<!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>Naive Bayes - MLlib - Spark 1.3.1 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">1.3.1</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>
</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>
</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">v1.3.1</span></p>-->
</div>
</div>
</div>
<div class="container" id="content">
<h1 class="title"><a href="mllib-guide.html">MLlib</a> - Regression</h1>
<h2 id="isotonic-regression">Isotonic regression</h2>
<p><a href="http://en.wikipedia.org/wiki/Isotonic_regression">Isotonic regression</a>
belongs to the family of regression algorithms. Formally isotonic regression is a problem where
given a finite set of real numbers <code>$Y = {y_1, y_2, ..., y_n}$</code> representing observed responses
and <code>$X = {x_1, x_2, ..., x_n}$</code> the unknown response values to be fitted
finding a function that minimises</p>
<p><code>\begin{equation}
f(x) = \sum_{i=1}^n w_i (y_i - x_i)^2
\end{equation}</code></p>
<p>with respect to complete order subject to
<code>$x_1\le x_2\le ...\le x_n$</code> where <code>$w_i$</code> are positive weights.
The resulting function is called isotonic regression and it is unique.
It can be viewed as least squares problem under order restriction.
Essentially isotonic regression is a
<a href="http://en.wikipedia.org/wiki/Monotonic_function">monotonic function</a>
best fitting the original data points.</p>
<p>MLlib supports a
<a href="http://doi.org/10.1198/TECH.2010.10111">pool adjacent violators algorithm</a>
which uses an approach to
<a href="http://doi.org/10.1007/978-3-642-99789-1_10">parallelizing isotonic regression</a>.
The training input is a RDD of tuples of three double values that represent
label, feature and weight in this order. Additionally IsotonicRegression algorithm has one
optional parameter called $isotonic$ defaulting to true.
This argument specifies if the isotonic regression is
isotonic (monotonically increasing) or antitonic (monotonically decreasing).</p>
<p>Training returns an IsotonicRegressionModel that can be used to predict
labels for both known and unknown features. The result of isotonic regression
is treated as piecewise linear function. The rules for prediction therefore are:</p>
<ul>
<li>If the prediction input exactly matches a training feature
then associated prediction is returned. In case there are multiple predictions with the same
feature then one of them is returned. Which one is undefined
(same as java.util.Arrays.binarySearch).</li>
<li>If the prediction input is lower or higher than all training features
then prediction with lowest or highest feature is returned respectively.
In case there are multiple predictions with the same feature
then the lowest or highest is returned respectively.</li>
<li>If the prediction input falls between two training features then prediction is treated
as piecewise linear function and interpolated value is calculated from the
predictions of the two closest features. In case there are multiple values
with the same feature then the same rules as in previous point are used.</li>
</ul>
<h3 id="examples">Examples</h3>
<div class="codetabs">
<div data-lang="scala">
<p>Data are read from a file where each line has a format label,feature
i.e. 4710.28,500.00. The data are split to training and testing set.
Model is created using the training set and a mean squared error is calculated from the predicted
labels and real labels in the test set.</p>
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.IsotonicRegression</span>
<span class="k">val</span> <span class="n">data</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="o">(</span><span class="s">"data/mllib/sample_isotonic_regression_data.txt"</span><span class="o">)</span>
<span class="c1">// Create label, feature, weight tuples from input data with weight set to default value 1.0.</span>
<span class="k">val</span> <span class="n">parsedData</span> <span class="k">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">line</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">parts</span> <span class="k">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">','</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="k">_</span><span class="o">.</span><span class="n">toDouble</span><span class="o">)</span>
<span class="o">(</span><span class="n">parts</span><span class="o">(</span><span class="mi">0</span><span class="o">),</span> <span class="n">parts</span><span class="o">(</span><span class="mi">1</span><span class="o">),</span> <span class="mf">1.0</span><span class="o">)</span>
<span class="o">}</span>
<span class="c1">// Split data into training (60%) and test (40%) sets.</span>
<span class="k">val</span> <span class="n">splits</span> <span class="k">=</span> <span class="n">parsedData</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">0.6</span><span class="o">,</span> <span class="mf">0.4</span><span class="o">),</span> <span class="n">seed</span> <span class="k">=</span> <span class="mi">11L</span><span class="o">)</span>
<span class="k">val</span> <span class="n">training</span> <span class="k">=</span> <span class="n">splits</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span>
<span class="k">val</span> <span class="n">test</span> <span class="k">=</span> <span class="n">splits</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span>
<span class="c1">// Create isotonic regression model from training data.</span>
<span class="c1">// Isotonic parameter defaults to true so it is only shown for demonstration</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">IsotonicRegression</span><span class="o">().</span><span class="n">setIsotonic</span><span class="o">(</span><span class="kc">true</span><span class="o">).</span><span class="n">run</span><span class="o">(</span><span class="n">training</span><span class="o">)</span>
<span class="c1">// Create tuples of predicted and real labels.</span>
<span class="k">val</span> <span class="n">predictionAndLabel</span> <span class="k">=</span> <span class="n">test</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">point</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">predictedLabel</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="n">_2</span><span class="o">)</span>
<span class="o">(</span><span class="n">predictedLabel</span><span class="o">,</span> <span class="n">point</span><span class="o">.</span><span class="n">_1</span><span class="o">)</span>
<span class="o">}</span>
<span class="c1">// Calculate mean squared error between predicted and real labels.</span>
<span class="k">val</span> <span class="n">meanSquaredError</span> <span class="k">=</span> <span class="n">predictionAndLabel</span><span class="o">.</span><span class="n">map</span><span class="o">{</span><span class="k">case</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="k">=></span> <span class="n">math</span><span class="o">.</span><span class="n">pow</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="mi">2</span><span class="o">)}.</span><span class="n">mean</span><span class="o">()</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="n">meanSquaredError</span><span class="o">)</span></code></pre></div>
</div>
<div data-lang="java">
<p>Data are read from a file where each line has a format label,feature
i.e. 4710.28,500.00. The data are split to training and testing set.
Model is created using the training set and a mean squared error is calculated from the predicted
labels and real labels in the test set.</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.JavaDoubleRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaPairRDD</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaRDD</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.api.java.function.Function</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.function.PairFunction</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.IsotonicRegressionModel</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">scala.Tuple2</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">scala.Tuple3</span><span class="o">;</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">String</span><span class="o">></span> <span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="na">textFile</span><span class="o">(</span><span class="s">"data/mllib/sample_isotonic_regression_data.txt"</span><span class="o">);</span>
<span class="c1">// Create label, feature, weight tuples from input data with weight set to default value 1.0.</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Tuple3</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>></span> <span class="n">parsedData</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="na">map</span><span class="o">(</span>
<span class="k">new</span> <span class="n">Function</span><span class="o"><</span><span class="n">String</span><span class="o">,</span> <span class="n">Tuple3</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>>()</span> <span class="o">{</span>
<span class="kd">public</span> <span class="n">Tuple3</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">></span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">line</span><span class="o">)</span> <span class="o">{</span>
<span class="n">String</span><span class="o">[]</span> <span class="n">parts</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">","</span><span class="o">);</span>
<span class="k">return</span> <span class="k">new</span> <span class="n">Tuple3</span><span class="o"><>(</span><span class="k">new</span> <span class="nf">Double</span><span class="o">(</span><span class="n">parts</span><span class="o">[</span><span class="mi">0</span><span class="o">]),</span> <span class="k">new</span> <span class="nf">Double</span><span class="o">(</span><span class="n">parts</span><span class="o">[</span><span class="mi">1</span><span class="o">]),</span> <span class="mf">1.0</span><span class="o">);</span>
<span class="o">}</span>
<span class="o">}</span>
<span class="o">);</span>
<span class="c1">// Split data into training (60%) and test (40%) sets.</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Tuple3</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>>[]</span> <span class="n">splits</span> <span class="o">=</span> <span class="n">parsedData</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">0.6</span><span class="o">,</span> <span class="mf">0.4</span><span class="o">},</span> <span class="mi">11L</span><span class="o">);</span>
<span class="n">JavaRDD</span><span class="o"><</span><span class="n">Tuple3</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>></span> <span class="n">training</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">JavaRDD</span><span class="o"><</span><span class="n">Tuple3</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>></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">// Create isotonic regression model from training data.</span>
<span class="c1">// Isotonic parameter defaults to true so it is only shown for demonstration</span>
<span class="n">IsotonicRegressionModel</span> <span class="n">model</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">IsotonicRegression</span><span class="o">().</span><span class="na">setIsotonic</span><span class="o">(</span><span class="kc">true</span><span class="o">).</span><span class="na">run</span><span class="o">(</span><span class="n">training</span><span class="o">);</span>
<span class="c1">// Create tuples of predicted and real labels.</span>
<span class="n">JavaPairRDD</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">></span> <span class="n">predictionAndLabel</span> <span class="o">=</span> <span class="n">test</span><span class="o">.</span><span class="na">mapToPair</span><span class="o">(</span>
<span class="k">new</span> <span class="n">PairFunction</span><span class="o"><</span><span class="n">Tuple3</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>()</span> <span class="o">{</span>
<span class="nd">@Override</span> <span class="kd">public</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">></span> <span class="nf">call</span><span class="o">(</span><span class="n">Tuple3</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">></span> <span class="n">point</span><span class="o">)</span> <span class="o">{</span>
<span class="n">Double</span> <span class="n">predictedLabel</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">predict</span><span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="na">_2</span><span class="o">());</span>
<span class="k">return</span> <span class="k">new</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>(</span><span class="n">predictedLabel</span><span class="o">,</span> <span class="n">point</span><span class="o">.</span><span class="na">_1</span><span class="o">());</span>
<span class="o">}</span>
<span class="o">}</span>
<span class="o">);</span>
<span class="c1">// Calculate mean squared error between predicted and real labels.</span>
<span class="n">Double</span> <span class="n">meanSquaredError</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaDoubleRDD</span><span class="o">(</span><span class="n">predictionAndLabel</span><span class="o">.</span><span class="na">map</span><span class="o">(</span>
<span class="k">new</span> <span class="n">Function</span><span class="o"><</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">>,</span> <span class="n">Object</span><span class="o">>()</span> <span class="o">{</span>
<span class="nd">@Override</span> <span class="kd">public</span> <span class="n">Object</span> <span class="nf">call</span><span class="o">(</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">Double</span><span class="o">,</span> <span class="n">Double</span><span class="o">></span> <span class="n">pl</span><span class="o">)</span> <span class="o">{</span>
<span class="k">return</span> <span class="n">Math</span><span class="o">.</span><span class="na">pow</span><span class="o">(</span><span class="n">pl</span><span class="o">.</span><span class="na">_1</span><span class="o">()</span> <span class="o">-</span> <span class="n">pl</span><span class="o">.</span><span class="na">_2</span><span class="o">(),</span> <span class="mi">2</span><span class="o">);</span>
<span class="o">}</span>
<span class="o">}</span>
<span class="o">).</span><span class="na">rdd</span><span class="o">()).</span><span class="na">mean</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">"Mean Squared Error = "</span> <span class="o">+</span> <span class="n">meanSquaredError</span><span class="o">);</span></code></pre></div>
</div>
</div>
</div> <!-- /container -->
<script src="js/vendor/jquery-1.8.0.min.js"></script>
<script src="js/vendor/bootstrap.min.js"></script>
<script src="js/main.js"></script>
<!-- MathJax Section -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
TeX: { equationNumbers: { autoNumber: "AMS" } }
});
</script>
<script>
// Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS.
// We could use "//cdn.mathjax...", but that won't support "file://".
(function(d, script) {
script = d.createElement('script');
script.type = 'text/javascript';
script.async = true;
script.onload = function(){
MathJax.Hub.Config({
tex2jax: {
inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ],
displayMath: [ ["$$","$$"], ["\\[", "\\]"] ],
processEscapes: true,
skipTags: ['script', 'noscript', 'style', 'textarea', 'pre']
}
});
};
script.src = ('https:' == 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>
