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| <h1 class="title"><a href="ml-guide.html">ML</a> - Ensembles</h1> |
| |
| |
| <p><strong>Table of Contents</strong></p> |
| |
| <ul id="markdown-toc"> |
| <li><a href="#tree-ensembles" id="markdown-toc-tree-ensembles">Tree Ensembles</a> <ul> |
| <li><a href="#random-forests" id="markdown-toc-random-forests">Random Forests</a> <ul> |
| <li><a href="#inputs-and-outputs" id="markdown-toc-inputs-and-outputs">Inputs and Outputs</a> <ul> |
| <li><a href="#input-columns" id="markdown-toc-input-columns">Input Columns</a></li> |
| <li><a href="#output-columns-predictions" id="markdown-toc-output-columns-predictions">Output Columns (Predictions)</a></li> |
| </ul> |
| </li> |
| <li><a href="#example-classification" id="markdown-toc-example-classification">Example: Classification</a></li> |
| <li><a href="#example-regression" id="markdown-toc-example-regression">Example: Regression</a></li> |
| </ul> |
| </li> |
| <li><a href="#gradient-boosted-trees-gbts" id="markdown-toc-gradient-boosted-trees-gbts">Gradient-Boosted Trees (GBTs)</a> <ul> |
| <li><a href="#inputs-and-outputs-1" id="markdown-toc-inputs-and-outputs-1">Inputs and Outputs</a> <ul> |
| <li><a href="#input-columns-1" id="markdown-toc-input-columns-1">Input Columns</a></li> |
| <li><a href="#output-columns-predictions-1" id="markdown-toc-output-columns-predictions-1">Output Columns (Predictions)</a></li> |
| </ul> |
| </li> |
| <li><a href="#example-classification-1" id="markdown-toc-example-classification-1">Example: Classification</a></li> |
| <li><a href="#example-regression-1" id="markdown-toc-example-regression-1">Example: Regression</a></li> |
| </ul> |
| </li> |
| </ul> |
| </li> |
| <li><a href="#one-vs-rest-aka-one-vs-all" id="markdown-toc-one-vs-rest-aka-one-vs-all">One-vs-Rest (a.k.a. One-vs-All)</a> <ul> |
| <li><a href="#example" id="markdown-toc-example">Example</a></li> |
| </ul> |
| </li> |
| </ul> |
| |
| <p>An <a href="http://en.wikipedia.org/wiki/Ensemble_learning">ensemble method</a> |
| is a learning algorithm which creates a model composed of a set of other base models.</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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