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
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
|
<!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 0.9.0 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">
<!-- Google analytics script -->
<script type="text/javascript">
/*
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-32518208-1']);
_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">0.9.0</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="scala-programming-guide.html">Spark in Scala</a></li>
<li><a href="java-programming-guide.html">Spark in Java</a></li>
<li><a href="python-programming-guide.html">Spark in Python</a></li>
<li class="divider"></li>
<li><a href="streaming-programming-guide.html">Spark Streaming</a></li>
<li><a href="mllib-guide.html">MLlib (Machine Learning)</a></li>
<li><a href="bagel-programming-guide.html">Bagel (Pregel on Spark)</a></li>
<li><a href="graphx-programming-guide.html">GraphX (Graph Processing)</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/core/index.html#org.apache.spark.package">Spark Core for Java/Scala</a></li>
<li><a href="api/pyspark/index.html">Spark Core for Python</a></li>
<li class="divider"></li>
<li><a href="api/streaming/index.html#org.apache.spark.streaming.package">Spark Streaming</a></li>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.package">MLlib (Machine Learning)</a></li>
<li><a href="api/bagel/index.html#org.apache.spark.bagel.package">Bagel (Pregel on Spark)</a></li>
<li><a href="api/graphx/index.html#org.apache.spark.graphx.package">GraphX (Graph Processing)</a></li>
<li class="divider"></li>
<li class="dropdown-submenu">
<a tabindex="-1" href="#">External Data Sources</a>
<ul class="dropdown-menu">
<li><a href="api/external/kafka/index.html#org.apache.spark.streaming.kafka.KafkaUtils$">Kafka</a></li>
<li><a href="api/external/flume/index.html#org.apache.spark.streaming.flume.FlumeUtils$">Flume</a></li>
<li><a href="api/external/twitter/index.html#org.apache.spark.streaming.twitter.TwitterUtils$">Twitter</a></li>
<li><a href="api/external/zeromq/index.html#org.apache.spark.streaming.zeromq.ZeroMQUtils$">ZeroMQ</a></li>
<li><a href="api/external/mqtt/index.html#org.apache.spark.streaming.mqtt.MQTTUtils$">MQTT</a></li>
</ul>
</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="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="hadoop-third-party-distributions.html">Running with CDH/HDP</a></li>
<li><a href="hardware-provisioning.html">Hardware Provisioning</a></li>
<li><a href="job-scheduling.html">Job Scheduling</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">v0.9.0</span></p>-->
</div>
</div>
</div>
<div class="container" id="content">
<h1 class="title">Machine Learning Library (MLlib)</h1>
<ul id="markdown-toc">
<li><a href="#dependencies">Dependencies</a></li>
<li><a href="#binary-classification">Binary Classification</a></li>
<li><a href="#linear-regression">Linear Regression</a></li>
<li><a href="#clustering">Clustering</a></li>
<li><a href="#collaborative-filtering">Collaborative Filtering</a> <ul>
<li><a href="#explicit-vs-implicit-feedback">Explicit vs Implicit Feedback</a></li>
</ul>
</li>
<li><a href="#gradient-descent-primitive">Gradient Descent Primitive</a></li>
<li><a href="#using-mllib-in-scala">Using MLLib in Scala</a> <ul>
<li><a href="#binary-classification-1">Binary Classification</a></li>
<li><a href="#linear-regression-1">Linear Regression</a></li>
<li><a href="#clustering-1">Clustering</a></li>
<li><a href="#collaborative-filtering-1">Collaborative Filtering</a></li>
</ul>
</li>
<li><a href="#using-mllib-in-java">Using MLLib in Java</a></li>
<li><a href="#using-mllib-in-python">Using MLLib in Python</a> <ul>
<li><a href="#binary-classification-2">Binary Classification</a></li>
<li><a href="#linear-regression-2">Linear Regression</a></li>
<li><a href="#clustering-2">Clustering</a></li>
<li><a href="#collaborative-filtering-2">Collaborative Filtering</a></li>
</ul>
</li>
</ul>
<p>MLlib is a Spark implementation of some common machine learning (ML)
functionality, as well associated tests and data generators. MLlib
currently supports four common types of machine learning problem settings,
namely, binary classification, regression, clustering and collaborative
filtering, as well as an underlying gradient descent optimization primitive.
This guide will outline the functionality supported in MLlib and also provides
an example of invoking MLlib.</p>
<h1 id="dependencies">Dependencies</h1>
<p>MLlib uses the <a href="https://github.com/mikiobraun/jblas">jblas</a> linear algebra library, which itself
depends on native Fortran routines. You may 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.</p>
<p>To use MLlib in Python, you will need <a href="http://www.numpy.org">NumPy</a> version 1.7 or newer
and Python 2.7.</p>
<h1 id="binary-classification">Binary Classification</h1>
<p>Binary classification is a supervised learning problem in which we want to
classify entities into one of two distinct categories or labels, e.g.,
predicting whether or not emails are spam. This problem involves executing a
learning <em>Algorithm</em> on a set of <em>labeled</em> examples, i.e., a set of entities
represented via (numerical) features along with underlying category labels.
The algorithm returns a trained <em>Model</em> that can predict the label for new
entities for which the underlying label is unknown. </p>
<p>MLlib currently supports two standard model families for binary classification,
namely <a href="http://en.wikipedia.org/wiki/Support_vector_machine">Linear Support Vector Machines
(SVMs)</a> and <a href="http://en.wikipedia.org/wiki/Logistic_regression">Logistic
Regression</a>, along with <a href="http://en.wikipedia.org/wiki/Regularization_(mathematics)">L1
and L2 regularized</a>
variants of each model family. The training algorithms all leverage an
underlying gradient descent primitive (described
<a href="#gradient-descent-primitive">below</a>), and take as input a regularization
parameter (<em>regParam</em>) along with various parameters associated with gradient
descent (<em>stepSize</em>, <em>numIterations</em>, <em>miniBatchFraction</em>). </p>
<p>Available algorithms for binary classification:</p>
<ul>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD">SVMWithSGD</a></li>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD">LogisticRegressionWithSGD</a></li>
</ul>
<h1 id="linear-regression">Linear Regression</h1>
<p>Linear regression is another classical supervised learning setting. In this
problem, each entity is associated with a real-valued label (as opposed to a
binary label as in binary classification), and we want to predict labels as
closely as possible given numerical features representing entities. MLlib
supports linear regression as well as L1
(<a href="http://en.wikipedia.org/wiki/Lasso_(statistics)#Lasso_method">lasso</a>) and L2
(<a href="http://en.wikipedia.org/wiki/Ridge_regression">ridge</a>) regularized variants.
The regression algorithms in MLlib also leverage the underlying gradient
descent primitive (described <a href="#gradient-descent-primitive">below</a>), and have
the same parameters as the binary classification algorithms described above. </p>
<p>Available algorithms for linear regression: </p>
<ul>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD">LinearRegressionWithSGD</a></li>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD">RidgeRegressionWithSGD</a></li>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.regression.LassoWithSGD">LassoWithSGD</a></li>
</ul>
<h1 id="clustering">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). 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
predfined 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>initializiationSteps</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>Available algorithms for clustering: </p>
<ul>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.clustering.KMeans">KMeans</a></li>
</ul>
<h1 id="collaborative-filtering">Collaborative Filtering</h1>
<p><a href="http://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering">Collaborative filtering</a>
is commonly used for recommender systems. These techniques aim to fill in the
missing entries of a user-item association matrix. MLlib currently supports
model-based collaborative filtering, in which users and products are described
by a small set of latent factors that can be used to predict missing entries.
In particular, we implement the <a href="http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf">alternating least squares
(ALS)</a>
algorithm to learn these latent factors. The implementation in MLlib has the
following parameters:</p>
<ul>
<li><em>numBlocks</em> is the number of blacks used to parallelize computation (set to -1 to auto-configure). </li>
<li><em>rank</em> is the number of latent factors in our model.</li>
<li><em>iterations</em> is the number of iterations to run.</li>
<li><em>lambda</em> specifies the regularization parameter in ALS.</li>
<li><em>implicitPrefs</em> specifies whether to use the <em>explicit feedback</em> ALS variant or one adapted for <em>implicit feedback</em> data</li>
<li><em>alpha</em> is a parameter applicable to the implicit feedback variant of ALS that governs the <em>baseline</em> confidence in preference observations</li>
</ul>
<h2 id="explicit-vs-implicit-feedback">Explicit vs Implicit Feedback</h2>
<p>The standard approach to matrix factorization based collaborative filtering treats
the entries in the user-item matrix as <em>explicit</em> preferences given by the user to the item.</p>
<p>It is common in many real-world use cases to only have access to <em>implicit feedback</em>
(e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with
such data is taken from
<a href="http://www2.research.att.com/~yifanhu/PUB/cf.pdf">Collaborative Filtering for Implicit Feedback Datasets</a>.
Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as
a combination of binary preferences and <em>confidence values</em>. The ratings are then related
to the level of confidence in observed user preferences, rather than explicit ratings given to items.
The model then tries to find latent factors that can be used to predict the expected preference of a user
for an item. </p>
<p>Available algorithms for collaborative filtering: </p>
<ul>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.recommendation.ALS">ALS</a></li>
</ul>
<h1 id="gradient-descent-primitive">Gradient Descent Primitive</h1>
<p><a href="http://en.wikipedia.org/wiki/Gradient_descent">Gradient descent</a> (along with
stochastic variants thereof) are first-order optimization methods that are
well-suited for large-scale and distributed computation. Gradient descent
methods aim to find a local minimum of a function by iteratively taking steps
in the direction of the negative gradient of the function at the current point,
i.e., the current parameter value. Gradient descent is included as a low-level
primitive in MLlib, upon which various ML algorithms are developed, and has the
following parameters:</p>
<ul>
<li><em>gradient</em> is a class that computes the stochastic gradient of the function
being optimized, i.e., with respect to a single training example, at the
current parameter value. MLlib includes gradient classes for common loss
functions, e.g., hinge, logistic, least-squares. The gradient class takes as
input a training example, its label, and the current parameter value. </li>
<li><em>updater</em> is a class that updates weights in each iteration of gradient
descent. MLlib includes updaters for cases without regularization, as well as
L1 and L2 regularizers.</li>
<li><em>stepSize</em> is a scalar value denoting the initial step size for gradient
descent. All updaters in MLlib use a step size at the t-th step equal to
stepSize / sqrt(t). </li>
<li><em>numIterations</em> is the number of iterations to run.</li>
<li><em>regParam</em> is the regularization parameter when using L1 or L2 regularization.</li>
<li><em>miniBatchFraction</em> is the fraction of the data used to compute the gradient
at each iteration.</li>
</ul>
<p>Available algorithms for gradient descent:</p>
<ul>
<li><a href="api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent">GradientDescent</a></li>
</ul>
<h1 id="using-mllib-in-scala">Using MLLib in Scala</h1>
<p>Following code snippets can be executed in <code>spark-shell</code>.</p>
<h2 id="binary-classification-1">Binary Classification</h2>
<p>The following code snippet illustrates how to load a sample dataset, execute a
training algorithm on this training data using a static method in the algorithm
object, and make predictions with the resulting model to compute the training
error.</p>
<div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.SparkContext</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.classification.SVMWithSGD</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span>
<span class="c1">// Load and parse the data file</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">"mllib/data/sample_svm_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">line</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">parts</span> <span class="k">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">' '</span><span class="o">)</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="n">parts</span><span class="o">(</span><span class="mi">0</span><span class="o">).</span><span class="n">toDouble</span><span class="o">,</span> <span class="n">parts</span><span class="o">.</span><span class="n">tail</span><span class="o">.</span><span class="n">map</span><span class="o">(</span><span class="n">x</span> <span class="k">=></span> <span class="n">x</span><span class="o">.</span><span class="n">toDouble</span><span class="o">).</span><span class="n">toArray</span><span class="o">)</span>
<span class="o">}</span>
<span class="c1">// Run training algorithm to build the model</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">model</span> <span class="k">=</span> <span class="nc">SVMWithSGD</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">numIterations</span><span class="o">)</span>
<span class="c1">// Evaluate model on training examples and compute training error</span>
<span class="k">val</span> <span class="n">labelAndPreds</span> <span class="k">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">point</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">prediction</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="n">features</span><span class="o">)</span>
<span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="n">label</span><span class="o">,</span> <span class="n">prediction</span><span class="o">)</span>
<span class="o">}</span>
<span class="k">val</span> <span class="n">trainErr</span> <span class="k">=</span> <span class="n">labelAndPreds</span><span class="o">.</span><span class="n">filter</span><span class="o">(</span><span class="n">r</span> <span class="k">=></span> <span class="n">r</span><span class="o">.</span><span class="n">_1</span> <span class="o">!=</span> <span class="n">r</span><span class="o">.</span><span class="n">_2</span><span class="o">).</span><span class="n">count</span><span class="o">.</span><span class="n">toDouble</span> <span class="o">/</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">count</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Training Error = "</span> <span class="o">+</span> <span class="n">trainErr</span><span class="o">)</span>
</code></pre></div>
<p>The <code>SVMWithSGD.train()</code> method by default performs L2 regularization with the
regularization parameter set to 1.0. If we want to configure this algorithm, we
can customize <code>SVMWithSGD</code> further by creating a new object directly and
calling setter methods. All other MLlib algorithms support customization in
this way as well. For example, the following code produces an L1 regularized
variant of SVMs with regularization parameter set to 0.1, and runs the training
algorithm for 200 iterations.</p>
<div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.optimization.L1Updater</span>
<span class="k">val</span> <span class="n">svmAlg</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SVMWithSGD</span><span class="o">()</span>
<span class="n">svmAlg</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">setNumIterations</span><span class="o">(</span><span class="mi">200</span><span class="o">)</span>
<span class="o">.</span><span class="n">setRegParam</span><span class="o">(</span><span class="mf">0.1</span><span class="o">)</span>
<span class="o">.</span><span class="n">setUpdater</span><span class="o">(</span><span class="k">new</span> <span class="n">L1Updater</span><span class="o">)</span>
<span class="k">val</span> <span class="n">modelL1</span> <span class="k">=</span> <span class="n">svmAlg</span><span class="o">.</span><span class="n">run</span><span class="o">(</span><span class="n">parsedData</span><span class="o">)</span>
</code></pre></div>
<h2 id="linear-regression-1">Linear Regression</h2>
<p>The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The
example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We
compute the Mean Squared Error at the end to evaluate
<a href="http://en.wikipedia.org/wiki/Goodness_of_fit">goodness of fit</a></p>
<div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LinearRegressionWithSGD</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</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">"mllib/data/ridge-data/lpsa.data"</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">line</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">parts</span> <span class="k">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">','</span><span class="o">)</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="n">parts</span><span class="o">(</span><span class="mi">0</span><span class="o">).</span><span class="n">toDouble</span><span class="o">,</span> <span class="n">parts</span><span class="o">(</span><span class="mi">1</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="n">x</span> <span class="k">=></span> <span class="n">x</span><span class="o">.</span><span class="n">toDouble</span><span class="o">).</span><span class="n">toArray</span><span class="o">)</span>
<span class="o">}</span>
<span class="c1">// Building the model</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">model</span> <span class="k">=</span> <span class="nc">LinearRegressionWithSGD</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">numIterations</span><span class="o">)</span>
<span class="c1">// Evaluate model on training examples and compute training error</span>
<span class="k">val</span> <span class="n">valuesAndPreds</span> <span class="k">=</span> <span class="n">parsedData</span><span class="o">.</span><span class="n">map</span> <span class="o">{</span> <span class="n">point</span> <span class="k">=></span>
<span class="k">val</span> <span class="n">prediction</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="n">features</span><span class="o">)</span>
<span class="o">(</span><span class="n">point</span><span class="o">.</span><span class="n">label</span><span class="o">,</span> <span class="n">prediction</span><span class="o">)</span>
<span class="o">}</span>
<span class="k">val</span> <span class="nc">MSE</span> <span class="k">=</span> <span class="n">valuesAndPreds</span><span class="o">.</span><span class="n">map</span><span class="o">{</span> <span class="k">case</span><span class="o">(</span><span class="n">v</span><span class="o">,</span> <span class="n">p</span><span class="o">)</span> <span class="k">=></span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="o">((</span><span class="n">v</span> <span class="o">-</span> <span class="n">p</span><span class="o">),</span> <span class="mi">2</span><span class="o">)}.</span><span class="n">reduce</span><span class="o">(</span><span class="k">_</span> <span class="o">+</span> <span class="k">_</span><span class="o">)/</span><span class="n">valuesAndPreds</span><span class="o">.</span><span class="n">count</span>
<span class="n">println</span><span class="o">(</span><span class="s">"training Mean Squared Error = "</span> <span class="o">+</span> <span class="nc">MSE</span><span class="o">)</span>
</code></pre></div>
<p>Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training
<a href="http://en.wikipedia.org/wiki/Mean_squared_error">Mean Squared Errors</a>.</p>
<h2 id="clustering-1">Clustering</h2>
<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="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.clustering.KMeans</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">"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="k">_</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">numIterations</span> <span class="k">=</span> <span class="mi">20</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">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>
<h2 id="collaborative-filtering-1">Collaborative Filtering</h2>
<p>In the following example we load rating data. Each row consists of a user, a product and a rating.
We use the default ALS.train() method which assumes ratings are explicit. We evaluate the recommendation
model by measuring the Mean Squared Error of rating prediction.</p>
<div class="highlight"><pre><code class="scala"><span class="k">import</span> <span class="nn">org.apache.spark.mllib.recommendation.ALS</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.recommendation.Rating</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">"mllib/data/als/test.data"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">ratings</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="k">_</span><span class="o">.</span><span class="n">split</span><span class="o">(</span><span class="sc">','</span><span class="o">)</span> <span class="k">match</span> <span class="o">{</span>
<span class="k">case</span> <span class="nc">Array</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">item</span><span class="o">,</span> <span class="n">rate</span><span class="o">)</span> <span class="k">=></span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">.</span><span class="n">toInt</span><span class="o">,</span> <span class="n">item</span><span class="o">.</span><span class="n">toInt</span><span class="o">,</span> <span class="n">rate</span><span class="o">.</span><span class="n">toDouble</span><span class="o">)</span>
<span class="o">})</span>
<span class="c1">// Build the recommendation model using ALS</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">model</span> <span class="k">=</span> <span class="nc">ALS</span><span class="o">.</span><span class="n">train</span><span class="o">(</span><span class="n">ratings</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="mi">20</span><span class="o">,</span> <span class="mf">0.01</span><span class="o">)</span>
<span class="c1">// Evaluate the model on rating data</span>
<span class="k">val</span> <span class="n">usersProducts</span> <span class="k">=</span> <span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="o">{</span> <span class="k">case</span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">,</span> <span class="n">rate</span><span class="o">)</span> <span class="k">=></span> <span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">)}</span>
<span class="k">val</span> <span class="n">predictions</span> <span class="k">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="o">(</span><span class="n">usersProducts</span><span class="o">).</span><span class="n">map</span><span class="o">{</span>
<span class="k">case</span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">,</span> <span class="n">rate</span><span class="o">)</span> <span class="k">=></span> <span class="o">((</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">),</span> <span class="n">rate</span><span class="o">)</span>
<span class="o">}</span>
<span class="k">val</span> <span class="n">ratesAndPreds</span> <span class="k">=</span> <span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="o">{</span>
<span class="k">case</span> <span class="nc">Rating</span><span class="o">(</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">,</span> <span class="n">rate</span><span class="o">)</span> <span class="k">=></span> <span class="o">((</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">),</span> <span class="n">rate</span><span class="o">)</span>
<span class="o">}.</span><span class="n">join</span><span class="o">(</span><span class="n">predictions</span><span class="o">)</span>
<span class="k">val</span> <span class="nc">MSE</span> <span class="k">=</span> <span class="n">ratesAndPreds</span><span class="o">.</span><span class="n">map</span><span class="o">{</span>
<span class="k">case</span> <span class="o">((</span><span class="n">user</span><span class="o">,</span> <span class="n">product</span><span class="o">),</span> <span class="o">(</span><span class="n">r1</span><span class="o">,</span> <span class="n">r2</span><span class="o">))</span> <span class="k">=></span> <span class="n">math</span><span class="o">.</span><span class="n">pow</span><span class="o">((</span><span class="n">r1</span><span class="o">-</span> <span class="n">r2</span><span class="o">),</span> <span class="mi">2</span><span class="o">)</span>
<span class="o">}.</span><span class="n">reduce</span><span class="o">(</span><span class="k">_</span> <span class="o">+</span> <span class="k">_</span><span class="o">)/</span><span class="n">ratesAndPreds</span><span class="o">.</span><span class="n">count</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="nc">MSE</span><span class="o">)</span>
</code></pre></div>
<p>If the rating matrix is derived from other source of information (i.e., it is inferred from
other signals), you can use the trainImplicit method to get better results.</p>
<div class="highlight"><pre><code class="scala"><span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="nc">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="o">(</span><span class="n">ratings</span><span class="o">,</span> <span class="mi">1</span><span class="o">,</span> <span class="mi">20</span><span class="o">,</span> <span class="mf">0.01</span><span class="o">)</span>
</code></pre></div>
<h1 id="using-mllib-in-java">Using MLLib in Java</h1>
<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.</p>
<h1 id="using-mllib-in-python">Using MLLib in Python</h1>
<p>Following examples can be tested in the PySpark shell.</p>
<h2 id="binary-classification-2">Binary Classification</h2>
<p>The following example shows how to load a sample dataset, build Logistic Regression model,
and make predictions with the resulting model to compute the training error.</p>
<div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.classification</span> <span class="kn">import</span> <span class="n">LogisticRegressionWithSGD</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">"mllib/data/sample_svm_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="n">model</span> <span class="o">=</span> <span class="n">LogisticRegressionWithSGD</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="c"># Build the model</span>
<span class="n">labelsAndPreds</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="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="mi">0</span><span class="p">)),</span>
<span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">size</span><span class="p">)))))</span>
<span class="c"># Evaluating the model on training data</span>
<span class="n">trainErr</span> <span class="o">=</span> <span class="n">labelsAndPreds</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span> <span class="n">v</span> <span class="o">!=</span> <span class="n">p</span><span class="p">)</span><span class="o">.</span><span class="n">count</span><span class="p">()</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">parsedData</span><span class="o">.</span><span class="n">count</span><span class="p">())</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Training Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">trainErr</span><span class="p">))</span>
</code></pre></div>
<h2 id="linear-regression-2">Linear Regression</h2>
<p>The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The
example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We
compute the Mean Squared Error at the end to evaluate
<a href="http://en.wikipedia.org/wiki/Goodness_of_fit">goodness of fit</a></p>
<div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.regression</span> <span class="kn">import</span> <span class="n">LinearRegressionWithSGD</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">"mllib/data/ridge-data/lpsa.data"</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">replace</span><span class="p">(</span><span class="s">','</span><span class="p">,</span> <span class="s">' '</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</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegressionWithSGD</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="c"># Evaluate the model on training data</span>
<span class="n">valuesAndPreds</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="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">item</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
<span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">point</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">point</span><span class="o">.</span><span class="n">size</span><span class="p">)))))</span>
<span class="n">MSE</span> <span class="o">=</span> <span class="n">valuesAndPreds</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">p</span><span class="p">):</span> <span class="p">(</span><span class="n">v</span> <span class="o">-</span> <span class="n">p</span><span class="p">)</span><span class="o">**</span><span class="mi">2</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="o">/</span><span class="n">valuesAndPreds</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">MSE</span><span class="p">))</span>
</code></pre></div>
<h2 id="clustering-2">Clustering</h2>
<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">"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">30</span><span class="p">,</span> <span class="n">initialization_mode</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>
<p>Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training Mean Squared
Errors.</p>
<h2 id="collaborative-filtering-2">Collaborative Filtering</h2>
<p>In the following example we load rating data. Each row consists of a user, a product and a rating.
We use the default ALS.train() method which assumes ratings are explicit. We evaluate the
recommendation by measuring the Mean Squared Error of rating prediction.</p>
<div class="highlight"><pre><code class="python"><span class="kn">from</span> <span class="nn">pyspark.mllib.recommendation</span> <span class="kn">import</span> <span class="n">ALS</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">"mllib/data/als/test.data"</span><span class="p">)</span>
<span class="n">ratings</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 recommendation model using Alternating Least Squares</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
<span class="c"># Evaluate the model on training data</span>
<span class="n">testdata</span> <span class="o">=</span> <span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">p</span><span class="p">:</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predictAll</span><span class="p">(</span><span class="n">testdata</span><span class="p">)</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="p">((</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="n">ratesAndPreds</span> <span class="o">=</span> <span class="n">ratings</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="p">((</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
<span class="n">MSE</span> <span class="o">=</span> <span class="n">ratesAndPreds</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">r</span><span class="p">:</span> <span class="p">(</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span><span class="o">**</span><span class="mi">2</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="o">/</span><span class="n">ratesAndPreds</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s">"Mean Squared Error = "</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">MSE</span><span class="p">))</span>
</code></pre></div>
<p>If the rating matrix is derived from other source of information (i.e., it is inferred from other
signals), you can use the trainImplicit method to get better results.</p>
<div class="highlight"><pre><code class="python"><span class="c"># Build the recommendation model using Alternating Least Squares based on implicit ratings</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">ALS</span><span class="o">.</span><span class="n">trainImplicit</span><span class="p">(</span><span class="n">ratings</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
</code></pre></div>
<!-- Main hero unit for a primary marketing message or call to action -->
<!--<div class="hero-unit">
<h1>Hello, world!</h1>
<p>This is a template for a simple marketing or informational website. It includes a large callout called the hero unit and three supporting pieces of content. Use it as a starting point to create something more unique.</p>
<p><a class="btn btn-primary btn-large">Learn more »</a></p>
</div>-->
<!-- Example row of columns -->
<!--<div class="row">
<div class="span4">
<h2>Heading</h2>
<p>Donec id elit non mi porta gravida at eget metus. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus. Etiam porta sem malesuada magna mollis euismod. Donec sed odio dui. </p>
<p><a class="btn" href="#">View details »</a></p>
</div>
<div class="span4">
<h2>Heading</h2>
<p>Donec id elit non mi porta gravida at eget metus. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus. Etiam porta sem malesuada magna mollis euismod. Donec sed odio dui. </p>
<p><a class="btn" href="#">View details »</a></p>
</div>
<div class="span4">
<h2>Heading</h2>
<p>Donec sed odio dui. Cras justo odio, dapibus ac facilisis in, egestas eget quam. Vestibulum id ligula porta felis euismod semper. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus.</p>
<p><a class="btn" href="#">View details »</a></p>
</div>
</div>
<hr>-->
<footer>
<hr>
<p style="text-align: center; veritcal-align: middle; color: #999;">
Apache Spark is an effort undergoing incubation at the Apache Software Foundation.
<a href="http://incubator.apache.org">
<img style="margin-left: 20px;" src="img/incubator-logo.png" />
</a>
</p>
</footer>
</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>
<!-- A script to fix internal hash links because we have an overlapping top bar.
Based on https://github.com/twitter/bootstrap/issues/193#issuecomment-2281510 -->
<script>
$(function() {
function maybeScrollToHash() {
if (window.location.hash && $(window.location.hash).length) {
var newTop = $(window.location.hash).offset().top - $('#topbar').height() - 5;
$(window).scrollTop(newTop);
}
}
$(window).bind('hashchange', function() {
maybeScrollToHash();
});
// Scroll now too in case we had opened the page on a hash, but wait 1 ms because some browsers
// will try to do *their* initial scroll after running the onReady handler.
setTimeout(function() { maybeScrollToHash(); }, 1)
})
</script>
</body>
</html>
|
