diff options
author | Reynold Xin <rxin@apache.org> | 2015-09-08 23:20:31 +0000 |
---|---|---|
committer | Reynold Xin <rxin@apache.org> | 2015-09-08 23:20:31 +0000 |
commit | 443d7fc272a34a818df4dd589bb251ec1087ae11 (patch) | |
tree | 58b176846888d8824cd113146bd59568ea5354f6 /site/docs/1.5.0/ml-ensembles.html | |
parent | 1037fcd3d980ca1bf8e79ccfecd1f5234545b6ff (diff) | |
download | spark-website-443d7fc272a34a818df4dd589bb251ec1087ae11.tar.gz spark-website-443d7fc272a34a818df4dd589bb251ec1087ae11.tar.bz2 spark-website-443d7fc272a34a818df4dd589bb251ec1087ae11.zip |
Added 1.5.0 docs.
Diffstat (limited to 'site/docs/1.5.0/ml-ensembles.html')
-rw-r--r-- | site/docs/1.5.0/ml-ensembles.html | 1243 |
1 files changed, 1243 insertions, 0 deletions
diff --git a/site/docs/1.5.0/ml-ensembles.html b/site/docs/1.5.0/ml-ensembles.html new file mode 100644 index 000000000..d2c6720c0 --- /dev/null +++ b/site/docs/1.5.0/ml-ensembles.html @@ -0,0 +1,1243 @@ +<!DOCTYPE html> +<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]--> +<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]--> +<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]--> + <head> + <meta charset="utf-8"> + <meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"> + <title>Ensembles - Spark 1.5.0 Documentation</title> + + + + + <link rel="stylesheet" href="css/bootstrap.min.css"> + <style> + body { + padding-top: 60px; + padding-bottom: 40px; + } + </style> + <meta name="viewport" content="width=device-width"> + <link rel="stylesheet" href="css/bootstrap-responsive.min.css"> + <link rel="stylesheet" href="css/main.css"> + + <script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script> + + <link rel="stylesheet" href="css/pygments-default.css"> + + + <!-- Google analytics script --> + <script type="text/javascript"> + var _gaq = _gaq || []; + _gaq.push(['_setAccount', 'UA-32518208-2']); + _gaq.push(['_trackPageview']); + + (function() { + var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; + ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; + var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); + })(); + </script> + + + </head> + <body> + <!--[if lt IE 7]> + <p class="chromeframe">You are using an outdated browser. <a href="http://browsehappy.com/">Upgrade your browser today</a> or <a href="http://www.google.com/chromeframe/?redirect=true">install Google Chrome Frame</a> to better experience this site.</p> + <![endif]--> + + <!-- This code is taken from http://twitter.github.com/bootstrap/examples/hero.html --> + + <div class="navbar navbar-fixed-top" id="topbar"> + <div class="navbar-inner"> + <div class="container"> + <div class="brand"><a href="index.html"> + <img src="img/spark-logo-hd.png" style="height:50px;"/></a><span class="version">1.5.0</span> + </div> + <ul class="nav"> + <!--TODO(andyk): Add class="active" attribute to li some how.--> + <li><a href="index.html">Overview</a></li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="quick-start.html">Quick Start</a></li> + <li><a href="programming-guide.html">Spark Programming Guide</a></li> + <li class="divider"></li> + <li><a href="streaming-programming-guide.html">Spark Streaming</a></li> + <li><a href="sql-programming-guide.html">DataFrames and SQL</a></li> + <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li> + <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li> + <li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li> + <li><a href="sparkr.html">SparkR (R on Spark)</a></li> + </ul> + </li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="api/scala/index.html#org.apache.spark.package">Scala</a></li> + <li><a href="api/java/index.html">Java</a></li> + <li><a href="api/python/index.html">Python</a></li> + <li><a href="api/R/index.html">R</a></li> + </ul> + </li> + + <li class="dropdown"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown">Deploying<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="cluster-overview.html">Overview</a></li> + <li><a href="submitting-applications.html">Submitting Applications</a></li> + <li class="divider"></li> + <li><a href="spark-standalone.html">Spark Standalone</a></li> + <li><a href="running-on-mesos.html">Mesos</a></li> + <li><a href="running-on-yarn.html">YARN</a></li> + <li class="divider"></li> + <li><a href="ec2-scripts.html">Amazon EC2</a></li> + </ul> + </li> + + <li class="dropdown"> + <a href="api.html" class="dropdown-toggle" data-toggle="dropdown">More<b class="caret"></b></a> + <ul class="dropdown-menu"> + <li><a href="configuration.html">Configuration</a></li> + <li><a href="monitoring.html">Monitoring</a></li> + <li><a href="tuning.html">Tuning Guide</a></li> + <li><a href="job-scheduling.html">Job Scheduling</a></li> + <li><a href="security.html">Security</a></li> + <li><a href="hardware-provisioning.html">Hardware Provisioning</a></li> + <li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li> + <li class="divider"></li> + <li><a href="building-spark.html">Building Spark</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects">Supplemental Projects</a></li> + </ul> + </li> + </ul> + <!--<p class="navbar-text pull-right"><span class="version-text">v1.5.0</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + + <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">Tree Ensembles</a> <ul> + <li><a href="#random-forests">Random Forests</a> <ul> + <li><a href="#inputs-and-outputs">Inputs and Outputs</a> <ul> + <li><a href="#input-columns">Input Columns</a></li> + <li><a href="#output-columns-predictions">Output Columns (Predictions)</a></li> + </ul> + </li> + <li><a href="#example-classification">Example: Classification</a></li> + <li><a href="#example-regression">Example: Regression</a></li> + </ul> + </li> + <li><a href="#gradient-boosted-trees-gbts">Gradient-Boosted Trees (GBTs)</a> <ul> + <li><a href="#inputs-and-outputs-1">Inputs and Outputs</a> <ul> + <li><a href="#input-columns-1">Input Columns</a></li> + <li><a href="#output-columns-predictions-1">Output Columns (Predictions)</a></li> + </ul> + </li> + <li><a href="#example-classification-1">Example: Classification</a></li> + <li><a href="#example-regression-1">Example: Regression</a></li> + </ul> + </li> + </ul> + </li> + <li><a href="#one-vs-rest-aka-one-vs-all">One-vs-Rest (a.k.a. One-vs-All)</a> <ul> + <li><a href="#example">Example</a></li> + </ul> + </li> +</ul> + +<p>An <a href="http://en.wikipedia.org/wiki/Ensemble_learning">ensemble method</a> +is a learning algorithm which creates a model composed of a set of other base models.</p> + +<h2 id="tree-ensembles">Tree Ensembles</h2> + +<p>The Pipelines API supports two major tree ensemble algorithms: <a href="http://en.wikipedia.org/wiki/Random_forest">Random Forests</a> and <a href="http://en.wikipedia.org/wiki/Gradient_boosting">Gradient-Boosted Trees (GBTs)</a>. +Both use <a href="ml-decision-tree.html">MLlib decision trees</a> as their base models.</p> + +<p>Users can find more information about ensemble algorithms in the <a href="mllib-ensembles.html">MLlib Ensemble guide</a>. In this section, we demonstrate the Pipelines API for ensembles.</p> + +<p>The main differences between this API and the <a href="mllib-ensembles.html">original MLlib ensembles API</a> are: +* support for ML Pipelines +* separation of classification vs. regression +* use of DataFrame metadata to distinguish continuous and categorical features +* a bit more functionality for random forests: estimates of feature importance, as well as the predicted probability of each class (a.k.a. class conditional probabilities) for classification.</p> + +<h3 id="random-forests">Random Forests</h3> + +<p><a href="http://en.wikipedia.org/wiki/Random_forest">Random forests</a> +are ensembles of <a href="ml-decision-tree.html">decision trees</a>. +Random forests combine many decision trees in order to reduce the risk of overfitting. +MLlib supports random forests for binary and multiclass classification and for regression, +using both continuous and categorical features.</p> + +<p>This section gives examples of using random forests with the Pipelines API. +For more information on the algorithm, please see the <a href="mllib-ensembles.html">main MLlib docs on random forests</a>.</p> + +<h4 id="inputs-and-outputs">Inputs and Outputs</h4> + +<p>We list the input and output (prediction) column types here. +All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.</p> + +<h5 id="input-columns">Input Columns</h5> + +<table class="table"> + <thead> + <tr> + <th align="left">Param name</th> + <th align="left">Type(s)</th> + <th align="left">Default</th> + <th align="left">Description</th> + </tr> + </thead> + <tbody> + <tr> + <td>labelCol</td> + <td>Double</td> + <td>"label"</td> + <td>Label to predict</td> + </tr> + <tr> + <td>featuresCol</td> + <td>Vector</td> + <td>"features"</td> + <td>Feature vector</td> + </tr> + </tbody> +</table> + +<h5 id="output-columns-predictions">Output Columns (Predictions)</h5> + +<table class="table"> + <thead> + <tr> + <th align="left">Param name</th> + <th align="left">Type(s)</th> + <th align="left">Default</th> + <th align="left">Description</th> + <th align="left">Notes</th> + </tr> + </thead> + <tbody> + <tr> + <td>predictionCol</td> + <td>Double</td> + <td>"prediction"</td> + <td>Predicted label</td> + <td></td> + </tr> + <tr> + <td>rawPredictionCol</td> + <td>Vector</td> + <td>"rawPrediction"</td> + <td>Vector of length # classes, with the counts of training instance labels at the tree node which makes the prediction</td> + <td>Classification only</td> + </tr> + <tr> + <td>probabilityCol</td> + <td>Vector</td> + <td>"probability"</td> + <td>Vector of length # classes equal to rawPrediction normalized to a multinomial distribution</td> + <td>Classification only</td> + </tr> + </tbody> +</table> + +<h4 id="example-classification">Example: Classification</h4> + +<p>The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. +We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the <code>DataFrame</code> which the tree-based algorithms can recognize.</p> + +<div class="codetabs"> +<div data-lang="scala"> + + <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.classification.RandomForestClassifier">Scala API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.RandomForestClassifier</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.RandomForestClassificationModel</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">StringIndexer</span><span class="o">,</span> <span class="nc">IndexToString</span><span class="o">,</span> <span class="nc">VectorIndexer</span><span class="o">}</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> + +<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span> +<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span> + +<span class="c1">// Index labels, adding metadata to the label column.</span> +<span class="c1">// Fit on whole dataset to include all labels in index.</span> +<span class="k">val</span> <span class="n">labelIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">StringIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> +<span class="c1">// Automatically identify categorical features, and index them.</span> +<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> + <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> + +<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> +<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">))</span> + +<span class="c1">// Train a RandomForest model.</span> +<span class="k">val</span> <span class="n">rf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RandomForestClassifier</span><span class="o">()</span> + <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setNumTrees</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> + +<span class="c1">// Convert indexed labels back to original labels.</span> +<span class="k">val</span> <span class="n">labelConverter</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">IndexToString</span><span class="o">()</span> + <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="n">labels</span><span class="o">)</span> + +<span class="c1">// Chain indexers and forest in a Pipeline</span> +<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span> + <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">rf</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">))</span> + +<span class="c1">// Train model. This also runs the indexers.</span> +<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> + +<span class="c1">// Make predictions.</span> +<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span> + +<span class="c1">// Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span> + +<span class="c1">// Select (prediction, true label) and compute test error</span> +<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">MulticlassClassificationEvaluator</span><span class="o">()</span> + <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"precision"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">accuracy</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Test Error = "</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">))</span> + +<span class="k">val</span> <span class="n">rfModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">2</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">RandomForestClassificationModel</span><span class="o">]</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Learned classification forest model:\n"</span> <span class="o">+</span> <span class="n">rfModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div> + + </div> + +<div data-lang="java"> + + <p>Refer to the <a href="api/java/org/apache/spark/ml/classification/RandomForestClassifier.html">Java API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.RandomForestClassifier</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.RandomForestClassificationModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.*</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.rdd.RDD</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span> + +<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span> +<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">rdd</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span> +<span class="n">DataFrame</span> <span class="n">data</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rdd</span><span class="o">,</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span> + +<span class="c1">// Index labels, adding metadata to the label column.</span> +<span class="c1">// Fit on whole dataset to include all labels in index.</span> +<span class="n">StringIndexerModel</span> <span class="n">labelIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StringIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> +<span class="c1">// Automatically identify categorical features, and index them.</span> +<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> + <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> + +<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> +<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span> +<span class="n">DataFrame</span> <span class="n">trainingData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> +<span class="n">DataFrame</span> <span class="n">testData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span> + +<span class="c1">// Train a RandomForest model.</span> +<span class="n">RandomForestClassifier</span> <span class="n">rf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RandomForestClassifier</span><span class="o">()</span> + <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">);</span> + +<span class="c1">// Convert indexed labels back to original labels.</span> +<span class="n">IndexToString</span> <span class="n">labelConverter</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">IndexToString</span><span class="o">()</span> + <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="na">labels</span><span class="o">());</span> + +<span class="c1">// Chain indexers and forest in a Pipeline</span> +<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span> + <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">rf</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">});</span> + +<span class="c1">// Train model. This also runs the indexers.</span> +<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span> + +<span class="c1">// Make predictions.</span> +<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span> + +<span class="c1">// Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span> + +<span class="c1">// Select (prediction, true label) and compute test error</span> +<span class="n">MulticlassClassificationEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">MulticlassClassificationEvaluator</span><span class="o">()</span> + <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"precision"</span><span class="o">);</span> +<span class="kt">double</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Test Error = "</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">));</span> + +<span class="n">RandomForestClassificationModel</span> <span class="n">rfModel</span> <span class="o">=</span> + <span class="o">(</span><span class="n">RandomForestClassificationModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">2</span><span class="o">]);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Learned classification forest model:\n"</span> <span class="o">+</span> <span class="n">rfModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div> + + </div> + +<div data-lang="python"> + + <p>Refer to the <a href="api/python/pyspark.ml.html#pyspark.ml.classification.RandomForestClassifier">Python API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.classification</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">StringIndexer</span><span class="p">,</span> <span class="n">VectorIndexer</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">MulticlassClassificationEvaluator</span> +<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span> + +<span class="c"># Load and parse the data file, converting it to a DataFrame.</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span> + +<span class="c"># Index labels, adding metadata to the label column.</span> +<span class="c"># Fit on whole dataset to include all labels in index.</span> +<span class="n">labelIndexer</span> <span class="o">=</span> <span class="n">StringIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"label"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> +<span class="c"># Automatically identify categorical features, and index them.</span> +<span class="c"># Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="n">featureIndexer</span> <span class="o">=</span>\ + <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> + +<span class="c"># Split the data into training and test sets (30% held out for testing)</span> +<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> + +<span class="c"># Train a RandomForest model.</span> +<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">labelCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">)</span> + +<span class="c"># Chain indexers and forest in a Pipeline</span> +<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">labelIndexer</span><span class="p">,</span> <span class="n">featureIndexer</span><span class="p">,</span> <span class="n">rf</span><span class="p">])</span> + +<span class="c"># Train model. This also runs the indexers.</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span> + +<span class="c"># Make predictions.</span> +<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span> + +<span class="c"># Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"prediction"</span><span class="p">,</span> <span class="s">"indexedLabel"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> + +<span class="c"># Select (prediction, true label) and compute test error</span> +<span class="n">evaluator</span> <span class="o">=</span> <span class="n">MulticlassClassificationEvaluator</span><span class="p">(</span> + <span class="n">labelCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"precision"</span><span class="p">)</span> +<span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> +<span class="k">print</span> <span class="s">"Test Error = </span><span class="si">%g</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">)</span> + +<span class="n">rfModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> +<span class="k">print</span> <span class="n">rfModel</span> <span class="c"># summary only</span></code></pre></div> + + </div> +</div> + +<h4 id="example-regression">Example: Regression</h4> + +<p>The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. +We use a feature transformer to index categorical features, adding metadata to the <code>DataFrame</code> which the tree-based algorithms can recognize.</p> + +<div class="codetabs"> +<div data-lang="scala"> + + <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.regression.RandomForestRegressor">Scala API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.RandomForestRegressor</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.RandomForestRegressionModel</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> + +<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span> +<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span> + +<span class="c1">// Automatically identify categorical features, and index them.</span> +<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> + <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> + +<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> +<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">))</span> + +<span class="c1">// Train a RandomForest model.</span> +<span class="k">val</span> <span class="n">rf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RandomForestRegressor</span><span class="o">()</span> + <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + +<span class="c1">// Chain indexer and forest in a Pipeline</span> +<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span> + <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">rf</span><span class="o">))</span> + +<span class="c1">// Train model. This also runs the indexer.</span> +<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> + +<span class="c1">// Make predictions.</span> +<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span> + +<span class="c1">// Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span> + +<span class="c1">// Select (prediction, true label) and compute test error</span> +<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RegressionEvaluator</span><span class="o">()</span> + <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"rmse"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">rmse</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Root Mean Squared Error (RMSE) on test data = "</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">)</span> + +<span class="k">val</span> <span class="n">rfModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">RandomForestRegressionModel</span><span class="o">]</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Learned regression forest model:\n"</span> <span class="o">+</span> <span class="n">rfModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div> + + </div> + +<div data-lang="java"> + + <p>Refer to the <a href="api/java/org/apache/spark/ml/regression/RandomForestRegressor.html">Java API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexerModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.RandomForestRegressionModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.RandomForestRegressor</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.rdd.RDD</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span> + +<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span> +<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">rdd</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span> +<span class="n">DataFrame</span> <span class="n">data</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rdd</span><span class="o">,</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span> + +<span class="c1">// Automatically identify categorical features, and index them.</span> +<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> + <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> + +<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> +<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span> +<span class="n">DataFrame</span> <span class="n">trainingData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> +<span class="n">DataFrame</span> <span class="n">testData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span> + +<span class="c1">// Train a RandomForest model.</span> +<span class="n">RandomForestRegressor</span> <span class="n">rf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RandomForestRegressor</span><span class="o">()</span> + <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">);</span> + +<span class="c1">// Chain indexer and forest in a Pipeline</span> +<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span> + <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">rf</span><span class="o">});</span> + +<span class="c1">// Train model. This also runs the indexer.</span> +<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span> + +<span class="c1">// Make predictions.</span> +<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span> + +<span class="c1">// Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span> + +<span class="c1">// Select (prediction, true label) and compute test error</span> +<span class="n">RegressionEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RegressionEvaluator</span><span class="o">()</span> + <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"rmse"</span><span class="o">);</span> +<span class="kt">double</span> <span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Root Mean Squared Error (RMSE) on test data = "</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">);</span> + +<span class="n">RandomForestRegressionModel</span> <span class="n">rfModel</span> <span class="o">=</span> + <span class="o">(</span><span class="n">RandomForestRegressionModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">1</span><span class="o">]);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Learned regression forest model:\n"</span> <span class="o">+</span> <span class="n">rfModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div> + + </div> + +<div data-lang="python"> + + <p>Refer to the <a href="api/python/pyspark.ml.html#pyspark.ml.regression.RandomForestRegressor">Python API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.regression</span> <span class="kn">import</span> <span class="n">RandomForestRegressor</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">VectorIndexer</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">RegressionEvaluator</span> +<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span> + +<span class="c"># Load and parse the data file, converting it to a DataFrame.</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span> + +<span class="c"># Automatically identify categorical features, and index them.</span> +<span class="c"># Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="n">featureIndexer</span> <span class="o">=</span>\ + <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> + +<span class="c"># Split the data into training and test sets (30% held out for testing)</span> +<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> + +<span class="c"># Train a RandomForest model.</span> +<span class="n">rf</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">(</span><span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">)</span> + +<span class="c"># Chain indexer and forest in a Pipeline</span> +<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">featureIndexer</span><span class="p">,</span> <span class="n">rf</span><span class="p">])</span> + +<span class="c"># Train model. This also runs the indexer.</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span> + +<span class="c"># Make predictions.</span> +<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span> + +<span class="c"># Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"prediction"</span><span class="p">,</span> <span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> + +<span class="c"># Select (prediction, true label) and compute test error</span> +<span class="n">evaluator</span> <span class="o">=</span> <span class="n">RegressionEvaluator</span><span class="p">(</span> + <span class="n">labelCol</span><span class="o">=</span><span class="s">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"rmse"</span><span class="p">)</span> +<span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> +<span class="k">print</span> <span class="s">"Root Mean Squared Error (RMSE) on test data = </span><span class="si">%g</span><span class="s">"</span> <span class="o">%</span> <span class="n">rmse</span> + +<span class="n">rfModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> +<span class="k">print</span> <span class="n">rfModel</span> <span class="c"># summary only</span></code></pre></div> + + </div> +</div> + +<h3 id="gradient-boosted-trees-gbts">Gradient-Boosted Trees (GBTs)</h3> + +<p><a href="http://en.wikipedia.org/wiki/Gradient_boosting">Gradient-Boosted Trees (GBTs)</a> +are ensembles of <a href="ml-decision-tree.html">decision trees</a>. +GBTs iteratively train decision trees in order to minimize a loss function. +MLlib supports GBTs for binary classification and for regression, +using both continuous and categorical features.</p> + +<p>This section gives examples of using GBTs with the Pipelines API. +For more information on the algorithm, please see the <a href="mllib-ensembles.html">main MLlib docs on GBTs</a>.</p> + +<h4 id="inputs-and-outputs-1">Inputs and Outputs</h4> + +<p>We list the input and output (prediction) column types here. +All output columns are optional; to exclude an output column, set its corresponding Param to an empty string.</p> + +<h5 id="input-columns-1">Input Columns</h5> + +<table class="table"> + <thead> + <tr> + <th align="left">Param name</th> + <th align="left">Type(s)</th> + <th align="left">Default</th> + <th align="left">Description</th> + </tr> + </thead> + <tbody> + <tr> + <td>labelCol</td> + <td>Double</td> + <td>"label"</td> + <td>Label to predict</td> + </tr> + <tr> + <td>featuresCol</td> + <td>Vector</td> + <td>"features"</td> + <td>Feature vector</td> + </tr> + </tbody> +</table> + +<p>Note that <code>GBTClassifier</code> currently only supports binary labels.</p> + +<h5 id="output-columns-predictions-1">Output Columns (Predictions)</h5> + +<table class="table"> + <thead> + <tr> + <th align="left">Param name</th> + <th align="left">Type(s)</th> + <th align="left">Default</th> + <th align="left">Description</th> + <th align="left">Notes</th> + </tr> + </thead> + <tbody> + <tr> + <td>predictionCol</td> + <td>Double</td> + <td>"prediction"</td> + <td>Predicted label</td> + <td></td> + </tr> + </tbody> +</table> + +<p>In the future, <code>GBTClassifier</code> will also output columns for <code>rawPrediction</code> and <code>probability</code>, just as <code>RandomForestClassifier</code> does.</p> + +<h4 id="example-classification-1">Example: Classification</h4> + +<p>The following examples load a dataset in LibSVM format, split it into training and test sets, train on the first dataset, and then evaluate on the held-out test set. +We use two feature transformers to prepare the data; these help index categories for the label and categorical features, adding metadata to the <code>DataFrame</code> which the tree-based algorithms can recognize.</p> + +<div class="codetabs"> +<div data-lang="scala"> + + <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.classification.GBTClassifier">Scala API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.GBTClassifier</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.GBTClassificationModel</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">StringIndexer</span><span class="o">,</span> <span class="nc">IndexToString</span><span class="o">,</span> <span class="nc">VectorIndexer</span><span class="o">}</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> + +<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span> +<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span> + +<span class="c1">// Index labels, adding metadata to the label column.</span> +<span class="c1">// Fit on whole dataset to include all labels in index.</span> +<span class="k">val</span> <span class="n">labelIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">StringIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> +<span class="c1">// Automatically identify categorical features, and index them.</span> +<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> + <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> + +<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> +<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">))</span> + +<span class="c1">// Train a GBT model.</span> +<span class="k">val</span> <span class="n">gbt</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">GBTClassifier</span><span class="o">()</span> + <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> + +<span class="c1">// Convert indexed labels back to original labels.</span> +<span class="k">val</span> <span class="n">labelConverter</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">IndexToString</span><span class="o">()</span> + <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="n">labels</span><span class="o">)</span> + +<span class="c1">// Chain indexers and GBT in a Pipeline</span> +<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span> + <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">gbt</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">))</span> + +<span class="c1">// Train model. This also runs the indexers.</span> +<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> + +<span class="c1">// Make predictions.</span> +<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span> + +<span class="c1">// Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span> + +<span class="c1">// Select (prediction, true label) and compute test error</span> +<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">MulticlassClassificationEvaluator</span><span class="o">()</span> + <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"precision"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">accuracy</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Test Error = "</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">))</span> + +<span class="k">val</span> <span class="n">gbtModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">2</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">GBTClassificationModel</span><span class="o">]</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Learned classification GBT model:\n"</span> <span class="o">+</span> <span class="n">gbtModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div> + + </div> + +<div data-lang="java"> + + <p>Refer to the <a href="api/java/org/apache/spark/ml/classification/GBTClassifier.html">Java API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.GBTClassifier</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.GBTClassificationModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.*</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.rdd.RDD</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span> + +<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span> +<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">rdd</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span> +<span class="n">DataFrame</span> <span class="n">data</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rdd</span><span class="o">,</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span> + +<span class="c1">// Index labels, adding metadata to the label column.</span> +<span class="c1">// Fit on whole dataset to include all labels in index.</span> +<span class="n">StringIndexerModel</span> <span class="n">labelIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">StringIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> +<span class="c1">// Automatically identify categorical features, and index them.</span> +<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> + <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> + +<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> +<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span> +<span class="n">DataFrame</span> <span class="n">trainingData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> +<span class="n">DataFrame</span> <span class="n">testData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span> + +<span class="c1">// Train a GBT model.</span> +<span class="n">GBTClassifier</span> <span class="n">gbt</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">GBTClassifier</span><span class="o">()</span> + <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span> + +<span class="c1">// Convert indexed labels back to original labels.</span> +<span class="n">IndexToString</span> <span class="n">labelConverter</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">IndexToString</span><span class="o">()</span> + <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setLabels</span><span class="o">(</span><span class="n">labelIndexer</span><span class="o">.</span><span class="na">labels</span><span class="o">());</span> + +<span class="c1">// Chain indexers and GBT in a Pipeline</span> +<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span> + <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">labelIndexer</span><span class="o">,</span> <span class="n">featureIndexer</span><span class="o">,</span> <span class="n">gbt</span><span class="o">,</span> <span class="n">labelConverter</span><span class="o">});</span> + +<span class="c1">// Train model. This also runs the indexers.</span> +<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span> + +<span class="c1">// Make predictions.</span> +<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span> + +<span class="c1">// Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"predictedLabel"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span> + +<span class="c1">// Select (prediction, true label) and compute test error</span> +<span class="n">MulticlassClassificationEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">MulticlassClassificationEvaluator</span><span class="o">()</span> + <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"indexedLabel"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"precision"</span><span class="o">);</span> +<span class="kt">double</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Test Error = "</span> <span class="o">+</span> <span class="o">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="o">));</span> + +<span class="n">GBTClassificationModel</span> <span class="n">gbtModel</span> <span class="o">=</span> + <span class="o">(</span><span class="n">GBTClassificationModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">2</span><span class="o">]);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Learned classification GBT model:\n"</span> <span class="o">+</span> <span class="n">gbtModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div> + + </div> + +<div data-lang="python"> + + <p>Refer to the <a href="api/python/pyspark.ml.html#pyspark.ml.classification.GBTClassifier">Python API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.classification</span> <span class="kn">import</span> <span class="n">GBTClassifier</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">StringIndexer</span><span class="p">,</span> <span class="n">VectorIndexer</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">MulticlassClassificationEvaluator</span> +<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span> + +<span class="c"># Load and parse the data file, converting it to a DataFrame.</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span> + +<span class="c"># Index labels, adding metadata to the label column.</span> +<span class="c"># Fit on whole dataset to include all labels in index.</span> +<span class="n">labelIndexer</span> <span class="o">=</span> <span class="n">StringIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"label"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> +<span class="c"># Automatically identify categorical features, and index them.</span> +<span class="c"># Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="n">featureIndexer</span> <span class="o">=</span>\ + <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> + +<span class="c"># Split the data into training and test sets (30% held out for testing)</span> +<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> + +<span class="c"># Train a GBT model.</span> +<span class="n">gbt</span> <span class="o">=</span> <span class="n">GBTClassifier</span><span class="p">(</span><span class="n">labelCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">,</span> <span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> + +<span class="c"># Chain indexers and GBT in a Pipeline</span> +<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">labelIndexer</span><span class="p">,</span> <span class="n">featureIndexer</span><span class="p">,</span> <span class="n">gbt</span><span class="p">])</span> + +<span class="c"># Train model. This also runs the indexers.</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span> + +<span class="c"># Make predictions.</span> +<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span> + +<span class="c"># Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"prediction"</span><span class="p">,</span> <span class="s">"indexedLabel"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> + +<span class="c"># Select (prediction, true label) and compute test error</span> +<span class="n">evaluator</span> <span class="o">=</span> <span class="n">MulticlassClassificationEvaluator</span><span class="p">(</span> + <span class="n">labelCol</span><span class="o">=</span><span class="s">"indexedLabel"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"precision"</span><span class="p">)</span> +<span class="n">accuracy</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> +<span class="k">print</span> <span class="s">"Test Error = </span><span class="si">%g</span><span class="s">"</span> <span class="o">%</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">accuracy</span><span class="p">)</span> + +<span class="n">gbtModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> +<span class="k">print</span> <span class="n">gbtModel</span> <span class="c"># summary only</span></code></pre></div> + + </div> +</div> + +<h4 id="example-regression-1">Example: Regression</h4> + +<p>Note: For this example dataset, <code>GBTRegressor</code> actually only needs 1 iteration, but that will not +be true in general.</p> + +<div class="codetabs"> +<div data-lang="scala"> + + <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.regression.GBTRegressor">Scala API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.GBTRegressor</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.regression.GBTRegressionModel</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span> +<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> + +<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span> +<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">).</span><span class="n">toDF</span><span class="o">()</span> + +<span class="c1">// Automatically identify categorical features, and index them.</span> +<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="k">val</span> <span class="n">featureIndexer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">VectorIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> + <span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">data</span><span class="o">)</span> + +<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> +<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">trainingData</span><span class="o">,</span> <span class="n">testData</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">))</span> + +<span class="c1">// Train a GBT model.</span> +<span class="k">val</span> <span class="n">gbt</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">GBTRegressor</span><span class="o">()</span> + <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span> + +<span class="c1">// Chain indexer and GBT in a Pipeline</span> +<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span> + <span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">gbt</span><span class="o">))</span> + +<span class="c1">// Train model. This also runs the indexer.</span> +<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> + +<span class="c1">// Make predictions.</span> +<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">)</span> + +<span class="c1">// Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="n">show</span><span class="o">(</span><span class="mi">5</span><span class="o">)</span> + +<span class="c1">// Select (prediction, true label) and compute test error</span> +<span class="k">val</span> <span class="n">evaluator</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">RegressionEvaluator</span><span class="o">()</span> + <span class="o">.</span><span class="n">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMetricName</span><span class="o">(</span><span class="s">"rmse"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">rmse</span> <span class="k">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Root Mean Squared Error (RMSE) on test data = "</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">)</span> + +<span class="k">val</span> <span class="n">gbtModel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="o">(</span><span class="mi">1</span><span class="o">).</span><span class="n">asInstanceOf</span><span class="o">[</span><span class="kt">GBTRegressionModel</span><span class="o">]</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Learned regression GBT model:\n"</span> <span class="o">+</span> <span class="n">gbtModel</span><span class="o">.</span><span class="n">toDebugString</span><span class="o">)</span></code></pre></div> + + </div> + +<div data-lang="java"> + + <p>Refer to the <a href="api/java/org/apache/spark/ml/regression/GBTRegressor.html">Java API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.RegressionEvaluator</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexer</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.VectorIndexerModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.GBTRegressionModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.regression.GBTRegressor</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.rdd.RDD</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span> + +<span class="c1">// Load and parse the data file, converting it to a DataFrame.</span> +<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">rdd</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="o">);</span> +<span class="n">DataFrame</span> <span class="n">data</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">rdd</span><span class="o">,</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span> + +<span class="c1">// Automatically identify categorical features, and index them.</span> +<span class="c1">// Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="n">VectorIndexerModel</span> <span class="n">featureIndexer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">VectorIndexer</span><span class="o">()</span> + <span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMaxCategories</span><span class="o">(</span><span class="mi">4</span><span class="o">)</span> + <span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">data</span><span class="o">);</span> + +<span class="c1">// Split the data into training and test sets (30% held out for testing)</span> +<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">});</span> +<span class="n">DataFrame</span> <span class="n">trainingData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> +<span class="n">DataFrame</span> <span class="n">testData</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span> + +<span class="c1">// Train a GBT model.</span> +<span class="n">GBTRegressor</span> <span class="n">gbt</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">GBTRegressor</span><span class="o">()</span> + <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setFeaturesCol</span><span class="o">(</span><span class="s">"indexedFeatures"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span> + +<span class="c1">// Chain indexer and GBT in a Pipeline</span> +<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span> + <span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">featureIndexer</span><span class="o">,</span> <span class="n">gbt</span><span class="o">});</span> + +<span class="c1">// Train model. This also runs the indexer.</span> +<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">trainingData</span><span class="o">);</span> + +<span class="c1">// Make predictions.</span> +<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">testData</span><span class="o">);</span> + +<span class="c1">// Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"features"</span><span class="o">).</span><span class="na">show</span><span class="o">(</span><span class="mi">5</span><span class="o">);</span> + +<span class="c1">// Select (prediction, true label) and compute test error</span> +<span class="n">RegressionEvaluator</span> <span class="n">evaluator</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">RegressionEvaluator</span><span class="o">()</span> + <span class="o">.</span><span class="na">setLabelCol</span><span class="o">(</span><span class="s">"label"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setPredictionCol</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMetricName</span><span class="o">(</span><span class="s">"rmse"</span><span class="o">);</span> +<span class="kt">double</span> <span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="na">evaluate</span><span class="o">(</span><span class="n">predictions</span><span class="o">);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Root Mean Squared Error (RMSE) on test data = "</span> <span class="o">+</span> <span class="n">rmse</span><span class="o">);</span> + +<span class="n">GBTRegressionModel</span> <span class="n">gbtModel</span> <span class="o">=</span> + <span class="o">(</span><span class="n">GBTRegressionModel</span><span class="o">)(</span><span class="n">model</span><span class="o">.</span><span class="na">stages</span><span class="o">()[</span><span class="mi">1</span><span class="o">]);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Learned regression GBT model:\n"</span> <span class="o">+</span> <span class="n">gbtModel</span><span class="o">.</span><span class="na">toDebugString</span><span class="o">());</span></code></pre></div> + + </div> + +<div data-lang="python"> + + <p>Refer to the <a href="api/python/pyspark.ml.html#pyspark.ml.regression.GBTRegressor">Python API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.regression</span> <span class="kn">import</span> <span class="n">GBTRegressor</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">VectorIndexer</span> +<span class="kn">from</span> <span class="nn">pyspark.ml.evaluation</span> <span class="kn">import</span> <span class="n">RegressionEvaluator</span> +<span class="kn">from</span> <span class="nn">pyspark.mllib.util</span> <span class="kn">import</span> <span class="n">MLUtils</span> + +<span class="c"># Load and parse the data file, converting it to a DataFrame.</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"data/mllib/sample_libsvm_data.txt"</span><span class="p">)</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span> + +<span class="c"># Automatically identify categorical features, and index them.</span> +<span class="c"># Set maxCategories so features with > 4 distinct values are treated as continuous.</span> +<span class="n">featureIndexer</span> <span class="o">=</span>\ + <span class="n">VectorIndexer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxCategories</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> + +<span class="c"># Split the data into training and test sets (30% held out for testing)</span> +<span class="p">(</span><span class="n">trainingData</span><span class="p">,</span> <span class="n">testData</span><span class="p">)</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">randomSplit</span><span class="p">([</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span> + +<span class="c"># Train a GBT model.</span> +<span class="n">gbt</span> <span class="o">=</span> <span class="n">GBTRegressor</span><span class="p">(</span><span class="n">featuresCol</span><span class="o">=</span><span class="s">"indexedFeatures"</span><span class="p">,</span> <span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> + +<span class="c"># Chain indexer and GBT in a Pipeline</span> +<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">featureIndexer</span><span class="p">,</span> <span class="n">gbt</span><span class="p">])</span> + +<span class="c"># Train model. This also runs the indexer.</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">trainingData</span><span class="p">)</span> + +<span class="c"># Make predictions.</span> +<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">testData</span><span class="p">)</span> + +<span class="c"># Select example rows to display.</span> +<span class="n">predictions</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"prediction"</span><span class="p">,</span> <span class="s">"label"</span><span class="p">,</span> <span class="s">"features"</span><span class="p">)</span><span class="o">.</span><span class="n">show</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> + +<span class="c"># Select (prediction, true label) and compute test error</span> +<span class="n">evaluator</span> <span class="o">=</span> <span class="n">RegressionEvaluator</span><span class="p">(</span> + <span class="n">labelCol</span><span class="o">=</span><span class="s">"label"</span><span class="p">,</span> <span class="n">predictionCol</span><span class="o">=</span><span class="s">"prediction"</span><span class="p">,</span> <span class="n">metricName</span><span class="o">=</span><span class="s">"rmse"</span><span class="p">)</span> +<span class="n">rmse</span> <span class="o">=</span> <span class="n">evaluator</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span> +<span class="k">print</span> <span class="s">"Root Mean Squared Error (RMSE) on test data = </span><span class="si">%g</span><span class="s">"</span> <span class="o">%</span> <span class="n">rmse</span> + +<span class="n">gbtModel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">stages</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> +<span class="k">print</span> <span class="n">gbtModel</span> <span class="c"># summary only</span></code></pre></div> + + </div> +</div> + +<h2 id="one-vs-rest-aka-one-vs-all">One-vs-Rest (a.k.a. One-vs-All)</h2> + +<p><a href="http://en.wikipedia.org/wiki/Multiclass_classification#One-vs.-rest">OneVsRest</a> is an example of a machine learning reduction for performing multiclass classification given a base classifier that can perform binary classification efficiently. It is also known as “One-vs-All.”</p> + +<p><code>OneVsRest</code> is implemented as an <code>Estimator</code>. For the base classifier it takes instances of <code>Classifier</code> and creates a binary classification problem for each of the k classes. The classifier for class i is trained to predict whether the label is i or not, distinguishing class i from all other classes.</p> + +<p>Predictions are done by evaluating each binary classifier and the index of the most confident classifier is output as label.</p> + +<h3 id="example">Example</h3> + +<p>The example below demonstrates how to load the +<a href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/iris.scale">Iris dataset</a>, parse it as a DataFrame and perform multiclass classification using <code>OneVsRest</code>. The test error is calculated to measure the algorithm accuracy.</p> + +<div class="codetabs"> +<div data-lang="scala"> + + <p>Refer to the <a href="api/scala/index.html#org.apache.spark.ml.classifier.OneVsRest">Scala API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.</span><span class="o">{</span><span class="nc">LogisticRegression</span><span class="o">,</span> <span class="nc">OneVsRest</span><span class="o">}</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.evaluation.MulticlassMetrics</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span> +<span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span> + +<span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span> + +<span class="c1">// parse data into dataframe</span> +<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="nc">MLUtils</span><span class="o">.</span><span class="n">loadLibSVMFile</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> + <span class="s">"data/mllib/sample_multiclass_classification_data.txt"</span><span class="o">)</span> +<span class="k">val</span> <span class="nc">Array</span><span class="o">(</span><span class="n">train</span><span class="o">,</span> <span class="n">test</span><span class="o">)</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">toDF</span><span class="o">().</span><span class="n">randomSplit</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">))</span> + +<span class="c1">// instantiate multiclass learner and train</span> +<span class="k">val</span> <span class="n">ovr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">OneVsRest</span><span class="o">().</span><span class="n">setClassifier</span><span class="o">(</span><span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">)</span> + +<span class="k">val</span> <span class="n">ovrModel</span> <span class="k">=</span> <span class="n">ovr</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">train</span><span class="o">)</span> + +<span class="c1">// score model on test data</span> +<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">ovrModel</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">test</span><span class="o">).</span><span class="n">select</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">predictionsAndLabels</span> <span class="k">=</span> <span class="n">predictions</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span><span class="k">case</span> <span class="nc">Row</span><span class="o">(</span><span class="n">p</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> <span class="n">l</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">=></span> <span class="o">(</span><span class="n">p</span><span class="o">,</span> <span class="n">l</span><span class="o">)}</span> + +<span class="c1">// compute confusion matrix</span> +<span class="k">val</span> <span class="n">metrics</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">MulticlassMetrics</span><span class="o">(</span><span class="n">predictionsAndLabels</span><span class="o">)</span> +<span class="n">println</span><span class="o">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">confusionMatrix</span><span class="o">)</span> + +<span class="c1">// the Iris DataSet has three classes</span> +<span class="k">val</span> <span class="n">numClasses</span> <span class="k">=</span> <span class="mi">3</span> + +<span class="n">println</span><span class="o">(</span><span class="s">"label\tfpr\n"</span><span class="o">)</span> +<span class="o">(</span><span class="mi">0</span> <span class="n">until</span> <span class="n">numClasses</span><span class="o">).</span><span class="n">foreach</span> <span class="o">{</span> <span class="n">index</span> <span class="k">=></span> + <span class="k">val</span> <span class="n">label</span> <span class="k">=</span> <span class="n">index</span><span class="o">.</span><span class="n">toDouble</span> + <span class="n">println</span><span class="o">(</span><span class="n">label</span> <span class="o">+</span> <span class="s">"\t"</span> <span class="o">+</span> <span class="n">metrics</span><span class="o">.</span><span class="n">falsePositiveRate</span><span class="o">(</span><span class="n">label</span><span class="o">))</span> +<span class="o">}</span></code></pre></div> + + </div> + +<div data-lang="java"> + + <p>Refer to the <a href="api/java/org/apache/spark/ml/classification/OneVsRest.html">Java API docs</a> for more details.</p> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.OneVsRest</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.OneVsRestModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.evaluation.MulticlassMetrics</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Matrix</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.util.MLUtils</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.rdd.RDD</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span> + +<span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">"JavaOneVsRestExample"</span><span class="o">);</span> +<span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span> +<span class="n">SQLContext</span> <span class="n">jsql</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span> + +<span class="n">RDD</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">data</span> <span class="o">=</span> <span class="n">MLUtils</span><span class="o">.</span><span class="na">loadLibSVMFile</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">sc</span><span class="o">(),</span> + <span class="s">"data/mllib/sample_multiclass_classification_data.txt"</span><span class="o">);</span> + +<span class="n">DataFrame</span> <span class="n">dataFrame</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">data</span><span class="o">,</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span> +<span class="n">DataFrame</span><span class="o">[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">dataFrame</span><span class="o">.</span><span class="na">randomSplit</span><span class="o">(</span><span class="k">new</span> <span class="kt">double</span><span class="o">[]</span> <span class="o">{</span><span class="mf">0.7</span><span class="o">,</span> <span class="mf">0.3</span><span class="o">},</span> <span class="mi">12345</span><span class="o">);</span> +<span class="n">DataFrame</span> <span class="n">train</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">0</span><span class="o">];</span> +<span class="n">DataFrame</span> <span class="n">test</span> <span class="o">=</span> <span class="n">splits</span><span class="o">[</span><span class="mi">1</span><span class="o">];</span> + +<span class="c1">// instantiate the One Vs Rest Classifier</span> +<span class="n">OneVsRest</span> <span class="n">ovr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">OneVsRest</span><span class="o">().</span><span class="na">setClassifier</span><span class="o">(</span><span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">());</span> + +<span class="c1">// train the multiclass model</span> +<span class="n">OneVsRestModel</span> <span class="n">ovrModel</span> <span class="o">=</span> <span class="n">ovr</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">train</span><span class="o">.</span><span class="na">cache</span><span class="o">());</span> + +<span class="c1">// score the model on test data</span> +<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">ovrModel</span> + <span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">test</span><span class="o">)</span> + <span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"prediction"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">);</span> + +<span class="c1">// obtain metrics</span> +<span class="n">MulticlassMetrics</span> <span class="n">metrics</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">MulticlassMetrics</span><span class="o">(</span><span class="n">predictions</span><span class="o">);</span> +<span class="n">Matrix</span> <span class="n">confusionMatrix</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="na">confusionMatrix</span><span class="o">();</span> + +<span class="c1">// output the Confusion Matrix</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Confusion Matrix"</span><span class="o">);</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="n">confusionMatrix</span><span class="o">);</span> + +<span class="c1">// compute the false positive rate per label</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">();</span> +<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"label\tfpr\n"</span><span class="o">);</span> + +<span class="c1">// the Iris DataSet has three classes</span> +<span class="kt">int</span> <span class="n">numClasses</span> <span class="o">=</span> <span class="mi">3</span><span class="o">;</span> +<span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">index</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">index</span> <span class="o"><</span> <span class="n">numClasses</span><span class="o">;</span> <span class="n">index</span><span class="o">++)</span> <span class="o">{</span> + <span class="kt">double</span> <span class="n">label</span> <span class="o">=</span> <span class="o">(</span><span class="kt">double</span><span class="o">)</span> <span class="n">index</span><span class="o">;</span> + <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">print</span><span class="o">(</span><span class="n">label</span><span class="o">);</span> + <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">print</span><span class="o">(</span><span class="s">"\t"</span><span class="o">);</span> + <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">print</span><span class="o">(</span><span class="n">metrics</span><span class="o">.</span><span class="na">falsePositiveRate</span><span class="o">(</span><span class="n">label</span><span class="o">));</span> + <span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">();</span> +<span class="o">}</span></code></pre></div> + + </div> +</div> + + + </div> <!-- /container --> + + <script src="js/vendor/jquery-1.8.0.min.js"></script> + <script src="js/vendor/bootstrap.min.js"></script> + <script src="js/vendor/anchor.min.js"></script> + <script src="js/main.js"></script> + + <!-- MathJax Section --> + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + TeX: { equationNumbers: { autoNumber: "AMS" } } + }); + </script> + <script> + // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS. + // We could use "//cdn.mathjax...", but that won't support "file://". + (function(d, script) { + script = d.createElement('script'); + script.type = 'text/javascript'; + script.async = true; + script.onload = function(){ + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ], + displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], + processEscapes: true, + skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'] + } + }); + }; + script.src = ('https:' == document.location.protocol ? 'https://' : 'http://') + + 'cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'; + d.getElementsByTagName('head')[0].appendChild(script); + }(document)); + </script> + </body> +</html> |