<!DOCTYPE html><html><head><title>Using Alternative Algorithm</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="Using Alternative Algorithm"/><link rel="canonical" href="https://docs.prediction.io/templates/classification/add-algorithm/"/><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 src="//cdnjs.cloudflare.com/ajax/libs/html5shiv/3.7.2/html5shiv.min.js"></script><script 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id="page-title"><h1>Using Alternative Algorithm</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#create-a-new-file-randomforestalgorithm-scala">Create a new file RandomForestAlgorithm.scala</a> </li> <li> <a href="#define-the-algorithm-class-and-parameters">Define the algorithm class and parameters</a> </li> <li> <a href="#update-engine-scala">Update Engine.scala</a> </li> <li> <a href="#update-engine-json">Update engine.json</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/templates/classification/add-algorithm.html.md"><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>Using Alternative Algorithm</h1></div></div><div class="content"><p>The classification template uses the Naive Bayes algorithm by default. You can easily add and use other MLlib classification algorithms. The following will demonstrate how to add the <a href="https://spark.apache.org/docs/latest/mllib-ensembles.html">MLlib Random Forests algorithm</a> into the engine.</p><h2 id='create-a-new-file-randomforestalgorithm.scala' class='header-anchors'>Create a new file RandomForestAlgorithm.scala</h2><p>Locate <code>src/main/scala/NaiveBayesAlgorithm.scala</code> under your engine directory, which should be /MyClassification if you are following the <a href="/templates/classification/quickstart/">Classification QuickStart</a>. Copy <code>NaiveBayesAlgorithm.scala</code> and create a new file <code>RandomForestAlgorithm.scala</code>. You will modify this file and follow the instructions below to define a new RandomForestAlgorithm class.</p><h2 id='define-the-algorithm-class-and-parameters' class='header-anchors'>Define the algorithm class and parameters</h2><p>In &#39;RandomForestAlgorithm.scala&#39;, import the MLlib Random Forests algorithm by changing the following lines:</p><p>Original</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2</pre></td><td class="code"><pre><span class="k">import</span> <span class="nn">org.apache.spark.mllib.classification.NaiveBayes</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.classification.NaiveBayesModel</span>
</pre></td></tr></tbody></table> </div> <p>Change to:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2</pre></td><td class="code"><pre><span class="k">import</span> <span class="nn">org.apache.spark.mllib.tree.RandomForest</span> <span class="c1">// CHANGED
</span><span class="k">import</span> <span class="nn">org.apache.spark.mllib.tree.model.RandomForestModel</span> <span class="c1">// CHANGED
</span></pre></td></tr></tbody></table> </div> <p>These are the necessary classes in order to use the MLLib&#39;s Random Forest algporithm.</p><p>Modify the <code>AlgorithmParams</code> class for the Random Forest algorithm:</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
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9</pre></td><td class="code"><pre><span class="c1">// CHANGED
</span><span class="k">case</span> <span class="k">class</span> <span class="nc">RandomForestAlgorithmParams</span><span class="o">(</span>
  <span class="n">numClasses</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
  <span class="n">numTrees</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
  <span class="n">featureSubsetStrategy</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
  <span class="n">impurity</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span>
  <span class="n">maxDepth</span><span class="k">:</span> <span class="kt">Int</span><span class="o">,</span>
  <span class="n">maxBins</span><span class="k">:</span> <span class="kt">Int</span>
<span class="o">)</span> <span class="k">extends</span> <span class="nc">Params</span>
</pre></td></tr></tbody></table> </div> <p>This class defines the parameters of the Random Forest algorithm (which later you can specify the value in engine.json). Please refer to <a href="https://spark.apache.org/docs/latest/mllib-ensembles.html">MLlib documentation</a> for the description and usage of these parameters.</p><p>Modify the <code>NaiveBayesAlgorithm</code> class to <code>RandomForestAlgorithm</code>. The changes are:</p> <ul> <li>The new <code>RandomForestAlgorithmParams</code> class is used as parameter.</li> <li><code>RandomForestModel</code> is used in type parameter. This is the model returned by the Random Forest algorithm.</li> <li>the <code>train()</code> function is modified and it returns the <code>RandomForestModel</code> instead of <code>NaiveBayesModel</code>.</li> <li>the <code>predict()</code> function takes the <code>RandomForestModel</code> as input.</li> </ul> <div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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33</pre></td><td class="code"><pre><span class="c1">// extends P2LAlgorithm because the MLlib's RandomForestModel doesn't
// contain RDD.
</span><span class="k">class</span> <span class="nc">RandomForestAlgorithm</span><span class="o">(</span><span class="k">val</span> <span class="n">ap</span><span class="k">:</span> <span class="kt">RandomForestAlgorithmParams</span><span class="o">)</span> <span class="c1">// CHANGED
</span>  <span class="k">extends</span> <span class="n">P2LAlgorithm</span><span class="o">[</span><span class="kt">PreparedData</span>, <span class="kt">RandomForestModel</span>, <span class="kt">//</span> <span class="kt">CHANGED</span>
  <span class="kt">Query</span>, <span class="kt">PredictedResult</span><span class="o">]</span> <span class="o">{</span>

  <span class="c1">// CHANGED
</span>  <span class="k">def</span> <span class="n">train</span><span class="o">(</span><span class="n">sc</span><span class="k">:</span> <span class="kt">SparkContext</span><span class="o">,</span> <span class="n">data</span><span class="k">:</span> <span class="kt">PreparedData</span><span class="o">)</span><span class="k">:</span> <span class="kt">RandomForestModel</span> <span class="o">=</span> <span class="o">{</span>
    <span class="c1">// CHANGED
</span>    <span class="c1">// Empty categoricalFeaturesInfo indicates all features are continuous.
</span>    <span class="k">val</span> <span class="n">categoricalFeaturesInfo</span> <span class="k">=</span> <span class="nc">Map</span><span class="o">[</span><span class="kt">Int</span>, <span class="kt">Int</span><span class="o">]()</span>
    <span class="nc">RandomForest</span><span class="o">.</span><span class="n">trainClassifier</span><span class="o">(</span>
      <span class="n">data</span><span class="o">.</span><span class="n">labeledPoints</span><span class="o">,</span>
      <span class="n">ap</span><span class="o">.</span><span class="n">numClasses</span><span class="o">,</span>
      <span class="n">categoricalFeaturesInfo</span><span class="o">,</span>
      <span class="n">ap</span><span class="o">.</span><span class="n">numTrees</span><span class="o">,</span>
      <span class="n">ap</span><span class="o">.</span><span class="n">featureSubsetStrategy</span><span class="o">,</span>
      <span class="n">ap</span><span class="o">.</span><span class="n">impurity</span><span class="o">,</span>
      <span class="n">ap</span><span class="o">.</span><span class="n">maxDepth</span><span class="o">,</span>
      <span class="n">ap</span><span class="o">.</span><span class="n">maxBins</span><span class="o">)</span>
  <span class="o">}</span>

  <span class="k">def</span> <span class="n">predict</span><span class="o">(</span>
    <span class="n">model</span><span class="k">:</span> <span class="kt">RandomForestModel</span><span class="o">,</span> <span class="c1">// CHANGED
</span>    <span class="n">query</span><span class="k">:</span> <span class="kt">Query</span><span class="o">)</span><span class="k">:</span> <span class="kt">PredictedResult</span> <span class="o">=</span> <span class="o">{</span>

    <span class="k">val</span> <span class="n">label</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span>
        <span class="n">query</span><span class="o">.</span><span class="n">attr0</span><span class="o">,</span> <span class="n">query</span><span class="o">.</span><span class="n">attr1</span><span class="o">,</span> <span class="n">query</span><span class="o">.</span><span class="n">attr2</span>
    <span class="o">))</span>
    <span class="k">new</span> <span class="nc">PredictedResult</span><span class="o">(</span><span class="n">label</span><span class="o">)</span>
  <span class="o">}</span>

<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p>Note that the MLlib Random Forest algorithm takes the same training data as the Navie Bayes algoithm (ie, RDD[LabeledPoint]) so you don&#39;t need to modify the <code>DataSource</code>, <code>TrainigData</code> and <code>PreparedData</code> classes. If the new algoritm to be added requires different types of training data, then you need to modify these classes accordingly to accomodate your new algorithm.</p><h2 id='update-engine.scala' class='header-anchors'>Update Engine.scala</h2><p>Modify the EngineFactory to add the new algorithm class <code>RandomForestAlgorithm</code> you just defined and give it a name <code>&quot;randomforest&quot;</code>. The name will be used in <code>engne.json</code> to specify which algorithm to use.</p><div class="highlight scala"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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10</pre></td><td class="code"><pre><span class="k">object</span> <span class="nc">ClassificationEngine</span> <span class="k">extends</span> <span class="nc">IEngineFactory</span> <span class="o">{</span>
  <span class="k">def</span> <span class="n">apply</span><span class="o">()</span> <span class="k">=</span> <span class="o">{</span>
    <span class="k">new</span> <span class="nc">Engine</span><span class="o">(</span>
      <span class="n">classOf</span><span class="o">[</span><span class="kt">DataSource</span><span class="o">],</span>
      <span class="n">classOf</span><span class="o">[</span><span class="kt">Preparator</span><span class="o">],</span>
      <span class="nc">Map</span><span class="o">(</span><span class="s">"naive"</span> <span class="o">-&gt;</span> <span class="n">classOf</span><span class="o">[</span><span class="kt">NaiveBayesAlgorithm</span><span class="o">],</span>
        <span class="s">"randomforest"</span> <span class="o">-&gt;</span> <span class="n">classOf</span><span class="o">[</span><span class="kt">RandomForestAlgorithm</span><span class="o">]),</span> <span class="c1">// ADDED
</span>      <span class="n">classOf</span><span class="o">[</span><span class="kt">Serving</span><span class="o">])</span>
  <span class="o">}</span>
<span class="o">}</span>
</pre></td></tr></tbody></table> </div> <p>This engine factory now returns an engine with two algorithms and they are named as <code>&quot;naive&quot;</code> and <code>&quot;randomforest&quot;</code> respectively.</p><h2 id='update-engine.json' class='header-anchors'>Update engine.json</h2><p>In order to use the new algorithm, you need to modify <code>engine.json</code> to specify the name of the algorithm and the parameters.</p><p>Update the engine.json to use <strong>randomforest</strong>:</p><div class="highlight json"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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15</pre></td><td class="code"><pre><span class="err">...</span><span class="w">
</span><span class="s2">"algorithms"</span><span class="err">:</span><span class="w"> </span><span class="p">[</span><span class="w">
  </span><span class="p">{</span><span class="w">
    </span><span class="s2">"name"</span><span class="p">:</span><span class="w"> </span><span class="s2">"randomforest"</span><span class="p">,</span><span class="w">
    </span><span class="s2">"params"</span><span class="p">:</span><span class="w"> </span><span class="p">{</span><span class="w">
      </span><span class="s2">"numClasses"</span><span class="p">:</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w">
      </span><span class="s2">"numTrees"</span><span class="p">:</span><span class="w"> </span><span class="mi">5</span><span class="p">,</span><span class="w">
      </span><span class="s2">"featureSubsetStrategy"</span><span class="p">:</span><span class="w"> </span><span class="s2">"auto"</span><span class="p">,</span><span class="w">
      </span><span class="s2">"impurity"</span><span class="p">:</span><span class="w"> </span><span class="s2">"gini"</span><span class="p">,</span><span class="w">
      </span><span class="s2">"maxDepth"</span><span class="p">:</span><span class="w"> </span><span class="mi">4</span><span class="p">,</span><span class="w">
      </span><span class="s2">"maxBins"</span><span class="p">:</span><span class="w"> </span><span class="mi">100</span><span class="w">
    </span><span class="p">}</span><span class="w">
  </span><span class="p">}</span><span class="w">
</span><span class="p">]</span><span class="w">
</span><span class="err">...</span><span class="w">
</span></pre></td></tr></tbody></table> </div> <p>The engine now uses <strong>MLlib Random Forests algorithm</strong> instead of the default Naive Bayes algorithm. You are ready to build, train and deploy the engine as described in <a href="/templates/classification/quickstart/">quickstart</a>.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
3</pre></td><td class="code"><pre><span class="gp">$ </span>pio build
<span class="gp">$ </span>pio train
<span class="gp">$ </span>pio deploy
</pre></td></tr></tbody></table> </div> <div class="alert-message info"><p>To switch back using Naive Bayes algorithm, simply modify engine.json.</p></div></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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