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
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
|
<!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>MLlib - Spark 1.5.1 Documentation</title>
<meta name="description" content="MLlib machine learning library overview for Spark 1.5.1">
<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">
<!-- Google analytics script -->
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-32518208-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</script>
</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">1.5.1</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">DataFrames and 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>
<li><a href="sparkr.html">SparkR (R 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">Scala</a></li>
<li><a href="api/java/index.html">Java</a></li>
<li><a href="api/python/index.html">Python</a></li>
<li><a href="api/R/index.html">R</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="spark-standalone.html">Spark Standalone</a></li>
<li><a href="running-on-mesos.html">Mesos</a></li>
<li><a href="running-on-yarn.html">YARN</a></li>
<li class="divider"></li>
<li><a href="ec2-scripts.html">Amazon EC2</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-spark.html">Building Spark</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">Contributing to Spark</a></li>
<li><a href="https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects">Supplemental Projects</a></li>
</ul>
</li>
</ul>
<!--<p class="navbar-text pull-right"><span class="version-text">v1.5.1</span></p>-->
</div>
</div>
</div>
<div class="container" id="content">
<h1 class="title">Machine Learning Library (MLlib) Guide</h1>
<p>MLlib is Spark’s machine learning (ML) library.
Its goal is to make practical machine learning scalable and easy.
It consists of common learning algorithms and utilities, including classification, regression,
clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization
primitives and higher-level pipeline APIs.</p>
<p>It divides into two packages:</p>
<ul>
<li><a href="mllib-guide.html#mllib-types-algorithms-and-utilities"><code>spark.mllib</code></a> contains the original API
built on top of <a href="programming-guide.html#resilient-distributed-datasets-rdds">RDDs</a>.</li>
<li><a href="mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines"><code>spark.ml</code></a> provides higher-level API
built on top of <a href="sql-programming-guide.html#dataframes">DataFrames</a> for constructing ML pipelines.</li>
</ul>
<p>Using <code>spark.ml</code> is recommended because with DataFrames the API is more versatile and flexible.
But we will keep supporting <code>spark.mllib</code> along with the development of <code>spark.ml</code>.
Users should be comfortable using <code>spark.mllib</code> features and expect more features coming.
Developers should contribute new algorithms to <code>spark.ml</code> if they fit the ML pipeline concept well,
e.g., feature extractors and transformers.</p>
<p>We list major functionality from both below, with links to detailed guides.</p>
<h1 id="sparkmllib-data-types-algorithms-and-utilities">spark.mllib: data types, algorithms, and utilities</h1>
<ul>
<li><a href="mllib-data-types.html">Data types</a></li>
<li><a href="mllib-statistics.html">Basic statistics</a>
<ul>
<li><a href="mllib-statistics.html#summary-statistics">summary statistics</a></li>
<li><a href="mllib-statistics.html#correlations">correlations</a></li>
<li><a href="mllib-statistics.html#stratified-sampling">stratified sampling</a></li>
<li><a href="mllib-statistics.html#hypothesis-testing">hypothesis testing</a></li>
<li><a href="mllib-statistics.html#random-data-generation">random data generation</a></li>
</ul>
</li>
<li><a href="mllib-classification-regression.html">Classification and regression</a>
<ul>
<li><a href="mllib-linear-methods.html">linear models (SVMs, logistic regression, linear regression)</a></li>
<li><a href="mllib-naive-bayes.html">naive Bayes</a></li>
<li><a href="mllib-decision-tree.html">decision trees</a></li>
<li><a href="mllib-ensembles.html">ensembles of trees (Random Forests and Gradient-Boosted Trees)</a></li>
<li><a href="mllib-isotonic-regression.html">isotonic regression</a></li>
</ul>
</li>
<li><a href="mllib-collaborative-filtering.html">Collaborative filtering</a>
<ul>
<li><a href="mllib-collaborative-filtering.html#collaborative-filtering">alternating least squares (ALS)</a></li>
</ul>
</li>
<li><a href="mllib-clustering.html">Clustering</a>
<ul>
<li><a href="mllib-clustering.html#k-means">k-means</a></li>
<li><a href="mllib-clustering.html#gaussian-mixture">Gaussian mixture</a></li>
<li><a href="mllib-clustering.html#power-iteration-clustering-pic">power iteration clustering (PIC)</a></li>
<li><a href="mllib-clustering.html#latent-dirichlet-allocation-lda">latent Dirichlet allocation (LDA)</a></li>
<li><a href="mllib-clustering.html#streaming-k-means">streaming k-means</a></li>
</ul>
</li>
<li><a href="mllib-dimensionality-reduction.html">Dimensionality reduction</a>
<ul>
<li><a href="mllib-dimensionality-reduction.html#singular-value-decomposition-svd">singular value decomposition (SVD)</a></li>
<li><a href="mllib-dimensionality-reduction.html#principal-component-analysis-pca">principal component analysis (PCA)</a></li>
</ul>
</li>
<li><a href="mllib-feature-extraction.html">Feature extraction and transformation</a></li>
<li><a href="mllib-frequent-pattern-mining.html">Frequent pattern mining</a>
<ul>
<li><a href="mllib-frequent-pattern-mining.html#fp-growth">FP-growth</a></li>
<li><a href="mllib-frequent-pattern-mining.html#association-rules">association rules</a></li>
<li><a href="mllib-frequent-pattern-mining.html#prefix-span">PrefixSpan</a></li>
</ul>
</li>
<li><a href="mllib-evaluation-metrics.html">Evaluation metrics</a></li>
<li><a href="mllib-pmml-model-export.html">PMML model export</a></li>
<li><a href="mllib-optimization.html">Optimization (developer)</a>
<ul>
<li><a href="mllib-optimization.html#stochastic-gradient-descent-sgd">stochastic gradient descent</a></li>
<li><a href="mllib-optimization.html#limited-memory-bfgs-l-bfgs">limited-memory BFGS (L-BFGS)</a></li>
</ul>
</li>
</ul>
<h1 id="sparkml-high-level-apis-for-ml-pipelines">spark.ml: high-level APIs for ML pipelines</h1>
<p><strong><a href="ml-guide.html">spark.ml programming guide</a></strong> provides an overview of the Pipelines API and major
concepts. It also contains sections on using algorithms within the Pipelines API, for example:</p>
<ul>
<li><a href="ml-features.html">Feature extraction, transformation, and selection</a></li>
<li><a href="ml-decision-tree.html">Decision trees for classification and regression</a></li>
<li><a href="ml-ensembles.html">Ensembles</a></li>
<li><a href="ml-linear-methods.html">Linear methods with elastic net regularization</a></li>
<li><a href="ml-ann.html">Multilayer perceptron classifier</a></li>
</ul>
<h1 id="dependencies">Dependencies</h1>
<p>MLlib uses the linear algebra package <a href="http://www.scalanlp.org/">Breeze</a>, which depends on
<a href="https://github.com/fommil/netlib-java">netlib-java</a> for optimised numerical processing.
If natives libraries<sup id="fnref:1"><a href="#fn:1" class="footnote">1</a></sup> are not available at runtime, you will see a warning message and a pure JVM
implementation will be used instead.</p>
<p>Due to licensing issues with runtime proprietary binaries, we do not include <code>netlib-java</code>’s native
proxies by default.
To configure <code>netlib-java</code> / Breeze to use system optimised binaries, include
<code>com.github.fommil.netlib:all:1.1.2</code> (or build Spark with <code>-Pnetlib-lgpl</code>) as a dependency of your
project and read the <a href="https://github.com/fommil/netlib-java">netlib-java</a> documentation for your
platform’s additional installation instructions.</p>
<p>To use MLlib in Python, you will need <a href="http://www.numpy.org">NumPy</a> version 1.4 or newer.</p>
<h1 id="migration-guide">Migration guide</h1>
<p>MLlib is under active development.
The APIs marked <code>Experimental</code>/<code>DeveloperApi</code> may change in future releases,
and the migration guide below will explain all changes between releases.</p>
<h2 id="from-14-to-15">From 1.4 to 1.5</h2>
<p>In the <code>spark.mllib</code> package, there are no break API changes but several behavior changes:</p>
<ul>
<li><a href="https://issues.apache.org/jira/browse/SPARK-9005">SPARK-9005</a>:
<code>RegressionMetrics.explainedVariance</code> returns the average regression sum of squares.</li>
<li><a href="https://issues.apache.org/jira/browse/SPARK-8600">SPARK-8600</a>: <code>NaiveBayesModel.labels</code> become
sorted.</li>
<li><a href="https://issues.apache.org/jira/browse/SPARK-3382">SPARK-3382</a>: <code>GradientDescent</code> has a default
convergence tolerance <code>1e-3</code>, and hence iterations might end earlier than 1.4.</li>
</ul>
<p>In the <code>spark.ml</code> package, there exists one break API change and one behavior change:</p>
<ul>
<li><a href="https://issues.apache.org/jira/browse/SPARK-9268">SPARK-9268</a>: Java’s varargs support is removed
from <code>Params.setDefault</code> due to a
<a href="https://issues.scala-lang.org/browse/SI-9013">Scala compiler bug</a>.</li>
<li><a href="https://issues.apache.org/jira/browse/SPARK-10097">SPARK-10097</a>: <code>Evaluator.isLargerBetter</code> is
added to indicate metric ordering. Metrics like RMSE no longer flip signs as in 1.4.</li>
</ul>
<h2 id="previous-spark-versions">Previous Spark versions</h2>
<p>Earlier migration guides are archived <a href="mllib-migration-guides.html">on this page</a>.</p>
<hr />
<div class="footnotes">
<ol>
<li id="fn:1">
<p>To learn more about the benefits and background of system optimised natives, you may wish to
watch Sam Halliday’s ScalaX talk on <a href="http://fommil.github.io/scalax14/#/">High Performance Linear Algebra in Scala</a>. <a href="#fnref:1" class="reversefootnote">↩</a></p>
</li>
</ol>
</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/vendor/anchor.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>
// 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>
|
