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Posted to commits@madlib.apache.org by fm...@apache.org on 2018/08/01 20:13:57 UTC
[02/18] madlib-site git commit: update jupyter notebooks for 1dot15
http://git-wip-us.apache.org/repos/asf/madlib-site/blob/acd339f6/community-artifacts/mlp-v3.ipynb
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diff --git a/community-artifacts/mlp-v3.ipynb b/community-artifacts/mlp-v3.ipynb
deleted file mode 100644
index 8c585a6..0000000
--- a/community-artifacts/mlp-v3.ipynb
+++ /dev/null
@@ -1,4584 +0,0 @@
-{
- "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",
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- " <td>5</td>\n",
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- " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')]</td>\n",
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- " <td>[Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')]</td>\n",
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- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
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- " <td>39</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
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- " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')]</td>\n",
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- " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')]</td>\n",
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- " <td>42</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')]</td>\n",
- " <td>Iris_versicolor</td>\n",
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- " <td>43</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
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- " <td>44</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris_versicolor</td>\n",
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- " <td>45</td>\n",
- " <td>[Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
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- " <td>46</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
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- " <tr>\n",
- " <td>47</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
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- " <td>48</td>\n",
- " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
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- " <td>49</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
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- " <td>50</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
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- " <td>51</td>\n",
- " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
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- " <td>52</td>\n",
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- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- "</table>"
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- " (9, [Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (10, [Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (11, [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (12, [Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (13, [Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (14, [Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (15, [Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (16, [Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (17, [Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (18, [Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (19, [Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (20, [Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (21, [Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (22, [Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (23, [Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (24, [Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (25, [Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')], u'Iris_versicolor', 2, 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>"
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- "[('',)]"
- ]
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- "execution_count": 5,
- "metadata": {},
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- "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>"
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- ]
- },
- "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>"
- ],
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- "[(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>"
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- "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",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('4.5'), Decimal('2.3'), 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>8</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('4.4'), Decimal('3.2'), 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>9</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>10</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>11</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>12</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>13</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>14</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>15</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>16</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>17</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>18</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>19</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.9'), Decimal('3.0'), Decimal('4.2'), Decimal('1.5')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>20</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.2'), Decimal('4.0'), Decimal('1.0')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>21</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.1'), Decimal('2.9'), Decimal('4.7'), Decimal('1.4')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>22</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.9'), Decimal('3.6'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>23</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.4'), Decimal('1.4')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>24</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>25</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>26</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.2'), Decimal('2.2'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>27</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.6'), Decimal('2.5'), Decimal('3.9'), Decimal('1.1')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Alaska</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>28</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('5.0'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>29</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('4.4'), Decimal('2.9'), Decimal('1.4'), Decimal('0.2')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>30</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>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>31</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.7'), Decimal('1.5'), Decimal('0.2')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>32</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.4'), Decimal('1.6'), Decimal('0.2')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>33</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.1')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>34</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('4.3'), Decimal('3.0'), Decimal('1.1'), Decimal('0.1')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>35</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('5.8'), Decimal('4.0'), Decimal('1.2'), Decimal('0.2')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>36</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('5.7'), Decimal('4.4'), Decimal('1.5'), Decimal('0.4')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>37</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.9'), Decimal('1.3'), Decimal('0.4')]</td>\n",
- " <td>Iris_setosa</td>\n",
- " <td>1</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>38</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.9'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>39</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.7'), Decimal('2.6'), Decimal('3.5'), Decimal('1.0')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>40</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.8'), Decimal('1.1')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>41</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.4'), Decimal('3.7'), Decimal('1.0')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>42</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('3.9'), Decimal('1.2')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>43</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.0'), Decimal('2.7'), Decimal('5.1'), Decimal('1.6')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>44</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.4'), Decimal('3.0'), Decimal('4.5'), Decimal('1.5')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>45</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.0'), Decimal('3.4'), Decimal('4.5'), Decimal('1.6')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>46</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.7'), Decimal('3.1'), Decimal('4.7'), Decimal('1.5')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>47</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.3'), Decimal('2.3'), Decimal('4.4'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>48</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.6'), Decimal('3.0'), Decimal('4.1'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>49</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.5'), Decimal('4.0'), Decimal('1.3')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>50</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.5'), Decimal('2.6'), Decimal('4.4'), Decimal('1.2')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>51</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('6.1'), Decimal('3.0'), Decimal('4.6'), Decimal('1.4')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <td>52</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.6'), Decimal('4.0'), Decimal('1.2')]</td>\n",
- " <td>Iris_versicolor</td>\n",
- " <td>2</td>\n",
- " <td>Tennessee</td>\n",
- " </tr>\n",
- "</table>"
- ],
- "text/plain": [
- "[(1, u'Iris_setosa', [Decimal('5.0'), Decimal('3.2'), Decimal('1.2'), Decimal('0.2')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (2, u'Iris_setosa', [Decimal('5.5'), Decimal('3.5'), Decimal('1.3'), Decimal('0.2')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (3, u'Iris_setosa', [Decimal('4.9'), Decimal('3.1'), Decimal('1.5'), Decimal('0.1')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (4, u'Iris_setosa', [Decimal('4.4'), Decimal('3.0'), Decimal('1.3'), Decimal('0.2')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (5, u'Iris_setosa', [Decimal('5.1'), Decimal('3.4'), Decimal('1.5'), Decimal('0.2')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (6, u'Iris_setosa', [Decimal('5.0'), Decimal('3.5'), Decimal('1.3'), Decimal('0.3')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (7, u'Iris_setosa', [Decimal('4.5'), Decimal('2.3'), Decimal('1.3'), Decimal('0.3')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (8, u'Iris_setosa', [Decimal('4.4'), Decimal('3.2'), Decimal('1.3'), Decimal('0.2')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (9, u'Iris_setosa', [Decimal('5.0'), Decimal('3.5'), Decimal('1.6'), Decimal('0.6')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (10, u'Iris_setosa', [Decimal('5.1'), Decimal('3.8'), Decimal('1.9'), Decimal('0.4')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (11, u'Iris_setosa', [Decimal('4.8'), Decimal('3.0'), Decimal('1.4'), Decimal('0.3')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (12, u'Iris_setosa', [Decimal('5.1'), Decimal('3.8'), Decimal('1.6'), Decimal('0.2')], u'Iris_setosa', 1, u'Alaska'),\n",
- " (13, u'Iris_versicolor', [Decimal('5.7'), Decimal('2.8'), Decimal('4.5'), Decimal('1.3')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (14, u'Iris_versicolor', [Decimal('6.3'), Decimal('3.3'), Decimal('4.7'), Decimal('1.6')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (15, u'Iris_versicolor', [Decimal('4.9'), Decimal('2.4'), Decimal('3.3'), Decimal('1.0')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (16, u'Iris_versicolor', [Decimal('6.6'), Decimal('2.9'), Decimal('4.6'), Decimal('1.3')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (17, u'Iris_versicolor', [Decimal('5.2'), Decimal('2.7'), Decimal('3.9'), Decimal('1.4')], u'Iris_versicolor', 2, u'Alaska'),\n",
- " (18, u'Iris_versicolor', [Decimal('5.0'), Decimal('2.0'), Decimal('3.5'), Decimal('1.0')], u'Iris_versicolor', 2, u'Alaska'),\n",
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- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "%%sql\n",
- "DROP TABLE IF 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"
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- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>count</th>\n",
- " </tr>\n",
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- " <td>0</td>\n",
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- "execution_count": 10,
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- "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"
- ]
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- "data": {
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- " <th>__id__</th>\n",
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- " </tr>\n",
- " <tr>\n",
- " <td>0</td>\n",
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- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
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- "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"
- ]
- },
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- "data": {
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- "<table>\n",
- " <tr>\n",
- " <th>mlp_classification</th>\n",
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- "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"
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- "<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",
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- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
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- ],
- "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": {
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- " <th>class_text</th>\n",
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- " <td>Iris_setosa</td>\n",
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- " <td>Iris_setosa</td>\n",
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- " <td>Iris_versicolor</td>\n",
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- " <td>Iris_versicolor</td>\n",
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- " <td>Iris_versicolor</td>\n",
- " <td>[Decimal('5.8'), Decimal('2.7'), Decimal('4.1'), Decimal('1.0')]</td>\n",
- " <td>Iris_versicolor</td>\n",
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- " <td>Alaska</td>\n",
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- " <tr>\n",
- " <td>26</td>\n",
- " <td>Iris_versicolo
<TRUNCATED>