http://git-wip-us.apache.org/repos/asf/madlib-site/blob/acd339f6/community-artifacts/Novelty-detection-demo-1.ipynb
----------------------------------------------------------------------
diff --git a/community-artifacts/Novelty-detection-demo-1.ipynb
b/community-artifacts/Novelty-detection-demo-1.ipynb
deleted file mode 100755
index 563bda4..0000000
--- a/community-artifacts/Novelty-detection-demo-1.ipynb
+++ /dev/null
@@ -1,478 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Novelty detection using 1-class SVM\n",
- "\n",
- "Classifies new data as similar or different to the training set. This
method is an unsupervised method that builds a decision boundary between the
data and origin in kernel space and can be used as a novelty detector."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "The sql extension is already loaded. To reload it, use:\n",
- " %reload_ext sql\n"
- ]
- }
- ],
- "source": [
- "# Setup\n",
- "%load_ext sql\n",
- "# %sql postgresql://gpdbchina@10.194.10.68:55000/madlib\n",
- "%sql postgresql://fmcquillan@localhost:5432/madlib\n",
- "%matplotlib inline\n",
- "\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "import matplotlib.font_manager"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "data": {
- "image/png":
"iVBORw0KGgoAAAANSUhEUgAAAW8AAAD7CAYAAAClvBX1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlVNSSFlKBY0xLShtIrJkSptIISVLm4jI8qUH3/cPD
gIwnEgocMu8Qj1JR0Sk2mkDYhGRKqLgLSIS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\nDaxcuZ6VK9cTjUZ54YX/k74S
0AU8xNGjr3LttbeQSp0PnAucy9TURk6deh94CBgF/hr4C+BDwLP+\neWcCv+c/d4FfE37KLF93vBQPlDaRZUgmk7YzGrWd0WhNfkVfKK+dnfZIJpM2Ho9bWJ053z2/0sKZ\nFtbmHE/4z8+ysM5/vm1O6sQd+4iF9VnvX28hWTBVI8uHct4i4TVfXru/v982Nm62kci5NhJZZxsb\nN1tjPphzfsIPzp/KE5Q3+wE4O2Anc4L82f41zi0Q1FfnHfyU5SsUvJU2EQmx9IzJ48e/Qir1NVIp\ny/Hjf4y19cBTWWc+A3wLl+rItQ64BvhnXKrEA2K43PhOXH77s8AQkG+i3a+Bm5mYOFicX0oWZdkD\nlsaYjcB3gfXANPCwtfaB+d8lIkuRbznXRGKYa6+9BTfVPZ519qh/bAcuDw7wsv/f7qxzD+MC8hrg\nJHAecCVwLXAO8BbwH4CngQNAJ/ATYHvWvdK59jeBnxXjV5VFKkbP+yTQY629CLgCuMUYc2ERrisi\nvlgsxsiIW641Gh1dVGVHQ0MD9fV3UV9/F5//fLM/qPkmcB2uauQRYBD4MvABXOB+BNgAvAPcCEwC\n/wnYBPwt8CX/9Xagx7/Wm6e1oJVmcS5P0dc2Mcb8LfCgtfaHOcdtse8lUuvmLjS1E9ezfpj+/rsA\nuO++RwH4whc+k1mA6ujRoxw5cgfM2s/9YeCnwDf9Y7244PwM8Aug3z/fA3ZTV/cSH/rQarZsuYCB\ngV1LKhPM3U8zEulVqWEBZVnP2xhzHnAp7ruViJRYX18fAPfdt4dU6l3AUF//fc45ZyOPPvo4R478\nM/BpAIaHn2Dz5n/HBRdcwOrVH8650mHgVVzgzk7BfA2XPpn2z/H8n+9jehpOnNjJiy++sOR2Dw7u\nz1tzruC9eEUbsDTGnAn8DbDdWvtOsa4rIrPlphv6+vp4++3XeO+9X9HXt513332XI0dW+4G7Afgz\n/3EGR45
8jrGx8zl06HncQORO//EwcEHOnQ4DbwD34QY7H8b1xK/D5dVHgThTUxeqxjsARel5G2Pq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
68/9q//z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\nH4NHrLUvLbtlIiJS0LLTJou+kdImIiJLVsq0iSyC53l0tbXR1da
G53lBN0dEQk497zLwPI94Rwf7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\nq1Z9zDY1tdj6+g9n/pGA1daYs21TU4v+Z5
SCOqNROzTzl8kOge2MRoNuVugVCt6apFOh8q1hvuHC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\n/p19g5k8dy9wHS7lc
gbwon+fp4AfUVdnOOOMOi688CIGBnYtue3ZqxvOtyCWdvMRmascOe+bgCeK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\n
/w235gn+8dtw9dzZg5Jx3EDlLmATbuAzxtTUTOojeyB1YuJ64H2mptw1VAooUh4K3iGUm1KJRHpJ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
aTgLSISQgreIiIh\npOAtIhJCCt4iIiFUlOBtjEkYY6aNMY3FuJ6IiMxv2cHbGLMRiAKvL785IiKlUW2bWCx7bRNjzPeB\ne4FR4DJr7fEC52ltExEJRJg3sSjJ2ibGmHbg59baw8u5johIKe0fHGRfKkUciAP7UqnMyoxhteB6\n3saYMWB99iHAAncDX8alTLJ/VtDu3bszz1tbW2ltbV18S0VEasD4+Djj4+MLnnfaaRNjzKeAp4H3\ncEF7I/BL4HJr7Vt5zlfaREQCUY1pk6Kt522M+Rmw1Vr7mwI/V/AWKRFt0LCwsH5G5QjeR4FmDViK\nlFeYe5WyMO2kI1KltKN8ddNOOiIiVUS7x4uEnHaUr01Km4hUgbAOxsnClPMWqSIK1rVDwVukSqi6\npLYoeItUCVWX1JaqrTaptpXCREQWI9TVJrlfH+OTk/r6KFVP1SUCIU+b6Ouj1CoNWNaOQmmTUPe8\nRWpVLBZTwK5xoQ7e+vooIrUq1GkT0NdHkWqm/79VKigiIaN6dkfBW0RCRQUJTtXWeS/FYrYWqhX6\nLGYU+ixqcQ6B/l7MqPTPQsG7RumzmJHvs0h/ZW8fG3O9v46OmgjglfT3ojuRcKkSXK+7NxKhu4wF\nCZX0WeQT6moTkVLJ3m0cAH+38VrLtwYpFosxPDKSGbAcrtEBy0IUvEWkYqmevbCyDliW5UYiIlUm\n0GoTEREpnpoasBQRqRYK3iIiIVSzwdsYkzDGTBtjGoNuS1CMMV83xrxkjHneGPOkMWZ10G0qN2PM\nVcaYl40xrxhjeoNuT1CMMRuNMT8yxrxojDlsjLk96DYFzRhTZ4w5aIwZDbot+dRk8DbGbASiwOtB\ntyVgB4CLrLWXAq8CuwJuT1kZY+qAPwdiwEXAF40xFwbbqsCcBHqstRcBVwC31PBnkbYd+KegG1FI\nTQZv4H7gzqAbETRr7dPW2mn/5bPAxiDbE4DLgVetta9b
a98HngD+KOA2BcJa+6a19nn/+TvAS8BH\ng21VcPwO3tXAd4JuSyE1F7yNMe3Az621h4NuS4W5CfhB0I0os48CP896/QtqOGClGWPOAy4FfhJs\nSwKV7uBVbDleVU7SMcaMAeuzD+H+EO4GvoxLmWT/rGrN81n0WWuf8s/pA9631j4eQBOlghhjzgT+\nBtju98BrjjHmD4F/sdY+b4xppUJjRFUGb2ttNN9xY8yngPOAF4wxBpcm+N/GmMuttW+VsYllU+iz\nSDPG3ID7evjZsjSosvwS+HjW643+sZpkjKnHBe7HrLV/F3R7AnQl0G6MuRqIAKuMMd+11v5pwO2a\npaYn6RhjfgZstdb+Jui2BMEYcxUwCPx7a+3bQben3IwxK4CfAp8DfgX8L+CL1tqXAm1YQIwx3wWO\nWWt7gm5LpTDGtAAJa2170G3JVXM57xyWCv1KVCYPAmcCY35J1F8G3aBystaeAm7FVd28CDxRw4H7\nSuBPgM8aYw75fx+uCrpdUlhN97xFRMKq1nveIiKhpOAtIhJCCt4iIiGk4C0iEkIK3iIiIaTgLSIS\nQgreIiIhpOAtIhJC/x+QMzAQu1m4swAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x10a034250>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Generate train data\n",
- "X = 0.3 * np.random.randn(100, 2)\n",
- "X_train = np.r_[X + 2, X - 2]\n",
- "X_train_D = pd.DataFrame(X_train, columns=['x1', 'x2'])\n",
- "\n",
- "# Generate some abnormal novel observations\n",
- "X_outliers = np.random.uniform(low=-7, high=7, size=(40, 2))\n",
- "X_outliers_D = pd.DataFrame(X_outliers, columns=['x1', 'x2'])\n",
- "\n",
- "b = plt.scatter(X_train[:, 0], X_train[:, 1], c='blue')\n",
- "c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')\n",
- "plt.axis('tight')\n",
- "plt.xlim((-5, 5))\n",
- "plt.ylim((-5, 5))\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "Done.\n",
- "200 rows affected.\n",
- "Done.\n",
- "Done.\n",
- "40 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Build tables\n",
- "%sql DROP TABLE IF EXISTS X_train_D CASCADE\n",
- "%sql PERSIST X_train_D\n",
- "%sql ALTER TABLE X_train_D add column X float[]\n",
- "%sql update X_train_D set X = array[x1, x2]::float[]\n",
- "\n",
- "%sql DROP TABLE IF EXISTS X_outliers_D CASCADE\n",
- "%sql PERSIST X_outliers_D\n",
- "%sql ALTER TABLE X_outliers_D add column X float[]\n",
- "%sql update X_outliers_D set X = array[x1, x2]::float[]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "collapsed": false
- },
- "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>[-0.0652579039751, -0.132031595902, -0.736674560761,
0.110776065553, -0.257405638109, 0.0592959032279, 0.394264859682,
-0.696044045897, 0.355378680096, -0.917519476201, 0.499471522957,
-0.050984665751, -0.134700659181, -0.0705623616132, -0.0440350483425,
-0.730837500515, -0.0321151797135, -0.893641152253, -0.686424263168,
-0.568143960852, -0.12323421025, -0.136522678866, -0.626743403999,
-0.256609467659, -0.285826104108, -0.117860781456, 0.318213569126,
0.465683621798, 0.569073368875, -0.319718151386, -0.032926380078,
0.330495474386, 0.0426841287321, 0.621705430279, 0.444381604771,
0.347966836495, 0.0573812330683, -0.103244905478, -0.575045339435,
-0.101783824252, 0.624406852044, 0.113168204922, 0.506933705459,
0.560256594355, 0.142564738705, 0.338469597153, -0.0531770790698,
0.107924238733, 0.665718502542, 0.712455036796, -0.265273701697,
-0.133616355952, 0.413526223018, -0.277088845804, -0.49433371065,
-1.00004534127]</td>\n",
- " <td>73.1866371844</td>\n",
- " <td>113.184300907</td>\n",
- " <td>100</td>\n",
- " <td>201</td>\n",
- " <td>-1</td>\n",
- " <td>[-1.0, 1.0]</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[([-0.0652579039751, -0.132031595902, -0.736674560761, 0.110776065553,
-0.257405638109, 0.0592959032279, 0.394264859682, -0.696044045897,
0.355378680096, -0.917519476201, 0.499471522957, -0.050984665751,
-0.134700659181, -0.0705623616132, -0.0440350483425, -0.730837500515,
-0.0321151797135, -0.893641152253, -0.686424263168, -0.568143960852,
-0.12323421025, -0.136522678866, -0.626743403999, -0.256609467659,
-0.285826104108, -0.117860781456, 0.318213569126, 0.465683621798,
0.569073368875, -0.319718151386, -0.032926380078, 0.330495474386,
0.0426841287321, 0.621705430279, 0.444381604771, 0.347966836495,
0.0573812330683, -0.103244905478, -0.575045339435, -0.101783824252,
0.624406852044, 0.113168204922, 0.506933705459, 0.560256594355, 0.142564738705,
0.338469597153, -0.0531770790698, 0.107924238733, 0.665718502542,
0.712455036796, -0.265273701697, -0.133616355952, 0.413526223018,
-0.277088845804, -0.49433371065, -1.00004534127], 73.1866371844,
113.184300907067, 100, 201L, -1L, [-1
.0, 1.0])]"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql \n",
- "-- Train the model\n",
- "DROP TABLE IF EXISTS svm_out1, svm_out1_summary, svm_out1_random
CASCADE;\n",
- "SELECT madlib.svm_one_class(\n",
- " 'X_train_D', -- source table\n",
- " 'svm_out1', -- output table\n",
- " 'X', -- features\n",
- " 'gaussian', -- kernel\n",
- " 'gamma=1, n_components=55, random_state=3', \n",
- " NULL, -- grouping \n",
- " 'init_stepsize=0.1, lambda=10, max_iter=100, tolerance=0' \n",
- " );\n",
- "SELECT * FROM svm_out1; "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "200 rows affected.\n",
- "Done.\n",
- "1 rows affected.\n",
- "40 rows affected.\n",
- "20 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/html": [
- "<table>\n",
- " <tr>\n",
- " <th>index</th>\n",
- " <th>prediction</th>\n",
- " <th>decision_function</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>0</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.185553475523</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>1</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.998404919902</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>2</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.338487093573</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>3</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.307355005857</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>4</td>\n",
- " <td>-1.0</td>\n",
- " <td>-1.07966449719</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>5</td>\n",
- " <td>-1.0</td>\n",
- " <td>-1.97175772304</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>6</td>\n",
- " <td>-1.0</td>\n",
- " <td>-2.20381705572</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>7</td>\n",
- " <td>-1.0</td>\n",
- " <td>-2.06660552658</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>8</td>\n",
- " <td>-1.0</td>\n",
- " <td>-1.44928341418</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>9</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.418765699618</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.624364032639</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.368526211192</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>12</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.304497177977</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>13</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.0421685610513</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>14</td>\n",
- " <td>-1.0</td>\n",
- " <td>-1.01890271929</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>15</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.669735104545</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>16</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.947241377137</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>17</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.576672641354</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>18</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.550855208608</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>19</td>\n",
- " <td>-1.0</td>\n",
- " <td>-0.315200233978</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(0L, -1.0, -0.185553475522983),\n",
- " (1L, -1.0, -0.998404919902401),\n",
- " (2L, -1.0, -0.338487093572938),\n",
- " (3L, -1.0, -0.307355005857271),\n",
- " (4L, -1.0, -1.07966449719332),\n",
- " (5L, -1.0, -1.97175772303719),\n",
- " (6L, -1.0, -2.20381705571656),\n",
- " (7L, -1.0, -2.06660552658075),\n",
- " (8L, -1.0, -1.44928341418456),\n",
- " (9L, -1.0, -0.418765699617653),\n",
- " (10L, -1.0, -0.624364032638881),\n",
- " (11L, -1.0, -0.368526211192035),\n",
- " (12L, -1.0, -0.304497177977408),\n",
- " (13L, -1.0, -0.0421685610512577),\n",
- " (14L, -1.0, -1.01890271928657),\n",
- " (15L, -1.0, -0.669735104544719),\n",
- " (16L, -1.0, -0.94724137713711),\n",
- " (17L, -1.0, -0.576672641354374),\n",
- " (18L, -1.0, -0.550855208607514),\n",
- " (19L, -1.0, -0.315200233978155)]"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Prediction\n",
- "# First for the training data\n",
- "%sql drop table if exists y_pred_train;\n",
- "%sql SELECT madlib.svm_predict('svm_out1', 'X_train_D', 'index',
'y_pred_train');\n",
- "y_pred_train = %sql SELECT * from y_pred_train; \n",
- "\n",
- "# Next for the outliers\n",
- "%sql drop table if exists y_pred_outliers;\n",
- "%sql SELECT madlib.svm_predict('svm_out1', 'X_outliers_D', 'index',
'y_pred_outliers');\n",
- "y_pred_outliers = %sql SELECT * from y_pred_outliers; \n",
- "\n",
- "%sql SELECT * FROM y_pred_outliers limit 20; -- Show the outliers\n",
- "#%sql SELECT * FROM y_pred_train limit 20; -- Show the training data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "Done.\n",
- "10000 rows affected.\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "[]"
- ]
- },
- "execution_count": 42,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Predict over the decision grid for plotting\n",
- "# xx, yy = np.meshgrid(np.linspace(-7, 7, 500), np.linspace(-7, 7,
500))\n",
- "xx, yy = np.meshgrid(np.linspace(-7, 7, 100), np.linspace(-7, 7, 100))\n",
- "grid_points = pd.DataFrame(np.c_[xx.ravel(), yy.ravel()], columns=['x1',
'x2'])\n",
- "\n",
- "%sql DROP TABLE IF EXISTS grid_points CASCADE\n",
- "%sql PERSIST grid_points\n",
- "%sql ALTER TABLE grid_points add column X float[]\n",
- "%sql update grid_points set X = array[x1, x2]::float[]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {
- "collapsed": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done.\n",
- "1 rows affected.\n",
- "10000 rows affected.\n"
- ]
- },
- {
- "data": {
- "image/png":
"iVBORw0KGgoAAAANSUhEUgAAAW8AAAEKCAYAAADdBdT9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9P5C+7ZLOj5/f5Skwwfsyz7tk/QtSZ+V9Gv5fu2Q9
KZCO56VdEbe5r+T9N91/53wGv+LyhuzdYWk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\nkv44Iq7P92NPvh/HKbuguEPZ
Py69ETZfUBaST9n+bj5vhaQDJd2vrOvlC5J+PV/fTZKulHSD7Z3K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\no7N8hDf
QME0K7H5mFeI8o7N0hDfQIE0P7qIZhzjP6CwV4Q00QEqhPRl94vXgRzpAzVIO7qK27Ecq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
QIIIbwBI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\nRrChVbj7Z2UIbwDl4O6flSK8azRd1V3WvUaagDHf7cfdP6tFn3d
D9e41ctYPs9eK1dk8AJCovGsz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\npYS37Qtt77F9WBnrAwBMbeSfx9teKOl0Sd
tGbw4AjEnLHmJRRuW9WtLFJawHAMYjf4jFNQ9nr/lr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\nGjscr5U/0rF9m6TXF
Gcp+5/wYUmXKesyKX7WWlMci8sj4iv5MpdLej4ibqihiWgQ2/Mk3STpvLwC\n7xzbyyU9GRF3236TGpoRrQzviDi933zbb5A0Ieke21bWTfA92ydExI4Km1iZQceix/Z7lJ0enlZJ\ng5rlUUlHFN4vzOd1ku25yoL78xFxc93tqdFJks6yfaakgyQdbHtNRLy75nbto9M/0rG9RdLxEfFM\n3W2pg+1lkj4t6eSIeKru9lTN9gGSfizpzZIel3SnpHMi4oFaG1YT22sk/TQiLqi7LU1h+xRJF0bE\nWXW3ZbLO9XlPEmroKVFFPitpnqTb8iFR19bdoCpFxAuSViobdXOfpLUdDu6TJL1T0mm2v5//PSyr\nu10YrNOVNwCkquuVNwAkifAGgAQR3gCQIMIbABJEeANAgghvAEgQ4Q0ACSK8ASBB/w+3pZ0Fhfm4\n5gAAAABJRU5ErkJggg==\n",
- "text/plain": [
- "<matplotlib.figure.Figure at 0x10a969f50>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Plot the decision grid\n",
- "%sql drop table if exists Z_D;\n",
- "%sql SELECT madlib.svm_predict('svm_out1', 'grid_points', 'index',
'Z_D');\n",
- "Z_D = %sql SELECT decision_function from Z_D order by index\n",
- "Z = np.array(Z_D)\n",
- "Z = Z.reshape(xx.shape)\n",
- "\n",
- "# Orange is not novel, green is novel\n",
- "plt.title(\"Novelty Detection\")\n",
- "plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='orange')\n",
- "plt.contourf(xx, yy, Z, levels=[Z.min(), 0], colors='green')\n",
- "#plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), Z.max(), 7),
cmap=plt.cm.Blues_r)\n",
- "#b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='blue')\n",
- "c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')\n",
- "plt.axis('tight')\n",
- "plt.xlim((-5, 5))\n",
- "plt.ylim((-5, 5))\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": true
- },
- "outputs": [],
- "source": []
- }
- ],
- "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": "2.7.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
