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| <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">K-means</a></li> |
| <li><a href="#gaussian-mixture">Gaussian mixture</a></li> |
| <li><a href="#power-iteration-clustering-pic">Power iteration clustering (PIC)</a></li> |
| <li><a href="#latent-dirichlet-allocation-lda">Latent Dirichlet allocation (LDA)</a></li> |
| <li><a href="#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.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/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></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="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="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></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.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">// 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">// 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">println</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.PowerIterationClustering</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">PowerIteartionClustering</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></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></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 learns clustering using <a href="http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm">expectation-maximization</a> |
| on the likelihood function. 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.</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> |
| |
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