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| <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>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="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="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 standalone application example |
| that is equivalent to the provided example in Scala is given below:</p> |
| |
| <div class="highlight"><pre><code class="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="n">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="n">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="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> |
| |
| <p>In order to run the above standalone application, follow the instructions |
| provided in the <a href="quick-start.html#standalone-applications">Standalone |
| Applications</a> section of the Spark |
| quick-start guide. Be sure to also include <em>spark-mllib</em> to your build file as |
| a dependency.</p> |
| </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="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> |
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| </script> |
| <script> |
| // Note that we load MathJax this way to work with local file (file://), HTTP and HTTPS. |
| // We could use "//cdn.mathjax...", but that won't support "file://". |
| (function(d, script) { |
| script = d.createElement('script'); |
| script.type = 'text/javascript'; |
| script.async = true; |
| script.onload = function(){ |
| MathJax.Hub.Config({ |
| tex2jax: { |
| inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ], |
| displayMath: [ ["$$","$$"], ["\\[", "\\]"] ], |
| processEscapes: true, |
| skipTags: ['script', 'noscript', 'style', 'textarea', 'pre'] |
| } |
| }); |
| }; |
| script.src = ('https:' == document.location.protocol ? 'https://' : 'http://') + |
| 'cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML'; |
| d.getElementsByTagName('head')[0].appendChild(script); |
| }(document)); |
| </script> |
| </body> |
| </html> |