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author | Patrick Wendell <pwendell@apache.org> | 2015-06-11 15:32:59 +0000 |
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committer | Patrick Wendell <pwendell@apache.org> | 2015-06-11 15:32:59 +0000 |
commit | 840d9f3df35e66c0032f7ac0c284ae4675a4f818 (patch) | |
tree | 9742b1bd3aa597e8f2c845959d42eb91170177a7 /site/docs/1.4.0/ml-ensembles.html | |
parent | 303c247e4604098518acaa2d8ebe63fc891706f0 (diff) | |
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Adding release 1.4.0
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Ensembles</h1> + + + <p><strong>Table of Contents</strong></p> + +<ul id="markdown-toc"> + <li><a href="#onevsrest" id="markdown-toc-onevsrest">OneVsRest</a> <ul> + <li><a href="#example" id="markdown-toc-example">Example</a></li> + </ul> + </li> +</ul> + +<p>An <a href="http://en.wikipedia.org/wiki/Ensemble_learning">ensemble method</a> +is a learning algorithm which creates a model composed of a set of other base models. +The Pipelines API supports the following ensemble algorithms: <a href="api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest"><code>OneVsRest</code></a></p> + +<h2 id="onevsrest">OneVsRest</h2> + +<p><a href="http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest">OneVsRest</a> is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently.</p> + +<p><code>OneVsRest</code> is implemented as an <code>Estimator</code>. For the base classifier it takes instances of <code>Classifier</code> and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.</p> + +<p>Predictions are done by evaluating each binary classifier and the index of the most confident classifier is output as label.</p> + +<h3 id="example">Example</h3> + +<p>The example below demonstrates how to load the +<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/iris.scale">Iris dataset</a>, parse it as a DataFrame and perform multiclass classification using <code>OneVsRest</code>. The test error is calculated to measure the algorithm accuracy.</p> + +<div class="codetabs"> +<div data-lang="scala"> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.</span><span class="o">{</span><span class="nc">LogisticRegression</span><span class="o">,</span> <span class="nc">OneVsRest</span><span class="o">}</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.evaluation.MulticlassMetrics</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> +<span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span> + +<span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span> + +<span class="c1">// parse data into dataframe</span> +<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> + <span class="s">"data/mllib/sample_multiclass_classification_data.txt"</span><span class="o">)</span> +<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">train</span><span class="o">,</span> <span class="n">test</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">toDF</span><span class="o">().</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">))</span> + +<span class="c1">// instantiate multiclass learner and train</span> +<span class="k">val</span> <span class="n">ovr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">OneVsRest</span><span class="o">().</span><span class="n">setClassifier</span><span class="o">(</span><span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">)</span> + +<span class="k">val</span> <span class="n">ovrModel</span> <span class="k">=</span> <span class="n">ovr</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">train</span><span class="o">)</span> + +<span class="c1">// score model on test data</span> +<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">ovrModel</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">test</span><span class="o">).</span><span class="n">select</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">predictionsAndLabels</span> <span class="k">=</span> <span class="n">predictions</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span><span class="k">case</span> <span class="nc">Row</span><span class="o">(</span><span class="n">p</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> <span class="n">l</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">=></span> <span class="o">(</span><span class="n">p</span><span class="o">,</span> <span class="n">l</span><span class="o">)}</span> + +<span class="c1">// compute confusion matrix</span> +<span class="k">val</span> <span class="n">metrics</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">MulticlassMetrics</span><span class="o">(</span><span class="n">predictionsAndLabels</span><span class="o">)</span> +<span class="n">println</span><span class="o">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">confusionMatrix</span><span class="o">)</span> + +<span class="c1">// the Iris DataSet has three classes</span> +<span class="k">val</span> <span class="n">numClasses</span> <span class="k">=</span> <span class="mi">3</span> + +<span class="n">println</span><span class="o">(</span><span class="s">"label\tfpr\n"</span><span class="o">)</span> +<span class="o">(</span><span class="mi">0</span> <span class="n">until</span> <span class="n">numClasses</span><span class="o">).</span><span class="n">foreach</span> <span class="o">{</span> <span class="n">index</span> <span class="k">=></span> + <span class="k">val</span> <span class="n">label</span> <span class="k">=</span> <span class="n">index</span><span class="o">.</span><span class="n">toDouble</span> + <span class="n">println</span><span class="o">(</span><span class="n">label</span> <span class="o">+</span> <span class="s">"\t"</span> <span class="o">+</span> <span class="n">metrics</span><span class="o">.</span><span class="n">falsePositiveRate</span><span class="o">(</span><span class="n">label</span><span class="o">))</span> +<span class="o">}</span></code></pre></div> + + </div> +<div data-lang="java"> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.OneVsRest</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.OneVsRestModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.evaluation.MulticlassMetrics</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Matrix</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.rdd.RDD</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span> + +<span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">"JavaOneVsRestExample"</span><span class="o">);</span> +<span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span> +<span class="n">SQLContext</span> <span class="n">jsql</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span> + +<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> + <span class="s">"data/mllib/sample_multiclass_classification_data.txt"</span><span class="o">);</span> + +<span class="n">DataFrame</span> <span class="n">dataFrame</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">data</span><span class="o">,</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span> +<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">dataFrame</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">},</span> <span class="mi">12345</span><span class="o">);</span> +<span class="n">DataFrame</span> <span class="n">train</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> +<span class="n">DataFrame</span> <span class="n">test</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span> + +<span class="c1">// instantiate the One Vs Rest Classifier</span> +<span class="n">OneVsRest</span> <span class="n">ovr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">OneVsRest</span><span class="o">().</span><span class="na">setClassifier</span><span class="o">(</span><span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">());</span> + +<span class="c1">// train the multiclass model</span> +<span class="n">OneVsRestModel</span> <span class="n">ovrModel</span> <span class="o">=</span> <span class="n">ovr</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">train</span><span class="o">.</span><span class="na">cache</span><span class="o">());</span> + +<span class="c1">// score the model on test data</span> +<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">ovrModel</span> + <span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">test</span><span class="o">)</span> + <span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">);</span> + +<span class="c1">// obtain metrics</span> +<span class="n">MulticlassMetrics</span> <span class="n">metrics</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">MulticlassMetrics</span><span class="o">(</span><span class="n">predictions</span><span class="o">);</span> +<span class="n">Matrix</span> <span class="n">confusionMatrix</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="na">confusionMatrix</span><span class="o">();</span> + +<span class="c1">// output the Confusion Matrix</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Confusion Matrix"</span><span class="o">);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="n">confusionMatrix</span><span class="o">);</span> + +<span class="c1">// compute the false positive rate per label</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">();</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"label\tfpr\n"</span><span class="o">);</span> + +<span class="c1">// the Iris DataSet has three classes</span> +<span class="kt">int</span> <span class="n">numClasses</span> <span class="o">=</span> <span class="mi">3</span><span class="o">;</span> +<span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">index</span> <span class="o"><</span> <span class="n">numClasses</span><span class="o">;</span> <span class="n">index</span><span class="o">++)</span> <span class="o">{</span> + <span class="kt">double</span> <span class="n">label</span> <span class="o">=</span> <span class="o">(</span><span class="kt">double</span><span class="o">)</span> <span class="n">index</span><span class="o">;</span> + <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">print</span><span class="o">(</span><span class="n">label</span><span class="o">);</span> + <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">print</span><span class="o">(</span><span class="s">"\t"</span><span class="o">);</span> + <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">print</span><span class="o">(</span><span class="n">metrics</span><span class="o">.</span><span class="na">falsePositiveRate</span><span class="o">(</span><span class="n">label</span><span class="o">));</span> + <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">();</span> +<span class="o">}</span></code></pre></div> + + </div> +</div> + + + </div> <!-- /container --> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.min.js"></script> + <script src="js/main.js"></script> + + <!-- MathJax Section --> + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + TeX: { equationNumbers: { autoNumber: "AMS" } } + }); + </script> + <script> + // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS. + // We could use "//cdn.mathjax...", but that won't support "file://". + (function(d, script) { + script = d.createElement('script'); + script.type = 'text/javascript'; + script.async = true; + script.onload = function(){ + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ], + displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], + processEscapes: true, + skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'] + } + }); + }; + script.src = ('https:' == 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