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author | Patrick Wendell <pwendell@apache.org> | 2015-06-11 15:32:59 +0000 |
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committer | Patrick Wendell <pwendell@apache.org> | 2015-06-11 15:32:59 +0000 |
commit | 840d9f3df35e66c0032f7ac0c284ae4675a4f818 (patch) | |
tree | 9742b1bd3aa597e8f2c845959d42eb91170177a7 /site/docs/1.4.0/mllib-clustering.html | |
parent | 303c247e4604098518acaa2d8ebe63fc891706f0 (diff) | |
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diff --git a/site/docs/1.4.0/mllib-clustering.html b/site/docs/1.4.0/mllib-clustering.html new file mode 100644 index 000000000..6533cdf0b --- /dev/null +++ b/site/docs/1.4.0/mllib-clustering.html @@ -0,0 +1,741 @@ +<!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>Clustering - MLlib - Spark 1.4.0 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"> + + + + </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">1.4.0</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">v1.4.0</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + + <h1 class="title"><a href="mllib-guide.html">MLlib</a> - Clustering</h1> + + + <p>Clustering is an unsupervised learning problem whereby we aim to group subsets +of entities with one another based on some notion of similarity. Clustering is +often used for exploratory analysis and/or as a component of a hierarchical +supervised learning pipeline (in which distinct classifiers or regression +models are trained for each cluster).</p> + +<p>MLlib supports the following models:</p> + +<ul id="markdown-toc"> + <li><a href="#k-means" id="markdown-toc-k-means">K-means</a></li> + <li><a href="#gaussian-mixture" id="markdown-toc-gaussian-mixture">Gaussian mixture</a></li> + <li><a href="#power-iteration-clustering-pic" id="markdown-toc-power-iteration-clustering-pic">Power iteration clustering (PIC)</a></li> + <li><a href="#latent-dirichlet-allocation-lda" id="markdown-toc-latent-dirichlet-allocation-lda">Latent Dirichlet allocation (LDA)</a></li> + <li><a href="#streaming-k-means" id="markdown-toc-streaming-k-means">Streaming k-means</a></li> +</ul> + +<h2 id="k-means">K-means</h2> + +<p><a href="http://en.wikipedia.org/wiki/K-means_clustering">k-means</a> is one of the +most commonly used clustering algorithms that clusters the data points into a +predefined number of clusters. The MLlib implementation includes a parallelized +variant of the <a href="http://en.wikipedia.org/wiki/K-means%2B%2B">k-means++</a> method +called <a href="http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf">kmeans||</a>. +The implementation in MLlib has the following parameters:</p> + +<ul> + <li><em>k</em> is the number of desired clusters.</li> + <li><em>maxIterations</em> is the maximum number of iterations to run.</li> + <li><em>initializationMode</em> specifies either random initialization or +initialization via k-means||.</li> + <li><em>runs</em> is the number of times to run the k-means algorithm (k-means is not +guaranteed to find a globally optimal solution, and when run multiple times on +a given dataset, the algorithm returns the best clustering result).</li> + <li><em>initializationSteps</em> determines the number of steps in the k-means|| algorithm.</li> + <li><em>epsilon</em> determines the distance threshold within which we consider k-means to have converged.</li> +</ul> + +<p><strong>Examples</strong></p> + +<div class="codetabs"> +<div data-lang="scala"> + <p>The following code snippets can be executed in <code>spark-shell</code>.</p> + + <p>In the following example after loading and parsing data, we use the +<a href="api/scala/index.html#org.apache.spark.mllib.clustering.KMeans"><code>KMeans</code></a> object to cluster the data +into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within +Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing <em>k</em>. In fact the +optimal <em>k</em> is usually one where there is an “elbow” in the WSSSE graph.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.</span><span class="o">{</span><span class="nc">KMeans</span><span class="o">,</span> <span class="nc">KMeansModel</span><span class="o">}</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span> + +<span class="c1">// Load and parse the data</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/kmeans_data.txt"</span><span class="o">)</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">s</span> <span class="k">=></span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="n">s</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="n">cache</span><span class="o">()</span> + +<span class="c1">// Cluster the data into two classes using KMeans</span> +<span class="k">val</span> <span class="n">numClusters</span> <span class="k">=</span> <span class="mi">2</span> +<span class="k">val</span> <span class="n">numIterations</span> <span class="k">=</span> <span class="mi">20</span> +<span class="k">val</span> <span class="n">clusters</span> <span class="k">=</span> <span class="nc">KMeans</span><span class="o">.</span><span class="n">train</span><span class="o">(</span><span class="n">parsedData</span><span class="o">,</span> <span class="n">numClusters</span><span class="o">,</span> <span class="n">numIterations</span><span class="o">)</span> + +<span class="c1">// Evaluate clustering by computing Within Set Sum of Squared Errors</span> +<span class="k">val</span> <span class="nc">WSSSE</span> <span class="k">=</span> <span class="n">clusters</span><span class="o">.</span><span class="n">computeCost</span><span class="o">(</span><span class="n">parsedData</span><span class="o">)</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Within Set Sum of Squared Errors = "</span> <span class="o">+</span> <span class="nc">WSSSE</span><span class="o">)</span> + +<span class="c1">// Save and load model</span> +<span class="n">clusters</span><span class="o">.</span><span class="n">save</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"myModelPath"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">sameModel</span> <span class="k">=</span> <span class="nc">KMeansModel</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"myModelPath"</span><span class="o">)</span></code></pre></div> + + </div> + +<div data-lang="java"> + <p>All of MLlib’s methods use Java-friendly types, so you can import and call them there the same +way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the +Spark Java API uses a separate <code>JavaRDD</code> class. You can convert a Java RDD to a Scala one by +calling <code>.rdd()</code> on your <code>JavaRDD</code> object. A self-contained application example +that is equivalent to the provided example in Scala is given below:</p> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.*</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.mllib.clustering.KMeans</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.clustering.KMeansModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span> + +<span class="kd">public</span> <span class="kd">class</span> <span class="nc">KMeansExample</span> <span class="o">{</span> + <span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</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">"K-means Example"</span><span class="o">);</span> + <span class="n">JavaSparkContext</span> <span class="n">sc</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="c1">// Load and parse data</span> + <span class="n">String</span> <span class="n">path</span> <span class="o">=</span> <span class="s">"data/mllib/kmeans_data.txt"</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="n">path</span><span class="o">);</span> + <span class="n">JavaRDD</span><span class="o"><</span><span class="n">Vector</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">Vector</span><span class="o">>()</span> <span class="o">{</span> + <span class="kd">public</span> <span class="n">Vector</span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">{</span> + <span class="n">String</span><span class="o">[]</span> <span class="n">sarray</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="na">split</span><span class="o">(</span><span class="s">" "</span><span class="o">);</span> + <span class="kt">double</span><span class="o">[]</span> <span class="n">values</span> <span class="o">=</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[</span><span class="n">sarray</span><span class="o">.</span><span class="na">length</span><span class="o">];</span> + <span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">sarray</span><span class="o">.</span><span class="na">length</span><span class="o">;</span> <span class="n">i</span><span class="o">++)</span> + <span class="n">values</span><span class="o">[</span><span class="n">i</span><span class="o">]</span> <span class="o">=</span> <span class="n">Double</span><span class="o">.</span><span class="na">parseDouble</span><span class="o">(</span><span class="n">sarray</span><span class="o">[</span><span class="n">i</span><span class="o">]);</span> + <span class="k">return</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="n">values</span><span class="o">);</span> + <span class="o">}</span> + <span class="o">}</span> + <span class="o">);</span> + <span class="n">parsedData</span><span class="o">.</span><span class="na">cache</span><span class="o">();</span> + + <span class="c1">// Cluster the data into two classes using KMeans</span> + <span class="kt">int</span> <span class="n">numClusters</span> <span class="o">=</span> <span class="mi">2</span><span class="o">;</span> + <span class="kt">int</span> <span class="n">numIterations</span> <span class="o">=</span> <span class="mi">20</span><span class="o">;</span> + <span class="n">KMeansModel</span> <span class="n">clusters</span> <span class="o">=</span> <span class="n">KMeans</span><span class="o">.</span><span class="na">train</span><span class="o">(</span><span class="n">parsedData</span><span class="o">.</span><span class="na">rdd</span><span class="o">(),</span> <span class="n">numClusters</span><span class="o">,</span> <span class="n">numIterations</span><span class="o">);</span> + + <span class="c1">// Evaluate clustering by computing Within Set Sum of Squared Errors</span> + <span class="kt">double</span> <span class="n">WSSSE</span> <span class="o">=</span> <span class="n">clusters</span><span class="o">.</span><span class="na">computeCost</span><span class="o">(</span><span class="n">parsedData</span><span class="o">.</span><span class="na">rdd</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">"Within Set Sum of Squared Errors = "</span> <span class="o">+</span> <span class="n">WSSSE</span><span class="o">);</span> + + <span class="c1">// Save and load model</span> + <span class="n">clusters</span><span class="o">.</span><span class="na">save</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">"myModelPath"</span><span class="o">);</span> + <span class="n">KMeansModel</span> <span class="n">sameModel</span> <span class="o">=</span> <span class="n">KMeansModel</span><span class="o">.</span><span class="na">load</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">"myModelPath"</span><span class="o">);</span> + <span class="o">}</span> +<span class="o">}</span></code></pre></div> + + </div> + +<div data-lang="python"> + <p>The following examples can be tested in the PySpark shell.</p> + + <p>In the following example after loading and parsing data, we use the KMeans object to cluster the +data into two clusters. The number of desired clusters is passed to the algorithm. We then compute +Within Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing <em>k</em>. In +fact the optimal <em>k</em> is usually one where there is an “elbow” in the WSSSE graph.</p> + + <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.clustering</span> <span class="kn">import</span> <span class="n">KMeans</span><span class="p">,</span> <span class="n">KMeansModel</span> +<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span> +<span class="kn">from</span> <span class="nn">math</span> <span class="kn">import</span> <span class="n">sqrt</span> + +<span class="c"># Load and parse the data</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"data/mllib/kmeans_data.txt"</span><span class="p">)</span> +<span class="n">parsedData</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">array</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">' '</span><span class="p">)]))</span> + +<span class="c"># Build the model (cluster the data)</span> +<span class="n">clusters</span> <span class="o">=</span> <span class="n">KMeans</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">parsedData</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">maxIterations</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> + <span class="n">runs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">initializationMode</span><span class="o">=</span><span class="s">"random"</span><span class="p">)</span> + +<span class="c"># Evaluate clustering by computing Within Set Sum of Squared Errors</span> +<span class="k">def</span> <span class="nf">error</span><span class="p">(</span><span class="n">point</span><span class="p">):</span> + <span class="n">center</span> <span class="o">=</span> <span class="n">clusters</span><span class="o">.</span><span class="n">centers</span><span class="p">[</span><span class="n">clusters</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">point</span><span class="p">)]</span> + <span class="k">return</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">sum</span><span class="p">([</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">(</span><span class="n">point</span> <span class="o">-</span> <span class="n">center</span><span class="p">)]))</span> + +<span class="n">WSSSE</span> <span class="o">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">point</span><span class="p">:</span> <span class="n">error</span><span class="p">(</span><span class="n">point</span><span class="p">))</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span> +<span class="k">print</span><span class="p">(</span><span class="s">"Within Set Sum of Squared Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">WSSSE</span><span class="p">))</span> + +<span class="c"># Save and load model</span> +<span class="n">clusters</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"myModelPath"</span><span class="p">)</span> +<span class="n">sameModel</span> <span class="o">=</span> <span class="n">KMeansModel</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">sc</span><span class="p">,</span> <span class="s">"myModelPath"</span><span class="p">)</span></code></pre></div> + + </div> + +</div> + +<h2 id="gaussian-mixture">Gaussian mixture</h2> + +<p>A <a href="http://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model">Gaussian Mixture Model</a> +represents a composite distribution whereby points are drawn from one of <em>k</em> Gaussian sub-distributions, +each with its own probability. The MLlib implementation uses the +<a href="http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm">expectation-maximization</a> + algorithm to induce the maximum-likelihood model given a set of samples. The implementation +has the following parameters:</p> + +<ul> + <li><em>k</em> is the number of desired clusters.</li> + <li><em>convergenceTol</em> is the maximum change in log-likelihood at which we consider convergence achieved.</li> + <li><em>maxIterations</em> is the maximum number of iterations to perform without reaching convergence.</li> + <li><em>initialModel</em> is an optional starting point from which to start the EM algorithm. If this parameter is omitted, a random starting point will be constructed from the data.</li> +</ul> + +<p><strong>Examples</strong></p> + +<div class="codetabs"> +<div data-lang="scala"> + <p>In the following example after loading and parsing data, we use a +<a href="api/scala/index.html#org.apache.spark.mllib.clustering.GaussianMixture">GaussianMixture</a> +object to cluster the data into two clusters. The number of desired clusters is passed +to the algorithm. We then output the parameters of the mixture model.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.GaussianMixture</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.GaussianMixtureModel</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span> + +<span class="c1">// Load and parse the data</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/gmm_data.txt"</span><span class="o">)</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">s</span> <span class="k">=></span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="n">s</span><span class="o">.</span><span class="n">trim</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="n">cache</span><span class="o">()</span> + +<span class="c1">// Cluster the data into two classes using GaussianMixture</span> +<span class="k">val</span> <span class="n">gmm</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">GaussianMixture</span><span class="o">().</span><span class="n">setK</span><span class="o">(</span><span class="mi">2</span><span class="o">).</span><span class="n">run</span><span class="o">(</span><span class="n">parsedData</span><span class="o">)</span> + +<span class="c1">// Save and load model</span> +<span class="n">gmm</span><span class="o">.</span><span class="n">save</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"myGMMModel"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">sameModel</span> <span class="k">=</span> <span class="nc">GaussianMixtureModel</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"myGMMModel"</span><span class="o">)</span> + +<span class="c1">// output parameters of max-likelihood model</span> +<span class="k">for</span> <span class="o">(</span><span class="n">i</span> <span class="k"><-</span> <span class="mi">0</span> <span class="n">until</span> <span class="n">gmm</span><span class="o">.</span><span class="n">k</span><span class="o">)</span> <span class="o">{</span> + <span class="n">println</span><span class="o">(</span><span class="s">"weight=%f\nmu=%s\nsigma=\n%s\n"</span> <span class="n">format</span> + <span class="o">(</span><span class="n">gmm</span><span class="o">.</span><span class="n">weights</span><span class="o">(</span><span class="n">i</span><span class="o">),</span> <span class="n">gmm</span><span class="o">.</span><span class="n">gaussians</span><span class="o">(</span><span class="n">i</span><span class="o">).</span><span class="n">mu</span><span class="o">,</span> <span class="n">gmm</span><span class="o">.</span><span class="n">gaussians</span><span class="o">(</span><span class="n">i</span><span class="o">).</span><span class="n">sigma</span><span class="o">))</span> +<span class="o">}</span></code></pre></div> + + </div> + +<div data-lang="java"> + <p>All of MLlib’s methods use Java-friendly types, so you can import and call them there the same +way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the +Spark Java API uses a separate <code>JavaRDD</code> class. You can convert a Java RDD to a Scala one by +calling <code>.rdd()</code> on your <code>JavaRDD</code> object. A self-contained application example +that is equivalent to the provided example in Scala is given below:</p> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">org.apache.spark.api.java.*</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.mllib.clustering.GaussianMixture</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.clustering.GaussianMixtureModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span> + +<span class="kd">public</span> <span class="kd">class</span> <span class="nc">GaussianMixtureExample</span> <span class="o">{</span> + <span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</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">"GaussianMixture Example"</span><span class="o">);</span> + <span class="n">JavaSparkContext</span> <span class="n">sc</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="c1">// Load and parse data</span> + <span class="n">String</span> <span class="n">path</span> <span class="o">=</span> <span class="s">"data/mllib/gmm_data.txt"</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="n">path</span><span class="o">);</span> + <span class="n">JavaRDD</span><span class="o"><</span><span class="n">Vector</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">Vector</span><span class="o">>()</span> <span class="o">{</span> + <span class="kd">public</span> <span class="n">Vector</span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">{</span> + <span class="n">String</span><span class="o">[]</span> <span class="n">sarray</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="na">trim</span><span class="o">().</span><span class="na">split</span><span class="o">(</span><span class="s">" "</span><span class="o">);</span> + <span class="kt">double</span><span class="o">[]</span> <span class="n">values</span> <span class="o">=</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[</span><span class="n">sarray</span><span class="o">.</span><span class="na">length</span><span class="o">];</span> + <span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">sarray</span><span class="o">.</span><span class="na">length</span><span class="o">;</span> <span class="n">i</span><span class="o">++)</span> + <span class="n">values</span><span class="o">[</span><span class="n">i</span><span class="o">]</span> <span class="o">=</span> <span class="n">Double</span><span class="o">.</span><span class="na">parseDouble</span><span class="o">(</span><span class="n">sarray</span><span class="o">[</span><span class="n">i</span><span class="o">]);</span> + <span class="k">return</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="n">values</span><span class="o">);</span> + <span class="o">}</span> + <span class="o">}</span> + <span class="o">);</span> + <span class="n">parsedData</span><span class="o">.</span><span class="na">cache</span><span class="o">();</span> + + <span class="c1">// Cluster the data into two classes using GaussianMixture</span> + <span class="n">GaussianMixtureModel</span> <span class="n">gmm</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">GaussianMixture</span><span class="o">().</span><span class="na">setK</span><span class="o">(</span><span class="mi">2</span><span class="o">).</span><span class="na">run</span><span class="o">(</span><span class="n">parsedData</span><span class="o">.</span><span class="na">rdd</span><span class="o">());</span> + + <span class="c1">// Save and load GaussianMixtureModel</span> + <span class="n">gmm</span><span class="o">.</span><span class="na">save</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">"myGMMModel"</span><span class="o">);</span> + <span class="n">GaussianMixtureModel</span> <span class="n">sameModel</span> <span class="o">=</span> <span class="n">GaussianMixtureModel</span><span class="o">.</span><span class="na">load</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">"myGMMModel"</span><span class="o">);</span> + <span class="c1">// Output the parameters of the mixture model</span> + <span class="k">for</span><span class="o">(</span><span class="kt">int</span> <span class="n">j</span><span class="o">=</span><span class="mi">0</span><span class="o">;</span> <span class="n">j</span><span class="o"><</span><span class="n">gmm</span><span class="o">.</span><span class="na">k</span><span class="o">();</span> <span class="n">j</span><span class="o">++)</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">printf</span><span class="o">(</span><span class="s">"weight=%f\nmu=%s\nsigma=\n%s\n"</span><span class="o">,</span> + <span class="n">gmm</span><span class="o">.</span><span class="na">weights</span><span class="o">()[</span><span class="n">j</span><span class="o">],</span> <span class="n">gmm</span><span class="o">.</span><span class="na">gaussians</span><span class="o">()[</span><span class="n">j</span><span class="o">].</span><span class="na">mu</span><span class="o">(),</span> <span class="n">gmm</span><span class="o">.</span><span class="na">gaussians</span><span class="o">()[</span><span class="n">j</span><span class="o">].</span><span class="na">sigma</span><span class="o">());</span> + <span class="o">}</span> + <span class="o">}</span> +<span class="o">}</span></code></pre></div> + + </div> + +<div data-lang="python"> + <p>In the following example after loading and parsing data, we use a +<a href="api/python/pyspark.mllib.html#pyspark.mllib.clustering.GaussianMixture">GaussianMixture</a> +object to cluster the data into two clusters. The number of desired clusters is passed +to the algorithm. We then output the parameters of the mixture model.</p> + + <div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.clustering</span> <span class="kn">import</span> <span class="n">GaussianMixture</span> +<span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">array</span> + +<span class="c"># Load and parse the data</span> +<span class="n">data</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">textFile</span><span class="p">(</span><span class="s">"data/mllib/gmm_data.txt"</span><span class="p">)</span> +<span class="n">parsedData</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">line</span><span class="p">:</span> <span class="n">array</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">' '</span><span class="p">)]))</span> + +<span class="c"># Build the model (cluster the data)</span> +<span class="n">gmm</span> <span class="o">=</span> <span class="n">GaussianMixture</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">parsedData</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> + +<span class="c"># output parameters of model</span> +<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span> + <span class="k">print</span> <span class="p">(</span><span class="s">"weight = "</span><span class="p">,</span> <span class="n">gmm</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="s">"mu = "</span><span class="p">,</span> <span class="n">gmm</span><span class="o">.</span><span class="n">gaussians</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">mu</span><span class="p">,</span> + <span class="s">"sigma = "</span><span class="p">,</span> <span class="n">gmm</span><span class="o">.</span><span class="n">gaussians</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">sigma</span><span class="o">.</span><span class="n">toArray</span><span class="p">())</span></code></pre></div> + + </div> + +</div> + +<h2 id="power-iteration-clustering-pic">Power iteration clustering (PIC)</h2> + +<p>Power iteration clustering (PIC) is a scalable and efficient algorithm for clustering vertices of a +graph given pairwise similarties as edge properties, +described in <a href="http://www.icml2010.org/papers/387.pdf">Lin and Cohen, Power Iteration Clustering</a>. +It computes a pseudo-eigenvector of the normalized affinity matrix of the graph via +<a href="http://en.wikipedia.org/wiki/Power_iteration">power iteration</a> and uses it to cluster vertices. +MLlib includes an implementation of PIC using GraphX as its backend. +It takes an <code>RDD</code> of <code>(srcId, dstId, similarity)</code> tuples and outputs a model with the clustering assignments. +The similarities must be nonnegative. +PIC assumes that the similarity measure is symmetric. +A pair <code>(srcId, dstId)</code> regardless of the ordering should appear at most once in the input data. +If a pair is missing from input, their similarity is treated as zero. +MLlib’s PIC implementation takes the following (hyper-)parameters:</p> + +<ul> + <li><code>k</code>: number of clusters</li> + <li><code>maxIterations</code>: maximum number of power iterations</li> + <li><code>initializationMode</code>: initialization model. This can be either “random”, which is the default, +to use a random vector as vertex properties, or “degree” to use normalized sum similarities.</li> +</ul> + +<p><strong>Examples</strong></p> + +<p>In the following, we show code snippets to demonstrate how to use PIC in MLlib.</p> + +<div class="codetabs"> +<div data-lang="scala"> + + <p><a href="api/scala/index.html#org.apache.spark.mllib.clustering.PowerIterationClustering"><code>PowerIterationClustering</code></a> +implements the PIC algorithm. +It takes an <code>RDD</code> of <code>(srcId: Long, dstId: Long, similarity: Double)</code> tuples representing the +affinity matrix. +Calling <code>PowerIterationClustering.run</code> returns a +<a href="api/scala/index.html#org.apache.spark.mllib.clustering.PowerIterationClusteringModel"><code>PowerIterationClusteringModel</code></a>, +which contains the computed clustering assignments.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.</span><span class="o">{</span><span class="nc">PowerIterationClustering</span><span class="o">,</span> <span class="nc">PowerIterationClusteringModel</span><span class="o">}</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span> + +<span class="k">val</span> <span class="n">similarities</span><span class="k">:</span> <span class="kt">RDD</span><span class="o">[(</span><span class="kt">Long</span>, <span class="kt">Long</span>, <span class="kt">Double</span><span class="o">)]</span> <span class="k">=</span> <span class="o">...</span> + +<span class="k">val</span> <span class="n">pic</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">PowerIterationClustering</span><span class="o">()</span> + <span class="o">.</span><span class="n">setK</span><span class="o">(</span><span class="mi">3</span><span class="o">)</span> + <span class="o">.</span><span class="n">setMaxIterations</span><span class="o">(</span><span class="mi">20</span><span class="o">)</span> +<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pic</span><span class="o">.</span><span class="n">run</span><span class="o">(</span><span class="n">similarities</span><span class="o">)</span> + +<span class="n">model</span><span class="o">.</span><span class="n">assignments</span><span class="o">.</span><span class="n">foreach</span> <span class="o">{</span> <span class="n">a</span> <span class="k">=></span> + <span class="n">println</span><span class="o">(</span><span class="n">s</span><span class="s">"${a.id} -> ${a.cluster}"</span><span class="o">)</span> +<span class="o">}</span> + +<span class="c1">// Save and load model</span> +<span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"myModelPath"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">sameModel</span> <span class="k">=</span> <span class="nc">PowerIterationClusteringModel</span><span class="o">.</span><span class="n">load</span><span class="o">(</span><span class="n">sc</span><span class="o">,</span> <span class="s">"myModelPath"</span><span class="o">)</span></code></pre></div> + + <p>A full example that produces the experiment described in the PIC paper can be found under +<a href="https://github.com/apache/spark/blob/master/examples/src/main/scala/org/apache/spark/examples/mllib/PowerIterationClusteringExample.scala"><code>examples/</code></a>.</p> + + </div> + +<div data-lang="java"> + + <p><a href="api/java/org/apache/spark/mllib/clustering/PowerIterationClustering.html"><code>PowerIterationClustering</code></a> +implements the PIC algorithm. +It takes an <code>JavaRDD</code> of <code>(srcId: Long, dstId: Long, similarity: Double)</code> tuples representing the +affinity matrix. +Calling <code>PowerIterationClustering.run</code> returns a +<a href="api/java/org/apache/spark/mllib/clustering/PowerIterationClusteringModel.html"><code>PowerIterationClusteringModel</code></a> +which contains the computed clustering assignments.</p> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><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="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.mllib.clustering.PowerIterationClustering</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.clustering.PowerIterationClusteringModel</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">Long</span><span class="o">,</span> <span class="n">Long</span><span class="o">,</span> <span class="n">Double</span><span class="o">>></span> <span class="n">similarities</span> <span class="o">=</span> <span class="o">...</span> + +<span class="n">PowerIterationClustering</span> <span class="n">pic</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">PowerIterationClustering</span><span class="o">()</span> + <span class="o">.</span><span class="na">setK</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span> + <span class="o">.</span><span class="na">setMaxIterations</span><span class="o">(</span><span class="mi">10</span><span class="o">);</span> +<span class="n">PowerIterationClusteringModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pic</span><span class="o">.</span><span class="na">run</span><span class="o">(</span><span class="n">similarities</span><span class="o">);</span> + +<span class="k">for</span> <span class="o">(</span><span class="n">PowerIterationClustering</span><span class="o">.</span><span class="na">Assignment</span> <span class="nl">a:</span> <span class="n">model</span><span class="o">.</span><span class="na">assignments</span><span class="o">().</span><span class="na">toJavaRDD</span><span class="o">().</span><span class="na">collect</span><span class="o">())</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">a</span><span class="o">.</span><span class="na">id</span><span class="o">()</span> <span class="o">+</span> <span class="s">" -> "</span> <span class="o">+</span> <span class="n">a</span><span class="o">.</span><span class="na">cluster</span><span class="o">());</span> +<span class="o">}</span> + +<span class="c1">// Save and load model</span> +<span class="n">model</span><span class="o">.</span><span class="na">save</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">"myModelPath"</span><span class="o">);</span> +<span class="n">PowerIterationClusteringModel</span> <span class="n">sameModel</span> <span class="o">=</span> <span class="n">PowerIterationClusteringModel</span><span class="o">.</span><span class="na">load</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">"myModelPath"</span><span class="o">);</span></code></pre></div> + + </div> + +</div> + +<h2 id="latent-dirichlet-allocation-lda">Latent Dirichlet allocation (LDA)</h2> + +<p><a href="http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation">Latent Dirichlet allocation (LDA)</a> +is a topic model which infers topics from a collection of text documents. +LDA can be thought of as a clustering algorithm as follows:</p> + +<ul> + <li>Topics correspond to cluster centers, and documents correspond to examples (rows) in a dataset.</li> + <li>Topics and documents both exist in a feature space, where feature vectors are vectors of word counts.</li> + <li>Rather than estimating a clustering using a traditional distance, LDA uses a function based + on a statistical model of how text documents are generated.</li> +</ul> + +<p>LDA takes in a collection of documents as vectors of word counts. +It supports different inference algorithms via <code>setOptimizer</code> function. EMLDAOptimizer learns clustering using <a href="http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm">expectation-maximization</a> +on the likelihood function and yields comprehensive results, while OnlineLDAOptimizer uses iterative mini-batch sampling for <a href="https://www.cs.princeton.edu/~blei/papers/HoffmanBleiBach2010b.pdf">online variational inference</a> and is generally memory friendly. After fitting on the documents, LDA provides:</p> + +<ul> + <li>Topics: Inferred topics, each of which is a probability distribution over terms (words).</li> + <li>Topic distributions for documents: For each document in the training set, LDA gives a probability distribution over topics. (EM only)</li> +</ul> + +<p>LDA takes the following parameters:</p> + +<ul> + <li><code>k</code>: Number of topics (i.e., cluster centers)</li> + <li><code>maxIterations</code>: Limit on the number of iterations of EM used for learning</li> + <li><code>docConcentration</code>: Hyperparameter for prior over documents’ distributions over topics. Currently must be > 1, where larger values encourage smoother inferred distributions.</li> + <li><code>topicConcentration</code>: Hyperparameter for prior over topics’ distributions over terms (words). Currently must be > 1, where larger values encourage smoother inferred distributions.</li> + <li><code>checkpointInterval</code>: If using checkpointing (set in the Spark configuration), this parameter specifies the frequency with which checkpoints will be created. If <code>maxIterations</code> is large, using checkpointing can help reduce shuffle file sizes on disk and help with failure recovery.</li> +</ul> + +<p><em>Note</em>: LDA is a new feature with some missing functionality. In particular, it does not yet +support prediction on new documents, and it does not have a Python API. These will be added in the future.</p> + +<p><strong>Examples</strong></p> + +<p>In the following example, we load word count vectors representing a corpus of documents. +We then use <a href="api/scala/index.html#org.apache.spark.mllib.clustering.LDA">LDA</a> +to infer three topics from the documents. The number of desired clusters is passed +to the algorithm. We then output the topics, represented as probability distributions over words.</p> + +<div class="codetabs"> +<div data-lang="scala"> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.LDA</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span> + +<span class="c1">// Load and parse the data</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_lda_data.txt"</span><span class="o">)</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">s</span> <span class="k">=></span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="n">s</span><span class="o">.</span><span class="n">trim</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="c1">// Index documents with unique IDs</span> +<span class="k">val</span> <span class="n">corpus</span> <span class="k">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">zipWithIndex</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">swap</span><span class="o">).</span><span class="n">cache</span><span class="o">()</span> + +<span class="c1">// Cluster the documents into three topics using LDA</span> +<span class="k">val</span> <span class="n">ldaModel</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LDA</span><span class="o">().</span><span class="n">setK</span><span class="o">(</span><span class="mi">3</span><span class="o">).</span><span class="n">run</span><span class="o">(</span><span class="n">corpus</span><span class="o">)</span> + +<span class="c1">// Output topics. Each is a distribution over words (matching word count vectors)</span> +<span class="n">println</span><span class="o">(</span><span class="s">"Learned topics (as distributions over vocab of "</span> <span class="o">+</span> <span class="n">ldaModel</span><span class="o">.</span><span class="n">vocabSize</span> <span class="o">+</span> <span class="s">" words):"</span><span class="o">)</span> +<span class="k">val</span> <span class="n">topics</span> <span class="k">=</span> <span class="n">ldaModel</span><span class="o">.</span><span class="n">topicsMatrix</span> +<span class="k">for</span> <span class="o">(</span><span class="n">topic</span> <span class="k"><-</span> <span class="nc">Range</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="mi">3</span><span class="o">))</span> <span class="o">{</span> + <span class="n">print</span><span class="o">(</span><span class="s">"Topic "</span> <span class="o">+</span> <span class="n">topic</span> <span class="o">+</span> <span class="s">":"</span><span class="o">)</span> + <span class="k">for</span> <span class="o">(</span><span class="n">word</span> <span class="k"><-</span> <span class="nc">Range</span><span class="o">(</span><span class="mi">0</span><span class="o">,</span> <span class="n">ldaModel</span><span class="o">.</span><span class="n">vocabSize</span><span class="o">))</span> <span class="o">{</span> <span class="n">print</span><span class="o">(</span><span class="s">" "</span> <span class="o">+</span> <span class="n">topics</span><span class="o">(</span><span class="n">word</span><span class="o">,</span> <span class="n">topic</span><span class="o">));</span> <span class="o">}</span> + <span class="n">println</span><span class="o">()</span> +<span class="o">}</span></code></pre></div> + + </div> + +<div data-lang="java"> + + <div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">scala.Tuple2</span><span class="o">;</span> + +<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.*</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.mllib.clustering.DistributedLDAModel</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.clustering.LDA</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.linalg.Vector</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span> +<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span> + +<span class="kd">public</span> <span class="kd">class</span> <span class="nc">JavaLDAExample</span> <span class="o">{</span> + <span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">main</span><span class="o">(</span><span class="n">String</span><span class="o">[]</span> <span class="n">args</span><span class="o">)</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">"LDA Example"</span><span class="o">);</span> + <span class="n">JavaSparkContext</span> <span class="n">sc</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="c1">// Load and parse the data</span> + <span class="n">String</span> <span class="n">path</span> <span class="o">=</span> <span class="s">"data/mllib/sample_lda_data.txt"</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="n">path</span><span class="o">);</span> + <span class="n">JavaRDD</span><span class="o"><</span><span class="n">Vector</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">Vector</span><span class="o">>()</span> <span class="o">{</span> + <span class="kd">public</span> <span class="n">Vector</span> <span class="nf">call</span><span class="o">(</span><span class="n">String</span> <span class="n">s</span><span class="o">)</span> <span class="o">{</span> + <span class="n">String</span><span class="o">[]</span> <span class="n">sarray</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="na">trim</span><span class="o">().</span><span class="na">split</span><span class="o">(</span><span class="s">" "</span><span class="o">);</span> + <span class="kt">double</span><span class="o">[]</span> <span class="n">values</span> <span class="o">=</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[</span><span class="n">sarray</span><span class="o">.</span><span class="na">length</span><span class="o">];</span> + <span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">i</span> <span class="o"><</span> <span class="n">sarray</span><span class="o">.</span><span class="na">length</span><span class="o">;</span> <span class="n">i</span><span class="o">++)</span> + <span class="n">values</span><span class="o">[</span><span class="n">i</span><span class="o">]</span> <span class="o">=</span> <span class="n">Double</span><span class="o">.</span><span class="na">parseDouble</span><span class="o">(</span><span class="n">sarray</span><span class="o">[</span><span class="n">i</span><span class="o">]);</span> + <span class="k">return</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="n">values</span><span class="o">);</span> + <span class="o">}</span> + <span class="o">}</span> + <span class="o">);</span> + <span class="c1">// Index documents with unique IDs</span> + <span class="n">JavaPairRDD</span><span class="o"><</span><span class="n">Long</span><span class="o">,</span> <span class="n">Vector</span><span class="o">></span> <span class="n">corpus</span> <span class="o">=</span> <span class="n">JavaPairRDD</span><span class="o">.</span><span class="na">fromJavaRDD</span><span class="o">(</span><span class="n">parsedData</span><span class="o">.</span><span class="na">zipWithIndex</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">Vector</span><span class="o">,</span> <span class="n">Long</span><span class="o">>,</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">Long</span><span class="o">,</span> <span class="n">Vector</span><span class="o">>>()</span> <span class="o">{</span> + <span class="kd">public</span> <span class="n">Tuple2</span><span class="o"><</span><span class="n">Long</span><span class="o">,</span> <span class="n">Vector</span><span class="o">></span> <span class="nf">call</span><span class="o">(</span><span class="n">Tuple2</span><span class="o"><</span><span class="n">Vector</span><span class="o">,</span> <span class="n">Long</span><span class="o">></span> <span class="n">doc_id</span><span class="o">)</span> <span class="o">{</span> + <span class="k">return</span> <span class="n">doc_id</span><span class="o">.</span><span class="na">swap</span><span class="o">();</span> + <span class="o">}</span> + <span class="o">}</span> + <span class="o">));</span> + <span class="n">corpus</span><span class="o">.</span><span class="na">cache</span><span class="o">();</span> + + <span class="c1">// Cluster the documents into three topics using LDA</span> + <span class="n">DistributedLDAModel</span> <span class="n">ldaModel</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LDA</span><span class="o">().</span><span class="na">setK</span><span class="o">(</span><span class="mi">3</span><span class="o">).</span><span class="na">run</span><span class="o">(</span><span class="n">corpus</span><span class="o">);</span> + + <span class="c1">// Output topics. Each is a distribution over words (matching word count vectors)</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 topics (as distributions over vocab of "</span> <span class="o">+</span> <span class="n">ldaModel</span><span class="o">.</span><span class="na">vocabSize</span><span class="o">()</span> + <span class="o">+</span> <span class="s">" words):"</span><span class="o">);</span> + <span class="n">Matrix</span> <span class="n">topics</span> <span class="o">=</span> <span class="n">ldaModel</span><span class="o">.</span><span class="na">topicsMatrix</span><span class="o">();</span> + <span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">topic</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">topic</span> <span class="o"><</span> <span class="mi">3</span><span class="o">;</span> <span class="n">topic</span><span class="o">++)</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">"Topic "</span> <span class="o">+</span> <span class="n">topic</span> <span class="o">+</span> <span class="s">":"</span><span class="o">);</span> + <span class="k">for</span> <span class="o">(</span><span class="kt">int</span> <span class="n">word</span> <span class="o">=</span> <span class="mi">0</span><span class="o">;</span> <span class="n">word</span> <span class="o"><</span> <span class="n">ldaModel</span><span class="o">.</span><span class="na">vocabSize</span><span class="o">();</span> <span class="n">word</span><span class="o">++)</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">" "</span> <span class="o">+</span> <span class="n">topics</span><span class="o">.</span><span class="na">apply</span><span class="o">(</span><span class="n">word</span><span class="o">,</span> <span class="n">topic</span><span class="o">));</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> + <span class="o">}</span> +<span class="o">}</span></code></pre></div> + + </div> + +</div> + +<h2 id="streaming-k-means">Streaming k-means</h2> + +<p>When data arrive in a stream, we may want to estimate clusters dynamically, +updating them as new data arrive. MLlib provides support for streaming k-means clustering, +with parameters to control the decay (or “forgetfulness”) of the estimates. The algorithm +uses a generalization of the mini-batch k-means update rule. For each batch of data, we assign +all points to their nearest cluster, compute new cluster centers, then update each cluster using:</p> + +<p><code>\begin{equation} + c_{t+1} = \frac{c_tn_t\alpha + x_tm_t}{n_t\alpha+m_t} +\end{equation}</code> +<code>\begin{equation} + n_{t+1} = n_t + m_t +\end{equation}</code></p> + +<p>Where <code>$c_t$</code> is the previous center for the cluster, <code>$n_t$</code> is the number of points assigned +to the cluster thus far, <code>$x_t$</code> is the new cluster center from the current batch, and <code>$m_t$</code> +is the number of points added to the cluster in the current batch. The decay factor <code>$\alpha$</code> +can be used to ignore the past: with <code>$\alpha$=1</code> all data will be used from the beginning; +with <code>$\alpha$=0</code> only the most recent data will be used. This is analogous to an +exponentially-weighted moving average.</p> + +<p>The decay can be specified using a <code>halfLife</code> parameter, which determines the +correct decay factor <code>a</code> such that, for data acquired +at time <code>t</code>, its contribution by time <code>t + halfLife</code> will have dropped to 0.5. +The unit of time can be specified either as <code>batches</code> or <code>points</code> and the update rule +will be adjusted accordingly.</p> + +<p><strong>Examples</strong></p> + +<p>This example shows how to estimate clusters on streaming data.</p> + +<div class="codetabs"> + +<div data-lang="scala"> + + <p>First we import the neccessary classes.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span> +<span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.StreamingKMeans</span></code></pre></div> + + <p>Then we make an input stream of vectors for training, as well as a stream of labeled data +points for testing. We assume a StreamingContext <code>ssc</code> has been created, see +<a href="streaming-programming-guide.html#initializing">Spark Streaming Programming Guide</a> for more info.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">trainingData</span> <span class="k">=</span> <span class="n">ssc</span><span class="o">.</span><span class="n">textFileStream</span><span class="o">(</span><span class="s">"/training/data/dir"</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="nc">Vectors</span><span class="o">.</span><span class="n">parse</span><span class="o">)</span> +<span class="k">val</span> <span class="n">testData</span> <span class="k">=</span> <span class="n">ssc</span><span class="o">.</span><span class="n">textFileStream</span><span class="o">(</span><span class="s">"/testing/data/dir"</span><span class="o">).</span><span class="n">map</span><span class="o">(</span><span class="nc">LabeledPoint</span><span class="o">.</span><span class="n">parse</span><span class="o">)</span></code></pre></div> + + <p>We create a model with random clusters and specify the number of clusters to find</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">val</span> <span class="n">numDimensions</span> <span class="k">=</span> <span class="mi">3</span> +<span class="k">val</span> <span class="n">numClusters</span> <span class="k">=</span> <span class="mi">2</span> +<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">StreamingKMeans</span><span class="o">()</span> + <span class="o">.</span><span class="n">setK</span><span class="o">(</span><span class="n">numClusters</span><span class="o">)</span> + <span class="o">.</span><span class="n">setDecayFactor</span><span class="o">(</span><span class="mf">1.0</span><span class="o">)</span> + <span class="o">.</span><span class="n">setRandomCenters</span><span class="o">(</span><span class="n">numDimensions</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">)</span></code></pre></div> + + <p>Now register the streams for training and testing and start the job, printing +the predicted cluster assignments on new data points as they arrive.</p> + + <div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="n">model</span><span class="o">.</span><span class="n">trainOn</span><span class="o">(</span><span class="n">trainingData</span><span class="o">)</span> +<span class="n">model</span><span class="o">.</span><span class="n">predictOnValues</span><span class="o">(</span><span class="n">testData</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">lp</span> <span class="k">=></span> <span class="o">(</span><span class="n">lp</span><span class="o">.</span><span class="n">label</span><span class="o">,</span> <span class="n">lp</span><span class="o">.</span><span class="n">features</span><span class="o">))).</span><span class="n">print</span><span class="o">()</span> + +<span class="n">ssc</span><span class="o">.</span><span class="n">start</span><span class="o">()</span> +<span class="n">ssc</span><span class="o">.</span><span class="n">awaitTermination</span><span class="o">()</span></code></pre></div> + + <p>As you add new text files with data the cluster centers will update. Each training +point should be formatted as <code>[x1, x2, x3]</code>, and each test data point +should be formatted as <code>(y, [x1, x2, x3])</code>, where <code>y</code> is some useful label or identifier +(e.g. a true category assignment). Anytime a text file is placed in <code>/training/data/dir</code> +the model will update. Anytime a text file is placed in <code>/testing/data/dir</code> +you will see predictions. With new data, the cluster centers will change!</p> + + </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 ? 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