1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
|
<!DOCTYPE html>
<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
<!--[if IE 7]> <html class="no-js lt-ie9 lt-ie8"> <![endif]-->
<!--[if IE 8]> <html class="no-js lt-ie9"> <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js"> <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">
<title>Machine Learning Library (MLlib) - Spark 1.0.0 Documentation</title>
<meta name="description" content="">
<link rel="stylesheet" href="css/bootstrap.min.css">
<style>
body {
padding-top: 60px;
padding-bottom: 40px;
}
</style>
<meta name="viewport" content="width=device-width">
<link rel="stylesheet" href="css/bootstrap-responsive.min.css">
<link rel="stylesheet" href="css/main.css">
<script src="js/vendor/modernizr-2.6.1-respond-1.1.0.min.js"></script>
<link rel="stylesheet" href="css/pygments-default.css">
</head>
<body>
<!--[if lt IE 7]>
<p class="chromeframe">You are using an outdated browser. <a href="http://browsehappy.com/">Upgrade your browser today</a> or <a href="http://www.google.com/chromeframe/?redirect=true">install Google Chrome Frame</a> to better experience this site.</p>
<![endif]-->
<!-- This code is taken from http://twitter.github.com/bootstrap/examples/hero.html -->
<div class="navbar navbar-fixed-top" id="topbar">
<div class="navbar-inner">
<div class="container">
<div class="brand"><a href="index.html">
<img src="img/spark-logo-hd.png" style="height:50px;"/></a><span class="version">1.0.0</span>
</div>
<ul class="nav">
<!--TODO(andyk): Add class="active" attribute to li some how.-->
<li><a href="index.html">Overview</a></li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Programming Guides<b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="quick-start.html">Quick Start</a></li>
<li><a href="programming-guide.html">Spark Programming Guide</a></li>
<li class="divider"></li>
<li><a href="streaming-programming-guide.html">Spark Streaming</a></li>
<li><a href="sql-programming-guide.html">Spark SQL</a></li>
<li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li>
<li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</a></li>
<li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">API Docs<b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="api/scala/index.html#org.apache.spark.package">Scaladoc</a></li>
<li><a href="api/java/index.html">Javadoc</a></li>
<li><a href="api/python/index.html">Python API</a></li>
</ul>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown">Deploying<b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="cluster-overview.html">Overview</a></li>
<li><a href="submitting-applications.html">Submitting Applications</a></li>
<li class="divider"></li>
<li><a href="ec2-scripts.html">Amazon EC2</a></li>
<li><a href="spark-standalone.html">Standalone Mode</a></li>
<li><a href="running-on-mesos.html">Mesos</a></li>
<li><a href="running-on-yarn.html">YARN</a></li>
</ul>
</li>
<li class="dropdown">
<a href="api.html" class="dropdown-toggle" data-toggle="dropdown">More<b class="caret"></b></a>
<ul class="dropdown-menu">
<li><a href="configuration.html">Configuration</a></li>
<li><a href="monitoring.html">Monitoring</a></li>
<li><a href="tuning.html">Tuning Guide</a></li>
<li><a href="job-scheduling.html">Job Scheduling</a></li>
<li><a href="security.html">Security</a></li>
<li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
<li><a href="hadoop-third-party-distributions.html">3<sup>rd</sup>-Party Hadoop Distros</a></li>
<li class="divider"></li>
<li><a href="building-with-maven.html">Building Spark with Maven</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li>
</ul>
</li>
</ul>
<!--<p class="navbar-text pull-right"><span class="version-text">v1.0.0</span></p>-->
</div>
</div>
</div>
<div class="container" id="content">
<h1 class="title">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>’s native libraries in MLlib’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$"><code>Vectors</code></a> provides factory methods to create sparse vectors.</p>
<p><em>Note</em>. Scala imports <code>scala.collection.immutable.Vector</code> by default, so you have to import <code>org.apache.spark.mllib.linalg.Vector</code> explicitly to use MLlib’s <code>Vector</code>.</p>
</div>
<div data-lang="java">
<p>We used to represent a feature vector by <code>double[]</code>, which is replaced by
<a href="api/java/index.html?org/apache/spark/mllib/linalg/Vector.html"><code>Vector</code></a> in v1.0. Algorithms that used
to accept <code>RDD<double[]></code> now take
<code>RDD<Vector></code>. <a href="api/java/index.html?org/apache/spark/mllib/regression/LabeledPoint.html"><code>LabeledPoint</code></a>
is now a wrapper of <code>(double, Vector)</code> instead of <code>(double, double[])</code>. Converting <code>double[]</code> to
<code>Vector</code> is straightforward:</p>
<div class="highlight"><pre><code class="java"><span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
<span class="kt">double</span><span class="o">[]</span> <span class="n">array</span> <span class="o">=</span> <span class="o">...</span> <span class="c1">// a double array</span>
<span class="n">Vector</span> <span class="n">vector</span> <span class="o">=</span> <span class="n">Vectors</span><span class="o">.</span><span class="na">dense</span><span class="o">(</span><span class="n">array</span><span class="o">);</span> <span class="c1">// a dense vector</span>
</code></pre></div>
<p><a href="api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$"><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>
</div> <!-- /container -->
<script src="js/vendor/jquery-1.8.0.min.js"></script>
<script src="js/vendor/bootstrap.min.js"></script>
<script src="js/main.js"></script>
<!-- MathJax Section -->
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
TeX: { equationNumbers: { autoNumber: "AMS" } }
});
</script>
<script type="text/javascript"
src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
<script>
MathJax.Hub.Config({
tex2jax: {
inlineMath: [ ["$", "$"], ["\\\\(","\\\\)"] ],
displayMath: [ ["$$","$$"], ["\\[", "\\]"] ],
processEscapes: true,
skipTags: ['script', 'noscript', 'style', 'textarea', 'pre']
}
});
</script>
</body>
</html>
|