Repository: incubator-madlib-site Updated Branches: refs/heads/asf-site d7cb76fa4 -> eaa6ce9d3
update K-means notebook with new array unnest function Project: http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/repo Commit: http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/commit/eaa6ce9d Tree: http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/tree/eaa6ce9d Diff: http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/diff/eaa6ce9d Branch: refs/heads/asf-site Commit: eaa6ce9d316d985bb3fc2ec342c1e0c1c69cf552 Parents: d7cb76f Author: Frank McQuillan <fmcquil...@pivotal.io> Authored: Thu Apr 27 15:20:36 2017 -0700 Committer: Frank McQuillan <fmcquil...@pivotal.io> Committed: Thu Apr 27 15:20:36 2017 -0700 ---------------------------------------------------------------------- community-artifacts/Kmeans-v1.ipynb | 977 -------------------------- community-artifacts/Kmeans-v2.ipynb | 1102 ++++++++++++++++++++++++++++++ 2 files changed, 1102 insertions(+), 977 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/eaa6ce9d/community-artifacts/Kmeans-v1.ipynb ---------------------------------------------------------------------- diff --git a/community-artifacts/Kmeans-v1.ipynb b/community-artifacts/Kmeans-v1.ipynb deleted file mode 100644 index 1706667..0000000 --- a/community-artifacts/Kmeans-v1.ipynb +++ /dev/null @@ -1,977 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# K-means (MADlib v1.10+)\n", - "Demonstrates k-means including new array input in MADlib v1.10" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The sql extension is already loaded. To reload it, use:\n", - " %reload_ext sql\n" - ] - } - ], - "source": [ - "%load_ext sql" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "u'Connected: gpdbchina@madlib'" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%sql postgresql://gpdbchina@10.194.10.68:55000/madlib\n", - "#%sql postgresql://fmcquillan@localhost:5432/madlib\n", - "#%sql postgresql://gpadmin@54.197.30.46:10432/gpadmin" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": false - }, - "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: 1.10.0-dev, git revision: rel/v1.9.1-47-g2d5a5ed, cmake configuration time: Tue Feb 7 19:45:19 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: 1.10.0-dev, git revision: rel/v1.9.1-47-g2d5a5ed, cmake configuration time: Tue Feb 7 19:45:19 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": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%sql select madlib.version();\n", - "#%sql select version();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Prepare some input data:" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done.\n", - "Done.\n", - "10 rows affected.\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "<table>\n", - " <tr>\n", - " <th>pid</th>\n", - " <th>points</th>\n", - " </tr>\n", - " <tr>\n", - " <td>1</td>\n", - " <td>[14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>2</td>\n", - " <td>[13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>3</td>\n", - " <td>[13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>4</td>\n", - " <td>[14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>5</td>\n", - " <td>[13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>6</td>\n", - " <td>[14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>7</td>\n", - " <td>[14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>8</td>\n", - " <td>[14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>9</td>\n", - " <td>[14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]</td>\n", - " </tr>\n", - " <tr>\n", - " <td>10</td>\n", - " <td>[13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0]</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[(1, [14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]),\n", - " (2, [13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]),\n", - " (3, [13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]),\n", - " (4, [14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]),\n", - " (5, [13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]),\n", - " (6, [14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]),\n", - " (7, [14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]),\n", - " (8, [14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]),\n", - " (9, [14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]),\n", - " (10, [13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0])]" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "DROP TABLE IF EXISTS km_sample;\n", - "\n", - "CREATE TABLE km_sample(pid int, points double precision[]);\n", - "\n", - "INSERT INTO km_sample VALUES\n", - "(1, '{14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.0600, 0.2800, 2.29, 5.64, 1.04, 3.92, 1065}'),\n", - "(2, '{13.2, 1.78, 2.14, 11.2, 1, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050}'),\n", - "(3, '{13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185}'),\n", - "(4, '{14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480}'),\n", - "(5, '{13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735}'),\n", - "(6, '{14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450}'),\n", - "(7, '{14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290}'),\n", - "(8, '{14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295}'),\n", - "(9, '{14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045}'),\n", - "(10, '{13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.8500, 7.2199, 1.01, 3.55, 1045}');\n", - "\n", - "SELECT * FROM km_sample ORDER BY pid;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Run k-means clustering using kmeans++ with centroid seeding:" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "<table>\n", - " <tr>\n", - " <th>centroids</th>\n", - " <th>cluster_variance</th>\n", - " <th>objective_fn</th>\n", - " <th>frac_reassigned</th>\n", - " <th>num_iterations</th>\n", - " </tr>\n", - " <tr>\n", - " <td>[[14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0], [13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0]]</td>\n", - " <td>[60672.638245208, 90512.324426408]</td>\n", - " <td>151184.962672</td>\n", - " <td>0.0</td>\n", - " <td>2</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[([[14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0], [13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0]], [60672.638245208, 90512.324426408], 151184.962671616, 0.0, 2)]" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM madlib.kmeanspp( 'km_sample', -- Table of source data\n", - " 'points', -- Column containing point co-ordinates \n", - " 2, -- Number of centroids to calculate\n", - " 'madlib.squared_dist_norm2', -- Distance function\n", - " 'madlib.avg', -- Aggregate function\n", - " 20, -- Number of iterations\n", - " 0.001 -- Fraction of centroids reassigned to keep iterating \n", - " );" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Calculate the simplified silhouette coefficient:" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "<table>\n", - " <tr>\n", - " <th>simple_silhouette</th>\n", - " </tr>\n", - " <tr>\n", - " <td>0.707360426139</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[(0.707360426138584,)]" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "SELECT * FROM madlib.simple_silhouette( 'km_sample',\n", - " 'points',\n", - " (SELECT centroids FROM\n", - " madlib.kmeanspp('km_sample',\n", - " 'points',\n", - " 2,\n", - " 'madlib.squared_dist_norm2',\n", - " 'madlib.avg',\n", - " 20,\n", - " 0.001)),\n", - " 'madlib.dist_norm2'\n", - " );" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Find the cluster assignment for each point:" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done.\n", - "1 rows affected.\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "<table>\n", - " <tr>\n", - " <th>pid</th>\n", - " <th>points</th>\n", - " <th>cluster_id</th>\n", - " </tr>\n", - " <tr>\n", - " <td>1</td>\n", - " <td>[14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]</td>\n", - " <td>0</td>\n", - " </tr>\n", - " <tr>\n", - " <td>2</td>\n", - " <td>[13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]</td>\n", - " <td>0</td>\n", - " </tr>\n", - " <tr>\n", - " <td>3</td>\n", - " <td>[13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <td>4</td>\n", - " <td>[14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <td>5</td>\n", - " <td>[13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]</td>\n", - " <td>0</td>\n", - " </tr>\n", - " <tr>\n", - " <td>6</td>\n", - " <td>[14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <td>7</td>\n", - " <td>[14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <td>8</td>\n", - " <td>[14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]</td>\n", - " <td>1</td>\n", - " </tr>\n", - " <tr>\n", - " <td>9</td>\n", - " <td>[14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]</td>\n", - " <td>0</td>\n", - " </tr>\n", - " <tr>\n", - " <td>10</td>\n", - " <td>[13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0]</td>\n", - " <td>0</td>\n", - " </tr>\n", - "</table>" - ], - "text/plain": [ - "[(1, [14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0], 0),\n", - " (2, [13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0], 0),\n", - " (3, [13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0], 1),\n", - " (4, [14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0], 1),\n", - " (5, [13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0], 0),\n", - " (6, [14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0], 1),\n", - " (7, [14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0], 1),\n", - " (8, [14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0], 1),\n", - " (9, [14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0], 0),\n", - " (10, [13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0], 0)]" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "DROP TABLE IF EXISTS km_result;\n", - "\n", - "-- Run kmeans algorithm\n", - "CREATE TABLE km_result AS\n", - "SELECT * FROM madlib.kmeanspp('km_sample', 'points', 2,\n", - " 'madlib.squared_dist_norm2',\n", - " 'madlib.avg', 20, 0.001); \n", - "\n", - "-- Get point assignment\n", - "SELECT data.*, (madlib.closest_column(centroids, points)).column_id as cluster_id\n", - "FROM km_sample as data, km_result\n", - "ORDER BY data.pid;" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5.0 Array input" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done.\n", - "Done.\n", - "10 rows affected.\n", - "10 rows affected.\n" - ] - }, - { - "data": { - "text/html": [ - "<table>\n", - " <tr>\n", - " <th>pid</th>\n", - 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" p13 float);\n", - "\n", - "\n", - "INSERT INTO km_arrayin VALUES\n", - "(1, 14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.0600, 0.2800, 2.29, 5.64, 1.04, 3.92, 1065),\n", - "(2, 13.2, 1.78, 2.14, 11.2, 1, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050),\n", - "(3, 13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185),\n", - "(4, 14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480),\n", - "(5, 13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735),\n", - "(6, 14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450),\n", - "(7, 14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290),\n", - "(8, 14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295),\n", - "(9, 14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045),\n", - "(10, 13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.8500, 7.2199, 1.01, 3.55, 1045);\n", - "\n", - "SELECT * FROM km_arrayin ORDER BY pid;" - 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" (10, 13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0, 1)]" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%%sql\n", - "DROP TABLE IF EXISTS km_result;\n", - "\n", - "-- Run kmeans algorithm\n", - "CREATE TABLE km_result AS\n", - "SELECT * FROM madlib.kmeans_random('km_arrayin', \n", - " 'ARRAY[p1, p2, p3, p4, p5, p6, \n", - " p7, p8, p9, p10, p11, p12, p13]', \n", - " 2,\n", - " 'madlib.squared_dist_norm2',\n", - " 'madlib.avg', \n", - " 20, \n", - " 0.001);\n", - "\n", - "-- Get point assignment\n", - "SELECT data.*, (madlib.closest_column(centroids, \n", - " ARRAY[p1, p2, p3, p4, p5, p6, \n", - " p7, p8, p9, p10, p11, p12, p13])).column_id as cluster_id\n", - "FROM km_arrayin as data, km_result\n", - "ORDER BY data.pid;" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} http://git-wip-us.apache.org/repos/asf/incubator-madlib-site/blob/eaa6ce9d/community-artifacts/Kmeans-v2.ipynb ---------------------------------------------------------------------- diff --git a/community-artifacts/Kmeans-v2.ipynb b/community-artifacts/Kmeans-v2.ipynb new file mode 100644 index 0000000..6d3e40e --- /dev/null +++ b/community-artifacts/Kmeans-v2.ipynb @@ -0,0 +1,1102 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# K-means (MADlib v1.11+)\n", + "Demonstrates k-means including new array input in 1.10 and new array unnest function in 1.11." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The sql extension is already loaded. To reload it, use:\n", + " %reload_ext sql\n" + ] + } + ], + "source": [ + "%load_ext sql" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "u'Connected: gpdbchina@madlib'" + ] + }, + "execution_count": 31, + "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": 32, + "metadata": { + "collapsed": false + }, + "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: 1.11-dev, git revision: rel/v1.10.0-26-ga3d54be, cmake configuration time: Thu Apr 27 01:01:30 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: 1.11-dev, git revision: rel/v1.10.0-26-ga3d54be, cmake configuration time: Thu Apr 27 01:01:30 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": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%sql select madlib.version();\n", + "#%sql select version();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Prepare some input data:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "Done.\n", + "10 rows affected.\n", + "10 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>pid</th>\n", + " <th>points</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>[14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>[13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>3</td>\n", + " <td>[13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>4</td>\n", + " <td>[14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>5</td>\n", + " <td>[13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>6</td>\n", + " <td>[14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>7</td>\n", + " <td>[14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>8</td>\n", + " <td>[14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>9</td>\n", + " <td>[14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>10</td>\n", + " <td>[13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0]</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(1, [14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]),\n", + " (2, [13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]),\n", + " (3, [13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]),\n", + " (4, [14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]),\n", + " (5, [13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]),\n", + " (6, [14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]),\n", + " (7, [14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]),\n", + " (8, [14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]),\n", + " (9, [14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]),\n", + " (10, [13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0])]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "DROP TABLE IF EXISTS km_sample;\n", + "\n", + "CREATE TABLE km_sample(pid int, points double precision[]);\n", + "\n", + "INSERT INTO km_sample VALUES\n", + "(1, '{14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.0600, 0.2800, 2.29, 5.64, 1.04, 3.92, 1065}'),\n", + "(2, '{13.2, 1.78, 2.14, 11.2, 1, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050}'),\n", + "(3, '{13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185}'),\n", + "(4, '{14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480}'),\n", + "(5, '{13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735}'),\n", + "(6, '{14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450}'),\n", + "(7, '{14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290}'),\n", + "(8, '{14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295}'),\n", + "(9, '{14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045}'),\n", + "(10, '{13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.8500, 7.2199, 1.01, 3.55, 1045}');\n", + "\n", + "SELECT * FROM km_sample ORDER BY pid;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Run k-means clustering using kmeans++ with centroid seeding:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "1 rows affected.\n", + "1 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>centroids</th>\n", + " <th>cluster_variance</th>\n", + " <th>objective_fn</th>\n", + " <th>frac_reassigned</th>\n", + " <th>num_iterations</th>\n", + " </tr>\n", + " <tr>\n", + " <td>[[13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0], [14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0]]</td>\n", + " <td>[90512.324426408, 60672.638245208]</td>\n", + " <td>151184.962672</td>\n", + " <td>0.0</td>\n", + " <td>2</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[([[13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0], [14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0]], [90512.324426408, 60672.638245208], 151184.962671616, 0.0, 2)]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "DROP TABLE IF EXISTS km_result;\n", + "\n", + "-- Run kmeans algorithm\n", + "CREATE TABLE km_result AS\n", + "SELECT * FROM madlib.kmeanspp( 'km_sample', -- Table of source data\n", + " 'points', -- Column containing point co-ordinates \n", + " 2, -- Number of centroids to calculate\n", + " 'madlib.squared_dist_norm2', -- Distance function\n", + " 'madlib.avg', -- Aggregate function\n", + " 20, -- Number of iterations\n", + " 0.001 -- Fraction of centroids reassigned to keep iterating \n", + " );\n", + "\n", + "SELECT * FROM km_result;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Calculate the simplified silhouette coefficient:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>simple_silhouette</th>\n", + " </tr>\n", + " <tr>\n", + " <td>0.689788048829</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(0.68978804882941,)]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "SELECT * FROM madlib.simple_silhouette( 'km_sample', -- Input points table\n", + " 'points', -- Column containing points\n", + " (SELECT centroids FROM km_result), -- Centroids\n", + " 'madlib.dist_norm2' -- Distance function\n", + " );" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Find the cluster assignment for each point:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>pid</th>\n", + " <th>points</th>\n", + " <th>cluster_id</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>[14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0]</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>[13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0]</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>3</td>\n", + " <td>[13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0]</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <td>4</td>\n", + " <td>[14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0]</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <td>5</td>\n", + " <td>[13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0]</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>6</td>\n", + " <td>[14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0]</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <td>7</td>\n", + " <td>[14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0]</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <td>8</td>\n", + " <td>[14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0]</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <td>9</td>\n", + " <td>[14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0]</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>10</td>\n", + " <td>[13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0]</td>\n", + " <td>0</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(1, [14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0], 0),\n", + " (2, [13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0], 0),\n", + " (3, [13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0], 1),\n", + " (4, [14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0], 1),\n", + " (5, [13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0], 0),\n", + " (6, [14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0], 1),\n", + " (7, [14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0], 1),\n", + " (8, [14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0], 1),\n", + " (9, [14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0], 0),\n", + " (10, [13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0], 0)]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "SELECT data.*, (madlib.closest_column(centroids, points)).column_id as cluster_id\n", + "FROM km_sample as data, km_result\n", + "ORDER BY data.pid;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Unnest the cluster centroids 2-D array to get a set of 1-D centroid arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "2 rows affected.\n", + "2 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>unnest_row_id</th>\n", + " <th>unnest_result</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>[13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0]</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>[14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0]</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(1, [13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0]),\n", + " (2, [14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0])]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "DROP TABLE IF EXISTS km_centroids_unnest;\n", + "\n", + "-- Run unnest function\n", + "CREATE TABLE km_centroids_unnest AS\n", + "SELECT (madlib.array_unnest_2d_to_1d(centroids)).*\n", + "FROM km_result;\n", + "\n", + "SELECT * FROM km_centroids_unnest ORDER BY 1;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the ID column returned by array_unnest_2d_to_1d() is not the same as the cluster ID assigned by k-means. See below to display the cluster IDs." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Display cluster ID\n", + "Create cluster IDs for 1-D centroid arrays so that cluster ID for any centroid can be matched to the cluster assignment for the data points:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>unnest_row_id</th>\n", + " <th>unnest_result</th>\n", + " <th>cluster_id</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>[13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0]</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>[14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0]</td>\n", + " <td>1</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(1, [13.872, 1.814, 2.376, 15.56, 88.2, 2.806, 2.928, 0.288, 1.844, 5.35198, 1.044, 3.348, 988.0], 0),\n", + " (2, [14.036, 2.018, 2.536, 16.56, 108.6, 3.004, 3.03, 0.298, 2.038, 6.10598, 1.004, 3.326, 1340.0], 1)]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "SELECT cent.*, (madlib.closest_column(centroids, unnest_result)).column_id as cluster_id\n", + "FROM km_centroids_unnest as cent, km_result\n", + "ORDER BY cent.unnest_row_id;" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Array input" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "Done.\n", + "10 rows affected.\n", + "10 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>pid</th>\n", + " <th>p1</th>\n", + " <th>p2</th>\n", + " <th>p3</th>\n", + " <th>p4</th>\n", + " <th>p5</th>\n", + " <th>p6</th>\n", + " <th>p7</th>\n", + " <th>p8</th>\n", + " <th>p9</th>\n", + " <th>p10</th>\n", + " <th>p11</th>\n", + " <th>p12</th>\n", + " <th>p13</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>14.23</td>\n", + " <td>1.71</td>\n", + " <td>2.43</td>\n", + " <td>15.6</td>\n", + " <td>127.0</td>\n", + " <td>2.8</td>\n", + " <td>3.06</td>\n", + " <td>0.28</td>\n", + " <td>2.29</td>\n", + " <td>5.64</td>\n", + " <td>1.04</td>\n", + " <td>3.92</td>\n", + " <td>1065.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>13.2</td>\n", + " <td>1.78</td>\n", + " <td>2.14</td>\n", + " <td>11.2</td>\n", + " <td>1.0</td>\n", + " <td>2.65</td>\n", + " <td>2.76</td>\n", + " <td>0.26</td>\n", + " <td>1.28</td>\n", + " <td>4.38</td>\n", + " <td>1.05</td>\n", + " <td>3.49</td>\n", + " <td>1050.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>3</td>\n", + " <td>13.16</td>\n", + " <td>2.36</td>\n", + " <td>2.67</td>\n", + " <td>18.6</td>\n", + " <td>101.0</td>\n", + " <td>2.8</td>\n", + " <td>3.24</td>\n", + " <td>0.3</td>\n", + " <td>2.81</td>\n", + " <td>5.6799</td>\n", + " <td>1.03</td>\n", + " <td>3.17</td>\n", + " <td>1185.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>4</td>\n", + " <td>14.37</td>\n", + " <td>1.95</td>\n", + " <td>2.5</td>\n", + " <td>16.8</td>\n", + " <td>113.0</td>\n", + " <td>3.85</td>\n", + " <td>3.49</td>\n", + " <td>0.24</td>\n", + " <td>2.18</td>\n", + " <td>7.8</td>\n", + " <td>0.86</td>\n", + " <td>3.45</td>\n", + " <td>1480.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>5</td>\n", + " <td>13.24</td>\n", + " <td>2.59</td>\n", + " <td>2.87</td>\n", + " <td>21.0</td>\n", + " <td>118.0</td>\n", + " <td>2.8</td>\n", + " <td>2.69</td>\n", + " <td>0.39</td>\n", + " <td>1.82</td>\n", + " <td>4.32</td>\n", + " <td>1.04</td>\n", + " <td>2.93</td>\n", + " <td>735.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>6</td>\n", + " <td>14.2</td>\n", + " <td>1.76</td>\n", + " <td>2.45</td>\n", + " <td>15.2</td>\n", + " <td>112.0</td>\n", + " <td>3.27</td>\n", + " <td>3.39</td>\n", + " <td>0.34</td>\n", + " <td>1.97</td>\n", + " <td>6.75</td>\n", + " <td>1.05</td>\n", + " <td>2.85</td>\n", + " <td>1450.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>7</td>\n", + " <td>14.39</td>\n", + " <td>1.87</td>\n", + " <td>2.45</td>\n", + " <td>14.6</td>\n", + " <td>96.0</td>\n", + " <td>2.5</td>\n", + " <td>2.52</td>\n", + " <td>0.3</td>\n", + " <td>1.98</td>\n", + " <td>5.25</td>\n", + " <td>1.02</td>\n", + " <td>3.58</td>\n", + " <td>1290.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>8</td>\n", + " <td>14.06</td>\n", + " <td>2.15</td>\n", + " <td>2.61</td>\n", + " <td>17.6</td>\n", + " <td>121.0</td>\n", + " <td>2.6</td>\n", + " <td>2.51</td>\n", + " <td>0.31</td>\n", + " <td>1.25</td>\n", + " <td>5.05</td>\n", + " <td>1.06</td>\n", + " <td>3.58</td>\n", + " <td>1295.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>9</td>\n", + " <td>14.83</td>\n", + " <td>1.64</td>\n", + " <td>2.17</td>\n", + " <td>14.0</td>\n", + " <td>97.0</td>\n", + " <td>2.8</td>\n", + " <td>2.98</td>\n", + " <td>0.29</td>\n", + " <td>1.98</td>\n", + " <td>5.2</td>\n", + " <td>1.08</td>\n", + " <td>2.85</td>\n", + " <td>1045.0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>10</td>\n", + " <td>13.86</td>\n", + " <td>1.35</td>\n", + " <td>2.27</td>\n", + " <td>16.0</td>\n", + " <td>98.0</td>\n", + " <td>2.98</td>\n", + " <td>3.15</td>\n", + " <td>0.22</td>\n", + " <td>1.85</td>\n", + " <td>7.2199</td>\n", + " <td>1.01</td>\n", + " <td>3.55</td>\n", + " <td>1045.0</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(1, 14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0),\n", + " (2, 13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0),\n", + " (3, 13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0),\n", + " (4, 14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0),\n", + " (5, 13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0),\n", + " (6, 14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0),\n", + " (7, 14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0),\n", + " (8, 14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0),\n", + " (9, 14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0),\n", + " (10, 13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0)]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "DROP TABLE IF EXISTS km_arrayin CASCADE;\n", + "\n", + "CREATE TABLE km_arrayin(pid int, \n", + " p1 float, \n", + " p2 float, \n", + " p3 float,\n", + " p4 float, \n", + " p5 float, \n", + " p6 float,\n", + " p7 float, \n", + " p8 float, \n", + " p9 float,\n", + " p10 float, \n", + " p11 float, \n", + " p12 float,\n", + " p13 float);\n", + "\n", + "INSERT INTO km_arrayin VALUES\n", + "(1, 14.23, 1.71, 2.43, 15.6, 127, 2.8, 3.0600, 0.2800, 2.29, 5.64, 1.04, 3.92, 1065),\n", + "(2, 13.2, 1.78, 2.14, 11.2, 1, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050),\n", + "(3, 13.16, 2.36, 2.67, 18.6, 101, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185),\n", + "(4, 14.37, 1.95, 2.5, 16.8, 113, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480),\n", + "(5, 13.24, 2.59, 2.87, 21, 118, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735),\n", + "(6, 14.2, 1.76, 2.45, 15.2, 112, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450),\n", + "(7, 14.39, 1.87, 2.45, 14.6, 96, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290),\n", + "(8, 14.06, 2.15, 2.61, 17.6, 121, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295),\n", + "(9, 14.83, 1.64, 2.17, 14, 97, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045),\n", + "(10, 13.86, 1.35, 2.27, 16, 98, 2.98, 3.15, 0.22, 1.8500, 7.2199, 1.01, 3.55, 1045);\n", + "\n", + "SELECT * FROM km_arrayin ORDER BY pid;" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Done.\n", + "1 rows affected.\n", + "10 rows affected.\n" + ] + }, + { + "data": { + "text/html": [ + "<table>\n", + " <tr>\n", + " <th>pid</th>\n", + " <th>p1</th>\n", + " <th>p2</th>\n", + " <th>p3</th>\n", + " <th>p4</th>\n", + " <th>p5</th>\n", + " <th>p6</th>\n", + " <th>p7</th>\n", + " <th>p8</th>\n", + " <th>p9</th>\n", + " <th>p10</th>\n", + " <th>p11</th>\n", + " <th>p12</th>\n", + " <th>p13</th>\n", + " <th>cluster_id</th>\n", + " </tr>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>14.23</td>\n", + " <td>1.71</td>\n", + " <td>2.43</td>\n", + " <td>15.6</td>\n", + " <td>127.0</td>\n", + " <td>2.8</td>\n", + " <td>3.06</td>\n", + " <td>0.28</td>\n", + " <td>2.29</td>\n", + " <td>5.64</td>\n", + " <td>1.04</td>\n", + " <td>3.92</td>\n", + " <td>1065.0</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>13.2</td>\n", + " <td>1.78</td>\n", + " <td>2.14</td>\n", + " <td>11.2</td>\n", + " <td>1.0</td>\n", + " <td>2.65</td>\n", + " <td>2.76</td>\n", + " <td>0.26</td>\n", + " <td>1.28</td>\n", + " <td>4.38</td>\n", + " <td>1.05</td>\n", + " <td>3.49</td>\n", + " <td>1050.0</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <td>3</td>\n", + " <td>13.16</td>\n", + " <td>2.36</td>\n", + " <td>2.67</td>\n", + " <td>18.6</td>\n", + " <td>101.0</td>\n", + " <td>2.8</td>\n", + " <td>3.24</td>\n", + " <td>0.3</td>\n", + " 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<td>3.27</td>\n", + " <td>3.39</td>\n", + " <td>0.34</td>\n", + " <td>1.97</td>\n", + " <td>6.75</td>\n", + " <td>1.05</td>\n", + " <td>2.85</td>\n", + " <td>1450.0</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>7</td>\n", + " <td>14.39</td>\n", + " <td>1.87</td>\n", + " <td>2.45</td>\n", + " <td>14.6</td>\n", + " <td>96.0</td>\n", + " <td>2.5</td>\n", + " <td>2.52</td>\n", + " <td>0.3</td>\n", + " <td>1.98</td>\n", + " <td>5.25</td>\n", + " <td>1.02</td>\n", + " <td>3.58</td>\n", + " <td>1290.0</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>8</td>\n", + " <td>14.06</td>\n", + " <td>2.15</td>\n", + " <td>2.61</td>\n", + " <td>17.6</td>\n", + " <td>121.0</td>\n", + " <td>2.6</td>\n", + " <td>2.51</td>\n", + " <td>0.31</td>\n", + " <td>1.25</td>\n", + " <td>5.05</td>\n", + " <td>1.06</td>\n", + " <td>3.58</td>\n", + " <td>1295.0</td>\n", + " <td>0</td>\n", + " </tr>\n", + " <tr>\n", + " <td>9</td>\n", + " <td>14.83</td>\n", + " <td>1.64</td>\n", + " <td>2.17</td>\n", + " <td>14.0</td>\n", + " <td>97.0</td>\n", + " <td>2.8</td>\n", + " <td>2.98</td>\n", + " <td>0.29</td>\n", + " <td>1.98</td>\n", + " <td>5.2</td>\n", + " <td>1.08</td>\n", + " <td>2.85</td>\n", + " <td>1045.0</td>\n", + " <td>1</td>\n", + " </tr>\n", + " <tr>\n", + " <td>10</td>\n", + " <td>13.86</td>\n", + " <td>1.35</td>\n", + " <td>2.27</td>\n", + " <td>16.0</td>\n", + " <td>98.0</td>\n", + " <td>2.98</td>\n", + " <td>3.15</td>\n", + " <td>0.22</td>\n", + " <td>1.85</td>\n", + " <td>7.2199</td>\n", + " <td>1.01</td>\n", + " <td>3.55</td>\n", + " <td>1045.0</td>\n", + " <td>1</td>\n", + " </tr>\n", + "</table>" + ], + "text/plain": [ + "[(1, 14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0, 1),\n", + " (2, 13.2, 1.78, 2.14, 11.2, 1.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.49, 1050.0, 1),\n", + " (3, 13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.6799, 1.03, 3.17, 1185.0, 1),\n", + " (4, 14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0, 0),\n", + " (5, 13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0, 1),\n", + " (6, 14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0, 0),\n", + " (7, 14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0, 0),\n", + " (8, 14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0, 0),\n", + " (9, 14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0, 1),\n", + " (10, 13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.2199, 1.01, 3.55, 1045.0, 1)]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%sql\n", + "DROP TABLE IF EXISTS km_result;\n", + "\n", + "-- Run kmeans algorithm\n", + "CREATE TABLE km_result AS\n", + "SELECT * FROM madlib.kmeans_random('km_arrayin', \n", + " 'ARRAY[p1, p2, p3, p4, p5, p6, \n", + " p7, p8, p9, p10, p11, p12, p13]', \n", + " 2,\n", + " 'madlib.squared_dist_norm2',\n", + " 'madlib.avg', \n", + " 20, \n", + " 0.001);\n", + "\n", + "-- Get point assignment\n", + "SELECT data.*, (madlib.closest_column(centroids, \n", + " ARRAY[p1, p2, p3, p4, p5, p6, \n", + " p7, p8, p9, p10, p11, p12, p13])).column_id as cluster_id\n", + "FROM km_arrayin as data, km_result\n", + "ORDER BY data.pid;" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}
