Docs for Spark 1.0.0

author: Patrick Wendell <pwendell@apache.org> 2014-05-30 08:55:36 +0000
committer: Patrick Wendell <pwendell@apache.org> 2014-05-30 08:55:36 +0000
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+                      <img src="img/spark-logo-hd.png" style="height:50px;"/></a><span class="version">1.0.0</span>
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+                        <!--TODO(andyk): Add class="active" attribute to li some how.-->
+                        <li><a href="index.html">Overview</a></li>
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+                            <a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a>
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+                                <li><a href="quick-start.html">Quick Start</a></li>
+                                <li><a href="programming-guide.html">Spark Programming Guide</a></li>
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+                                <li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li>
+                                <li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li>
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+                                <li><a href="building-with-maven.html">Building Spark with Maven</a></li>
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+                    <!--<p class="navbar-text pull-right"><span class="version-text">v1.0.0</span></p>-->
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+        </div>
+
+        <div class="container" id="content">
+          
+            <h1 class="title">Machine Learning Library (MLlib)</h1>
+          
+
+          <p>MLlib is a Spark implementation of some common machine learning algorithms and utilities,
+including classification, regression, clustering, collaborative
+filtering, dimensionality reduction, as well as underlying optimization primitives:</p>
+
+<ul>
+  <li><a href="mllib-basics.html">Basics</a>
+    <ul>
+      <li>data types </li>
+      <li>summary statistics</li>
+    </ul>
+  </li>
+  <li>Classification and regression
+    <ul>
+      <li><a href="mllib-linear-methods.html#linear-support-vector-machine-svm">linear support vector machine (SVM)</a></li>
+      <li><a href="mllib-linear-methods.html#logistic-regression">logistic regression</a></li>
+      <li><a href="mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression">linear least squares, Lasso, and ridge regression</a></li>
+      <li><a href="mllib-decision-tree.html">decision tree</a></li>
+      <li><a href="mllib-naive-bayes.html">naive Bayes</a></li>
+    </ul>
+  </li>
+  <li><a href="mllib-collaborative-filtering.html">Collaborative filtering</a>
+    <ul>
+      <li>alternating least squares (ALS)</li>
+    </ul>
+  </li>
+  <li><a href="mllib-clustering.html">Clustering</a>
+    <ul>
+      <li>k-means</li>
+    </ul>
+  </li>
+  <li><a href="mllib-dimensionality-reduction.html">Dimensionality reduction</a>
+    <ul>
+      <li>singular value decomposition (SVD)</li>
+      <li>principal component analysis (PCA)</li>
+    </ul>
+  </li>
+  <li><a href="mllib-optimization.html">Optimization</a>
+    <ul>
+      <li>stochastic gradient descent</li>
+      <li>limited-memory BFGS (L-BFGS)</li>
+    </ul>
+  </li>
+</ul>
+
+<p>MLlib is a new component under active development.
+The APIs marked <code>Experimental</code>/<code>DeveloperApi</code> may change in future releases, 
+and we will provide migration guide between releases.</p>
+
+<h1 id="dependencies">Dependencies</h1>
+
+<p>MLlib uses linear algebra packages <a href="http://www.scalanlp.org/">Breeze</a>, which depends on
+<a href="https://github.com/fommil/netlib-java">netlib-java</a>, and
+<a href="https://github.com/mikiobraun/jblas">jblas</a>. 
+<code>netlib-java</code> and <code>jblas</code> depend on native Fortran routines.
+You need to install the
+<a href="https://github.com/mikiobraun/jblas/wiki/Missing-Libraries">gfortran runtime library</a> if it is not
+already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries
+automatically.  Due to license issues, we do not include <code>netlib-java</code>&#8217;s native libraries in MLlib&#8217;s
+dependency set. If no native library is available at runtime, you will see a warning message.  To
+use native libraries from <code>netlib-java</code>, please include artifact
+<code>com.github.fommil.netlib:all:1.1.2</code> as a dependency of your project or build your own (see
+<a href="https://github.com/fommil/netlib-java/blob/master/README.md#machine-optimised-system-libraries">instructions</a>).</p>
+
+<p>To use MLlib in Python, you will need <a href="http://www.numpy.org">NumPy</a> version 1.4 or newer.</p>
+
+<hr />
+
+<h1 id="migration-guide">Migration Guide</h1>
+
+<h2 id="from-09-to-10">From 0.9 to 1.0</h2>
+
+<p>In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
+breaking changes.  If your data is sparse, please store it in a sparse format instead of dense to
+take advantage of sparsity in both storage and computation.</p>
+
+<div class="codetabs">
+<div data-lang="scala">
+
+    <p>We used to represent a feature vector by <code>Array[Double]</code>, which is replaced by
+<a href="api/scala/index.html#org.apache.spark.mllib.linalg.Vector"><code>Vector</code></a> in v1.0. Algorithms that used
+to accept <code>RDD[Array[Double]]</code> now take
+<code>RDD[Vector]</code>. <a href="api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint"><code>LabeledPoint</code></a>
+is now a wrapper of <code>(Double, Vector)</code> instead of <code>(Double, Array[Double])</code>. Converting
+<code>Array[Double]</code> to <code>Vector</code> is straightforward:</p>
+
+    <div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.</span><span class="o">{</span><span class="nc">Vector</span><span class="o">,</span> <span class="nc">Vectors</span><span class="o">}</span>
+
+<span class="k">val</span> <span class="n">array</span><span class="k">:</span> <span class="kt">Array</span><span class="o">[</span><span class="kt">Double</span><span class="o">]</span> <span class="k">=</span> <span class="o">...</span> <span class="c1">// a double array</span>
+<span class="k">val</span> <span class="n">vector</span><span class="k">:</span> <span class="kt">Vector</span> <span class="o">=</span> <span class="nc">Vectors</span><span class="o">.</span><span class="n">dense</span><span class="o">(</span><span class="n">array</span><span class="o">)</span> <span class="c1">// a dense vector</span>
+</code></pre></div>
+
+    <p><a href="api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$"><code>Vectors</code></a> provides factory methods to create sparse vectors.</p>
+
+    <p><em>Note</em>. Scala imports <code>scala.collection.immutable.Vector</code> by default, so you have to import <code>org.apache.spark.mllib.linalg.Vector</code> explicitly to use MLlib&#8217;s <code>Vector</code>.</p>
+
+  </div>
+
+<div data-lang="java">
+
+    <p>We used to represent a feature vector by <code>double[]</code>, which is replaced by
+<a href="api/java/index.html?org/apache/spark/mllib/linalg/Vector.html"><code>Vector</code></a> in v1.0. Algorithms that used
+to accept <code>RDD&lt;double[]&gt;</code> now take
+<code>RDD&lt;Vector&gt;</code>. <a href="api/java/index.html?org/apache/spark/mllib/regression/LabeledPoint.html"><code>LabeledPoint</code></a>
+is now a wrapper of <code>(double, Vector)</code> instead of <code>(double, double[])</code>. Converting <code>double[]</code> to
+<code>Vector</code> is straightforward:</p>
+
+    <div class="highlight"><pre><code class="java"><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span>
+<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
+
+<span class="kt">double</span><span class="o">[]</span> <span class="n">array</span> <span class="o">=</span> <span class="o">...</span> <span class="c1">// a double array</span>
+<span class="n">Vector</span> <span class="n">vector</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="n">array</span><span class="o">);</span> <span class="c1">// a dense vector</span>
+</code></pre></div>
+
+    <p><a href="api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$"><code>Vectors</code></a> provides factory methods to
+create sparse vectors.</p>
+
+  </div>
+
+<div data-lang="python">
+
+    <p>We used to represent a labeled feature vector in a NumPy array, where the first entry corresponds to
+the label and the rest are features.  This representation is replaced by class
+<a href="api/python/pyspark.mllib.regression.LabeledPoint-class.html"><code>LabeledPoint</code></a>, which takes both
+dense and sparse feature vectors.</p>
+
+    <div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.linalg</span> <span class="kn">import</span> <span class="n">SparseVector</span>
+<span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LabeledPoint</span>
+
+<span class="c"># Create a labeled point with a positive label and a dense feature vector.</span>
+<span class="n">pos</span> <span class="o">=</span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">1.0</span><span class="p">,</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">])</span>
+
+<span class="c"># Create a labeled point with a negative label and a sparse feature vector.</span>
+<span class="n">neg</span> <span class="o">=</span> <span class="n">LabeledPoint</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">SparseVector</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">]))</span>
+</code></pre></div>
+
+  </div>
+</div>
+
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author	Patrick Wendell <pwendell@apache.org>	2014-05-30 08:55:36 +0000
committer	Patrick Wendell <pwendell@apache.org>	2014-05-30 08:55:36 +0000
commit	66fb4b11cbae79d9044b2bbf2e53351642a58ff5 (patch)
tree	2a3238ae425816d2296c2972987e56b3e949ea0b /site/docs/1.0.0/mllib-guide.html
parent	3936fd5d223549bfa06bd6eeba6becfc88daee52 (diff)
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