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blob: 471da2c11401cf7f3c9c0c1d8729537ee1c8ada1 [file] [log] [blame]
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machines\n",
"Support Vector Machines (SVMs) are models for regression and classification tasks. SVM models have two particularly desirable features: robustness in the presence of noisy data and applicability to a variety of data configurations. At its core, a linear SVM model is a hyperplane separating two distinct classes of data (in the case of classification problems), in such a way that the distance between the hyperplane and the nearest training data point (called the margin) is maximized. Vectors that lie on this margin are called support vectors. With the support vectors fixed, perturbations of vectors beyond the margin will not affect the model; this contributes to the model’s robustness. By substituting a kernel function for the usual inner product, one can approximate a large variety of decision boundaries in addition to linear hyperplanes."
]
},
{
"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 (PM demo machine) - via tunnel\n",
"%sql postgresql://gpadmin@localhost:8000/madlib\n",
" \n",
"# PostgreSQL local\n",
"#%sql postgresql://fmcquillan@localhost:5432/madlib"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>version</th>\n",
" </tr>\n",
" <tr>\n",
" <td>MADlib version: 1.17-dev, git revision: rel/v1.16-29-g9fa27e5, cmake configuration time: Mon Oct 7 17:04:14 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/v1.16-29-g9fa27e5, cmake configuration time: Mon Oct 7 17:04:14 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();"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Classification\n",
"# 1. Create input data set"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"Done.\n",
"15 rows affected.\n",
"15 rows affected.\n"
]
},
{
"data": {
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" <th>price</th>\n",
" <th>size</th>\n",
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},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql \n",
"DROP TABLE IF EXISTS houses;\n",
"\n",
"CREATE TABLE houses (id INT, tax INT, bedroom INT, bath FLOAT, price INT,\n",
" size INT, lot INT);\n",
"\n",
"INSERT INTO houses VALUES \n",
" (1 , 590 , 2 , 1 , 50000 , 770 , 22100),\n",
" (2 , 1050 , 3 , 2 , 85000 , 1410 , 12000),\n",
" (3 , 20 , 3 , 1 , 22500 , 1060 , 3500),\n",
" (4 , 870 , 2 , 2 , 90000 , 1300 , 17500),\n",
" (5 , 1320 , 3 , 2 , 133000 , 1500 , 30000),\n",
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" (14 , 2070 , 2 , 3 , 148000 , 1550 , 14000),\n",
" (15 , 650 , 3 , 1.5 , 65000 , 1450 , 12000);\n",
" \n",
"SELECT * FROM houses ORDER BY id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# 2. Train linear classification model\n",
"Categorical variable is price < $100,0000."
]
},
{
"cell_type": "code",
"execution_count": 5,
"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>coef</th>\n",
" <th>loss</th>\n",
" <th>norm_of_gradient</th>\n",
" <th>num_iterations</th>\n",
" <th>num_rows_processed</th>\n",
" <th>num_rows_skipped</th>\n",
" <th>dep_var_mapping</th>\n",
" </tr>\n",
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" <td>[0.12229100715556, -0.00311209904999331, 0.0729255891679728, 0.00159299038324124]</td>\n",
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]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS houses_svm, houses_svm_summary;\n",
"\n",
"SELECT madlib.svm_classification('houses',\n",
" 'houses_svm',\n",
" 'price < 100000',\n",
" 'ARRAY[1, tax, bath, size]'\n",
" );\n",
"SELECT * FROM houses_svm;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Predict using linear model\n",
"We want to predict if house price is less than $100,000. We use the training data set for prediction as well, which is not usual but serves to show the syntax. The predicted results are in the \"prediction\" column and the actual data is in the \"actual\" column."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"15 rows affected.\n"
]
},
{
"data": {
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]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS houses_pred;\n",
"\n",
"SELECT madlib.svm_predict('houses_svm', \n",
" 'houses', \n",
" 'id', \n",
" 'houses_pred');\n",
"\n",
"SELECT *, price < 100000 AS actual FROM houses JOIN houses_pred USING (id) ORDER BY id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count the miss-classifications:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
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"1 rows affected.\n"
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"source": [
"%%sql\n",
"SELECT COUNT(*) FROM houses_pred JOIN houses USING (id) \n",
"WHERE houses_pred.prediction != (houses.price < 100000);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Train using Gaussian kernel\n",
"Next generate a nonlinear model using a Gaussian kernel. This time we specify the initial step size and maximum number of iterations to run. As part of the kernel parameter, we choose 10 as the dimension of the space where we train SVM. A larger number will lead to a more powerful model but run the risk of overfitting. As a result, the model will be a 10 dimensional vector, instead of 4 as in the case of linear model."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
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"execution_count": 8,
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],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS houses_svm_gaussian, houses_svm_gaussian_summary, houses_svm_gaussian_random;\n",
"\n",
"SELECT madlib.svm_classification( 'houses',\n",
" 'houses_svm_gaussian',\n",
" 'price < 100000',\n",
" 'ARRAY[1, tax, bath, size]',\n",
" 'gaussian',\n",
" 'n_components=100',\n",
" '',\n",
" 'init_stepsize=1, max_iter=200'\n",
" );\n",
"\n",
"SELECT * FROM houses_svm_gaussian;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 5. Predict using Gaussian model\n",
"The predicted results are in the \"prediction\" column and the actual data is in the \"actual\" column."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"15 rows affected.\n"
]
},
{
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},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS houses_pred_gaussian;\n",
"\n",
"SELECT madlib.svm_predict('houses_svm_gaussian', \n",
" 'houses', \n",
" 'id', \n",
" 'houses_pred_gaussian');\n",
"\n",
"SELECT *, price < 100000 AS actual FROM houses JOIN houses_pred_gaussian USING (id) ORDER BY id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Count the miss classifications. Note this produces a more accurate result than the linear case for this small data set:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
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],
"source": [
"%%sql\n",
"SELECT COUNT(*) FROM houses_pred_gaussian JOIN houses USING (id) \n",
"WHERE houses_pred_gaussian.prediction != (houses.price < 100000);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 6. Balancing data sets\n",
"In the case of an unbalanced class-size dataset, use the 'balanced' parameter to classify when building the model:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
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"[([0.0304283785262617, -0.31252256022358, -0.786594823395088, -0.213462165447043, -0.205921127201806, -0.160495098525847, -0.0572558143143236, 0.183220396715039, 0.464792217394048, 0.481483244258389, -0.517643614876286, -0.978683507901074, 0.16817691490251, -0.646816511296802, 0.186375390577986, 0.680958110801916, -1.08232636410793, -0.945175946274317, -0.491574430145303, -0.0814842224959727, -0.0190008166655649, 0.0481772284935738, -0.163702000177582, -0.674004897487282, 0.113518490341767, -0.638187403343937, 0.526552063250668, -0.274100343388661, 0.354431317955514, -0.428444014517539, 0.0946683130713131, -0.239646558966188, -0.288975110225114, 0.277634287723891, 0.109083762491799, -0.590472152297871, 0.30239084357163, -0.644378259476824, 0.518616701508965, -0.0310448850251757, -0.0616074328876328, -0.815709238025655, 0.533952545147382, -0.27885652806791, -0.169816368423772, 0.501969605761016, -0.0453904283532783, -0.296542126733526, -0.6744641877468, -1.04295422716397, 0.0998805368884473, -0.0581387461992054, 0.226951693444486, 0.183293767588985, -0.506636260378821, -0.182587120340722, -0.632707861890101, 0.165980897095258, -0.918139789600548, -0.770637770944717, 0.945310986017951, -1.02669717302724, 0.608102258578773, 0.22613081465608, 0.141010992575908, -0.154732938082785, 0.673276713057085, -0.252979879432229, 0.373696371450733, -0.204550990421942, 0.934207962184333, -0.306025798509274, 0.812798367835215, -0.455532753255022, 0.125622634105054, 0.367604443133276, 0.55994486569804, 0.0753886324541624, 0.77813576524246, -0.166034479090781, 0.94565093122238, -0.296006132277826, -0.265234940757184, 0.416497397310383, 0.731437706958231, 0.270984781299447, -0.428663581410429, -0.312686040931128, -0.773226583138831, 0.598583108690796, -1.09057460676235, -0.685811206980829, 0.320672030715493, -0.216001011689699, -0.467434588986512, -0.412477454386901, 0.437774085441529, 0.177397734130052, -0.621856758100873, 0.965785439434402], 0.00892975733073064, 0.0517583539075499, 177, 15L, 0L, [False, True])]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS houses_svm_gaussian, houses_svm_gaussian_summary, houses_svm_gaussian_random;\n",
"\n",
"SELECT madlib.svm_classification( 'houses',\n",
" 'houses_svm_gaussian',\n",
" 'price < 150000',\n",
" 'ARRAY[1, tax, bath, size]',\n",
" 'gaussian',\n",
" 'n_components=100',\n",
" '',\n",
" 'init_stepsize=1, max_iter=200, class_weight=balanced'\n",
" );\n",
"\n",
"SELECT * FROM houses_svm_gaussian;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Regression\n",
"# 1. Create input data set\n",
"For regression we use part of the well known abalone data set https://archive.ics.uci.edu/ml/datasets/abalone :"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"Done.\n",
"20 rows affected.\n",
"20 rows affected.\n"
]
},
{
"data": {
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" <tr>\n",
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" <th>diameter</th>\n",
" <th>height</th>\n",
" <th>rings</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>M</td>\n",
" <td>0.455</td>\n",
" <td>0.365</td>\n",
" <td>0.095</td>\n",
" <td>15</td>\n",
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" <td>8</td>\n",
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" <td>0.475</td>\n",
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" <td>0.125</td>\n",
" <td>9</td>\n",
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" <td>10</td>\n",
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" <td>0.55</td>\n",
" <td>0.44</td>\n",
" <td>0.15</td>\n",
" <td>19</td>\n",
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" <td>11</td>\n",
" <td>F</td>\n",
" <td>0.525</td>\n",
" <td>0.38</td>\n",
" <td>0.14</td>\n",
" <td>14</td>\n",
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" <td>0.43</td>\n",
" <td>0.35</td>\n",
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" <td>10</td>\n",
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" <td>M</td>\n",
" <td>0.5</td>\n",
" <td>0.4</td>\n",
" <td>0.13</td>\n",
" <td>12</td>\n",
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" <td>9</td>\n",
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]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS abalone;\n",
"\n",
"CREATE TABLE abalone (id INT, sex TEXT, length FLOAT, diameter FLOAT, height FLOAT, rings INT);\n",
"\n",
"INSERT INTO abalone VALUES\n",
"(1,'M',0.455,0.365,0.095,15),\n",
"(2,'M',0.35,0.265,0.09,7),\n",
"(3,'F',0.53,0.42,0.135,9),\n",
"(4,'M',0.44,0.365,0.125,10),\n",
"(5,'I',0.33,0.255,0.08,7),\n",
"(6,'I',0.425,0.3,0.095,8),\n",
"(7,'F',0.53,0.415,0.15,20),\n",
"(8,'F',0.545,0.425,0.125,16),\n",
"(9,'M',0.475,0.37,0.125,9),\n",
"(10,'F',0.55,0.44,0.15,19),\n",
"(11,'F',0.525,0.38,0.14,14),\n",
"(12,'M',0.43,0.35,0.11,10),\n",
"(13,'M',0.49,0.38,0.135,11),\n",
"(14,'F',0.535,0.405,0.145,10),\n",
"(15,'F',0.47,0.355,0.1,10),\n",
"(16,'M',0.5,0.4,0.13,12),\n",
"(17,'I',0.355,0.28,0.085,7),\n",
"(18,'F',0.44,0.34,0.1,10),\n",
"(19,'M',0.365,0.295,0.08,7),\n",
"(20,'M',0.45,0.32,0.1,9);\n",
"\n",
"SELECT * FROM abalone ORDER BY id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Train linear regression model"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
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"<table>\n",
" <tr>\n",
" <th>coef</th>\n",
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" <th>num_rows_processed</th>\n",
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" <tr>\n",
" <td>[1.998949892503, 0.918517335088235, 0.712125758488304, 0.229379426728093]</td>\n",
" <td>8.29033306424</td>\n",
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]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS abalone_svm_regression, abalone_svm_regression_summary;\n",
"\n",
"SELECT madlib.svm_regression('abalone',\n",
" 'abalone_svm_regression',\n",
" 'rings',\n",
" 'ARRAY[1, length, diameter, height]'\n",
" );\n",
"\n",
"SELECT * FROM abalone_svm_regression;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Predict using linear model"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"20 rows affected.\n"
]
},
{
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"</table>"
],
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"[(1, u'M', 0.455, 0.365, 0.095, 15, 2.69859222735555, 2.69859222735555),\n",
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" (3, u'F', 0.53, 0.42, 0.135, 9, 2.81582312127315, 2.81582312127315),\n",
" (4, u'M', 0.44, 0.365, 0.125, 10, 2.69169585013107, 2.69169585013107),\n",
" (5, u'I', 0.33, 0.255, 0.08, 7, 2.50200303563489, 2.50200303563489),\n",
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" (10, u'F', 0.55, 0.44, 0.15, 19, 2.8518766745456, 2.8518766745456),\n",
" (11, u'F', 0.525, 0.38, 0.14, 14, 2.78389240139182, 2.78389240139182),\n",
" (12, u'M', 0.43, 0.35, 0.11, 10, 2.66838809900194, 2.66838809900194),\n",
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" (17, u'I', 0.355, 0.28, 0.085, 7, 2.54391601010794, 2.54391601010794),\n",
" (18, u'F', 0.44, 0.34, 0.1, 10, 2.66815822050066, 2.66815822050066),\n",
" (19, u'M', 0.365, 0.295, 0.08, 7, 2.56263617270251, 2.56263617270251),\n",
" (20, u'M', 0.45, 0.32, 0.1, 9, 2.66310087868178, 2.66310087868178)]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS abalone_regr;\n",
"\n",
"SELECT madlib.svm_predict('abalone_svm_regression',\n",
" 'abalone', \n",
" 'id', \n",
" 'abalone_regr');\n",
"\n",
"SELECT * FROM abalone JOIN abalone_regr USING (id) ORDER BY id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"RMS error:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>rms_error</th>\n",
" </tr>\n",
" <tr>\n",
" <td>9.08842735715</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(9.08842735715349,)]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT SQRT(AVG((rings-prediction)*(rings-prediction))) as rms_error FROM abalone \n",
"JOIN abalone_regr USING (id);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Train using Gaussian model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>coef</th>\n",
" <th>loss</th>\n",
" <th>norm_of_gradient</th>\n",
" <th>num_iterations</th>\n",
" <th>num_rows_processed</th>\n",
" <th>num_rows_skipped</th>\n",
" <th>dep_var_mapping</th>\n",
" </tr>\n",
" <tr>\n",
" <td>[1.43663248508461, 0.69548130777053, -0.667820070034776, 0.353629960777887, 0.90572382703018, -1.35192985512522, 1.33643594466947, -1.71808935250261, 0.244907459899655, -1.16083721399717, 0.529533556029462, -1.47565163746093, 0.980976366290674, -1.57625949227502, 0.725799354053906, 0.621521219305506, -0.460388482856766, 1.62409092636038, -1.57065529672944, -1.04776403679369, -1.57215274912702, 0.414786913802637, 0.338630545155005, 1.67994043684046, 0.510271334529243, 1.56926726571838, -0.266055554149799, -1.58512365036022, -0.537619986488822, -1.53123126187998, 0.432059794750374, 1.10164971751016, -1.63470052461906, 1.37210730848619, 0.69195070273926, -1.51550206095593, -0.879588483736812, -0.37806986511409, -1.02110166282495, -0.905287480716121, -0.0893285495393008, -0.576435902131671, 1.24002228080107, -0.817209795372, 0.522281410113176, 0.156847005593254, -0.872148823590253, -1.43633009947157, -0.804330453999928, -0.926921599832849, -0.211959168386152, -0.804358663674064, -0.616861771161596, 1.74018724759497, -1.16264563996159, -1.63930487981442, -1.82413119548892, 0.794011977984717, 1.1698395008002, 1.61482847146405, 0.205981267231647, -0.458375714158526, -0.867267996489895, -0.308286903981698, 1.58324830425497, -0.827831442995643, -1.47580575745441, -0.445332216554912, 1.16538304159597, 1.37221102799479, 1.57289281402231, -0.66649715533837, -0.476569227105178, 0.29665562590112, 1.15590670076679, 1.46291613083677, 0.79992035125813, 1.09721250793166, -1.39887016649551, 0.190329304832203, 1.28663304442308, 1.42061537940844, -1.03946229472351, -0.556121905835198, 0.341622218455033, -0.899369783669295, 1.61878187535374, 1.57583095826418, -0.71553739367934, -1.59696136184546, -1.56192177099981, -0.174158730591109, 1.19626739396989, -1.37221525222703, 1.59932192137657, 0.435584312299149, 1.38585223624462, 0.449219561014516, -1.61927162148616, -1.76886002780722]</td>\n",
" <td>2.60162532006</td>\n",
" <td>1.04369725716</td>\n",
" <td>166</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
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"</table>"
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]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS abalone_svm_gaussian_regression, abalone_svm_gaussian_regression_summary, abalone_svm_gaussian_regression_random;\n",
"\n",
"SELECT madlib.svm_regression( 'abalone',\n",
" 'abalone_svm_gaussian_regression',\n",
" 'rings',\n",
" 'ARRAY[1, length, diameter, height]',\n",
" 'gaussian',\n",
" 'n_components=100',\n",
" '',\n",
" 'init_stepsize=1, max_iter=200'\n",
" );\n",
"\n",
"SELECT * FROM abalone_svm_gaussian_regression;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 5. Predict using Gaussian model"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"20 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>id</th>\n",
" <th>sex</th>\n",
" <th>length</th>\n",
" <th>diameter</th>\n",
" <th>height</th>\n",
" <th>rings</th>\n",
" <th>prediction</th>\n",
" <th>decision_function</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>M</td>\n",
" <td>0.455</td>\n",
" <td>0.365</td>\n",
" <td>0.095</td>\n",
" <td>15</td>\n",
" <td>9.98603457721</td>\n",
" <td>9.98603457721</td>\n",
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" <tr>\n",
" <td>2</td>\n",
" <td>M</td>\n",
" <td>0.35</td>\n",
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" <td>9.57759650003</td>\n",
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" <td>3</td>\n",
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" <td>0.53</td>\n",
" <td>0.42</td>\n",
" <td>0.135</td>\n",
" <td>9</td>\n",
" <td>10.2223107905</td>\n",
" <td>10.2223107905</td>\n",
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" <tr>\n",
" <td>4</td>\n",
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" <td>9.97689991817</td>\n",
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" <td>5</td>\n",
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" <td>9.49509568417</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>I</td>\n",
" <td>0.425</td>\n",
" <td>0.3</td>\n",
" <td>0.095</td>\n",
" <td>8</td>\n",
" <td>9.82953290649</td>\n",
" <td>9.82953290649</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>F</td>\n",
" <td>0.53</td>\n",
" <td>0.415</td>\n",
" <td>0.15</td>\n",
" <td>20</td>\n",
" <td>10.2265451261</td>\n",
" <td>10.2265451261</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>F</td>\n",
" <td>0.545</td>\n",
" <td>0.425</td>\n",
" <td>0.125</td>\n",
" <td>16</td>\n",
" <td>10.2457829829</td>\n",
" <td>10.2457829829</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>M</td>\n",
" <td>0.475</td>\n",
" <td>0.37</td>\n",
" <td>0.125</td>\n",
" <td>9</td>\n",
" <td>10.0580169046</td>\n",
" <td>10.0580169046</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>F</td>\n",
" <td>0.55</td>\n",
" <td>0.44</td>\n",
" <td>0.15</td>\n",
" <td>19</td>\n",
" <td>10.2825919797</td>\n",
" <td>10.2825919797</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>F</td>\n",
" <td>0.525</td>\n",
" <td>0.38</td>\n",
" <td>0.14</td>\n",
" <td>14</td>\n",
" <td>10.1755652941</td>\n",
" <td>10.1755652941</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>M</td>\n",
" <td>0.43</td>\n",
" <td>0.35</td>\n",
" <td>0.11</td>\n",
" <td>10</td>\n",
" <td>9.92374820403</td>\n",
" <td>9.92374820403</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>M</td>\n",
" <td>0.49</td>\n",
" <td>0.38</td>\n",
" <td>0.135</td>\n",
" <td>11</td>\n",
" <td>10.1066593545</td>\n",
" <td>10.1066593545</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>F</td>\n",
" <td>0.535</td>\n",
" <td>0.405</td>\n",
" <td>0.145</td>\n",
" <td>10</td>\n",
" <td>10.2224287437</td>\n",
" <td>10.2224287437</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>F</td>\n",
" <td>0.47</td>\n",
" <td>0.355</td>\n",
" <td>0.1</td>\n",
" <td>10</td>\n",
" <td>10.0099999803</td>\n",
" <td>10.0099999803</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>M</td>\n",
" <td>0.5</td>\n",
" <td>0.4</td>\n",
" <td>0.13</td>\n",
" <td>12</td>\n",
" <td>10.1445256879</td>\n",
" <td>10.1445256879</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>I</td>\n",
" <td>0.355</td>\n",
" <td>0.28</td>\n",
" <td>0.085</td>\n",
" <td>7</td>\n",
" <td>9.61132344417</td>\n",
" <td>9.61132344417</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>F</td>\n",
" <td>0.44</td>\n",
" <td>0.34</td>\n",
" <td>0.1</td>\n",
" <td>10</td>\n",
" <td>9.92513613473</td>\n",
" <td>9.92513613473</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>M</td>\n",
" <td>0.365</td>\n",
" <td>0.295</td>\n",
" <td>0.08</td>\n",
" <td>7</td>\n",
" <td>9.65721889547</td>\n",
" <td>9.65721889547</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>M</td>\n",
" <td>0.45</td>\n",
" <td>0.32</td>\n",
" <td>0.1</td>\n",
" <td>9</td>\n",
" <td>9.92028835238</td>\n",
" <td>9.92028835238</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, u'M', 0.455, 0.365, 0.095, 15, 9.98603457720518, 9.98603457720518),\n",
" (2, u'M', 0.35, 0.265, 0.09, 7, 9.57759650002543, 9.57759650002543),\n",
" (3, u'F', 0.53, 0.42, 0.135, 9, 10.2223107904678, 10.2223107904678),\n",
" (4, u'M', 0.44, 0.365, 0.125, 10, 9.97689991817358, 9.97689991817358),\n",
" (5, u'I', 0.33, 0.255, 0.08, 7, 9.49509568416916, 9.49509568416916),\n",
" (6, u'I', 0.425, 0.3, 0.095, 8, 9.82953290649331, 9.82953290649331),\n",
" (7, u'F', 0.53, 0.415, 0.15, 20, 10.2265451260768, 10.2265451260768),\n",
" (8, u'F', 0.545, 0.425, 0.125, 16, 10.2457829829034, 10.2457829829034),\n",
" (9, u'M', 0.475, 0.37, 0.125, 9, 10.0580169045793, 10.0580169045793),\n",
" (10, u'F', 0.55, 0.44, 0.15, 19, 10.282591979716, 10.282591979716),\n",
" (11, u'F', 0.525, 0.38, 0.14, 14, 10.1755652940929, 10.1755652940929),\n",
" (12, u'M', 0.43, 0.35, 0.11, 10, 9.92374820403203, 9.92374820403203),\n",
" (13, u'M', 0.49, 0.38, 0.135, 11, 10.1066593545246, 10.1066593545246),\n",
" (14, u'F', 0.535, 0.405, 0.145, 10, 10.2224287437228, 10.2224287437228),\n",
" (15, u'F', 0.47, 0.355, 0.1, 10, 10.0099999802503, 10.0099999802503),\n",
" (16, u'M', 0.5, 0.4, 0.13, 12, 10.1445256879087, 10.1445256879087),\n",
" (17, u'I', 0.355, 0.28, 0.085, 7, 9.61132344416669, 9.61132344416669),\n",
" (18, u'F', 0.44, 0.34, 0.1, 10, 9.92513613472998, 9.92513613472998),\n",
" (19, u'M', 0.365, 0.295, 0.08, 7, 9.65721889547397, 9.65721889547397),\n",
" (20, u'M', 0.45, 0.32, 0.1, 9, 9.9202883523785, 9.9202883523785)]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS abalone_gaussian_regr;\n",
"\n",
"SELECT madlib.svm_predict('abalone_svm_gaussian_regression', \n",
" 'abalone', \n",
" 'id', \n",
" 'abalone_gaussian_regr');\n",
"\n",
"SELECT * FROM abalone JOIN abalone_gaussian_regr USING (id) ORDER BY id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the RMS error. Note this produces a more accurate result than the linear case for this small data set:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>rms_error</th>\n",
" </tr>\n",
" <tr>\n",
" <td>3.75666107851</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(3.75666107851157,)]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT SQRT(AVG((rings-prediction)*(rings-prediction))) as rms_error FROM abalone \n",
"JOIN abalone_gaussian_regr USING (id);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 6. Cross validation\n",
"Let's run cross validation for different initial step sizes and lambda values:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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>coef</th>\n",
" <th>loss</th>\n",
" <th>norm_of_gradient</th>\n",
" <th>num_iterations</th>\n",
" <th>num_rows_processed</th>\n",
" <th>num_rows_skipped</th>\n",
" <th>dep_var_mapping</th>\n",
" </tr>\n",
" <tr>\n",
" <td>[1.43663141048166, 0.69590902120895, -0.667071782085283, 0.353774339959869, 0.905828289634807, -1.35169013920124, 1.33646358226853, -1.7180201996699, 0.24481068329437, -1.16069594203275, 0.529737373827505, -1.47546535187187, 0.980691636646386, -1.57624210352963, 0.725851837240663, 0.621725612776311, -0.460138729733476, 1.62396471237433, -1.57063525564175, -1.04781069512703, -1.57213080053901, 0.415306056428304, 0.338679918908941, 1.6801320999485, 0.510208152821366, 1.56936887737261, -0.266001406651365, -1.58507523905296, -0.537812259067129, -1.5312628382147, 0.432237469443731, 1.10170029776598, -1.63469708889792, 1.3721951919766, 0.691837885175724, -1.5155853305037, -0.879575274457664, -0.378046928285422, -1.02084583660482, -0.905052544480774, -0.0895711922931681, -0.576177025418454, 1.23999477285708, -0.817661938486405, 0.522699926739854, 0.157003593221511, -0.871879320669594, -1.43641645044609, -0.804548716520958, -0.926662211340699, -0.211709523145134, -0.804204137088408, -0.616360942183748, 1.74003514481556, -1.16277414575498, -1.63929118823194, -1.82407340498873, 0.794042280007498, 1.17011142340466, 1.61469087973011, 0.206231124021691, -0.458287050727443, -0.86716709482106, -0.308198290202033, 1.58323228771844, -0.827377112621467, -1.47575759821081, -0.445203431415296, 1.16551153170496, 1.37211944359085, 1.57289635294413, -0.666315273550231, -0.476600127294968, 0.296662747500788, 1.15584301004967, 1.46263374958392, 0.799907772688504, 1.09708642804713, -1.39887637044597, 0.190356100346786, 1.28636855084754, 1.42039838933069, -1.03949687018857, -0.555861325085124, 0.341382237642365, -0.899593443791824, 1.61885754858329, 1.57586783372196, -0.71510056870506, -1.59701588256911, -1.56188454682269, -0.174291105678036, 1.19615194057151, -1.37224827664802, 1.59933543202374, 0.435995073036911, 1.38582746743955, 0.449261405728548, -1.61911544479704, -1.7689529702645]</td>\n",
" <td>2.60165100186</td>\n",
" <td>1.04370217846</td>\n",
" <td>165</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>[None]</td>\n",
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"</table>"
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]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS abalone_svm_gaussian_regression, abalone_svm_gaussian_regression_summary, \n",
"abalone_svm_gaussian_regression_random, abalone_svm_gaussian_regression_cv;\n",
"\n",
"SELECT madlib.svm_regression( 'abalone',\n",
" 'abalone_svm_gaussian_regression',\n",
" 'rings',\n",
" 'ARRAY[1, length, diameter, height]',\n",
" 'gaussian',\n",
" 'n_components=100',\n",
" '',\n",
" 'init_stepsize=[0.01,1], n_folds=3, max_iter=200, lambda=[0.01, 0.1, 0.5], validation_result=abalone_svm_gaussian_regression_cv'\n",
" );\n",
"\n",
"SELECT * FROM abalone_svm_gaussian_regression;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View the summary table showing the final model parameters are those that produced \n",
"the lowest error in the cross validation runs:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>method</th>\n",
" <th>version_number</th>\n",
" <th>source_table</th>\n",
" <th>model_table</th>\n",
" <th>dependent_varname</th>\n",
" <th>independent_varname</th>\n",
" <th>kernel_func</th>\n",
" <th>kernel_params</th>\n",
" <th>grouping_col</th>\n",
" <th>optim_params</th>\n",
" <th>reg_params</th>\n",
" <th>num_all_groups</th>\n",
" <th>num_failed_groups</th>\n",
" <th>total_rows_processed</th>\n",
" <th>total_rows_skipped</th>\n",
" </tr>\n",
" <tr>\n",
" <td>SVR</td>\n",
" <td>1.17-dev</td>\n",
" <td>abalone</td>\n",
" <td>abalone_svm_gaussian_regression</td>\n",
" <td>rings</td>\n",
" <td>ARRAY[1, length, diameter, height]</td>\n",
" <td>gaussian</td>\n",
" <td>gamma=0.25, n_components=100,random_state=1, fit_intercept=False, fit_in_memory=True</td>\n",
" <td>NULL</td>\n",
" <td> init_stepsize=1.0,<br> decay_factor=0.9,<br> max_iter=200,<br> tolerance=1e-10,<br> epsilon=0.01,<br> eps_table=,<br> class_weight=<br> </td>\n",
" <td>lambda=0.01, norm=l2, n_folds=3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(u'SVR', u'1.17-dev', u'abalone', u'abalone_svm_gaussian_regression', u'rings', u'ARRAY[1, length, diameter, height]', u'gaussian', u'gamma=0.25, n_components=100,random_state=1, fit_intercept=False, fit_in_memory=True', u'NULL', u' init_stepsize=1.0,\\n decay_factor=0.9,\\n max_iter=200,\\n tolerance=1e-10,\\n epsilon=0.01,\\n eps_table=,\\n class_weight=\\n ', u'lambda=0.01, norm=l2, n_folds=3', 1, 0, 20L, 0L)]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT * FROM abalone_svm_gaussian_regression_summary;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View the values for cross validation:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>init_stepsize</th>\n",
" <th>lambda</th>\n",
" <th>mean_score</th>\n",
" <th>std_dev_score</th>\n",
" </tr>\n",
" <tr>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>-10.6826786731</td>\n",
" <td>2.89736952916</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.01</td>\n",
" <td>0.5</td>\n",
" <td>-10.6896583532</td>\n",
" <td>2.89700539715</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1.0</td>\n",
" <td>0.1</td>\n",
" <td>-3.81154741847</td>\n",
" <td>2.20160973525</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0.01</td>\n",
" <td>0.1</td>\n",
" <td>-10.6839690886</td>\n",
" <td>2.89730222282</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1.0</td>\n",
" <td>0.01</td>\n",
" <td>-3.62237265846</td>\n",
" <td>2.30389631902</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1.0</td>\n",
" <td>0.5</td>\n",
" <td>-3.9659217506</td>\n",
" <td>2.24231164049</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('0.01'), Decimal('0.01'), Decimal('-10.6826786731'), Decimal('2.89736952916')),\n",
" (Decimal('0.01'), Decimal('0.5'), Decimal('-10.6896583532'), Decimal('2.89700539715')),\n",
" (Decimal('1.0'), Decimal('0.1'), Decimal('-3.81154741847'), Decimal('2.20160973525')),\n",
" (Decimal('0.01'), Decimal('0.1'), Decimal('-10.6839690886'), Decimal('2.89730222282')),\n",
" (Decimal('1.0'), Decimal('0.01'), Decimal('-3.62237265846'), Decimal('2.30389631902')),\n",
" (Decimal('1.0'), Decimal('0.5'), Decimal('-3.9659217506'), Decimal('2.24231164049'))]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT * FROM abalone_svm_gaussian_regression_cv;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 7. Predict using cross-validated Gaussian regression model:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>svm_predict</th>\n",
" </tr>\n",
" <tr>\n",
" <td></td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[('',)]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS abalone_gaussian_regr;\n",
"SELECT madlib.svm_predict('abalone_svm_gaussian_regression', \n",
" 'abalone', \n",
" 'id', \n",
" 'abalone_gaussian_regr');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute the RMS error. Note this produces a more accurate result than the previous run with the Gaussian kernel:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>rms_error</th>\n",
" </tr>\n",
" <tr>\n",
" <td>3.7567001982</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(3.7567001982019,)]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT SQRT(AVG((rings-prediction)*(rings-prediction))) as rms_error FROM abalone \n",
"JOIN abalone_gaussian_regr USING (id);"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Novelty detection \n",
"# 1. Train a non-linear one-class SVM\n",
"Use a Gaussian kernel using the housing data set. Note that the dependent variable is not a parameter for one-class:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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>coef</th>\n",
" <th>loss</th>\n",
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" <th>num_iterations</th>\n",
" <th>num_rows_processed</th>\n",
" <th>num_rows_skipped</th>\n",
" <th>dep_var_mapping</th>\n",
" </tr>\n",
" <tr>\n",
" <td>[0.0207901288823711, -0.00103437489314969, 0.00407820868429805, 0.0274910360546609, 0.0105696547048294, -0.00313332466259033, -0.0216703145014011, 0.0363248037825208, -0.0211400498166549, -0.00827402232219555, 0.0265909439934851, 0.0282462482323058, -0.0407407195393746, 0.0191290942177852, -0.00313542082923064, -0.0191740603622109, 0.0143626646548982, -0.0620527674181034, -0.000319831622794402, 0.00388104709972051, 0.00248129433065678, 0.00764915273571186, 0.014492283562898, 0.0184730815984353, -0.00745840880633255, -0.0232208663374367, -0.010724056217189, 0.00541494627043399, 0.0150679846777238, 0.0204022414812525, -0.0294626167089617, -0.00399506510201406, -0.0231139983460727, 0.0242203153309423, -0.0421196963278802, 0.0112202149916885, -0.00720876723524249, 0.0213674589734111, -0.00260107056222295, -0.0130652059444514, 0.0710580616012718, 0.0519822855717347, 0.00961050532247376, 0.0390561950837254, -0.0152620688050253, 0.0100336750737295, 0.0632488712630204, -0.0549714494076944, -0.007684860916257, 0.0322104572263339, -0.00832311210931705, 0.0279669244721609, 0.0455147539995411, -0.0639670005155479, -0.00965055072583972, 0.00648588125681694]</td>\n",
" <td>0.944029805394</td>\n",
" <td>14.5264894419</td>\n",
" <td>100</td>\n",
" <td>16</td>\n",
" <td>-1</td>\n",
" <td>[-1.0, 1.0]</td>\n",
" </tr>\n",
"</table>"
],
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"[([0.0207901288823711, -0.00103437489314969, 0.00407820868429805, 0.0274910360546609, 0.0105696547048294, -0.00313332466259033, -0.0216703145014011, 0.0363248037825208, -0.0211400498166549, -0.00827402232219555, 0.0265909439934851, 0.0282462482323058, -0.0407407195393746, 0.0191290942177852, -0.00313542082923064, -0.0191740603622109, 0.0143626646548982, -0.0620527674181034, -0.000319831622794402, 0.00388104709972051, 0.00248129433065678, 0.00764915273571186, 0.014492283562898, 0.0184730815984353, -0.00745840880633255, -0.0232208663374367, -0.010724056217189, 0.00541494627043399, 0.0150679846777238, 0.0204022414812525, -0.0294626167089617, -0.00399506510201406, -0.0231139983460727, 0.0242203153309423, -0.0421196963278802, 0.0112202149916885, -0.00720876723524249, 0.0213674589734111, -0.00260107056222295, -0.0130652059444514, 0.0710580616012718, 0.0519822855717347, 0.00961050532247376, 0.0390561950837254, -0.0152620688050253, 0.0100336750737295, 0.0632488712630204, -0.0549714494076944, -0.007684860916257, 0.0322104572263339, -0.00832311210931705, 0.0279669244721609, 0.0455147539995411, -0.0639670005155479, -0.00965055072583972, 0.00648588125681694], 0.944029805394336, 14.5264894418914, 100, 16L, -1L, [-1.0, 1.0])]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS houses_one_class_gaussian, houses_one_class_gaussian_summary, houses_one_class_gaussian_random;\n",
"\n",
"SELECT madlib.svm_one_class('houses',\n",
" 'houses_one_class_gaussian',\n",
" 'ARRAY[1,tax,bedroom,bath,size,lot,price]',\n",
" 'gaussian',\n",
" 'gamma=0.5,n_components=55, random_state=3',\n",
" NULL,\n",
" 'max_iter=100, init_stepsize=10,lambda=10, tolerance=0'\n",
" );\n",
"\n",
"SELECT * FROM houses_one_class_gaussian;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Create test data\n",
"For the novelty detection using one-class, let's create a test data set using the last 3 values from the training set plus an outlier at the end (10x price):"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"Done.\n",
"4 rows affected.\n",
"4 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>id</th>\n",
" <th>tax</th>\n",
" <th>bedroom</th>\n",
" <th>bath</th>\n",
" <th>price</th>\n",
" <th>size</th>\n",
" <th>lot</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>3100</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" <td>140000</td>\n",
" <td>1760</td>\n",
" <td>38000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2070</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" <td>148000</td>\n",
" <td>1550</td>\n",
" <td>14000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>650</td>\n",
" <td>3</td>\n",
" <td>1.5</td>\n",
" <td>65000</td>\n",
" <td>1450</td>\n",
" <td>12000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>650</td>\n",
" <td>3</td>\n",
" <td>1.5</td>\n",
" <td>650000</td>\n",
" <td>1450</td>\n",
" <td>12000</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, 3100, 3, 2.0, 140000, 1760, 38000),\n",
" (2, 2070, 2, 3.0, 148000, 1550, 14000),\n",
" (3, 650, 3, 1.5, 65000, 1450, 12000),\n",
" (4, 650, 3, 1.5, 650000, 1450, 12000)]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"DROP TABLE IF EXISTS houses_one_class_test;\n",
"\n",
"CREATE TABLE houses_one_class_test (id INT, tax INT, bedroom INT, bath FLOAT, price INT,\n",
" size INT, lot INT);\n",
"\n",
"INSERT INTO houses_one_class_test VALUES \n",
" (1 , 3100 , 3 , 2 , 140000 , 1760 , 38000),\n",
" (2 , 2070 , 2 , 3 , 148000 , 1550 , 14000),\n",
" (3 , 650 , 3 , 1.5 , 65000 , 1450 , 12000),\n",
" (4 , 650 , 3 , 1.5 , 650000 , 1450 , 12000);\n",
" \n",
"SELECT * FROM houses_one_class_test ORDER BY id;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Predict using Gaussian one-class novelty detection model\n",
"Result shows the last row predicted to be novel:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done.\n",
"1 rows affected.\n",
"4 rows affected.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>id</th>\n",
" <th>tax</th>\n",
" <th>bedroom</th>\n",
" <th>bath</th>\n",
" <th>price</th>\n",
" <th>size</th>\n",
" <th>lot</th>\n",
" <th>prediction</th>\n",
" <th>decision_function</th>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>3100</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" <td>140000</td>\n",
" <td>1760</td>\n",
" <td>38000</td>\n",
" <td>1.0</td>\n",
" <td>0.0662278474212</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2070</td>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" <td>148000</td>\n",
" <td>1550</td>\n",
" <td>14000</td>\n",
" <td>1.0</td>\n",
" <td>0.092124936453</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>650</td>\n",
" <td>3</td>\n",
" <td>1.5</td>\n",
" <td>65000</td>\n",
" <td>1450</td>\n",
" <td>12000</td>\n",
" <td>1.0</td>\n",
" <td>0.03415206006</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>650</td>\n",
" <td>3</td>\n",
" <td>1.5</td>\n",
" <td>650000</td>\n",
" <td>1450</td>\n",
" <td>12000</td>\n",
" <td>-1.0</td>\n",
" <td>-0.0131918729845</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(1, 3100, 3, 2.0, 140000, 1760, 38000, 1.0, 0.066227847421185),\n",
" (2, 2070, 2, 3.0, 148000, 1550, 14000, 1.0, 0.0921249364529948),\n",
" (3, 650, 3, 1.5, 65000, 1450, 12000, 1.0, 0.0341520600599523),\n",
" (4, 650, 3, 1.5, 650000, 1450, 12000, -1.0, -0.0131918729845241)]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql \n",
"DROP TABLE IF EXISTS houses_pred;\n",
"\n",
"SELECT madlib.svm_predict('houses_one_class_gaussian', \n",
" 'houses_one_class_test', \n",
" 'id', \n",
" 'houses_pred');\n",
"\n",
"SELECT * FROM houses_one_class_test JOIN houses_pred USING (id) ORDER BY id;"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
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"file_extension": ".py",
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