Repository: madlib-site Updated Branches: refs/heads/notebook-updates-1dot14 [created] 3f849b9e4
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/3f849b9e/community-artifacts/mlp-v3.ipynb ---------------------------------------------------------------------- diff --git a/community-artifacts/mlp-v3.ipynb b/community-artifacts/mlp-v3.ipynb new file mode 100644 index 0000000..8c585a6 --- /dev/null +++ b/community-artifacts/mlp-v3.ipynb @@ -0,0 +1,4584 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multilayer Perceptron\n", + "\n", + "Multilayer Perceptron (MLP) is a type of neural network that can be used for regression and classification.\n", + "\n", + "This version of the workbook includes mini-batching which was added in the 1.14 release." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/fmcquillan/anaconda/lib/python2.7/site-packages/IPython/config.py:13: ShimWarning: The `IPython.config` package has been deprecated. 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\n", + "\n", + "# Greenplum Database 4.3.10.0\n", + "#%sql postgresql://gpdbchina@10.194.10.68:61000/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: 1.14-dev, git revision: rc/1.13-rc1-66-g4cced1b, cmake configuration time: Mon Apr 23 16:26:17 UTC 2018, build type: release, build system: Linux-2.6.32-696.20.1.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(u'MADlib version: 1.14-dev, git revision: rc/1.13-rc1-66-g4cced1b, cmake configuration time: Mon Apr 23 16:26:17 UTC 2018, build type: release, build system: Linux-2.6.32-696.20.1.el6.x86_64, C compiler: gcc 4.4.7, C++ compiler: g++ 4.4.7',)]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%sql select madlib.version();\n", + "#%sql select version();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classification without Mini-Batching\n", + "\n", + "# 1. Create input table for classification" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "Done.\n", + "52 rows affected.\n", + "52 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>id</th>\n", + " <th>attributes</th>\n", + " <th>class_text</th>\n", + " <th>class</th>\n", + " <th>state</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>3</td>\n", + " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n", + " 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u'Alaska'),\n", + " (26, [Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')], u'Iris_versicolor', 2, u'Alaska'),\n", + " (27, [Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')], u'Iris_versicolor', 2, u'Alaska'),\n", + " (28, [Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (29, [Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (30, [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (31, [Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (32, [Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (33, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (34, [Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (35, [Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (36, [Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (37, [Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')], u'Iris_setosa', 1, u'Tennessee'),\n", + " (38, [Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (39, [Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (40, [Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (41, [Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (42, [Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (43, [Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (44, [Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (45, [Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (46, [Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (47, [Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (48, [Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (49, [Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (50, [Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (51, [Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')], u'Iris_versicolor', 2, u'Tennessee'),\n", + " (52, [Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')], u'Iris_versicolor', 2, u'Tennessee')]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql \n", + "DROP TABLE IF EXISTS iris_data;\n", + "\n", + "CREATE TABLE iris_data(\n", + " id serial,\n", + " attributes numeric[],\n", + " class_text varchar,\n", + " class integer,\n", + " state varchar\n", + ");\n", + "\n", + "INSERT INTO iris_data(id, attributes, class_text, class, state) VALUES\n", + "(1,ARRAY[5.0,3.2,1.2,0.2],'Iris_setosa',1,'Alaska'),\n", + "(2,ARRAY[5.5,3.5,1.3,0.2],'Iris_setosa',1,'Alaska'),\n", + "(3,ARRAY[4.9,3.1,1.5,0.1],'Iris_setosa',1,'Alaska'),\n", + "(4,ARRAY[4.4,3.0,1.3,0.2],'Iris_setosa',1,'Alaska'),\n", + "(5,ARRAY[5.1,3.4,1.5,0.2],'Iris_setosa',1,'Alaska'),\n", + "(6,ARRAY[5.0,3.5,1.3,0.3],'Iris_setosa',1,'Alaska'),\n", + "(7,ARRAY[4.5,2.3,1.3,0.3],'Iris_setosa',1,'Alaska'),\n", + "(8,ARRAY[4.4,3.2,1.3,0.2],'Iris_setosa',1,'Alaska'),\n", + "(9,ARRAY[5.0,3.5,1.6,0.6],'Iris_setosa',1,'Alaska'),\n", + "(10,ARRAY[5.1,3.8,1.9,0.4],'Iris_setosa',1,'Alaska'),\n", + "(11,ARRAY[4.8,3.0,1.4,0.3],'Iris_setosa',1,'Alaska'),\n", + "(12,ARRAY[5.1,3.8,1.6,0.2],'Iris_setosa',1,'Alaska'),\n", + "(13,ARRAY[5.7,2.8,4.5,1.3],'Iris_versicolor',2,'Alaska'),\n", + "(14,ARRAY[6.3,3.3,4.7,1.6],'Iris_versicolor',2,'Alaska'),\n", + "(15,ARRAY[4.9,2.4,3.3,1.0],'Iris_versicolor',2,'Alaska'),\n", + "(16,ARRAY[6.6,2.9,4.6,1.3],'Iris_versicolor',2,'Alaska'),\n", + "(17,ARRAY[5.2,2.7,3.9,1.4],'Iris_versicolor',2,'Alaska'),\n", + "(18,ARRAY[5.0,2.0,3.5,1.0],'Iris_versicolor',2,'Alaska'),\n", + "(19,ARRAY[5.9,3.0,4.2,1.5],'Iris_versicolor',2,'Alaska'),\n", + "(20,ARRAY[6.0,2.2,4.0,1.0],'Iris_versicolor',2,'Alaska'),\n", + "(21,ARRAY[6.1,2.9,4.7,1.4],'Iris_versicolor',2,'Alaska'),\n", + "(22,ARRAY[5.6,2.9,3.6,1.3],'Iris_versicolor',2,'Alaska'),\n", + "(23,ARRAY[6.7,3.1,4.4,1.4],'Iris_versicolor',2,'Alaska'),\n", + "(24,ARRAY[5.6,3.0,4.5,1.5],'Iris_versicolor',2,'Alaska'),\n", + "(25,ARRAY[5.8,2.7,4.1,1.0],'Iris_versicolor',2,'Alaska'),\n", + "(26,ARRAY[6.2,2.2,4.5,1.5],'Iris_versicolor',2,'Alaska'),\n", + "(27,ARRAY[5.6,2.5,3.9,1.1],'Iris_versicolor',2,'Alaska'),\n", + "(28,ARRAY[5.0,3.4,1.5,0.2],'Iris_setosa',1,'Tennessee'),\n", + "(29,ARRAY[4.4,2.9,1.4,0.2],'Iris_setosa',1,'Tennessee'),\n", + "(30,ARRAY[4.9,3.1,1.5,0.1],'Iris_setosa',1,'Tennessee'),\n", + "(31,ARRAY[5.4,3.7,1.5,0.2],'Iris_setosa',1,'Tennessee'),\n", + "(32,ARRAY[4.8,3.4,1.6,0.2],'Iris_setosa',1,'Tennessee'),\n", + "(33,ARRAY[4.8,3.0,1.4,0.1],'Iris_setosa',1,'Tennessee'),\n", + "(34,ARRAY[4.3,3.0,1.1,0.1],'Iris_setosa',1,'Tennessee'),\n", + "(35,ARRAY[5.8,4.0,1.2,0.2],'Iris_setosa',1,'Tennessee'),\n", + "(36,ARRAY[5.7,4.4,1.5,0.4],'Iris_setosa',1,'Tennessee'),\n", + "(37,ARRAY[5.4,3.9,1.3,0.4],'Iris_setosa',1,'Tennessee'),\n", + "(38,ARRAY[6.0,2.9,4.5,1.5],'Iris_versicolor',2,'Tennessee'),\n", + "(39,ARRAY[5.7,2.6,3.5,1.0],'Iris_versicolor',2,'Tennessee'),\n", + "(40,ARRAY[5.5,2.4,3.8,1.1],'Iris_versicolor',2,'Tennessee'),\n", + "(41,ARRAY[5.5,2.4,3.7,1.0],'Iris_versicolor',2,'Tennessee'),\n", + "(42,ARRAY[5.8,2.7,3.9,1.2],'Iris_versicolor',2,'Tennessee'),\n", + "(43,ARRAY[6.0,2.7,5.1,1.6],'Iris_versicolor',2,'Tennessee'),\n", + "(44,ARRAY[5.4,3.0,4.5,1.5],'Iris_versicolor',2,'Tennessee'),\n", + "(45,ARRAY[6.0,3.4,4.5,1.6],'Iris_versicolor',2,'Tennessee'),\n", + "(46,ARRAY[6.7,3.1,4.7,1.5],'Iris_versicolor',2,'Tennessee'),\n", + "(47,ARRAY[6.3,2.3,4.4,1.3],'Iris_versicolor',2,'Tennessee'),\n", + "(48,ARRAY[5.6,3.0,4.1,1.3],'Iris_versicolor',2,'Tennessee'),\n", + "(49,ARRAY[5.5,2.5,4.0,1.3],'Iris_versicolor',2,'Tennessee'),\n", + "(50,ARRAY[5.5,2.6,4.4,1.2],'Iris_versicolor',2,'Tennessee'),\n", + "(51,ARRAY[6.1,3.0,4.6,1.4],'Iris_versicolor',2,'Tennessee'),\n", + "(52,ARRAY[5.8,2.6,4.0,1.2],'Iris_versicolor',2,'Tennessee');\n", + "\n", + "SELECT * FROM iris_data ORDER BY id;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Classification model\n", + "\n", + "Generate a multilayer perceptron with a single hidden layer of 5 units. Use the attributes column as the independent variables, and use the class column as the classification. Set the tolerance to 0 so that 500 iterations will be run. Use a hyperbolic tangent activation function. The model will be written to mlp_model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "1 rows affected.\n", + "1 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>mlp_classification</th>\n", + " </tr>\n", + " <tr>\n", + " <td></td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[('',)]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "DROP TABLE IF EXISTS mlp_model, mlp_model_summary, mlp_model_standardization;\n", + "\n", + "-- Set seed so results are reproducible\n", + "SELECT setseed(0);\n", + "\n", + "SELECT madlib.mlp_classification(\n", + " 'iris_data', -- Source table\n", + " 'mlp_model', -- Destination table\n", + " 'attributes', -- Input features\n", + " 'class_text', -- Label\n", + " ARRAY[5], -- Number of units per layer\n", + " 'learning_rate_init=0.003,\n", + " n_iterations=500,\n", + " tolerance=0', -- Optimizer params\n", + " 'tanh', -- Activation function\n", + " NULL, -- Default weight (1)\n", + " FALSE, -- No warm start\n", + " FALSE -- Not verbose\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "View the classification model:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>coeff</th>\n", + " <th>loss</th>\n", + " <th>num_iterations</th>\n", + " </tr>\n", + " <tr>\n", + " <td>[-0.133201972097394, -0.118636963016892, 0.259474215534915, -0.528433916977086, -0.429596986146934, 0.0280938067215663, -0.144466640003299, 0.286988287730496, -0.357379109879214, -0.66607966047993, -0.362686864800844, -0.445850222259492, 0.346304179999463, -0.522045614901381, -0.374150944327096, -0.0495674317777646, -0.0832467361276034, 0.279692771132311, -0.517927322791807, -0.2809929864922, -0.183324281085865, -0.243554165368264, 0.349093795759817, -0.713886988614578, -0.432209713264906, -0.450592821509468, 0.43151079821727, 0.472695001337664, 0.612226054527927, 0.490963383058475, 0.724327654407535, -0.103407228476278, -0.851088815290572, -0.837299697527914, -0.899486162064411, -0.486098700715332, -1.08911290004112]</td>\n", + " <td>0.00734471737812</td>\n", + " <td>500</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[([-0.133201972097394, -0.118636963016892, 0.259474215534915, -0.528433916977086, -0.429596986146934, 0.0280938067215663, -0.144466640003299, 0.286988287730496, -0.357379109879214, -0.66607966047993, -0.362686864800844, -0.445850222259492, 0.346304179999463, -0.522045614901381, -0.374150944327096, -0.0495674317777646, -0.0832467361276034, 0.279692771132311, -0.517927322791807, -0.2809929864922, -0.183324281085865, -0.243554165368264, 0.349093795759817, -0.713886988614578, -0.432209713264906, -0.450592821509468, 0.43151079821727, 0.472695001337664, 0.612226054527927, 0.490963383058475, 0.724327654407535, -0.103407228476278, -0.851088815290572, -0.837299697527914, -0.899486162064411, -0.486098700715332, -1.08911290004112], 0.00734471737812046, 500)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "SELECT * FROM mlp_model;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "View the model summary table:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>source_table</th>\n", + " <th>independent_varname</th>\n", + " <th>dependent_varname</th>\n", + " <th>dependent_vartype</th>\n", + " <th>tolerance</th>\n", + " <th>learning_rate_init</th>\n", + " <th>learning_rate_policy</th>\n", + " <th>n_iterations</th>\n", + " <th>n_tries</th>\n", + " <th>layer_sizes</th>\n", + " <th>activation</th>\n", + " <th>is_classification</th>\n", + " <th>classes</th>\n", + " <th>weights</th>\n", + " <th>grouping_col</th>\n", + " </tr>\n", + " <tr>\n", + " <td>iris_data</td>\n", + " <td>attributes</td>\n", + " <td>class_text</td>\n", + " <td>character varying</td>\n", + " <td>0.0</td>\n", + " <td>0.003</td>\n", + " <td>constant</td>\n", + " <td>500</td>\n", + " <td>1</td>\n", + " <td>[4, 5, 2]</td>\n", + " <td>tanh</td>\n", + " <td>True</td>\n", + " <td>[u'Iris_setosa', u'Iris_versicolor']</td>\n", + " <td>1</td>\n", + " <td>NULL</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(u'iris_data', u'attributes', u'class_text', u'character varying', 0.0, 0.003, u'constant', 500, 1, [4, 5, 2], u'tanh', True, [u'Iris_setosa', u'Iris_versicolor'], u'1', u'NULL')]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "SELECT * FROM mlp_model_summary;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "View the standardization table:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>mean</th>\n", + " <th>std</th>\n", + " </tr>\n", + " <tr>\n", + " <td>[5.45961538461539, 2.99807692307692, 3.025, 0.851923076923077]</td>\n", + " <td>[0.598799958694505, 0.498262513685689, 1.41840579525043, 0.550346179381454]</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[([5.45961538461539, 2.99807692307692, 3.025, 0.851923076923077], [0.598799958694505, 0.498262513685689, 1.41840579525043, 0.550346179381454])]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "SELECT * FROM mlp_model_standardization;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Prediction for classification\n", + "\n", + "Now let's use the model to predict. In the following example we will use the training data set for prediction as well, which is not usual but serves to show the syntax. The prediction is in the estimated_class_text column with the actual value in the class_text column." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "1 rows affected.\n", + "52 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>id</th>\n", + " <th>estimated_class_text</th>\n", + " <th>attributes</th>\n", + " <th>class_text</th>\n", + " <th>class</th>\n", + " <th>state</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>Iris_setosa</td>\n", + " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>Iris_setosa</td>\n", + " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>3</td>\n", + " <td>Iris_setosa</td>\n", + " <td>[Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>4</td>\n", + " <td>Iris_setosa</td>\n", + " <td>[Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>5</td>\n", + " <td>Iris_setosa</td>\n", + " <td>[Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>6</td>\n", + " <td>Iris_setosa</td>\n", + " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>7</td>\n", + " 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EXISTS mlp_prediction;\n", + "\n", + "SELECT madlib.mlp_predict(\n", + " 'mlp_model', -- Model table\n", + " 'iris_data', -- Test data table\n", + " 'id', -- Id column in test table\n", + " 'mlp_prediction', -- Output table for predictions\n", + " 'response' -- Output classes, not probabilities\n", + " );\n", + "\n", + "SELECT * FROM mlp_prediction JOIN iris_data USING (id) ORDER BY id;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Count the misclassifications:" + ] + }, + { + "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>count</th>\n", + " </tr>\n", + " <tr>\n", + " <td>0</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(0L,)]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "SELECT COUNT(*) FROM mlp_prediction JOIN iris_data USING (id) \n", + "WHERE mlp_prediction.estimated_class_text != iris_data.class_text;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classification with Mini-Batching\n", + "\n", + "# 1. Call mini-batch preprocessor\n", + "\n", + "Use the same data set as above." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "1 rows affected.\n", + "2 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>__id__</th>\n", + " <th>dependent_varname</th>\n", + " <th>independent_varname</th>\n", + " </tr>\n", + " <tr>\n", + " <td>0</td>\n", + " <td>[[0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0], [0.0, 1.0]]</td>\n", + " <td>[[0.0674425821070736, -0.798930106411465, 0.969398182526215, 0.632469046062059], [0.902445979718656, -0.196837852308928, 1.03989986852812, 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[1.90445005685255, -0.196837852308928, 1.11040155453003, 0.81417286040029], [0.0674425821070736, -1.20032494247982, 0.546388066514775, 0.450765231723828]])]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "DROP TABLE IF EXISTS iris_data_packed, iris_data_packed_summary, iris_data_packed_standardization;\n", + "\n", + "SELECT madlib.minibatch_preprocessor('iris_data', -- Source table\n", + " 'iris_data_packed', -- Output table\n", + " 'class_text', -- Dependent variable\n", + " 'attributes' -- Independent variables\n", + " );\n", + "\n", + "SELECT * FROM iris_data_packed ORDER BY __id__;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Classification model with mini-batching\n", + "Use similar parameters as before:" + ] + }, + { + "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>mlp_classification</th>\n", + " </tr>\n", + " <tr>\n", + " <td></td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[('',)]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "DROP TABLE IF EXISTS mlp_model, mlp_model_summary, mlp_model_standardization;\n", + "-- Set seed so results are reproducible\n", + "SELECT setseed(0);\n", + "\n", + "SELECT madlib.mlp_classification(\n", + " 'iris_data_packed', -- Ouput table from mini-batch preprocessor\n", + " 'mlp_model', -- Destination table\n", + " 'independent_varname', -- Hardcode to this, from table iris_data_packed\n", + " 'dependent_varname', -- Hardcode to this, from table iris_data_packed\n", + " ARRAY[5], -- Number of units per layer\n", + " 'learning_rate_init=0.1,\n", + " n_iterations=500,\n", + " tolerance=0', -- Optimizer params\n", + " 'tanh', -- Activation function\n", + " NULL, -- Default weight (1)\n", + " FALSE, -- No warm start\n", + " FALSE -- Not verbose\n", + ");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "View the classification model:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>coeff</th>\n", + " <th>loss</th>\n", + " <th>num_iterations</th>\n", + " </tr>\n", + " <tr>\n", + " <td>[-0.0309749079415527, -0.238585231565989, 0.516459799389647, -0.46368579959856, -0.673732925023756, 0.0778619338393474, 0.197305073296416, -0.249692782186095, 0.439028430336739, 0.626376493360924, -0.0882970823387187, -0.0797679288590299, 0.397818124348168, -0.123298748840092, -0.207996015482563, -0.225365582875435, -0.311523866687928, 0.323570097811814, -0.369573237920434, -0.364962791069138, 0.233034700676389, 0.220908774672402, -0.292167680244236, 0.695958014086603, 0.619175545678452, -0.164066120012632, 0.951372990648697, -0.819801614646947, 0.290676073162377, 0.785234769097514, -1.04842776221521, 0.172123879960554, -0.94312904330421, 0.658608739158027, -0.264472191827765, -0.37546065538845, 0.923108343066969]</td>\n", + " <td>0.00603272550899</td>\n", + " <td>500</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[([-0.0309749079415527, -0.238585231565989, 0.516459799389647, -0.46368579959856, -0.673732925023756, 0.0778619338393474, 0.197305073296416, -0.249692782186095, 0.439028430336739, 0.626376493360924, -0.0882970823387187, -0.0797679288590299, 0.397818124348168, -0.123298748840092, -0.207996015482563, -0.225365582875435, -0.311523866687928, 0.323570097811814, -0.369573237920434, -0.364962791069138, 0.233034700676389, 0.220908774672402, -0.292167680244236, 0.695958014086603, 0.619175545678452, -0.164066120012632, 0.951372990648697, -0.819801614646947, 0.290676073162377, 0.785234769097514, -1.04842776221521, 0.172123879960554, -0.94312904330421, 0.658608739158027, -0.264472191827765, -0.37546065538845, 0.923108343066969], 0.00603272550899296, 500)]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "SELECT * FROM mlp_model;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Prediction for classification\n", + "\n", + "Now predict using model we built using mini-batching. As before we will use the training data set for prediction as well, which is not usual but serves to show the syntax. The prediction is in the estimated_class_text column with the actual value in the class_text column. Note that the prediction function is exactly the same whether you use mini batching or not:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "1 rows affected.\n", + "52 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>id</th>\n", + " <th>estimated_class_text</th>\n", + " <th>attributes</th>\n", + " <th>class_text</th>\n", + " <th>class</th>\n", + " <th>state</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>Iris_setosa</td>\n", + " <td>[Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>Iris_setosa</td>\n", + " <td>[Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')]</td>\n", + " <td>Iris_setosa</td>\n", + " <td>1</td>\n", + " <td>Alaska</td>\n", + 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