| { |
| "cells": [ |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Random forest\n", |
| "\n", |
| "Random forests build an ensemble of classifiers, each of which is a tree model constructed using bootstrapped samples from the input data. The results of these models are then combined to yield a single prediction, which, at the expense of some loss in interpretation, have been found to be highly accurate.\n", |
| "\n", |
| "Please also refer to the decision tree user documentation for information relevant to the implementation of random forests in MADlib.\n", |
| "\n", |
| "This notebook includes impurity importance which was added in 1.15." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 1, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stderr", |
| "output_type": "stream", |
| "text": [ |
| "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. You should import from traitlets.config instead.\n", |
| " \"You should import from traitlets.config instead.\", ShimWarning)\n", |
| "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/utils/traitlets.py:5: UserWarning: IPython.utils.traitlets has moved to a top-level traitlets package.\n", |
| " warn(\"IPython.utils.traitlets has moved to a top-level traitlets package.\")\n" |
| ] |
| } |
| ], |
| "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 5.4.0 on GCP (demo machine)\n", |
| "%sql postgresql://gpadmin@35.184.253.255:5432/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: 1.15-dev, git revision: rc/1.14-rc1-45-g3ab7554, cmake configuration time: Wed Aug 1 18:34:10 UTC 2018, build type: release, build system: Linux-2.6.32-696.20.1.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'MADlib version: 1.15-dev, git revision: rc/1.14-rc1-45-g3ab7554, cmake configuration time: Wed Aug 1 18:34:10 UTC 2018, build type: release, build system: Linux-2.6.32-696.20.1.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]" |
| ] |
| }, |
| "execution_count": 3, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%sql select madlib.version();\n", |
| "#%sql select version();" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Random forest classification examples" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 1. Load data\n", |
| "Data set related to whether to play golf or not." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 5, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "Done.\n", |
| "14 rows affected.\n", |
| "14 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>id</th>\n", |
| " <th>OUTLOOK</th>\n", |
| " <th>temperature</th>\n", |
| " <th>humidity</th>\n", |
| " <th>Temp_Humidity</th>\n", |
| " <th>clouds_airquality</th>\n", |
| " <th>windy</th>\n", |
| " <th>class</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>sunny</td>\n", |
| " <td>85.0</td>\n", |
| " <td>85.0</td>\n", |
| " <td>[85.0, 85.0]</td>\n", |
| " <td>[u'none', u'unhealthy']</td>\n", |
| " <td>False</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>2</td>\n", |
| " <td>sunny</td>\n", |
| " <td>80.0</td>\n", |
| " <td>90.0</td>\n", |
| " <td>[80.0, 90.0]</td>\n", |
| " <td>[u'none', u'moderate']</td>\n", |
| " <td>True</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>3</td>\n", |
| " <td>overcast</td>\n", |
| " <td>83.0</td>\n", |
| " <td>78.0</td>\n", |
| " <td>[83.0, 78.0]</td>\n", |
| " <td>[u'low', u'moderate']</td>\n", |
| " <td>False</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>4</td>\n", |
| " <td>rain</td>\n", |
| " <td>70.0</td>\n", |
| " <td>96.0</td>\n", |
| " <td>[70.0, 96.0]</td>\n", |
| " <td>[u'low', u'moderate']</td>\n", |
| " <td>False</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>5</td>\n", |
| " <td>rain</td>\n", |
| " <td>68.0</td>\n", |
| " <td>80.0</td>\n", |
| " <td>[68.0, 80.0]</td>\n", |
| " <td>[u'medium', u'good']</td>\n", |
| " <td>False</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>6</td>\n", |
| " <td>rain</td>\n", |
| " <td>65.0</td>\n", |
| " <td>70.0</td>\n", |
| " <td>[65.0, 70.0]</td>\n", |
| " <td>[u'low', u'unhealthy']</td>\n", |
| " <td>True</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>7</td>\n", |
| " <td>overcast</td>\n", |
| " <td>64.0</td>\n", |
| " <td>65.0</td>\n", |
| " <td>[64.0, 65.0]</td>\n", |
| " <td>[u'medium', u'moderate']</td>\n", |
| " <td>True</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>8</td>\n", |
| " <td>sunny</td>\n", |
| " <td>72.0</td>\n", |
| " <td>95.0</td>\n", |
| " <td>[72.0, 95.0]</td>\n", |
| " <td>[u'high', u'unhealthy']</td>\n", |
| " <td>False</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>9</td>\n", |
| " <td>sunny</td>\n", |
| " <td>69.0</td>\n", |
| " <td>70.0</td>\n", |
| " <td>[69.0, 70.0]</td>\n", |
| " <td>[u'high', u'good']</td>\n", |
| " <td>False</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>10</td>\n", |
| " <td>rain</td>\n", |
| " <td>75.0</td>\n", |
| " <td>80.0</td>\n", |
| " <td>[75.0, 80.0]</td>\n", |
| " <td>[u'medium', u'good']</td>\n", |
| " <td>False</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>11</td>\n", |
| " <td>sunny</td>\n", |
| " <td>75.0</td>\n", |
| " <td>70.0</td>\n", |
| " <td>[75.0, 70.0]</td>\n", |
| " <td>[u'none', u'good']</td>\n", |
| " <td>True</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>12</td>\n", |
| " <td>overcast</td>\n", |
| " <td>72.0</td>\n", |
| " <td>90.0</td>\n", |
| " <td>[72.0, 90.0]</td>\n", |
| " <td>[u'medium', u'moderate']</td>\n", |
| " <td>True</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>13</td>\n", |
| " <td>overcast</td>\n", |
| " <td>81.0</td>\n", |
| " <td>75.0</td>\n", |
| " <td>[81.0, 75.0]</td>\n", |
| " <td>[u'medium', u'moderate']</td>\n", |
| " <td>False</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>14</td>\n", |
| " <td>rain</td>\n", |
| " <td>71.0</td>\n", |
| " <td>80.0</td>\n", |
| " <td>[71.0, 80.0]</td>\n", |
| " <td>[u'low', u'unhealthy']</td>\n", |
| " <td>True</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, u'sunny', 85.0, 85.0, [85.0, 85.0], [u'none', u'unhealthy'], False, u\"Don't Play\"),\n", |
| " (2, u'sunny', 80.0, 90.0, [80.0, 90.0], [u'none', u'moderate'], True, u\"Don't Play\"),\n", |
| " (3, u'overcast', 83.0, 78.0, [83.0, 78.0], [u'low', u'moderate'], False, u'Play'),\n", |
| " (4, u'rain', 70.0, 96.0, [70.0, 96.0], [u'low', u'moderate'], False, u'Play'),\n", |
| " (5, u'rain', 68.0, 80.0, [68.0, 80.0], [u'medium', u'good'], False, u'Play'),\n", |
| " (6, u'rain', 65.0, 70.0, [65.0, 70.0], [u'low', u'unhealthy'], True, u\"Don't Play\"),\n", |
| " (7, u'overcast', 64.0, 65.0, [64.0, 65.0], [u'medium', u'moderate'], True, u'Play'),\n", |
| " (8, u'sunny', 72.0, 95.0, [72.0, 95.0], [u'high', u'unhealthy'], False, u\"Don't Play\"),\n", |
| " (9, u'sunny', 69.0, 70.0, [69.0, 70.0], [u'high', u'good'], False, u'Play'),\n", |
| " (10, u'rain', 75.0, 80.0, [75.0, 80.0], [u'medium', u'good'], False, u'Play'),\n", |
| " (11, u'sunny', 75.0, 70.0, [75.0, 70.0], [u'none', u'good'], True, u'Play'),\n", |
| " (12, u'overcast', 72.0, 90.0, [72.0, 90.0], [u'medium', u'moderate'], True, u'Play'),\n", |
| " (13, u'overcast', 81.0, 75.0, [81.0, 75.0], [u'medium', u'moderate'], False, u'Play'),\n", |
| " (14, u'rain', 71.0, 80.0, [71.0, 80.0], [u'low', u'unhealthy'], True, u\"Don't Play\")]" |
| ] |
| }, |
| "execution_count": 5, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS rf_golf CASCADE;\n", |
| "\n", |
| "CREATE TABLE rf_golf (\n", |
| " id integer NOT NULL,\n", |
| " \"OUTLOOK\" text,\n", |
| " temperature double precision,\n", |
| " humidity double precision,\n", |
| " \"Temp_Humidity\" double precision[],\n", |
| " clouds_airquality text[],\n", |
| " windy boolean,\n", |
| " class text\n", |
| ");\n", |
| "\n", |
| "INSERT INTO rf_golf VALUES\n", |
| "(1,'sunny', 85, 85, ARRAY[85, 85],ARRAY['none', 'unhealthy'], 'false','Don''t Play'),\n", |
| "(2, 'sunny', 80, 90, ARRAY[80, 90], ARRAY['none', 'moderate'], 'true', 'Don''t Play'),\n", |
| "(3, 'overcast', 83, 78, ARRAY[83, 78], ARRAY['low', 'moderate'], 'false', 'Play'),\n", |
| "(4, 'rain', 70, 96, ARRAY[70, 96], ARRAY['low', 'moderate'], 'false', 'Play'),\n", |
| "(5, 'rain', 68, 80, ARRAY[68, 80], ARRAY['medium', 'good'], 'false', 'Play'),\n", |
| "(6, 'rain', 65, 70, ARRAY[65, 70], ARRAY['low', 'unhealthy'], 'true', 'Don''t Play'),\n", |
| "(7, 'overcast', 64, 65, ARRAY[64, 65], ARRAY['medium', 'moderate'], 'true', 'Play'),\n", |
| "(8, 'sunny', 72, 95, ARRAY[72, 95], ARRAY['high', 'unhealthy'], 'false', 'Don''t Play'),\n", |
| "(9, 'sunny', 69, 70, ARRAY[69, 70], ARRAY['high', 'good'], 'false', 'Play'),\n", |
| "(10, 'rain', 75, 80, ARRAY[75, 80], ARRAY['medium', 'good'], 'false', 'Play'),\n", |
| "(11, 'sunny', 75, 70, ARRAY[75, 70], ARRAY['none', 'good'], 'true', 'Play'),\n", |
| "(12, 'overcast', 72, 90, ARRAY[72, 90], ARRAY['medium', 'moderate'], 'true', 'Play'),\n", |
| "(13, 'overcast', 81, 75, ARRAY[81, 75], ARRAY['medium', 'moderate'], 'false', 'Play'),\n", |
| "(14, 'rain', 71, 80, ARRAY[71, 80], ARRAY['low', 'unhealthy'], 'true', 'Don''t Play');\n", |
| "\n", |
| "SELECT * FROM rf_golf ORDER BY id;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 2. Train random forest\n", |
| "Train random forest and view the summary table:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 6, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "1 rows affected.\n", |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>method</th>\n", |
| " <th>is_classification</th>\n", |
| " <th>source_table</th>\n", |
| " <th>model_table</th>\n", |
| " <th>id_col_name</th>\n", |
| " <th>dependent_varname</th>\n", |
| " <th>independent_varnames</th>\n", |
| " <th>cat_features</th>\n", |
| " <th>con_features</th>\n", |
| " <th>grouping_cols</th>\n", |
| " <th>num_trees</th>\n", |
| " <th>num_random_features</th>\n", |
| " <th>max_tree_depth</th>\n", |
| " <th>min_split</th>\n", |
| " <th>min_bucket</th>\n", |
| " <th>num_splits</th>\n", |
| " <th>verbose</th>\n", |
| " <th>importance</th>\n", |
| " <th>num_permutations</th>\n", |
| " <th>num_all_groups</th>\n", |
| " <th>num_failed_groups</th>\n", |
| " <th>total_rows_processed</th>\n", |
| " <th>total_rows_skipped</th>\n", |
| " <th>dependent_var_levels</th>\n", |
| " <th>dependent_var_type</th>\n", |
| " <th>independent_var_types</th>\n", |
| " <th>null_proxy</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>forest_train</td>\n", |
| " <td>True</td>\n", |
| " <td>rf_golf</td>\n", |
| " <td>train_output</td>\n", |
| " <td>id</td>\n", |
| " <td>class</td>\n", |
| " <td>\"OUTLOOK\",windy,temperature,humidity</td>\n", |
| " <td>\"OUTLOOK\",windy</td>\n", |
| " <td>temperature,humidity</td>\n", |
| " <td>None</td>\n", |
| " <td>20</td>\n", |
| " <td>2</td>\n", |
| " <td>8</td>\n", |
| " <td>3</td>\n", |
| " <td>1</td>\n", |
| " <td>10</td>\n", |
| " <td>False</td>\n", |
| " <td>True</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>14</td>\n", |
| " <td>0</td>\n", |
| " <td>\"Don't Play\",\"Play\"</td>\n", |
| " <td>text</td>\n", |
| " <td>text, boolean, double precision, double precision</td>\n", |
| " <td>None</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'forest_train', True, u'rf_golf', u'train_output', u'id', u'class', u'\"OUTLOOK\",windy,temperature,humidity', u'\"OUTLOOK\",windy', u'temperature,humidity', None, 20, 2, 8, 3, 1, 10, False, True, 1, 1, 0, 14, 0, u'\"Don\\'t Play\",\"Play\"', u'text', u'text, boolean, double precision, double precision', u'None')]" |
| ] |
| }, |
| "execution_count": 6, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS train_output, train_output_group, train_output_summary;\n", |
| "\n", |
| "SELECT madlib.forest_train('rf_golf', -- source table\n", |
| " 'train_output', -- output model table\n", |
| " 'id', -- id column\n", |
| " 'class', -- response\n", |
| " '\"OUTLOOK\", temperature, humidity, windy', -- features\n", |
| " NULL, -- exclude columns\n", |
| " NULL, -- grouping columns\n", |
| " 20::integer, -- number of trees\n", |
| " 2::integer, -- number of random features\n", |
| " TRUE::boolean, -- variable importance\n", |
| " 1::integer, -- num_permutations\n", |
| " 8::integer, -- max depth\n", |
| " 3::integer, -- min split\n", |
| " 1::integer, -- min bucket\n", |
| " 10::integer -- number of splits per continuous variable\n", |
| " );\n", |
| "\n", |
| "SELECT * FROM train_output_summary;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Review the group table for variable importance:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 7, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>gid</th>\n", |
| " <th>success</th>\n", |
| " <th>cat_n_levels</th>\n", |
| " <th>cat_levels_in_text</th>\n", |
| " <th>oob_error</th>\n", |
| " <th>oob_var_importance</th>\n", |
| " <th>impurity_var_importance</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>True</td>\n", |
| " <td>[3, 2]</td>\n", |
| " <td>[u'overcast', u'sunny', u'rain', u'False', u'True']</td>\n", |
| " <td>0.28571428571428571429</td>\n", |
| " <td>[0.06625, 0.0233928571428572, 0.0, 0.0145833333333333]</td>\n", |
| " <td>[0.27116534244345, 0.10438655409181, 0.307256009757555, 0.184889160052295]</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, True, [3, 2], [u'overcast', u'sunny', u'rain', u'False', u'True'], Decimal('0.28571428571428571429'), [0.06625, 0.0233928571428572, 0.0, 0.0145833333333333], [0.27116534244345, 0.10438655409181, 0.307256009757555, 0.184889160052295])]" |
| ] |
| }, |
| "execution_count": 7, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT * FROM train_output_group;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "The 'cat_levels_in_text' array shows the levels of the categorical variables \"OUTLOOK\" and windy, which have 3 and 2 levels respectively. \n", |
| "\n", |
| "Out-of-bag and impurity variable importance arrays are ordered according to the order of the variables in 'independent_varnames' in model summary table. A higher value means higher importance for the variable. \n", |
| "\n", |
| "We can use the helper function to get a normalized view of variable importance:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "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>feature</th>\n", |
| " <th>oob_var_importance</th>\n", |
| " <th>impurity_var_importance</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>\"OUTLOOK\"</td>\n", |
| " <td>63.5636778983</td>\n", |
| " <td>31.2511535374</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>windy</td>\n", |
| " <td>22.4443175328</td>\n", |
| " <td>12.0302992992</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>humidity</td>\n", |
| " <td>13.9920045688</td>\n", |
| " <td>21.3080310195</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>temperature</td>\n", |
| " <td>0.0</td>\n", |
| " <td>35.4105161438</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'\"OUTLOOK\"', 63.5636778983438, 31.2511535374489),\n", |
| " (u'windy', 22.4443175328384, 12.0302992992133),\n", |
| " (u'humidity', 13.9920045688178, 21.3080310195216),\n", |
| " (u'temperature', 0.0, 35.4105161438162)]" |
| ] |
| }, |
| "execution_count": 9, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS imp_output;\n", |
| "\n", |
| "SELECT madlib.get_var_importance('train_output','imp_output');\n", |
| "SELECT * FROM imp_output ORDER BY oob_var_importance DESC;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": { |
| "scrolled": true |
| }, |
| "source": [ |
| "# 3. Predict\n", |
| "Predict output categories. For the purpose of this example, we use the same data that was used for training:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "1 rows affected.\n", |
| "14 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>id</th>\n", |
| " <th>class</th>\n", |
| " <th>estimated_class</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>2</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>3</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>4</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>5</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>6</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>7</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>8</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>9</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>10</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>11</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>12</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>13</td>\n", |
| " <td>Play</td>\n", |
| " <td>Play</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>14</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>Don't Play</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, u\"Don't Play\", u\"Don't Play\"),\n", |
| " (2, u\"Don't Play\", u\"Don't Play\"),\n", |
| " (3, u'Play', u'Play'),\n", |
| " (4, u'Play', u'Play'),\n", |
| " (5, u'Play', u'Play'),\n", |
| " (6, u\"Don't Play\", u\"Don't Play\"),\n", |
| " (7, u'Play', u'Play'),\n", |
| " (8, u\"Don't Play\", u\"Don't Play\"),\n", |
| " (9, u'Play', u'Play'),\n", |
| " (10, u'Play', u'Play'),\n", |
| " (11, u'Play', u'Play'),\n", |
| " (12, u'Play', u'Play'),\n", |
| " (13, u'Play', u'Play'),\n", |
| " (14, u\"Don't Play\", u\"Don't Play\")]" |
| ] |
| }, |
| "execution_count": 10, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS prediction_results;\n", |
| "\n", |
| "SELECT madlib.forest_predict('train_output', -- tree model \n", |
| " 'rf_golf', -- new data table\n", |
| " 'prediction_results', -- output table\n", |
| " 'response'); -- show response\n", |
| "\n", |
| "SELECT g.id, class, estimated_class FROM prediction_results p, \n", |
| "rf_golf g WHERE p.id = g.id ORDER BY g.id;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "To display the probabilities associated with each value of the dependent variable, set the 'type' parameter to 'prob':" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "1 rows affected.\n", |
| "14 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>id</th>\n", |
| " <th>class</th>\n", |
| " <th>estimated_prob_Don't Play</th>\n", |
| " <th>estimated_prob_Play</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>0.95</td>\n", |
| " <td>0.05</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>2</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>0.9</td>\n", |
| " <td>0.1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>3</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.05</td>\n", |
| " <td>0.95</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>4</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.25</td>\n", |
| " <td>0.75</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>5</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.2</td>\n", |
| " <td>0.8</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>6</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>0.7</td>\n", |
| " <td>0.3</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>7</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.1</td>\n", |
| " <td>0.9</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>8</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>0.8</td>\n", |
| " <td>0.2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>9</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.05</td>\n", |
| " <td>0.95</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>10</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.3</td>\n", |
| " <td>0.7</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>11</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.15</td>\n", |
| " <td>0.85</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>12</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.4</td>\n", |
| " <td>0.6</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>13</td>\n", |
| " <td>Play</td>\n", |
| " <td>0.05</td>\n", |
| " <td>0.95</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>14</td>\n", |
| " <td>Don't Play</td>\n", |
| " <td>0.85</td>\n", |
| " <td>0.15</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, u\"Don't Play\", 0.95, 0.05),\n", |
| " (2, u\"Don't Play\", 0.9, 0.1),\n", |
| " (3, u'Play', 0.05, 0.95),\n", |
| " (4, u'Play', 0.25, 0.75),\n", |
| " (5, u'Play', 0.2, 0.8),\n", |
| " (6, u\"Don't Play\", 0.7, 0.3),\n", |
| " (7, u'Play', 0.1, 0.9),\n", |
| " (8, u\"Don't Play\", 0.8, 0.2),\n", |
| " (9, u'Play', 0.05, 0.95),\n", |
| " (10, u'Play', 0.3, 0.7),\n", |
| " (11, u'Play', 0.15, 0.85),\n", |
| " (12, u'Play', 0.4, 0.6),\n", |
| " (13, u'Play', 0.05, 0.95),\n", |
| " (14, u\"Don't Play\", 0.85, 0.15)]" |
| ] |
| }, |
| "execution_count": 11, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS prediction_results;\n", |
| "\n", |
| "SELECT madlib.forest_predict('train_output', -- tree model \n", |
| " 'rf_golf', -- new data table\n", |
| " 'prediction_results', -- output table\n", |
| " 'prob'); -- show probability\n", |
| "\n", |
| "SELECT g.id, class, \"estimated_prob_Don't Play\", \"estimated_prob_Play\" \n", |
| "FROM prediction_results p, rf_golf g WHERE p.id = g.id ORDER BY g.id;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 4. View tree in text format\n", |
| "View a single tree within the forest identified by 'gid' and 'sample_id', out of the several that were created:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 9, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>get_tree</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>-------------------------------------<br> - Each node represented by 'id' inside ().<br> - Each internal nodes has the split condition at the end, while each<br> leaf node has a * at the end.<br> - For each internal node (i), its child nodes are indented by 1 level<br> with ids (2i+1) for True node and (2i+2) for False node.<br> - Number of (weighted) rows for each response variable inside [].'<br> The response label order is given as ['\"Don\\'t Play\"', '\"Play\"'].<br> For each leaf, the prediction is given after the '-->'<br> <br>-------------------------------------<br>(0)[ 4 12] temperature <= 65<br> (1)[2 0] * --> \"Don't Play\"<br> (2)[ 2 12] humidity <= 78<br> (5)[0 8] * --> \"Play\"<br> (6)[2 4] windy in {False}<br> (13)[1 4] temperature <= 70<br> (27)[0 3] * --> \"Play\"<br> (28)[1 1] * --> \"Don't Play\"<br> (14)[1 0] * --> \"Don't Play\"<br></td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'-------------------------------------\\n - Each node represented by \\'id\\' inside ().\\n - Each internal nodes has the split condition at the end, while each\\n leaf node has a * at the end.\\n - For each internal node (i), its child nodes are indented by 1 level\\n with ids (2i+1) for True node and (2i+2) for False node.\\n - Number of (weighted) rows for each response variable inside [].\\'\\n The response label order is given as [\\'\"Don\\\\\\'t Play\"\\', \\'\"Play\"\\'].\\n For each leaf, the prediction is given after the \\'-->\\'\\n \\n-------------------------------------\\n(0)[ 4 12] temperature <= 65\\n (1)[2 0] * --> \"Don\\'t Play\"\\n (2)[ 2 12] humidity <= 78\\n (5)[0 8] * --> \"Play\"\\n (6)[2 4] windy in {False}\\n (13)[1 4] temperature <= 70\\n (27)[0 3] * --> \"Play\"\\n (28)[1 1] * --> \"Don\\'t Play\"\\n (14)[1 0] * --> \"Don\\'t Play\"\\n',)]" |
| ] |
| }, |
| "execution_count": 9, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT madlib.get_tree('train_output',1,7, FALSE);" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Please see the decision tree user docs for an explanation on how to interpret the tree display above." |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 5. View tree in dot format" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 10, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>get_tree</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>digraph \"Classification tree for rf_golf\" {<br>\"0\" [label=\"temperature <= 65\", shape=ellipse];<br>\"0\" -> \"1\"[label=\"yes\"];<br>\"1\" [label=\"\\\"Don't Play\\\"\",shape=box];<br>\"0\" -> \"2\"[label=\"no\"];<br>\"2\" [label=\"humidity <= 78\", shape=ellipse];<br>\"2\" -> \"5\"[label=\"yes\"];<br>\"5\" [label=\"\\\"Play\\\"\",shape=box];<br>\"2\" -> \"6\"[label=\"no\"];<br>\"6\" [label=\"windy <= False\", shape=ellipse];<br>\"6\" -> \"13\"[label=\"yes\"];<br>\"6\" -> \"14\"[label=\"no\"];<br>\"14\" [label=\"\\\"Don't Play\\\"\",shape=box];<br>\"13\" [label=\"temperature <= 70\", shape=ellipse];<br>\"13\" -> \"27\"[label=\"yes\"];<br>\"27\" [label=\"\\\"Play\\\"\",shape=box];<br>\"13\" -> \"28\"[label=\"no\"];<br>\"28\" [label=\"\\\"Don't Play\\\"\",shape=box];<br><br>} //---end of digraph--------- </td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'digraph \"Classification tree for rf_golf\" {\\n\"0\" [label=\"temperature <= 65\", shape=ellipse];\\n\"0\" -> \"1\"[label=\"yes\"];\\n\"1\" [label=\"\\\\\"Don\\'t Play\\\\\"\",shape=box];\\n\"0\" -> \"2\"[label=\"no\"];\\n\"2\" [label=\"humidity <= 78\", shape=ellipse];\\n\"2\" -> \"5\"[label=\"yes\"];\\n\"5\" [label=\"\\\\\"Play\\\\\"\",shape=box];\\n\"2\" -> \"6\"[label=\"no\"];\\n\"6\" [label=\"windy <= False\", shape=ellipse];\\n\"6\" -> \"13\"[label=\"yes\"];\\n\"6\" -> \"14\"[label=\"no\"];\\n\"14\" [label=\"\\\\\"Don\\'t Play\\\\\"\",shape=box];\\n\"13\" [label=\"temperature <= 70\", shape=ellipse];\\n\"13\" -> \"27\"[label=\"yes\"];\\n\"27\" [label=\"\\\\\"Play\\\\\"\",shape=box];\\n\"13\" -> \"28\"[label=\"no\"];\\n\"28\" [label=\"\\\\\"Don\\'t Play\\\\\"\",shape=box];\\n\\n} //---end of digraph--------- ',)]" |
| ] |
| }, |
| "execution_count": 10, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT madlib.get_tree('train_output',1,7);" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 6. View tree in dot format with additional information" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 11, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>get_tree</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>digraph \"Classification tree for rf_golf\" {<br>\"0\" [label=\"temperature <= 65\\n impurity = 0.375\\n samples = 16\\n value = [ 4 12]\\n class = \\\"Play\\\"\", shape=ellipse];<br>\"0\" -> \"1\"[label=\"yes\"];<br>\"1\" [label=\"\\\"Don't Play\\\"\\n impurity = 0\\n samples = 2\\n value = [2 0]\",shape=box];<br>\"0\" -> \"2\"[label=\"no\"];<br>\"2\" [label=\"humidity <= 78\\n impurity = 0.244898\\n samples = 14\\n value = [ 2 12]\\n class = \\\"Play\\\"\", shape=ellipse];<br>\"2\" -> \"5\"[label=\"yes\"];<br>\"5\" [label=\"\\\"Play\\\"\\n impurity = 0\\n samples = 8\\n value = [0 8]\",shape=box];<br>\"2\" -> \"6\"[label=\"no\"];<br>\"6\" [label=\"windy <= False\\n impurity = 0.444444\\n samples = 6\\n value = [2 4]\\n class = \\\"Play\\\"\", shape=ellipse];<br>\"6\" -> \"13\"[label=\"yes\"];<br>\"6\" -> \"14\"[label=\"no\"];<br>\"14\" [label=\"\\\"Don't Play\\\"\\n impurity = 0\\n samples = 1\\n value = [1 0]\",shape=box];<br>\"13\" [label=\"temperature <= 70\\n impurity = 0.32\\n samples = 5\\n value = [1 4]\\n class = \\\"Play\\\"\", shape=ellipse];<br>\"13\" -> \"27\"[label=\"yes\"];<br>\"27\" [label=\"\\\"Play\\\"\\n impurity = 0\\n samples = 3\\n value = [0 3]\",shape=box];<br>\"13\" -> \"28\"[label=\"no\"];<br>\"28\" [label=\"\\\"Don't Play\\\"\\n impurity = 0.5\\n samples = 2\\n value = [1 1]\",shape=box];<br><br>} //---end of digraph--------- </td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'digraph \"Classification tree for rf_golf\" {\\n\"0\" [label=\"temperature <= 65\\\\n impurity = 0.375\\\\n samples = 16\\\\n value = [ 4 12]\\\\n class = \\\\\"Play\\\\\"\", shape=ellipse];\\n\"0\" -> \"1\"[label=\"yes\"];\\n\"1\" [label=\"\\\\\"Don\\'t Play\\\\\"\\\\n impurity = 0\\\\n samples = 2\\\\n value = [2 0]\",shape=box];\\n\"0\" -> \"2\"[label=\"no\"];\\n\"2\" [label=\"humidity <= 78\\\\n impurity = 0.244898\\\\n samples = 14\\\\n value = [ 2 12]\\\\n class = \\\\\"Play\\\\\"\", shape=ellipse];\\n\"2\" -> \"5\"[label=\"yes\"];\\n\"5\" [label=\"\\\\\"Play\\\\\"\\\\n impurity = 0\\\\n samples = 8\\\\n value = [0 8]\",shape=box];\\n\"2\" -> \"6\"[label=\"no\"];\\n\"6\" [label=\"windy <= False\\\\n impurity = 0.444444\\\\n samples = 6\\\\n value = [2 4]\\\\n class = \\\\\"Play\\\\\"\", shape=ellipse];\\n\"6\" -> \"13\"[label=\"yes\"];\\n\"6\" -> \"14\"[label=\"no\"];\\n\"14\" [label=\"\\\\\"Don\\'t Play\\\\\"\\\\n impurity = 0\\\\n samples = 1\\\\n value = [1 0]\",shape=box];\\n\"13\" [label=\"temperature <= 70\\\\n impurity = 0.32\\\\n samples = 5\\\\n value = [1 4]\\\\n class = \\\\\"Play\\\\\"\", shape=ellipse];\\n\"13\" -> \"27\"[label=\"yes\"];\\n\"27\" [label=\"\\\\\"Play\\\\\"\\\\n impurity = 0\\\\n samples = 3\\\\n value = [0 3]\",shape=box];\\n\"13\" -> \"28\"[label=\"no\"];\\n\"28\" [label=\"\\\\\"Don\\'t Play\\\\\"\\\\n impurity = 0.5\\\\n samples = 2\\\\n value = [1 1]\",shape=box];\\n\\n} //---end of digraph--------- ',)]" |
| ] |
| }, |
| "execution_count": 11, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT madlib.get_tree('train_output',1,7, TRUE, TRUE);" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "You may wish to visualize the tree using pygraphviz or another program that can handle dot format:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 12, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n", |
| "True\n" |
| ] |
| }, |
| { |
| "data": { |
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C56ahl0WTZV9SZ9uc7X0NrDmWoUOHel6g7M+MOZ2HDTbYIJBaAKHMh2Widr+l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Q3hKMfXKyiBvSUnZZ5IXDHMou9651GmdlgHiSFDKTIxBNqGQPaZ/NV7zJqHqPrZqu+VGArlGvEc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++OMOd9kRRxxR0fHW2BBoJAILLrigMidz3e69995KD2FxRfU5I5AwDh8+PBfHBe5mIaoN9qYQ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\n0z/wlrNMDUQdcmB5WbRokWvZsqXTLOFOlSOHY00Eazmv9I+XUwXMF7V3tad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|
| "text/plain": [ |
| "<IPython.core.display.Image object>" |
| ] |
| }, |
| "execution_count": 12, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# set paths for your environment\n", |
| "from os.path import expanduser\n", |
| "home = expanduser(\"/Users/fmcquillan/Documents/Product/MADlib/Demos/1 dot 14 demos\")\n", |
| "\n", |
| "dot_output = %sql select madlib.get_tree('train_output',1,7, TRUE, TRUE);\n", |
| "with open('tree_out.dot', 'w') as f: \n", |
| " f.write(dot_output[0][0])\n", |
| " \n", |
| "import pygraphviz as pgv\n", |
| "from IPython.display import Image\n", |
| "graph = pgv.AGraph(\"tree_out.dot\")\n", |
| "print bool(graph)\n", |
| "graph.draw('tree_out.png',prog='dot')\n", |
| "Image('tree_out.png')" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Please see the decision tree user docs for an explanation on an important difference about how categorical variable tests are displayed in text and dot format." |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 7. Arrays of features\n", |
| "Categorical and continuous features can be array columns, in which case the array is expanded to treat each element of the array as a feature." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 12, |
| "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>method</th>\n", |
| " <th>is_classification</th>\n", |
| " <th>source_table</th>\n", |
| " <th>model_table</th>\n", |
| " <th>id_col_name</th>\n", |
| " <th>dependent_varname</th>\n", |
| " <th>independent_varnames</th>\n", |
| " <th>cat_features</th>\n", |
| " <th>con_features</th>\n", |
| " <th>grouping_cols</th>\n", |
| " <th>num_trees</th>\n", |
| " <th>num_random_features</th>\n", |
| " <th>max_tree_depth</th>\n", |
| " <th>min_split</th>\n", |
| " <th>min_bucket</th>\n", |
| " <th>num_splits</th>\n", |
| " <th>verbose</th>\n", |
| " <th>importance</th>\n", |
| " <th>num_permutations</th>\n", |
| " <th>num_all_groups</th>\n", |
| " <th>num_failed_groups</th>\n", |
| " <th>total_rows_processed</th>\n", |
| " <th>total_rows_skipped</th>\n", |
| " <th>dependent_var_levels</th>\n", |
| " <th>dependent_var_type</th>\n", |
| " <th>independent_var_types</th>\n", |
| " <th>null_proxy</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>forest_train</td>\n", |
| " <td>True</td>\n", |
| " <td>rf_golf</td>\n", |
| " <td>train_output</td>\n", |
| " <td>id</td>\n", |
| " <td>class</td>\n", |
| " <td>(clouds_airquality)[1],(clouds_airquality)[2],(\"Temp_Humidity\")[1],(\"Temp_Humidity\")[2]</td>\n", |
| " <td>(clouds_airquality)[1],(clouds_airquality)[2]</td>\n", |
| " <td>(\"Temp_Humidity\")[1],(\"Temp_Humidity\")[2]</td>\n", |
| " <td>None</td>\n", |
| " <td>20</td>\n", |
| " <td>2</td>\n", |
| " <td>8</td>\n", |
| " <td>3</td>\n", |
| " <td>1</td>\n", |
| " <td>10</td>\n", |
| " <td>False</td>\n", |
| " <td>True</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>14</td>\n", |
| " <td>0</td>\n", |
| " <td>\"Don't Play\",\"Play\"</td>\n", |
| " <td>text</td>\n", |
| " <td>text, text, double precision, double precision</td>\n", |
| " <td>None</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'forest_train', True, u'rf_golf', u'train_output', u'id', u'class', u'(clouds_airquality)[1],(clouds_airquality)[2],(\"Temp_Humidity\")[1],(\"Temp_Humidity\")[2]', u'(clouds_airquality)[1],(clouds_airquality)[2]', u'(\"Temp_Humidity\")[1],(\"Temp_Humidity\")[2]', None, 20, 2, 8, 3, 1, 10, False, True, 1, 1, 0, 14, 0, u'\"Don\\'t Play\",\"Play\"', u'text', u'text, text, double precision, double precision', u'None')]" |
| ] |
| }, |
| "execution_count": 12, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS train_output, train_output_group, train_output_summary;\n", |
| "\n", |
| "SELECT madlib.forest_train('rf_golf', -- source table\n", |
| " 'train_output', -- output model table\n", |
| " 'id', -- id column\n", |
| " 'class', -- response\n", |
| " '\"Temp_Humidity\", clouds_airquality', -- features\n", |
| " NULL, -- exclude columns\n", |
| " NULL, -- grouping columns\n", |
| " 20::integer, -- number of trees\n", |
| " 2::integer, -- number of random features\n", |
| " TRUE::boolean, -- variable importance\n", |
| " 1::integer, -- num_permutations\n", |
| " 8::integer, -- max depth\n", |
| " 3::integer, -- min split\n", |
| " 1::integer, -- min bucket\n", |
| " 10::integer -- number of splits per continuous variable\n", |
| " );\n", |
| "\n", |
| "SELECT * FROM train_output_summary;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 8. Sample ratio\n", |
| "Use the sample ratio parameter to train on a subset of the data:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 13, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "1 rows affected.\n", |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>gid</th>\n", |
| " <th>success</th>\n", |
| " <th>cat_n_levels</th>\n", |
| " <th>cat_levels_in_text</th>\n", |
| " <th>oob_error</th>\n", |
| " <th>oob_var_importance</th>\n", |
| " <th>impurity_var_importance</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>True</td>\n", |
| " <td>[3, 2]</td>\n", |
| " <td>[u'overcast', u'sunny', u'rain', u'False', u'True']</td>\n", |
| " <td>0.64285714285714285714</td>\n", |
| " <td>[0.0, 0.015625, 0.015625, 0.0208333333333333]</td>\n", |
| " <td>[0.04719444444445, 0.060156462585, 0.11203471740695, 0.103798888701435]</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, True, [3, 2], [u'overcast', u'sunny', u'rain', u'False', u'True'], Decimal('0.64285714285714285714'), [0.0, 0.015625, 0.015625, 0.0208333333333333], [0.04719444444445, 0.060156462585, 0.11203471740695, 0.103798888701435])]" |
| ] |
| }, |
| "execution_count": 13, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS train_output, train_output_group, train_output_summary;\n", |
| "\n", |
| "SELECT madlib.forest_train('rf_golf', -- source table\n", |
| " 'train_output', -- output model table\n", |
| " 'id', -- id column\n", |
| " 'class', -- response\n", |
| " '\"OUTLOOK\", temperature, humidity, windy', -- features\n", |
| " NULL, -- exclude columns\n", |
| " NULL, -- grouping columns\n", |
| " 20::integer, -- number of trees\n", |
| " 2::integer, -- number of random features\n", |
| " TRUE::boolean, -- variable importance\n", |
| " 1::integer, -- num_permutations\n", |
| " 8::integer, -- max depth\n", |
| " 3::integer, -- min split\n", |
| " 1::integer, -- min bucket\n", |
| " 10::integer, -- number of splits per continuous variable\n", |
| " NULL, -- NULL handling\n", |
| " FALSE, -- Verbose\n", |
| " 0.5 -- Sample ratio\n", |
| " );\n", |
| "\n", |
| "SELECT * FROM train_output_group;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Notice the tree is different than a previous one trained without sampling." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 16, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n", |
| "True\n" |
| ] |
| }, |
| { |
| "data": { |
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XrcwQMATyIzDaDCHzg1TWGm+//bYuRGIDoItYWTurYOMsmLLLSzFCa926dcDhibkL6xxE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84Zl3HxQZ9kdTsMgTzNAeJvVw4CAWBlHGMpYG3xjlw+HN98GEuCVYfeOuyCeY8ykKxjFcc/jw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\nVwsWLFC+JPrh2GOPzcRfUkdrbRD4GZ8U6jtI+ml344036jXFninZKcIHwko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|
| "text/plain": [ |
| "<IPython.core.display.Image object>" |
| ] |
| }, |
| "execution_count": 16, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# set paths for your environment\n", |
| "from os.path import expanduser\n", |
| "home = expanduser(\"/Users/fmcquillan/Documents/Product/MADlib/Demos/1 dot 14 demos\")\n", |
| "\n", |
| "dot_output = %sql select madlib.get_tree('train_output',1,11, TRUE, TRUE);\n", |
| "with open('tree_out.dot', 'w') as f: \n", |
| " f.write(dot_output[0][0])\n", |
| " \n", |
| "import pygraphviz as pgv\n", |
| "from IPython.display import Image\n", |
| "graph = pgv.AGraph(\"tree_out.dot\")\n", |
| "print bool(graph)\n", |
| "graph.draw('tree_out.png',prog='dot')\n", |
| "Image('tree_out.png')" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# Random forest regression examples" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 1. Load data\n", |
| "Data set related to fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models). Data was extracted from the 1974 Motor Trend US magazine." |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 14, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "Done.\n", |
| "32 rows affected.\n", |
| "32 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>id</th>\n", |
| " <th>mpg</th>\n", |
| " <th>cyl</th>\n", |
| " <th>disp</th>\n", |
| " <th>hp</th>\n", |
| " <th>drat</th>\n", |
| " <th>wt</th>\n", |
| " <th>qsec</th>\n", |
| " <th>vs</th>\n", |
| " <th>am</th>\n", |
| " <th>gear</th>\n", |
| " <th>carb</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>29</td>\n", |
| " <td>13.3</td>\n", |
| " <td>8</td>\n", |
| " <td>350.0</td>\n", |
| " <td>245</td>\n", |
| " <td>3.73</td>\n", |
| " <td>3.84</td>\n", |
| " <td>15.41</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>27</td>\n", |
| " <td>15.5</td>\n", |
| " <td>8</td>\n", |
| " <td>318.0</td>\n", |
| " <td>150</td>\n", |
| " <td>2.768</td>\n", |
| " <td>3.52</td>\n", |
| " <td>16.87</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>25</td>\n", |
| " <td>21.5</td>\n", |
| " <td>4</td>\n", |
| " <td>120.1</td>\n", |
| " <td>97</td>\n", |
| " <td>3.7</td>\n", |
| " <td>2.465</td>\n", |
| " <td>20.01</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>9</td>\n", |
| " <td>21.4</td>\n", |
| " <td>None</td>\n", |
| " <td>258.0</td>\n", |
| " <td>110</td>\n", |
| " <td>3.08</td>\n", |
| " <td>3.215</td>\n", |
| " <td>19.44</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>11</td>\n", |
| " <td>18.1</td>\n", |
| " <td>6</td>\n", |
| " <td>225.0</td>\n", |
| " <td>105</td>\n", |
| " <td>2.768</td>\n", |
| " <td>3.46</td>\n", |
| " <td>20.22</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>13</td>\n", |
| " <td>14.3</td>\n", |
| " <td>8</td>\n", |
| " <td>360.0</td>\n", |
| " <td>245</td>\n", |
| " <td>3.21</td>\n", |
| " <td>3.578</td>\n", |
| " <td>15.84</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>3</td>\n", |
| " <td>24.4</td>\n", |
| " <td>4</td>\n", |
| " <td>146.7</td>\n", |
| " <td>62</td>\n", |
| " <td>3.69</td>\n", |
| " <td>3.19</td>\n", |
| " <td>20.0</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>4</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>5</td>\n", |
| " <td>17.8</td>\n", |
| " <td>6</td>\n", |
| " <td>167.6</td>\n", |
| " <td>123</td>\n", |
| " <td>3.92</td>\n", |
| " <td>3.44</td>\n", |
| " <td>18.9</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>4</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>19</td>\n", |
| " <td>10.4</td>\n", |
| " <td>8</td>\n", |
| " <td>472.0</td>\n", |
| " <td>205</td>\n", |
| " <td>2.93</td>\n", |
| " <td>5.25</td>\n", |
| " <td>17.98</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>21</td>\n", |
| " <td>10.4</td>\n", |
| " <td>8</td>\n", |
| " <td>460.0</td>\n", |
| " <td>215</td>\n", |
| " <td>3.0</td>\n", |
| " <td>5.424</td>\n", |
| " <td>17.82</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>23</td>\n", |
| " <td>14.7</td>\n", |
| " <td>8</td>\n", |
| " <td>440.0</td>\n", |
| " <td>230</td>\n", |
| " <td>3.23</td>\n", |
| " <td>5.345</td>\n", |
| " <td>17.42</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>18.7</td>\n", |
| " <td>8</td>\n", |
| " <td>360.0</td>\n", |
| " <td>175</td>\n", |
| " <td>3.15</td>\n", |
| " <td>3.44</td>\n", |
| " <td>17.02</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>30</td>\n", |
| " <td>19.2</td>\n", |
| " <td>8</td>\n", |
| " <td>400.0</td>\n", |
| " <td>175</td>\n", |
| " <td>3.08</td>\n", |
| " <td>3.845</td>\n", |
| " <td>17.05</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>16</td>\n", |
| " <td>19.2</td>\n", |
| " <td>6</td>\n", |
| " <td>167.6</td>\n", |
| " <td>123</td>\n", |
| " <td>3.92</td>\n", |
| " <td>3.44</td>\n", |
| " <td>18.3</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>4</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>14</td>\n", |
| " <td>22.8</td>\n", |
| " <td>4</td>\n", |
| " <td>140.8</td>\n", |
| " <td>95</td>\n", |
| " <td>3.92</td>\n", |
| " <td>3.15</td>\n", |
| " <td>22.9</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>4</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>6</td>\n", |
| " <td>16.4</td>\n", |
| " <td>8</td>\n", |
| " <td>275.8</td>\n", |
| " <td>180</td>\n", |
| " <td>3.078</td>\n", |
| " <td>4.07</td>\n", |
| " <td>17.4</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>3</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>10</td>\n", |
| " <td>15.2</td>\n", |
| " <td>8</td>\n", |
| " <td>275.8</td>\n", |
| " <td>180</td>\n", |
| " <td>3.078</td>\n", |
| " <td>3.78</td>\n", |
| " <td>18.0</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>3</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>8</td>\n", |
| " <td>17.3</td>\n", |
| " <td>8</td>\n", |
| " <td>275.8</td>\n", |
| " <td>180</td>\n", |
| " <td>3.078</td>\n", |
| " <td>3.73</td>\n", |
| " <td>17.6</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>3</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>18</td>\n", |
| " <td>15.2</td>\n", |
| " <td>None</td>\n", |
| " <td>304.0</td>\n", |
| " <td>150</td>\n", |
| " <td>3.15</td>\n", |
| " <td>3.435</td>\n", |
| " <td>17.3</td>\n", |
| " <td>0</td>\n", |
| " <td>0</td>\n", |
| " <td>3</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>32</td>\n", |
| " <td>21.4</td>\n", |
| " <td>4</td>\n", |
| " <td>121.0</td>\n", |
| " <td>109</td>\n", |
| " <td>4.11</td>\n", |
| " <td>2.78</td>\n", |
| " <td>18.6</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>26</td>\n", |
| " <td>15.8</td>\n", |
| " <td>8</td>\n", |
| " <td>351.0</td>\n", |
| " <td>264</td>\n", |
| " <td>4.22</td>\n", |
| " <td>3.17</td>\n", |
| " <td>14.5</td>\n", |
| " <td>0</td>\n", |
| " <td>1</td>\n", |
| " <td>5</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>24</td>\n", |
| " <td>30.4</td>\n", |
| " <td>4</td>\n", |
| " <td>95.14</td>\n", |
| " <td>113</td>\n", |
| " <td>3.77</td>\n", |
| " <td>1.513</td>\n", |
| " <td>16.9</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>5</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>22</td>\n", |
| " <td>26.0</td>\n", |
| " <td>4</td>\n", |
| " <td>120.3</td>\n", |
| " <td>91</td>\n", |
| " <td>4.43</td>\n", |
| " <td>2.14</td>\n", |
| " <td>16.7</td>\n", |
| " <td>0</td>\n", |
| " <td>1</td>\n", |
| " <td>5</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>20</td>\n", |
| " <td>27.3</td>\n", |
| " <td>4</td>\n", |
| " <td>79.0</td>\n", |
| " <td>66</td>\n", |
| " <td>4.08</td>\n", |
| " <td>1.935</td>\n", |
| " <td>18.9</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>12</td>\n", |
| " <td>32.4</td>\n", |
| " <td>4</td>\n", |
| " <td>78.7</td>\n", |
| " <td>66</td>\n", |
| " <td>4.08</td>\n", |
| " <td>2.2</td>\n", |
| " <td>19.47</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>4</td>\n", |
| " <td>21.0</td>\n", |
| " <td>6</td>\n", |
| " <td>160.0</td>\n", |
| " <td>110</td>\n", |
| " <td>3.9</td>\n", |
| " <td>2.875</td>\n", |
| " <td>17.02</td>\n", |
| " <td>0</td>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>2</td>\n", |
| " <td>21.0</td>\n", |
| " <td>6</td>\n", |
| " <td>160.0</td>\n", |
| " <td>110</td>\n", |
| " <td>3.9</td>\n", |
| " <td>2.62</td>\n", |
| " <td>16.46</td>\n", |
| " <td>0</td>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>4</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>28</td>\n", |
| " <td>15.0</td>\n", |
| " <td>8</td>\n", |
| " <td>301.0</td>\n", |
| " <td>335</td>\n", |
| " <td>3.54</td>\n", |
| " <td>3.578</td>\n", |
| " <td>14.6</td>\n", |
| " <td>0</td>\n", |
| " <td>1</td>\n", |
| " <td>5</td>\n", |
| " <td>8</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>17</td>\n", |
| " <td>33.9</td>\n", |
| " <td>4</td>\n", |
| " <td>71.14</td>\n", |
| " <td>65</td>\n", |
| " <td>4.22</td>\n", |
| " <td>1.835</td>\n", |
| " <td>19.9</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>15</td>\n", |
| " <td>30.4</td>\n", |
| " <td>4</td>\n", |
| " <td>75.7</td>\n", |
| " <td>52</td>\n", |
| " <td>4.93</td>\n", |
| " <td>1.615</td>\n", |
| " <td>18.52</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>2</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>7</td>\n", |
| " <td>22.8</td>\n", |
| " <td>4</td>\n", |
| " <td>108.0</td>\n", |
| " <td>93</td>\n", |
| " <td>3.85</td>\n", |
| " <td>2.32</td>\n", |
| " <td>18.61</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>1</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>31</td>\n", |
| " <td>19.7</td>\n", |
| " <td>6</td>\n", |
| " <td>145.0</td>\n", |
| " <td>175</td>\n", |
| " <td>3.62</td>\n", |
| " <td>2.77</td>\n", |
| " <td>15.5</td>\n", |
| " <td>0</td>\n", |
| " <td>1</td>\n", |
| " <td>5</td>\n", |
| " <td>6</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(29, 13.3, 8, 350.0, 245, 3.73, 3.84, 15.41, 0, 0, 3, 4),\n", |
| " (27, 15.5, 8, 318.0, 150, 2.768, 3.52, 16.87, 0, 0, 3, 2),\n", |
| " (25, 21.5, 4, 120.1, 97, 3.7, 2.465, 20.01, 1, 0, 3, 1),\n", |
| " (9, 21.4, None, 258.0, 110, 3.08, 3.215, 19.44, 1, 0, 3, 1),\n", |
| " (11, 18.1, 6, 225.0, 105, 2.768, 3.46, 20.22, 1, 0, 3, 1),\n", |
| " (13, 14.3, 8, 360.0, 245, 3.21, 3.578, 15.84, 0, 0, 3, 4),\n", |
| " (3, 24.4, 4, 146.7, 62, 3.69, 3.19, 20.0, 1, 0, 4, 2),\n", |
| " (5, 17.8, 6, 167.6, 123, 3.92, 3.44, 18.9, 1, 0, 4, 4),\n", |
| " (19, 10.4, 8, 472.0, 205, 2.93, 5.25, 17.98, 0, 0, 3, 4),\n", |
| " (21, 10.4, 8, 460.0, 215, 3.0, 5.424, 17.82, 0, 0, 3, 4),\n", |
| " (23, 14.7, 8, 440.0, 230, 3.23, 5.345, 17.42, 0, 0, 3, 4),\n", |
| " (1, 18.7, 8, 360.0, 175, 3.15, 3.44, 17.02, 0, 0, 3, 2),\n", |
| " (30, 19.2, 8, 400.0, 175, 3.08, 3.845, 17.05, 0, 0, 3, 2),\n", |
| " (16, 19.2, 6, 167.6, 123, 3.92, 3.44, 18.3, 1, 0, 4, 4),\n", |
| " (14, 22.8, 4, 140.8, 95, 3.92, 3.15, 22.9, 1, 0, 4, 2),\n", |
| " (6, 16.4, 8, 275.8, 180, 3.078, 4.07, 17.4, 0, 0, 3, 3),\n", |
| " (10, 15.2, 8, 275.8, 180, 3.078, 3.78, 18.0, 0, 0, 3, 3),\n", |
| " (8, 17.3, 8, 275.8, 180, 3.078, 3.73, 17.6, 0, 0, 3, 3),\n", |
| " (18, 15.2, None, 304.0, 150, 3.15, 3.435, 17.3, 0, 0, 3, 2),\n", |
| " (32, 21.4, 4, 121.0, 109, 4.11, 2.78, 18.6, 1, 1, 4, 2),\n", |
| " (26, 15.8, 8, 351.0, 264, 4.22, 3.17, 14.5, 0, 1, 5, 4),\n", |
| " (24, 30.4, 4, 95.14, 113, 3.77, 1.513, 16.9, 1, 1, 5, 2),\n", |
| " (22, 26.0, 4, 120.3, 91, 4.43, 2.14, 16.7, 0, 1, 5, 2),\n", |
| " (20, 27.3, 4, 79.0, 66, 4.08, 1.935, 18.9, 1, 1, 4, 1),\n", |
| " (12, 32.4, 4, 78.7, 66, 4.08, 2.2, 19.47, 1, 1, 4, 1),\n", |
| " (4, 21.0, 6, 160.0, 110, 3.9, 2.875, 17.02, 0, 1, 4, 4),\n", |
| " (2, 21.0, 6, 160.0, 110, 3.9, 2.62, 16.46, 0, 1, 4, 4),\n", |
| " (28, 15.0, 8, 301.0, 335, 3.54, 3.578, 14.6, 0, 1, 5, 8),\n", |
| " (17, 33.9, 4, 71.14, 65, 4.22, 1.835, 19.9, 1, 1, 4, 1),\n", |
| " (15, 30.4, 4, 75.7, 52, 4.93, 1.615, 18.52, 1, 1, 4, 2),\n", |
| " (7, 22.8, 4, 108.0, 93, 3.85, 2.32, 18.61, 1, 1, 4, 1),\n", |
| " (31, 19.7, 6, 145.0, 175, 3.62, 2.77, 15.5, 0, 1, 5, 6)]" |
| ] |
| }, |
| "execution_count": 14, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS mt_cars;\n", |
| "\n", |
| "CREATE TABLE mt_cars (\n", |
| " id integer NOT NULL,\n", |
| " mpg double precision,\n", |
| " cyl integer,\n", |
| " disp double precision,\n", |
| " hp integer,\n", |
| " drat double precision,\n", |
| " wt double precision,\n", |
| " qsec double precision,\n", |
| " vs integer,\n", |
| " am integer,\n", |
| " gear integer,\n", |
| " carb integer\n", |
| ");\n", |
| "\n", |
| "INSERT INTO mt_cars VALUES\n", |
| "(1,18.7,8,360,175,3.15,3.44,17.02,0,0,3,2),\n", |
| "(2,21,6,160,110,3.9,2.62,16.46,0,1,4,4),\n", |
| "(3,24.4,4,146.7,62,3.69,3.19,20,1,0,4,2),\n", |
| "(4,21,6,160,110,3.9,2.875,17.02,0,1,4,4),\n", |
| "(5,17.8,6,167.6,123,3.92,3.44,18.9,1,0,4,4),\n", |
| "(6,16.4,8,275.8,180,3.078,4.07,17.4,0,0,3,3),\n", |
| "(7,22.8,4,108,93,3.85,2.32,18.61,1,1,4,1),\n", |
| "(8,17.3,8,275.8,180,3.078,3.73,17.6,0,0,3,3),\n", |
| "(9,21.4,null,258,110,3.08,3.215,19.44,1,0,3,1),\n", |
| "(10,15.2,8,275.8,180,3.078,3.78,18,0,0,3,3),\n", |
| "(11,18.1,6,225,105,2.768,3.46,20.22,1,0,3,1),\n", |
| "(12,32.4,4,78.7,66,4.08,2.20,19.47,1,1,4,1),\n", |
| "(13,14.3,8,360,245,3.21,3.578,15.84,0,0,3,4),\n", |
| "(14,22.8,4,140.8,95,3.92,3.15,22.9,1,0,4,2),\n", |
| "(15,30.4,4,75.7,52,4.93,1.615,18.52,1,1,4,2),\n", |
| "(16,19.2,6,167.6,123,3.92,3.44,18.3,1,0,4,4),\n", |
| "(17,33.9,4,71.14,65,4.22,1.835,19.9,1,1,4,1),\n", |
| "(18,15.2,null,304,150,3.15,3.435,17.3,0,0,3,2),\n", |
| "(19,10.4,8,472,205,2.93,5.25,17.98,0,0,3,4),\n", |
| "(20,27.3,4,79,66,4.08,1.935,18.9,1,1,4,1),\n", |
| "(21,10.4,8,460,215,3,5.424,17.82,0,0,3,4),\n", |
| "(22,26,4,120.3,91,4.43,2.14,16.7,0,1,5,2),\n", |
| "(23,14.7,8,440,230,3.23,5.345,17.42,0,0,3,4),\n", |
| "(24,30.4,4,95.14,113,3.77,1.513,16.9,1,1,5,2),\n", |
| "(25,21.5,4,120.1,97,3.70,2.465,20.01,1,0,3,1),\n", |
| "(26,15.8,8,351,264,4.22,3.17,14.5,0,1,5,4),\n", |
| "(27,15.5,8,318,150,2.768,3.52,16.87,0,0,3,2),\n", |
| "(28,15,8,301,335,3.54,3.578,14.6,0,1,5,8),\n", |
| "(29,13.3,8,350,245,3.73,3.84,15.41,0,0,3,4),\n", |
| "(30,19.2,8,400,175,3.08,3.845,17.05,0,0,3,2),\n", |
| "(31,19.7,6,145,175,3.62,2.77,15.5,0,1,5,6),\n", |
| "(32,21.4,4,121,109,4.11,2.78,18.6,1,1,4,2);\n", |
| "\n", |
| "SELECT * FROM mt_cars ORDER BY am;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 2. Train random forest with grouping and surrogates\n", |
| "We train a regression random forest tree with grouping on transmission type (0 = automatic, 1 = manual) and use surrogates for NULL handling:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 15, |
| "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>method</th>\n", |
| " <th>is_classification</th>\n", |
| " <th>source_table</th>\n", |
| " <th>model_table</th>\n", |
| " <th>id_col_name</th>\n", |
| " <th>dependent_varname</th>\n", |
| " <th>independent_varnames</th>\n", |
| " <th>cat_features</th>\n", |
| " <th>con_features</th>\n", |
| " <th>grouping_cols</th>\n", |
| " <th>num_trees</th>\n", |
| " <th>num_random_features</th>\n", |
| " <th>max_tree_depth</th>\n", |
| " <th>min_split</th>\n", |
| " <th>min_bucket</th>\n", |
| " <th>num_splits</th>\n", |
| " <th>verbose</th>\n", |
| " <th>importance</th>\n", |
| " <th>num_permutations</th>\n", |
| " <th>num_all_groups</th>\n", |
| " <th>num_failed_groups</th>\n", |
| " <th>total_rows_processed</th>\n", |
| " <th>total_rows_skipped</th>\n", |
| " <th>dependent_var_levels</th>\n", |
| " <th>dependent_var_type</th>\n", |
| " <th>independent_var_types</th>\n", |
| " <th>null_proxy</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>forest_train</td>\n", |
| " <td>False</td>\n", |
| " <td>mt_cars</td>\n", |
| " <td>mt_cars_output</td>\n", |
| " <td>id</td>\n", |
| " <td>mpg</td>\n", |
| " <td>vs,cyl,disp,qsec,wt</td>\n", |
| " <td>vs,cyl</td>\n", |
| " <td>disp,qsec,wt</td>\n", |
| " <td>am</td>\n", |
| " <td>10</td>\n", |
| " <td>2</td>\n", |
| " <td>10</td>\n", |
| " <td>8</td>\n", |
| " <td>3</td>\n", |
| " <td>10</td>\n", |
| " <td>False</td>\n", |
| " <td>True</td>\n", |
| " <td>1</td>\n", |
| " <td>2</td>\n", |
| " <td>0</td>\n", |
| " <td>32</td>\n", |
| " <td>0</td>\n", |
| " <td>None</td>\n", |
| " <td>double precision</td>\n", |
| " <td>integer, integer, double precision, double precision, double precision</td>\n", |
| " <td>None</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'forest_train', False, u'mt_cars', u'mt_cars_output', u'id', u'mpg', u'vs,cyl,disp,qsec,wt', u'vs,cyl', u'disp,qsec,wt', u'am', 10, 2, 10, 8, 3, 10, False, True, 1, 2, 0, 32, 0, None, u'double precision', u'integer, integer, double precision, double precision, double precision', u'None')]" |
| ] |
| }, |
| "execution_count": 15, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS mt_cars_output, mt_cars_output_group, mt_cars_output_summary;\n", |
| "\n", |
| "SELECT madlib.forest_train('mt_cars', -- source table\n", |
| " 'mt_cars_output', -- output model table\n", |
| " 'id', -- id column\n", |
| " 'mpg', -- response\n", |
| " '*', -- features\n", |
| " 'id, hp, drat, am, gear, carb', -- exclude columns\n", |
| " 'am', -- grouping columns\n", |
| " 10::integer, -- number of trees\n", |
| " 2::integer, -- number of random features\n", |
| " TRUE::boolean, -- variable importance\n", |
| " 1, -- num_permutations\n", |
| " 10, -- max depth\n", |
| " 8, -- min split\n", |
| " 3, -- min bucket\n", |
| " 10, -- number of splits per continuous variable\n", |
| " 'max_surrogates=2' -- NULL handling\n", |
| " );\n", |
| "\n", |
| "SELECT * FROM mt_cars_output_summary;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Review the group table to see variable importance by group:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 16, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "2 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>gid</th>\n", |
| " <th>am</th>\n", |
| " <th>success</th>\n", |
| " <th>cat_n_levels</th>\n", |
| " <th>cat_levels_in_text</th>\n", |
| " <th>oob_error</th>\n", |
| " <th>oob_var_importance</th>\n", |
| " <th>impurity_var_importance</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>True</td>\n", |
| " <td>[2, 3]</td>\n", |
| " <td>[u'0', u'1', u'4', u'6', u'8']</td>\n", |
| " <td>9.02518813679</td>\n", |
| " <td>[3.27437852678571, 5.11869678113842, 3.456005, 1.69090166666667, 2.64476081632653]</td>\n", |
| " <td>[3.34409180528, 1.180789912157, 4.0143746624229, 1.987039338848, 2.595222156443]</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>2</td>\n", |
| " <td>1</td>\n", |
| " <td>True</td>\n", |
| " <td>[2, 3]</td>\n", |
| " <td>[u'0', u'1', u'4', u'6', u'8']</td>\n", |
| " <td>12.3234806919</td>\n", |
| " <td>[7.67188583333333, 11.7029212868481, 15.69645390625, 0.0, 18.2606219444444]</td>\n", |
| " <td>[7.93109211433, 9.112014009263, 14.112089342763, 2.836969504704, 11.291365053425]</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, 0, True, [2, 3], [u'0', u'1', u'4', u'6', u'8'], 9.02518813678994, [3.27437852678571, 5.11869678113842, 3.456005, 1.69090166666667, 2.64476081632653], [3.34409180528, 1.180789912157, 4.0143746624229, 1.987039338848, 2.595222156443]),\n", |
| " (2, 1, True, [2, 3], [u'0', u'1', u'4', u'6', u'8'], 12.3234806918696, [7.67188583333333, 11.7029212868481, 15.69645390625, 0.0, 18.2606219444444], [7.93109211433, 9.112014009263, 14.112089342763, 2.836969504704, 11.291365053425])]" |
| ] |
| }, |
| "execution_count": 16, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT * FROM mt_cars_output_group ORDER BY gid;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Use the helper function to display normalized variable importance:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 17, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "1 rows affected.\n", |
| "10 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>am</th>\n", |
| " <th>feature</th>\n", |
| " <th>oob_var_importance</th>\n", |
| " <th>impurity_var_importance</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>0</td>\n", |
| " <td>cyl</td>\n", |
| " <td>31.6266798136</td>\n", |
| " <td>8.99888201496</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>0</td>\n", |
| " <td>disp</td>\n", |
| " <td>21.3534749649</td>\n", |
| " <td>30.5938284017</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>0</td>\n", |
| " <td>vs</td>\n", |
| " <td>20.2312669969</td>\n", |
| " <td>25.485556146</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>0</td>\n", |
| " <td>wt</td>\n", |
| " <td>16.3410741245</td>\n", |
| " <td>19.7783684871</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>0</td>\n", |
| " <td>qsec</td>\n", |
| " <td>10.4475041001</td>\n", |
| " <td>15.1433649503</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>wt</td>\n", |
| " <td>34.2395972676</td>\n", |
| " <td>24.9348163611</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>disp</td>\n", |
| " <td>29.4316514473</td>\n", |
| " <td>31.1638455198</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>cyl</td>\n", |
| " <td>21.9435741529</td>\n", |
| " <td>20.122137131</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>vs</td>\n", |
| " <td>14.3851771323</td>\n", |
| " <td>17.5142973837</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>qsec</td>\n", |
| " <td>0.0</td>\n", |
| " <td>6.2649036044</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(0, u'cyl', 31.6266798136018, 8.99888201496216),\n", |
| " (0, u'disp', 21.3534749649495, 30.5938284017064),\n", |
| " (0, u'vs', 20.2312669968611, 25.4855561460076),\n", |
| " (0, u'wt', 16.3410741245189, 19.7783684870616),\n", |
| " (0, u'qsec', 10.4475041000687, 15.1433649502623),\n", |
| " (1, u'wt', 34.239597267579, 24.9348163610914),\n", |
| " (1, u'disp', 29.4316514472623, 31.1638455198447),\n", |
| " (1, u'cyl', 21.9435741528927, 20.1221371309527),\n", |
| " (1, u'vs', 14.3851771322661, 17.5142973837102),\n", |
| " (1, u'qsec', 0.0, 6.26490360440106)]" |
| ] |
| }, |
| "execution_count": 17, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS mt_imp_output;\n", |
| "\n", |
| "SELECT madlib.get_var_importance('mt_cars_output','mt_imp_output');\n", |
| "SELECT * FROM mt_imp_output ORDER BY am, oob_var_importance DESC;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 3. Predict\n", |
| "\n", |
| "Predict regression output for the same data and compare with original:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 18, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "1 rows affected.\n", |
| "32 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>am</th>\n", |
| " <th>id</th>\n", |
| " <th>mpg</th>\n", |
| " <th>estimated_mpg</th>\n", |
| " <th>delta</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>0</td>\n", |
| " <td>1</td>\n", |
| " <td>18.7</td>\n", |
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| " <td>2.89681677019</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>0</td>\n", |
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| " <tr>\n", |
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| " <tr>\n", |
| " <td>0</td>\n", |
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| " <td>0</td>\n", |
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| " <td>3.66981677019</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>2</td>\n", |
| " <td>21.0</td>\n", |
| " <td>20.8864007937</td>\n", |
| " <td>0.113599206349</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>4</td>\n", |
| " <td>21.0</td>\n", |
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| " <td>-0.108900793651</td>\n", |
| " </tr>\n", |
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| " <td>1</td>\n", |
| " <td>7</td>\n", |
| " <td>22.8</td>\n", |
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| " <td>3.22194444444</td>\n", |
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| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>15</td>\n", |
| " <td>30.4</td>\n", |
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| " <td>0.496194444444</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>17</td>\n", |
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| " <td>4.72194444444</td>\n", |
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| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>20</td>\n", |
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| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>22</td>\n", |
| " <td>26.0</td>\n", |
| " <td>25.6858888889</td>\n", |
| " <td>0.314111111111</td>\n", |
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| " <td>24</td>\n", |
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| " <td>26</td>\n", |
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| " <td>19.7780674603</td>\n", |
| " <td>-3.97806746032</td>\n", |
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| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>28</td>\n", |
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| " <td>19.7780674603</td>\n", |
| " <td>-4.77806746032</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>31</td>\n", |
| " <td>19.7</td>\n", |
| " <td>20.8864007937</td>\n", |
| " <td>-1.18640079365</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>32</td>\n", |
| " <td>21.4</td>\n", |
| " <td>23.3028888889</td>\n", |
| " <td>-1.90288888889</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
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| " (0, 9, 21.4, 18.4258975155279, 2.97410248447205),\n", |
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| " (1, 32, 21.4, 23.3028888888889, -1.90288888888889)]" |
| ] |
| }, |
| "execution_count": 18, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS prediction_results;\n", |
| "\n", |
| "SELECT madlib.forest_predict('mt_cars_output',\n", |
| " 'mt_cars',\n", |
| " 'prediction_results',\n", |
| " 'response');\n", |
| "\n", |
| "SELECT s.am, s.id, mpg, estimated_mpg, mpg-estimated_mpg as delta FROM prediction_results p, \n", |
| "mt_cars s WHERE s.id = p.id ORDER BY s.am, s.id;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 4. View tree" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 22, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>get_tree</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>digraph \"Regression tree for mt_cars\" {<br>\"0\" [label=\"disp <= 258\", shape=ellipse];<br>\"0\" -> \"1\"[label=\"yes\"];<br>\"0\" -> \"2\"[label=\"no\"];<br>\"1\" [label=\"disp <= 146.7\", shape=ellipse];<br>\"1\" -> \"3\"[label=\"yes\"];<br>\"3\" [label=\"23.3833\",shape=box];<br>\"1\" -> \"4\"[label=\"no\"];<br>\"4\" [label=\"19.3143\",shape=box];<br>\"2\" [label=\"wt <= 3.44\", shape=ellipse];<br>\"2\" -> \"5\"[label=\"yes\"];<br>\"5\" [label=\"17.3\",shape=box];<br>\"2\" -> \"6\"[label=\"no\"];<br>\"6\" [label=\"13.72\",shape=box];<br><br>} //---end of digraph--------- </td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'digraph \"Regression tree for mt_cars\" {\\n\"0\" [label=\"disp <= 258\", shape=ellipse];\\n\"0\" -> \"1\"[label=\"yes\"];\\n\"0\" -> \"2\"[label=\"no\"];\\n\"1\" [label=\"disp <= 146.7\", shape=ellipse];\\n\"1\" -> \"3\"[label=\"yes\"];\\n\"3\" [label=\"23.3833\",shape=box];\\n\"1\" -> \"4\"[label=\"no\"];\\n\"4\" [label=\"19.3143\",shape=box];\\n\"2\" [label=\"wt <= 3.44\", shape=ellipse];\\n\"2\" -> \"5\"[label=\"yes\"];\\n\"5\" [label=\"17.3\",shape=box];\\n\"2\" -> \"6\"[label=\"no\"];\\n\"6\" [label=\"13.72\",shape=box];\\n\\n} //---end of digraph--------- ',)]" |
| ] |
| }, |
| "execution_count": 22, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT madlib.get_tree('mt_cars_output',1,7);" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 24, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n", |
| "True\n" |
| ] |
| }, |
| { |
| "data": { |
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jjz7SzbtNezIWf2GxebKh1aANj5Lc6+lJUfgP1logXp/JyqLw5rBLJBBCAsH7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CL64Zk/Z7ebLFFltoO/Q1/5NBUZti1zPPIYKWF/s1f+SRR3rRwuvz5PtClFHqOeuss9STR2w5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\nBgwDQ4KB/w+ZpdaHJHq+LwAAAABJRU5ErkJggg==\n", |
| "text/plain": [ |
| "<IPython.core.display.Image object>" |
| ] |
| }, |
| "execution_count": 24, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "# set paths for your environment\n", |
| "from os.path import expanduser\n", |
| "home = expanduser(\"/Users/fmcquillan/Documents/Product/MADlib/Demos/1 dot 14 demos\")\n", |
| "\n", |
| "dot_output = %sql select madlib.get_tree('mt_cars_output',1,7, TRUE, TRUE);\n", |
| "with open('tree_out.dot', 'w') as f: \n", |
| " f.write(dot_output[0][0])\n", |
| " \n", |
| "import pygraphviz as pgv\n", |
| "from IPython.display import Image\n", |
| "graph = pgv.AGraph(\"tree_out.dot\")\n", |
| "print bool(graph)\n", |
| "graph.draw('tree_out.png',prog='dot')\n", |
| "Image('tree_out.png')" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "Display the surrogate variables that are used to compute the split for each node when the primary variable is NULL:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 25, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>get_tree_surr</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td><br> -------------------------------------<br> Surrogates for internal nodes<br> -------------------------------------<br> <br>(0) disp <= 258<br> 1: vs in {1} [common rows = 23]<br> 2: qsec > 17.42 [common rows = 22]<br> [Majority branch = 13 ]<br><br>(1) disp <= 146.7<br> 1: wt <= 3.19 [common rows = 13]<br> 2: cyl in {4} [common rows = 11]<br> [Majority branch = 7 ]<br><br>(2) wt <= 3.44<br> 1: qsec <= 17.3 [common rows = 8]<br> 2: vs in {0} [common rows = 5]<br> [Majority branch = 5 ]<br><br></td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'\\n -------------------------------------\\n Surrogates for internal nodes\\n -------------------------------------\\n \\n(0) disp <= 258\\n 1: vs in {1} [common rows = 23]\\n 2: qsec > 17.42 [common rows = 22]\\n [Majority branch = 13 ]\\n\\n(1) disp <= 146.7\\n 1: wt <= 3.19 [common rows = 13]\\n 2: cyl in {4} [common rows = 11]\\n [Majority branch = 7 ]\\n\\n(2) wt <= 3.44\\n 1: qsec <= 17.3 [common rows = 8]\\n 2: vs in {0} [common rows = 5]\\n [Majority branch = 5 ]\\n\\n',)]" |
| ] |
| }, |
| "execution_count": 25, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT madlib.get_tree_surr('mt_cars_output',1,7);" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# NULL handling example" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 1. Load data\n", |
| "Create toy example to illustrate null-as-category handling for categorical features:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 19, |
| "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>country</th>\n", |
| " <th>city</th>\n", |
| " <th>weather</th>\n", |
| " <th>response</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>None</td>\n", |
| " <td>None</td>\n", |
| " <td>None</td>\n", |
| " <td>a</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>2</td>\n", |
| " <td>US</td>\n", |
| " <td>None</td>\n", |
| " <td>None</td>\n", |
| " <td>b</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>3</td>\n", |
| " <td>US</td>\n", |
| " <td>NY</td>\n", |
| " <td>None</td>\n", |
| " <td>c</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>4</td>\n", |
| " <td>US</td>\n", |
| " <td>NY</td>\n", |
| " <td>rainy</td>\n", |
| " <td>d</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, None, None, None, u'a'),\n", |
| " (2, u'US', None, None, u'b'),\n", |
| " (3, u'US', u'NY', None, u'c'),\n", |
| " (4, u'US', u'NY', u'rainy', u'd')]" |
| ] |
| }, |
| "execution_count": 19, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS null_handling_example;\n", |
| "\n", |
| "CREATE TABLE null_handling_example (\n", |
| " id integer,\n", |
| " country text,\n", |
| " city text,\n", |
| " weather text,\n", |
| " response text\n", |
| ");\n", |
| " \n", |
| "INSERT INTO null_handling_example VALUES\n", |
| "(1,null,null,null,'a'),\n", |
| "(2,'US',null,null,'b'),\n", |
| "(3,'US','NY',null,'c'),\n", |
| "(4,'US','NY','rainy','d');\n", |
| " \n", |
| "SELECT * FROM null_handling_example ORDER BY id;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 2. Train random forest\n", |
| "Note that '__NULL__' is set as a valid level for the categorical features country, weather and city:" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 20, |
| "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>method</th>\n", |
| " <th>is_classification</th>\n", |
| " <th>source_table</th>\n", |
| " <th>model_table</th>\n", |
| " <th>id_col_name</th>\n", |
| " <th>dependent_varname</th>\n", |
| " <th>independent_varnames</th>\n", |
| " <th>cat_features</th>\n", |
| " <th>con_features</th>\n", |
| " <th>grouping_cols</th>\n", |
| " <th>num_trees</th>\n", |
| " <th>num_random_features</th>\n", |
| " <th>max_tree_depth</th>\n", |
| " <th>min_split</th>\n", |
| " <th>min_bucket</th>\n", |
| " <th>num_splits</th>\n", |
| " <th>verbose</th>\n", |
| " <th>importance</th>\n", |
| " <th>num_permutations</th>\n", |
| " <th>num_all_groups</th>\n", |
| " <th>num_failed_groups</th>\n", |
| " <th>total_rows_processed</th>\n", |
| " <th>total_rows_skipped</th>\n", |
| " <th>dependent_var_levels</th>\n", |
| " <th>dependent_var_type</th>\n", |
| " <th>independent_var_types</th>\n", |
| " <th>null_proxy</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>forest_train</td>\n", |
| " <td>True</td>\n", |
| " <td>null_handling_example</td>\n", |
| " <td>train_output</td>\n", |
| " <td>id</td>\n", |
| " <td>response</td>\n", |
| " <td>country,weather,city</td>\n", |
| " <td>country,weather,city</td>\n", |
| " <td></td>\n", |
| " <td>None</td>\n", |
| " <td>10</td>\n", |
| " <td>2</td>\n", |
| " <td>3</td>\n", |
| " <td>2</td>\n", |
| " <td>1</td>\n", |
| " <td>3</td>\n", |
| " <td>False</td>\n", |
| " <td>True</td>\n", |
| " <td>1</td>\n", |
| " <td>1</td>\n", |
| " <td>0</td>\n", |
| " <td>4</td>\n", |
| " <td>0</td>\n", |
| " <td>\"a\",\"b\",\"c\",\"d\"</td>\n", |
| " <td>text</td>\n", |
| " <td>text, text, text</td>\n", |
| " <td>__NULL__</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(u'forest_train', True, u'null_handling_example', u'train_output', u'id', u'response', u'country,weather,city', u'country,weather,city', u'', None, 10, 2, 3, 2, 1, 3, False, True, 1, 1, 0, 4, 0, u'\"a\",\"b\",\"c\",\"d\"', u'text', u'text, text, text', u'__NULL__')]" |
| ] |
| }, |
| "execution_count": 20, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS train_output, train_output_group, train_output_summary;\n", |
| "\n", |
| "SELECT madlib.forest_train('null_handling_example', -- source table\n", |
| " 'train_output', -- output model table\n", |
| " 'id', -- id column\n", |
| " 'response', -- response\n", |
| " 'country, weather, city', -- features\n", |
| " NULL, -- exclude columns\n", |
| " NULL, -- grouping columns\n", |
| " 10::integer, -- number of trees\n", |
| " 2::integer, -- number of random features\n", |
| " TRUE::boolean, -- variable importance\n", |
| " 1::integer, -- num_permutations\n", |
| " 3::integer, -- max depth\n", |
| " 2::integer, -- min split\n", |
| " 1::integer, -- min bucket\n", |
| " 3::integer, -- number of splits per continuous variable\n", |
| " 'null_as_category=TRUE'\n", |
| " );\n", |
| "\n", |
| "SELECT * FROM train_output_summary;" |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 21, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "1 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>gid</th>\n", |
| " <th>success</th>\n", |
| " <th>cat_n_levels</th>\n", |
| " <th>cat_levels_in_text</th>\n", |
| " <th>oob_error</th>\n", |
| " <th>oob_var_importance</th>\n", |
| " <th>impurity_var_importance</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>True</td>\n", |
| " <td>[2, 2, 2]</td>\n", |
| " <td>[u'US', u'__NULL__', u'rainy', u'__NULL__', u'NY', u'__NULL__']</td>\n", |
| " <td>1.00000000000000000000</td>\n", |
| " <td>[0.0, 0.0, 0.0]</td>\n", |
| " <td>[0.125, 0.0944444444444, 0.1836666666667]</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, True, [2, 2, 2], [u'US', u'__NULL__', u'rainy', u'__NULL__', u'NY', u'__NULL__'], Decimal('1.00000000000000000000'), [0.0, 0.0, 0.0], [0.125, 0.0944444444444, 0.1836666666667])]" |
| ] |
| }, |
| "execution_count": 21, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "SELECT * FROM train_output_group;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "# 3. Predict\n", |
| "\n", |
| "Predict for data not previously seen by assuming NULL value as the default. " |
| ] |
| }, |
| { |
| "cell_type": "code", |
| "execution_count": 22, |
| "metadata": {}, |
| "outputs": [ |
| { |
| "name": "stdout", |
| "output_type": "stream", |
| "text": [ |
| "Done.\n", |
| "Done.\n", |
| "4 rows affected.\n", |
| "Done.\n", |
| "1 rows affected.\n", |
| "4 rows affected.\n" |
| ] |
| }, |
| { |
| "data": { |
| "text/html": [ |
| "<table>\n", |
| " <tr>\n", |
| " <th>id</th>\n", |
| " <th>expected_response</th>\n", |
| " <th>estimated_response</th>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>1</td>\n", |
| " <td>a</td>\n", |
| " <td>a</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>2</td>\n", |
| " <td>b</td>\n", |
| " <td>a</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>3</td>\n", |
| " <td>c</td>\n", |
| " <td>c</td>\n", |
| " </tr>\n", |
| " <tr>\n", |
| " <td>4</td>\n", |
| " <td>d</td>\n", |
| " <td>c</td>\n", |
| " </tr>\n", |
| "</table>" |
| ], |
| "text/plain": [ |
| "[(1, u'a', u'a'), (2, u'b', u'a'), (3, u'c', u'c'), (4, u'd', u'c')]" |
| ] |
| }, |
| "execution_count": 22, |
| "metadata": {}, |
| "output_type": "execute_result" |
| } |
| ], |
| "source": [ |
| "%%sql\n", |
| "DROP TABLE IF EXISTS table_test;\n", |
| "\n", |
| "CREATE TABLE table_test (\n", |
| " id integer,\n", |
| " country text,\n", |
| " city text,\n", |
| " weather text,\n", |
| " expected_response text\n", |
| ");\n", |
| "\n", |
| "INSERT INTO table_test VALUES\n", |
| "(1,'IN','MUM','cloudy','a'),\n", |
| "(2,'US','HOU','humid','b'),\n", |
| "(3,'US','NY','sunny','c'),\n", |
| "(4,'US','NY','rainy','d');\n", |
| " \n", |
| "DROP TABLE IF EXISTS prediction_results;\n", |
| "\n", |
| "SELECT madlib.forest_predict('train_output',\n", |
| " 'table_test',\n", |
| " 'prediction_results',\n", |
| " 'response');\n", |
| "\n", |
| "SELECT s.id, expected_response, estimated_response\n", |
| "FROM prediction_results p, table_test s\n", |
| "WHERE s.id = p.id ORDER BY id;" |
| ] |
| }, |
| { |
| "cell_type": "markdown", |
| "metadata": {}, |
| "source": [ |
| "There is only training data for country 'US' so the response for country 'IN' is 'a' as expected, corresponding to a NULL (not 'US') country level. \n", |
| "\n", |
| "Likewise, any city in the 'US' that is not 'NY' will predict response 'b' as expected, corresponding to a NULL (not 'NY') city level. " |
| ] |
| } |
| ], |
| "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": 1 |
| } |
