http://git-wip-us.apache.org/repos/asf/madlib-site/blob/418f361c/community-artifacts/mlp-v2.ipynb ---------------------------------------------------------------------- diff --git a/community-artifacts/mlp-v2.ipynb b/community-artifacts/mlp-v2.ipynb deleted file mode 100644 index 145b3e2..0000000 --- a/community-artifacts/mlp-v2.ipynb +++ /dev/null @@ -1,3755 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Multilayer Perceptron" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The sql extension is already loaded. To reload it, use:\n", - " %reload_ext sql\n" - ] - } - ], - "source": [ - "%load_ext sql" - ] - }, - { - "cell_type": "code", - "execution_count": 118, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "u'Connected: gpdbchina@madlib'" - ] - }, - "execution_count": 118, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Greenplum 4.3.10.0\n", - "%sql postgresql://gpdbchina@10.194.10.68:61000/madlib\n", - " \n", - "# PostgreSQL local\n", - "#%sql postgresql://fmcquillan@localhost:5432/madlib\n", - "\n", - "# Greenplum 4.2.3.0\n", - "#%sql postgresql://gpdbchina@10.194.10.68:55000/madlib" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "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: 2.0-dev, git revision: rel/v1.12-9-gf790a61, cmake configuration time: Tue Sep 19 17:56:02 UTC 2017, build type: Release, build system: Linux-2.6.18-238.27.1.el5.hotfix.bz516490, C compiler: gcc 4.4.0, C++ compiler: g++ 4.4.0</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[(u'MADlib version: 2.0-dev, git revision: rel/v1.12-9-gf790a61, cmake configuration time: Tue Sep 19 17:56:02 UTC 2017, build type: Release, build system: Linux-2.6.18-238.27.1.el5.hotfix.bz516490, C compiler: gcc 4.4.0, C++ compiler: g++ 4.4.0',)]" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%sql select madlib.version();\n", - "#%sql select version();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1. Create input table for classification" - ] - }, - { - "cell_type": "code", - "execution_count": 120, - "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", - " <td>Iris_setosa</td>\n", - 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" (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": 120, - "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": 121, - "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": 121, - "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": [ - "# 3. View the classification model" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "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>[0.418378455147, 0.0769385374681, -0.146527483484, 0.998888382223, 1.12987089952, -0.437290718473, -0.409273157364, 0.498986904082, -0.544260518783, -0.0914464021734, 0.0390525613337, 0.288450666036, -0.327040616284, 0.16574382417, 0.872753267348, 0.675971654762, 0.318207842514, 0.290765083843, -0.468849343505, 0.288355834648, -0.773220988598, -0.277246576994, -0.676925299898, 0.669871211106, -0.391874491507, 0.0738458598585, -1.85544863789, -0.0153495613681, -0.111982901002, -0.225221594881, -0.217849875552, 0.392310858189, 1.37852218837, -1.08667184295, 1.29050890203, -0.251494108369, 0.229914016792]</td>\n", - " <td>0.0105868627707</td>\n", - " <td>500</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[([0.418378455147, 0.0769385374681, -0.146527483484, 0.998888382223, 1.12987089952, -0.437290718473, -0.409273157364, 0.498986904082, -0.544260518783, -0.0914464021734, 0.0390525613337, 0.288450666036, -0.327040616284, 0.16574382417, 0.872753267348, 0.675971654762, 0.318207842514, 0.290765083843, -0.468849343505, 0.288355834648, -0.773220988598, -0.277246576994, -0.676925299898, 0.669871211106, -0.391874491507, 0.0738458598585, -1.85544863789, -0.0153495613681, -0.111982901002, -0.225221594881, -0.217849875552, 0.392310858189, 1.37852218837, -1.08667184295, 1.29050890203, -0.251494108369, 0.229914016792], 0.0105868627707, 500)]" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM mlp_model;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 4. Classification with n_tries\n", - "Now, use the n_tries optimizer parameter to learn and choose the best model among n_tries number of models learned by the algorithm. Run only for 100 iterations and choose the best model from this short run." - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "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": 123, - "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=50,\n", - " tolerance=0,\n", - " n_tries=3', -- Optimizer params, with n_tries\n", - " 'tanh', -- Activation function\n", - " NULL, -- Default weight (1)\n", - " FALSE, -- No warm start\n", - " FALSE -- Not verbose\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 5. View the classification model with n_tries" - ] - }, - { - "cell_type": "code", - "execution_count": 124, - "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>[0.386129091572, 0.0230804544453, 0.0360373350657, 0.85734404404, 1.00624019669, -0.511165317414, -0.256735693588, 0.424597795202, -0.344940825214, 0.105701317173, -0.0239838319549, 0.19433093383, -0.198067795405, -0.00423615288994, 0.714494593542, 0.67620771672, 0.31692178694, 0.291634997959, -0.470338692309, 0.286903795447, -0.797640757541, -0.36833411352, -0.623711536473, 0.556232364597, -0.501937062704, 0.0664559736029, -1.29169165842, -0.39640660159, 0.349410636066, -0.266301858512, -0.12933320224, 0.399700744444, 0.814765208894, -0.705614802744, 0.829115365015, -0.210413844738, 0.14139734348]</td>\n", - " <td>0.169187585083</td>\n", - " <td>50</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[([0.386129091572, 0.0230804544453, 0.0360373350657, 0.85734404404, 1.00624019669, -0.511165317414, -0.256735693588, 0.424597795202, -0.344940825214, 0.105701317173, -0.0239838319549, 0.19433093383, -0.198067795405, -0.00423615288994, 0.714494593542, 0.67620771672, 0.31692178694, 0.291634997959, -0.470338692309, 0.286903795447, -0.797640757541, -0.36833411352, -0.623711536473, 0.556232364597, -0.501937062704, 0.0664559736029, -1.29169165842, -0.39640660159, 0.349410636066, -0.266301858512, -0.12933320224, 0.399700744444, 0.814765208894, -0.705614802744, 0.829115365015, -0.210413844738, 0.14139734348], 0.169187585083, 50)]" - ] - }, - "execution_count": 124, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM mlp_model;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 6. Classification with warm start\n", - "Next, use the warm_start parameter to start learning a new model, using the coefficients already present in mlp_model. Note that we must not drop the mlp_model table, and cannot use the n_tries parameter if warm_start is used." - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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": 126, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\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=450,\n", - " tolerance=0', -- Optimizer params\n", - " 'tanh', -- Activation function\n", - " NULL, -- Default weight (1)\n", - " TRUE, -- Warm start\n", - " FALSE -- Not verbose\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 7. View the classification model with warm_start\n", - "Notice that the loss is lower compared to the previous example run of 500 iterations without n_tries, despite having the same values for every other parameter. This is because the algorithm learnt three different models starting with a different set of initial weights for the coefficients, and chose the best model among them." - ] - }, - { - "cell_type": "code", - "execution_count": 127, - "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>[0.43089489035, 0.0840880267225, -0.180463790458, 1.02521696861, 1.15327056306, -0.425505368961, -0.445320886704, 0.51333393744, -0.594555309569, -0.140856773835, 0.0616746727506, 0.306834150297, -0.359872964248, 0.206830852263, 0.911186393447, 0.675568040494, 0.316470613819, 0.291871023206, -0.471193550262, 0.286075681893, -0.759796508813, -0.252763378948, -0.690187333341, 0.70213228757, -0.360741864941, 0.0646874010451, -1.97963455591, 0.0877591861911, -0.220936503136, -0.222359882677, -0.244113625762, 0.401469317001, 1.50270810639, -1.18978059055, 1.39946250414, -0.254355820573, 0.256177767002]</td>\n", - " <td>0.00513279402477</td>\n", - " <td>450</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[([0.43089489035, 0.0840880267225, -0.180463790458, 1.02521696861, 1.15327056306, -0.425505368961, -0.445320886704, 0.51333393744, -0.594555309569, -0.140856773835, 0.0616746727506, 0.306834150297, -0.359872964248, 0.206830852263, 0.911186393447, 0.675568040494, 0.316470613819, 0.291871023206, -0.471193550262, 0.286075681893, -0.759796508813, -0.252763378948, -0.690187333341, 0.70213228757, -0.360741864941, 0.0646874010451, -1.97963455591, 0.0877591861911, -0.220936503136, -0.222359882677, -0.244113625762, 0.401469317001, 1.50270810639, -1.18978059055, 1.39946250414, -0.254355820573, 0.256177767002], 0.00513279402477, 450)]" - ] - }, - "execution_count": 127, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM mlp_model;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 8. Classification with grouping\n", - "Next, group the training data by state, and learn a different model for each state." - ] - }, - { - "cell_type": "code", - "execution_count": 128, - "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": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "DROP TABLE IF EXISTS mlp_model_group, mlp_model_group_summary, mlp_model_group_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_group', -- 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=50,\n", - " tolerance=0,\n", - " n_tries=3', -- Optimizer params, with n_tries\n", - " 'tanh', -- Activation function\n", - " NULL, -- Default weight (1)\n", - " FALSE, -- No warm start\n", - " FALSE, -- Not verbose\n", - " 'state' -- Grouping column\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 9. View the classification model with grouping\n", - "A separate model is learnt for each state, and the result table displays the name of the state (grouping column) associated with the model." - ] - }, - { - "cell_type": "code", - "execution_count": 129, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "<table>\n", - " <tr>\n", - " <th>state</th>\n", - " <th>coeff</th>\n", - " <th>loss</th>\n", - " <th>num_iterations</th>\n", - " </tr>\n", - " <tr>\n", - " <td>Alaska</td>\n", - " <td>[0.361442205117, 0.0437833983169, 0.0228267239333, 0.86051976995, 1.00898866341, -0.509493068163, -0.259717661563, 0.423128001757, -0.348520276299, 0.102569227429, -0.023820253367, 0.194242907683, -0.194973841963, -0.00354279848888, 0.715171987578, 0.679803198566, 0.30085527398, 0.290398225819, -0.481339584921, 0.273581567191, -0.798021118384, -0.364181329645, -0.631718355895, 0.568550659826, -0.492243523831, 0.0678832140837, -1.29021370112, -0.396961890891, 0.347728139082, -0.247139454296, -0.165073883437, 0.398273503965, 0.813287251616, -0.705059513443, 0.830797861999, -0.229576248952, 0.177138024677]</td>\n", - " <td>0.169694760351</td>\n", - " <td>50</td>\n", - " </tr>\n", - " <tr>\n", - " <td>Tennessee</td>\n", - " <td>[0.286460719233, -0.391554641774, 0.647611140251, -0.670341290996, 0.111261651571, -0.323445227135, -0.362568050061, 0.556843820772, 0.542593756362, -0.849092718814, 0.639022610897, 0.289608013322, -0.771514626336, -0.225524049129, 0.55654170876, 0.699286372946, -0.0971044516431, -0.856859889972, -0.53385894952, 0.181718585915, -0.635491855087, -0.334708753092, 0.676444860354, 0.798824989025, 0.712555678327, -0.362929653511, 0.347434351706, 0.177418782808, 0.225111014926, 0.178241297519, -0.0229063368163, 0.0404201612098, -1.12689954392, -0.523963295766, 1.0563791555, 0.642674420838, 0.742594634114]</td>\n", - " <td>0.159575522026</td>\n", - " <td>50</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[(u'Alaska', [0.361442205117, 0.0437833983169, 0.0228267239333, 0.86051976995, 1.00898866341, -0.509493068163, -0.259717661563, 0.423128001757, -0.348520276299, 0.102569227429, -0.023820253367, 0.194242907683, -0.194973841963, -0.00354279848888, 0.715171987578, 0.679803198566, 0.30085527398, 0.290398225819, -0.481339584921, 0.273581567191, -0.798021118384, -0.364181329645, -0.631718355895, 0.568550659826, -0.492243523831, 0.0678832140837, -1.29021370112, -0.396961890891, 0.347728139082, -0.247139454296, -0.165073883437, 0.398273503965, 0.813287251616, -0.705059513443, 0.830797861999, -0.229576248952, 0.177138024677], 0.169694760351, 50),\n", - " (u'Tennessee', [0.286460719233, -0.391554641774, 0.647611140251, -0.670341290996, 0.111261651571, -0.323445227135, -0.362568050061, 0.556843820772, 0.542593756362, -0.849092718814, 0.639022610897, 0.289608013322, -0.771514626336, -0.225524049129, 0.55654170876, 0.699286372946, -0.0971044516431, -0.856859889972, -0.53385894952, 0.181718585915, -0.635491855087, -0.334708753092, 0.676444860354, 0.798824989025, 0.712555678327, -0.362929653511, 0.347434351706, 0.177418782808, 0.225111014926, 0.178241297519, -0.0229063368163, 0.0404201612098, -1.12689954392, -0.523963295766, 1.0563791555, 0.642674420838, 0.742594634114], 0.159575522026, 50)]" - ] - }, - "execution_count": 129, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM mlp_model_group ORDER BY state;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 10. Classification with warm start and grouping\n", - "Next use warm_start to improve the models that are already present in the mlp_model_group table. Note again that we cannot use n_tries with warm_start, and the model table must already be present." - ] - }, - { - "cell_type": "code", - "execution_count": 130, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "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": 130, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT madlib.mlp_classification(\n", - " 'iris_data', -- Source table\n", - " 'mlp_model_group', -- 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=450,\n", - " tolerance=0', -- Optimizer params\n", - " 'tanh', -- Activation function\n", - " NULL, -- Default weight (1)\n", - " TRUE, -- Warm start\n", - " FALSE, -- Not verbose\n", - " 'state' -- Grouping column\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 11. View the classification model with warm start and grouping\n" - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "<table>\n", - " <tr>\n", - " <th>state</th>\n", - " <th>coeff</th>\n", - " <th>loss</th>\n", - " <th>num_iterations</th>\n", - " </tr>\n", - " <tr>\n", - " <td>Alaska</td>\n", - " <td>[0.372342289818, 0.0954960560128, -0.153810124763, 1.00725097032, 1.13495556432, -0.429533598061, -0.409198527269, 0.478752722727, -0.551271316257, -0.0950828556324, 0.0237081636546, 0.297325734546, -0.307808869913, 0.17901470615, 0.882685356116, 0.67682803433, 0.28787018652, 0.297557142865, -0.499522323052, 0.256295844717, -0.768404886254, -0.266707916954, -0.676330194506, 0.699875226902, -0.36878024444, 0.0723594672762, -1.86161184866, -0.0295697622013, -0.116698438374, -0.188190989233, -0.253894999257, 0.393797250761, 1.38468539912, -1.07245164218, 1.29522443949, -0.288524714015, 0.265959140497]</td>\n", - " <td>0.0106816837098</td>\n", - " <td>450</td>\n", - " </tr>\n", - " <tr>\n", - " <td>Tennessee</td>\n", - " <td>[0.101408120906, -0.504692476376, 0.754172746712, -0.859366119317, -0.0883419348625, -0.322217245475, -0.488849126018, 0.640836955763, 0.379240951714, -1.01591620805, 0.561633386233, 0.474881549745, -0.820848160685, -0.0367588935663, 0.756738298906, 0.690992107944, -0.0117938780722, -0.911609074051, -0.417367078805, 0.303153350896, -0.502205494032, -0.262138409583, 0.531455906745, 0.931199625293, 0.837077534104, -0.383150148885, 0.722094737958, 0.54110242729, -0.140304542646, 0.107821427424, -0.229319721716, 0.0606406565821, -1.50155993006, -0.887646940248, 1.42179471312, 0.713094290933, 0.949008019012]</td>\n", - " <td>0.00853723754598</td>\n", - " <td>450</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[(u'Alaska', [0.372342289818, 0.0954960560128, -0.153810124763, 1.00725097032, 1.13495556432, -0.429533598061, -0.409198527269, 0.478752722727, -0.551271316257, -0.0950828556324, 0.0237081636546, 0.297325734546, -0.307808869913, 0.17901470615, 0.882685356116, 0.67682803433, 0.28787018652, 0.297557142865, -0.499522323052, 0.256295844717, -0.768404886254, -0.266707916954, -0.676330194506, 0.699875226902, -0.36878024444, 0.0723594672762, -1.86161184866, -0.0295697622013, -0.116698438374, -0.188190989233, -0.253894999257, 0.393797250761, 1.38468539912, -1.07245164218, 1.29522443949, -0.288524714015, 0.265959140497], 0.0106816837098, 450),\n", - " (u'Tennessee', [0.101408120906, -0.504692476376, 0.754172746712, -0.859366119317, -0.0883419348625, -0.322217245475, -0.488849126018, 0.640836955763, 0.379240951714, -1.01591620805, 0.561633386233, 0.474881549745, -0.820848160685, -0.0367588935663, 0.756738298906, 0.690992107944, -0.0117938780722, -0.911609074051, -0.417367078805, 0.303153350896, -0.502205494032, -0.262138409583, 0.531455906745, 0.931199625293, 0.837077534104, -0.383150148885, 0.722094737958, 0.54110242729, -0.140304542646, 0.107821427424, -0.229319721716, 0.0606406565821, -1.50155993006, -0.887646940248, 1.42179471312, 0.713094290933, 0.949008019012], 0.00853723754598, 450)]" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM mlp_model_group ORDER BY state;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 12. Create input data for regression\n", - "\n", - "This dataset contains housing prices." - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done.\n", - "Done.\n", - "20 rows affected.\n", - "20 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "<table>\n", - " <tr>\n", - " <th>id</th>\n", - " <th>x</th>\n", - " <th>zipcode</th>\n", - " <th>y</th>\n", - " </tr>\n", - " <tr>\n", - " <td>1</td>\n", - " <td>[Decimal('1'), Decimal('0.00632'), Decimal('18.00'), Decimal('2.310'), Decimal('0'), Decimal('0.5380'), Decimal('6.5750'), Decimal('65.20'), Decimal('4.0900'), Decimal('1'), Decimal('296.0'), Decimal('15.30'), Decimal('396.90'), Decimal('4.98')]</td>\n", - " <td>94016</td>\n", - " <td>24.0</td>\n", - " </tr>\n", - " <tr>\n", - " <td>2</td>\n", - " <td>[Decimal('1'), Decimal('0.02731'), Decimal('0.00'), Decimal('7.070'), Decimal('0'), Decimal('0.4690'), Decimal('6.4210'), Decimal('78.90'), Decimal('4.9671'), Decimal('2'), Decimal('242.0'), Decimal('17.80'), Decimal('396.90'), Decimal('9.14')]</td>\n", - 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" <td>94016</td>\n", - " <td>36.2</td>\n", - " </tr>\n", - " <tr>\n", - " <td>6</td>\n", - " <td>[Decimal('1'), Decimal('0.02985'), Decimal('0.00'), Decimal('2.180'), Decimal('0'), Decimal('0.4580'), Decimal('6.4300'), Decimal('58.70'), Decimal('6.0622'), Decimal('3'), Decimal('222.0'), Decimal('18.70'), Decimal('394.12'), Decimal('5.21')]</td>\n", - " <td>94016</td>\n", - " <td>28.7</td>\n", - " </tr>\n", - " <tr>\n", - " <td>7</td>\n", - " <td>[Decimal('1'), Decimal('0.08829'), Decimal('12.50'), Decimal('7.870'), Decimal('0'), Decimal('0.5240'), Decimal('6.0120'), Decimal('66.60'), Decimal('5.5605'), Decimal('5'), Decimal('311.0'), Decimal('15.20'), Decimal('395.60'), Decimal('12.43')]</td>\n", - " <td>94016</td>\n", - " <td>22.9</td>\n", - " </tr>\n", - " <tr>\n", - " <td>8</td>\n", - " <td>[Decimal('1'), Decimal('0.14455'), Decimal('12.50'), Decimal('7.870'), Decimal('0'), Decimal('0.5240'), Decimal('6.1720'), Decimal('96.10'), Decimal('5.9505'), Decimal('5'), Decimal('311.0'), Decimal('15.20'), Decimal('396.90'), Decimal('19.15')]</td>\n", - " <td>94016</td>\n", - " <td>27.1</td>\n", - " </tr>\n", - " <tr>\n", - " <td>9</td>\n", - " <td>[Decimal('1'), Decimal('0.21124'), Decimal('12.50'), Decimal('7.870'), Decimal('0'), Decimal('0.5240'), Decimal('5.6310'), Decimal('100.00'), Decimal('6.0821'), Decimal('5'), Decimal('311.0'), Decimal('15.20'), Decimal('386.63'), Decimal('29.93')]</td>\n", - " <td>94016</td>\n", - " <td>16.5</td>\n", - " </tr>\n", - " <tr>\n", - " <td>10</td>\n", - " <td>[Decimal('1'), Decimal('0.17004'), Decimal('12.50'), Decimal('7.870'), Decimal('0'), Decimal('0.5240'), Decimal('6.0040'), Decimal('85.90'), Decimal('6.5921'), Decimal('5'), Decimal('311.0'), Decimal('15.20'), Decimal('386.71'), Decimal('17.10')]</td>\n", - " <td>94016</td>\n", - " <td>18.9</td>\n", - " </tr>\n", - " <tr>\n", - " <td>11</td>\n", - " <td>[Decimal('1'), Decimal('0.22489'), Decimal('12.50'), Decimal('7.870'), Decimal('0'), Decimal('0.5240'), Decimal('6.3770'), Decimal('94.30'), Decimal('6.3467'), Decimal('5'), Decimal('311.0'), Decimal('15.20'), Decimal('392.52'), Decimal('20.45')]</td>\n", - " <td>94016</td>\n", - " <td>15.0</td>\n", - " </tr>\n", - " <tr>\n", - " <td>12</td>\n", - " <td>[Decimal('1'), Decimal('0.11747'), Decimal('12.50'), Decimal('7.870'), Decimal('0'), Decimal('0.5240'), Decimal('6.0090'), Decimal('82.90'), Decimal('6.2267'), Decimal('5'), Decimal('311.0'), Decimal('15.20'), Decimal('396.90'), Decimal('13.27')]</td>\n", - " <td>20001</td>\n", - " <td>18.9</td>\n", - " </tr>\n", - " <tr>\n", - " <td>13</td>\n", - " <td>[Decimal('1'), Decimal('0.09378'), Decimal('12.50'), Decimal('7.870'), Decimal('0'), Decimal('0.5240'), Decimal('5.8890'), Decimal('39.00'), Decimal('5.4509'), Decimal('5'), Decimal('311.0'), Decimal('15.20'), Decimal('390.50'), Decimal('15.71')]</td>\n", - " <td>20001</td>\n", - " <td>21.7</td>\n", - " </tr>\n", - " <tr>\n", - " <td>14</td>\n", - " <td>[Decimal('1'), Decimal('0.62976'), Decimal('0.00'), Decimal('8.140'), Decimal('0'), Decimal('0.5380'), Decimal('5.9490'), Decimal('61.80'), Decimal('4.7075'), Decimal('4'), Decimal('307.0'), Decimal('21.00'), Decimal('396.90'), Decimal('8.26')]</td>\n", - " <td>20001</td>\n", - " <td>20.4</td>\n", - " </tr>\n", - " <tr>\n", - " <td>15</td>\n", - " <td>[Decimal('1'), Decimal('0.63796'), Decimal('0.00'), Decimal('8.140'), Decimal('0'), Decimal('0.5380'), Decimal('6.0960'), Decimal('84.50'), Decimal('4.4619'), Decimal('4'), Decimal('307.0'), Decimal('21.00'), Decimal('380.02'), Decimal('10.26')]</td>\n", - " <td>20001</td>\n", - " <td>18.2</td>\n", - " </tr>\n", - " <tr>\n", - " <td>16</td>\n", - " <td>[Decimal('1'), Decimal('0.62739'), Decimal('0.00'), Decimal('8.140'), Decimal('0'), Decimal('0.5380'), Decimal('5.8340'), Decimal('56.50'), Decimal('4.4986'), Decimal('4'), Decimal('307.0'), Decimal('21.00'), Decimal('395.62'), Decimal('8.47')]</td>\n", - " <td>20001</td>\n", - " <td>19.9</td>\n", - " </tr>\n", - " <tr>\n", - " <td>17</td>\n", - " <td>[Decimal('1'), Decimal('1.05393'), Decimal('0.00'), Decimal('8.140'), Decimal('0'), Decimal('0.5380'), Decimal('5.9350'), Decimal('29.30'), Decimal('4.4986'), Decimal('4'), Decimal('307.0'), Decimal('21.00'), Decimal('386.85'), Decimal('6.58')]</td>\n", - " <td>20001</td>\n", - " <td>23.1</td>\n", - " </tr>\n", - " <tr>\n", - " <td>18</td>\n", - " <td>[Decimal('1'), Decimal('0.78420'), Decimal('0.00'), Decimal('8.140'), Decimal('0'), Decimal('0.5380'), Decimal('5.9900'), Decimal('81.70'), Decimal('4.2579'), Decimal('4'), Decimal('307.0'), Decimal('21.00'), Decimal('386.75'), Decimal('14.67')]</td>\n", - " <td>20001</td>\n", - " <td>17.5</td>\n", - " </tr>\n", - " <tr>\n", - " <td>19</td>\n", - " <td>[Decimal('1'), Decimal('0.80271'), Decimal('0.00'), Decimal('8.140'), Decimal('0'), Decimal('0.5380'), Decimal('5.4560'), Decimal('36.60'), Decimal('3.7965'), Decimal('4'), Decimal('307.0'), Decimal('21.00'), Decimal('288.99'), Decimal('11.69')]</td>\n", - " <td>20001</td>\n", - " <td>20.2</td>\n", - " </tr>\n", - " <tr>\n", - " <td>20</td>\n", - " <td>[Decimal('1'), Decimal('0.72580'), Decimal('0.00'), Decimal('8.140'), Decimal('0'), Decimal('0.5380'), Decimal('5.7270'), Decimal('69.50'), Decimal('3.7965'), Decimal('4'), Decimal('307.0'), Decimal('21.00'), Decimal('390.95'), Decimal('11.28')]</td>\n", - 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] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "DROP TABLE IF EXISTS lin_housing;\n", - "\n", - "CREATE TABLE lin_housing (id serial, \n", - " x numeric[], \n", - " zipcode int, \n", - " y float8);\n", - "\n", - "INSERT INTO lin_housing(id, x, zipcode, y) VALUES\n", - "(1,ARRAY[1,0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98],94016,24.00),\n", - "(2,ARRAY[1,0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14],94016,21.60),\n", - "(3,ARRAY[1,0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03],94016,34.70),\n", - "(4,ARRAY[1,0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94],94016,33.40),\n", - "(5,ARRAY[1,0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33],94016,36.20),\n", - "(6,ARRAY[1,0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21],94016,28.70),\n", - "(7,ARRAY[1,0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43],94016,22.90),\n", - 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"(18,ARRAY[1,0.78420,0.00,8.140,0,0.5380,5.9900,81.70,4.2579,4,307.0,21.00,386.75,14.67],20001,17.50),\n", - "(19,ARRAY[1,0.80271,0.00,8.140,0,0.5380,5.4560,36.60,3.7965,4,307.0,21.00,288.99,11.69],20001,20.20),\n", - "(20,ARRAY[1,0.72580,0.00,8.140,0,0.5380,5.7270,69.50,3.7965,4,307.0,21.00,390.95,11.28],20001,18.20);\n", - "\n", - "SELECT * FROM lin_housing ORDER BY id;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 13. Regression model\n", - "Now train a regression model using a multilayer perceptron with 2 hidden layers of 25 nodes each." - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "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_regression</th>\n", - " </tr>\n", - " <tr>\n", - " <td></td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[('',)]" - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "DROP TABLE IF EXISTS mlp_regress, mlp_regress_summary, mlp_regress_standardization;\n", - "\n", - "SELECT setseed(0);\n", - "SELECT madlib.mlp_regression(\n", - " 'lin_housing', -- Source table\n", - " 'mlp_regress', -- Desination table\n", - " 'x', -- Input features\n", - " 'y', -- Dependent variable\n", - " ARRAY[25,25], -- Number of units per layer\n", - " 'learning_rate_init=0.001,\n", - " n_iterations=500,\n", - " lambda=0.001,\n", - " tolerance=0', -- Optimizer params\n", - " 'relu', -- Activation function\n", - " NULL, -- Default weight (1)\n", - " FALSE, -- No warm start\n", - " FALSE -- Not verbose\n", - ");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 14. View the regression model" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "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>[0.752109182538, -0.0824025290857, 0.0410059928624, 0.337021944674, 0.65416507111, -0.235970675026, 0.105710990669, 0.0477997938709, -0.25104991458, -0.603068658656, 0.383645101517, 0.401628129242, -0.162802028669, -0.156557103874, 0.2105522042, 0.44411313857, 0.105882403567, 0.267663469827, -0.399241363581, -0.0170604141314, -0.377382699093, -0.299853370639, 0.126338917041, 0.210676270596, -0.070565829362, -0.0757525460553, -0.401460560938, -0.210582542703, 0.383367516914, -0.268075915715, -0.173442916036, 0.264575649181, 0.184456779405, -0.0097165532605, 0.00997431792943, 0.0189488858382, 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<TRUNCATED>
