++ b/community-artifacts/Deep-learning/automl/.ipynb_checkpoints/hyperband_diag_v2_mnist-checkpoint.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Hyperband diagonal using MNIST\n",
     "\n",
-    "Implemention of Hyperband https://arxiv.org/pdf/1603.06560.pdf for MPP - uses the Hyperband schedule but runs it on a diagonal across brackets, instead of one bracket at a time. \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",
     "Model architecture based on https://keras.io/examples/mnist_transfer_cnn/ \n",
     "\n",
@@ -25,22 +25,24 @@
     "\n",
     "<a href=\"#plot\">6. Plot results</a>\n",
     "\n",
-    "<a href=\"#print\">7. Print run schedules</a>"
+    "<a href=\"#print\">7. Print run schedules (display only)</a>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "The sql extension is already loaded. To reload it, use:\n",
-      "  %reload_ext sql\n"
+      "/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"
      ]
     }
    ],
@@ -50,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -90,15 +92,15 @@
        "        <th>version</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "        <td>MADlib version: 1.17-dev, git revision: rel/v1.16-47-g5a1717e, cmake configuration time: Tue Nov 19 01:02:39 UTC 2019, build type: release, build system: Linux-3.10.0-957.27.2.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+       "        <td>MADlib version: 1.17-dev, git revision: rel/v1.16-50-g5abfb79, cmake configuration time: Tue Nov 26 01:00:01 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/v1.16-47-g5a1717e, cmake configuration time: Tue Nov 19 01:02:39 UTC 2019, build type: release, build system: Linux-3.10.0-957.27.2.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+       "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-50-g5abfb79, cmake configuration time: Tue Nov 26 01:00:01 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": 19,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -119,9 +121,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "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": [
     "from __future__ import print_function\n",
     "\n",
@@ -163,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -183,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -215,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -638,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -648,29 +665,29 @@
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
+      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
       "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 26, 26, 32)        0         \n",
+      "activation_1 (Activation)    (None, 26, 26, 32)        0         \n",
       "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 24, 24, 32)        9248      \n",
+      "conv2d_2 (Conv2D)            (None, 24, 24, 32)        9248      \n",
       "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 24, 24, 32)        0         \n",
+      "activation_2 (Activation)    (None, 24, 24, 32)        0         \n",
       "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 32)        0         \n",
+      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
       "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 12, 12, 32)        0         \n",
+      "dropout_1 (Dropout)          (None, 12, 12, 32)        0         \n",
       "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 4608)              0         \n",
+      "flatten_1 (Flatten)          (None, 4608)              0         \n",
       "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 128)               589952    \n",
+      "dense_1 (Dense)              (None, 128)               589952    \n",
       "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 128)               0         \n",
+      "activation_3 (Activation)    (None, 128)               0         \n",
       "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 128)               0         \n",
+      "dropout_2 (Dropout)          (None, 128)               0         \n",
       "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 10)                1290      \n",
+      "dense_2 (Dense)              (None, 10)                1290      \n",
       "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 10)                0         \n",
+      "activation_4 (Activation)    (None, 10)                0         \n",
       "=================================================================\n",
       "Total params: 600,810\n",
       "Trainable params: 600,810\n",
@@ -716,7 +733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -745,7 +762,7 @@
        "[(1, u'feature + classification layers trainable')]"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -765,7 +782,7 @@
    "metadata": {},
    "source": [
     "<a id=\"hyperband\"></a>\n",
-    "# 5.  Hyperband"
+    "# 5.  Hyperband diagonal"
    ]
   },
   {
@@ -777,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -804,7 +821,7 @@
        "[]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -874,12 +891,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Table names"
+    "Generalize table names"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -902,12 +919,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Hyperband diagonal"
+    "Hyperband diagonal logic"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -923,15 +940,19 @@
     "        self.try_params = try_params_function\n",
     "\n",
     "        self.max_iter = 9  # maximum iterations per configuration\n",
-    "        self.eta = 3        # defines configuration downsampling rate (default = 3)\n",
+    "        self.eta = 3        # defines downsampling rate (default = 3)\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",
     "        self.setup_full_schedule()\n",
     "        self.create_mst_superset()\n",
+    "        \n",
+    "        self.best_loss = np.inf\n",
+    "        self.best_accuracy = 0.0\n",
+    "\n",
     "    \n",
-    "    # create full Hyperband schedule for all brackets\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",
@@ -940,7 +961,7 @@
     "        print (\" \")\n",
     "        print (\"Hyperband brackets\")\n",
     "\n",
-    "        #### Begin Finite Horizon Hyperband outlerloop. Repeat indefinitely.\n",
+    "        # loop through each bracket in reverse order\n",
     "        for s in reversed(range(self.s_max+1)):\n",
     "            \n",
     "            print (\" \")\n",
@@ -953,9 +974,8 @@
     "            r = self.max_iter*self.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):\n",
-    "                # Run each of the n_i configs for r_i iterations and keep best n_i/eta\n",
+    "                # n_i configs for r_i iterations\n",
     "                n_i = n*self.eta**(-i)\n",
     "                r_i = r*self.eta**(i)\n",
     "\n",
@@ -968,15 +988,14 @@
     "                if counter == s:\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",
+    "                \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)\")\n",
     "        \n",
+    "        \n",
     "    # generate model selection tuples for all brackets\n",
     "    def create_mst_superset(self):\n",
     "        # get hyper parameter configs for each bracket s\n",
@@ -984,7 +1003,6 @@
     "            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",
-    "            \n",
     "            print (\" \")\n",
     "            print (\"Create superset of MSTs, i.e., i=0 for for each bracket s\")\n",
     "            print (\" \")\n",
@@ -997,10 +1015,13 @@
     "            self.get_params(n, s)\n",
     "        \n",
     "            \n",
-    "    # run Hyperband diagonal logic\n",
-    "    # can be called multiple times\n",
-    "    def run( self, skip_last = 0, dry_run = False ):    \n",
+    "    # Hyperband diagonal logic\n",
+    "    def run( self, skip_last = 0, dry_run = False ):   \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",
     "            print (\" \")\n",
@@ -1015,20 +1036,24 @@
     "\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]) # r_i is the same for all diagonal elements\n",
-    "\n",
-    "            # select a number of best configurations for the next loop\n",
-    "            # filter out early stops, if any\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",
     "                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",
+    "                    \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",
@@ -1054,14 +1079,36 @@
     "                    \"\"\".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_mnist WHERE s=1 AND mst_key NOT IN (SELECT mnist_multi_model_info.mst_key FROM mnist_multi_model_info JOIN mst_table_hb_mnist ON mnist_multi_model_info.mst_key=mst_table_hb_mnist.mst_key WHERE s=1 ORDER BY validation_loss_final ASC LIMIT 1);\n",
+    "        \n",
+    "            # keep track of best loss so far (for display purposes only)\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_loss_final ASC LIMIT 1;\n",
+    "                    \n",
+    "            if loss < self.best_loss:\n",
+    "                self.best_loss = loss\n",
+    "                self.best_accuracy = accuracy\n",
+    "                        \n",
+    "            print (\" \")\n",
+    "            print (\"best validation loss so far = \" + str(loss))\n",
+    "            print (\"best validation accuracy so far = \" + str(accuracy))\n",
+    "            \n",
     "        return"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate params and insert into MST table"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1087,7 +1134,7 @@
     "\n",
     "    # fit params\n",
     "    # batch size\n",
-    "    batch_size = [64, 128]\n",
+    "    batch_size = [32, 64, 128]\n",
     "    # epochs\n",
     "    epochs = [1]\n",
     "\n",
@@ -1116,21 +1163,32 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run model hopper for candidates in MST table"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def try_params(i, r):\n",
+    "def try_params(i, r, first_pass):\n",
     "    \n",
     "    # multi-model fit\n",
-    "    # TO DO:  use warm start to continue from where left off after if not 1st time thru for this s value\n",
-    "    %sql DROP TABLE IF EXISTS $output_table, $output_table_summary, $output_table_info;\n",
-    "    \n",
-    "    # passing vars as madlib args does not seem to work\n",
-    "    #%sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', $output_table, $mst_diag_table, $r_i::INT, 0);\n",
-    "    %sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', 'mnist_multi_model', 'mst_diag_table_hb_mnist', $r::INT, 0, 'test_mnist_packed');\n",
-    "   \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('train_mnist_packed', $output_table, $mst_diag_table, $r_i::INT, 0);\n",
+    "        %sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', 'mnist_multi_model', 'mst_diag_table_hb_mnist', $r::INT, FALSE, 'test_mnist_packed');\n",
+    "\n",
+    "    else:\n",
+    "        # warm start to continue from previous run\n",
+    "        %sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', 'mnist_multi_model', 'mst_diag_table_hb_mnist', $r::INT, FALSE, 'test_mnist_packed', NULL, True);\n",
+    "\n",
     "    # save results via temp table\n",
     "    # add everything from info table\n",
     "    %sql DROP TABLE IF EXISTS temp_results;\n",
@@ -1150,8 +1208,15 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call Hyperband diagonal"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -1216,61 +1281,17 @@
       "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",
       "9 rows affected.\n",
+      " \n",
       "try params for i = 0\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "9 rows affected.\n",
-      "Done.\n",
-      "9 rows affected.\n",
-      "9 rows affected.\n",
-      "9 rows affected.\n",
-      "loop on s desc to prune mst table:\n",
-      "pruning s = 2 with k = 3\n",
-      " \n",
-      "i=1\n",
-      "Done.\n",
-      "loop on s desc to create diagonal table:\n",
-      "3 rows affected.\n",
-      "3 rows affected.\n",
-      "try params for i = 1\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "6 rows affected.\n",
-      "Done.\n",
-      "6 rows affected.\n",
-      "6 rows affected.\n",
-      "6 rows affected.\n",
-      "loop on s desc to prune mst table:\n",
-      "pruning s = 2 with k = 1\n",
-      "pruning s = 1 with k = 1\n",
-      " \n",
-      "i=2\n",
-      "Done.\n",
-      "loop on s desc to create diagonal table:\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "3 rows affected.\n",
-      "try params for i = 2\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "5 rows affected.\n",
-      "Done.\n",
-      "5 rows affected.\n",
-      "5 rows affected.\n",
-      "5 rows affected.\n",
-      "loop on s desc to prune mst table:\n",
-      "pruning s = 2 with k = 0\n",
-      "pruning s = 1 with k = 0\n",
-      "pruning s = 0 with k = 1\n"
+      "Done.\n"
      ]
     }
    ],
@@ -1290,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1321,7 +1342,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 rows affected.\n"
+      "8 rows affected.\n"
      ]
     },
     {
@@ -2107,7 +2128,7 @@
     {
      "data": {
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\" 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\" width=\"999.9999783255842\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2127,15 +2148,13 @@
       "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 ASC LIMIT 10;\n",
+    "df_results = %sql SELECT * FROM $results_table ORDER BY training_loss ASC LIMIT 12;\n",
     "df_results = df_results.DataFrame()\n",
     "\n",
     "#set up plots\n",
@@ -2185,7 +2204,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "10 rows affected.\n"
+      "8 rows affected.\n"
      ]
     },
     {
@@ -2971,7 +2990,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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\" width=\"999.9999783255842\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2991,15 +3010,13 @@
       "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 validation_loss ASC LIMIT 10;\n",
+    "df_results = %sql SELECT * FROM $results_table ORDER BY validation_loss ASC LIMIT 12;\n",
     "df_results = df_results.DataFrame()\n",
     "\n",
     "#set up plots\n",
@@ -3038,7 +3055,7 @@
    "metadata": {},
    "source": [
     "<a id=\"print\"></a>\n",
-    "# 7. Print run schedules"
+    "# 7. Print run schedules (display only)"
    ]
   },
   {
@@ -3051,7 +3068,9 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",

diff --git a/community-artifacts/Deep-learning/automl/hyperband_diag_v1.ipynb b/community-artifacts/Deep-learning/automl/hyperband_diag_v1.ipynb
deleted file mode 100644
index c485a81..0000000
--- a/community-artifacts/Deep-learning/automl/hyperband_diag_v1.ipynb
+++ /dev/null

@@ -1,382 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "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"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext sql"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "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",
-    "# PostgreSQL local\n",
-    "#%sql postgresql://fmcquillan@localhost:5432/madlib\n",
-    "\n",
-    "# psycopg2 connection\n",
-    "import psycopg2 as p2\n",
-    "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
-    "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
-    "cur = conn.cursor()"
-   ]
-  },
-  {
-   "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/v1.16-46-g77ee745, cmake configuration time: Thu Nov 14 17:59:26 UTC 2019, build type: release, build system: Linux-3.10.0-957.27.2.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: 1.17-dev, git revision: rel/v1.16-46-g77ee745, cmake configuration time: Thu Nov 14 17:59:26 UTC 2019, build type: release, build system: Linux-3.10.0-957.27.2.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": [
-    "Pretty print run schedule"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter = 81\n",
-      "eta = 3\n",
-      "B = 5*max_iter = 405\n",
-      " \n",
-      "s=4\n",
-      "n_i      r_i\n",
-      "------------\n",
-      "81     1.0\n",
-      "27.0     3.0\n",
-      "9.0     9.0\n",
-      "3.0     27.0\n",
-      "1.0     81.0\n",
-      " \n",
-      "s=3\n",
-      "n_i      r_i\n",
-      "------------\n",
-      "27     3.0\n",
-      "9.0     9.0\n",
-      "3.0     27.0\n",
-      "1.0     81.0\n",
-      " \n",
-      "s=2\n",
-      "n_i      r_i\n",
-      "------------\n",
-      "9     9.0\n",
-      "3.0     27.0\n",
-      "1.0     81.0\n",
-      " \n",
-      "s=1\n",
-      "n_i      r_i\n",
-      "------------\n",
-      "6     27.0\n",
-      "2.0     81.0\n",
-      " \n",
-      "s=0\n",
-      "n_i      r_i\n",
-      "------------\n",
-      "5     81\n",
-      " \n",
-      "sum of configurations at leaf nodes across all s = 10.0\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 = 81  # maximum iterations/epochs per configuration\n",
-    "eta = 3  # defines downsampling rate (default=3)\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",
-    "\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):\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:\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 diagonal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "echo input:\n",
-      "max_iter = 81\n",
-      "eta = 3\n",
-      "s_max = 4\n",
-      "B = 5*max_iter = 405\n",
-      " \n",
-      "initial n, r values for each s:\n",
-      "s=4\n",
-      "n=81\n",
-      "r=1.0\n",
-      " \n",
-      "s=3\n",
-      "n=27\n",
-      "r=3.0\n",
-      " \n",
-      "s=2\n",
-      "n=9\n",
-      "r=9.0\n",
-      " \n",
-      "s=1\n",
-      "n=6\n",
-      "r=27.0\n",
-      " \n",
-      "s=0\n",
-      "n=5\n",
-      "r=81\n",
-      " \n",
-      "outer loop on diagonal:\n",
-      " \n",
-      "i=0\n",
-      "inner loop on s desc:\n",
-      "s=4\n",
-      "n_i=81\n",
-      "r_i=1.0\n",
-      " \n",
-      "i=1\n",
-      "inner loop on s desc:\n",
-      "s=4\n",
-      "n_i=27.0\n",
-      "r_i=3.0\n",
-      "s=3\n",
-      "n_i=27\n",
-      "r_i=3.0\n",
-      " \n",
-      "i=2\n",
-      "inner loop on s desc:\n",
-      "s=4\n",
-      "n_i=9.0\n",
-      "r_i=9.0\n",
-      "s=3\n",
-      "n_i=9.0\n",
-      "r_i=9.0\n",
-      "s=2\n",
-      "n_i=9\n",
-      "r_i=9.0\n",
-      " \n",
-      "i=3\n",
-      "inner loop on s desc:\n",
-      "s=4\n",
-      "n_i=3.0\n",
-      "r_i=27.0\n",
-      "s=3\n",
-      "n_i=3.0\n",
-      "r_i=27.0\n",
-      "s=2\n",
-      "n_i=3.0\n",
-      "r_i=27.0\n",
-      "s=1\n",
-      "n_i=6\n",
-      "r_i=27.0\n",
-      " \n",
-      "i=4\n",
-      "inner loop on s desc:\n",
-      "s=4\n",
-      "n_i=1.0\n",
-      "r_i=81.0\n",
-      "s=3\n",
-      "n_i=1.0\n",
-      "r_i=81.0\n",
-      "s=2\n",
-      "n_i=1.0\n",
-      "r_i=81.0\n",
-      "s=1\n",
-      "n_i=2.0\n",
-      "r_i=81.0\n",
-      "s=0\n",
-      "n_i=5\n",
-      "r_i=81\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from math import log, ceil\n",
-    "\n",
-    "#input\n",
-    "max_iter = 81  # maximum iterations/epochs per configuration\n",
-    "eta = 3  # defines downsampling rate (default=3)\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):\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))"
-   ]
-  }
- ],
- "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/automl/hyperband_diag_v2_mnist.ipynb b/community-artifacts/Deep-learning/automl/hyperband_diag_v2_mnist.ipynb
index 171c9cd..fa92b05 100644
--- a/community-artifacts/Deep-learning/automl/hyperband_diag_v2_mnist.ipynb
+++ b/community-artifacts/Deep-learning/automl/hyperband_diag_v2_mnist.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# Hyperband diagonal using MNIST\n",
     "\n",
-    "Implemention of Hyperband https://arxiv.org/pdf/1603.06560.pdf for MPP - uses the Hyperband schedule but runs it on a diagonal across brackets, instead of one bracket at a time. \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",
     "Model architecture based on https://keras.io/examples/mnist_transfer_cnn/ \n",
     "\n",
@@ -25,12 +25,12 @@
     "\n",
     "<a href=\"#plot\">6. Plot results</a>\n",
     "\n",
-    "<a href=\"#print\">7. Print run schedules</a>"
+    "<a href=\"#print\">7. Print run schedules (display only)</a>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -92,15 +92,15 @@
        "        <th>version</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "        <td>MADlib version: 1.17-dev, git revision: rel/v1.16-47-g5a1717e, cmake configuration time: Tue Nov 19 01:02:39 UTC 2019, build type: release, build system: Linux-3.10.0-957.27.2.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5</td>\n",
+       "        <td>MADlib version: 1.17-dev, git revision: rel/v1.16-50-g5abfb79, cmake configuration time: Tue Nov 26 01:00:01 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/v1.16-47-g5a1717e, cmake configuration time: Tue Nov 19 01:02:39 UTC 2019, build type: release, build system: Linux-3.10.0-957.27.2.el7.x86_64, C compiler: gcc 4.8.5, C++ compiler: g++ 4.8.5',)]"
+       "[(u'MADlib version: 1.17-dev, git revision: rel/v1.16-50-g5abfb79, cmake configuration time: Tue Nov 26 01:00:01 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": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -232,7 +232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -655,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -665,29 +665,29 @@
       "_________________________________________________________________\n",
       "Layer (type)                 Output Shape              Param #   \n",
       "=================================================================\n",
-      "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
+      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
       "_________________________________________________________________\n",
-      "activation_5 (Activation)    (None, 26, 26, 32)        0         \n",
+      "activation_1 (Activation)    (None, 26, 26, 32)        0         \n",
       "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 24, 24, 32)        9248      \n",
+      "conv2d_2 (Conv2D)            (None, 24, 24, 32)        9248      \n",
       "_________________________________________________________________\n",
-      "activation_6 (Activation)    (None, 24, 24, 32)        0         \n",
+      "activation_2 (Activation)    (None, 24, 24, 32)        0         \n",
       "_________________________________________________________________\n",
-      "max_pooling2d_2 (MaxPooling2 (None, 12, 12, 32)        0         \n",
+      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
       "_________________________________________________________________\n",
-      "dropout_3 (Dropout)          (None, 12, 12, 32)        0         \n",
+      "dropout_1 (Dropout)          (None, 12, 12, 32)        0         \n",
       "_________________________________________________________________\n",
-      "flatten_2 (Flatten)          (None, 4608)              0         \n",
+      "flatten_1 (Flatten)          (None, 4608)              0         \n",
       "_________________________________________________________________\n",
-      "dense_3 (Dense)              (None, 128)               589952    \n",
+      "dense_1 (Dense)              (None, 128)               589952    \n",
       "_________________________________________________________________\n",
-      "activation_7 (Activation)    (None, 128)               0         \n",
+      "activation_3 (Activation)    (None, 128)               0         \n",
       "_________________________________________________________________\n",
-      "dropout_4 (Dropout)          (None, 128)               0         \n",
+      "dropout_2 (Dropout)          (None, 128)               0         \n",
       "_________________________________________________________________\n",
-      "dense_4 (Dense)              (None, 10)                1290      \n",
+      "dense_2 (Dense)              (None, 10)                1290      \n",
       "_________________________________________________________________\n",
-      "activation_8 (Activation)    (None, 10)                0         \n",
+      "activation_4 (Activation)    (None, 10)                0         \n",
       "=================================================================\n",
       "Total params: 600,810\n",
       "Trainable params: 600,810\n",
@@ -733,7 +733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -762,7 +762,7 @@
        "[(1, u'feature + classification layers trainable')]"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -782,7 +782,7 @@
    "metadata": {},
    "source": [
     "<a id=\"hyperband\"></a>\n",
-    "# 5.  Hyperband"
+    "# 5.  Hyperband diagonal"
    ]
   },
   {
@@ -794,7 +794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -821,7 +821,7 @@
        "[]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -891,12 +891,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Table names"
+    "Generalize table names"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -919,12 +919,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Hyperband diagonal"
+    "Hyperband diagonal logic"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -940,15 +940,19 @@
     "        self.try_params = try_params_function\n",
     "\n",
     "        self.max_iter = 9  # maximum iterations per configuration\n",
-    "        self.eta = 3        # defines configuration downsampling rate (default = 3)\n",
+    "        self.eta = 3        # defines downsampling rate (default = 3)\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",
     "        self.setup_full_schedule()\n",
     "        self.create_mst_superset()\n",
+    "        \n",
+    "        self.best_loss = np.inf\n",
+    "        self.best_accuracy = 0.0\n",
+    "\n",
     "    \n",
-    "    # create full Hyperband schedule for all brackets\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",
@@ -957,7 +961,7 @@
     "        print (\" \")\n",
     "        print (\"Hyperband brackets\")\n",
     "\n",
-    "        #### Begin Finite Horizon Hyperband outlerloop. Repeat indefinitely.\n",
+    "        # loop through each bracket in reverse order\n",
     "        for s in reversed(range(self.s_max+1)):\n",
     "            \n",
     "            print (\" \")\n",
@@ -970,9 +974,8 @@
     "            r = self.max_iter*self.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):\n",
-    "                # Run each of the n_i configs for r_i iterations and keep best n_i/eta\n",
+    "                # n_i configs for r_i iterations\n",
     "                n_i = n*self.eta**(-i)\n",
     "                r_i = r*self.eta**(i)\n",
     "\n",
@@ -985,15 +988,14 @@
     "                if counter == s:\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",
+    "                \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)\")\n",
     "        \n",
+    "        \n",
     "    # generate model selection tuples for all brackets\n",
     "    def create_mst_superset(self):\n",
     "        # get hyper parameter configs for each bracket s\n",
@@ -1001,7 +1003,6 @@
     "            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",
-    "            \n",
     "            print (\" \")\n",
     "            print (\"Create superset of MSTs, i.e., i=0 for for each bracket s\")\n",
     "            print (\" \")\n",
@@ -1014,10 +1015,13 @@
     "            self.get_params(n, s)\n",
     "        \n",
     "            \n",
-    "    # run Hyperband diagonal logic\n",
-    "    # can be called multiple times\n",
-    "    def run( self, skip_last = 0, dry_run = False ):    \n",
+    "    # Hyperband diagonal logic\n",
+    "    def run( self, skip_last = 0, dry_run = False ):   \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",
     "            print (\" \")\n",
@@ -1032,20 +1036,24 @@
     "\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]) # r_i is the same for all diagonal elements\n",
-    "\n",
-    "            # select a number of best configurations for the next loop\n",
-    "            # filter out early stops, if any\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",
     "                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",
+    "                    \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",
@@ -1071,14 +1079,36 @@
     "                    \"\"\".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_mnist WHERE s=1 AND mst_key NOT IN (SELECT mnist_multi_model_info.mst_key FROM mnist_multi_model_info JOIN mst_table_hb_mnist ON mnist_multi_model_info.mst_key=mst_table_hb_mnist.mst_key WHERE s=1 ORDER BY validation_loss_final ASC LIMIT 1);\n",
+    "        \n",
+    "            # keep track of best loss so far (for display purposes only)\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_loss_final ASC LIMIT 1;\n",
+    "                    \n",
+    "            if loss < self.best_loss:\n",
+    "                self.best_loss = loss\n",
+    "                self.best_accuracy = accuracy\n",
+    "                        \n",
+    "            print (\" \")\n",
+    "            print (\"best validation loss so far = \" + str(loss))\n",
+    "            print (\"best validation accuracy so far = \" + str(accuracy))\n",
+    "            \n",
     "        return"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate params and insert into MST table"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1104,7 +1134,7 @@
     "\n",
     "    # fit params\n",
     "    # batch size\n",
-    "    batch_size = [64, 128]\n",
+    "    batch_size = [32, 64, 128]\n",
     "    # epochs\n",
     "    epochs = [1]\n",
     "\n",
@@ -1133,21 +1163,32 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Run model hopper for candidates in MST table"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def try_params(i, r):\n",
+    "def try_params(i, r, first_pass):\n",
     "    \n",
     "    # multi-model fit\n",
-    "    # TO DO:  use warm start to continue from where left off after if not 1st time thru for this s value\n",
-    "    %sql DROP TABLE IF EXISTS $output_table, $output_table_summary, $output_table_info;\n",
-    "    \n",
-    "    # passing vars as madlib args does not seem to work\n",
-    "    #%sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', $output_table, $mst_diag_table, $r_i::INT, 0);\n",
-    "    %sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', 'mnist_multi_model', 'mst_diag_table_hb_mnist', $r::INT, 0, 'test_mnist_packed');\n",
-    "   \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('train_mnist_packed', $output_table, $mst_diag_table, $r_i::INT, 0);\n",
+    "        %sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', 'mnist_multi_model', 'mst_diag_table_hb_mnist', $r::INT, FALSE, 'test_mnist_packed');\n",
+    "\n",
+    "    else:\n",
+    "        # warm start to continue from previous run\n",
+    "        %sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', 'mnist_multi_model', 'mst_diag_table_hb_mnist', $r::INT, FALSE, 'test_mnist_packed', NULL, True);\n",
+    "\n",
     "    # save results via temp table\n",
     "    # add everything from info table\n",
     "    %sql DROP TABLE IF EXISTS temp_results;\n",
@@ -1167,8 +1208,15 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Call Hyperband diagonal"
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "metadata": {
     "scrolled": false
    },
@@ -1233,61 +1281,17 @@
       "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",
       "9 rows affected.\n",
+      " \n",
       "try params for i = 0\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "9 rows affected.\n",
-      "Done.\n",
-      "9 rows affected.\n",
-      "9 rows affected.\n",
-      "9 rows affected.\n",
-      "loop on s desc to prune mst table:\n",
-      "pruning s = 2 with k = 3\n",
-      " \n",
-      "i=1\n",
-      "Done.\n",
-      "loop on s desc to create diagonal table:\n",
-      "3 rows affected.\n",
-      "3 rows affected.\n",
-      "try params for i = 1\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "6 rows affected.\n",
-      "Done.\n",
-      "6 rows affected.\n",
-      "6 rows affected.\n",
-      "6 rows affected.\n",
-      "loop on s desc to prune mst table:\n",
-      "pruning s = 2 with k = 1\n",
-      "pruning s = 1 with k = 1\n",
-      " \n",
-      "i=2\n",
-      "Done.\n",
-      "loop on s desc to create diagonal table:\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "3 rows affected.\n",
-      "try params for i = 2\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "5 rows affected.\n",
-      "Done.\n",
-      "5 rows affected.\n",
-      "5 rows affected.\n",
-      "5 rows affected.\n",
-      "loop on s desc to prune mst table:\n",
-      "pruning s = 2 with k = 0\n",
-      "pruning s = 1 with k = 0\n",
-      "pruning s = 0 with k = 1\n"
+      "Done.\n"
      ]
     }
    ],
@@ -1307,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1331,14 +1335,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12 rows affected.\n"
+      "8 rows affected.\n"
      ]
     },
     {
@@ -2124,7 +2128,7 @@
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\" 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1B2ohu6u6fMvueSSqk3oiu5/PDQIAQQQiBQgTvkv1OaMU9IlTO6///4a+2lU7866Eib6vh4B0GOuuvmiuVCrLHRoFYj2i6v9C5jK6K0w+lsb3ivZohUUigu1ujjdoQ3UNd82ZsJEq371iHbteLi+mLR2HfXmPd001B16xUhqT2pPtNqrUqvftFOcmW6FjOpQ+6UF6Yz4fmkK6A2TuhFZ/Q2OsRIatxqLtV/iUft8EiaxopRzL9CYgUh9d9WFoOy4/ieuyS5JCRPVXUkS7eSuVyXrDSa1j9SjQYVOmGhprB7F0WM52mE99shnwiRfj+QocIrZ8E2PhumNNLWfCU7Z6DEx7U+SbcJEn1P7bpEeo1LQUnuXe5XV67EVZOqxHAVGHAgggAACDQsQpzScMMlXnNKYj+TU18N6tFiPquox2/POO88mTJhQ72DQ3l56XEWJCiUZtOGpVqk0dGTzSE7qrYh1zeG6lhI1N998c9YJE31G6gaiEjpaNZP6d+1VqSqrjWC1AkWPT9TefJ7/dyCQrYDGk47aeyjGfF7suSRMYjQpkwiBxgxE9Ayc9qnQBKVVBdUPvQK3T58+4UtJS5ikVsDojSyaJKsfSgRpYzOtVCh0wkR3bJQY0L4oWnpa374btQdmKqmgxMITTzxR57jNdoWJPkx3dxQU6E0/2nMml0OPfGnTO23++stf/jLtR2k5sBJ5Cjg++OCDTUxSr/DLJWGiTe30+mZNOnpF4c477xyWOVbf5T5VUe0fo0RJJmMlbSMpgAACCBSxAHHKfzu3viRGPuKUdJu+6lEblam9Z0ZqtUT1TV8bGp5acaK3ymiPOO0Vl+7QozmKcfRotR5rTnekNn2t7w1/tc+/7bbbwgpP7bOiGxzVD92RV7vU7mxXmOjz9NZIvaxA8YBuumjFSV2rUlVWj/goUVTfaut07ef7CNQlEJv0yOVcEiaMvaIRaMxARDugKzuuSUCbdaUmUW1UdeSRR4ZfWHUkLWGivSm0D4U21VJCofoeHqNGjQqbrOrI5JfgdKs6snkkR3XQ6/K0Ka9WMii5U/ttRloOq83atMN96kglFfQLv37JryvRkkvCRGNMY0OPZmllRe1nnjN5rbDuRilY0Z0mBRxazVG9vrrjpLcwpV4rrOd/dQdKm1TVfj2hnr/WqwYVAOWSMJGjrqekmQIfXae+FS3anE3118bIl112WVgNVPt5Y21qqyXItV85WDT/06EhCCCAQAYCxCnpEyb5iFNiXiusfTg0j6XiPd1A0J4humlQPUGhVaGahxUn6c1yqUM3nc4///zw1jvtY6I3xejQPKqytW++6aaE4hG9UKD6/igNDafUjQq9snjq1KkhHql+aHWL9itLPQ6ka2iPFMUs2mQ3teee5m9tRjtmzJhwei4JE71IQDdXZKHEjG4E1beiRftFKG5QPKO9T7Qip/aeE3oz4vvvv1/nG/0y+FGjaAkJkDApoc6mqbkLNGYgolUESozoF1DdedBKB+2MrpUPmkC1+kR3BZKWMJGykiJKjuiXcyVMtJpGvyDrF3itUtAk1tArd2v3VL4SJkqIHH/88eFZX+1erUlWSzi114aSIUpOKLDRc7HVAxYFB2qLVmPo7o0CBSUBlJDQkUvCROfrERUlCDQ29txzz/DZej2ZAgEl1PS6Xd0hizm00ar2a9HjPnp+UnelFEiofeoT9VP1PW+UpFGAoUNldY6uqT/q15EjR+acMEndLUrVv/Yu99XbpWc/jz322OCtZImW4mo/Ez2upq8pWFOQmHq2O8aEMggggECxChCn/LdnG9pfq7HjlIaupT7RZq16W4Y2jVRSQUkAJW4UX2hzV70NLnWkNsfXo9lakalkgW58aM8v/aKvPU+0h4liEB2pVSHa20Tx5NZbbx0SLtrLRImL2FWmqeunbPRvxUXa5003VxQH6EZL7bi0b9++dvfdd4eEhmI+XV91VaJGN6Ruv/32nBImqoc2dX/ooYdCFRWX1bUqNVX/xx9/PNxsUp1lp5s8bdq0CXvCKO6RjVbgKvHEgUCMAAmTGCXKIPAfgcYMRPSRmnT0nnhtaqWJRVl7bVSlZz4PPvjgMOEkMWGitmmHd9310F0NTaLaQ0N3VzRZKWmS7vnb6oMuXwkTXUPBhOqqzWiVoNIEqzsrSvJo4ldiR8/MVj+UuFBAoZ3vNQErAVF9xUyuCRNdS32vt/NozGkD3RYtWoRkjjafVWJGj83EHqqvlv3OmDEjvGZYO8sr6aDEnO76VH9ttT5TCRklUhQAKhmkvWj0eJACDiX2cl1hoj1uFMTIXoGVnjduaOM1JUf0rLbudmk86WdFddFn6O6X3rxT1345sT6UQwABBIpFgDjlvz2ZbkPyxoxT0l1LSX29yU+P9eqGmOY+JUOULNFqkeqHVhgrAaEbaLoxoBhAN3W0AlQ3EPQIjOa/1KEbI0oSaO84zfErV64MMYKSJ/379w8rKTJ9fa5u6ulxGyVdtAJGcZEeg1HiRzFIy5Ytq66vpI9upmh1ijbHVEJH8cXw4cPDRvkqn8sKE11Ib8JTPKajvlWptQ0Vg2qls2686AaUVhGrDTKUOXucFMv/9fLfDhIm+TfmCgggUE1Ayym1jHTcuHFhIudAAAEEEEAAAQS8CBCneOkJ6oGADwESJj76gVogUFQCqc08tSoidWgVhpac6s6I7qxoWWnr1q2Lqt00BgEEEEAAAQT8CxCn+O8jaoiAFwESJl56gnogUEQC2n9DK0i03FSPfqQ2HFOSRJtvaampHj3iQAABBBBAAAEENrcAccrmFud6CCRXQI+a6bEuPdKV6aFHwpo2bRr2BGzo4C05mcpSHoGEC+jtP1pNoudptZGtNlHVjurdu3cPbwViv4mEdzDVRwABBBBAIMECxCkJ7jyqjsBmFtA+QtrLR5s2137rUkNV0f4+2kdwu+22q3qzVH3lSZhs5k7lcggggAACCCCAAAIIIIAAAgggkJuA3jSpx3L0ggK9FCImaaIVKXojk96O1alTp7Bxc0MHCZPc+oizEUAAAQQQQAABBBBAAAEEEECgAAJ6K6ZeB65Db/8sLy+vtxbat1GvAtexww47RO3ZSMKkAJ3KJRFAAAEEEEAAAQQQQAABBBBAIHcBrTRZsWJFSIYoKVLfoWSKkip6/Xa6lSWpzyBhknv/8AkIIIAAAggggAACCCCAAAIIIFBkAiWVMFmzZo3NnTvX2rRpE3bE5UAAAQQQQACBeAFtkvbJJ59Y165drVmzZvEnllhJ4o0S63CaiwACCCDQqAKe4o2SSpj8+c9/toMOOqhRO5MPQwABBBBAoNQE9JYt3qhVf68Tb5TaTwTtRQABBBDIh4CHeKOkEibaQVfvaBZ827Zt89GnfCYCCCCAAAJFK7B48eJw4+Hdd98NO8tz1C1AvMHIQAABBBBAIHsBT/FGSSVMPvjgA+vQoYMtWrTI2rdvn30PciYCCCCAAAIlKMA8GtfpOMU5UQoBBBBAAIG6BDzNoyRMGKMIIIAAAgggECXgKYCJqnCBCuFUIHguiwACCCBQFAKe5lESJkUxpGgEAggggAAC+RfwFMDkv7XZXwGn7O04EwEEEEAAAU/zKAkTxiMCCCCAAAIIRAl4CmCiKlygQjgVCJ7LIoAAAggUhYCneZSESVEMKRqBAAIIIIBA/gU8BTD5b232V8ApezvORAABBBBAwNM8SsKE8YgAAggggAACUQKeApioCheoEE4FgueyCCCAAAJFIeBpHiVhUhRDikYggAACCCCQfwFPAUz+W5v9FXDK3o4zEUAAAQQQ8DSPkjBhPCKAAAIIIIBAlICnACaqwgUqhFOB4LksAggggEBRCHiaR0mYFMWQohEIIIAAAgjkX8BTAJP/1mZ/BZyyt+NMBBBAAAEEPM2jJEwYjwgggAACCCAQJeApgImqcIEK4VQgeC6LAAIIIFAUAp7mURImRTGkaAQCCCCAAAL5F/AUwOS/tdlfAafs7TgTAQQQQAABT/MoCRPGIwIIIIAAAghECXgKYKIqXKBCOBUInssigAACCBSFgKd5lIRJUQwpGoEAAggggED+BTwFMPlvbfZXwCl7O85EAAEEEEDA0zxKwoTxiAACCCCAAAJRAp4CmKgKF6gQTgWC57IIIIAAAkUh4GkeJWFSFEOKRiCAAAIIIJB/AU8BTP5bm/0VcMrejjMRQAABBBDwNI+SMGE8IoBAwQU2rl1jK1+YZSufn2UbVqywJi1bWotDuluLg7tb+ZbNCl4/KoAAAl8KeApgPPcJTp57h7qVssCK9Svsvn//xl75bJ6trVxvW5Y1tQO23sdO3/5/rWXTlqVMQ9sRcCXgaR4lYeJqaFAZBEpP4IvFH9mHo4fbhqVLzMrKzCorq/5usl1r2/mKwVbRtl3pwdBiBBwKeApgHPJUVQknz71D3UpV4PXP5trPFo+3DVZmZpVm1f5uYpV2Vdv+1m3rrqXKQ7sRcCXgaR4lYeJqaFAZBEpLQCtL3v+/gbZh2dIvEyW1j7Iya7LtdtZx5E2sNCmtoUFrnQp4CmCcEoVq4eS5d6hbKQpoZcmF711tG0LjlTCpfVRaEzO7o9MIVpqU4gChze4EPM2jJEzcDQ8qhEDpCCyf+ZR9MvnOtA1u0/c8a3V4r7TlKIAAAvkV8BTA5LeluX06Trn5cTYCjS0w7uM7beaq19J+bM+v7Gf9dzonbTkKIIBAfgU8zaMkTPLb13w6Agg0IPDBiGG25h9/r3t1Seq8sjJr9rW9rP3/DcUSAQQKLOApgCkwRYOXx8lz71C3UhQ4b8EAW1m5rp7VJSmRSmtRtoX9crdbSpGINiPgSsDTPErCxNXQoDIIlJbA+4N+bOs+/ihto7fYqZ11/OnNactRAAEE8ivgKYDJb0tz+3SccvPjbAQaW+DMt39oX0R8aIWZ/Wr3sRElKYIAAvkU8DSPkjDJZ0/z2Qgg0KAAK0wYIAgkS8BTAONZDifPvUPdSlGAFSal2Ou0OckCnuZREiZJHknUHYGEC1TtYaL91/Q2P/3RrmvalW3Ff/5UmrGHScI7muoXjYCnAMYzKk6ee4e6laIAe5iUYq/T5iQLeJpHSZgkeSRRdwQSLqC35Lz3k8tsY4tPzbbY5C1/ZuvMylduY51uuJW35CS8r6l+cQh4CmA8i+LkuXeoWykK8JacUux12pxkAU/zKAmTJI8k6o5AwgU2frHG3r/jMtvw2af1veXPmmy9jXW88FYrr2iW8NZSfQSSL+ApgPGsiZPn3qFupSrw7JIH7ZfLnrSNYSlr5X82gP3y73LbYOdte6T1bH1SqfLQbgRcCXiaR0mYuBoaVAaB0hJY/upT9skTEa8VPuY8a7UfrxUurdFBaz0KeApgPPqk6oST596hbqUo8Pn65Tb5vQtttW20D6yZLbEtbYOVWxPbaK1trbW3NbaVlVvfTndY86atSpGINiPgSsDTPErCxNXQoDIIlJbAB/cOszUfRLxWuP1e1v4MXitcWqOD1noU8BTAePQhYeK5V6hbKQv86eNf2KurZqUl+PpXeliPnS5OW44CCCCQXwFP8QYJk/z2NZ+OAAINCLw//se2bmnEa4W3a2cd+/NaYQYTAoUW8BTAFNqioevj5Ll3qFspCkxa0M9WVn6etuktyppbv90mpS1HAQQQyK+Ap3mUhEl++5pPRwCBBgRYYcLwQCBZAp4CGM9yOHnuHepWigLj3z7D1tr6tE3f0ppa/93vTVuOAgggkF8BT/MoCZP89jWfjgACDQiwhwnDA4FkCXgKYDzL4eS5d6hbKQqwwqQUe502J1nA0zxKwiTJI4m6I5BwgfCWnAkDbcOqpWaV2qm+1lFWZk2+sp11PP8m3pKT8L6m+sUh4CmA8SyKk+feoW6lKMAeJqXY67Q5yQKe5lESJkkeSdQdgSIQ+GLJR/bhlOG2YeUSs7KyLxMn//m7SYvWtvP/G2wVrdsVQUtpAgLJF/AUwHjWxMlz71C3UhRIvSVnnW2st/lb8JacUhwatNmpgKd5lJCIATAAACAASURBVISJ00FCtRAoJQGtNFk5f7atfGOWbfhsuTXZupW16NLdWnQ+lJUlpTQQaKt7AU8BjGcsnDz3DnUrVYEPP/urPbR4lNWVNFGy5Dttr7Sdt96vVHloNwKuBDzNoyRMXA0NKoMAAggggIBfAU8BjF8lM5w89w51K2UBrTR5+d/32tufvWxfVK6zirItbPetv2Hf2P4Ma960VSnT0HYEXAl4mkdJmLgaGlQGAQQQQAABvwKeAhi/SiRMPPcNdUMAAQQQ8C/gKd4gYeJ/vFBDBBBAAAEEXAh4CmBcgNRTCZw89w51QwABBBDwLuBpHiVh4n20UD8EEEAAAQScCHgKYJyQ1FkNnDz3DnVDAAEEEPAu4GkeJWHifbRQPwQQQAABBJwIeApgnJCQMPHcEdQNAQQQQCCRAp7iDRImiRxCVBoBBBBAAIHNL+ApgNn8rY+/Ik7xVpREAAEEEECgtoCneZSECeMTAQQQQAABBKIEPAUwURUuUCGcCgTPZRFAAAEEikLA0zxKwqQohhSNQAABBBBAIP8CngKY/Lc2+yvglL0dZyKAAAIIIOBpHiVhwnhEAAEEEEAAgSgBTwFMVIULVAinAsFzWQQQQACBohDwNI+SMCmKIUUjEEAAAQQQyL+ApwAm/63N/go4ZW/HmQgggAACCHiaR0mYMB4RQAABBBBAIErAUwATVeECFcKpQPBcFgEEEECgKAQ8zaMkTIpiSNEIBBBAAAEE8i/gKYDJf2uzvwJO2dtxJgIIIIAAAp7mURImjEcEEEAAAQQQiBLwFMBEVbhAhXAqEDyXRQABBBAoCgFP8ygJk6IYUjQCAQQQQACB/At4CmDy39rsr4BT9naciQACCCCAgKd5lIQJ4xEBBBBAAAEEogQ8BTBRFS5QIZwKBM9lEUAAAQSKQsDTPErCpCiGFI1AAAEEEEAg/wKeApj8tzb7K+CUvR1nIoAAAggg4GkeJWHCeEQAAQQQQACBKAFPAUxUhQtUCKcCwXNZBBBAAIGiEPA0j5IwKYohRSMQQAABBBDIv4CnACb/rc3+Cjhlb8eZCCCAAAIIeJpHSZgwHhFAAAEEEEAgSsBTABNV4QIVwqlA8FwWAQQQQKAoBDzNoyRMimJI0QgEEEAAAQTyL+ApgMl/a7O/Ak7Z23EmAggggAACnuZREiaMRwQQQAABBBCIEvAUwERVuECFcCoQPJdFAAEEECgKAU/zKAmTohhSNAIBBBBAAIH8C3gKYPLf2uyvgFP2dpyJAAIIIICAp3k0o4TJ9OnTbdSoUTZ37lyrqKiwHj162IgRI6xLly5Rvfrcc8+F8nPmzLG1a9faHnvsYeeff75ddNFFVl5eXuMzNm7caPfee6+NGzfO3nrrLVuzZo116NDBTjrpJPvxj39sbdq0ibpm9UKe4DOuPCcggAACCCBQYAHm0bgOwCnOiVIIIIAAAgjUJeBpHo1OmEycONHOPffckBzp379/SGCMHTvWli1bZrNnz7auXbs22NsPPPCAnX766bb99tuHBIkSHjNmzLAHH3ww/Pu2226rcf6AAQPs1ltvtZ49e4YkyZZbbmnPP/+83XPPPbb77rvba6+9Zs2bN89ohHmCz6jiFEYAAQQQQMCBAPNoXCfgFOdEKQQQQAABBIoiYaKkSKdOnaxly5Y2b9688LeOhQsXWufOne2ggw6yZ555pt7eXr9+vbVr185WrVplb7zxhu26665VZZV8mTBhQki6HHLIIeHrq1evtm222cb222+/sBqlrKysqvyll14aEjWPPPKIHXfccRmNMAKYjLgojAACCCCAQA0B5tG4AYFTnBOlEEAAAQQQKIqEyeTJk61fv342bNgwGzp0aI029e3b1+6+++6QPNEjM3Udr776qu2///521FFH2RNPPFGjyAsvvBASJeecc47deeed4XtLliwJK1FOOOEEe+ihh2qU/9nPfmaDBg0KCRqtPsnkIIDJRIuyCCCAAAII1BRgHo0bETjFOVEKAQQQQACBokiYXHjhhWEvkSeffNJ69+5do01aHaJVItOmTbM+ffrU2eMvvviiHXzwwXbyySeb9kGpfujRGq0k2WeffcLqk9TxjW98w5Ro0Z4n+lztmaJHci655BLT9x5//PFN9j1JN9wIYNIJ8X0EEEAAAQTqF2AejRsdOMU5UQoBBBBAAIGiSJhopYcegZk/f77tvffeNdr02GOPhUdjxowZY3pcpq7j008/DStGdthhB1uwYEGNvUe0T4n2K2nRooWtWLGi6vT33nsvrGqZOXNmjY+8+OKLw94mTZs2TTu69HnVP3Px4sXh8aFFixZZ+/bt055PAQQQQAABBBD4rwCJgLjRgFOcE6UQQAABBBAoioRJr169wiMwSnZU339EjdPX9f2RI0eGR2XqOy644AIbP368HX300XbdddeFBMpTTz1ll19+edizpLKy0rTXSer417/+ZVdffbX985//tFNPPdW22mor+8Mf/mB33XVXSKSkHt9paIjpEaJrr712kyIkTPjBRAABBBBAIHMBEgFxZjjFOVEKAQQQQACBokiY5LrCRAh6jfDAgQPDBq/r1q0LLto89pZbbgmJFiVLli5dGr7+2WefWbdu3WzHHXcMm8FW3/RVZbWPyaOPPmrHHntsgyOMFSb8ACKAAAIIINB4AiQC4ixxinOiFAIIIIAAAkWRMMl1D5PqCEpi6E07SoLsu+++tmHDhpA40R4nSo7o+NWvfmVnnXWW3XjjjSHJUv145ZVXwh4mWpkyevTojEYYAUxGXBRGAAEEEECghgDzaNyAwCnOiVIIIIAAAggURcJk0qRJdvbZZ4fHW4YMGVKjTXo8Rm/RaegtOQ0Ng6lTp4ZHbm644QYbPHhwKKrHe/Q4jlaSXHnllTVO12uGv/Wtb9lll10WVqdkchDAZKJFWQQQQAABBGoKMI/GjQic4pwohQACCCCAQFEkTJYtW2YdO3a0Vq1ahdUhWhGiQ0mSzp0724EHHmjPPvts+Jr2I9HXVbZt27YNjgC9PlgrS7Qp7JtvvmmtW7cO5fUq4RNPPDE8lvPyyy/bFltsUfU5qdUuU6ZMsdNOOy2jEUYAkxEXhRFAAAEEEKghwDwaNyBwinOiFAIIIIAAAkWRMFEjtGGrNm7t0qVLeI2w9iQZO3asKekxa9as8HiNDr3VpmfPnuGRGq08SR1KcOjfhx9+eNib5N1337WJEyeGt9g8/PDD4ZzUocd0unfvbnodsZImZ5xxRtWmryr7zW9+M1wz5k051TuAAIYfSAQQQAABBLIXYB6Ns8MpzolSCCCAAAIIFE3CRA3R4zPaN2Tu3LlWUVFhPXr0sOHDh4ekRuqoL2GivUe0Yevrr79uWrHSpk0bO/LII8NjOLvvvvsmTtr4VY/kTJs2LbydR2/R+epXv2qnnHJKeFxn6623znh0EcBkTMYJCCCAAAIIVAkwj8YNBpzinCiFAAIIIIBAUSVMkt6dBDBJ70HqjwACCCBQSAHm0Th9nOKcKIUAAggggAAJE0djgADGUWdQFQQQQACBxAkwj8Z1GU5xTpRCAAEEEECAhImjMUAA46gzqAoCCCCAQOIEmEfjugynOCdKIYAAAgggQMLE0RgggHHUGVQFAQQQQCBxAsyjcV2GU5wTpRBAAAEEECBh4mgMEMA46gyqggACCCCQOAHm0bguwynOiVIIIIAAAgiQMHE0BghgHHUGVUEAAQQQSJwA82hcl+EU50QpBBBAAAEESJg4GgMEMI46g6oggAACCCROgHk0rstwinOiFAIIIIAAAiRMHI0BAhhHnUFVEEAAAQQSJ8A8GtdlOMU5UQoBBBBAAAESJo7GAAGMo86gKggggAACiRNgHo3rMpzinCiFAAIIIIAACRNHY4AAxlFnUBUEEEAAgcQJMI/GdRlOcU6UQgABBBBAgISJozFAAOOoM6gKAggggEDiBJhH47oMpzgnSiGAAAIIIEDCxNEYIIBx1BlUBQEEEEAgcQLMo3FdhlOcE6UQQAABBBAgYeJoDBDAOOoMqoIAAgggkDgB5tG4LsMpzolSCCCAAAIIkDBxNAYIYBx1BlVBAAEEEEicAPNoXJfhFOdEKQQQQAABBEiYOBoDBDCOOoOqIIAAAggkToB5NK7LcIpzohQCCCCAAAIkTByNAQIYR51BVRBAAAEEEifAPBrXZTjFOVEKAQQQQAABEiaOxgABjKPOoCoIIIAAAokT8DqPTp8+3UaNGmVz5861iooK69Gjh40YMcK6dOmSsfFf//pXO/DAA239+vV2zz332BlnnJHxZ3h1yrghnIAAAggggEABBDzNo2WVlZWVBTAoyCU9wRcEgIsigAACCCCQg4DHeXTixIl27rnnhuRI//79bc2aNTZ27FhbtmyZzZ4927p27RrdYiVJvvnNb9pbb71lq1atImESLUdBBBBAAAEEGk/AU7xBwqTx+pVPQgABBBBAoKgFPAUwglZSpFOnTtayZUubN29e+FvHwoULrXPnznbQQQfZM888E90nP/3pT8PKlCuvvNKuueYaEibRchREAAEEEECg8QQ8xRskTBqvX/kkBBBAAAEEilrAUwAj6MmTJ1u/fv1s2LBhNnTo0Br2ffv2tbvvvjskTzp06JC2X7SqZN999w2P9rRo0SJ8Lo/kpGWjAAIIIIAAAo0u4CneIGHS6N3LByKAAAIIIFCcAp4CGAlfeOGFNm7cOHvyySetd+/eNdAnTJgQHtGZNm2a9enTp8EO0dPJhx12mK1bt86ef/55+9WvfkXCpDiHMK1CAAEEEEiAgKd4g4RJAgYMVUQAAQQQQMCDgKcARh4nnHCCPfLIIzZ//nzbe++9axA99thjdtxxx9mYMWPs0ksvbZDvtttuswEDBtgrr7wS9jxJrVyJXWGyYsUK05/UsXjx4vA40KJFi6x9+/Yeuo46IIAAAgggkBgBT/EGCZPEDBsqigACCCCAQGEFPAUwkujVq1fYo2TBggW266671sDR1/X9kSNH2qBBg+qF0yM72jD2kksuCfuX6Mg0YaJHgq699tpNrkHCpLDjlasjgAACCCRTwFO8QcIkmWOIWiNQVAIb16+xlYtmhT8b1q6wJlu2tBYduoc/5U2bFVVbaQwCSRbwFMDIsTFWmBxzzDH29ttvh1cSN2v25f9vMk2YsMIkyaOauiOAAAIIeBPwFG+QMPE2OqgPAiUm8MXKj+zD2cNtw+dLzKzMzPSm8y//btK8te186GCraNGuxFRoLgI+BTwFMBLKdQ+T3/3ud2F/k/Hjx9sRRxxRhT59+nS76qqr7MYbb7QTTzzR2rVrZ1tttVV0p3hziq44BRFAAAEEEHAg4GkeJWHiYEBQBQRKVUArS95/aqBt+HzpfxIltSXKrEnz7azj/9zESpNSHSS025WApwBGMJMmTbKzzz47PA4zZMiQGlZ6y41WijT0lpxbb7017F2S7nj88cft6KOPTles6vvenKIrTkEEEEAAAQQcCHiaR0mYOBgQVAGBUhVY/u5T9slf70zb/Db7nWetvtorbTkKIIBAfgU8BTBq6bJly6xjx47WqlUrmzdvnrVs2TIAKEnSuXNnO/DAA+3ZZ58NX1u9enX4usq2bds2fE2P4vz1r3/dBG3mzJmmjWC1WWyPHj3Cnx133DEa15tTdMUpiAACCCCAgAMBT/MoCRMHA4IqIFCqAh/8cZitWfL3elaXpFTKrFnrvaz9YUNLlYl2I+BGwFMAk0LR4zQXXHBB2LhVrxFeu3atjR071pYsWWKzZs2yfffdNxRVEqRnz5521llnhZUnDR2Z7mFS+7M8OrkZRFQEAQQQQACBNAKe5lESJgxXBBAomMD7M35s61Z9lPb6W3ylnXXsfXPachRAAIH8CngKYKq3dOrUqTZ69OiwcWtFRUVYETJ8+HDr1q1bVTESJvkdG3w6AggggAACjSXgKd4gYdJYvcrnIIBAxgKsMMmYjBMQKKiApwCmoBAJujPm2Ym6IYAAAgggUJeAp3iDhAljFAEECibAHiYFo+fCCGQl4CmAyaoBm+kknDYTNJdBAAEEEChKAU/zKAmTohxiNAqBZAjwlpxk9BO1RCAl4CmA8dwrOHnuHeqGAAIIIOBdwNM8SsLE+2ihfggUucAXKz+yD2cPtw2fLzGzsv9sAPvl302at7adDx1sFS3aFbkCzUMgGQKeAhjPYjh57h3qhgACCCDgXcDTPErCxPtooX4IlICAVpqsXDTbVi6aZRvWLrcmW7ayFh26W4sOh1p502YlIEATEUiGgKcAxrMYTp57h7ohgAACCHgX8DSPkjDxPlqoHwIIIIAAAk4EPAUwTkjqrAZOnnuHuiGAAAIIeBfwNI+SMPE+WqgfAggggAACTgQ8BTBOSEiYeO4I6oYAAgggkEgBT/EGCZNEDiEqjQACCCCAwOYX8BTAbP7Wx18Rp3grSiKAAAIIIFBbwNM8SsKE8YkAAggggAACUQKeApioCheoEE4FgueyCCCAAAJFIeBpHiVhUhRDikYggAACCCCQfwFPAUz+W5v9FXDK3o4zEUAAAQQQ8DSPkjBhPCKAAAIIIIBAlICnACaqwgUqhFOB4LksAggggEBRCHiaR0mYFMWQohEIIIAAAgjkX8BTAJP/1mZ/BZyyt+NMBBBAAAEEPM2jJEwYjwgggAACCCAQJeApgImqcIEK4VQgeC6LAAIIIFAUAp7mURImRTGkaAQCCCCAAAL5F/AUwOS/tdlfAafs7TgTAQQQQAABT/MoCRPGIwIIIIAAAghECXgKYKIqXKBCOBUInssigAACCBSFgKd5lIRJUQwpGoEAAggggED+BTwFMPlvbfZXwCl7O85EAAEEEEDA0zxKwoTxiAACCCCAAAJRAp4CmKgKF6gQTgWC57IIIIAAAkUh4GkeJWFSFEOKRiCAAAIIIJB/AU8BTP5bm/0VcMrejjMRQAABBBDwNI+SMGE8IoAAAggggECUgKcAJqrCBSqEU4HguSwCCCCAQFEIeJpHSZgUxZCiEQgggAACCORfwFMAk//WZn8FnLK340wEEEAAAQQ8zaMkTBiPCCCAAAIIIBAl4CmAiapwgQrhVCB4LosAAgggUBQCnuZREiZFMaRoBAIIIIAAAvkX8BTA5L+12V8Bp+ztOBMBBBBAAAFP8ygJE8YjAggggAACCEQJeApgoipcoEI4FQieyyKAAAIIFIWAp3mUhElRDCkagQACCCCAQP4FPAUw+W9t9lfAKXs7zkQAAQQQQMDTPErChPGIAAIIIIAAAlECngKYqAoXqBBOBYLnsggggAACRSHgaR4lYVIUQ4pGIIAAAgggkH8BTwFM/lub/RVwyt6OMxFAAAEEEPA0j5IwYTwigAACCCCAQJSApwAmqsIFKoRTgeC5LAIIIIBAUQh4mkdJmBTFkKIRCCCAAAII5F/AUwCT/9ZmfwWcsrfjTAQQQAABBDzNoyRMGI8IIIAAAgggECXgKYCJqnCBCuFUIHguiwACCCBQFAKe5lESJkUxpGgEAggggAAC+RfwFMDkv7XZXwGn7O04EwEEEEAAAU/zKAkTxiMCCCCAAAIIRAl4CmCiKlygQjgVCJ7LIoAAAggUhYCneTSjhMn06dNt1KhRNnfuXKuoqLAePXrYiBEjrEuXLlEd89xzz4Xyc+bMsbVr19oee+xh559/vl100UVWXl6+yWds3LjR7rzzTrvrrrts3rx5VllZabvssot95zvfsZ/+9KdR16xeyBN8xpXnBAQQQAABBAoswDwa1wE4xTlRCgEEEEAAgboEPM2j0QmTiRMn2rnnnhuSI/3797c1a9bY2LFjbdmyZTZ79mzr2rVrg739wAMP2Omnn27bb799SJC0adPGZsyYYQ8++GD492233Vbj/PXr19spp5xijz32mH3ve9+z7t27W1lZmb333nvhz5QpUzIeXZ7gM648JyCAAAIIIFBgAebRuA7AKc6JUggggAACCBRFwkRJkU6dOlnLli3DSg/9rWPhwoXWuXNnO+igg+yZZ56pt7eV/GjXrp2tWrXK3njjDdt1112ryir5MmHChJB0OeSQQ6q+fv3119uwYcNCwuSoo45qlJFEANMojHwIAggggECJCjCPxnU8TnFOlEIAAQQQQKAoEiaTJ0+2fv36hQTG0KFDa7Spb9++dvfdd4fkSYcOHers8VdffdX233//kPh44oknapR54YUXQqLknHPOCY/f6Fi9enVIsBx++OFhBYoexVm5cqW1aNEirDLJ9iCAyVaO8xBAAAEEEDBjHo0bBTjFOVEKAQQQQACBokiYXHjhhTZu3Dh78sknrXfv3jXapNUhWiUybdo069OnT509/uKLL9rBBx9sJ598smkflOrHa6+9Zvvtt5/ts88+YfWJDl1HyZXhw4eHR35++ctf2vLly0PCRI/p3Hjjjda6deuMRxcBTMZknIAAAggggECVAPNo3GDAKc6JUggggAACCBRFwuSEE06wRx55xObPn2977713jTbpkZnjjjvOxowZY5deemmdPf7pp5+GvUt22GEHW7BggTVv3ryq3K233moDBgwIyZAVK1aEr+uzLrvssrDPSZMmTezqq68OK0602uTee+8N+6W89NJL1qxZswZHmD4v9ZkquHjx4vD40KJFi6x9+/aMTgQQQAABBBDIQIBEQBwWTnFOlEIAAQQQQKAoEia9evUKe5Qo2VF9/xE1Tl/X90eOHGmDBg2qt8cvuOACGz9+vB199NF23XXXhQTKU089ZZdffnl4BEeP3WivEx033HCDXXPNNSFZ8vrrr4d9UlLHD37wg5A00WfpDTsNHXqE6Nprr92kCAkTfjARQAABBBDIXIBEQJwZTnFOlEIAAQQQQKAoEia5rjARgl4jPHDgwLDB67p164KLNo+95ZZbQqJFyZKlS5eGr990000hkaK9TbQZbPXj2WeftSOOOCK8Oef+++9vcISxwoQfQAQQQAABBBpPgERAnCVOcU6UQgABBBBAoCgSJrnuYVIdQUkMvWlHm7fuu+++tmHDhpA40R4nqeTIb37zm5AQ0X4lU6dOrWH4t7/9LTwWpL1UtNdJJgcBTCZalEUAAQQQQKCmAPNo3IjAKc6JUggggAACCBRFwmTSpEl29tlnh8dbhgwZUqNNenuO3qLT0FtyGhoGSoiceuqp4TGcwYMHh6Lvv/9+eI2x9huZM2dOjdNnzJhhRx55pJ1xxhl2zz33ZDTCCGAy4qIwAggggAACNQSYR+MGBE5xTpRCAAEEEECgKBImelNNx44drVWrVmF1iFaE6FCSRPuLHHjggaZHZXRoPxJ9XWXbtm3b4AhYsmRJWFmiTWHffPPNGm++6dmzpz333HMhYaLPTx160442f9XjOFqFkslBAJOJFmURQAABBBCoKcA8GjcicIpzohQCCCCAAAJFkTBRI7TJqjZu7dKlS3iNsPYkGTt2rCnpMWvWrPB4jY6ZM2eakh1nnXVWWHmSOqZMmRL+ffjhh9uOO+5o7777rk2cODG8xebhhx8O51Q/9Irh7t27h81gf/jDH4a35Dz00EP2hz/8IWwc++ijj1p5eXlGI4wAJiMuCiOAAAIIIFBDgHk0bkDgFOdEKQQQQAABBIomYaKG6PGZ0aNH29y5c62iosJ69Ohhw4cPt27dulW1s76EySuvvBI2d9Vbb7RiRa8M1qM1egxn9913r3OkaL8SvS1Hq1eUWNFjOnpLzlVXXRWun+lBAJOpGOURQAABBBD4rwDzaNxowCnOiVIIIIAAAggUVcIk6d1JAJP0HqT+CCCAAAKFFGAejdPHKc6JUggggAACCJAwcTQGCGAcdQZVQQABBBBInADzaFyX4RTnRCkEEEAAAQRImDgaAwQwjjqDqiCAAAIIJE6AeTSuy3CKc6IUAggggAACJEwcjQECGEedQVUQQAABBBInwDwa12U4xTlRCgEEEEAAARImjsYAAYyjzqAqCCCAAAKJE2AejesynOKcKIUAAggggAAJE0djgADGUWdQFQQQQACBxAkwj8Z1GU5xTpRCAAEEEECAhImjMUAA46gzqAoCCCCAQOIEmEfjugynOCdKIYAAAgggQMLE0RgggHHUGVQFAQQQQCBxAsyjcV2GU5wTpRBAAAEEECBh4mgMEMA46gyqggACCCCQOAHm0bguwynOiVIIIIAAAgiQMHE0BghgHHUGVUEAAQQQSJwA82hcl+EU50QpBBBAAAEESJg4GgMEMI46g6oggAACCCROgHk0rstwinOiFAIIIIAAAiRMHI0BAhhHnUFVEEAAAQQSJ8A8GtdlOMU5UQoBBBBAAAESJo7GAAGMo86gKggggAACiRNgHo3rMpzinCiFAAIIIIAACRNHY4AAxlFnUBUEEEAAgcQJMI/GdRlOcU6UQgABBBBAgISJozFAAOOoM6gKAggggEDiBJhH47oMpzgnSiGAAAIIIEDCxNEYIIBx1BlUBQEEEEAgcQLMo3FdhlOcE6UQQAABBBAgYeJoDBDAOOoMqoIAAgggkDgB5tG4LsMpzolSCCCAAAIIkDBxNAYIYBx1BlVBAAEEEEicAPNoXJfhFOdEKQQQQAABBEiYOBoDBDCOOoOqIIAAAggkToB5NK7LcIpzohQCCCCAAAIkTByNAQIYR51BVRBAAAEEEifAPBrXZTjFOVEKAQQQQAABEiaOxgABjKPOoCoIIIAAAokTYB6N6zKc4pwohQACCCCAAAkTR2OAAMZRZ1AVBBBAAIHECTCPxnUZTnFOlEIAAQQQQICEiaMxQADjqDOoCgIIIIBA4gSYR+O6DKc4J0ohgAACCCBAwsTRGCCAcdQZVAUBBBBAIHECzKNxXYZTnBOlEEAAAQQQIGHiaAwQwDjqDKqCAAIIIJA4AebRuC7DKc6JUggggAACCJAwcTQGCGAcdQZVQQABBBBInADzaFyX4RTnRCkEEEAAAQRImDgaAwQwEOqkuQAAIABJREFUjjqDqiCAAAIIJE6AeTSuy3CKc6IUAggggAACJEwcjQECGEedQVUQQAABBBInwDwa12U4xTlRCgEEEEAAARImjsYAAYyjzqAqCCCAAAKJE2AejesynOKcKIUAAggggAAJE0djgADGUWdQFQQQQACBxAkwj8Z1GU5xTpRCAAEEEECAhImjMUAA46gzqAoCCCCAQOIEmEfjugynOCdKIYAAAgggQMLE0RgggHHUGVQFAQQQQCBxAsyjcV2GU5wTpRBAAAEEECBh4mgMEMA46gyqggACCCCQOAHm0bguwynOiVIIIIAAAgiQMHE0BghgHHUGVUEAAQQQSJwA82hcl+EU50QpBBBAAAEESJg4GgMEMI46g6oggAACCCROgHk0rstwinOiFAIIIIAAAiRMHI0BAhhHnUFVEEAAAQQSJ8A8GtdlOMU5UQoBBBBAAAESJo7GAAGMo86gKggggAACiRNgHo3rMpzinCiFAAIIIIAACRNHY4AAxlFnUBUEEEAAgcQJMI/GdRlOcU6UQgABBBBAgISJozFAAOOoM6gKAggggEDiBJhH47oMpzgnSiGAAAIIIEDCxNEYIIBx1BlUBQEEEEAgcQLMo3FdhlOcE6UQQAABBBAgYeJoDBDAOOoMqoIAAgggkDgBr/Po9OnTbdSoUTZ37lyrqKiwHj162IgRI6xLly5pjR9//HEbN26cvf766/bJJ59YeXm5dezY0b773e/aj370I9tmm23SfkbtAl6dMm4IJyCAAAIIIFAAAU/zaFllZWVlAQwKcklP8AUB4KIIIIAAAgjkIOBxHp04caKde+65ITnSv39/W7NmjY0dO9aWLVtms2fPtq5duzbY4tGjR9usWbPsgAMOsLZt29q6devspZdesl//+te2++6728svv2xbb711RmoenTJqAIURQAABBBAooICneZSESQEHApdGAIEvBTZuXGMrl88KfzZsWGFNmrS0Fq26hz/l5c1gQgABJwKeAhiRKCnSqVMna9mypc2bNy/8rWPhwoXWuXNnO+igg+yZZ57JSk8rVq666iq7++677cwzz8zoM7w5ZVR5CiOAAAIIIFBgAU/zKAmTAg8GLo9AqQt8sfYj+3DhcNuwfomZlZmZFr19+XeTpq1t510GW8WW7UqdifYj4ELAUwAjkMmTJ1u/fv1s2LBhNnTo0BpGffv2DckOJU86dOiQsd9vfvMb+973vmdjxoyxSy+9NKPzvTllVHkKI4AAAgggUGABT/MoCZMCDwYuj0ApC2hlyfsLBtqG9Uv/kyiprVFmTZpuZx13u4mVJqU8UGi7GwFPAYxQLrzwwrD/yJNPPmm9e/eu4TRhwoTwiM60adOsT58+aQ1XrVoVHufR33/5y19s4MCB9uGHH4a9Tfbaa6+051cv4M0po8pTGAEEEEAAgQILeJpHSZgUeDBweQRKWWD5sqfsk4/vTEvQZqfzrNW2vdKWowACCORXwFMAo5aecMIJ9sgjj9j8+fNt7733rtH4xx57zI477rjoFSKpFSmpD9lnn33sxhtvtKOPPjot6ooVK0x/UsfixYvD40CLFi2y9u3bpz2fAggggAACCCDwXwFP8QYJE0YmAggUTOCD94bZms//Xs/qklS1yqxZ872sfaeay+0LVmkujEAJC3gKYNQNvXr1CnuULFiwwHbdddcaPaOv6/sjR460QYMGpe01JV0++ugjW7JkSdgs9k9/+pNdfvnl9v3vfz/tuXok6Nprr92kHAmTtHQUQAABBBBAYBMBT/EGCRMGKAIIFEzg/QU/tnVffJT2+ltUtLOOu92cthwFEEAgvwKeAhi1tDFXmNSW06M8erXwlClT7LTTTmsQlhUm+R13fDoCCCCAQGkJeIo3SJiU1tijtQi4EmCFiavuoDIIpBXwFMCoso25h0ntxldWVlqrVq1s//33t5kzZ6a1qV7Am1NGlacwAggggAACBRbwNI+SMCnwYODyCJSyAHuYlHLv0/YkCngKYOQ3adIkO/vss8PjMEOGDKlBqrfn6C062b4lZ/369faVr3zF9txzT3vttdcy6i5vThlVnsIIIIAAAggUWMDTPErCpMCDgcsjUMoCvCWnlHuftidRwFMAI79ly5ZZx44dw0qQefPmWcuWLQOrkiSdO3e2Aw880J599tnwtdWrV4evq2zbtm2r+D/++GPbaaedNumOsWPHhtcJ6007ehNPJoc3p0zqTlkEEEAAAQQKLeBpHiVhUujRwPURKHGBL9Z+ZB8uHG4b1i8xs7L/bAD75d9Nmra2nXcZbBVbtitxJZqPgA8BTwFMSmT8+PF2wQUXWJcuXUJyY+3ataZkhzZvnTVrlu27776hqB6r6dmzp5111llh5Unq2H777e2QQw6xAw44ILzRZunSpaGs3rLTqVMne/7552skWGJ6wqNTTL0pgwACCCCAgAcBT/MoCRMPI4I6IFDiAlppsnL5bFu5fJZt2LDcmjRpZS1adbcWrQ618vJmJa5D8xHwI+ApgKmuMnXqVBs9erTNnTvXKioqrEePHjZ8+HDr1q1bVbH6EibXX3+9zZgxw956662QZNlyyy1tjz32CBvKDhgwwLbddtuMO8CrU8YN4QQEEEAAAQQKIOBpHiVhUoABwCURQAABBBBIooCnAMazH06ee4e6IYAAAgh4F/A0j5Iw8T5aqB8CCCCAAAJOBDwFME5I6qwGTp57h7ohgAACCHgX8DSPkjDxPlqoHwIIIIAAAk4EPAUwTkhImHjuCOqGAAIIIJBIAU/xBgmTRA4hKo0AAggggMDmF/AUwGz+1sdfEad4K0oigAACCCBQW8DTPErChPGJAAIIIIAAAlECngKYqAoXqBBOBYLnsggggAACRSHgaR4lYVIUQ4pGIIAAAgggkH8BTwFM/lub/RVwyt6OMxFAAAEEEPA0j5IwYTwigAACCCCAQJSApwAmqsIFKoRTgeC5LAIIIIBAUQh4mkczSphMnz7dRo0aZXPnzrWKigrr0aOHjRgxwrp06RLVMc8991woP2fOHFu7dq3tsccedv7559tFF11k5eXlDX7GFVdcYTfeeKM1adLE1q9fH3W92oU8wWfVAE5CAAEEEECggALMo3H4OMU5UQoBBBBAAIG6BDzNo9EJk4kTJ9q5554bkiP9+/e3NWvW2NixY23ZsmU2e/Zs69q1a4O9/cADD9jpp59u22+/fUiQtGnTxmbMmGEPPvhg+Pdtt91W7/l//vOf7eCDD7bmzZvb559/TsKEnysEEEAAAQQKIOApgClA86MviVM0FQURQAABBBDYRMDTPBqVMFFSpFOnTtayZUubN29e+FvHwoULrXPnznbQQQfZM888U29Xa0VIu3btbNWqVfbGG2/YrrvuWlVWyZcJEyaEpMshhxyyyWesW7fODjjgANttt91CcmbWrFkkTPihQgABBBBAoAACngKYAjQ/+pI4RVNREAEEEEAAgeQnTCZPnmz9+vWzYcOG2dChQ2s0qG/fvnb33XeH5EmHDh3q7O5XX33V9t9/fzvqqKPsiSeeqFHmhRdeCImSc845x+68885Nzr/uuuvspptusvnz59v3v/99Eib8QCGAAAIIIFAgARIBcfA4xTlRCgEEEEAAgboEPM2jUStMLrzwQhs3bpw9+eST1rt37xpt0uoQrRKZNm2a9enTp84ef/HFF8MjNSeffLJpH5Tqx2uvvWb77bef7bPPPmH1SfVDq1mUaLn55pvt4osvtsMPP5yECT9TCCCAAAIIFEjAUwBTIIKoy+IUxUQhBBBAAAEE6hTwNI9GJUxOOOEEe+SRR8Iqj7333rtGox577DE77rjjbMyYMXbppZfW2eBPP/007F2yww472IIFC8JeJKnj1ltvtQEDBliLFi1sxYoVVV/fuHFjWHmizWD1GI7+zjRhos+r/pmLFy8Ojw8tWrTI2rdvz/BEAAEEEEAAgQwEPAUwGVR7sxfFabOTc0EEEEAAgSIS8DSPRiVMevXqFfYoUbKj+v4j6hN9Xd8fOXKkDRo0qN5uuuCCC2z8+PF29NFHmx6zUQLlqaeesssvv9xWr15tlZWVNfYmueWWW+yqq64yPc6j1Sc6Mk2Y6BGia6+9dpM6kTApop8mmoIAAgggsNkEPAUwm63RWVwIpyzQOAUBBBBAAIH/CHiaR6MSJrmuMFG79RrhgQMHhg1etZGrDm0eq8SIEi3aGHbp0qXh6++88054645Wntxwww1VAyfThAkrTPiZQwABBBBAoPEEPAUwjdeqxv8knBrflE9EAAEEECgdAU/zaFTCJNc9TKp3rZIY2pukrKzM9t13X9uwYUNInGiPE70pR8eJJ55oc+bMCStQmjVrVnW6Nn195ZVX7G9/+5s1bdo0vLknk8MTfCb1piwCCCCAAAIeBJhH43oBpzgnSiGAAAIIIFCXgKd5NCphMmnSJDv77LPD4y1Dhgyp0Sa9PUdv0WnoLTkNDYOpU6faqaeeGlaSDB48OBTVJrDaDLahY8cdd7SPP/44oxHmCT6jilMYAQQQQAABBwLMo3GdgFOcE6UQQAABBBAoioTJsmXLrGPHjtaqVauwOkQrQnQoSdK5c2c78MAD7dlnnw1f034k+rrKtm3btsERsGTJkrCyRJvCvvnmm9a6detQXitL9LXah15prNUlDzzwQFh5cvzxx2c0wghgMuKiMAIIIIAAAjUEmEfjBgROcU6UQgABBBBAoCgSJmqENmzVxq1dunQJrxHWniRjx441JT30Fhs9XqNj5syZ1rNnTzvrrLPCypPUMWXKlPBv7UOi1SHvvvuuTZw4MbzF5uGHHw7npDsy3cOk9ucRwKQT5vsIIIAAAgjUL8A8Gjc6cIpzohQCCCCAAAJFkzBRQ/T4zOjRo23u3LlWUVFhPXr0sOHDh1u3bt2q2llfwkR7j2hz19dff920YqVNmzZ25JFHhsdwdt9996iRQsIkiolCCCCAAAII5EWAREAcK05xTpRCAAEEEECgqBImSe9OApik9yD1RwABBBAopADzaJw+TnFOlEIAAQQQQICEiaMxQADjqDOoCgIIIIBA4gSYR+O6DKc4J0ohgAACCCBAwsTRGCCAcdQZVAUBBBBAIHECzKNxXYZTnBOlEEAAAQQQIGHiaAwQwDjqDKqCAAIIIJA4AebRuC7DKc6JUggggAACCJAwcTQGCGAcdQZVQQABBBBInEBjz6NffPGF3X///eGNe3369LGOHTsmzsR7oFcUoDQCAQQQQKCkBBo73sgFr6yysrIylw9I0rme4JPkRl0RQAABBBCQQC7z6IABA+yZZ56x1157LWBu3LjRDj30UHvppZdMoUirVq1szpw59rWvfS3x2Lk4Jb7xNAABBBBAAIEcBTzNoyRMcuxMTkcAAQQQQKBUBHIJYLp162a9e/e2m266KXD97ne/s1NOOcUGDRpkX//61+2iiy6yE0880e68887Ec+bilPjG0wAEEEAAAQRyFPA0j5IwybEzOR0BBBBAAIFSEcglgNluu+3shhtuCIkRHeedd549/fTT9s4774R//+QnP7H77ruv6t9JNs3FKcntpu4IIIAAAgg0hoCneZSESWP0KJ+BAAIIIIBACQjkEsBstdVW9vOf/9zOPffcILXXXntZ9+7dq1aUTJo0yS6++GJbvXp14iVzcUp842kAAggggAACOQp4mkdJmOTYmZyOAAIIIIBAqQjkEsBob5KjjjrKxo4da++++67ttttuNnnyZDvzzDMD36hRo8Kff//734nnzMUp8Y2nAQgggAACCOQo4GkeJWGSY2dyOgIIIIAAAqUikEsAc8UVV9iYMWPCozja3HX+/Pn2/vvvW5s2bQLfWWedZfPmzbOXX3458Zy5OCW+8TQAAQQQQACBHAU8zaMkTHLsTE5HAAEEEECgVARyCWA+/fRTO/XUU8O+Jc2aNavxeM7nn39uO+20U0im3HjjjYnnzMUp8Y2nAQgggAACCOQo4GkeJWGSY2dyOgIIIIAAAqUi0BgBzIoVK6x58+a2xRZbVLEpYfLWW29Zhw4dTJvDJv1oDKekG1B/BBBAAAEEshXwNI+SMMm2FzkPAQQQQACBEhPwFMB4psfJc+9QNwQQQAAB7wKe5lESJt5HC/VDAAEEEEDAiUAuAczs2bPt1VdftUsuuaSqNb/97W/tqquusiVLlli/fv3s1ltvddLS3KqRi1NuV+ZsBBBAAAEEki/gaR4lYZL88UQLEEAAAQQQ2CwCuQQwxx57rDVt2tQeeuihUNf33nvP9txzT2vVqpXtuOOOYRPY8ePHV712eLM0KE8XycUpT1XiYxFAAAEEEEiMgKd5lIRJYoYNFUUAAQQQQKCwArkEMNqf5OKLL7ZBgwaFRowYMcKuu+46W7Bgge288852zDHHmDaGfeGFFwrbyEa4ei5OjXB5PgIBBBBAAIFEC3iaR0mYJHooUXkEEEAAAQQ2n0AuAYw2er3jjjusb9++ocJHHnmkVVZW2owZM8K/b7/9dhsyZIj9+9//3nwNytOVcnHKU5X4WAQQQAABBBIj4GkeJWGSmGFDRRFAAAEEECisQC4BjB67ufLKK23gwIG2fv1623bbbe2KK64ISRIdEyZMsMsuu8xWr15d2EY2wtVzcWqEy/MRCCCAAAIIJFrA0zxKwiTRQ4nKI4AAAgggsPkEcglgevXqFTZ3feqpp+yBBx6wSy+91P70pz/ZIYccEhpw9dVX23333Rf2Nkn6kYtT0ttO/RFAAAEEEMhVwNM8SsIk197kfAQQQAABBEpEIJcA5oknnrATTjjBNm7cGLS+8Y1v2Jw5c6rk9O9ddtnFpk+fnnjNXJwS33gagAACCCCAQI4CnuZREiY5dianI4AAAgggUCoCuQYwWlHy4IMP2jbbbBNeL6zHcnRo35Lzzz/fzjzzTDvppJMSz5mrU+IBaAACCCCAAAI5CHiaR0mY5NCRnIoAAggggEApCXgKYDy74+S5d6gbAggggIB3AU/zKAkT76OF+iGAAAIIIOBEoLECmLlz54bXCevYbbfdrGvXrk5a2DjVaCynxqkNn4IAAggggECyBDzNoyRMkjV2qC0CCCCAAAIFE8g1gHnuueesf//+9o9//KNGG772ta/Z+PHj7bDDDitY2xrzwrk6NWZd+CwEEEAAAQSSJuBpHiVhkrTRQ30RQAABBBAokEAuAcwrr7xi3bt3DzU//fTTq1aVaLXJlClTwtdnzZpl+++/f4Fa13iXzcWp8WrBJyGAAAIIIJBMAU/zKAmTZI4hao0AAggggMBmF8glgPnOd75jL7zwgj3//PO2xx571Kj722+/bQcffLAdeuihYVPYpB+5OCW97dQfAQQQQACBXAU8zaMkTHLtTc5HAAEEEECgRARyCWBat25tF198sV133XV1al1zzTV2++2325IlSxKvmYtT4htPAxBAAAEEEMhRwNM8SsIkx87kdAQQQAABBEpFIJcApnnz5nbTTTfZRRddVCeXkiUDBw60zz//PPGcuTglvvE0AAEEEEAAgRwFPM2jJExy7ExORwABBBBAoFQEcglg9t577/AozkMPPVQn14knnmhvvfWWvfnmm4nnzMUp8Y2nAQgggAACCOQo4GkeJWGSY2dyOgIIIIAAAqUikEsAc+2115r+6C05w4YNsx133DGwffLJJ3b99dfbbbfdZkOHDrUhQ4YknjMXp8Q3ngYggAACCCCQo4CneZSESY6dyekIIIAAAgiUikAuAczatWvtmGOOsZkzZ1pZWZlpTxMd2rOksrLSevbsaY8//rhVVFQknjMXp8Q3ngYggAACCCCQo4CneZSESY6dyekIIIAAAgiUikCuAczGjRtt8uTJNn36dHvnnXcC22677WZ9+vSxM888MyRSysvLE8+Zq1PiAWgAAggggAACOQh4mkdJmOTQkZyKAAIIIIBAKQnkM4AZPnx4eCRn/fr1iSfNp1PicWgAAggggAACaQQ8zaMkTBiuCCCAAAIIIBAlkM8ARgkT7V+yYcOGqLp4LpRPJ8/tpm4IIIAAAgg0hoCneZSESWP0KJ+BAAIIIIBACQjkM4AhYVICA4gmIoAAAgggECGQz3gj4vI1ipAwyVSM8ggggAACCJSoQD4DGBImJTqoaDYCCCCAAAK1BPIZb2SKTcIkUzHKI4AAAgggUKIC+QxgSJiU6KCi2QgggAACCJAw8TEG8hno+WghtUAAAQQQQCB/AvmcR0mY5K/f+GQEEEAAAQSSJJDPeCNTB1aYZCpGeQQQQAABBEpUINMAZsSIEdFSM2fOtKeffppNX6PFKIgAAggggEBxCmQab+RTgYRJPnX5bAQQQAABBIpIINMApry8PKPWl5WVkTDJSIzCCCCAAAIIFJ9ApvFGPgVImORTl89GAAEEEECgiAQyDWCee+65jFv/7W9/O+NzvJ2QqZO3+lMfBBBAAAEECingaR4lYVLIkcC1EUAAAQQQSJCApwDGMxtOnnuHuiGAAAIIeBfwNI+SMPE+WqgfAggggAACTgQ8BTBOSOqsBk6ee4e6IYAAAgh4F/A0j5Iw8T5aqB8CCCCAAAJOBDwFME5ISJh47gjqhgACCCCQSAFP8QYJk0QOISqNAAIIIIDA5hfwFMBs/tbHXxGneCtKIoAAAgggUFvA0zxKwoTxiQACCCCAAAJRAp4CmKgKF6gQTgWC57IIIIAAAkUh4GkeJWFSFEOKRiCAAAIIIJB/AU8BTP5bm/0VcMrejjMRQAABBBDwNI+SMGE8IoAAAggggECUgKcAJqrCBSqEU4HguSwCCCCAQFEIeJpHSZgUxZCiEQgggAACCORfwFMAk//WZn8FnLK340wEEEAAAQQ8zaMkTBiPCCCAAAIIIBAl4CmAiapwgQrhVCB4LosAAgggUBQCnuZREiZFMaRoBAIIIIAAAvkX8BTA5L+12V8Bp+ztOBMBBBBAAAFP8ygJE8YjAggggAACCEQJeApgoipcoEI4FQieyyKAAAIIFIWAp3mUhElRDCkagQACCCCAQP4FPAUw+W9t9lfAKXs7zkQAAQQQQMDTPErChPGIAAIIIIAAAlECngKYqAoXqBBOBYLnsggggAACRSHgaR4lYVIUQ4pGIIAAAgggkH8BTwFM/lub/RVwyt6OMxFAAAEEEPA0j5IwYTwigAACCCCAQJSApwAmqsIFKoRTgeC5LAIIIIBAUQh4mkdJmBTFkKIRCCCAAAII5F/AUwCT/9ZmfwWcsrfjTAQQQAABBDzNoyRMGI8IIIAAAgggECXgKYCJqnCBCuFUIHguiwACCCBQFAKe5lESJkUxpGgEAggggAAC+RfwFMDkv7XZXwGn7O04EwEEEEAAAU/zaEYJk+nTp9uoUaNs7ty5VlFRYT169LARI0ZYly5donr1ueeeC+XnzJlja9eutT322MPOP/98u+iii6y8vLzqM5YtW2b33nuvPf744zZv3jz75z//aW3btrUDDjjABg8ebF//+tejrle7kCf4rBrASQgggAACCBRQgHk0Dh+nOCdKIYAAAgggUJeAp3k0OmEyceJEO/fcc0NypH///rZmzRobO3asKbkxe/Zs69q1a4O9/cADD9jpp59u22+/fUiQtGnTxmbMmGEPPvhg+Pdtt91Wdf4TTzxhxx13nB1xxBHhzw477GD/+Mc/bNy4cbZixQq777777LTTTst4dHmCz7jynIAAAggggECBBZhH4zoApzgnSiGAAAIIIFAUCRMlRTp16mQtW7YMKz70t46FCxda586d7aCDDrJnnnmm3t5ev369tWvXzlatWmVvvPGG7brrrlVllXyZMGFCSLoccsgh4evvvfee6Zzdd9+9xmfq3P3339+22247++ijj2qsSokZagQwMUqUQQABBBBAoG4B5tG4kYFTnBOlEEAAAQQQKIqEyeTJk61fv342bNgwGzp0aI029e3b1+6+++6QPOnQoUOdPf7qq6+GRMdRRx1lWj1S/XjhhRdCouScc86xO++8M+2I0efo8xYvXmw77bRT2vLVCxDAZMRFYQQQQAABBGoIMI/GDQic4pwohQACCCCAQFEkTC688MLwOMyTTz5pvXv3rtEmrQ7RKpFp06ZZnz596uzxF1980Q4++GA7+eSTTfugVD9ee+0122+//WyfffYJq08aOjZu3Gjt27e3JUuW2PLly61Zs2YZjTACmIy4KIwAAggggEAiEia57LH28MMP2+9//3vTDRzd/FFsoRWu5513np155pnWtGnTjEcB8UbGZJyAAAIIIIBAlYCneTRqD5MTTjjBHnnkEZs/f77tvffeNbryscceC/uNjBkzxi699NI6u/nTTz8Ne5doL5IFCxZY8+bNq8rdeuutNmDAAGvRokXYn6ShQ3um6BpnnXWWadVLukOfV/0ztSpFjw8tWrQoJF44EEAAAQQQQCBewFMAk6p1rnusabXq1ltvbSeddFKIcXRD5v7777eXX37Zjj322BD/lJWVxSOZmUenjBpAYQQQQAABBAoo4GkejUqY9OrVK+xRomRH9f1HZKiv6/sjR460QYMG1ct6wQUX2Pjx4+3oo4+26667LiRQnnrqKbv88stt9erVVllZGfYtqe+YOXNmeKRn5513DkGM9jFJd+gRomuvvXaTYiRM0snxfQQQQAABBDYV8BTAqHa57rGmz3j66afDBvPVkyIbNmywww8/3GbNmmWPPvpoSJxkcnhzyqTulEUAAQQQQKDQAp7m0aiESa4rTASu1wgPHDgwbPC6bt260AfaPPaWW24JiRYlS5YuXVpn32hDWCVaWrVqZUqc1N4Mtr4OZYVJoYc610cAAQQQKCYBTwGMXHPdY62hvvn5z39uP/rRj9LeEKrrM7w5FdMYpC0IIIAAAsUv4GkejUqY5LqHSfUuVRJDb9rRnZx9993XdBdHiRPtcaLESO3jj3/8Y3jkZ9tttw2rWWKTJQQwxf92icGPAAAgAElEQVSDRAsRQAABBDavgKcARi1vzPiktuRVV11lo0aNsrvuuitsfJ/J4c0pk7pTFgEEEEAAgUILeJpHoxImkyZNsrPPPjs83jJkyJAafgoidIenobfkNAQ+depUO/XUU+2GG26wwYMH1yj67LPP2vHHHx/2PlGy5Ktf/WpOfecJPqeGcDICCCCAAAIFEPA2jzbGCtj6brB06dLFysvL7e233077GDArWgswGLkkAggggEDRCniKN6ISJnpGuGPHjuGRGK0O0YoQHUqSdO7c2Q488EBTckOH9iPR11W2bdu2DXai3najlSXaFPbNN9+01q1bV5XXM8UKhNq1axeSJbvsskvOA8ITfM6N4QMQQAABBBDYzALe5tHG2GOtNuGqVavs29/+tr366qvhDYB6w1+6gz3T0gnxfQQQQAABBOIFPMUbUQkTNU0btmrjVt1x0WuEtSeJ3lqjpIc2RdPjNTq0x0jPnj03eZPNlClTwkoUbaK244472rvvvmva2V53ZfRKP52TOrSp62GHHRb2OtHKE230WvtQAKNd7TM5PMFnUm/KIoAAAggg4EHA2zza2CtMlCzRBq+Ka37xi1/YRRddFMXOCpMoJgohgAACCCAQJeAp3ohOmKhlenxm9OjRNnfuXKuoqLAePXrY8OHDrVu3blUNry9h8sorr4TNXV9//fWwq32bNm3syCOPDI/h1N6XJLWJW0OaSrh06tQpCjxVyBN8RhWnMAIIIIAAAg4EvM2jjbmHycqVK+2YY46x559/3u64445wcyjbw5tTtu3gPAQQQAABBAoh4GkezShhUgisxrymJ/jGbBefhQACCCCAwOYQ8DaPNtYea8uXLw9v43vppZfsl7/8Zdi3LZfDm1MubeFcBBBAAAEENreAp3mUhMnm7n2uhwACCCCAQEIFPAUwImyMPdaULNGKV62EVQLmBz/4Qc69480p5wbxAQgggAACCGxGAU/zKAmTzdjxXAoBBBBAAIEkC3gKYFKOue6xpo3rtXfaiSeeaN/97nc36R49dlz90eOY/vPoFFNvyiCAAAIIIOBBwNM8SsLEw4igDggggAACCCRAwFMAU50rlz3WysrKGpQfOnSo6S04mRxenTJpA2URQAABBBAolICneZSESaFGAddFAAEEEEAgYQKeAhjPdDh57h3qhgACCCDgXcDTPErCxPtooX4IIIAAAgg4EfAUwDghqbMaOHnuHepWygIbN66xlctnhT8bNqywJk1aWotW3cOf8vJmpUxD2xFwJeBpHiVh4mpoUBkEEEAAAQT8CngKYPwqmeHkuXeoW6kKfLH2I/tw4XDbsH6JmelRvMqqv5s0bW077zLYKrZsV6o8tBsBVwKe5lESJq6GBpVBAAEEEEDAr4CnAMavEgkTz31D3UpTQCtL3l8w0DasX/qfRElthzJr0nQ767jbTaw0Kc0hQqudCXiKN0iYOBscVAcBBBBAAAGvAp4CGK9GqhdOnnuHupWiwPJlT9knH9+ZtultdjrPWm3bK205CiCAQH4FPM2jJEzy29d8OgIIIIAAAkUj4CmA8YyKk+feoW6lKPDBe8Nszed/r2d1SUqkzJo138vadxpaikS0GQFXAp7mURImroYGlUEAAQQQQMCvgKcAxq8SK0w89w11K02B9xf82NZ98VHaxm9R0c467nZz2nIUQACB/Ap4ijdImOS3r/l0BBBAAAEEikbAUwDjGRUnz71D3UpRgBUmpdjrtDnJAp7mURImSR5J1B0BBBBAAIHNKOApgNmMzc74UjhlTMYJCORVgD1M8srLhyPQ6AKe5lESJo3evXwgAggggAACxSngKYDxLIyT596hbqUowFtySrHXaXOSBTzNoyRMkjySqDsCCCCAAAKbUcBTALMZm53xpXDKmIwTEMi7wBdrP7IPFw63DeuXmFnZfzaA/fLvJk1b2867DLaKLdvlvR5cAAEE0gt4mkdJmKTvL0oggAACCCCAAK/LjR4DngK96EpTEIESENBKk5XLZ9vK5bNsw4bl1qRJK2vRqru1aHWolZc3KwEBmohAMgQ8zaMkTJIxZqglAggggAACBRfwFMAUHKOBCuDkuXeoGwIIIICAdwFP8ygJE++jhfohgAACCCDgRMBTAOOEpM5q4OS5d6gbAggggIB3AU/zKAkT76OF+iGAAAIIIOBEwFMA44SEhInnjqBuCCCAAAKJFPAUb5AwSeQQotIIIIAAAghsfgFPAczmb338FXGKt6IkAggggAACtQU8zaMkTBifCCCAAAIIIBAl4CmAiapwgQrhVCB4LosAAgggUBQCnuZREiZFMaRoBAIIIIAAAvkX8BTA5L+12V8Bp+ztOBMBBBBAAAFP8ygJE8YjAggggAACCEQJeApgoipcoEI4FQieyyKAAAIIFIWAp3mUhElRDCkagQACCCCAQP4FPAUw+W9t9lfAKXs7zkQAAQQQQMDTPErChPGIAAIIIIAAAlECngKYqAoXqBBOBYLnsggggAACRSHgaR4lYVIUQ4pGIIAAAgggkH8BTwFM/lub/RVwyt6OMxFAAAEEEPA0j5IwYTwigAACCCCAQJSApwAmqsIFKoRTgeC5LAIIIIBAUQh4mkdJmBTFkKIRCCCAAAII5F/AUwCT/9ZmfwWcsrfjTAQQQAABBDzNoyRMGI8IIIAAAgggECXgKYCJqnCBCuFUIHguiwACCCBQFAKe5lESJkUxpGgEAggggAAC+RfwFMDkv7XZXwGn7O04EwEEEEAAAU/zKAkTxiMCCCCAAAIIRAl4CmCiKlygQjgVCJ7LIoAAAggUhYCneZSESVEMKRqBAAIIIIBA/gU8BTD5b232V8ApezvORAABBBBAwNM8SsKE8YgAAggggAACUQKeApioCheoEE4FgueyCCCAAAJFIeBpHiVhUhRDikYggAACCCCQfwFPAUz+W5v9FXD6/+3dCZgUxdnA8Xc5FhBYlEMFIRzxREADAmoAJYgXISLRRA0KeCHxiAIaPMKhAVSCFx6gEMAjaATip3gbQAUPVBAQEjVEEAWMLsgp937PW9rrzO7MTnXvznRNz7+fhwddqrurflUzVf1uVXVwO85EAAEEEEDApX6UgAntEQEEEEAAAQSsBFwawFhlOKREOIUEz20RQAABBCIh4FI/SsAkEk2KQiCAAAIIIJB+AZcGMOkvbfA74BTcjjMRQAABBBBwqR8lYEJ7RAABBBBAAAErAZcGMFYZDikRTiHBc1sEEEAAgUgIuNSPEjCJRJOiEAgggAACCKRfwKUBTPpLG/wOOAW340wEEEAAAQRc6kcJmNAeEUAAAQQQQMBKwKUBjFWGQ0qEU0jw3BYBBBBAIBICLvWjBEwi0aQoBAIIIIAAAukXcGkAk/7SBr8DTsHtOBMBBBBAAAGX+lECJrRHBBBAAAEEELAScGkAY5XhkBLhFBI8t0UAAQQQiISAS/0oAZNINCkKgQACCCCAQPoFXBrApL+0we+AU3A7zkQAAQQQQMClfpSACe0RAQQQQAABBKwEXBrAWGU4pEQ4hQTPbRFAAAEEIiHgUj9KwCQSTYpCIIAAAgggkH4BlwYw6S9t8DvgFNyOMxFAAAEEEHCpHyVgQntEAAEEEEAAASsBlwYwVhkOKRFOIcFzWwQQQACBSAi41I8SMIlEk6IQCCCAAAIIpF/ApQFM+ksb/A44BbfjTAQQQAABBFzqRwmY0B4RQAABBBBAwErApQGMVYZDSoRTSPDcFgEEEEAgEgIu9aMETCLRpCgEAggggAAC6RdwaQCT/tIGvwNOwe04EwEEEEAAAZf6UQImtEcEEEAAAQQQsBJwaQBjleGQEuEUEjy3RQABBBCIhIBL/SgBk0g0KQqBAAIIIIBA+gVcGsCkv7TB74BTcDvORAABBBBAwKV+lIAJ7REBBBBAAAEErARcGsBYZTikRDiFBM9tEUAAAQQiIeBSP0rAJBJNikIggAACCCCQfgGXBjDpL23wO+AU3I4zEUAAAQQQcKkfJWBCe0QAAQQQQAABKwGXBjBWGQ4pEU4hwXNbBBBAAIFICLjUjxIwiUSTohAIIIAAAgikX8ClAUz6Sxv8DjgFt+NMBBBAAAEEXOpHCZjQHhFAAAEEEEDASsClAYxVhkNKhFNI8NwWAQQQQCASAi71owRMItGkKAQCCCCAAALpF3BpAJP+0ga/A07B7TgTAQQQQAABl/pRAia0RwQQQAABBBCwEnBpAGOV4ZAS4RQSPLdFAAEEEIiEgEv9KAGTSDQpCoEAAggggED6BVwawKS/tMHvgFNwO85EAAEEEEDApX6UgAntEQEEEEAAAQSsBFwawFhlOKREOIUEz20RQAABBCIh4FI/SsAkEk2KQiCAAAIIIJB+AZcGMOkvbfA74BTcjjMRQAABBBBwqR/1FTCZNWuW3HnnnbJs2TLJz8+Xzp07y+jRo6VVq1ZWtfr666+b9O+++67s3LlTDjvsMLn88svl97//vVSqVKnUNZYuXSo333yzzJ8/X3bt2iWtW7eWoUOHSq9evazuVzKRS/CBCsBJCCCAAAIIhChAP2qHj5OdE6kQQAABBBBIJOBSP2odMJk8ebJceumlJjgyYMAA2bFjh4wfP142btwoCxYsMMGMso6nnnpKLrjgAqlfv74JkDRo0EBeffVVeeaZZ8z/P/DAA3GnL1myRDp16iTVqlWTa6+91pz3+OOPm3tNmTJF+vXr57t1uQTvO/OcgAACCCCAQMgC9KN2FYCTnROpEEAAAQQQiETARIMizZo1k4KCAlm+fLn5W4/PP/9cWrZsKR06dJA5c+Ykre09e/ZIo0aNZOvWrfLRRx9JixYtitNq8OXhhx82gZATTzyx+OddunQxM0sWLlwoxx13nPn57t27pWPHjvLZZ5/J6tWri/Nh28wYwNhKkQ4BBBBAAIHSAvSjdq0CJzsnUiGAAAIIIBCJgMnUqVOlf//+MmLECBk+fHhcmXSmx7Rp00zwpEmTJglrfPHixdK2bVs57bTT5KWXXopL8/bbb5tAySWXXCKTJk0y/7Zq1Spp3ry5nHzyyTJ37ty49F5eHnvsMenTp4+vFsYAxhcXiRFAAAEEEIgToB+1axA42TmRCgEEEEAAgUgETAYOHCgTJkyQV155Rbp37x5XJp0dorNEZs6cKb17905Y4++8846ccMIJcvbZZ4vugxJ76NKbY489Vo4++mgz+0QPXb5z3nnnyU033SSjRo2KS//JJ5/IEUccIVdffbXcd999vloYAxhfXCRGAAEEEECAgEmANsB4IwAapyCAAAIIIPCDgEv9qNUeJj179pTZs2fLihUr5KijjoqryBdeeEF69Ogh9957r1xzzTUJK/nbb781e5AceOCBsnLlSqlRo0ZxunvuuUeuu+46qV27tmzevNn8fNy4cTJkyBB58MEHRYM1scf27dulZs2aCYMvJW+u1/Ouqf+2bt06s3xozZo10rhxYxokAggggAACCPgQcGkA4yPbGU+KU8bJuSECCCCAQIQEXOpHrQIm3bp1M3uUaLAjdv8RrRP9uf77mDFjzBtskh1XXHGFTJw4UU4//XS59dZbTQDltddeM4ERDYIUFRWJ7nWix2233SbDhg0T3Wj24osvjrvkvn37pHLlygmX95S8ty4hGjlyZKksETCJ0KeJoiCAAAIIZEzApQFMxgod4EY4BUDjFAQQQAABBH4QcKkftQqYlHeGiZZbXyM8ePBgs8Grbt6qh24ee/fdd5tAiwZLNmzYYH7ODBM+KwgggAACCLgn4NIAxj2dH3OEk8u1Q94QQAABBFwXcKkftQqYlHcPk9gK0SUy+qadvLw8OeaYY2Tv3r0mcKJ7nOibcvRgDxPXmzD5QwABBBDIRQGXBjAu++Pkcu2QNwQQQAAB1wVc6ketAiZTpkwxS2N0eYsulYk99O05+uaast6SU1aFzJgxQ84991z585//LDfffLNJqq8N1qU/Xbt2LfW6Yn0jj76Zh7fkuN7MyR8CCCCAQNQEXBrAuGyLk8u1Q94QQAABBFwXcKkftQqYbNy4UZo2bSp16tQxs0N0RogeGiRp2bKltG/fvvj1v7ofif5c0zZs2LDMuigsLDQzS3RT2H/9619Sr1694vSdOnWSt956S9577z1p166d+bku2+nYsaPZS2X16tXmHn4Ol+D95Ju0CCCAAAIIuCBAP2pXCzjZOZEKAQQQQACBRAIu9aNWARMthG7Yqhu3tmrVyrxGWPckGT9+vGjQY/78+WZ5jR7z5s0zM0P69u1rZp54x/Tp083/n3zyyXLQQQeZWSS6qasu0XnuuefMObHHokWLpEuXLlK9enXzFh3dJFZnleiynUSbwdo0NZfgbfJLGgQQQAABBFwSoB+1qw2c7JxIhQACCCCAQGQCJloQXT4zduxYWbZsmeTn50vnzp1l1KhR0qZNm+JyJguYfPDBB2Zz16VLl4rOWGnQoIGceuqpZhnOoYcemrClLFmyxPy7BmR27dolrVu3lj/+8Y/Su3fvQC2LAUwgNk5CAAEEEEDACNCP2jUEnOycSIUAAggggECkAibZXp0MYLK9Bsk/AggggECYAvSjdvo42TmRCgEEEEAAAQImDrUBBjAOVQZZQQABBBDIOgH6Ubsqw8nOiVQIIIAAAggQMHGoDTCAcagyyAoCCCCAQNYJ0I/aVRlOdk6kQgABBBBAgICJQ22AAYxDlUFWEEAAAQSyToB+1K7KcLJzIhUCCCCAAAIETBxqAwxgHKoMsoIAAgggkHUC9KN2VYaTnROpEEAAAQQQIGDiUBtgAONQZZAVBBBAAIGsE6AftasynOycSIUAAggggAABE4faAAMYhyqDrCCAAAIIZJ0A/ahdleFk50QqBBBAAAEECJg41AYYwDhUGWQFAQQQQCDrBOhH7aoMJzsnUiGAAAIIIEDAxKE2wADGocogKwgggAACWSdAP2pXZTjZOZEKAQQQQAABAiYOtQEGMA5VBllBAAEEEMg6AfpRuyrDyc6JVAgggAACCBAwcagNMIBxqDLICgIIIIBA1gnQj9pVGU52TqRCAAEEEECAgIlDbYABjEOVQVYQQAABBLJOgH7UrspwsnMiFQIIIIAAAgRMHGoDDGAcqgyyggACCCCQdQL0o3ZVhpOdE6kQQAABBBAgYOJQG2AA41BlkBUEEEAAgawToB+1qzKc7JxIhUCmBfbt2SFb1sw3f/bu3CyVqxVI7SadzJ9KVapnOjvcDwEEkgi41I/mFRUVFeVKTbkEnyvmlBMBBBBAIDoC9KN2dYmTnROpEMikwK4ta+XLBaNk73eFIpInIvoI9P3flWvUk0N+frPk126UySxxLwQQIGDiVhtgAONWfZAbBBBAAIHsEnC1H501a5bceeedsmzZMsnPz5fOnTvL6NGjpVWrVimBP/74Y5k0aZIsXrzY/NmwYYNccskl5mdBD1edgpaH8xDIdgGdWbL6tcGy97sNPwRKSpYoTyrXqCtNTxnHTJNsr2zyHwkBl/pRZphEoklRCAQQQAABBNIv4NIAxivt5MmT5dJLLzXBkQEDBsiOHTtk/PjxsnHjRlmwYIG0bt26TJipU6dK//79pXnz5nL44YfLyy+/TMAk/U2JOyCQUYFNn70mX3+YOgja4NjLpE7zbhnNGzdDAIHSAi6NNwiY0EIRQAABBBBAwErApQGMZliDIs2aNZOCggJZvny5+VuPzz//XFq2bCkdOnSQOXPmlFm2wsJCqVSpkhxwwAGyatUqEzhhholVcyARAlkj8MUbI2RH4cdJZpd4xciT6vWOlMZdhmdNucgoAlEVcGm8QcAkqq2MciGAAAIIIFDBAi4NYLRo3uyQESNGyPDh8Q85/fr1k2nTppngSZMmTawkCJhYMZEIgawTWP3qINm9dW3KfFet1Uiadr8rZToSIIBAegVcGm8QMElvXXN1BBBAAAEEIiPg0gBGUQcOHCgTJkyQV155Rbp37x7n/PDDD5slOjNnzpTevXtb1QEBEysmEiGQdQLMMMm6KiPDOS7g0niDgEmON0aKjwACCCCAgK2ASwMYzXPPnj1l9uzZsmLFCjnqqKPiivHCCy9Ijx495N5775VrrrnGqohBAyabN28W/eMd69atM8uB1qxZI40bN7a6N4kQQCB9Auxhkj5broxAOgRcGm8QMElHDXNNBBBAAAEEIijg0gBGebt162b2KFm5cqW0aNEiTlx/rv8+ZswYGTp0qFVtBA2Y6JKgkSNHlroHARMrdhIhkHYB3pKTdmJugECFCrg03iBgUqFVy8UQQAABBBCIroBLAxhVZoZJdNsaJUOgogV2bVkrXy4YJXu/KxSRvB82gP3+78o16skhP79Z8ms3qujbcj0EEAgg4NJ4g4BJgArkFAQQQAABBHJRwKUBjPqzh0kutkLKjEBwAZ1psmXNAtmyZr7s3blJKlerI7WbdJLaTX4ulapUD35hzkQAgQoVcGm8QcCkQquWiyGAAAIIIBBdAZcGMKo8ZcoUufjii81ymGHDhsXB9+/f37xFh7fkRLc9UjIEEEAAgWgKuDTeIGASzTZGqRBAAAEEEKhwAZcGMFq4jRs3StOmTaVOnTqyfPlyKSgoMGXWIEnLli2lffv2MnfuXPOz7du3m59r2oYNGya0CbqHScmLueZU4Q2BCyKAAAIIIJBGAZf6UQImaaxoLo0AAggggECUBFwawHiuEydOlCuuuEJatWplXiO8c+dOGT9+vBQWFsr8+fPlmGOOMUnnzZsnXbt2lb59+5qZJ96xadMmk16Pb7/9VsaNGyft2rWTXr16mZ/p+bpXip/DRSc/+SctAggggAACYQq41I8SMAmzJXBvBBBAAAEEskjApQFMLNuMGTNk7NixsmzZMsnPz5fOnTvLqFGjpE2bNsXJkgVMvFklyaqhZIDFprpcdbLJO2kQQAABBBAIW8ClfpSASditgfsjgAACCCCQJQIuDWBcJsPJ5dohbwgggAACrgu41I8SMHG9tZA/BBBAAAEEHBFwaQDjCEnCbODkcu2QNwQQQAAB1wVc6kcJmLjeWsgfAggggAACjgi4NIBxhISAicsVQd4QQAABBLJSwKXxBgGTrGxCZBoBBBBAAIHMC7g0gMl86e3viJO9FSkRQAABBBAoKeBSP0rAhPaJAAIIIIAAAlYCLg1grDIcUiKcQoLntggggAACkRBwqR8lYBKJJkUhEEAAAQQQSL+ASwOY9Jc2+B1wCm7HmQgggAACCLjUjxIwoT0igAACCCCAgJWASwMYqwyHlAinkOC5LQIIIIBAJARc6kcJmESiSVEIBBBAAAEE0i/g0gAm/aUNfgecgttxJgIIIIAAAi71owRMaI8IIIAAAgggYCXg0gDGKsMhJcIpJHhuiwACCCAQCQGX+lECJpFoUhQCAQQQQACB9Au4NIBJf2mD3wGn4HaciQACCCCAgEv9KAET2iMCCCCAAAIIWAm4NICxynBIiXAKCZ7bIoAAAghEQsClfpSASSSaFIVAAAEEEEAg/QIuDWDSX9rgd8ApuB1nIoAAAggg4FI/SsCE9ogAAggggAACVgIuDWCsMhxSIpxCgue2CCCAAAKREHCpHyVgEokmRSEQQAABBBBIv4BLA5j0lzb4HXAKbseZCCCAAAIIuNSPEjChPSKAAAIIIICAlYBLAxirDIeUCKeQ4LktAggggEAkBFzqRwmYRKJJUQgEEEAAAQTSL+DSACb9pQ1+B5yC23EmAggggAACLvWjBExojwgggAACCCBgJeDSAMYqwyElwikkeG6LAAIIIBAJAZf6UQImkWhSFAIBBBBAAIH0C7g0gEl/aYPfAafgdpyJAAIIIICAS/0oARPaIwIIIIAAAghYCbg0gLHKcEiJcAoJntsigAACCERCwKV+lIBJJJoUhUAAAQQQQCD9Ai4NYNJf2uB3wCm4HWcigAACCCDgUj9KwIT2iAACCCCAAAJWAi4NYKwyHFIinEKC57YIIIAAApEQcKkfJWASiSZFIRBAAAEEEEi/gEsDmPSXNvgdcApux5kIIIAAAgi41I8SMKE9IoAAAggggICVgEsDGKsMh5QIp5DguS0CCCCAQCQEXOpHCZhEoklRCAQQQAABBNIv4NIAJv2lDX4HnILbcSYCCCCAAAIu9aMETGiPCCCAAAIIIGAl4NIAxirDISXCKSR4bosAAgggEAkBl/pRAiaRaFIUAgEEEEAAgfQLuDSASX9pg98Bp+B2nIkAAggggIBL/SgBE9ojAggggAACCFgJuDSAscpwSIlwCgme2yKAAAIIRELApX6UgEkkmhSFQAABBBBAIP0CLg1g0l/a4HfAKbgdZyKAAAIIIOBSP0rAhPaIAAIIIIAAAlYCLg1grDIcUiKcQoLntggggAACkRBwqR8lYBKJJkUhEEAAAQQQSL+ASwOY9Jc2+B1wCm7HmQgggAACCLjUjxIwoT0igAACCCCAgJWASwMYqwyHlAinkOC5LQIIIIBAJARc6kcJmESiSVEIBBBAAAEE0i/g0gAm/aUNfgecgttxJgIIIIAAAi71o74CJrNmzZI777xTli1bJvn5+dK5c2cZPXq0tGrVyqpWly5datK/8847sn79ejnwwAOlXbt2cv3118uJJ54Yd419+/bJ448/LhMmTJBPPvlEduzYIU2aNJFevXrJoEGDpEGDBlb3jE3kErzvzHMCAggggAACIQvQj9pVAE52TqRCAAEEEEAgkYBL/ah1wGTy5Mly6aWXmuDIgAEDTABj/PjxsnHjRlmwYIG0bt26zNpeuHChdOnSRerVqyeXXXaZCX6sXr1aHn74Yfn666/lxRdflFNPPbX4Gtddd53cc8890rVrVxMkqVatmrz11lvy2GOPyaGHHipLliyRGjVq+GphLsH7yjiJEUAAAQQQcECAftSuEnCycyIVAggggAACkQiYaFCkWbNmUlBQIMuXLzd/6/H555CTC8AAACAASURBVJ9Ly5YtpUOHDjJnzpwya7tPnz7yxBNPmNkpsTNSFi1aZGaZnHPOOfL000+ba2zfvl32339/OfbYY+Xdd9+VvLy84mtfc801JlAze/Zs6dGjh68WxgDGFxeJEUAAAQQQiBOgH7VrEDjZOZEKAQQQQACBSARMpk6dKv3795cRI0bI8OHD48rUr18/mTZtmgme6KyRZEfPnj1NkKOwsFDq1q1bnOyrr76Sgw8+WC666CJzHT00Tf369UXPefbZZ+Mueccdd8jQoUNNgEZnn/g5GMD40SItAggggAAC8QL0o3YtAic7J1IhgAACCCAQiYDJwIEDzV4ir7zyinTv3j2uTLqkRpfozJw5U3r37p20xu+//365+uqr5bTTTpORI0cWL8n505/+JDrL5PXXX49b1nPcccfJ4sWLzZ4nel3dM0WX5Fx11VWi/6ZLeCpVquSrhTGA8cVFYgQQQAABBOIE6EftGgROdk6kQgABBBBAIBIBE292yIoVK+Soo46KK9MLL7xglsbce++9ostlkh179+6VW265RTRwsnXr1uJkujxHN5M97LDD4k5dtWqVmdUyb968uJ9feeWVZm+TKlWqpGxdmzdvFv3jHevWrTPLh9asWSONGzdOeT4JEEAAAQQQQOBHAQIBdq0BJzsnUiGAAAIIIBCJgEm3bt3MEpiVK1dKixYt4sqkP9d/HzNmjFkqk+woKiqSBx54QJ588kmzievhhx9u3n4zduxYqVWrlrl+06ZNi0//3//+JzfddJPokp1zzz1X9ttvP3n55Zflr3/9qwmkTJo0KWXr0iVEOpul5EHAJCUdCRBAAAEEECglQCDArlHgZOdEKgQQQAABBCIRMKmIGSYaTBk3bpxZZhO76atuAtu2bVs5++yz5e9//7vx2rZtm7Rp00YOOugg8wae2E1f9Tq6j8nzzz8vZ555ZpktjBkmfAARQAABBBCoOAECAXaWONk5kQoBBBBAAIFIBEzKu4fJ7t27zSwSnVWiAZKSh76SeP369eb1wno8+uij0rdvX/nLX/4igwcPjkv+wQcfmD1MhgwZYman+DkYwPjRIi0CCCCAAALxAvSjdi0CJzsnUiGAAAIIIBCJgMmUKVPk4osvNstbhg0bFlcmXR6jb9Ep6y05undIo0aNzP4nug9KyUN/rgETfX2xHrq8R5fj6EySG264IS65vmb4+OOPl2uvvVbuvvtuXy2MAYwvLhIjgAACCCAQJ0A/atcgcLJzIhUCCCCAAAKRCJhoIEP3F6lTp44sX75cCgoKTLk0SNKyZUtp3769zJ071/xs+/bt5ueatmHDhuZn+/btM8trNmzYYJbYaMDDO95++23p1KmTefvOSy+9ZH6srxI+66yzzLKc999/X6pWrVqc3pvtMn36dDnvvPN8tTAGML64SIwAAggggAABkwBtgPFGADROQQABBBBA4AcBl/rRvCLdjdXimDhxolxxxRVm/xF9jfDOnTtl/PjxUlhYKPPnz5djjjnGXEXfatO1a1ezpEZnnnjHgw8+KPqGG12ao9fRt+J8+umn8tBDD4m+QUdfK6xvsNFD/1+DKO+8844JmvTp06d409fnnntOOnbsaO5p86ac2KK5BG9BThIEEEAAAQScEqAftasOnOycSIUAAggggEAiAZf6UeuAiRZkxowZZt8Q3YckPz9fOnfuLKNGjTJBDe9IFjDRf3/mmWdMkGXRokWyZcsWqVevnnTp0sW8btgLuHjX0Y1fdUnOzJkzzdt5NK7TvHlz+fWvf22W69SsWdN363IJ3nfmOQEBBBBAAIGQBehH7SoAJzsnUiGAAAIIIBCpgEm2VycDmGyvQfKPAAIIIBCmAP2onT5Odk6kQgABBBBAgICJQ22AAYxDlUFWEEAAAQSyToB+1K7KcLJzIhUCCCCAAAIETBxqAwxgHKoMsoIAAgggkHUC9KN2VYaTnROpEEAAAQQQIGDiUBtgAONQZZAVBBBAAIGsE6AftasynOycSIUAAggggAABE4faAAMYhyqDrCAQI/Ddnk3y/jePyX+2fSC7inZLfl5VObRmOzmu/oVSo0odrBBAwBEB+lG7isDJzolUCCCAAAIIEDBxqA0wgHGoMsgKAj8IfLntQ3l23Z2yW/aVMqkqleRXDW+QQ2oeixcCCDggQD9qVwk42TmRCoFMC+zbtUO2LJ9v/uzdtlkq1yyQ2kd3Mn8q5VfPdHa4HwIIJBFwqR/19VrhbK9Rl+Cz3ZL8I1ARAjqzZOqqgQmDJd71NWjSr9lDzDSpCHCugUA5BehH7QBxsnMiFQKZFNhVuFa+nD5K9m4pFMnLEykqKv67cu16csj5N0t+vUaZzBL3QgABAiZutQEGMG7VB7lB4M3198virfNTQvysVmfpfPCVKdORAAEE0itAP2rni5OdE6kQyJSAzixZ/fBg2bt1w/eBkpJHXp5UrlVXml4+jpkmmaoU7oNAGQIu9aPMMKGpIoBAaAJTVvaXLUXfpbx/7bwa0v+nU1KmIwECCKRXwKUBTHpLWr6r41Q+P85GoKIFNi1+Tb5+aVLKyzY44zKpc2y3lOlIgAAC6RVwqR8lYJLeuubqCCBQhsDE//SRnbInpVE1qSIDDn08ZToSIIBAegVcGsCkt6TluzpO5fPjbAQqWuCLx0fIji8+Tjy7xLtZXp5Ub3ykNO4zvKJvz/UQQMCngEv9KAETn5VHcgQQqDgBZphUnCVXQiATAi4NYDJR3qD3wCmoHOchkB6B1RMHye4Na1NevGrdRtJ0wF0p05EAAQTSK+BSP0rAJL11zdURQKAMAW8Pk90i8oVUl0KpJnulklSWfVJPdkpj2SFVRYQ9TGhGCLgh4NIAxg2RxLnAyeXaIW+5KMAMk1ysdcqczQIu9aMETLK5JZF3BLJcQN+Sc8+qK2WZFMg+qSwiuhFbXvHflWSvtJbNcm2zB3hLTpbXNdmPhoBLAxiXRXFyuXbIWy4KsIdJLtY6Zc5mAZf6UQIm2dySyDsCWS6wec9mGbjqJtlryqGBkpJHkQmjPNRstBRUKcjy0pJ9BLJfwKUBjMuaOLlcO+QtFwX2bN0kq+4ZKFJpX7Lhhsi+StLs2oekSq06uUhEmRFwSsClfpSAiVNNg8wgkFsCE9ZPknlbl6QsdNdax8qAgy9JmY4ECCCQXgGXBjDpLWn5ro5T+fw4G4GKFtg07zX5+olJIk1EzFrf+AmtIro2eI1Ig99dJnVO5i05Fe3P9RDwK+BSP0rAxG/tkR4BBCpM4LKV18mWIh2lJJpd4t2mSGrnVZVHfnp3hd2XCyGAQDABlwYwwUqQmbNwyowzd0HAVuCL0SNkx6cffx8p0Qmr+kensOoU180//JE8qX74kdL4Rt6SY+tKOgTSJeBSP0rAJF21zHURQCClwEX/uVp2pUwlki8ijx463iIlSRBAIJ0CLg1g0lnO8l4bp/IKcj4CFSuweugg2b3e4i05BzeSprfzlpyK1edqCPgXcKkfJWDiv/44AwEEKkiAGSYVBMllEMiQgEsDmAwVOdBtcArExkkIpE2geIZJka7FSXLkMcMkbRXAhRHwKeBSP0rAxGflkRwBBCpOgD1MKs6SKyGQCQGXBjCZKG/Qe+AUVI7zEEiPgNnDZOqklBdv0I89TFIikQCBDAi41I8SMMlAhXMLBBBILMBbcmgZCGSXgEsDGJflcHK5dshbLgrs27lDVt84WPZu3CCSaJZJXp5UPqCuNB0zTipVq56LRJQZAacEXOpHCZg41TTIDAK5J7B02zK5Y91E2Ws2fo3ftr6yFMkfGw6QNjVb5x4MJUbAQQGXBjAO8hRnCSeXa4e85arArnVr5cuxo2TvhkKRvLzvAyc//F25bj055PqbJb9ho1zlodwIOCXgUj9KwMSppkFmEMhNAZ1pMv2bp+X9bR/JzqI9Ui2vihxXs5WcX/9cKaiiW9lzIICACwIuDWBc8EiWB5xcrh3ylssCOtNky9sLZMvb82Xvpk1SuU4dqX1CJ6l9ws+ZWZLLDYOyOyfgUj9KwMS55kGGEEAAAQQQcFPApQGMm0Lf5wonl2uHvCGAAAIIuC7gUj9KwMT11kL+EEAAAQQQcETApQGMIyQJs4GTy7VD3hBAAAEEXBdwqR8lYOJ6ayF/CCCAAAIIOCLg0gDGERICJi5XBHlDAAEEEMhKAZfGGwRMsrIJkWkEEEAAAQQyL+DSACbzpbe/I072VqREAAEEEECgpIBL/SgBE9onAggggAACCFgJuDSAscpwSIlwCgme2yKAAAIIRELApX6UgEkkmhSFQAABBBBAIP0CLg1g0l/a4HfAKbgdZyKAAAIIIOBSP0rAhPaIAAIIIIAAAlYCLg1grDIcUiKcQoLntggggAACkRBwqR8lYBKJJkUhEEAAAQQQSL+ASwOY9Jc2+B1wCm7HmQgggAACCLjUjxIwoT0igAACCCCAgJWASwMYqwyHlAinkOC5LQIIIIBAJARc6kcJmESiSVEIBBBAAAEE0i/g0gAm/aUNfgecgttxJgIIIIAAAi71owRMaI8IIIAAAgggYCXg0gDGKsMhJcIpJHhuiwACCCAQCQGX+lECJpFoUhQCAQQQQACB9Au4NIBJf2mD3wGn4HaciQACCCCAgEv9KAET2iMCCCCAAAIIWAm4NICxynBIiXAKCZ7bIoAAAghEQsClfpSASSSaFIVAAAEEEEAg/QIuDWDSX9rgd8ApuB1nIoAAAggg4FI/SsCE9ogAAggggAACVgIuDWCsMhxSIpxCgue2CCCAAAKREHCpH82pgMmqVaukefPmsnDhQmnYsGEkGhOFQAABBBBAIFMC69atkw4dOshnn30mzZo1y9Rts+4+jDeyrsrIMAIIIICAQwIujTdyKmDy3nvvmYEeBwIIIIAAAggEF9BfPLRv3z74BSJ+JuONiFcwxUMAAQQQyIiAC+ONnAqY7NixQ5YtWyYNGjSQKlWqZKSSg9zEi6jl4kyYXC67thXK//1vr2n7uTcDLpfbfjaVfc+ePfL1119L69atpXr16kG6uJw4h/FGdlRzNn32Klo0l8ue6+Mt6p6xZjaMs10ab+RUwKSiO5t0Xc+lNVvpKmOy6+Zy2dWE8n8hTZo0kTVr1kjjxo0z3fxCvR91T93nYrsP9UPHzelzvuB7J1e/d3K5z83lsuf6WDvX6z5ot0/AJKhcGs/L5cacy2XP9S/xXC8/bZ8Hl1x9cEljd8qlUwjwvcP3Tq5+7+Ry28/lsjPWzN3vvPIMCAiYlEcvTefm8hdZLpc917/Ec738tP3c7cRzve7T1JVyWQuBXG97uVz+XC47443c7W+p+9yue4tuMWESAiZB5dJ43ubNm+Wuu+6SQYMGSUFBQRrv5N6lc7nsWhuUn7afi5/7XG/7uf65d68nyp0c5Xrby+Xy53LZ6XNyd6xF3ed23Qft3QmYBJXjPAQQQAABBBBAAAEEEEAAAQQQiKwAAZPIVi0FQwABBBBAAAEEEEAAAQQQQACBoAIETILKcR4CCCCAAAIIIIAAAggggAACCERWgIBJZKuWgiGAAAIIIIAAAggggAACCCCAQFABAiZB5TgPAQQQQAABBBBAAAEEEEAAAQQiK0DAJE1Vu3r1arnxxhvl1Vdfla1bt8oRRxwhV111lVx66aW+7jhr1iy58847ZdmyZZKfny+dO3eW0aNHS6tWrcq8ztq1a6Vly5ayadMmue222+SWW26JS9+vXz+ZNm1awmsMHjxY/vKXv/jKZ2ziTJd94cKFMnbsWPnwww/lq6++kr1798pPfvITOeOMM2TIkCHSqFGjUmUpLCw0Jv/3f/8n+t/NmjWTSy65xLyZqEqVKoHLridmuvzr1q2TBx54QBYtWiSLFy+W9evXS7du3eS1115LWI4RI0bIyJEjE/7br3/9a5kxY0aZ5Q/aJr2Lbt++XW699VZ58sknRfPesGFDOf/88+VPf/qT7LfffqXu7dfzjTfeEC3je++9Z67Vvn17U1797FTEkcny6/eH3k/rdenSpfLdd9/JY489Jn369ElYFG3H6pXoeO655+SXv/xluQgyWfZ77rlHnn32Wfn3v/8tGzZsMG8MO/TQQ+Wyyy6Tiy66SCpXrlyqLFGq+5KF08+49iF6rFmzRho3bhyXJC8vL2ndav+Rqs8oV8Pg5FAF/H5HJsts0M834w3GG4w3GG/Efq9UxHhDrxf0OynIeNO1MUcmy854I3UXTsAktZHvFPpue31I02DFtddeK82bNzcP5s8//7x5kBs+fLjVNSdPnmwCLDrQHTBggOzYsUPGjx8vGzdulAULFkjr1q2TXuess86SOXPmmGBNWQETffgqeWigpW3btlZ5LJkojLI/8cQT5iGyQ4cO5uG7UqVK5uFy6tSpUrt2bRNIiA2abNmyRY4//nj5+OOP5fe//720adNG9EFLr6GBpClTpgQqu54URvnnzZsnXbt2lUMOOUTatWtnHjJtAiZ333231K9fP66sTZs2LTOwUJ42qTfSYJbm7fXXX5cLL7xQunTpIkuWLJGHHnpITjrpJBNg1PrzDr+eL7/8sgkKqIU+XFarVk0efvhh89D94osvyimnnBK4bvXETJdf26O2b/1MVq9eXTQ4mCpgUqNGDbn55ptLldNrI0EBMl32Cy64wAQv9ftP26m+AnP27Nnyz3/+0wSMSn53Ra3uY+vp888/l6OPPtr8SL/TkwVMNCh4+eWXl6rinj17Sp06dYJWPec5LOD3OzJZUcrz+Wa8wXgjVcCE8Yb/L5HyfCaDjLdcGm+EMd5yacyR6bpnvJH680nAJLWR7xT6208dzM+cOVN69+5dfP6vfvUr89CmD+otWrQo87oaFNHfFutvVZcvX27+1kMHzvrwpMEBDYgkOvQ39/pAoTNTdLZIWQGToqIi3+Ur64Swyx6bt7///e/y29/+1swuGDZsWPE/6X+rybhx48yMEu+4+uqr5f777zcP8/ogH+QIo/waANKZBwceeKDJsv6m2SZg8tlnn5k2ZnuUp0169/jrX/9qZvKo9X333Vd8a60LnQ2ks57U0Dv8eGowRmcgfP3117JixQozy0gPDVzqw6YGEvSzFxuQsS27pguj/F9++aXUq1fPBEs0ANi/f/+UAROtUw2iVeQRRtmT5f/MM88036P6W3WvjqNY97Hl19ly33zzjRx55JHy+OOPJw2Y9O3b17QTjtwR8PMdmUylPJ9vxhvfqzLeKHtGK+MNf99J5flMBh1vuTLeCGu85cqYI4y6Z7yR+vNJwCS1ka8UutxAfxt68MEHy3//+9+4c72ZAIkCGCVv4j0cJZqR4i2n0eBJkyZN4k7VQbUGVPS39/pbRf2tclkBk3379ok+cNesWTPhFHc/hQ+77CXzqr+N79ixo1x33XVy1113Ff+zPlDqQ7Va6UO0d6xatcrMBtIH+kmTJvkpuknrSvn9BEy8h/GqVaumLG/QNhl74ZNPPtkEpNRaZ7N4hwZ8NC8nnnhi8VIiv57e5yvRLCFvGdKbb74pnTp1SlnWRAkyXf5k3wmpZpho+9bf9qmfzrAqa6mGLUTYZY/Np84K0xlJOjNJZ4fpEeW6f/TRR+Xiiy82S8zuvfdeE1RMNsNEAyaPPPKImY2odc8RbQG/35HJNIJ+vhlv/CjKeCN1wITxhv33UdDPZNDxlkvjDc1L2OUPc8wRZtkZbyT/jBIwsf/+skr57rvvmuUeOrVLp9LHHjt37jQP6LpkQJdNlHUMHDhQJkyYIK+88op07949LqkuMdAlOiVnsGii3/3ud/LWW2/JRx99ZAbYqQImOnNFp7rrfgDHHXec/PGPf5Szzz7bqqwlE4Vddh086h99+NYZBkOHDjX7mujDo8640EP3ONFglj6Y67Kmkocu3dGAly7p8XuEXX4vv7YBE6/uNb0u79JZH2XtsRO0TXr50tlMtWrVkv3331/0NxklD60TnU2lM0L08Ot5xx13mDrXz4fucxF76OfotNNOKzWryE8dZ7r8QQcw2sY1ELpr1y6zJ4wuQ9I9Y4455hg/xY1LG2bZ9bctOoNE9zHRZTfXX3+9WXKly6y8QF9U617rUgPgGjDRfZq8YHmygIkGvjVYol66BEf7mj//+c++ZpIFbiScmHEBv9+RyTIY9PPNeIPxBuMNxhsVPd7Q76mg30lBx5ux341hjznCKjvjjbK7cAImFTzE0SDGOeecIzfccIPoIL7kocsmdLM+3VejrENnh+h6fX3wP+qoo+KSvvDCC9KjRw/z28Zrrrmm+N+8n+t09dNPP734t66JZphoYEQDCxok0QfYTz75xOyPorNWdCmPPpT4PcIsu+a15GamOltEl+PobBvv+OCDD0yZf/Ob38hTTz1Vqoi61Ok///mPeTjze4Rdfi+/qQYwurGVbgKpMy00OKSzPSZOnGiCFfrbe91cMtERpE3GXkc319X7qbEO9EseWidPP/20CZhoMMevp7ekSj8HuoQh9tDPkS7LKTnbyE8dZ7r8JfNmsyRHl6vorCr9ztClR++//75ZZqYP0Bo0CrrxbZhlj93IVtu2BoAefPBBs/zKO6Ja9+eee67od5YGwDX4VVbARPfN0k2bDz/8cNHg/Pz5881sE51posFhXc7DES0Bv9+RyUof5PPNeCN+83TGG4lnmDDe+HF2s59vnyCfyfKMt1wab2hewix/2GOOsMrOeKPsTygBkyQ++vBte+gyA/2jh06X1zXF+sYP/a1uyUPX3OvAV387WtahMyJ0j5KVK1eW2u9Ef67/PmbMGPMbdT10log+EOoD0d/+9jfzMz9LgDS9bib4s5/9zCwl0kCMzSaBLpTdc9R86x+10IcM3aVbfzOrG+96hy7J0P1JNIiiU89KHvpvOkOn5FuFktWVS+X38pgqYJKoLHv27DFtWB+s3n77bTNLquTht02WPF9/K67tX9uobrJb8vDW4uubc3QWkN/Pki6l0j1SdFPQX/ziF3GX13bx05/+1MzM0plbQY5Ml79kHm0CJonKpUtXNEil+yb961//ClJ0833j5/uovHUfe762SZ05pm/i0M2zNZipe97oBsfeEcW6/8c//mH2wNJZNaeeeqopalkBk0QVq8FzDaLp7KqXXnopUN1zUvoFGG8w3rBpZYw3vldKNAYub5/DeCNeMMzxhuYkl8ccYZSd8Ubqb2ACJkmM/Kz717feeAOesH7jc8UVV5jfzusDkbf5p9+AiVLow6ROB7M9XCh7srzqmmJd5qFT0r3Aks0ME+91tDYGLpY/SMBEy6oPVDozQ9+womYlj0xHvf1+lqI6y8Crh6ADGD3/vPPOMzOqPv3007iZGTZtXNNkuu7LypfWswbGdJaUt3l21Or+22+/NUtxNPCnm7x6h9+AiZ6nwU/93tO9qnTzYA73BBhv2NWJi/2tl3PGG4lnmCSrWcYbZbf5sPvcMMcbuT7myHTdM96w638ImNg5WacKY02xLu/RZSY6KyL2DSOaF31bji5D0KUWGkjx3raTrED620xdzjNq1Ci56aabrMutCcMoe6oM6itJdeaMLjvRgz1MkovpG2R02r7u/6H7gJQ8Mr2u0m97iuo+FhURMLnxxhvl9ttvNzOINIjo98h03ZeVP11mpMtPYh+eolb3f/jDH8xyGp0t1aBBg2IOXUo5a9Ys0ZlyOgtLZ02letg+//zzRd9kovsGxb5e3W8bIL17An6/I5OVwM/nm/FG8nbAeMP+M8J4o2wrP5/JRFfyu2dcyWuUJ2BS3vGG5iXs8sd6ZHrMkemyM96w+94iYGLnZJ1Kp47rTuANGzYs9ZYcfTuITqm0eUvOlClTzHKSkq/E1Yzoq0X1y8x7S84zzzxjtVGrvllCZ6KUdeg+JrocR98Brvf3c4RR9lT50wcK3cnf20hU0+vbWfRnJd+So68p1bWL5XlLTqbrPlH5g84w0SVM+urrZMvJ/LTJZPVy0kknmeU4tm/J8eM5d+5c8xt5/XzoDITYQz9HOgusPG/JyXT5K3IAo3tb6IN2oiV+qT5D+u9hlz02j96yOv2e0n2c9Iha3ffq1cssP0p16D5UqWaN6NIlnY2jM0yqVauW6pL8exYJhNHnMt5I3kAYb9h/eBhvlG0Vdp9bnoBJeccbuT7myHTdM96w+94iYGLn5CuVzurQN+SUfIuNPozqmnLdv0Q7Vu/Qh5jdu3fHbcqnuzTrg73uI6KbcXozQzRIolO19Tes+pCgh67t1303Sh56nj4k6nR8/QJr27atmcK+bds281ackgNtfdWuptG/dc+HIL+NzHTZtczr1683v20teeibiM4666xS6/d1Jo7OoNF9EAYNGlR8mj6AacBIlzLpg32QI4zyl8xnWQET3atE67/k/jQ68NZNYBcvXmw2Co3dH8K7vp82qdfTtqr30eChd+jrmnUGiy6huO+++4p/rq99Hjx4sAkE6qtRvcOPp25s6g1YdWma98ptb3+f/Px8syRFN0MNcoRR/th8phrA6Ka6GmAqeWiAQd+Wpd8bQd7+pNfLdNm1jXq/IYstj/5MNybT79bp06eb7zY9olb3uo9QojdJ6YbM+v2kmzTXrVvX7HGi7TlZ3auRvrFNp/imejNbkM8E54Qv4Oc7UnPLeCP4WIvxRun2znjjG7MUnvHG922jIsYbuT7myPR4i/GGXT9OwMTOyVcqfVDUTRb1N3q64ajunq6/LdS33iT67b23I7M+DMQeOijWGSE6zVM3q9Q3H+gDvQ6O9Q0IqV4TmmwPE33Vrm4CqMGEww47rPgtOfpbef2g6ls1dAlPkCOMsmuQ54ADDjBLDXRTUV2Co1OVZ8yYYR7YdWaPGnqHPkB7b8PRcqqjptFNv5JtBmtrEUb5NW+xe45oG9PAge53oIcG3rw3BelaRW1vGrzTN6nodH+dWaMP41988YV5rbQu3Uh22LZJr+1p8EOv7R36YKsP79qp6vIx3WRXuuy4zwAACwVJREFUNyXVt57oZrD6CmgN5nmHX099c4OWTd9EpQEwDZJonjVI+fzzzxdvnmlbnyXTZbr8GuDwHnQ1mKWzRPQtXN5nX8vapk0bk019G4EupdIldfqdow/SuneFtmt9nbku79BAa9Ajk2XX7ygNWmqg94gjjjBvV9LAsO7TpLMltIxan7HBr6jVfaJ6SraHiS671OVWOsNKvwP1ldL6/xpY0mCy9hfefi9B65/z3BTw+x3JeGO1CcYGHWsx3mC84bWdqPU5Lo031DiXxxyZLHuyno3xRrwMAZM0jYE+++wzswfIq6++ah7g9VWPV111lVx++eWl7phsAKMJ9aF/7Nix5iFBH/70oVJnR3gPSWVlP1nARGdk6GuPdSaBPoTob3P1N9MnnHCC2e9EH2LLc2S67LrUSB8kdUaNBpP0gVtNNSg0ZMgQOeSQQ0oVR2fR6EwTfRjVt25oUEGXIGn6KlWqlKf4kunya2bL2sdAHzy1LeihQTdth7pBnb61RoNH+lppnVGiwSMNoqU6bNpksoCJXls/D/oGKd2EVN+IozNQdKbAsGHDpGbNmqVu78dTT9aZV3p9b/NeDRLoTKugs4ZKZiiT5fdmlSSrE5266QXG9AFZvys02KDtW2cTadvv3r272fRYgyjlPTJVdl0up8uo9EFfA3raTjX42bp1azNjQpfNxQbWvHJFqe79BEz0e0y/B/X1w2qnD4T6Haivn9cgqLcReHnrn/PdFPDzHcl4o3TAxM9Yi/EG443Yb4Eo9TmujTdsP5cVMd50ccyRqfGW34BJro43CJi4Of4hVwgggAACCCCAAAIIIIAAAgggEKIAAZMQ8bk1AggggAACCCCAAAIIIIAAAgi4KUDAxM16IVcIIIAAAggggAACCCCAAAIIIBCiAAGTEPG5NQIIIIAAAggggAACCCCAAAIIuClAwMTNeiFXCCCAAAIIIIAAAggggAACCCAQogABkxDxuTUCCCCAAAIIIIAAAggggAACCLgpQMDEzXohVwgggAACCCCAAAIIIIAAAgggEKIAAZMQ8bk1AggggAACCCCAAAIIIIAAAgi4KUDAxM16IVcIIIAAAggggAACCCCAAAIIIBCiAAGTEPG5NQIIIIAAAggggAACCCCAAAIIuClAwMTNeiFXCCCAAAIIIIAAAggggAACCCAQogABkxDxuTUCCJRPYOrUqdK/f3+ZO3eunHzyyeW7GGcjgAACCCCAAAIJBBhv0CwQyF0BAia5W/eUPMcF5s2bJ127dpXbbrtNbrnlFqMxYsQIOfbYY6VXr17O6DzzzDPy4YcfmryVPBjAOFNNZAQBBBBAAIGEAow3aBgIIJDNAgRMsrn2yDsC5RBINIDJy8uTvn37igYiXDn69esn06ZNk6KiolJZ2rt3r+zevVvy8/OlUqVKrmSZfCCAAAIIIIDADwKMN2gKCCCQzQIETLK59sg7AuUQyPQAZteuXbJv3z6pXr26r1yXFTDxdSESI4AAAggggEDGBRhvZJycGyKAQAUKEDCpQEwuhUA2CcQOYDp16mSW5yQ6Ymd2LF68WEaNGiVvvPGGfPvtt9K4cWP5zW9+I8OGDZP99tuv+HQvyPHNN9/IjTfeKM8995z873//k3/+859mr5GnnnpKpk+fLnq9r776ypzboUMHszRI8+IdzZo1k9WrV5fK1pQpU0TvkWxJjubt1ltvlX/84x+ydu1a2X///eUXv/iF+dlhhx1WfL1Vq1ZJ8+bNZfjw4dKxY0cZOXKkLFmyRGrVqiW9e/eWu+66S2rWrJlN1UpeEUAAAQQQcEqA8YYI4w2nmiSZQcCXAAETX1wkRiA6ArEDmMsuu0xeffVVufDCC6Vz585y+eWXFxe0T58+5r9feukls7dJkyZN5KKLLpKDDjrIBBceeeQRE2zQjVerVKli0noBE90PpV69etKzZ08zu+SMM86QI4880tzjgAMOkPbt20vDhg1lzZo1MnnyZBM8ef311+XEE08019H9SzRo8eabb8pjjz1WnCf99xYtWiQMmGzZskWOP/54WbFihZx//vkmALNy5Up58MEHzeyWBQsWSMuWLc21vAGMBms0zYABA0z5NLAzY8YM8/8TJkyITqVTEgQQQAABBDIswHiD8UaGmxy3Q6BCBQiYVCgnF0MgewT8TJHdsWOHmYnxk5/8xMwuqVatWnFBZ86cKeecc44JXuj+J7EBk/POO0/+9re/ie6NEnts27at1MyN9evXS6tWrUzw5fnnny9OXtaSnEQzTHS2i25kqzNhbrrppuLraCBGZ7d069ZNXnvttbiASY0aNeSjjz4yQRjvOP3002XOnDmyceNGZplkT7MmpwgggAACjgkw3vgxYMJ4w7HGSXYQsBAgYGKBRBIEoijgZwAze/ZsM0tEZ2mce+65cRy6ZEeXzpx11lkmOBIbMNElNzrLpKxDZ4To/iZ6HQ24vPvuu6JLebzDb8Dk6KOPli+++MLMVim5X4ouy9FyFxYWmhku3gyTCy64QJ544om4bI4bN06GDBkiy5YtM4EcDgQQQAABBBDwL8B4g/GG/1bDGQi4I0DAxJ26ICcIZFTAzwBm7NixcsMNN5SZPw1G6FKW2ICJziSJ3dvEu8DSpUvNvic6g0MDJrGHzkbR5TtBAyb62xsNmrz//vul8vuHP/xB7rvvPvnggw+kbdu2xQET3TtFZ6XEHt7sFXU66aSTMlo33AwBBBBAAIGoCDDeYLwRlbZMOXJTgIBJbtY7pUbAzLTQjV41UKABAz2SvVb4jjvukKFDh5plLrrfR6JDZ2y0a9cuLmCS6FXAOvtD9xDRjVWvvvpqs6eJbqyqrwUeM2aMCaLEnud3hkmQgIlu+jpixIiEARPdm0WX8nAggAACCCCAgH8Bxhs/BkwYb/hvP5yBQNgCBEzCrgHuj0BIAn4GMPq2GX1rjC5TGTRoUMoclxXk0BkeOtNDZ6PorJTYQ/cvWbhwYVzApH///mZ/lETBl0R7mOjyGW9JTuxeK3of3b9EAyAll+QwgElZpSRAAAEEEEAgkADjjR+X5DDeCNSEOAmBUAUImITKz80RCE8g0QCmdu3aZtbJs88+G5ex7du3mw1RdQaK7kty8MEHx/37nj17ZPPmzVK3bl3z87ICJg888IBcddVVZuNVDWB4x4svvihnnnmm+d/Y4IjOQrn//vtNkMO7vndOWZu+6jIi3YPEO/RNO126dEm46SsDmPDaIXdGAAEEEIi2AOON+NcKM6M12u2d0kVPgIBJ9OqUEiFgJZBoANO9e3fz2l0NIOgbcTRAom+60UNfO6wbu+qsDZ31oUtpdP8RfR3vrFmz5PbbbzeBklQBk//+97/Spk0bKSgokCuvvFLq168vixYtMpuualBGN1mNDZjoz/XVxr/97W+lR48eUrVqVfMmHX1rT6KASexrhfU8fQWx91rh/Px8eeutt0q9VpiAiVWTIRECCCCAAAK+BRhvEDDx3Wg4AQGHBAiYOFQZZAWBTAokGsB8+umnJojxzjvvFG/GGhu8+Pe//20CI7qcRt9CU6dOHWnatKmceuqpMnDgQGnSpEnKgIkm0KCMvvL3ww8/NMGR9u3by6233iqPPPKITJs2LS5gohvA6oazTz75pKxbt85sCDtlyhQTnEkUMNHr66uA9Xq6lGjt2rUmn6eccoqMHDlSDj/88GJm7y05BEwy2fK4FwIIIIBALgkw3iBgkkvtnbJGT4CASfTqlBIhgAACCCCAAAIIIIAAAggggEA5Bf4f7KhYaQjf/wUAAAAASUVORK5CYII=\" width=\"999.9999783255842\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -2144,10 +2148,6 @@
       "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"
      ]
     }
@@ -2197,14 +2197,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "12 rows affected.\n"
+      "8 rows affected.\n"
      ]
     },
     {
@@ -2990,7 +2990,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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+       "<img 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\" width=\"999.9999783255842\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -3010,10 +3010,6 @@
       "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"
      ]
     }
@@ -3059,7 +3055,7 @@
    "metadata": {},
    "source": [
     "<a id=\"print\"></a>\n",
-    "# 7. Print run schedules"
+    "# 7. Print run schedules (display only)"
    ]
   },
   {
@@ -3072,7 +3068,9 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",

diff --git a/community-artifacts/Deep-learning/automl/hyperband_v0.ipynb b/community-artifacts/Deep-learning/automl/hyperband_v0.ipynb
deleted file mode 100644
index 4cf7293..0000000
--- a/community-artifacts/Deep-learning/automl/hyperband_v0.ipynb
+++ /dev/null

@@ -1,259 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "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": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "u'Connected: fmcquillan@madlib'"
-      ]
-     },
-     "execution_count": 3,
-     "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": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from random import random\n",
-    "from math import log, ceil\n",
-    "from time import time, ctime\n",
-    "\n",
-    "\n",
-    "class Hyperband:\n",
-    "\n",
-    "\tdef __init__( self, get_params_function, try_params_function ):\n",
-    "\t\tself.get_params = get_params_function\n",
-    "\t\tself.try_params = try_params_function\n",
-    "\n",
-    "\t\tself.max_iter = 27  \t# maximum iterations per configuration\n",
-    "\t\tself.eta = 3\t\t\t# defines configuration downsampling rate (default = 3)\n",
-    "\n",
-    "\t\tself.logeta = lambda x: log( x ) / log( self.eta )\n",
-    "\t\tself.s_max = int( self.logeta( self.max_iter ))\n",
-    "\t\tself.B = ( self.s_max + 1 ) * self.max_iter\n",
-    "\n",
-    "\t\tself.results = []\t# list of dicts\n",
-    "\t\tself.counter = 0\n",
-    "\t\tself.best_loss = np.inf\n",
-    "\t\tself.best_counter = -1\n",
-    "\n",
-    "\n",
-    "\t# can be called multiple times\n",
-    "\tdef run( self, skip_last = 0, dry_run = False ):\n",
-    "\n",
-    "\t\tfor s in reversed( range( self.s_max + 1 )):\n",
-    "            \n",
-    "\t\t\tprint (\" \")            \n",
-    "\t\t\tprint (\"s = \", s)\n",
-    "\n",
-    "\t\t\t# initial number of configurations\n",
-    "\t\t\tn = int( ceil( self.B / self.max_iter / ( s + 1 ) * self.eta ** s ))\n",
-    "\n",
-    "\t\t\t# initial number of iterations per config\n",
-    "\t\t\tr = self.max_iter * self.eta ** ( -s )\n",
-    "\n",
-    "\t\t\t# n random configurations\n",
-    "\t\t\tT = [ self.get_params() for i in range( n )]\n",
-    "\n",
-    "\t\t\tfor i in range(( s + 1 ) - int( skip_last )):\t# changed from s + 1\n",
-    "\n",
-    "\t\t\t\t# Run each of the n configs for <iterations>\n",
-    "\t\t\t\t# and keep best (n_configs / eta) configurations\n",
-    "\n",
-    "\t\t\t\tn_configs = n * self.eta ** ( -i )\n",
-    "\t\t\t\tn_iterations = r * self.eta ** ( i )\n",
-    "\n",
-    "\t\t\t\tprint \"\\n*** {} configurations x {:.1f} iterations each\".format(\n",
-    "\t\t\t\t\tn_configs, n_iterations )\n",
-    "\n",
-    "\t\t\t\tval_losses = []\n",
-    "\t\t\t\tearly_stops = []\n",
-    "\n",
-    "\t\t\t\tfor t in T:\n",
-    "\n",
-    "\t\t\t\t\tself.counter += 1\n",
-    "\t\t\t\t\t#print \"\\n{} | {} | lowest loss so far: {:.4f} (run {})\\n\".format(\n",
-    "\t\t\t\t\t#\tself.counter, ctime(), self.best_loss, self.best_counter )\n",
-    "\n",
-    "\t\t\t\t\tstart_time = time()\n",
-    "\n",
-    "\t\t\t\t\tif dry_run:\n",
-    "\t\t\t\t\t\tresult = { 'loss': random(), 'log_loss': random(), 'auc': random()}\n",
-    "\t\t\t\t\telse:\n",
-    "\t\t\t\t\t\tresult = self.try_params( n_iterations, t )\t\t# <---\n",
-    "\n",
-    "\t\t\t\t\tassert( type( result ) == dict )\n",
-    "\t\t\t\t\tassert( 'loss' in result )\n",
-    "\n",
-    "\t\t\t\t\tseconds = int( round( time() - start_time ))\n",
-    "\t\t\t\t\t#print \"\\n{} seconds.\".format( seconds )\n",
-    "\n",
-    "\t\t\t\t\tloss = result['loss']\n",
-    "\t\t\t\t\tval_losses.append( loss )\n",
-    "\n",
-    "\t\t\t\t\tearly_stop = result.get( 'early_stop', False )\n",
-    "\t\t\t\t\tearly_stops.append( early_stop )\n",
-    "\n",
-    "\t\t\t\t\t# keeping track of the best result so far (for display only)\n",
-    "\t\t\t\t\t# could do it be checking results each time, but hey\n",
-    "\t\t\t\t\tif loss < self.best_loss:\n",
-    "\t\t\t\t\t\tself.best_loss = loss\n",
-    "\t\t\t\t\t\tself.best_counter = self.counter\n",
-    "\n",
-    "\t\t\t\t\tresult['counter'] = self.counter\n",
-    "\t\t\t\t\tresult['seconds'] = seconds\n",
-    "\t\t\t\t\tresult['params'] = t\n",
-    "\t\t\t\t\tresult['iterations'] = n_iterations\n",
-    "\n",
-    "\t\t\t\t\tself.results.append( result )\n",
-    "\n",
-    "\t\t\t\t# select a number of best configurations for the next loop\n",
-    "\t\t\t\t# filter out early stops, if any\n",
-    "\t\t\t\tindices = np.argsort( val_losses )\n",
-    "\t\t\t\tT = [ T[i] for i in indices if not early_stops[i]]\n",
-    "\t\t\t\tT = T[ 0:int( n_configs / self.eta )]\n",
-    "\n",
-    "\t\treturn self.results\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_params():\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def try_params():\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " \n",
-      "('s = ', 3)\n",
-      "\n",
-      "*** 27 configurations x 1.0 iterations each\n",
-      "\n",
-      "*** 9.0 configurations x 3.0 iterations each\n",
-      "\n",
-      "*** 3.0 configurations x 9.0 iterations each\n",
-      "\n",
-      "*** 1.0 configurations x 27.0 iterations each\n",
-      " \n",
-      "('s = ', 2)\n",
-      "\n",
-      "*** 9 configurations x 3.0 iterations each\n",
-      "\n",
-      "*** 3.0 configurations x 9.0 iterations each\n",
-      "\n",
-      "*** 1.0 configurations x 27.0 iterations each\n",
-      " \n",
-      "('s = ', 1)\n",
-      "\n",
-      "*** 6 configurations x 9.0 iterations each\n",
-      "\n",
-      "*** 2.0 configurations x 27.0 iterations each\n",
-      " \n",
-      "('s = ', 0)\n",
-      "\n",
-      "*** 4 configurations x 27.0 iterations each\n"
-     ]
-    }
-   ],
-   "source": [
-    "#!/usr/bin/env python\n",
-    "\n",
-    "\"bare-bones demonstration of using hyperband to tune sklearn GBT\"\n",
-    "\n",
-    "#from hyperband import Hyperband\n",
-    "#from defs.gb import get_params, try_params\n",
-    "\n",
-    "hb = Hyperband( get_params, try_params )\n",
-    "\n",
-    "# no actual tuning, doesn't call try_params()\n",
-    "results = hb.run( dry_run = True )\n",
-    "\n",
-    "#results = hb.run( skip_last = 1 ) # shorter run\n",
-    "#results = hb.run()"
-   ]
-  }
- ],
- "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": 2
-}

diff --git a/community-artifacts/Deep-learning/automl/hyperband_v1.ipynb b/community-artifacts/Deep-learning/automl/hyperband_v1.ipynb
deleted file mode 100644
index 106fd45..0000000
--- a/community-artifacts/Deep-learning/automl/hyperband_v1.ipynb
+++ /dev/null

@@ -1,3424 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hyperband\n",
-    "\n",
-    "Impelementation of Hyperband https://arxiv.org/pdf/1603.06560.pdf"
-   ]
-  },
-  {
-   "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": [
-      "Done.\n",
-      "Done.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%sql\n",
-    "DROP TABLE IF EXISTS results;\n",
-    "\n",
-    "CREATE TABLE results ( \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, \n",
-    "                      n INTEGER, \n",
-    "                      r INTEGER\n",
-    "                     );"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from random import random\n",
-    "from math import log, ceil\n",
-    "from time import time, ctime\n",
-    "\n",
-    "\n",
-    "class Hyperband:\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 = 81  # maximum iterations per configuration\n",
-    "        self.max_iter = 9  # maximum iterations per configuration\n",
-    "        self.eta = 3        # defines configuration downsampling rate (default = 3)\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",
-    "        self.results = []    # list of dicts\n",
-    "        self.counter = 0\n",
-    "        self.best_loss = np.inf\n",
-    "        self.best_counter = -1\n",
-    "\n",
-    "    # can be called multiple times\n",
-    "    def run( self, skip_last = 0, dry_run = False ):\n",
-    "\n",
-    "        for s in reversed( range( self.s_max + 1 )):\n",
-    "            \n",
-    "            # initial number of configurations\n",
-    "            n = int( ceil( self.B / self.max_iter / ( s + 1 ) * self.eta ** s ))\n",
-    "\n",
-    "            # initial number of iterations per config\n",
-    "            r = self.max_iter * self.eta ** ( -s )\n",
-    "            \n",
-    "            print (\"s = \", s)\n",
-    "            print (\"n = \", n)\n",
-    "            print (\"r = \", r)\n",
-    "\n",
-    "            # n random configurations\n",
-    "            T = self.get_params(n) # what to return from function if anything?\n",
-    "            \n",
-    "            for i in range(( s + 1 ) - int( skip_last )): # changed from s + 1\n",
-    "\n",
-    "                # Run each of the n configs for <iterations>\n",
-    "                # and keep best (n_configs / eta) configurations\n",
-    "\n",
-    "                n_configs = n * self.eta ** ( -i )\n",
-    "                n_iterations = r * self.eta ** ( i )\n",
-    "\n",
-    "                print \"\\n*** {} configurations x {:.1f} iterations each\".format(\n",
-    "                    n_configs, n_iterations )\n",
-    "                \n",
-    "                # multi-model training\n",
-    "                U = self.try_params(s, n_configs, n_iterations) # what to return from function if anything?\n",
-    "\n",
-    "                # select a number of best configurations for the next loop\n",
-    "                # filter out early stops, if any\n",
-    "                k = int( n_configs / self.eta)\n",
-    "                %sql DELETE FROM mst_table_hb WHERE mst_key NOT IN (SELECT mst_key from iris_multi_model_info ORDER BY training_loss_final ASC LIMIT $k::INT);\n",
-    "                        \n",
-    "        #return self.results\n",
-    "        \n",
-    "        return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_params(n):\n",
-    "    \n",
-    "    from sklearn.model_selection import ParameterSampler\n",
-    "    from scipy.stats.distributions import expon, uniform, lognorm\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 = ['Adam', 'SGD']\n",
-    "    # learning rate\n",
-    "    lr = [0.01, 0.1]\n",
-    "    # metrics\n",
-    "    metrics = ['accuracy']\n",
-    "\n",
-    "    # fit params\n",
-    "    # batch size\n",
-    "    batch_size = [4, 8]\n",
-    "    # epochs\n",
-    "    epochs = [1]\n",
-    "\n",
-    "    # create random param list\n",
-    "    param_grid = {\n",
-    "        'model_id': model_id,\n",
-    "        'loss': loss,\n",
-    "        'optimizer': optimizer,\n",
-    "        'lr': uniform(lr[0], lr[1]),\n",
-    "        'metrics': metrics,\n",
-    "        'batch_size': batch_size,\n",
-    "        'epochs': epochs\n",
-    "    }\n",
-    "    param_list = list(ParameterSampler(param_grid, n_iter=n))\n",
-    "    \n",
-    "    import psycopg2 as p2\n",
-    "\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",
-    "    %sql DROP TABLE IF EXISTS mst_table_hb, mst_table_auto_hb;\n",
-    "\n",
-    "    %sql CREATE TABLE mst_table_hb(mst_key serial, model_id integer, compile_params varchar, fit_params varchar);\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 = \"(\" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
-    "        \n",
-    "        %sql INSERT INTO mst_table_hb (model_id, compile_params, fit_params) VALUES $row_content\n",
-    "        \n",
-    "    %sql DROP TABLE IF EXISTS mst_table_hb_summary;\n",
-    "    %sql CREATE TABLE mst_table_hb_summary (model_arch_table varchar);\n",
-    "    %sql INSERT INTO mst_table_hb_summary VALUES ('model_arch_library');\n",
-    "    \n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def try_params(s, n_configs, n_iterations):\n",
-    "    \n",
-    "    print (\"s = \", s)\n",
-    "    print (\"n_configs aka n = \", n_configs)\n",
-    "    print (\"n_iterations aka r = \", n_iterations)\n",
-    "    \n",
-    "    import psycopg2 as p2\n",
-    "\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",
-    "    # multi-model fit\n",
-    "    # TO DO:  use warm start to continue from where left off after if not 1st time thru for this s value\n",
-    "    %sql DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;\n",
-    "    %sql SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', 'iris_multi_model', 'mst_table_hb', $n_iterations::INT, 0);\n",
-    "   \n",
-    "    # save results\n",
-    "    %sql DROP TABLE IF EXISTS temp_results;\n",
-    "    %sql CREATE TABLE temp_results AS (SELECT * FROM iris_multi_model_info);\n",
-    "    %sql ALTER TABLE temp_results DROP COLUMN mst_key, 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 n INTEGER, ADD COLUMN r INTEGER;\n",
-    "    %sql UPDATE temp_results SET model_arch_table = (SELECT model_arch_table FROM iris_multi_model_summary), num_iterations = (SELECT num_iterations FROM iris_multi_model_summary), start_training_time = (SELECT start_training_time FROM iris_multi_model_summary), end_training_time = (SELECT end_training_time FROM iris_multi_model_summary), s = $s, n = $n_configs, r = $n_iterations;\n",
-    "    %sql INSERT INTO results (SELECT * FROM temp_results);\n",
-    "\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def top_k(k):\n",
-    "    \n",
-    "    print (\"k = \", k)\n",
-    "    %sql DELETE FROM mst_table_hb WHERE mst_key NOT IN (SELECT mst_key from iris_multi_model_info ORDER BY training_loss_final ASC LIMIT $k::INT);\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n"
-     ]
-    }
-   ],
-   "source": [
-    "get_params(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "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": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%sql\n",
-    "SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', 'iris_multi_model', 'mst_table_hb', 3.0::INT, 0);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('s = ', 2)\n",
-      "('n = ', 9)\n",
-      "('r = ', 1.0)\n",
-      "Done.\n",
-      "Done.\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",
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "\n",
-      "*** 9 configurations x 1.0 iterations each\n",
-      "('s = ', 2)\n",
-      "('n_configs aka n = ', 9)\n",
-      "('n_iterations aka r = ', 1.0)\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "9 rows affected.\n",
-      "Done.\n",
-      "9 rows affected.\n",
-      "9 rows affected.\n",
-      "6 rows affected.\n",
-      "\n",
-      "*** 3.0 configurations x 3.0 iterations each\n",
-      "('s = ', 2)\n",
-      "('n_configs aka n = ', 3.0)\n",
-      "('n_iterations aka r = ', 3.0)\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "3 rows affected.\n",
-      "Done.\n",
-      "3 rows affected.\n",
-      "3 rows affected.\n",
-      "2 rows affected.\n",
-      "\n",
-      "*** 1.0 configurations x 9.0 iterations each\n",
-      "('s = ', 2)\n",
-      "('n_configs aka n = ', 1.0)\n",
-      "('n_iterations aka r = ', 9.0)\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "('s = ', 1)\n",
-      "('n = ', 3)\n",
-      "('r = ', 3.0)\n",
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "\n",
-      "*** 3 configurations x 3.0 iterations each\n",
-      "('s = ', 1)\n",
-      "('n_configs aka n = ', 3)\n",
-      "('n_iterations aka r = ', 3.0)\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "3 rows affected.\n",
-      "Done.\n",
-      "3 rows affected.\n",
-      "3 rows affected.\n",
-      "2 rows affected.\n",
-      "\n",
-      "*** 1.0 configurations x 9.0 iterations each\n",
-      "('s = ', 1)\n",
-      "('n_configs aka n = ', 1.0)\n",
-      "('n_iterations aka r = ', 9.0)\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "('s = ', 0)\n",
-      "('n = ', 3)\n",
-      "('r = ', 9)\n",
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "\n",
-      "*** 3 configurations x 9.0 iterations each\n",
-      "('s = ', 0)\n",
-      "('n_configs aka n = ', 3)\n",
-      "('n_iterations aka r = ', 9)\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "3 rows affected.\n",
-      "Done.\n",
-      "3 rows affected.\n",
-      "3 rows affected.\n",
-      "2 rows affected.\n"
-     ]
-    }
-   ],
-   "source": [
-    "hp = Hyperband( get_params, try_params )\n",
-    "results = hp.run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('s = ', 4)\n",
-      "('n = ', 81)\n",
-      "('r = ', 1.0)\n",
-      "\n",
-      "*** 81 configurations x 1.0 iterations each\n",
-      "\n",
-      "1 | Mon Nov  4 11:31:06 2019 | lowest loss so far: inf (run -1)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "2 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.8345 (run 1)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "3 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.6510 (run 2)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "4 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "5 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "6 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "7 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "8 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "9 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "10 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "11 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "12 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "13 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "14 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "15 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "16 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "17 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "18 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "19 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "20 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "21 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "22 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "23 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "24 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "25 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "26 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "27 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "28 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "29 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "30 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "31 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "32 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "33 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "34 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "35 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "36 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "37 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "38 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "39 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "40 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "41 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "42 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "43 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "44 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "45 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "46 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "47 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "48 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "49 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "50 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "51 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "52 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "53 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "54 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "55 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "56 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "57 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "58 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "59 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "60 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "61 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "62 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "63 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "64 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "65 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "66 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "67 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "68 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "69 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "70 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "71 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "72 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "73 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "74 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "75 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "76 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "77 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "78 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "79 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "80 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "81 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "*** 27.0 configurations x 3.0 iterations each\n",
-      "\n",
-      "82 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "83 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "84 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "85 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "86 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "87 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
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-      "88 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
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-      "\n",
-      "89 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
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-      "90 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
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-      "\n",
-      "91 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "92 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
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-      "\n",
-      "93 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "94 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "95 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "96 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "97 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "98 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "99 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
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-      "\n",
-      "100 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "101 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "102 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "103 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "104 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "105 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
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-      "\n",
-      "106 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "107 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
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-      "('s = ', 0)\n",
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-      "\n",
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-     ]
-    }
-   ],
-   "source": [
-    "#!/usr/bin/env python\n",
-    "\n",
-    "\"bare-bones demonstration of using hyperband to tune sklearn GBT\"\n",
-    "\n",
-    "#from hyperband import Hyperband\n",
-    "#from defs.gb import get_params, try_params\n",
-    "\n",
-    "hb = Hyperband( get_params, try_params )\n",
-    "\n",
-    "# no actual tuning, doesn't call try_params()\n",
-    "results = hb.run( dry_run = True )\n",
-    "\n",
-    "#results = hb.run( skip_last = 1 ) # shorter run\n",
-    "#results = hb.run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
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-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.5297427251128467,\n",
-       "  'counter': 2,\n",
-       "  'iterations': 1.0,\n",
-       "  'log_loss': 0.6810161167234852,\n",
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-       "  'params': None,\n",
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-       " {'auc': 0.6457699181279158,\n",
-       "  'counter': 3,\n",
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-       "  'params': None,\n",
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-       "  'params': None,\n",
-       "  'seconds': 0},\n",
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-       "  'counter': 7,\n",
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-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.8321190560222416,\n",
-       "  'counter': 10,\n",
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-       "  'params': None,\n",
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-       " {'auc': 0.061062929867188864,\n",
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-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.5834570772207172,\n",
-       "  'counter': 13,\n",
-       "  'iterations': 1.0,\n",
-       "  'log_loss': 0.8877445886262226,\n",
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-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.21304684547405073,\n",
-       "  'counter': 14,\n",
-       "  'iterations': 1.0,\n",
-       "  'log_loss': 0.7469669529997487,\n",
-       "  'loss': 0.23330551995179538,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.6763383197387115,\n",
-       "  'counter': 15,\n",
-       "  'iterations': 1.0,\n",
-       "  'log_loss': 0.7302057912871137,\n",
-       "  'loss': 0.7292961849015884,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.08100918401803436,\n",
-       "  'counter': 16,\n",
-       "  'iterations': 1.0,\n",
-       "  'log_loss': 0.2476607780064477,\n",
-       "  'loss': 0.22020659312661672,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.6772648893392582,\n",
-       "  'counter': 17,\n",
-       "  'iterations': 1.0,\n",
-       "  'log_loss': 0.2635059351107911,\n",
-       "  'loss': 0.3548780552739327,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.38959696020655343,\n",
-       "  'counter': 18,\n",
-       "  'iterations': 1.0,\n",
-       "  'log_loss': 0.8074850148212164,\n",
-       "  'loss': 0.6831797045398854,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.28214128210394984,\n",
-       "  'counter': 19,\n",
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-       "  'counter': 154,\n",
-       "  'iterations': 9.0,\n",
-       "  'log_loss': 0.642407933520737,\n",
-       "  'loss': 0.25987622452527404,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.1219267252329107,\n",
-       "  'counter': 155,\n",
-       "  'iterations': 9.0,\n",
-       "  'log_loss': 0.6545156950387196,\n",
-       "  'loss': 0.32292023554125926,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.6042193527835271,\n",
-       "  'counter': 156,\n",
-       "  'iterations': 9.0,\n",
-       "  'log_loss': 0.40200498422921305,\n",
-       "  'loss': 0.8421553561696671,\n",
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-       "  'seconds': 0},\n",
-       " {'auc': 0.3493005856626694,\n",
-       "  'counter': 157,\n",
-       "  'iterations': 9.0,\n",
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-       " {'auc': 0.13196247808805261,\n",
-       "  'counter': 158,\n",
-       "  'iterations': 27.0,\n",
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-       "  'loss': 0.30410196671776657,\n",
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-       " {'auc': 0.6093129479295774,\n",
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-       " {'auc': 0.9969193634649506,\n",
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-       " {'auc': 0.357386674867637,\n",
-       "  'counter': 164,\n",
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-       "  'loss': 0.9693066933917974,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.7535755450807958,\n",
-       "  'counter': 165,\n",
-       "  'iterations': 9.0,\n",
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-       "  'loss': 0.07652694221842082,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.9577803131411142,\n",
-       "  'counter': 166,\n",
-       "  'iterations': 9.0,\n",
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-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.1449007354101708,\n",
-       "  'counter': 167,\n",
-       "  'iterations': 9.0,\n",
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-       "  'seconds': 0},\n",
-       " {'auc': 0.6787455152608078,\n",
-       "  'counter': 168,\n",
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-       "  'seconds': 0},\n",
-       " {'auc': 0.13165584552000364,\n",
-       "  'counter': 169,\n",
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-       " {'auc': 0.06201807035142759,\n",
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-       " {'auc': 0.8624386730545928,\n",
-       "  'counter': 171,\n",
-       "  'iterations': 27.0,\n",
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-       " {'auc': 0.5715381698905945,\n",
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-       " {'auc': 0.9504349501608724,\n",
-       "  'counter': 173,\n",
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-       "  'counter': 174,\n",
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-       " {'auc': 0.21820227121120184,\n",
-       "  'counter': 175,\n",
-       "  'iterations': 27.0,\n",
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-       " {'auc': 0.38119137963197636,\n",
-       "  'counter': 176,\n",
-       "  'iterations': 27.0,\n",
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-       "  'seconds': 0},\n",
-       " {'auc': 0.22770017353156757,\n",
-       "  'counter': 177,\n",
-       "  'iterations': 27.0,\n",
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-       "  'loss': 0.6351536093891014,\n",
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-       "  'seconds': 0},\n",
-       " {'auc': 0.07157251719815028,\n",
-       "  'counter': 178,\n",
-       "  'iterations': 27.0,\n",
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-       "  'seconds': 0},\n",
-       " {'auc': 0.8214789406991464,\n",
-       "  'counter': 179,\n",
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-       "  'seconds': 0},\n",
-       " {'auc': 0.8359682498265789,\n",
-       "  'counter': 180,\n",
-       "  'iterations': 27.0,\n",
-       "  'log_loss': 0.4715081057970302,\n",
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-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.9772179053215029,\n",
-       "  'counter': 181,\n",
-       "  'iterations': 81.0,\n",
-       "  'log_loss': 0.06464750664889651,\n",
-       "  'loss': 0.8809809023370188,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.5735130758927682,\n",
-       "  'counter': 182,\n",
-       "  'iterations': 81.0,\n",
-       "  'log_loss': 0.3034499002530837,\n",
-       "  'loss': 0.1269187021236282,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.1512467303346996,\n",
-       "  'counter': 183,\n",
-       "  'iterations': 81,\n",
-       "  'log_loss': 0.06311948439063264,\n",
-       "  'loss': 0.39857231746894706,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.8624041903201188,\n",
-       "  'counter': 184,\n",
-       "  'iterations': 81,\n",
-       "  'log_loss': 0.4487021577153334,\n",
-       "  'loss': 0.482329681305285,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.7276284122996118,\n",
-       "  'counter': 185,\n",
-       "  'iterations': 81,\n",
-       "  'log_loss': 0.6673161280963441,\n",
-       "  'loss': 0.2502910767343628,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.7599292676148188,\n",
-       "  'counter': 186,\n",
-       "  'iterations': 81,\n",
-       "  'log_loss': 0.8642048547707212,\n",
-       "  'loss': 0.714166849575351,\n",
-       "  'params': None,\n",
-       "  'seconds': 0},\n",
-       " {'auc': 0.7396708566431607,\n",
-       "  'counter': 187,\n",
-       "  'iterations': 81,\n",
-       "  'log_loss': 0.8495002542298392,\n",
-       "  'loss': 0.14587931909618035,\n",
-       "  'params': None,\n",
-       "  'seconds': 0}]"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'a': 2, 'b': 0.3388081749546307, 'c': 0.704635960884642},\n",
-       " {'a': 1, 'b': 0.4904175136129263, 'c': 0.8971084273807718},\n",
-       " {'a': 1, 'b': 1.2386463990117793, 'c': 0.21568311690580266},\n",
-       " {'a': 1, 'b': 1.91007461806631, 'c': 0.17778124867596956},\n",
-       " {'a': 1, 'b': 1.2563450220231427, 'c': 0.002076412746974121}]"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import ParameterSampler\n",
-    "from scipy.stats.distributions import expon, uniform, lognorm\n",
-    "import numpy as np\n",
-    "#rng = np.random.RandomState()\n",
-    "param_grid = {'a':[1, 2], 'b': expon(), 'c': uniform()}\n",
-    "#param_list = list(ParameterSampler(param_grid, n_iter=5, random_state=rng))\n",
-    "param_list = list(ParameterSampler(param_grid, n_iter=5))\n",
-    "rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items()) for d in param_list]\n",
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'a': 2, 'b': 0.37954129345633403, 'c': 0.3742154014629032},\n",
-       " {'a': 2, 'b': 1.2830633021262747, 'c': 0.4373122879029032},\n",
-       " {'a': 1, 'b': 0.22037072550727527, 'c': 0.26397341600176616},\n",
-       " {'a': 1, 'b': 0.549444485603122, 'c': 0.8317686948528791},\n",
-       " {'a': 1, 'b': 1.0567787144413414, 'c': 0.9560841093558743}]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'a': 2.0, 'b': 0.379541, 'c': 0.374215},\n",
-       " {'a': 2.0, 'b': 1.283063, 'c': 0.437312},\n",
-       " {'a': 1.0, 'b': 0.220371, 'c': 0.263973},\n",
-       " {'a': 1.0, 'b': 0.549444, 'c': 0.831769},\n",
-       " {'a': 1.0, 'b': 1.056779, 'c': 0.956084}]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rounded_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'d': 2.9713720038716116},\n",
-       " {'d': 10.275052606706604},\n",
-       " {'d': 4.211836333907813},\n",
-       " {'d': 3.6005371688499834},\n",
-       " {'d': 14.68709362771547}]"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#rng = np.random.RandomState(0)\n",
-    "param_grid = {'d': lognorm(1, 2, 3)}\n",
-    "#param_list = list(ParameterSampler(param_grid, n_iter=5, random_state=rng))\n",
-    "param_list = list(ParameterSampler(param_grid, n_iter=5))\n",
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.07983433464722507,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 4,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.03805362658279962,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 1,\n",
-       "  'optimizer': 'SGD'},\n",
-       " {'batch_size': 4,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.09043633721868387,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.02775811670911417,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 1,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.104019113296403,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.06986494800074812,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'SGD'},\n",
-       " {'batch_size': 4,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.010449656955883938,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 4,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.04915490422264339,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'SGD'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.05257644929029893,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 1,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.02993608422766151,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'SGD'}]"
-      ]
-     },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# model architecture\n",
-    "model_id = [1, 2]\n",
-    "\n",
-    "# compile params\n",
-    "\n",
-    "# loss function\n",
-    "loss = ['categorical_crossentropy']\n",
-    "# optimizer\n",
-    "optimizer = ['Adam', 'SGD']\n",
-    "# learning rate\n",
-    "lr = [0.01, 0.1]\n",
-    "# metrics\n",
-    "metrics = ['accuracy']\n",
-    "\n",
-    "# fit params\n",
-    "\n",
-    "# batch size\n",
-    "batch_size = [4, 8]\n",
-    "# epochs\n",
-    "epochs = [1]\n",
-    "\n",
-    "# create random param list\n",
-    "param_grid = {\n",
-    "    'model_id': model_id,\n",
-    "    'loss': loss,\n",
-    "    'optimizer': optimizer,\n",
-    "    'lr': uniform(lr[0], lr[1]),\n",
-    "    'metrics': metrics,\n",
-    "    'batch_size': batch_size,\n",
-    "    'epochs': epochs\n",
-    "}\n",
-    "param_list = list(ParameterSampler(param_grid, n_iter=10))\n",
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'batch_size': 8,\n",
-       " 'epochs': 1,\n",
-       " 'loss': 'categorical_crossentropy',\n",
-       " 'lr': 0.03396784466820144,\n",
-       " 'optimizer': 'Adam'}"
-      ]
-     },
-     "execution_count": 212,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_list[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.07983433464722507)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='SGD(lr=0.03805362658279962)',metrics=['accuracy']$$\n",
-      "batch_size=4,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.09043633721868387)',metrics=['accuracy']$$\n",
-      "batch_size=4,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.02775811670911417)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.104019113296403)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='SGD(lr=0.06986494800074812)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.010449656955883938)',metrics=['accuracy']$$\n",
-      "batch_size=4,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='SGD(lr=0.04915490422264339)',metrics=['accuracy']$$\n",
-      "batch_size=4,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.05257644929029893)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='SGD(lr=0.02993608422766151)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for params in param_list:\n",
-    "#    for key, value in params.items():\n",
-    "#        print (key, value)\n",
-    "\n",
-    "    compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
-    "    print (compile_params)\n",
-    "        \n",
-    "    fit_params = \"batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\"))\n",
-    "    print (fit_params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done.\n",
-      "Done.\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": [
-    "import psycopg2 as p2\n",
-    "\n",
-    "#conn = p2.connect('postgresql://gpadmin@35.239.240.26:5432/madlib')\n",
-    "conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
-    "cur = conn.cursor()\n",
-    "\n",
-    "%sql DROP TABLE IF EXISTS mst_table_hb, mst_table_auto_hb;\n",
-    "\n",
-    "%sql CREATE TABLE mst_table_hb(mst_key serial, model_id integer, compile_params varchar, fit_params varchar);\n",
-    "\n",
-    "for params in param_list:\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 = \"(\" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
-    "    \n",
-    "    %sql INSERT INTO mst_table_hb (model_id, compile_params, fit_params) VALUES $row_content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 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>2</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.07983433464722507)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=8,epochs=1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>2</td>\n",
-       "        <td>1</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='SGD(lr=0.03805362658279962)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=4,epochs=1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>3</td>\n",
-       "        <td>2</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.09043633721868387)',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=0.02775811670911417)',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=0.104019113296403)',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='SGD(lr=0.06986494800074812)',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=0.010449656955883938)',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='SGD(lr=0.04915490422264339)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=4,epochs=1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>9</td>\n",
-       "        <td>1</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.05257644929029893)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=8,epochs=1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>10</td>\n",
-       "        <td>2</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='SGD(lr=0.02993608422766151)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=8,epochs=1</td>\n",
-       "    </tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "[(1, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.07983433464722507)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (2, 1, u\"loss='categorical_crossentropy',optimizer='SGD(lr=0.03805362658279962)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
-       " (3, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.09043633721868387)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
-       " (4, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.02775811670911417)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (5, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.104019113296403)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (6, 2, u\"loss='categorical_crossentropy',optimizer='SGD(lr=0.06986494800074812)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.010449656955883938)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
-       " (8, 2, u\"loss='categorical_crossentropy',optimizer='SGD(lr=0.04915490422264339)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
-       " (9, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.05257644929029893)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (10, 2, u\"loss='categorical_crossentropy',optimizer='SGD(lr=0.02993608422766151)',metrics=['accuracy']\", u'batch_size=8,epochs=1')]"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%sql\n",
-    "SELECT * FROM mst_table_hb ORDER BY mst_key;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from random import random\n",
-    "from math import log, ceil\n",
-    "from time import time, ctime\n",
-    "\n",
-    "\n",
-    "class Hyperband:\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 = 81  # maximum iterations per configuration\n",
-    "        self.eta = 3        # defines configuration downsampling rate (default = 3)\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",
-    "        self.results = []    # list of dicts\n",
-    "        self.counter = 0\n",
-    "        self.best_loss = np.inf\n",
-    "        self.best_counter = -1\n",
-    "\n",
-    "\n",
-    "    # can be called multiple times\n",
-    "    def run( self, skip_last = 0, dry_run = False ):\n",
-    "\n",
-    "        for s in reversed( range( self.s_max + 1 )):\n",
-    "            \n",
-    "            # initial number of configurations\n",
-    "            n = int( ceil( self.B / self.max_iter / ( s + 1 ) * self.eta ** s ))\n",
-    "\n",
-    "            # initial number of iterations per config\n",
-    "            r = self.max_iter * self.eta ** ( -s )\n",
-    "            \n",
-    "            print (\"s = \", s)\n",
-    "            print (\"n = \", n)\n",
-    "            print (\"r = \", r)\n",
-    "\n",
-    "            # n random configurations\n",
-    "            T = self.get_params(n) # what to return from function if anything?\n",
-    "            \n",
-    "            return\n",
-    "\n",
-    "            for i in range(( s + 1 ) - int( skip_last )): # changed from s + 1\n",
-    "\n",
-    "                # Run each of the n configs for <iterations>\n",
-    "                # and keep best (n_configs / eta) configurations\n",
-    "\n",
-    "                n_configs = n * self.eta ** ( -i )\n",
-    "                n_iterations = r * self.eta ** ( i )\n",
-    "\n",
-    "                print \"\\n*** {} configurations x {:.1f} iterations each\".format(\n",
-    "                    n_configs, n_iterations )\n",
-    "\n",
-    "                val_losses = []\n",
-    "                early_stops = []\n",
-    "                \n",
-    "                \n",
-    "                \n",
-    "                \n",
-    "\n",
-    "                for t in T:\n",
-    "\n",
-    "                    self.counter += 1\n",
-    "                    print \"\\n{} | {} | lowest loss so far: {:.4f} (run {})\\n\".format(\n",
-    "                        self.counter, ctime(), self.best_loss, self.best_counter )\n",
-    "\n",
-    "                    start_time = time()\n",
-    "\n",
-    "                    if dry_run:\n",
-    "                        result = { 'loss': random(), 'log_loss': random(), 'auc': random()}\n",
-    "                    else:\n",
-    "                        result = self.try_params( n_iterations, t )  # <---\n",
-    "\n",
-    "                    assert( type( result ) == dict )\n",
-    "                    assert( 'loss' in result )\n",
-    "\n",
-    "                    seconds = int( round( time() - start_time ))\n",
-    "                    print \"\\n{} seconds.\".format( seconds )\n",
-    "\n",
-    "                    loss = result['loss']\n",
-    "                    val_losses.append( loss )\n",
-    "\n",
-    "                    early_stop = result.get( 'early_stop', False )\n",
-    "                    early_stops.append( early_stop )\n",
-    "\n",
-    "                    # keeping track of the best result so far (for display only)\n",
-    "                    # could do it be checking results each time, but hey\n",
-    "                    if loss < self.best_loss:\n",
-    "                        self.best_loss = loss\n",
-    "                        self.best_counter = self.counter\n",
-    "\n",
-    "                    result['counter'] = self.counter\n",
-    "                    result['seconds'] = seconds\n",
-    "                    result['params'] = t\n",
-    "                    result['iterations'] = n_iterations\n",
-    "                        \n",
-    "                    self.results.append( result )\n",
-    "\n",
-    "                # select a number of best configurations for the next loop\n",
-    "                # filter out early stops, if any\n",
-    "                indices = np.argsort( val_losses )\n",
-    "                T = [ T[i] for i in indices if not early_stops[i]]\n",
-    "                T = T[ 0:int( n_configs / self.eta )]\n",
-    "                        \n",
-    "        return self.results"
-   ]
-  }
- ],
- "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": 2
-}

diff --git a/community-artifacts/Deep-learning/automl/hyperband_v1.py b/community-artifacts/Deep-learning/automl/hyperband_v1.py
deleted file mode 100644
index 9bbf0f0..0000000
--- a/community-artifacts/Deep-learning/automl/hyperband_v1.py
+++ /dev/null

@@ -1,99 +0,0 @@
-import numpy as np
-
-from random import random
-from math import log, ceil
-from time import time, ctime
-
-
-class Hyperband:
-
-	def __init__( self, get_params_function, try_params_function ):
-		self.get_params = get_params_function
-		self.try_params = try_params_function
-
-		self.max_iter = 81  	# maximum iterations per configuration
-		self.eta = 3			# defines configuration downsampling rate (default = 3)
-
-		self.logeta = lambda x: log( x ) / log( self.eta )
-		self.s_max = int( self.logeta( self.max_iter ))
-		self.B = ( self.s_max + 1 ) * self.max_iter
-
-		self.results = []	# list of dicts
-		self.counter = 0
-		self.best_loss = np.inf
-		self.best_counter = -1
-
-
-	# can be called multiple times
-	def run( self, skip_last = 0, dry_run = False ):
-
-		for s in reversed( range( self.s_max + 1 )):
-
-			# initial number of configurations
-			n = int( ceil( self.B / self.max_iter / ( s + 1 ) * self.eta ** s ))
-
-			# initial number of iterations per config
-			r = self.max_iter * self.eta ** ( -s )
-
-			# n random configurations
-			T = [ self.get_params() for i in range( n )]
-
-			for i in range(( s + 1 ) - int( skip_last )):	# changed from s + 1
-
-				# Run each of the n configs for <iterations>
-				# and keep best (n_configs / eta) configurations
-
-				n_configs = n * self.eta ** ( -i )
-				n_iterations = r * self.eta ** ( i )
-
-				print "\n*** {} configurations x {:.1f} iterations each".format(
-					n_configs, n_iterations )
-
-				val_losses = []
-				early_stops = []
-
-				for t in T:
-
-					self.counter += 1
-					print "\n{} | {} | lowest loss so far: {:.4f} (run {})\n".format(
-						self.counter, ctime(), self.best_loss, self.best_counter )
-
-					start_time = time()
-
-					if dry_run:
-						result = { 'loss': random(), 'log_loss': random(), 'auc': random()}
-					else:
-						result = self.try_params( n_iterations, t )		# <---
-
-					assert( type( result ) == dict )
-					assert( 'loss' in result )
-
-					seconds = int( round( time() - start_time ))
-					print "\n{} seconds.".format( seconds )
-
-					loss = result['loss']
-					val_losses.append( loss )
-
-					early_stop = result.get( 'early_stop', False )
-					early_stops.append( early_stop )
-
-					# keeping track of the best result so far (for display only)
-					# could do it be checking results each time, but hey
-					if loss < self.best_loss:
-						self.best_loss = loss
-						self.best_counter = self.counter
-
-					result['counter'] = self.counter
-					result['seconds'] = seconds
-					result['params'] = t
-					result['iterations'] = n_iterations
-
-					self.results.append( result )
-
-				# select a number of best configurations for the next loop
-				# filter out early stops, if any
-				indices = np.argsort( val_losses )
-				T = [ T[i] for i in indices if not early_stops[i]]
-				T = T[ 0:int( n_configs / self.eta )]
-
-		return self.results

diff --git a/community-artifacts/Deep-learning/automl/hyperband_v2.ipynb b/community-artifacts/Deep-learning/automl/hyperband_v2.ipynb
deleted file mode 100644
index d1a2de6..0000000
--- a/community-artifacts/Deep-learning/automl/hyperband_v2.ipynb
+++ /dev/null

@@ -1,3043 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hyperband\n",
-    "\n",
-    "Impelementation of Hyperband https://arxiv.org/pdf/1603.06560.pdf with ideas from blog post by the same authors https://homes.cs.washington.edu/~jamieson/hyperband.html"
-   ]
-  },
-  {
-   "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": [],
-   "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\n",
-    "\n",
-    "#psycopg2 connection\n",
-    "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()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pretty print run schedule"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter = 27\n",
-      "eta = 3\n",
-      "B = 4*max_iter = 108\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",
-    "\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",
-    "\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):\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:\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": [
-    "Create tables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done.\n",
-      "Done.\n",
-      "Done.\n",
-      "Done.\n",
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%sql\n",
-    "-- overall results table\n",
-    "DROP TABLE IF EXISTS results;\n",
-    "CREATE TABLE results ( \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, \n",
-    "                      n INTEGER, \n",
-    "                      r INTEGER,\n",
-    "                      run_id SERIAL\n",
-    "                     );\n",
-    "\n",
-    "-- model selection table\n",
-    "DROP TABLE IF EXISTS mst_table_hb, mst_table_auto_hb;\n",
-    "CREATE TABLE mst_table_hb (\n",
-    "                           mst_key SERIAL, \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_summary;\n",
-    "CREATE TABLE mst_table_hb_summary (model_arch_table varchar);\n",
-    "INSERT INTO mst_table_hb_summary VALUES ('model_arch_library');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Hyperband main "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from random import random\n",
-    "from math import log, ceil\n",
-    "from time import time, ctime\n",
-    "\n",
-    "\n",
-    "class Hyperband:\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 = 3  # maximum iterations per configuration\n",
-    "        self.eta = 3        # defines configuration downsampling rate (default = 3)\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",
-    "        self.results = []    # list of dicts\n",
-    "        self.counter = 0\n",
-    "        self.best_loss = np.inf\n",
-    "        self.best_counter = -1\n",
-    "\n",
-    "    # can be called multiple times\n",
-    "    def run( self, skip_last = 0, dry_run = False ):\n",
-    "\n",
-    "        for s in reversed( range( self.s_max + 1 )):\n",
-    "            \n",
-    "            # initial number of configurations\n",
-    "            n = int( ceil( self.B / self.max_iter / ( s + 1 ) * self.eta ** s ))\n",
-    "\n",
-    "            # initial number of iterations per config\n",
-    "            r = self.max_iter * self.eta ** ( -s )\n",
-    "            \n",
-    "            print (\"s = \", s)\n",
-    "            print (\"n = \", n)\n",
-    "            print (\"r = \", r)\n",
-    "\n",
-    "            # n random configurations\n",
-    "            T = self.get_params(n) # what to return from function if anything?\n",
-    "            \n",
-    "            for i in range(( s + 1 ) - int( skip_last )): # changed from s + 1\n",
-    "\n",
-    "                # Run each of the n configs for <iterations>\n",
-    "                # and keep best (n_configs / eta) configurations\n",
-    "\n",
-    "                n_configs = n * self.eta ** ( -i )\n",
-    "                n_iterations = r * self.eta ** ( i )\n",
-    "\n",
-    "                print \"\\n*** {} configurations x {:.1f} iterations each\".format(\n",
-    "                    n_configs, n_iterations )\n",
-    "                \n",
-    "                # multi-model training\n",
-    "                U = self.try_params(s, n_configs, n_iterations) # what to return from function if anything?\n",
-    "\n",
-    "                # select a number of best configurations for the next loop\n",
-    "                # filter out early stops, if any\n",
-    "                # drop from model selection table, model table and info table to keep all in sync\n",
-    "                k = int( n_configs / self.eta)\n",
-    "                \n",
-    "                %sql DELETE FROM iris_multi_model_info WHERE mst_key NOT IN (SELECT mst_key FROM iris_multi_model_info ORDER BY training_loss_final ASC LIMIT $k::INT);\n",
-    "                %sql DELETE FROM iris_multi_model WHERE mst_key NOT IN (SELECT mst_key FROM iris_multi_model_info);\n",
-    "                %sql DELETE FROM mst_table_hb WHERE mst_key NOT IN (SELECT mst_key FROM iris_multi_model_info);\n",
-    "\n",
-    "        #return self.results\n",
-    "        \n",
-    "        return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_params(n):\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 = ['Adam', 'SGD']\n",
-    "    # learning rate (sample on log scale here not in ParameterSampler)\n",
-    "    lr_range = [0.01, 0.1]\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 = [4, 8]\n",
-    "    # epochs\n",
-    "    epochs = [1]\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 = \"(\" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
-    "        \n",
-    "        %sql INSERT INTO mst_table_hb (model_id, compile_params, fit_params) VALUES $row_content\n",
-    "    \n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def try_params(s, n_configs, n_iterations):\n",
-    "\n",
-    "    # multi-model fit\n",
-    "    # TO DO:  use warm start to continue from where left off after if not 1st time thru for this s value\n",
-    "    %sql DROP TABLE IF EXISTS iris_multi_model, iris_multi_model_summary, iris_multi_model_info;\n",
-    "    %sql SELECT madlib.madlib_keras_fit_multiple_model('iris_train_packed', 'iris_multi_model', 'mst_table_hb', $n_iterations::INT, 0);\n",
-    "   \n",
-    "    # save results\n",
-    "    %sql DROP TABLE IF EXISTS temp_results;\n",
-    "    %sql CREATE TABLE temp_results AS (SELECT * FROM iris_multi_model_info);\n",
-    "    %sql ALTER TABLE temp_results DROP COLUMN mst_key, 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 n INTEGER, ADD COLUMN r INTEGER;\n",
-    "    %sql UPDATE temp_results SET model_arch_table = (SELECT model_arch_table FROM iris_multi_model_summary), num_iterations = (SELECT num_iterations FROM iris_multi_model_summary), start_training_time = (SELECT start_training_time FROM iris_multi_model_summary), end_training_time = (SELECT end_training_time FROM iris_multi_model_summary), s = $s, n = $n_configs, r = $n_iterations;\n",
-    "    %sql INSERT INTO results (SELECT * FROM temp_results);\n",
-    "\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('s = ', 1)\n",
-      "('n = ', 3)\n",
-      "('r = ', 1.0)\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "\n",
-      "*** 3 configurations x 1.0 iterations each\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "3 rows affected.\n",
-      "Done.\n",
-      "3 rows affected.\n",
-      "3 rows affected.\n",
-      "2 rows affected.\n",
-      "2 rows affected.\n",
-      "2 rows affected.\n",
-      "\n",
-      "*** 1.0 configurations x 3.0 iterations each\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "('s = ', 0)\n",
-      "('n = ', 2)\n",
-      "('r = ', 3)\n",
-      "1 rows affected.\n",
-      "1 rows affected.\n",
-      "\n",
-      "*** 2 configurations x 3.0 iterations each\n",
-      "Done.\n",
-      "1 rows affected.\n",
-      "Done.\n",
-      "2 rows affected.\n",
-      "Done.\n",
-      "2 rows affected.\n",
-      "2 rows affected.\n",
-      "2 rows affected.\n",
-      "2 rows affected.\n",
-      "2 rows affected.\n"
-     ]
-    }
-   ],
-   "source": [
-    "hp = Hyperband( get_params, try_params )\n",
-    "results = hp.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib notebook\n",
-    "import matplotlib.pyplot as plt\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": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 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"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'run_id'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-195f08b29212>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mrun_id\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'run_id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mdf_output_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu'sql SELECT training_metrics,training_loss FROM $output_root_name WHERE run_id = $run_id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mdf_output_info\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_output_info\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fmcquillan/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1967\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1968\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1969\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1970\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1971\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fmcquillan/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1974\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1975\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1976\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1977\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1978\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fmcquillan/anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1089\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1090\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1091\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1092\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1093\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fmcquillan/anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3210\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3211\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3212\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3213\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fmcquillan/anaconda/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   1757\u001b[0m                                  'backfill or nearest lookups')\n\u001b[1;32m   1758\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1759\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1761\u001b[0m         indexer = self.get_indexer([key], method=method,\n",
-      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3979)\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3843)\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12265)\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32mpandas/hashtable.pyx\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12216)\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'run_id'"
-     ]
-    }
-   ],
-   "source": [
-    "output_root_name = 'results'\n",
-    "df_results = %sql SELECT * FROM $output_root_name ORDER BY run_id;\n",
-    "df_results = df_results.DataFrame()\n",
-    "\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n",
-    "for run_id in df_results['run_id']:\n",
-    "    df_output_info = %sql SELECT training_metrics,training_loss FROM $output_root_name 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 = axs[0]\n",
-    "    ax_loss = axs[1]\n",
-    "    ax_metric.set_xticks(X[::1])\n",
-    "    ax_metric.plot(X, training_metrics, label=run_id)\n",
-    "    ax_metric.set_xlabel('Iteration')\n",
-    "    ax_metric.set_ylabel('Metric')\n",
-    "    ax_metric.set_title('Training metric curve')\n",
-    "\n",
-    "    ax_loss.set_xticks(X[::1])\n",
-    "    ax_loss.plot(X, training_loss, label=run_id)\n",
-    "    ax_loss.set_xlabel('Iteration')\n",
-    "    ax_loss.set_ylabel('Loss')\n",
-    "    ax_loss.set_title('Training loss curve')\n",
-    "    \n",
-    "fig.legend(ncol=4)\n",
-    "fig.tight_layout()\n",
-    "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ------------------ Scratch ---------------------"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "best_configs = %sql SELECT ARRAY(SELECT mst_key FROM iris_multi_model_info ORDER BY training_loss_final ASC LIMIT $k::INT);\n",
-    "                %sql DELETE FROM mst_table_hb WHERE mst_key NOT IN $best_configs;\n",
-    "                %sql DELETE FROM iris_multi_model WHERE mst_key NOT IN $best_configs;\n",
-    "                %sql DELETE FROM iris_multi_model_info WHERE mst_key NOT IN $best_configs;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('s = ', 4)\n",
-      "('n = ', 81)\n",
-      "('r = ', 1.0)\n",
-      "\n",
-      "*** 81 configurations x 1.0 iterations each\n",
-      "\n",
-      "1 | Mon Nov  4 11:31:06 2019 | lowest loss so far: inf (run -1)\n",
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-      "0 seconds.\n",
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-      "2 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.8345 (run 1)\n",
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-      "3 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.6510 (run 2)\n",
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-      "4 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
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-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "31 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "32 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
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-      "33 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "34 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "35 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "36 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "37 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "38 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "39 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "40 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "41 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "42 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "43 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "44 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "45 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "46 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
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-      "47 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
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-      "48 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "49 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "50 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "51 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "52 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "53 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "54 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "55 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "56 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
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-      "57 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "58 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "59 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0176 (run 3)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "60 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "61 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "62 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "63 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "64 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "65 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "66 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "67 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "68 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "69 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "70 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "71 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "72 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "73 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "74 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "75 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "76 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "77 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "78 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "79 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "80 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "81 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "*** 27.0 configurations x 3.0 iterations each\n",
-      "\n",
-      "82 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "83 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "84 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "85 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "86 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "87 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "88 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "89 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "90 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "91 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "92 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "93 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "94 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "95 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "96 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "97 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "98 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "99 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "100 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "101 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "102 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "103 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "104 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "105 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "106 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "107 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "108 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "*** 9.0 configurations x 9.0 iterations each\n",
-      "\n",
-      "109 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "110 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "111 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "112 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "113 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "114 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "115 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "116 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "117 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "*** 3.0 configurations x 27.0 iterations each\n",
-      "\n",
-      "118 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "119 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "120 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "*** 1.0 configurations x 81.0 iterations each\n",
-      "\n",
-      "121 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "('s = ', 3)\n",
-      "('n = ', 27)\n",
-      "('r = ', 3.0)\n",
-      "\n",
-      "*** 27 configurations x 3.0 iterations each\n",
-      "\n",
-      "122 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "123 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "124 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "125 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "126 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "127 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "128 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "129 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "130 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "131 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "132 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "133 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "134 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "135 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "136 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "137 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "138 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "139 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "140 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "141 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "142 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "143 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "144 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "145 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "146 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "147 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "148 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "*** 9.0 configurations x 9.0 iterations each\n",
-      "\n",
-      "149 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "150 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "151 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "152 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "153 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "154 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "\n",
-      "155 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
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-      "\n",
-      "156 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
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-      "\n",
-      "157 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
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-      "\n",
-      "*** 3.0 configurations x 27.0 iterations each\n",
-      "\n",
-      "158 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
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-      "\n",
-      "*** 1.0 configurations x 81.0 iterations each\n",
-      "\n",
-      "161 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "('s = ', 2)\n",
-      "('n = ', 9)\n",
-      "('r = ', 9.0)\n",
-      "\n",
-      "*** 9 configurations x 9.0 iterations each\n",
-      "\n",
-      "162 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
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-      "170 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
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-      "\n",
-      "*** 3.0 configurations x 27.0 iterations each\n",
-      "\n",
-      "171 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
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-      "\n",
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-      "174 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
-      "\n",
-      "\n",
-      "0 seconds.\n",
-      "('s = ', 1)\n",
-      "('n = ', 6)\n",
-      "('r = ', 27.0)\n",
-      "\n",
-      "*** 6 configurations x 27.0 iterations each\n",
-      "\n",
-      "175 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
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-      "\n",
-      "*** 2.0 configurations x 81.0 iterations each\n",
-      "\n",
-      "181 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
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-      "('s = ', 0)\n",
-      "('n = ', 5)\n",
-      "('r = ', 81)\n",
-      "\n",
-      "*** 5 configurations x 81.0 iterations each\n",
-      "\n",
-      "183 | Mon Nov  4 11:31:06 2019 | lowest loss so far: 0.0156 (run 59)\n",
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-     ]
-    }
-   ],
-   "source": [
-    "#!/usr/bin/env python\n",
-    "\n",
-    "\"bare-bones demonstration of using hyperband to tune sklearn GBT\"\n",
-    "\n",
-    "#from hyperband import Hyperband\n",
-    "#from defs.gb import get_params, try_params\n",
-    "\n",
-    "hb = Hyperband( get_params, try_params )\n",
-    "\n",
-    "# no actual tuning, doesn't call try_params()\n",
-    "results = hb.run( dry_run = True )\n",
-    "\n",
-    "#results = hb.run( skip_last = 1 ) # shorter run\n",
-    "#results = hb.run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table>\n",
-       "    <tr>\n",
-       "        <th>?column?</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>[5]</td>\n",
-       "    </tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "[([5],)]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "best_configs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'a': 2, 'b': 0.3388081749546307, 'c': 0.704635960884642},\n",
-       " {'a': 1, 'b': 0.4904175136129263, 'c': 0.8971084273807718},\n",
-       " {'a': 1, 'b': 1.2386463990117793, 'c': 0.21568311690580266},\n",
-       " {'a': 1, 'b': 1.91007461806631, 'c': 0.17778124867596956},\n",
-       " {'a': 1, 'b': 1.2563450220231427, 'c': 0.002076412746974121}]"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import ParameterSampler\n",
-    "from scipy.stats.distributions import expon, uniform, lognorm\n",
-    "import numpy as np\n",
-    "#rng = np.random.RandomState()\n",
-    "param_grid = {'a':[1, 2], 'b': expon(), 'c': uniform()}\n",
-    "#param_list = list(ParameterSampler(param_grid, n_iter=5, random_state=rng))\n",
-    "param_list = list(ParameterSampler(param_grid, n_iter=5))\n",
-    "rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items()) for d in param_list]\n",
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'a': 2, 'b': 0.37954129345633403, 'c': 0.3742154014629032},\n",
-       " {'a': 2, 'b': 1.2830633021262747, 'c': 0.4373122879029032},\n",
-       " {'a': 1, 'b': 0.22037072550727527, 'c': 0.26397341600176616},\n",
-       " {'a': 1, 'b': 0.549444485603122, 'c': 0.8317686948528791},\n",
-       " {'a': 1, 'b': 1.0567787144413414, 'c': 0.9560841093558743}]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'a': 2.0, 'b': 0.379541, 'c': 0.374215},\n",
-       " {'a': 2.0, 'b': 1.283063, 'c': 0.437312},\n",
-       " {'a': 1.0, 'b': 0.220371, 'c': 0.263973},\n",
-       " {'a': 1.0, 'b': 0.549444, 'c': 0.831769},\n",
-       " {'a': 1.0, 'b': 1.056779, 'c': 0.956084}]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rounded_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'d': 2.9713720038716116},\n",
-       " {'d': 10.275052606706604},\n",
-       " {'d': 4.211836333907813},\n",
-       " {'d': 3.6005371688499834},\n",
-       " {'d': 14.68709362771547}]"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#rng = np.random.RandomState(0)\n",
-    "param_grid = {'d': lognorm(1, 2, 3)}\n",
-    "#param_list = list(ParameterSampler(param_grid, n_iter=5, random_state=rng))\n",
-    "param_list = list(ParameterSampler(param_grid, n_iter=5))\n",
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.20984381 1.42136262 0.81160104 0.038913   0.22006219 6.32888505\n",
-      " 0.09113144]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[{'lr': 0.22006218937865862, 'optimizer': 'Adam'},\n",
-       " {'lr': 1.4213626223774578, 'optimizer': 'SGD'},\n",
-       " {'lr': 0.09113143553155685, 'optimizer': 'SGD'},\n",
-       " {'lr': 0.09113143553155685, 'optimizer': 'Adam'},\n",
-       " {'lr': 0.038913004274499154, 'optimizer': 'Adam'},\n",
-       " {'lr': 0.038913004274499154, 'optimizer': 'SGD'},\n",
-       " {'lr': 6.328885051196006, 'optimizer': 'Adam'}]"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import ParameterSampler\n",
-    "from scipy.stats.distributions import uniform\n",
-    "import numpy as np\n",
-    "\n",
-    "#s = np.random.uniform(-1,1,7)\n",
-    "#print (s)\n",
-    "   \n",
-    "# optimizer\n",
-    "optimizer = ['Adam', 'SGD']\n",
-    "# learning rate (log scale)\n",
-    "lr_range = [0.001, 10]\n",
-    "lr = 10**np.random.uniform(np.log10(lr_range[0]), np.log10(lr_range[1]), 7)\n",
-    "print (lr)\n",
-    "\n",
-    "# create random param list\n",
-    "param_grid = {\n",
-    "        'optimizer': optimizer,\n",
-    "        'lr': lr\n",
-    "}\n",
-    "param_list = list(ParameterSampler(param_grid, n_iter=7))\n",
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.07983433464722507,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 4,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.03805362658279962,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 1,\n",
-       "  'optimizer': 'SGD'},\n",
-       " {'batch_size': 4,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.09043633721868387,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.02775811670911417,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 1,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.104019113296403,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.06986494800074812,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'SGD'},\n",
-       " {'batch_size': 4,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.010449656955883938,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 4,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.04915490422264339,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'SGD'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.05257644929029893,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 1,\n",
-       "  'optimizer': 'Adam'},\n",
-       " {'batch_size': 8,\n",
-       "  'epochs': 1,\n",
-       "  'loss': 'categorical_crossentropy',\n",
-       "  'lr': 0.02993608422766151,\n",
-       "  'metrics': 'accuracy',\n",
-       "  'model_id': 2,\n",
-       "  'optimizer': 'SGD'}]"
-      ]
-     },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# model architecture\n",
-    "model_id = [1, 2]\n",
-    "\n",
-    "# compile params\n",
-    "\n",
-    "# loss function\n",
-    "loss = ['categorical_crossentropy']\n",
-    "# optimizer\n",
-    "optimizer = ['Adam', 'SGD']\n",
-    "# learning rate\n",
-    "lr = [0.01, 0.1]\n",
-    "# metrics\n",
-    "metrics = ['accuracy']\n",
-    "\n",
-    "# fit params\n",
-    "\n",
-    "# batch size\n",
-    "batch_size = [4, 8]\n",
-    "# epochs\n",
-    "epochs = [1]\n",
-    "\n",
-    "# create random param list\n",
-    "param_grid = {\n",
-    "    'model_id': model_id,\n",
-    "    'loss': loss,\n",
-    "    'optimizer': optimizer,\n",
-    "    'lr': uniform(lr[0], lr[1]),\n",
-    "    'metrics': metrics,\n",
-    "    'batch_size': batch_size,\n",
-    "    'epochs': epochs\n",
-    "}\n",
-    "param_list = list(ParameterSampler(param_grid, n_iter=10))\n",
-    "param_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'batch_size': 8,\n",
-       " 'epochs': 1,\n",
-       " 'loss': 'categorical_crossentropy',\n",
-       " 'lr': 0.03396784466820144,\n",
-       " 'optimizer': 'Adam'}"
-      ]
-     },
-     "execution_count": 212,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "param_list[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.07983433464722507)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='SGD(lr=0.03805362658279962)',metrics=['accuracy']$$\n",
-      "batch_size=4,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.09043633721868387)',metrics=['accuracy']$$\n",
-      "batch_size=4,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.02775811670911417)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.104019113296403)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='SGD(lr=0.06986494800074812)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.010449656955883938)',metrics=['accuracy']$$\n",
-      "batch_size=4,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='SGD(lr=0.04915490422264339)',metrics=['accuracy']$$\n",
-      "batch_size=4,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='Adam(lr=0.05257644929029893)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n",
-      "$$loss='categorical_crossentropy',optimizer='SGD(lr=0.02993608422766151)',metrics=['accuracy']$$\n",
-      "batch_size=8,epochs=1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for params in param_list:\n",
-    "#    for key, value in params.items():\n",
-    "#        print (key, value)\n",
-    "\n",
-    "    compile_params = \"$$loss='\" + str(params.get(\"loss\")) + \"',optimizer='\" + str(params.get(\"optimizer\")) + \"(lr=\" + str(params.get(\"lr\")) + \")',metrics=['\" + str(params.get(\"metrics\")) + \"']$$\" \n",
-    "    print (compile_params)\n",
-    "        \n",
-    "    fit_params = \"batch_size=\" + str(params.get(\"batch_size\")) + \",epochs=\" + str(params.get(\"epochs\"))\n",
-    "    print (fit_params)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done.\n",
-      "Done.\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": [
-    "%sql DROP TABLE IF EXISTS mst_table_hb, mst_table_auto_hb;\n",
-    "\n",
-    "%sql CREATE TABLE mst_table_hb(mst_key serial, model_id integer, compile_params varchar, fit_params varchar);\n",
-    "\n",
-    "for params in param_list:\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 = \"(\" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
-    "    \n",
-    "    %sql INSERT INTO mst_table_hb (model_id, compile_params, fit_params) VALUES $row_content"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "10 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>2</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.07983433464722507)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=8,epochs=1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>2</td>\n",
-       "        <td>1</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='SGD(lr=0.03805362658279962)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=4,epochs=1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>3</td>\n",
-       "        <td>2</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.09043633721868387)',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=0.02775811670911417)',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=0.104019113296403)',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='SGD(lr=0.06986494800074812)',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=0.010449656955883938)',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='SGD(lr=0.04915490422264339)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=4,epochs=1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>9</td>\n",
-       "        <td>1</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='Adam(lr=0.05257644929029893)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=8,epochs=1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>10</td>\n",
-       "        <td>2</td>\n",
-       "        <td>loss='categorical_crossentropy',optimizer='SGD(lr=0.02993608422766151)',metrics=['accuracy']</td>\n",
-       "        <td>batch_size=8,epochs=1</td>\n",
-       "    </tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "[(1, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.07983433464722507)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (2, 1, u\"loss='categorical_crossentropy',optimizer='SGD(lr=0.03805362658279962)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
-       " (3, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.09043633721868387)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
-       " (4, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.02775811670911417)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (5, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.104019113296403)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (6, 2, u\"loss='categorical_crossentropy',optimizer='SGD(lr=0.06986494800074812)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (7, 2, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.010449656955883938)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
-       " (8, 2, u\"loss='categorical_crossentropy',optimizer='SGD(lr=0.04915490422264339)',metrics=['accuracy']\", u'batch_size=4,epochs=1'),\n",
-       " (9, 1, u\"loss='categorical_crossentropy',optimizer='Adam(lr=0.05257644929029893)',metrics=['accuracy']\", u'batch_size=8,epochs=1'),\n",
-       " (10, 2, u\"loss='categorical_crossentropy',optimizer='SGD(lr=0.02993608422766151)',metrics=['accuracy']\", u'batch_size=8,epochs=1')]"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%sql\n",
-    "SELECT * FROM mst_table_hb ORDER BY mst_key;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from random import random\n",
-    "from math import log, ceil\n",
-    "from time import time, ctime\n",
-    "\n",
-    "\n",
-    "class Hyperband:\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 = 81  # maximum iterations per configuration\n",
-    "        self.eta = 3        # defines configuration downsampling rate (default = 3)\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",
-    "        self.results = []    # list of dicts\n",
-    "        self.counter = 0\n",
-    "        self.best_loss = np.inf\n",
-    "        self.best_counter = -1\n",
-    "\n",
-    "\n",
-    "    # can be called multiple times\n",
-    "    def run( self, skip_last = 0, dry_run = False ):\n",
-    "\n",
-    "        for s in reversed( range( self.s_max + 1 )):\n",
-    "            \n",
-    "            # initial number of configurations\n",
-    "            n = int( ceil( self.B / self.max_iter / ( s + 1 ) * self.eta ** s ))\n",
-    "\n",
-    "            # initial number of iterations per config\n",
-    "            r = self.max_iter * self.eta ** ( -s )\n",
-    "            \n",
-    "            print (\"s = \", s)\n",
-    "            print (\"n = \", n)\n",
-    "            print (\"r = \", r)\n",
-    "\n",
-    "            # n random configurations\n",
-    "            T = self.get_params(n) # what to return from function if anything?\n",
-    "            \n",
-    "            return\n",
-    "\n",
-    "            for i in range(( s + 1 ) - int( skip_last )): # changed from s + 1\n",
-    "\n",
-    "                # Run each of the n configs for <iterations>\n",
-    "                # and keep best (n_configs / eta) configurations\n",
-    "\n",
-    "                n_configs = n * self.eta ** ( -i )\n",
-    "                n_iterations = r * self.eta ** ( i )\n",
-    "\n",
-    "                print \"\\n*** {} configurations x {:.1f} iterations each\".format(\n",
-    "                    n_configs, n_iterations )\n",
-    "\n",
-    "                val_losses = []\n",
-    "                early_stops = []\n",
-    "                \n",
-    "                \n",
-    "                \n",
-    "                \n",
-    "\n",
-    "                for t in T:\n",
-    "\n",
-    "                    self.counter += 1\n",
-    "                    print \"\\n{} | {} | lowest loss so far: {:.4f} (run {})\\n\".format(\n",
-    "                        self.counter, ctime(), self.best_loss, self.best_counter )\n",
-    "\n",
-    "                    start_time = time()\n",
-    "\n",
-    "                    if dry_run:\n",
-    "                        result = { 'loss': random(), 'log_loss': random(), 'auc': random()}\n",
-    "                    else:\n",
-    "                        result = self.try_params( n_iterations, t )  # <---\n",
-    "\n",
-    "                    assert( type( result ) == dict )\n",
-    "                    assert( 'loss' in result )\n",
-    "\n",
-    "                    seconds = int( round( time() - start_time ))\n",
-    "                    print \"\\n{} seconds.\".format( seconds )\n",
-    "\n",
-    "                    loss = result['loss']\n",
-    "                    val_losses.append( loss )\n",
-    "\n",
-    "                    early_stop = result.get( 'early_stop', False )\n",
-    "                    early_stops.append( early_stop )\n",
-    "\n",
-    "                    # keeping track of the best result so far (for display only)\n",
-    "                    # could do it be checking results each time, but hey\n",
-    "                    if loss < self.best_loss:\n",
-    "                        self.best_loss = loss\n",
-    "                        self.best_counter = self.counter\n",
-    "\n",
-    "                    result['counter'] = self.counter\n",
-    "                    result['seconds'] = seconds\n",
-    "                    result['params'] = t\n",
-    "                    result['iterations'] = n_iterations\n",
-    "                        \n",
-    "                    self.results.append( result )\n",
-    "\n",
-    "                # select a number of best configurations for the next loop\n",
-    "                # filter out early stops, if any\n",
-    "                indices = np.argsort( val_losses )\n",
-    "                T = [ T[i] for i in indices if not early_stops[i]]\n",
-    "                T = T[ 0:int( n_configs / self.eta )]\n",
-    "                        \n",
-    "        return self.results"
-   ]
-  }
- ],
- "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": 2
-}

diff --git a/community-artifacts/Deep-learning/automl/hyperband_v3_mnist.ipynb b/community-artifacts/Deep-learning/automl/hyperband_v3_mnist.ipynb
deleted file mode 100644
index aa290ba..0000000
--- a/community-artifacts/Deep-learning/automl/hyperband_v3_mnist.ipynb
+++ /dev/null

@@ -1,2928 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hyperband using MNIST\n",
-    "\n",
-    "Model architecture based on https://keras.io/examples/mnist_transfer_cnn/ \n",
-    "\n",
-    "To load images into tables we use the script called <em>madlib_image_loader.py</em> located at https://github.com/apache/madlib-site/tree/asf-site/community-artifacts/Deep-learning which uses the Python Imaging Library so supports multiple formats http://www.pythonware.com/products/pil/\n",
-    "\n",
-    "## Table of contents\n",
-    "<a href=\"#import_libraries\">1. Import libraries</a>\n",
-    "\n",
-    "<a href=\"#load_and_prepare_data\">2. Load and prepare data</a>\n",
-    "\n",
-    "<a href=\"#image_preproc\">3. Call image preprocessor</a>\n",
-    "\n",
-    "<a href=\"#define_and_load_model\">4. Define and load model architecture</a>\n",
-    "\n",
-    "<a href=\"#hyperband\">5. Hyperband</a>\n",
-    "\n",
-    "<a href=\"#plot\">6. Plot results</a>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "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"
-     ]
-    }
-   ],
-   "source": [
-    "%load_ext sql"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "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",
-    "# PostgreSQL local\n",
-    "#%sql postgresql://fmcquillan@localhost:5432/madlib\n",
-    "\n",
-    "# psycopg2 connection\n",
-    "import psycopg2 as p2\n",
-    "#conn = p2.connect('postgresql://fmcquillan@localhost:5432/madlib')\n",
-    "conn = p2.connect('postgresql://gpadmin@localhost:8000/madlib')\n",
-    "cur = conn.cursor()"
-   ]
-  },
-  {
-   "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/v1.16-47-g5a1717e, cmake configuration time: Tue Nov 19 01:02:39 UTC 2019, build type: release, build system: Linux-3.10.0-957.27.2.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: 1.17-dev, git revision: rel/v1.16-47-g5a1717e, cmake configuration time: Tue Nov 19 01:02:39 UTC 2019, build type: release, build system: Linux-3.10.0-957.27.2.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=\"import_libraries\"></a>\n",
-    "# 1.  Import libraries\n",
-    "From https://keras.io/examples/mnist_transfer_cnn/ import libraries and define some params"
-   ]
-  },
-  {
-   "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"
-     ]
-    }
-   ],
-   "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",
-    "\n",
-    "now = datetime.datetime.now\n",
-    "\n",
-    "#batch_size = 128\n",
-    "num_classes = 10\n",
-    "#epochs = 5\n",
-    "\n",
-    "# input image dimensions\n",
-    "img_rows, img_cols = 28, 28\n",
-    "# number of convolutional filters to use\n",
-    "filters = 32\n",
-    "# size of pooling area for max pooling\n",
-    "pool_size = 2\n",
-    "# convolution kernel size\n",
-    "kernel_size = 3\n",
-    "\n",
-    "if K.image_data_format() == 'channels_first':\n",
-    "    input_shape = (1, img_rows, img_cols)\n",
-    "else:\n",
-    "    input_shape = (img_rows, img_cols, 1)"
-   ]
-  },
-  {
-   "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"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id=\"load_and_prepare_data\"></a>\n",
-    "# 2.  Load and prepare data\n",
-    "\n",
-    "First load MNIST data from Keras, consisting of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(10000, 28, 28)\n",
-      "(10000, 28, 28, 1)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# the data, split between train and test sets\n",
-    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
-    "\n",
-    "# reshape to match model architecture\n",
-    "print(x_test.shape)\n",
-    "x_train = x_train.reshape(len(x_train), *input_shape)\n",
-    "x_test = x_test.reshape(len(x_test), *input_shape)\n",
-    "print(x_test.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": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# MADlib tools directory\n",
-    "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"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Specify database credentials, for connecting to db\n",
-    "db_creds = DbCredentials(user='gpadmin',\n",
-    "                         host='localhost',\n",
-    "                         port='8000',\n",
-    "                         password='')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize ImageLoader (increase num_workers to run faster)\n",
-    "iloader = ImageLoader(num_workers=5, db_creds=db_creds)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done.\n",
-      "MainProcess: Connected to madlib db.\n",
-      "Executing: CREATE TABLE train_mnist (id SERIAL, x REAL[], y TEXT)\n",
-      "CREATE TABLE\n",
-      "Created table train_mnist in madlib db\n",
-      "Spawning 5 workers...\n",
-      "Initializing PoolWorker-11 [pid 34068]\n",
-      "PoolWorker-11: Created temporary directory /tmp/madlib_RbuQlbqxI5\n",
-      "Initializing PoolWorker-12 [pid 34069]\n",
-      "PoolWorker-12: Created temporary directory /tmp/madlib_tEyH9GMFGV\n",
-      "Initializing PoolWorker-13 [pid 34070]\n",
-      "PoolWorker-13: Created temporary directory /tmp/madlib_TyYs4viAVD\n",
-      "Initializing PoolWorker-14 [pid 34071]\n",
-      "Initializing PoolWorker-15 [pid 34072]\n",
-      "PoolWorker-14: Created temporary directory /tmp/madlib_KTwnncRsaq\n",
-      "PoolWorker-15: Created temporary directory /tmp/madlib_jtG9zAC8HU\n",
-      "PoolWorker-11: Connected to madlib db.\n",
-      "PoolWorker-13: Connected to madlib db.\n",
-      "PoolWorker-14: 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_RbuQlbqxI5/train_mnist0000.tmp\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0000.tmp\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0000.tmp\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0000.tmp\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0000.tmp\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0001.tmp\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0001.tmp\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0001.tmp\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0001.tmp\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0001.tmp\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0002.tmp\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0002.tmp\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0002.tmp\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0002.tmp\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0002.tmp\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0003.tmp\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0003.tmp\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0003.tmp\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0003.tmp\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0003.tmp\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0004.tmp\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0004.tmp\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0004.tmp\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0004.tmp\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0004.tmp\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0005.tmp\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0005.tmp\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0005.tmp\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0005.tmp\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0005.tmp\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0006.tmp\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0006.tmp\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0006.tmp\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0006.tmp\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0006.tmp\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0007.tmp\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0007.tmp\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0007.tmp\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0007.tmp\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0007.tmp\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0008.tmp\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0008.tmp\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0008.tmp\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0008.tmp\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0008.tmp\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0009.tmp\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0009.tmp\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0009.tmp\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0009.tmp\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0009.tmp\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0010.tmp\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0010.tmp\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0010.tmp\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0010.tmp\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0010.tmp\n",
-      "PoolWorker-13: Wrote 1000 images to /tmp/madlib_TyYs4viAVD/train_mnist0011.tmp\n",
-      "PoolWorker-11: Wrote 1000 images to /tmp/madlib_RbuQlbqxI5/train_mnist0011.tmp\n",
-      "PoolWorker-14: Wrote 1000 images to /tmp/madlib_KTwnncRsaq/train_mnist0011.tmp\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PoolWorker-12: Wrote 1000 images to /tmp/madlib_tEyH9GMFGV/train_mnist0011.tmp\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-13: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-14: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Wrote 1000 images to /tmp/madlib_jtG9zAC8HU/train_mnist0011.tmp\n",
-      "PoolWorker-12: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-15: Loaded 1000 images into train_mnist\n",
-      "PoolWorker-11: Removed temporary directory /tmp/madlib_RbuQlbqxI5\n",
-      "PoolWorker-15: Removed temporary directory /tmp/madlib_jtG9zAC8HU\n",
-      "PoolWorker-12: Removed temporary directory /tmp/madlib_tEyH9GMFGV\n",
-      "PoolWorker-13: Removed temporary directory /tmp/madlib_TyYs4viAVD\n",
-      "PoolWorker-14: Removed temporary directory /tmp/madlib_KTwnncRsaq\n",
-      "Done!  Loaded 60000 images in 45.7068669796s\n",
-      "5 workers terminated.\n",
-      "MainProcess: Connected to madlib db.\n",
-      "Executing: CREATE TABLE test_mnist (id SERIAL, x REAL[], y TEXT)\n",
-      "CREATE TABLE\n",
-      "Created table test_mnist in madlib db\n",
-      "Spawning 5 workers...\n",
-      "Initializing PoolWorker-16 [pid 34074]\n",
-      "PoolWorker-16: Created temporary directory /tmp/madlib_MjwU1yRoMW\n",
-      "Initializing PoolWorker-17 [pid 34075]\n",
-      "PoolWorker-17: Created temporary directory /tmp/madlib_kTezv88uWu\n",
-      "Initializing PoolWorker-18 [pid 34076]\n",
-      "PoolWorker-18: Created temporary directory /tmp/madlib_TFIofbewK1\n",
-      "Initializing PoolWorker-19 [pid 34077]\n",
-      "PoolWorker-19: Created temporary directory /tmp/madlib_QUIRxlckvj\n",
-      "PoolWorker-20: Created temporary directory /tmp/madlib_Eii5YFUzCZ\n",
-      "Initializing PoolWorker-20 [pid 34078]\n",
-      "PoolWorker-17: Connected to madlib db.\n",
-      "PoolWorker-18: 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: Wrote 1000 images to /tmp/madlib_TFIofbewK1/test_mnist0000.tmp\n",
-      "PoolWorker-19: Wrote 1000 images to /tmp/madlib_QUIRxlckvj/test_mnist0000.tmp\n",
-      "PoolWorker-17: Wrote 1000 images to /tmp/madlib_kTezv88uWu/test_mnist0000.tmp\n",
-      "PoolWorker-16: Wrote 1000 images to /tmp/madlib_MjwU1yRoMW/test_mnist0000.tmp\n",
-      "PoolWorker-20: Wrote 1000 images to /tmp/madlib_Eii5YFUzCZ/test_mnist0000.tmp\n",
-      "PoolWorker-18: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-17: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-19: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-18: Wrote 1000 images to /tmp/madlib_TFIofbewK1/test_mnist0001.tmp\n",
-      "PoolWorker-16: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-20: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-18: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-19: Wrote 1000 images to /tmp/madlib_QUIRxlckvj/test_mnist0001.tmp\n",
-      "PoolWorker-17: Wrote 1000 images to /tmp/madlib_kTezv88uWu/test_mnist0001.tmp\n",
-      "PoolWorker-16: Wrote 1000 images to /tmp/madlib_MjwU1yRoMW/test_mnist0001.tmp\n",
-      "PoolWorker-20: Wrote 1000 images to /tmp/madlib_Eii5YFUzCZ/test_mnist0001.tmp\n",
-      "PoolWorker-19: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-17: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-16: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-20: Loaded 1000 images into test_mnist\n",
-      "PoolWorker-16: Removed temporary directory /tmp/madlib_MjwU1yRoMW\n",
-      "PoolWorker-19: Removed temporary directory /tmp/madlib_QUIRxlckvj\n",
-      "PoolWorker-17: Removed temporary directory /tmp/madlib_kTezv88uWu\n",
-      "PoolWorker-20: Removed temporary directory /tmp/madlib_Eii5YFUzCZ\n",
-      "PoolWorker-18: Removed temporary directory /tmp/madlib_TFIofbewK1\n",
-      "Done!  Loaded 10000 images in 6.80017995834s\n",
-      "5 workers terminated.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Drop tables\n",
-    "%sql DROP TABLE IF EXISTS train_mnist, test_mnist\n",
-    "\n",
-    "# Save images to temporary directories and load into database\n",
-    "iloader.load_dataset_from_np(x_train, y_train, 'train_mnist', append=False)\n",
-    "iloader.load_dataset_from_np(x_test, y_test, 'test_mnist', append=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id=\"image_preproc\"></a>\n",
-    "# 3. Call image preprocessor\n",
-    "\n",
-    "Transforms from one image per row to multiple images per row for batch optimization.  Also normalizes and one-hot encodes.\n",
-    "\n",
-    "Training dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "scrolled": true
-   },
-   "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_values</th>\n",
-       "        <th>buffer_size</th>\n",
-       "        <th>normalizing_const</th>\n",
-       "        <th>num_classes</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>train_mnist</td>\n",
-       "        <td>train_mnist_packed</td>\n",
-       "        <td>y</td>\n",
-       "        <td>x</td>\n",
-       "        <td>text</td>\n",
-       "        <td>[u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9']</td>\n",
-       "        <td>1000</td>\n",
-       "        <td>255.0</td>\n",
-       "        <td>10</td>\n",
-       "    </tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "[(u'train_mnist', u'train_mnist_packed', u'y', u'x', u'text', [u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9'], 1000, 255.0, 10)]"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%sql\n",
-    "DROP TABLE IF EXISTS train_mnist_packed, train_mnist_packed_summary;\n",
-    "\n",
-    "SELECT madlib.training_preprocessor_dl('train_mnist',             -- Source table\n",
-    "                                       'train_mnist_packed',      -- Output table\n",
-    "                                       'y',                       -- Dependent variable\n",
-    "                                       'x',                       -- Independent variable\n",
-    "                                        1000,                     -- Buffer size\n",
-    "                                        255                       -- Normalizing constant\n",
-    "                                        );\n",
-    "\n",
-    "SELECT * FROM train_mnist_packed_summary;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "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_values</th>\n",
-       "        <th>buffer_size</th>\n",
-       "        <th>normalizing_const</th>\n",
-       "        <th>num_classes</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "        <td>test_mnist</td>\n",
-       "        <td>test_mnist_packed</td>\n",
-       "        <td>y</td>\n",
-       "        <td>x</td>\n",
-       "        <td>text</td>\n",
-       "        <td>[u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9']</td>\n",
-       "        <td>5000</td>\n",
-       "        <td>255.0</td>\n",
-       "        <td>10</td>\n",
-       "    </tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "[(u'test_mnist', u'test_mnist_packed', u'y', u'x', u'text', [u'0', u'1', u'2', u'3', u'4', u'5', u'6', u'7', u'8', u'9'], 5000, 255.0, 10)]"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%sql\n",
-    "DROP TABLE IF EXISTS test_mnist_packed, test_mnist_packed_summary;\n",
-    "\n",
-    "SELECT madlib.validation_preprocessor_dl('test_mnist',              -- Source table\n",
-    "                                         'test_mnist_packed',       -- Output table\n",
-    "                                         'y',                       -- Dependent variable\n",
-    "                                         'x',                       -- Independent variable\n",
-    "                                         'train_mnist_packed'       -- Training preproc table\n",
-    "                                        );\n",
-    "\n",
-    "SELECT * FROM test_mnist_packed_summary;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id=\"define_and_load_model\"></a>\n",
-    "# 4. Define and load model architecture\n",
-    "\n",
-    "Model with feature and classification layers trainable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       \n",
-      "_________________________________________________________________\n",
-      "activation_1 (Activation)    (None, 26, 26, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_2 (Conv2D)            (None, 24, 24, 32)        9248      \n",
-      "_________________________________________________________________\n",
-      "activation_2 (Activation)    (None, 24, 24, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "dropout_1 (Dropout)          (None, 12, 12, 32)        0         \n",
-      "_________________________________________________________________\n",
-      "flatten_1 (Flatten)          (None, 4608)              0         \n",
-      "_________________________________________________________________\n",
-      "dense_1 (Dense)              (None, 128)               589952    \n",
-      "_________________________________________________________________\n",
-      "activation_3 (Activation)    (None, 128)               0         \n",
-      "_________________________________________________________________\n",
-      "dropout_2 (Dropout)          (None, 128)               0         \n",
-      "_________________________________________________________________\n",
-      "dense_2 (Dense)              (None, 10)                1290      \n",
-      "_________________________________________________________________\n",
-      "activation_4 (Activation)    (None, 10)                0         \n",
-      "=================================================================\n",
-      "Total params: 600,810\n",
-      "Trainable params: 600,810\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
-    }
-   ],
-   "source": [
-    "# define two groups of layers: feature (convolutions) and classification (dense)\n",
-    "feature_layers = [\n",
-    "    Conv2D(filters, kernel_size,\n",
-    "           padding='valid',\n",
-    "           input_shape=input_shape),\n",
-    "    Activation('relu'),\n",
-    "    Conv2D(filters, kernel_size),\n",
-    "    Activation('relu'),\n",
-    "    MaxPooling2D(pool_size=pool_size),\n",
-    "    Dropout(0.25),\n",
-    "    Flatten(),\n",
-    "]\n",
-    "\n",
-    "classification_layers = [\n",
-    "    Dense(128),\n",
-    "    Activation('relu'),\n",
-    "    Dropout(0.5),\n",
-    "    Dense(num_classes),\n",
-    "    Activation('softmax')\n",
-    "]\n",
-    "\n",
-    "# create complete model\n",
-    "model = Sequential(feature_layers + classification_layers)\n",
-    "\n",
-    "model.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load into model architecture table using psycopg2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done.\n",
-      "1 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>feature + classification layers trainable</td>\n",
-       "    </tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "[(1, u'feature + classification layers trainable')]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%sql DROP TABLE IF EXISTS model_arch_table_mnist;\n",
-    "query = \"SELECT madlib.load_keras_model('model_arch_table_mnist', %s, NULL, %s)\"\n",
-    "cur.execute(query,[model.to_json(), \"feature + classification layers trainable\"])\n",
-    "conn.commit()\n",
-    "\n",
-    "# check model loaded OK\n",
-    "%sql SELECT model_id, name FROM model_arch_table_mnist;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id=\"hyperband\"></a>\n",
-    "# 5.  Hyperband"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create tables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Done.\n",
-      "Done.\n",
-      "Done.\n",
-      "Done.\n",
-      "Done.\n",
-      "Done.\n",
-      "1 rows affected.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "%%sql\n",
-    "-- overall results table\n",
-    "DROP TABLE IF EXISTS results_mnist;\n",
-    "CREATE TABLE results_mnist ( \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, \n",
-    "                      n INTEGER, \n",
-    "                      r INTEGER,\n",
-    "                      run_id SERIAL\n",
-    "                     );\n",
-    "\n",
-    "-- model selection table\n",
-    "DROP TABLE IF EXISTS mst_table_hb_mnist;\n",
-    "CREATE TABLE mst_table_hb_mnist (\n",
-    "                           mst_key SERIAL, \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_mnist_summary;\n",
-    "CREATE TABLE mst_table_hb_mnist_summary (model_arch_table VARCHAR);\n",
-    "INSERT INTO mst_table_hb_mnist_summary VALUES ('model_arch_table_mnist');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Table names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_table = 'results_mnist'\n",
-    "\n",
-    "output_table = 'mnist_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_mnist'\n",
-    "mst_table_summary = '_'.join([mst_table, 'summary'])\n",
-    "\n",
-    "model_arch_table = 'model_arch_library_mnist'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Pretty print run schedule"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter = 9\n",
-      "eta = 3\n",
-      "B = 3*max_iter = 27\n",
-      " \n",
-      "s=2\n",
-      "n_i      r_i\n",
-      "------------\n",
-      "9     1.0\n",
-      "3.0     3.0\n",
-      "1.0     9.0\n",
-      " \n",
-      "s=1\n",
-      "n_i      r_i\n",
-      "------------\n",
-      "3     3.0\n",
-      "1.0     9.0\n",
-      " \n",
-      "s=0\n",
-      "n_i      r_i\n",
-      "------------\n",
-      "3     9\n",
-      " \n",
-      "sum of configurations at leaf nodes across all s = 5.0\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 = 9  # maximum iterations/epochs per configuration\n",
-    "eta = 3  # defines downsampling rate (default=3)\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",
-    "\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):\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:\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": [
-    "Hyperband "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "from random import random\n",
-    "from math import log, ceil\n",
-    "from time import time, ctime\n",
-    "\n",
-    "\n",
-    "class Hyperband:\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 = 9  # maximum iterations per configuration\n",
-    "        self.eta = 3        # defines configuration downsampling rate (default = 3)\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",
-    "        self.results = []    # list of dicts\n",
-    "        self.counter = 0\n",
-    "        self.best_loss = np.inf\n",
-    "        self.best_counter = -1\n",
-    "\n",
-    "    # can be called multiple times\n",
-    "    def run( self, skip_last = 0, dry_run = False ):\n",
-    "\n",
-    "        for s in reversed( range( self.s_max + 1 )):\n",
-    "            \n",
-    "            # initial number of configurations\n",
-    "            n = int( ceil( self.B / self.max_iter / ( s + 1 ) * self.eta ** s ))\n",
-    "\n",
-    "            # initial number of iterations per config\n",
-    "            r = self.max_iter * self.eta ** ( -s )\n",
-    "            \n",
-    "            print (\"s = \", s)\n",
-    "            print (\"n = \", n)\n",
-    "            print (\"r = \", r)\n",
-    "\n",
-    "            # n random configurations\n",
-    "            T = self.get_params(n) # what to return from function if anything?\n",
-    "            \n",
-    "            for i in range(( s + 1 ) - int( skip_last )): # changed from s + 1\n",
-    "\n",
-    "                # Run each of the n configs for <iterations>\n",
-    "                # and keep best (n_configs / eta) configurations\n",
-    "\n",
-    "                n_configs = n * self.eta ** ( -i )\n",
-    "                n_iterations = r * self.eta ** ( i )\n",
-    "\n",
-    "                print (\"\\n*** {} configurations x {:.1f} iterations each\".format(\n",
-    "                    n_configs, n_iterations ))\n",
-    "                \n",
-    "                # multi-model training\n",
-    "                U = self.try_params(s, n_configs, n_iterations) # what to return from function if anything?\n",
-    "\n",
-    "                # select a number of best configurations for the next loop\n",
-    "                # filter out early stops, if any\n",
-    "                # drop from model selection table\n",
-    "                k = int( n_configs / self.eta)\n",
-    "                \n",
-    "                # prune mst_table for next try\n",
-    "                %sql DELETE FROM $mst_table WHERE mst_key NOT IN (SELECT mst_key FROM $output_table_info ORDER BY validation_loss_final ASC LIMIT $k::INT);\n",
-    "\n",
-    "        #return self.results\n",
-    "        \n",
-    "        return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_params(n):\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]\n",
-    "\n",
-    "    # compile params\n",
-    "    # loss function\n",
-    "    loss = ['categorical_crossentropy']\n",
-    "    # optimizer\n",
-    "    optimizer = ['Adam', 'SGD']\n",
-    "    # learning rate (sample on log scale here not in ParameterSampler)\n",
-    "    lr_range = [0.001, 0.1]\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 = [64, 128]\n",
-    "    # epochs\n",
-    "    epochs = [1]\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 = \"(\" + model_id + \", \" + compile_params + \", \" + fit_params + \");\"\n",
-    "        \n",
-    "        %sql INSERT INTO $mst_table (model_id, compile_params, fit_params) VALUES $row_content\n",
-    "    \n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def try_params(s, n_configs, n_iterations):\n",
-    "    \n",
-    "    # multi-model fit\n",
-    "    # TO DO:  use warm start to continue from where left off after if not 1st time thru for this s value\n",
-    "    %sql DROP TABLE IF EXISTS $output_table, $output_table_summary, $output_table_info;\n",
-    "    \n",
-    "    # passing vars as madlib args does not work???\n",
-    "    #%sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', $output_table, $mst_table, $n_iterations::INT, 0);\n",
-    "    %sql SELECT madlib.madlib_keras_fit_multiple_model('train_mnist_packed', 'mnist_multi_model', 'mst_table_hb_mnist', $n_iterations::INT, 0, 'test_mnist_packed');\n",
-    "   \n",
-    "    # save results\n",
-    "    %sql DROP TABLE IF EXISTS temp_results;\n",
-    "    %sql CREATE TABLE temp_results AS (SELECT * FROM $output_table_info);\n",
-    "    %sql ALTER TABLE temp_results DROP COLUMN mst_key, 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 n INTEGER, ADD COLUMN r INTEGER;\n",
-    "    %sql UPDATE temp_results SET model_arch_table = (SELECT model_arch_table FROM $output_table_summary), num_iterations = (SELECT num_iterations FROM iris_multi_model_summary), start_training_time = (SELECT start_training_time FROM iris_multi_model_summary), end_training_time = (SELECT end_training_time FROM iris_multi_model_summary), s = $s, n = $n_configs, r = $n_iterations;\n",
-    "    %sql INSERT INTO $results_table (SELECT * FROM temp_results);\n",
-    "\n",
-    "    return"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "s =  2\n",
-      "n =  9\n",
-      "r =  1.0\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",
-      "*** 9 configurations x 1.0 iterations each\n",
-      "Done.\n"
-     ]
-    }
-   ],
-   "source": [
-    "hp = Hyperband( get_params, try_params )\n",
-    "results = hp.run()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<a id=\"plot\"></a>\n",
-    "# 6. Plot results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib notebook\n",
-    "import matplotlib.pyplot as plt\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": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7 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"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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\" width=\"999.9999783255842\">"
-      ],
-      "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 $results_table ORDER BY run_id;\n",
-    "df_results = %sql SELECT * FROM $results_table ORDER BY training_loss ASC LIMIT 7;\n",
-    "df_results = df_results.DataFrame()\n",
-    "\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\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 = axs[0]\n",
-    "    ax_loss = axs[1]\n",
-    "    ax_metric.set_xticks(X[::1])\n",
-    "    ax_metric.plot(X, training_metrics, label=run_id)\n",
-    "    ax_metric.set_xlabel('Iteration')\n",
-    "    ax_metric.set_ylabel('Metric')\n",
-    "    ax_metric.set_title('Training metric curve')\n",
-    "\n",
-    "    ax_loss.set_xticks(X[::1])\n",
-    "    ax_loss.plot(X, training_loss, label=run_id)\n",
-    "    ax_loss.set_xlabel('Iteration')\n",
-    "    ax_loss.set_ylabel('Loss')\n",
-    "    ax_loss.set_title('Training loss curve')\n",
-    "    \n",
-    "fig.legend(ncol=4)\n",
-    "fig.tight_layout()\n",
-    "# fig.savefig('./lc_keras_fit.png', dpi = 300)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5 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"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
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\" width=\"999.9999783255842\">"
-      ],
-      "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"
-     ]
-    }
-   ],
-   "source": [
-    "#df_results = %sql SELECT * FROM $results_table ORDER BY run_id;\n",
-    "df_results = %sql SELECT * FROM $results_table ORDER BY validation_loss ASC LIMIT 5;\n",
-    "df_results = df_results.DataFrame()\n",
-    "\n",
-    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\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 = axs[0]\n",
-    "    ax_loss = axs[1]\n",
-    "    ax_metric.set_xticks(X[::1])\n",
-    "    ax_metric.plot(X, validation_metrics, label=run_id)\n",
-    "    ax_metric.set_xlabel('Iteration')\n",
-    "    ax_metric.set_ylabel('Metric')\n",
-    "    ax_metric.set_title('Validation metric curve')\n",
-    "\n",
-    "    ax_loss.set_xticks(X[::1])\n",
-    "    ax_loss.plot(X, validation_loss, label=run_id)\n",
-    "    ax_loss.set_xlabel('Iteration')\n",
-    "    ax_loss.set_ylabel('Loss')\n",
-    "    ax_loss.set_title('Validation loss curve')\n",
-    "    \n",
-    "fig.legend(ncol=4)\n",
-    "fig.tight_layout()\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": "2.7.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}