<!DOCTYPE html><html><head><title>Machine Learning Analytics with IPython Notebook</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Machine Learning Analytics with IPython Notebook"/><link rel="canonical" href="https://docs.prediction.io/datacollection/analytics-ipynb/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet"/><link href="/stylesheets/application-3598c7d7.css" rel="stylesheet" type="text/css"/><script 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hidden-lg"><div id="page-title"><h1>Machine Learning Analytics with IPython Notebook</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#prerequisites">Prerequisites</a> </li> <li> <a href="#preparing-ipython-notebook">Preparing IPython Notebook</a> </li> <li> <a href="#performing-analysis-with-spark-sql">Performing Analysis with Spark SQL</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/datacollection/analytics-ipynb.html.md.erb"><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Machine Learning Analytics with IPython Notebook</h1></div></div><div class="content"><p><a href="http://ipython.org/notebook.html">IPython Notebook</a> is a very powerful interactive computational environment, and with <a href="http://predictionio.incubator.apache.org">Apache PredictionIO (incubating)</a>, <a href="http://spark.apache.org/docs/latest/api/python/">PySpark</a> and <a href="https://spark.apache.org/sql/">Spark SQL</a>, you can easily analyze your collected events when you are developing or tuning your engine.</p><h2 id='prerequisites' class='header-anchors'>Prerequisites</h2><p>Before you begin, please make sure you have the latest stable IPython installed, and that the command <code>ipython</code> can be accessed from your shell&#39;s search path.</p><p><h2 id='export-events-to-apache-parquet' class='header-anchors'>Export Events to Apache Parquet</h2><p>PredictionIO supports exporting your events to <a href="http://parquet.incubator.apache.org/">Apache Parquet</a>, a columnar storage format that allows you to query quickly.</p><p>Let&#39;s export the data we imported in <a href="/templates/recommendation/quickstart/#import-sample-data">Recommendation Engine Template Quick Start</a>, and assume the App ID is 1.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nv">$PIO_HOME</span>/bin/pio <span class="nb">export</span> --appid 1 --output /tmp/movies --format parquet
</pre></td></tr></tbody></table> </div> <p>After the command has finished successfully, you should see something similar to the following.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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 |-- creationTime: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- entityId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- entityType: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- event: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- eventId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- eventTime: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- properties: struct <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |    |-- rating: double <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- targetEntityId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- targetEntityType: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
</pre></td></tr></tbody></table> </div></p><h2 id='preparing-ipython-notebook' class='header-anchors'>Preparing IPython Notebook</h2><p>Launch IPython Notebook with PySpark using the following command, with <code>$SPARK_HOME</code> replaced by the location of Apache Spark.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nv">PYSPARK_DRIVER_PYTHON</span><span class="o">=</span>ipython <span class="nv">PYSPARK_DRIVER_PYTHON_OPTS</span><span class="o">=</span><span class="s2">"notebook --pylab inline"</span> <span class="nv">$SPARK_HOME</span>/bin/pyspark
</pre></td></tr></tbody></table> </div> <p>By default, you should be able to access your IPython Notebook via web browser at <a href="http://localhost:8888">http://localhost:8888</a>.</p><p>Let&#39;s initialize our notebook for the following code in the first cell.</p><div class="highlight python"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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<span class="k">def</span> <span class="nf">rows_to_df</span><span class="p">(</span><span class="n">rows</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">e</span><span class="p">:</span> <span class="n">e</span><span class="o">.</span><span class="n">asDict</span><span class="p">(),</span> <span class="n">rows</span><span class="p">))</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">SQLContext</span>
<span class="n">sqlc</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
<span class="n">rdd</span> <span class="o">=</span> <span class="n">sqlc</span><span class="o">.</span><span class="n">parquetFile</span><span class="p">(</span><span class="s">"/tmp/movies"</span><span class="p">)</span>
<span class="n">rdd</span><span class="o">.</span><span class="n">registerTempTable</span><span class="p">(</span><span class="s">"events"</span><span class="p">)</span>
</pre></td></tr></tbody></table> </div> <p><img alt="Initialization for IPython Notebook" src="/images/datacollection/ipynb-01-004d791e.png"/></p><p><code>rows_to_df(rows)</code> will come in handy when we want to dump the results from Spark SQL using IPython Notebook&#39;s native table rendering.</p><h2 id='performing-analysis-with-spark-sql' class='header-anchors'>Performing Analysis with Spark SQL</h2><p>If all steps above ran successfully, you should have a ready-to-use analytics environment by now. Let&#39;s try a few examples to see if everything is functional.</p><p>In the second cell, put in this piece of code and run it.</p><div class="highlight python"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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5</pre></td><td class="code"><pre><span class="n">summary</span> <span class="o">=</span> <span class="n">sqlc</span><span class="o">.</span><span class="n">sql</span><span class="p">(</span><span class="s">"SELECT "</span>
                   <span class="s">"entityType, event, targetEntityType, COUNT(*) AS c "</span>
                   <span class="s">"FROM events "</span>
                   <span class="s">"GROUP BY entityType, event, targetEntityType"</span><span class="p">)</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
<span class="n">rows_to_df</span><span class="p">(</span><span class="n">summary</span><span class="p">)</span>
</pre></td></tr></tbody></table> </div> <p>You should see the following screen.</p><p><img alt="Summary of Events" src="/images/datacollection/ipynb-02-cd8b12e4.png"/></p><p>We can also plot our data, in the next two cells.</p><div class="highlight python"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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7</pre></td><td class="code"><pre><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="n">count</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">e</span><span class="p">:</span> <span class="n">e</span><span class="o">.</span><span class="n">c</span><span class="p">,</span> <span class="n">summary</span><span class="p">)</span>
<span class="n">event</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">e</span><span class="p">:</span> <span class="s">"</span><span class="si">%</span><span class="s">s (</span><span class="si">%</span><span class="s">d)"</span> <span class="o">%</span> <span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">event</span><span class="p">,</span> <span class="n">e</span><span class="o">.</span><span class="n">c</span><span class="p">),</span> <span class="n">summary</span><span class="p">)</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s">'gold'</span><span class="p">,</span> <span class="s">'lightskyblue'</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">event</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span> <span class="n">startangle</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span> <span class="n">autopct</span><span class="o">=</span><span class="s">"</span><span class="si">%1.1</span><span class="s">f</span><span class="si">%%</span><span class="s">"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s">'equal'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></td></tr></tbody></table> </div> <p><img alt="Summary in Pie Chart" src="/images/datacollection/ipynb-03-28f3aa3d.png"/></p><div class="highlight python"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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                   <span class="s">"FROM events "</span>
                   <span class="s">"WHERE properties.rating IS NOT NULL "</span>
                   <span class="s">"GROUP BY properties.rating "</span>
                   <span class="s">"ORDER BY r"</span><span class="p">)</span><span class="o">.</span><span class="n">collect</span><span class="p">()</span>
<span class="n">count</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">e</span><span class="p">:</span> <span class="n">e</span><span class="o">.</span><span class="n">c</span><span class="p">,</span> <span class="n">ratings</span><span class="p">)</span>
<span class="n">rating</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">e</span><span class="p">:</span> <span class="s">"</span><span class="si">%</span><span class="s">s (</span><span class="si">%</span><span class="s">d)"</span> <span class="o">%</span> <span class="p">(</span><span class="n">e</span><span class="o">.</span><span class="n">r</span><span class="p">,</span> <span class="n">e</span><span class="o">.</span><span class="n">c</span><span class="p">),</span> <span class="n">ratings</span><span class="p">)</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s">'yellowgreen'</span><span class="p">,</span> <span class="s">'plum'</span><span class="p">,</span> <span class="s">'gold'</span><span class="p">,</span> <span class="s">'lightskyblue'</span><span class="p">,</span> <span class="s">'lightcoral'</span><span class="p">]</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">rating</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors</span><span class="p">,</span> <span class="n">startangle</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span>
        <span class="n">autopct</span><span class="o">=</span><span class="s">"</span><span class="si">%1.1</span><span class="s">f</span><span class="si">%%</span><span class="s">"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s">'equal'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></td></tr></tbody></table> </div> <p><img alt="Breakdown of Ratings" src="/images/datacollection/ipynb-04-797d73f1.png"/></p><p>Happy analyzing!</p></div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//docs.prediction.io/install/" target="blank">Download</a></li><li><a href="//docs.prediction.io/" target="blank">Docs</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">GitHub</a></li><li><a href="mailto:user-subscribe@predictionio.incubator.apache.org" target="blank">Subscribe to User Mailing List</a></li><li><a href="//stackoverflow.com/questions/tagged/predictionio" target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.incubator.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">Source Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:dev-subscribe@predictionio.incubator.apache.org" target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div></div><div id="footer-bottom"><div class="container"><div class="row"><div class="col-md-12"><div id="footer-logo-wrapper"><img alt="PredictionIO" src="/images/logos/logo-white-d1e9c6e6.png"/></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/incubator-predictionio" data-style="mega" data-count-href="/apache/incubator-predictionio/stargazers" data-count-api="/repos/apache/incubator-predictionio#stargazers_count" data-count-aria-label="# stargazers on GitHub" aria-label="Star apache/incubator-predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/incubator-predictionio/fork" data-icon="octicon-git-branch" data-style="mega" data-count-href="/apache/incubator-predictionio/network" data-count-api="/repos/apache/incubator-predictionio#forks_count" data-count-aria-label="# forks on GitHub" aria-label="Fork apache/incubator-predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js"></script><a href="//www.facebook.com/predictionio" target="blank"><img alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> <a href="//twitter.com/predictionio" target="blank"><img alt="PredictionIO on Facebook" src="/images/icons/facebook-5c57939c.png"/></a> </div></div></div></div></div></footer></div><script>(function(w,d,t,u,n,s,e){w['SwiftypeObject']=n;w[n]=w[n]||function(){
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