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author | Patrick Wendell <pwendell@apache.org> | 2014-05-30 08:55:36 +0000 |
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committer | Patrick Wendell <pwendell@apache.org> | 2014-05-30 08:55:36 +0000 |
commit | 66fb4b11cbae79d9044b2bbf2e53351642a58ff5 (patch) | |
tree | 2a3238ae425816d2296c2972987e56b3e949ea0b /site/docs/1.0.0/mllib-clustering.html | |
parent | 3936fd5d223549bfa06bd6eeba6becfc88daee52 (diff) | |
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Docs for Spark 1.0.0
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diff --git a/site/docs/1.0.0/mllib-clustering.html b/site/docs/1.0.0/mllib-clustering.html new file mode 100644 index 000000000..c8ef3f1ac --- /dev/null +++ b/site/docs/1.0.0/mllib-clustering.html @@ -0,0 +1,241 @@ +<!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 1.0.0 Documentation</title> + <meta name="description" content=""> + + + + <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">1.0.0</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">Spark 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">Scaladoc</a></li> + <li><a href="api/java/index.html">Javadoc</a></li> + <li><a href="api/python/index.html">Python API</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="ec2-scripts.html">Amazon EC2</a></li> + <li><a href="spark-standalone.html">Standalone Mode</a></li> + <li><a href="running-on-mesos.html">Mesos</a></li> + <li><a href="running-on-yarn.html">YARN</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-with-maven.html">Building Spark with Maven</a></li> + <li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li> + </ul> + </li> + </ul> + <!--<p class="navbar-text pull-right"><span class="version-text">v1.0.0</span></p>--> + </div> + </div> + </div> + + <div class="container" id="content"> + + <h1 class="title"><a href="mllib-guide.html">MLlib</a> - Clustering</h1> + + + <ul id="markdown-toc"> + <li><a href="#clustering">Clustering</a></li> + <li><a href="#examples">Examples</a></li> +</ul> + +<h2 id="clustering">Clustering</h2> + +<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 +<a href="http://en.wikipedia.org/wiki/K-means_clustering">k-means</a> clustering, one of +the most commonly used clustering algorithms that clusters the data points into +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> + +<h2 id="examples">Examples</h2> + +<div class="codetabs"> +<div data-lang="scala"> + <p>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="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.KMeans</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/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="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> +</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.</p> + </div> + +<div data-lang="python"> + <p>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="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="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/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> +</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 type="text/javascript" + src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script> + <script> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ], + displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], + processEscapes: true, + skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'] + } + }); + </script> + </body> +</html> |