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| author | Reynold Xin <rxin@apache.org> | 2015-09-17 22:07:42 +0000 |
|---|---|---|
| committer | Reynold Xin <rxin@apache.org> | 2015-09-17 22:07:42 +0000 |
| commit | ee9ffe89d608e7640a2487406b618d27e58026d6 (patch) | |
| tree | 50ec819abb41a9a769d7f64eed1f0ab2084aa6ff /site/docs/1.5.0/ml-ensembles.html | |
| parent | c7104724b279f09486ea62f4a24252e8d06f5c96 (diff) | |
| download | spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.tar.gz spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.tar.bz2 spark-website-ee9ffe89d608e7640a2487406b618d27e58026d6.zip | |
delete 1.5.0
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| -rw-r--r-- | site/docs/1.5.0/ml-ensembles.html | 1243 |
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diff --git a/site/docs/1.5.0/ml-ensembles.html b/site/docs/1.5.0/ml-ensembles.html deleted file mode 100644 index d2c6720c0..000000000 --- a/site/docs/1.5.0/ml-ensembles.html +++ /dev/null @@ -1,1243 +0,0 @@ -<!DOCTYPE html> -<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> -<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> -<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> -<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> - <head> - <meta charset="utf-8"> - <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> - <title>Ensembles - Spark 1.5.0 Documentation</title> - - - - - <link rel="stylesheet" href="css/bootstrap.min.css"> - <style> - body { - padding-top: 60px; - padding-bottom: 40px; - } - </style> - <meta name="viewport" content="width=device-width"> - <link rel="stylesheet" href="css/bootstrap-responsive.min.css"> - <link rel="stylesheet" href="css/main.css"> - - <script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script> - - <link rel="stylesheet" href="css/pygments-default.css"> - - - <!-- Google analytics script --> - <script type="text/javascript"> - var _gaq = _gaq || []; - _gaq.push(['_setAccount', 'UA-32518208-2']); - _gaq.push(['_trackPageview']); - - (function() { - var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; - ga.src = ('https:' == document.location.protocol ? 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One-vs-All)</a> <ul> - <li><a href="#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.</p> - -<h2 id="tree-ensembles">Tree Ensembles</h2> - -<p>The Pipelines API supports two major tree ensemble algorithms: <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forests</a> and <a href="http://en.wikipedia.org/wiki/Gradient_boosting">Gradient-Boosted Trees (GBTs)</a>. -Both use <a href="ml-decision-tree.html">MLlib decision trees</a> as their base models.</p> - -<p>Users can find more information about ensemble algorithms in the <a href="mllib-ensembles.html">MLlib Ensemble guide</a>. In this section, we demonstrate the Pipelines API for ensembles.</p> - -<p>The main differences between this API and the <a href="mllib-ensembles.html">original MLlib ensembles API</a> are: -* support for ML Pipelines -* separation of classification vs. regression -* use of DataFrame metadata to distinguish continuous and categorical features -* a bit more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification.</p> - -<h3 id="random-forests">Random Forests</h3> - -<p><a href="http://en.wikipedia.org/wiki/Random_forest">Random forests</a> -are ensembles of <a href="ml-decision-tree.html">decision trees</a>. -Random forests combine many decision trees in order to reduce the risk of overfitting. -MLlib supports random forests for binary and multiclass classification and for regression, -using both continuous and categorical features.</p> - -<p>This section gives examples of using random forests with the Pipelines API. -For more information on the algorithm, please see the <a href="mllib-ensembles.html">main MLlib docs on random forests</a>.</p> - -<h4 id="inputs-and-outputs">Inputs and Outputs</h4> - -<p>We list the input and output (prediction) column types here. -All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.</p> - -<h5 id="input-columns">Input Columns</h5> - -<table class="table"> - <thead> - <tr> - <th align="left">Param name</th> - <th align="left">Type(s)</th> - <th align="left">Default</th> - <th align="left">Description</th> - </tr> - </thead> - <tbody> - <tr> - <td>labelCol</td> - <td>Double</td> - <td>"label"</td> - <td>Label to predict</td> - </tr> - <tr> - <td>featuresCol</td> - <td>Vector</td> - <td>"features"</td> - <td>Feature vector</td> - </tr> - </tbody> -</table> - -<h5 id="output-columns-predictions">Output Columns (Predictions)</h5> - -<table class="table"> - <thead> - <tr> - <th align="left">Param name</th> - <th align="left">Type(s)</th> - <th align="left">Default</th> - <th align="left">Description</th> - <th align="left">Notes</th> - </tr> - </thead> - <tbody> - <tr> - <td>predictionCol</td> - <td>Double</td> - <td>"prediction"</td> - <td>Predicted label</td> - <td></td> - </tr> - <tr> - <td>rawPredictionCol</td> - <td>Vector</td> - <td>"rawPrediction"</td> - <td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td> - <td>Classification only</td> - </tr> - <tr> - <td>probabilityCol</td> - <td>Vector</td> - <td>"probability"</td> - <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td> - <td>Classification only</td> - </tr> - </tbody> -</table> - -<h4 id="example-classification">Example: Classification</h4> - -<p>The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. -We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the <code>DataFrame</code> which the tree-based algorithms can recognize.</p> - -<div class="codetabs"> -<div data-lang="scala"> - - <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier">Scala API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.RandomForestClassifier</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.RandomForestClassificationModel</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">StringIndexer</span><span class="o">,</span> <span class="nc">IndexToString</span><span class="o">,</span> <span class="nc">VectorIndexer</span><span class="o">}</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span> -<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> - -<span class="c1">// Load and parse the data file, converting it to a 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_libsvm_data.txt"</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span> - -<span class="c1">// Index labels, adding metadata to the label column.</span> -<span class="c1">// Fit on whole dataset to include all labels in index.</span> -<span class="k">val</span> <span class="n">labelIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">StringIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> -<span class="c1">// Automatically identify categorical features, and index them.</span> -<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> - <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> - -<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> -<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</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">// Train a RandomForest model.</span> -<span class="k">val</span> <span class="n">rf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RandomForestClassifier</span><span class="o">()</span> - <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setNumTrees</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> - -<span class="c1">// Convert indexed labels back to original labels.</span> -<span class="k">val</span> <span class="n">labelConverter</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">IndexToString</span><span class="o">()</span> - <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="n">labels</span><span class="o">)</span> - -<span class="c1">// Chain indexers and forest in a Pipeline</span> -<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span> - <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">rf</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">))</span> - -<span class="c1">// Train model. This also runs the indexers.</span> -<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> - -<span class="c1">// Make predictions.</span> -<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span> - -<span class="c1">// Select example rows to display.</span> -<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span> - -<span class="c1">// Select (prediction, true label) and compute test error</span> -<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">MulticlassClassificationEvaluator</span><span class="o">()</span> - <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"precision"</span><span class="o">)</span> -<span class="k">val</span> <span class="n">accuracy</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Test Error = "</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">))</span> - -<span class="k">val</span> <span class="n">rfModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">2</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">RandomForestClassificationModel</span><span class="o">]</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Learned classification forest model:\n"</span> <span class="o">+</span> <span class="n">rfModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div> - - </div> - -<div data-lang="java"> - - <p>Refer to the <a href="api/java/org/apache/spark/ml/classification/RandomForestClassifier.html">Java API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.RandomForestClassifier</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.RandomForestClassificationModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.*</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="c1">// Load and parse the data file, converting it to a DataFrame.</span> -<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">rdd</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">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span> -<span class="n">DataFrame</span> <span class="n">data</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">rdd</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="c1">// Index labels, adding metadata to the label column.</span> -<span class="c1">// Fit on whole dataset to include all labels in index.</span> -<span class="n">StringIndexerModel</span> <span class="n">labelIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StringIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> -<span class="c1">// Automatically identify categorical features, and index them.</span> -<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> - <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> - -<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> -<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</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="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span> -<span class="n">DataFrame</span> <span class="n">trainingData</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">testData</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">// Train a RandomForest model.</span> -<span class="n">RandomForestClassifier</span> <span class="n">rf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RandomForestClassifier</span><span class="o">()</span> - <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">);</span> - -<span class="c1">// Convert indexed labels back to original labels.</span> -<span class="n">IndexToString</span> <span class="n">labelConverter</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">IndexToString</span><span class="o">()</span> - <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="na">labels</span><span class="o">());</span> - -<span class="c1">// Chain indexers and forest in a Pipeline</span> -<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span> - <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">rf</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">});</span> - -<span class="c1">// Train model. This also runs the indexers.</span> -<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span> - -<span class="c1">// Make predictions.</span> -<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span> - -<span class="c1">// Select example rows to display.</span> -<span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span> - -<span class="c1">// Select (prediction, true label) and compute test error</span> -<span class="n">MulticlassClassificationEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">MulticlassClassificationEvaluator</span><span class="o">()</span> - <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"precision"</span><span class="o">);</span> -<span class="kt">double</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</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">"Test Error = "</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">));</span> - -<span class="n">RandomForestClassificationModel</span> <span class="n">rfModel</span> <span class="o">=</span> - <span class="o">(</span><span class="n">RandomForestClassificationModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">2</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">"Learned classification forest model:\n"</span> <span class="o">+</span> <span class="n">rfModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div> - - </div> - -<div data-lang="python"> - - <p>Refer to the <a href="api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier">Python API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.classification</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">StringIndexer</span><span class="p">,</span> <span class="n">VectorIndexer</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">MulticlassClassificationEvaluator</span> -<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span> - -<span class="c"># Load and parse the data file, converting it to a DataFrame.</span> -<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span> - -<span class="c"># Index labels, adding metadata to the label column.</span> -<span class="c"># Fit on whole dataset to include all labels in index.</span> -<span class="n">labelIndexer</span> <span class="o">=</span> <span class="n">StringIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"label"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> -<span class="c"># Automatically identify categorical features, and index them.</span> -<span class="c"># Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="n">featureIndexer</span> <span class="o">=</span>\ - <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> - -<span class="c"># Split the data into training and test sets (30% held out for testing)</span> -<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> - -<span class="c"># Train a RandomForest model.</span> -<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">labelCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">)</span> - -<span class="c"># Chain indexers and forest in a Pipeline</span> -<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">labelIndexer</span><span class="p">,</span> <span class="n">featureIndexer</span><span class="p">,</span> <span class="n">rf</span><span class="p">])</span> - -<span class="c"># Train model. This also runs the indexers.</span> -<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span> - -<span class="c"># Make predictions.</span> -<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span> - -<span class="c"># Select example rows to display.</span> -<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"prediction"</span><span class="p">,</span> <span class="s">"indexedLabel"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> - -<span class="c"># Select (prediction, true label) and compute test error</span> -<span class="n">evaluator</span> <span class="o">=</span> <span class="n">MulticlassClassificationEvaluator</span><span class="p">(</span> - <span class="n">labelCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"precision"</span><span class="p">)</span> -<span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> -<span class="k">print</span> <span class="s">"Test Error = </span><span class="si">%g</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">)</span> - -<span class="n">rfModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> -<span class="k">print</span> <span class="n">rfModel</span> <span class="c"># summary only</span></code></pre></div> - - </div> -</div> - -<h4 id="example-regression">Example: Regression</h4> - -<p>The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. -We use a feature transformer to index categorical features, adding metadata to the <code>DataFrame</code> which the tree-based algorithms can recognize.</p> - -<div class="codetabs"> -<div data-lang="scala"> - - <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.regression.RandomForestRegressor">Scala API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.RandomForestRegressor</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.RandomForestRegressionModel</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span> -<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> - -<span class="c1">// Load and parse the data file, converting it to a 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_libsvm_data.txt"</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span> - -<span class="c1">// Automatically identify categorical features, and index them.</span> -<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> - <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> - -<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> -<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</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">// Train a RandomForest model.</span> -<span class="k">val</span> <span class="n">rf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RandomForestRegressor</span><span class="o">()</span> - <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - -<span class="c1">// Chain indexer and forest in a Pipeline</span> -<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span> - <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">rf</span><span class="o">))</span> - -<span class="c1">// Train model. This also runs the indexer.</span> -<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> - -<span class="c1">// Make predictions.</span> -<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span> - -<span class="c1">// Select example rows to display.</span> -<span class="n">predictions</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="s">"features"</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span> - -<span class="c1">// Select (prediction, true label) and compute test error</span> -<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RegressionEvaluator</span><span class="o">()</span> - <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"rmse"</span><span class="o">)</span> -<span class="k">val</span> <span class="n">rmse</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Root Mean Squared Error (RMSE) on test data = "</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">)</span> - -<span class="k">val</span> <span class="n">rfModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">RandomForestRegressionModel</span><span class="o">]</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Learned regression forest model:\n"</span> <span class="o">+</span> <span class="n">rfModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div> - - </div> - -<div data-lang="java"> - - <p>Refer to the <a href="api/java/org/apache/spark/ml/regression/RandomForestRegressor.html">Java API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexerModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.RandomForestRegressionModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.RandomForestRegressor</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="c1">// Load and parse the data file, converting it to a DataFrame.</span> -<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">rdd</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">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span> -<span class="n">DataFrame</span> <span class="n">data</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">rdd</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="c1">// Automatically identify categorical features, and index them.</span> -<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> - <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> - -<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> -<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</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="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span> -<span class="n">DataFrame</span> <span class="n">trainingData</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">testData</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">// Train a RandomForest model.</span> -<span class="n">RandomForestRegressor</span> <span class="n">rf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RandomForestRegressor</span><span class="o">()</span> - <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">);</span> - -<span class="c1">// Chain indexer and forest in a Pipeline</span> -<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span> - <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">rf</span><span class="o">});</span> - -<span class="c1">// Train model. This also runs the indexer.</span> -<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span> - -<span class="c1">// Make predictions.</span> -<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span> - -<span class="c1">// Select example rows to display.</span> -<span class="n">predictions</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="s">"features"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span> - -<span class="c1">// Select (prediction, true label) and compute test error</span> -<span class="n">RegressionEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RegressionEvaluator</span><span class="o">()</span> - <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"rmse"</span><span class="o">);</span> -<span class="kt">double</span> <span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</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">"Root Mean Squared Error (RMSE) on test data = "</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">);</span> - -<span class="n">RandomForestRegressionModel</span> <span class="n">rfModel</span> <span class="o">=</span> - <span class="o">(</span><span class="n">RandomForestRegressionModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">1</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">"Learned regression forest model:\n"</span> <span class="o">+</span> <span class="n">rfModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div> - - </div> - -<div data-lang="python"> - - <p>Refer to the <a href="api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor">Python API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.regression</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">VectorIndexer</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">RegressionEvaluator</span> -<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span> - -<span class="c"># Load and parse the data file, converting it to a DataFrame.</span> -<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span> - -<span class="c"># Automatically identify categorical features, and index them.</span> -<span class="c"># Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="n">featureIndexer</span> <span class="o">=</span>\ - <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> - -<span class="c"># Split the data into training and test sets (30% held out for testing)</span> -<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> - -<span class="c"># Train a RandomForest model.</span> -<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">)</span> - -<span class="c"># Chain indexer and forest in a Pipeline</span> -<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">featureIndexer</span><span class="p">,</span> <span class="n">rf</span><span class="p">])</span> - -<span class="c"># Train model. This also runs the indexer.</span> -<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span> - -<span class="c"># Make predictions.</span> -<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span> - -<span class="c"># Select example rows to display.</span> -<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"prediction"</span><span class="p">,</span> <span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> - -<span class="c"># Select (prediction, true label) and compute test error</span> -<span class="n">evaluator</span> <span class="o">=</span> <span class="n">RegressionEvaluator</span><span class="p">(</span> - <span class="n">labelCol</span><span class="o">=</span><span class="s">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"rmse"</span><span class="p">)</span> -<span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> -<span class="k">print</span> <span class="s">"Root Mean Squared Error (RMSE) on test data = </span><span class="si">%g</span><span class="s">"</span> <span class="o">%</span> <span class="n">rmse</span> - -<span class="n">rfModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> -<span class="k">print</span> <span class="n">rfModel</span> <span class="c"># summary only</span></code></pre></div> - - </div> -</div> - -<h3 id="gradient-boosted-trees-gbts">Gradient-Boosted Trees (GBTs)</h3> - -<p><a href="http://en.wikipedia.org/wiki/Gradient_boosting">Gradient-Boosted Trees (GBTs)</a> -are ensembles of <a href="ml-decision-tree.html">decision trees</a>. -GBTs iteratively train decision trees in order to minimize a loss function. -MLlib supports GBTs for binary classification and for regression, -using both continuous and categorical features.</p> - -<p>This section gives examples of using GBTs with the Pipelines API. -For more information on the algorithm, please see the <a href="mllib-ensembles.html">main MLlib docs on GBTs</a>.</p> - -<h4 id="inputs-and-outputs-1">Inputs and Outputs</h4> - -<p>We list the input and output (prediction) column types here. -All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.</p> - -<h5 id="input-columns-1">Input Columns</h5> - -<table class="table"> - <thead> - <tr> - <th align="left">Param name</th> - <th align="left">Type(s)</th> - <th align="left">Default</th> - <th align="left">Description</th> - </tr> - </thead> - <tbody> - <tr> - <td>labelCol</td> - <td>Double</td> - <td>"label"</td> - <td>Label to predict</td> - </tr> - <tr> - <td>featuresCol</td> - <td>Vector</td> - <td>"features"</td> - <td>Feature vector</td> - </tr> - </tbody> -</table> - -<p>Note that <code>GBTClassifier</code> currently only supports binary labels.</p> - -<h5 id="output-columns-predictions-1">Output Columns (Predictions)</h5> - -<table class="table"> - <thead> - <tr> - <th align="left">Param name</th> - <th align="left">Type(s)</th> - <th align="left">Default</th> - <th align="left">Description</th> - <th align="left">Notes</th> - </tr> - </thead> - <tbody> - <tr> - <td>predictionCol</td> - <td>Double</td> - <td>"prediction"</td> - <td>Predicted label</td> - <td></td> - </tr> - </tbody> -</table> - -<p>In the future, <code>GBTClassifier</code> will also output columns for <code>rawPrediction</code> and <code>probability</code>, just as <code>RandomForestClassifier</code> does.</p> - -<h4 id="example-classification-1">Example: Classification</h4> - -<p>The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. -We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the <code>DataFrame</code> which the tree-based algorithms can recognize.</p> - -<div class="codetabs"> -<div data-lang="scala"> - - <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.classification.GBTClassifier">Scala API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.GBTClassifier</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.GBTClassificationModel</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">StringIndexer</span><span class="o">,</span> <span class="nc">IndexToString</span><span class="o">,</span> <span class="nc">VectorIndexer</span><span class="o">}</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span> -<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> - -<span class="c1">// Load and parse the data file, converting it to a 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_libsvm_data.txt"</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span> - -<span class="c1">// Index labels, adding metadata to the label column.</span> -<span class="c1">// Fit on whole dataset to include all labels in index.</span> -<span class="k">val</span> <span class="n">labelIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">StringIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> -<span class="c1">// Automatically identify categorical features, and index them.</span> -<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> - <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> - -<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> -<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</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">// Train a GBT model.</span> -<span class="k">val</span> <span class="n">gbt</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">GBTClassifier</span><span class="o">()</span> - <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> - -<span class="c1">// Convert indexed labels back to original labels.</span> -<span class="k">val</span> <span class="n">labelConverter</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">IndexToString</span><span class="o">()</span> - <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="n">labels</span><span class="o">)</span> - -<span class="c1">// Chain indexers and GBT in a Pipeline</span> -<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span> - <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">gbt</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">))</span> - -<span class="c1">// Train model. This also runs the indexers.</span> -<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> - -<span class="c1">// Make predictions.</span> -<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span> - -<span class="c1">// Select example rows to display.</span> -<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span> - -<span class="c1">// Select (prediction, true label) and compute test error</span> -<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">MulticlassClassificationEvaluator</span><span class="o">()</span> - <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"precision"</span><span class="o">)</span> -<span class="k">val</span> <span class="n">accuracy</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Test Error = "</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">))</span> - -<span class="k">val</span> <span class="n">gbtModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">2</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">GBTClassificationModel</span><span class="o">]</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Learned classification GBT model:\n"</span> <span class="o">+</span> <span class="n">gbtModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div> - - </div> - -<div data-lang="java"> - - <p>Refer to the <a href="api/java/org/apache/spark/ml/classification/GBTClassifier.html">Java API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.GBTClassifier</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.GBTClassificationModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.*</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="c1">// Load and parse the data file, converting it to a DataFrame.</span> -<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">rdd</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">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span> -<span class="n">DataFrame</span> <span class="n">data</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">rdd</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="c1">// Index labels, adding metadata to the label column.</span> -<span class="c1">// Fit on whole dataset to include all labels in index.</span> -<span class="n">StringIndexerModel</span> <span class="n">labelIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StringIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> -<span class="c1">// Automatically identify categorical features, and index them.</span> -<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> - <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> - -<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> -<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</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="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span> -<span class="n">DataFrame</span> <span class="n">trainingData</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">testData</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">// Train a GBT model.</span> -<span class="n">GBTClassifier</span> <span class="n">gbt</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">GBTClassifier</span><span class="o">()</span> - <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span> - -<span class="c1">// Convert indexed labels back to original labels.</span> -<span class="n">IndexToString</span> <span class="n">labelConverter</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">IndexToString</span><span class="o">()</span> - <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="na">labels</span><span class="o">());</span> - -<span class="c1">// Chain indexers and GBT in a Pipeline</span> -<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span> - <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">gbt</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">});</span> - -<span class="c1">// Train model. This also runs the indexers.</span> -<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span> - -<span class="c1">// Make predictions.</span> -<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span> - -<span class="c1">// Select example rows to display.</span> -<span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span> - -<span class="c1">// Select (prediction, true label) and compute test error</span> -<span class="n">MulticlassClassificationEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">MulticlassClassificationEvaluator</span><span class="o">()</span> - <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"precision"</span><span class="o">);</span> -<span class="kt">double</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</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">"Test Error = "</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">));</span> - -<span class="n">GBTClassificationModel</span> <span class="n">gbtModel</span> <span class="o">=</span> - <span class="o">(</span><span class="n">GBTClassificationModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">2</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">"Learned classification GBT model:\n"</span> <span class="o">+</span> <span class="n">gbtModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div> - - </div> - -<div data-lang="python"> - - <p>Refer to the <a href="api/python/pyspark.ml.html#pyspark.ml.classification.GBTClassifier">Python API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.classification</span> <span class="kn">import</span> <span class="n">GBTClassifier</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">StringIndexer</span><span class="p">,</span> <span class="n">VectorIndexer</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">MulticlassClassificationEvaluator</span> -<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span> - -<span class="c"># Load and parse the data file, converting it to a DataFrame.</span> -<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span> - -<span class="c"># Index labels, adding metadata to the label column.</span> -<span class="c"># Fit on whole dataset to include all labels in index.</span> -<span class="n">labelIndexer</span> <span class="o">=</span> <span class="n">StringIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"label"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> -<span class="c"># Automatically identify categorical features, and index them.</span> -<span class="c"># Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="n">featureIndexer</span> <span class="o">=</span>\ - <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> - -<span class="c"># Split the data into training and test sets (30% held out for testing)</span> -<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> - -<span class="c"># Train a GBT model.</span> -<span class="n">gbt</span> <span class="o">=</span> <span class="n">GBTClassifier</span><span class="p">(</span><span class="n">labelCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> - -<span class="c"># Chain indexers and GBT in a Pipeline</span> -<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">labelIndexer</span><span class="p">,</span> <span class="n">featureIndexer</span><span class="p">,</span> <span class="n">gbt</span><span class="p">])</span> - -<span class="c"># Train model. This also runs the indexers.</span> -<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span> - -<span class="c"># Make predictions.</span> -<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span> - -<span class="c"># Select example rows to display.</span> -<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"prediction"</span><span class="p">,</span> <span class="s">"indexedLabel"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> - -<span class="c"># Select (prediction, true label) and compute test error</span> -<span class="n">evaluator</span> <span class="o">=</span> <span class="n">MulticlassClassificationEvaluator</span><span class="p">(</span> - <span class="n">labelCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"precision"</span><span class="p">)</span> -<span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> -<span class="k">print</span> <span class="s">"Test Error = </span><span class="si">%g</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">)</span> - -<span class="n">gbtModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> -<span class="k">print</span> <span class="n">gbtModel</span> <span class="c"># summary only</span></code></pre></div> - - </div> -</div> - -<h4 id="example-regression-1">Example: Regression</h4> - -<p>Note: For this example dataset, <code>GBTRegressor</code> actually only needs 1 iteration, but that will not -be true in general.</p> - -<div class="codetabs"> -<div data-lang="scala"> - - <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.regression.GBTRegressor">Scala API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.GBTRegressor</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.GBTRegressionModel</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span> -<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span> -<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> - -<span class="c1">// Load and parse the data file, converting it to a 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_libsvm_data.txt"</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span> - -<span class="c1">// Automatically identify categorical features, and index them.</span> -<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> - <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> - -<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> -<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</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">// Train a GBT model.</span> -<span class="k">val</span> <span class="n">gbt</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">GBTRegressor</span><span class="o">()</span> - <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> - -<span class="c1">// Chain indexer and GBT in a Pipeline</span> -<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span> - <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">gbt</span><span class="o">))</span> - -<span class="c1">// Train model. This also runs the indexer.</span> -<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> - -<span class="c1">// Make predictions.</span> -<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span> - -<span class="c1">// Select example rows to display.</span> -<span class="n">predictions</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="s">"features"</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span> - -<span class="c1">// Select (prediction, true label) and compute test error</span> -<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RegressionEvaluator</span><span class="o">()</span> - <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"rmse"</span><span class="o">)</span> -<span class="k">val</span> <span class="n">rmse</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Root Mean Squared Error (RMSE) on test data = "</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">)</span> - -<span class="k">val</span> <span class="n">gbtModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">GBTRegressionModel</span><span class="o">]</span> -<span class="n">println</span><span class="o">(</span><span class="s">"Learned regression GBT model:\n"</span> <span class="o">+</span> <span class="n">gbtModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div> - - </div> - -<div data-lang="java"> - - <p>Refer to the <a href="api/java/org/apache/spark/ml/regression/GBTRegressor.html">Java API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexerModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.GBTRegressionModel</span><span class="o">;</span> -<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.GBTRegressor</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="c1">// Load and parse the data file, converting it to a DataFrame.</span> -<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">rdd</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">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span> -<span class="n">DataFrame</span> <span class="n">data</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">rdd</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="c1">// Automatically identify categorical features, and index them.</span> -<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span> - <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> - <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> - -<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> -<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</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="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span> -<span class="n">DataFrame</span> <span class="n">trainingData</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">testData</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">// Train a GBT model.</span> -<span class="n">GBTRegressor</span> <span class="n">gbt</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">GBTRegressor</span><span class="o">()</span> - <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span> - -<span class="c1">// Chain indexer and GBT in a Pipeline</span> -<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span> - <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">gbt</span><span class="o">});</span> - -<span class="c1">// Train model. This also runs the indexer.</span> -<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span> - -<span class="c1">// Make predictions.</span> -<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span> - -<span class="c1">// Select example rows to display.</span> -<span class="n">predictions</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="s">"features"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span> - -<span class="c1">// Select (prediction, true label) and compute test error</span> -<span class="n">RegressionEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RegressionEvaluator</span><span class="o">()</span> - <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> - <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"rmse"</span><span class="o">);</span> -<span class="kt">double</span> <span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</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">"Root Mean Squared Error (RMSE) on test data = "</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">);</span> - -<span class="n">GBTRegressionModel</span> <span class="n">gbtModel</span> <span class="o">=</span> - <span class="o">(</span><span class="n">GBTRegressionModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">1</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">"Learned regression GBT model:\n"</span> <span class="o">+</span> <span class="n">gbtModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div> - - </div> - -<div data-lang="python"> - - <p>Refer to the <a href="api/python/pyspark.ml.html#pyspark.ml.regression.GBTRegressor">Python API docs</a> for more details.</p> - - <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.regression</span> <span class="kn">import</span> <span class="n">GBTRegressor</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">VectorIndexer</span> -<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">RegressionEvaluator</span> -<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span> - -<span class="c"># Load and parse the data file, converting it to a DataFrame.</span> -<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span> - -<span class="c"># Automatically identify categorical features, and index them.</span> -<span class="c"># Set maxCategories so features with > 4 distinct values are treated as continuous.</span> -<span class="n">featureIndexer</span> <span class="o">=</span>\ - <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> - -<span class="c"># Split the data into training and test sets (30% held out for testing)</span> -<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> - -<span class="c"># Train a GBT model.</span> -<span class="n">gbt</span> <span class="o">=</span> <span class="n">GBTRegressor</span><span class="p">(</span><span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> - -<span class="c"># Chain indexer and GBT in a Pipeline</span> -<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">featureIndexer</span><span class="p">,</span> <span class="n">gbt</span><span class="p">])</span> - -<span class="c"># Train model. This also runs the indexer.</span> -<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span> - -<span class="c"># Make predictions.</span> -<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span> - -<span class="c"># Select example rows to display.</span> -<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"prediction"</span><span class="p">,</span> <span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> - -<span class="c"># Select (prediction, true label) and compute test error</span> -<span class="n">evaluator</span> <span class="o">=</span> <span class="n">RegressionEvaluator</span><span class="p">(</span> - <span class="n">labelCol</span><span class="o">=</span><span class="s">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"rmse"</span><span class="p">)</span> -<span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> -<span class="k">print</span> <span class="s">"Root Mean Squared Error (RMSE) on test data = </span><span class="si">%g</span><span class="s">"</span> <span class="o">%</span> <span class="n">rmse</span> - -<span class="n">gbtModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> -<span class="k">print</span> <span class="n">gbtModel</span> <span class="c"># summary only</span></code></pre></div> - - </div> -</div> - -<h2 id="one-vs-rest-aka-one-vs-all">One-vs-Rest (a.k.a. One-vs-All)</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. It is also known as “One-vs-All.”</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"> - - <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest">Scala API docs</a> for more details.</p> - - <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"> - - <p>Refer to the <a href="api/java/org/apache/spark/ml/classification/OneVsRest.html">Java API docs</a> for more details.</p> - - <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="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> - 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