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
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
|
<!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>Spark ML Programming Guide - Spark 1.4.0 Documentation</title>
<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">1.4.0</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">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">v1.4.0</span></p>-->
</div>
</div>
</div>
<div class="container" id="content">
<h1 class="title">Spark ML Programming Guide</h1>
<p><code>spark.ml</code> is a new package introduced in Spark 1.2, which aims to provide a uniform set of
high-level APIs that help users create and tune practical machine learning pipelines.
It is currently an alpha component, and we would like to hear back from the community about
how it fits real-world use cases and how it could be improved.</p>
<p>Note that we will keep supporting and adding features to <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.mllib</code> and can optionally contribute
to <code>spark.ml</code>.</p>
<p><strong>Table of Contents</strong></p>
<ul id="markdown-toc">
<li><a href="#main-concepts" id="markdown-toc-main-concepts">Main Concepts</a> <ul>
<li><a href="#ml-dataset" id="markdown-toc-ml-dataset">ML Dataset</a></li>
<li><a href="#ml-algorithms" id="markdown-toc-ml-algorithms">ML Algorithms</a> <ul>
<li><a href="#transformers" id="markdown-toc-transformers">Transformers</a></li>
<li><a href="#estimators" id="markdown-toc-estimators">Estimators</a></li>
<li><a href="#properties-of-ml-algorithms" id="markdown-toc-properties-of-ml-algorithms">Properties of ML Algorithms</a></li>
</ul>
</li>
<li><a href="#pipeline" id="markdown-toc-pipeline">Pipeline</a> <ul>
<li><a href="#how-it-works" id="markdown-toc-how-it-works">How It Works</a></li>
<li><a href="#details" id="markdown-toc-details">Details</a></li>
</ul>
</li>
<li><a href="#parameters" id="markdown-toc-parameters">Parameters</a></li>
</ul>
</li>
<li><a href="#algorithm-guides" id="markdown-toc-algorithm-guides">Algorithm Guides</a></li>
<li><a href="#code-examples" id="markdown-toc-code-examples">Code Examples</a> <ul>
<li><a href="#example-estimator-transformer-and-param" id="markdown-toc-example-estimator-transformer-and-param">Example: Estimator, Transformer, and Param</a></li>
<li><a href="#example-pipeline" id="markdown-toc-example-pipeline">Example: Pipeline</a></li>
<li><a href="#example-model-selection-via-cross-validation" id="markdown-toc-example-model-selection-via-cross-validation">Example: Model Selection via Cross-Validation</a></li>
</ul>
</li>
<li><a href="#dependencies" id="markdown-toc-dependencies">Dependencies</a></li>
<li><a href="#migration-guide" id="markdown-toc-migration-guide">Migration Guide</a> <ul>
<li><a href="#from-12-to-13" id="markdown-toc-from-12-to-13">From 1.2 to 1.3</a></li>
</ul>
</li>
</ul>
<h1 id="main-concepts">Main Concepts</h1>
<p>Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple algorithms into a single pipeline, or workflow. This section covers the key concepts introduced by the Spark ML API.</p>
<ul>
<li>
<p><strong><a href="ml-guide.html#ml-dataset">ML Dataset</a></strong>: Spark ML uses the <a href="api/scala/index.html#org.apache.spark.sql.DataFrame"><code>DataFrame</code></a> from Spark SQL as a dataset which can hold a variety of data types.
E.g., a dataset could have different columns storing text, feature vectors, true labels, and predictions.</p>
</li>
<li>
<p><strong><a href="ml-guide.html#transformers"><code>Transformer</code></a></strong>: A <code>Transformer</code> is an algorithm which can transform one <code>DataFrame</code> into another <code>DataFrame</code>.
E.g., an ML model is a <code>Transformer</code> which transforms an RDD with features into an RDD with predictions.</p>
</li>
<li>
<p><strong><a href="ml-guide.html#estimators"><code>Estimator</code></a></strong>: An <code>Estimator</code> is an algorithm which can be fit on a <code>DataFrame</code> to produce a <code>Transformer</code>.
E.g., a learning algorithm is an <code>Estimator</code> which trains on a dataset and produces a model.</p>
</li>
<li>
<p><strong><a href="ml-guide.html#pipeline"><code>Pipeline</code></a></strong>: A <code>Pipeline</code> chains multiple <code>Transformer</code>s and <code>Estimator</code>s together to specify an ML workflow.</p>
</li>
<li>
<p><strong><a href="ml-guide.html#parameters"><code>Param</code></a></strong>: All <code>Transformer</code>s and <code>Estimator</code>s now share a common API for specifying parameters.</p>
</li>
</ul>
<h2 id="ml-dataset">ML Dataset</h2>
<p>Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
Spark ML adopts the <a href="api/scala/index.html#org.apache.spark.sql.DataFrame"><code>DataFrame</code></a> from Spark SQL in order to support a variety of data types under a unified Dataset concept.</p>
<p><code>DataFrame</code> supports many basic and structured types; see the <a href="sql-programming-guide.html#spark-sql-datatype-reference">Spark SQL datatype reference</a> for a list of supported types.
In addition to the types listed in the Spark SQL guide, <code>DataFrame</code> can use ML <a href="api/scala/index.html#org.apache.spark.mllib.linalg.Vector"><code>Vector</code></a> types.</p>
<p>A <code>DataFrame</code> can be created either implicitly or explicitly from a regular <code>RDD</code>. See the code examples below and the <a href="sql-programming-guide.html">Spark SQL programming guide</a> for examples.</p>
<p>Columns in a <code>DataFrame</code> are named. The code examples below use names such as “text,” “features,” and “label.”</p>
<h2 id="ml-algorithms">ML Algorithms</h2>
<h3 id="transformers">Transformers</h3>
<p>A <a href="api/scala/index.html#org.apache.spark.ml.Transformer"><code>Transformer</code></a> is an abstraction which includes feature transformers and learned models. Technically, a <code>Transformer</code> implements a method <code>transform()</code> which converts one <code>DataFrame</code> into another, generally by appending one or more columns.
For example:</p>
<ul>
<li>A feature transformer might take a dataset, read a column (e.g., text), convert it into a new column (e.g., feature vectors), append the new column to the dataset, and output the updated dataset.</li>
<li>A learning model might take a dataset, read the column containing feature vectors, predict the label for each feature vector, append the labels as a new column, and output the updated dataset.</li>
</ul>
<h3 id="estimators">Estimators</h3>
<p>An <a href="api/scala/index.html#org.apache.spark.ml.Estimator"><code>Estimator</code></a> abstracts the concept of a learning algorithm or any algorithm which fits or trains on data. Technically, an <code>Estimator</code> implements a method <code>fit()</code> which accepts a <code>DataFrame</code> and produces a <code>Transformer</code>.
For example, a learning algorithm such as <code>LogisticRegression</code> is an <code>Estimator</code>, and calling <code>fit()</code> trains a <code>LogisticRegressionModel</code>, which is a <code>Transformer</code>.</p>
<h3 id="properties-of-ml-algorithms">Properties of ML Algorithms</h3>
<p><code>Transformer</code>s and <code>Estimator</code>s are both stateless. In the future, stateful algorithms may be supported via alternative concepts.</p>
<p>Each instance of a <code>Transformer</code> or <code>Estimator</code> has a unique ID, which is useful in specifying parameters (discussed below).</p>
<h2 id="pipeline">Pipeline</h2>
<p>In machine learning, it is common to run a sequence of algorithms to process and learn from data.
E.g., a simple text document processing workflow might include several stages:</p>
<ul>
<li>Split each document’s text into words.</li>
<li>Convert each document’s words into a numerical feature vector.</li>
<li>Learn a prediction model using the feature vectors and labels.</li>
</ul>
<p>Spark ML represents such a workflow as a <a href="api/scala/index.html#org.apache.spark.ml.Pipeline"><code>Pipeline</code></a>,
which consists of a sequence of <a href="api/scala/index.html#org.apache.spark.ml.PipelineStage"><code>PipelineStage</code>s</a> (<code>Transformer</code>s and <code>Estimator</code>s) to be run in a specific order. We will use this simple workflow as a running example in this section.</p>
<h3 id="how-it-works">How It Works</h3>
<p>A <code>Pipeline</code> is specified as a sequence of stages, and each stage is either a <code>Transformer</code> or an <code>Estimator</code>.
These stages are run in order, and the input dataset is modified as it passes through each stage.
For <code>Transformer</code> stages, the <code>transform()</code> method is called on the dataset.
For <code>Estimator</code> stages, the <code>fit()</code> method is called to produce a <code>Transformer</code> (which becomes part of the <code>PipelineModel</code>, or fitted <code>Pipeline</code>), and that <code>Transformer</code>’s <code>transform()</code> method is called on the dataset.</p>
<p>We illustrate this for the simple text document workflow. The figure below is for the <em>training time</em> usage of a <code>Pipeline</code>.</p>
<p style="text-align: center;">
<img src="img/ml-Pipeline.png" title="Spark ML Pipeline Example" alt="Spark ML Pipeline Example" width="80%" />
</p>
<p>Above, the top row represents a <code>Pipeline</code> with three stages.
The first two (<code>Tokenizer</code> and <code>HashingTF</code>) are <code>Transformer</code>s (blue), and the third (<code>LogisticRegression</code>) is an <code>Estimator</code> (red).
The bottom row represents data flowing through the pipeline, where cylinders indicate <code>DataFrame</code>s.
The <code>Pipeline.fit()</code> method is called on the original dataset which has raw text documents and labels.
The <code>Tokenizer.transform()</code> method splits the raw text documents into words, adding a new column with words into the dataset.
The <code>HashingTF.transform()</code> method converts the words column into feature vectors, adding a new column with those vectors to the dataset.
Now, since <code>LogisticRegression</code> is an <code>Estimator</code>, the <code>Pipeline</code> first calls <code>LogisticRegression.fit()</code> to produce a <code>LogisticRegressionModel</code>.
If the <code>Pipeline</code> had more stages, it would call the <code>LogisticRegressionModel</code>’s <code>transform()</code> method on the dataset before passing the dataset to the next stage.</p>
<p>A <code>Pipeline</code> is an <code>Estimator</code>.
Thus, after a <code>Pipeline</code>’s <code>fit()</code> method runs, it produces a <code>PipelineModel</code> which is a <code>Transformer</code>. This <code>PipelineModel</code> is used at <em>test time</em>; the figure below illustrates this usage.</p>
<p style="text-align: center;">
<img src="img/ml-PipelineModel.png" title="Spark ML PipelineModel Example" alt="Spark ML PipelineModel Example" width="80%" />
</p>
<p>In the figure above, the <code>PipelineModel</code> has the same number of stages as the original <code>Pipeline</code>, but all <code>Estimator</code>s in the original <code>Pipeline</code> have become <code>Transformer</code>s.
When the <code>PipelineModel</code>’s <code>transform()</code> method is called on a test dataset, the data are passed through the <code>Pipeline</code> in order.
Each stage’s <code>transform()</code> method updates the dataset and passes it to the next stage.</p>
<p><code>Pipeline</code>s and <code>PipelineModel</code>s help to ensure that training and test data go through identical feature processing steps.</p>
<h3 id="details">Details</h3>
<p><em>DAG <code>Pipeline</code>s</em>: A <code>Pipeline</code>’s stages are specified as an ordered array. The examples given here are all for linear <code>Pipeline</code>s, i.e., <code>Pipeline</code>s in which each stage uses data produced by the previous stage. It is possible to create non-linear <code>Pipeline</code>s as long as the data flow graph forms a Directed Acyclic Graph (DAG). This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters). If the <code>Pipeline</code> forms a DAG, then the stages must be specified in topological order.</p>
<p><em>Runtime checking</em>: Since <code>Pipeline</code>s can operate on datasets with varied types, they cannot use compile-time type checking. <code>Pipeline</code>s and <code>PipelineModel</code>s instead do runtime checking before actually running the <code>Pipeline</code>. This type checking is done using the dataset <em>schema</em>, a description of the data types of columns in the <code>DataFrame</code>.</p>
<h2 id="parameters">Parameters</h2>
<p>Spark ML <code>Estimator</code>s and <code>Transformer</code>s use a uniform API for specifying parameters.</p>
<p>A <a href="api/scala/index.html#org.apache.spark.ml.param.Param"><code>Param</code></a> is a named parameter with self-contained documentation.
A <a href="api/scala/index.html#org.apache.spark.ml.param.ParamMap"><code>ParamMap</code></a> is a set of (parameter, value) pairs.</p>
<p>There are two main ways to pass parameters to an algorithm:</p>
<ol>
<li>Set parameters for an instance. E.g., if <code>lr</code> is an instance of <code>LogisticRegression</code>, one could call <code>lr.setMaxIter(10)</code> to make <code>lr.fit()</code> use at most 10 iterations. This API resembles the API used in MLlib.</li>
<li>Pass a <code>ParamMap</code> to <code>fit()</code> or <code>transform()</code>. Any parameters in the <code>ParamMap</code> will override parameters previously specified via setter methods.</li>
</ol>
<p>Parameters belong to specific instances of <code>Estimator</code>s and <code>Transformer</code>s.
For example, if we have two <code>LogisticRegression</code> instances <code>lr1</code> and <code>lr2</code>, then we can build a <code>ParamMap</code> with both <code>maxIter</code> parameters specified: <code>ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)</code>.
This is useful if there are two algorithms with the <code>maxIter</code> parameter in a <code>Pipeline</code>.</p>
<h1 id="algorithm-guides">Algorithm Guides</h1>
<p>There are now several algorithms in the Pipelines API which are not in the lower-level MLlib API, so we link to documentation for them here. These algorithms are mostly feature transformers, which fit naturally into the <code>Transformer</code> abstraction in Pipelines, and ensembles, which fit naturally into the <code>Estimator</code> abstraction in the Pipelines.</p>
<p><strong>Pipelines API Algorithm Guides</strong></p>
<ul>
<li><a href="ml-features.html">Feature Extraction, Transformation, and Selection</a></li>
<li><a href="ml-ensembles.html">Ensembles</a></li>
</ul>
<h1 id="code-examples">Code Examples</h1>
<p>This section gives code examples illustrating the functionality discussed above.
There is not yet documentation for specific algorithms in Spark ML. For more info, please refer to the <a href="api/scala/index.html#org.apache.spark.ml.package">API Documentation</a>. Spark ML algorithms are currently wrappers for MLlib algorithms, and the <a href="mllib-guide.html">MLlib programming guide</a> has details on specific algorithms.</p>
<h2 id="example-estimator-transformer-and-param">Example: Estimator, Transformer, and Param</h2>
<p>This example covers the concepts of <code>Estimator</code>, <code>Transformer</code>, and <code>Param</code>.</p>
<div class="codetabs">
<div data-lang="scala">
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.</span><span class="o">{</span><span class="nc">SparkConf</span><span class="o">,</span> <span class="nc">SparkContext</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.param.ParamMap</span>
<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">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span>
<span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span>
<span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">().</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"SimpleParamsExample"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>
<span class="k">import</span> <span class="nn">sqlContext.implicits._</span>
<span class="c1">// Prepare training data.</span>
<span class="c1">// We use LabeledPoint, which is a case class. Spark SQL can convert RDDs of case classes</span>
<span class="c1">// into DataFrames, where it uses the case class metadata to infer the schema.</span>
<span class="k">val</span> <span class="n">training</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</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="mf">0.0</span><span class="o">,</span> <span class="mf">1.1</span><span class="o">,</span> <span class="mf">0.1</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</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="mf">2.0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</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="mf">2.0</span><span class="o">,</span> <span class="mf">1.3</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</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="mf">0.0</span><span class="o">,</span> <span class="mf">1.2</span><span class="o">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="o">))))</span>
<span class="c1">// Create a LogisticRegression instance. This instance is an Estimator.</span>
<span class="k">val</span> <span class="n">lr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">()</span>
<span class="c1">// Print out the parameters, documentation, and any default values.</span>
<span class="n">println</span><span class="o">(</span><span class="s">"LogisticRegression parameters:\n"</span> <span class="o">+</span> <span class="n">lr</span><span class="o">.</span><span class="n">explainParams</span><span class="o">()</span> <span class="o">+</span> <span class="s">"\n"</span><span class="o">)</span>
<span class="c1">// We may set parameters using setter methods.</span>
<span class="n">lr</span><span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="n">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">)</span>
<span class="c1">// Learn a LogisticRegression model. This uses the parameters stored in lr.</span>
<span class="k">val</span> <span class="n">model1</span> <span class="k">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="c1">// Since model1 is a Model (i.e., a Transformer produced by an Estimator),</span>
<span class="c1">// we can view the parameters it used during fit().</span>
<span class="c1">// This prints the parameter (name: value) pairs, where names are unique IDs for this</span>
<span class="c1">// LogisticRegression instance.</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Model 1 was fit using parameters: "</span> <span class="o">+</span> <span class="n">model1</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">extractParamMap</span><span class="o">)</span>
<span class="c1">// We may alternatively specify parameters using a ParamMap,</span>
<span class="c1">// which supports several methods for specifying parameters.</span>
<span class="k">val</span> <span class="n">paramMap</span> <span class="k">=</span> <span class="nc">ParamMap</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">maxIter</span> <span class="o">-></span> <span class="mi">20</span><span class="o">)</span>
<span class="n">paramMap</span><span class="o">.</span><span class="n">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">maxIter</span><span class="o">,</span> <span class="mi">30</span><span class="o">)</span> <span class="c1">// Specify 1 Param. This overwrites the original maxIter.</span>
<span class="n">paramMap</span><span class="o">.</span><span class="n">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">regParam</span> <span class="o">-></span> <span class="mf">0.1</span><span class="o">,</span> <span class="n">lr</span><span class="o">.</span><span class="n">threshold</span> <span class="o">-></span> <span class="mf">0.55</span><span class="o">)</span> <span class="c1">// Specify multiple Params.</span>
<span class="c1">// One can also combine ParamMaps.</span>
<span class="k">val</span> <span class="n">paramMap2</span> <span class="k">=</span> <span class="nc">ParamMap</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">probabilityCol</span> <span class="o">-></span> <span class="s">"myProbability"</span><span class="o">)</span> <span class="c1">// Change output column name</span>
<span class="k">val</span> <span class="n">paramMapCombined</span> <span class="k">=</span> <span class="n">paramMap</span> <span class="o">++</span> <span class="n">paramMap2</span>
<span class="c1">// Now learn a new model using the paramMapCombined parameters.</span>
<span class="c1">// paramMapCombined overrides all parameters set earlier via lr.set* methods.</span>
<span class="k">val</span> <span class="n">model2</span> <span class="k">=</span> <span class="n">lr</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="n">toDF</span><span class="o">,</span> <span class="n">paramMapCombined</span><span class="o">)</span>
<span class="n">println</span><span class="o">(</span><span class="s">"Model 2 was fit using parameters: "</span> <span class="o">+</span> <span class="n">model2</span><span class="o">.</span><span class="n">parent</span><span class="o">.</span><span class="n">extractParamMap</span><span class="o">)</span>
<span class="c1">// Prepare test data.</span>
<span class="k">val</span> <span class="n">test</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</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="mf">1.0</span><span class="o">,</span> <span class="mf">1.5</span><span class="o">,</span> <span class="mf">1.3</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</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="mf">3.0</span><span class="o">,</span> <span class="mf">2.0</span><span class="o">,</span> <span class="o">-</span><span class="mf">0.1</span><span class="o">)),</span>
<span class="nc">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</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="mf">0.0</span><span class="o">,</span> <span class="mf">2.2</span><span class="o">,</span> <span class="o">-</span><span class="mf">1.5</span><span class="o">))))</span>
<span class="c1">// Make predictions on test data using the Transformer.transform() method.</span>
<span class="c1">// LogisticRegression.transform will only use the 'features' column.</span>
<span class="c1">// Note that model2.transform() outputs a 'myProbability' column instead of the usual</span>
<span class="c1">// 'probability' column since we renamed the lr.probabilityCol parameter previously.</span>
<span class="n">model2</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">test</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"features"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"myProbability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">collect</span><span class="o">()</span>
<span class="o">.</span><span class="n">foreach</span> <span class="o">{</span> <span class="k">case</span> <span class="nc">Row</span><span class="o">(</span><span class="n">features</span><span class="k">:</span> <span class="kt">Vector</span><span class="o">,</span> <span class="n">label</span><span class="k">:</span> <span class="kt">Double</span><span class="o">,</span> <span class="n">prob</span><span class="k">:</span> <span class="kt">Vector</span><span class="o">,</span> <span class="n">prediction</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">=></span>
<span class="n">println</span><span class="o">(</span><span class="n">s</span><span class="s">"($features, $label) -> prob=$prob, prediction=$prediction"</span><span class="o">)</span>
<span class="o">}</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="o">()</span></code></pre></div>
</div>
<div data-lang="java">
<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">java.util.List</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">com.google.common.collect.Lists</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegressionModel</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.param.ParamMap</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vectors</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.mllib.regression.LabeledPoint</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>
<span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">"JavaSimpleParamsExample"</span><span class="o">);</span>
<span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span>
<span class="n">SQLContext</span> <span class="n">jsql</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span>
<span class="c1">// Prepare training data.</span>
<span class="c1">// We use LabeledPoint, which is a JavaBean. Spark SQL can convert RDDs of JavaBeans</span>
<span class="c1">// into DataFrames, where it uses the bean metadata to infer the schema.</span>
<span class="n">List</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">localTraining</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</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="mf">0.0</span><span class="o">,</span> <span class="mf">1.1</span><span class="o">,</span> <span class="mf">0.1</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</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="mf">2.0</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">,</span> <span class="o">-</span><span class="mf">1.0</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</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="mf">2.0</span><span class="o">,</span> <span class="mf">1.3</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</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="mf">0.0</span><span class="o">,</span> <span class="mf">1.2</span><span class="o">,</span> <span class="o">-</span><span class="mf">0.5</span><span class="o">)));</span>
<span class="n">DataFrame</span> <span class="n">training</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTraining</span><span class="o">),</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Create a LogisticRegression instance. This instance is an Estimator.</span>
<span class="n">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">();</span>
<span class="c1">// Print out the parameters, documentation, and any default values.</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"LogisticRegression parameters:\n"</span> <span class="o">+</span> <span class="n">lr</span><span class="o">.</span><span class="na">explainParams</span><span class="o">()</span> <span class="o">+</span> <span class="s">"\n"</span><span class="o">);</span>
<span class="c1">// We may set parameters using setter methods.</span>
<span class="n">lr</span><span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="na">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">);</span>
<span class="c1">// Learn a LogisticRegression model. This uses the parameters stored in lr.</span>
<span class="n">LogisticRegressionModel</span> <span class="n">model1</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">training</span><span class="o">);</span>
<span class="c1">// Since model1 is a Model (i.e., a Transformer produced by an Estimator),</span>
<span class="c1">// we can view the parameters it used during fit().</span>
<span class="c1">// This prints the parameter (name: value) pairs, where names are unique IDs for this</span>
<span class="c1">// LogisticRegression instance.</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Model 1 was fit using parameters: "</span> <span class="o">+</span> <span class="n">model1</span><span class="o">.</span><span class="na">parent</span><span class="o">().</span><span class="na">extractParamMap</span><span class="o">());</span>
<span class="c1">// We may alternatively specify parameters using a ParamMap.</span>
<span class="n">ParamMap</span> <span class="n">paramMap</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">ParamMap</span><span class="o">();</span>
<span class="n">paramMap</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">maxIter</span><span class="o">().</span><span class="na">w</span><span class="o">(</span><span class="mi">20</span><span class="o">));</span> <span class="c1">// Specify 1 Param.</span>
<span class="n">paramMap</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">maxIter</span><span class="o">(),</span> <span class="mi">30</span><span class="o">);</span> <span class="c1">// This overwrites the original maxIter.</span>
<span class="n">paramMap</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">regParam</span><span class="o">().</span><span class="na">w</span><span class="o">(</span><span class="mf">0.1</span><span class="o">),</span> <span class="n">lr</span><span class="o">.</span><span class="na">threshold</span><span class="o">().</span><span class="na">w</span><span class="o">(</span><span class="mf">0.55</span><span class="o">));</span> <span class="c1">// Specify multiple Params.</span>
<span class="c1">// One can also combine ParamMaps.</span>
<span class="n">ParamMap</span> <span class="n">paramMap2</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">ParamMap</span><span class="o">();</span>
<span class="n">paramMap2</span><span class="o">.</span><span class="na">put</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">probabilityCol</span><span class="o">().</span><span class="na">w</span><span class="o">(</span><span class="s">"myProbability"</span><span class="o">));</span> <span class="c1">// Change output column name</span>
<span class="n">ParamMap</span> <span class="n">paramMapCombined</span> <span class="o">=</span> <span class="n">paramMap</span><span class="o">.</span><span class="na">$plus$plus</span><span class="o">(</span><span class="n">paramMap2</span><span class="o">);</span>
<span class="c1">// Now learn a new model using the paramMapCombined parameters.</span>
<span class="c1">// paramMapCombined overrides all parameters set earlier via lr.set* methods.</span>
<span class="n">LogisticRegressionModel</span> <span class="n">model2</span> <span class="o">=</span> <span class="n">lr</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">training</span><span class="o">,</span> <span class="n">paramMapCombined</span><span class="o">);</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"Model 2 was fit using parameters: "</span> <span class="o">+</span> <span class="n">model2</span><span class="o">.</span><span class="na">parent</span><span class="o">().</span><span class="na">extractParamMap</span><span class="o">());</span>
<span class="c1">// Prepare test documents.</span>
<span class="n">List</span><span class="o"><</span><span class="n">LabeledPoint</span><span class="o">></span> <span class="n">localTest</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</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="mf">1.0</span><span class="o">,</span> <span class="mf">1.5</span><span class="o">,</span> <span class="mf">1.3</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">0.0</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="mf">3.0</span><span class="o">,</span> <span class="mf">2.0</span><span class="o">,</span> <span class="o">-</span><span class="mf">0.1</span><span class="o">)),</span>
<span class="k">new</span> <span class="nf">LabeledPoint</span><span class="o">(</span><span class="mf">1.0</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="mf">0.0</span><span class="o">,</span> <span class="mf">2.2</span><span class="o">,</span> <span class="o">-</span><span class="mf">1.5</span><span class="o">)));</span>
<span class="n">DataFrame</span> <span class="n">test</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTest</span><span class="o">),</span> <span class="n">LabeledPoint</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Make predictions on test documents using the Transformer.transform() method.</span>
<span class="c1">// LogisticRegression.transform will only use the 'features' column.</span>
<span class="c1">// Note that model2.transform() outputs a 'myProbability' column instead of the usual</span>
<span class="c1">// 'probability' column since we renamed the lr.probabilityCol parameter previously.</span>
<span class="n">DataFrame</span> <span class="n">results</span> <span class="o">=</span> <span class="n">model2</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">test</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="n">Row</span> <span class="nl">r:</span> <span class="n">results</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"features"</span><span class="o">,</span> <span class="s">"label"</span><span class="o">,</span> <span class="s">"myProbability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">).</span><span class="na">collect</span><span class="o">())</span> <span class="o">{</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"("</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">", "</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> <span class="o">+</span> <span class="s">") -> prob="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span>
<span class="o">+</span> <span class="s">", prediction="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">3</span><span class="o">));</span>
<span class="o">}</span>
<span class="n">jsc</span><span class="o">.</span><span class="na">stop</span><span class="o">();</span></code></pre></div>
</div>
</div>
<h2 id="example-pipeline">Example: Pipeline</h2>
<p>This example follows the simple text document <code>Pipeline</code> illustrated in the figures above.</p>
<div class="codetabs">
<div data-lang="scala">
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.</span><span class="o">{</span><span class="nc">SparkConf</span><span class="o">,</span> <span class="nc">SparkContext</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">HashingTF</span><span class="o">,</span> <span class="nc">Tokenizer</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span>
<span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span>
<span class="c1">// Labeled and unlabeled instance types.</span>
<span class="c1">// Spark SQL can infer schema from case classes.</span>
<span class="k">case</span> <span class="k">class</span> <span class="nc">LabeledDocument</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">label</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span>
<span class="k">case</span> <span class="k">class</span> <span class="nc">Document</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">)</span>
<span class="c1">// Set up contexts. Import implicit conversions to DataFrame from sqlContext.</span>
<span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">().</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"SimpleTextClassificationPipeline"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>
<span class="k">import</span> <span class="nn">sqlContext.implicits._</span>
<span class="c1">// Prepare training documents, which are labeled.</span>
<span class="k">val</span> <span class="n">training</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">0L</span><span class="o">,</span> <span class="s">"a b c d e spark"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">1L</span><span class="o">,</span> <span class="s">"b d"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">2L</span><span class="o">,</span> <span class="s">"spark f g h"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">3L</span><span class="o">,</span> <span class="s">"hadoop mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">)))</span>
<span class="c1">// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.</span>
<span class="k">val</span> <span class="n">tokenizer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Tokenizer</span><span class="o">()</span>
<span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">hashingTF</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">HashingTF</span><span class="o">()</span>
<span class="o">.</span><span class="n">setNumFeatures</span><span class="o">(</span><span class="mi">1000</span><span class="o">)</span>
<span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">getOutputCol</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">lr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">()</span>
<span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="n">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">)</span>
<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span>
<span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">,</span> <span class="n">hashingTF</span><span class="o">,</span> <span class="n">lr</span><span class="o">))</span>
<span class="c1">// Fit the pipeline to training documents.</span>
<span class="k">val</span> <span class="n">model</span> <span class="k">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="c1">// Prepare test documents, which are unlabeled.</span>
<span class="k">val</span> <span class="n">test</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"spark i j k"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"mapreduce spark"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"apache hadoop"</span><span class="o">)))</span>
<span class="c1">// Make predictions on test documents.</span>
<span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">test</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"text"</span><span class="o">,</span> <span class="s">"probability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">collect</span><span class="o">()</span>
<span class="o">.</span><span class="n">foreach</span> <span class="o">{</span> <span class="k">case</span> <span class="nc">Row</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">prob</span><span class="k">:</span> <span class="kt">Vector</span><span class="o">,</span> <span class="n">prediction</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">=></span>
<span class="n">println</span><span class="o">(</span><span class="n">s</span><span class="s">"($id, $text) --> prob=$prob, prediction=$prediction"</span><span class="o">)</span>
<span class="o">}</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="o">()</span></code></pre></div>
</div>
<div data-lang="java">
<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">java.util.List</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">com.google.common.collect.Lists</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineModel</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.HashingTF</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.Tokenizer</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span>
<span class="c1">// Labeled and unlabeled instance types.</span>
<span class="c1">// Spark SQL can infer schema from Java Beans.</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">Document</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
<span class="kd">private</span> <span class="kt">long</span> <span class="n">id</span><span class="o">;</span>
<span class="kd">private</span> <span class="n">String</span> <span class="n">text</span><span class="o">;</span>
<span class="kd">public</span> <span class="nf">Document</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">,</span> <span class="n">String</span> <span class="n">text</span><span class="o">)</span> <span class="o">{</span>
<span class="k">this</span><span class="o">.</span><span class="na">id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span>
<span class="k">this</span><span class="o">.</span><span class="na">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kt">long</span> <span class="nf">getId</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">id</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setId</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="n">String</span> <span class="nf">getText</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">text</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setText</span><span class="o">(</span><span class="n">String</span> <span class="n">text</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">;</span> <span class="o">}</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">LabeledDocument</span> <span class="kd">extends</span> <span class="n">Document</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
<span class="kd">private</span> <span class="kt">double</span> <span class="n">label</span><span class="o">;</span>
<span class="kd">public</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">,</span> <span class="n">String</span> <span class="n">text</span><span class="o">,</span> <span class="kt">double</span> <span class="n">label</span><span class="o">)</span> <span class="o">{</span>
<span class="kd">super</span><span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="n">text</span><span class="o">);</span>
<span class="k">this</span><span class="o">.</span><span class="na">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kt">double</span> <span class="nf">getLabel</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">label</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setLabel</span><span class="o">(</span><span class="kt">double</span> <span class="n">label</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">;</span> <span class="o">}</span>
<span class="o">}</span>
<span class="c1">// Set up contexts.</span>
<span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">"JavaSimpleTextClassificationPipeline"</span><span class="o">);</span>
<span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span>
<span class="n">SQLContext</span> <span class="n">jsql</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span>
<span class="c1">// Prepare training documents, which are labeled.</span>
<span class="n">List</span><span class="o"><</span><span class="n">LabeledDocument</span><span class="o">></span> <span class="n">localTraining</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">0L</span><span class="o">,</span> <span class="s">"a b c d e spark"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">1L</span><span class="o">,</span> <span class="s">"b d"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">2L</span><span class="o">,</span> <span class="s">"spark f g h"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">3L</span><span class="o">,</span> <span class="s">"hadoop mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">));</span>
<span class="n">DataFrame</span> <span class="n">training</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTraining</span><span class="o">),</span> <span class="n">LabeledDocument</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.</span>
<span class="n">Tokenizer</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Tokenizer</span><span class="o">()</span>
<span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">);</span>
<span class="n">HashingTF</span> <span class="n">hashingTF</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">HashingTF</span><span class="o">()</span>
<span class="o">.</span><span class="na">setNumFeatures</span><span class="o">(</span><span class="mi">1000</span><span class="o">)</span>
<span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="na">getOutputCol</span><span class="o">())</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">);</span>
<span class="n">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">()</span>
<span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="na">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">);</span>
<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span>
<span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">tokenizer</span><span class="o">,</span> <span class="n">hashingTF</span><span class="o">,</span> <span class="n">lr</span><span class="o">});</span>
<span class="c1">// Fit the pipeline to training documents.</span>
<span class="n">PipelineModel</span> <span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">training</span><span class="o">);</span>
<span class="c1">// Prepare test documents, which are unlabeled.</span>
<span class="n">List</span><span class="o"><</span><span class="n">Document</span><span class="o">></span> <span class="n">localTest</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"spark i j k"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"mapreduce spark"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"apache hadoop"</span><span class="o">));</span>
<span class="n">DataFrame</span> <span class="n">test</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTest</span><span class="o">),</span> <span class="n">Document</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Make predictions on test documents.</span>
<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">test</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="n">Row</span> <span class="nl">r:</span> <span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"text"</span><span class="o">,</span> <span class="s">"probability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">).</span><span class="na">collect</span><span class="o">())</span> <span class="o">{</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"("</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">", "</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> <span class="o">+</span> <span class="s">") --> prob="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span>
<span class="o">+</span> <span class="s">", prediction="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">3</span><span class="o">));</span>
<span class="o">}</span>
<span class="n">jsc</span><span class="o">.</span><span class="na">stop</span><span class="o">();</span></code></pre></div>
</div>
<div data-lang="python">
<div class="highlight"><pre><code class="language-python" data-lang="python"><span class="kn">from</span> <span class="nn">pyspark</span> <span class="kn">import</span> <span class="n">SparkContext</span>
<span class="kn">from</span> <span class="nn">pyspark.ml</span> <span class="kn">import</span> <span class="n">Pipeline</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.classification</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
<span class="kn">from</span> <span class="nn">pyspark.ml.feature</span> <span class="kn">import</span> <span class="n">HashingTF</span><span class="p">,</span> <span class="n">Tokenizer</span>
<span class="kn">from</span> <span class="nn">pyspark.sql</span> <span class="kn">import</span> <span class="n">Row</span><span class="p">,</span> <span class="n">SQLContext</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">SparkContext</span><span class="p">(</span><span class="n">appName</span><span class="o">=</span><span class="s">"SimpleTextClassificationPipeline"</span><span class="p">)</span>
<span class="n">sqlContext</span> <span class="o">=</span> <span class="n">SQLContext</span><span class="p">(</span><span class="n">sc</span><span class="p">)</span>
<span class="c"># Prepare training documents, which are labeled.</span>
<span class="n">LabeledDocument</span> <span class="o">=</span> <span class="n">Row</span><span class="p">(</span><span class="s">"id"</span><span class="p">,</span> <span class="s">"text"</span><span class="p">,</span> <span class="s">"label"</span><span class="p">)</span>
<span class="n">training</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([(</span><span class="il">0L</span><span class="p">,</span> <span class="s">"a b c d e spark"</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="p">(</span><span class="il">1L</span><span class="p">,</span> <span class="s">"b d"</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">),</span>
<span class="p">(</span><span class="il">2L</span><span class="p">,</span> <span class="s">"spark f g h"</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span>
<span class="p">(</span><span class="il">3L</span><span class="p">,</span> <span class="s">"hadoop mapreduce"</span><span class="p">,</span> <span class="mf">0.0</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">x</span><span class="p">:</span> <span class="n">LabeledDocument</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
<span class="c"># Configure an ML pipeline, which consists of tree stages: tokenizer, hashingTF, and lr.</span>
<span class="n">tokenizer</span> <span class="o">=</span> <span class="n">Tokenizer</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="s">"text"</span><span class="p">,</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"words"</span><span class="p">)</span>
<span class="n">hashingTF</span> <span class="o">=</span> <span class="n">HashingTF</span><span class="p">(</span><span class="n">inputCol</span><span class="o">=</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">getOutputCol</span><span class="p">(),</span> <span class="n">outputCol</span><span class="o">=</span><span class="s">"features"</span><span class="p">)</span>
<span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">maxIter</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">regParam</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="n">pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="n">stages</span><span class="o">=</span><span class="p">[</span><span class="n">tokenizer</span><span class="p">,</span> <span class="n">hashingTF</span><span class="p">,</span> <span class="n">lr</span><span class="p">])</span>
<span class="c"># Fit the pipeline to training documents.</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">pipeline</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
<span class="c"># Prepare test documents, which are unlabeled.</span>
<span class="n">Document</span> <span class="o">=</span> <span class="n">Row</span><span class="p">(</span><span class="s">"id"</span><span class="p">,</span> <span class="s">"text"</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="p">([(</span><span class="il">4L</span><span class="p">,</span> <span class="s">"spark i j k"</span><span class="p">),</span>
<span class="p">(</span><span class="il">5L</span><span class="p">,</span> <span class="s">"l m n"</span><span class="p">),</span>
<span class="p">(</span><span class="il">6L</span><span class="p">,</span> <span class="s">"mapreduce spark"</span><span class="p">),</span>
<span class="p">(</span><span class="il">7L</span><span class="p">,</span> <span class="s">"apache hadoop"</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">x</span><span class="p">:</span> <span class="n">Document</span><span class="p">(</span><span class="o">*</span><span class="n">x</span><span class="p">))</span><span class="o">.</span><span class="n">toDF</span><span class="p">()</span>
<span class="c"># Make predictions on test documents and print columns of interest.</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
<span class="n">selected</span> <span class="o">=</span> <span class="n">prediction</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="s">"id"</span><span class="p">,</span> <span class="s">"text"</span><span class="p">,</span> <span class="s">"prediction"</span><span class="p">)</span>
<span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">selected</span><span class="o">.</span><span class="n">collect</span><span class="p">():</span>
<span class="k">print</span> <span class="n">row</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="p">()</span></code></pre></div>
</div>
</div>
<h2 id="example-model-selection-via-cross-validation">Example: Model Selection via Cross-Validation</h2>
<p>An important task in ML is <em>model selection</em>, or using data to find the best model or parameters for a given task. This is also called <em>tuning</em>.
<code>Pipeline</code>s facilitate model selection by making it easy to tune an entire <code>Pipeline</code> at once, rather than tuning each element in the <code>Pipeline</code> separately.</p>
<p>Currently, <code>spark.ml</code> supports model selection using the <a href="api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator"><code>CrossValidator</code></a> class, which takes an <code>Estimator</code>, a set of <code>ParamMap</code>s, and an <a href="api/scala/index.html#org.apache.spark.ml.Evaluator"><code>Evaluator</code></a>.
<code>CrossValidator</code> begins by splitting the dataset into a set of <em>folds</em> which are used as separate training and test datasets; e.g., with <code>$k=3$</code> folds, <code>CrossValidator</code> will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
<code>CrossValidator</code> iterates through the set of <code>ParamMap</code>s. For each <code>ParamMap</code>, it trains the given <code>Estimator</code> and evaluates it using the given <code>Evaluator</code>.
The <code>ParamMap</code> which produces the best evaluation metric (averaged over the <code>$k$</code> folds) is selected as the best model.
<code>CrossValidator</code> finally fits the <code>Estimator</code> using the best <code>ParamMap</code> and the entire dataset.</p>
<p>The following example demonstrates using <code>CrossValidator</code> to select from a grid of parameters.
To help construct the parameter grid, we use the <a href="api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder"><code>ParamGridBuilder</code></a> utility.</p>
<p>Note that cross-validation over a grid of parameters is expensive.
E.g., in the example below, the parameter grid has 3 values for <code>hashingTF.numFeatures</code> and 2 values for <code>lr.regParam</code>, and <code>CrossValidator</code> uses 2 folds. This multiplies out to <code>$(3 \times 2) \times 2 = 12$</code> different models being trained.
In realistic settings, it can be common to try many more parameters and use more folds (<code>$k=3$</code> and <code>$k=10$</code> are common).
In other words, using <code>CrossValidator</code> can be very expensive.
However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning.</p>
<div class="codetabs">
<div data-lang="scala">
<div class="highlight"><pre><code class="language-scala" data-lang="scala"><span class="k">import</span> <span class="nn">org.apache.spark.</span><span class="o">{</span><span class="nc">SparkConf</span><span class="o">,</span> <span class="nc">SparkContext</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.evaluation.BinaryClassificationEvaluator</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.feature.</span><span class="o">{</span><span class="nc">HashingTF</span><span class="o">,</span> <span class="nc">Tokenizer</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.ml.tuning.</span><span class="o">{</span><span class="nc">ParamGridBuilder</span><span class="o">,</span> <span class="nc">CrossValidator</span><span class="o">}</span>
<span class="k">import</span> <span class="nn">org.apache.spark.mllib.linalg.Vector</span>
<span class="k">import</span> <span class="nn">org.apache.spark.sql.</span><span class="o">{</span><span class="nc">Row</span><span class="o">,</span> <span class="nc">SQLContext</span><span class="o">}</span>
<span class="c1">// Labeled and unlabeled instance types.</span>
<span class="c1">// Spark SQL can infer schema from case classes.</span>
<span class="k">case</span> <span class="k">class</span> <span class="nc">LabeledDocument</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">label</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span>
<span class="k">case</span> <span class="k">class</span> <span class="nc">Document</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">)</span>
<span class="k">val</span> <span class="n">conf</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkConf</span><span class="o">().</span><span class="n">setAppName</span><span class="o">(</span><span class="s">"CrossValidatorExample"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sc</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">)</span>
<span class="k">val</span> <span class="n">sqlContext</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">SQLContext</span><span class="o">(</span><span class="n">sc</span><span class="o">)</span>
<span class="k">import</span> <span class="nn">sqlContext.implicits._</span>
<span class="c1">// Prepare training documents, which are labeled.</span>
<span class="k">val</span> <span class="n">training</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">0L</span><span class="o">,</span> <span class="s">"a b c d e spark"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">1L</span><span class="o">,</span> <span class="s">"b d"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">2L</span><span class="o">,</span> <span class="s">"spark f g h"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">3L</span><span class="o">,</span> <span class="s">"hadoop mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"b spark who"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"g d a y"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"spark fly"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"was mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">8L</span><span class="o">,</span> <span class="s">"e spark program"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">9L</span><span class="o">,</span> <span class="s">"a e c l"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">10L</span><span class="o">,</span> <span class="s">"spark compile"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="nc">LabeledDocument</span><span class="o">(</span><span class="mi">11L</span><span class="o">,</span> <span class="s">"hadoop software"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">)))</span>
<span class="c1">// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.</span>
<span class="k">val</span> <span class="n">tokenizer</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Tokenizer</span><span class="o">()</span>
<span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">hashingTF</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">HashingTF</span><span class="o">()</span>
<span class="o">.</span><span class="n">setInputCol</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">getOutputCol</span><span class="o">)</span>
<span class="o">.</span><span class="n">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">)</span>
<span class="k">val</span> <span class="n">lr</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">LogisticRegression</span><span class="o">()</span>
<span class="o">.</span><span class="n">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="k">val</span> <span class="n">pipeline</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">Pipeline</span><span class="o">()</span>
<span class="o">.</span><span class="n">setStages</span><span class="o">(</span><span class="nc">Array</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">,</span> <span class="n">hashingTF</span><span class="o">,</span> <span class="n">lr</span><span class="o">))</span>
<span class="c1">// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.</span>
<span class="c1">// This will allow us to jointly choose parameters for all Pipeline stages.</span>
<span class="c1">// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.</span>
<span class="k">val</span> <span class="n">crossval</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">CrossValidator</span><span class="o">()</span>
<span class="o">.</span><span class="n">setEstimator</span><span class="o">(</span><span class="n">pipeline</span><span class="o">)</span>
<span class="o">.</span><span class="n">setEvaluator</span><span class="o">(</span><span class="k">new</span> <span class="nc">BinaryClassificationEvaluator</span><span class="o">)</span>
<span class="c1">// We use a ParamGridBuilder to construct a grid of parameters to search over.</span>
<span class="c1">// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,</span>
<span class="c1">// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.</span>
<span class="k">val</span> <span class="n">paramGrid</span> <span class="k">=</span> <span class="k">new</span> <span class="nc">ParamGridBuilder</span><span class="o">()</span>
<span class="o">.</span><span class="n">addGrid</span><span class="o">(</span><span class="n">hashingTF</span><span class="o">.</span><span class="n">numFeatures</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mi">10</span><span class="o">,</span> <span class="mi">100</span><span class="o">,</span> <span class="mi">1000</span><span class="o">))</span>
<span class="o">.</span><span class="n">addGrid</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="n">regParam</span><span class="o">,</span> <span class="nc">Array</span><span class="o">(</span><span class="mf">0.1</span><span class="o">,</span> <span class="mf">0.01</span><span class="o">))</span>
<span class="o">.</span><span class="n">build</span><span class="o">()</span>
<span class="n">crossval</span><span class="o">.</span><span class="n">setEstimatorParamMaps</span><span class="o">(</span><span class="n">paramGrid</span><span class="o">)</span>
<span class="n">crossval</span><span class="o">.</span><span class="n">setNumFolds</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span> <span class="c1">// Use 3+ in practice</span>
<span class="c1">// Run cross-validation, and choose the best set of parameters.</span>
<span class="k">val</span> <span class="n">cvModel</span> <span class="k">=</span> <span class="n">crossval</span><span class="o">.</span><span class="n">fit</span><span class="o">(</span><span class="n">training</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="c1">// Prepare test documents, which are unlabeled.</span>
<span class="k">val</span> <span class="n">test</span> <span class="k">=</span> <span class="n">sc</span><span class="o">.</span><span class="n">parallelize</span><span class="o">(</span><span class="nc">Seq</span><span class="o">(</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"spark i j k"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"mapreduce spark"</span><span class="o">),</span>
<span class="nc">Document</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"apache hadoop"</span><span class="o">)))</span>
<span class="c1">// Make predictions on test documents. cvModel uses the best model found (lrModel).</span>
<span class="n">cvModel</span><span class="o">.</span><span class="n">transform</span><span class="o">(</span><span class="n">test</span><span class="o">.</span><span class="n">toDF</span><span class="o">)</span>
<span class="o">.</span><span class="n">select</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"text"</span><span class="o">,</span> <span class="s">"probability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">)</span>
<span class="o">.</span><span class="n">collect</span><span class="o">()</span>
<span class="o">.</span><span class="n">foreach</span> <span class="o">{</span> <span class="k">case</span> <span class="nc">Row</span><span class="o">(</span><span class="n">id</span><span class="k">:</span> <span class="kt">Long</span><span class="o">,</span> <span class="n">text</span><span class="k">:</span> <span class="kt">String</span><span class="o">,</span> <span class="n">prob</span><span class="k">:</span> <span class="kt">Vector</span><span class="o">,</span> <span class="n">prediction</span><span class="k">:</span> <span class="kt">Double</span><span class="o">)</span> <span class="k">=></span>
<span class="n">println</span><span class="o">(</span><span class="n">s</span><span class="s">"($id, $text) --> prob=$prob, prediction=$prediction"</span><span class="o">)</span>
<span class="o">}</span>
<span class="n">sc</span><span class="o">.</span><span class="n">stop</span><span class="o">()</span></code></pre></div>
</div>
<div data-lang="java">
<div class="highlight"><pre><code class="language-java" data-lang="java"><span class="kn">import</span> <span class="nn">java.util.List</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">com.google.common.collect.Lists</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.SparkConf</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.api.java.JavaSparkContext</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.Pipeline</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.PipelineStage</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.classification.LogisticRegression</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.evaluation.BinaryClassificationEvaluator</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.HashingTF</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.feature.Tokenizer</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.param.ParamMap</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.tuning.CrossValidator</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.tuning.CrossValidatorModel</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.ml.tuning.ParamGridBuilder</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.DataFrame</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.Row</span><span class="o">;</span>
<span class="kn">import</span> <span class="nn">org.apache.spark.sql.SQLContext</span><span class="o">;</span>
<span class="c1">// Labeled and unlabeled instance types.</span>
<span class="c1">// Spark SQL can infer schema from Java Beans.</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">Document</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
<span class="kd">private</span> <span class="kt">long</span> <span class="n">id</span><span class="o">;</span>
<span class="kd">private</span> <span class="n">String</span> <span class="n">text</span><span class="o">;</span>
<span class="kd">public</span> <span class="nf">Document</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">,</span> <span class="n">String</span> <span class="n">text</span><span class="o">)</span> <span class="o">{</span>
<span class="k">this</span><span class="o">.</span><span class="na">id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span>
<span class="k">this</span><span class="o">.</span><span class="na">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kt">long</span> <span class="nf">getId</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">id</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setId</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">id</span> <span class="o">=</span> <span class="n">id</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="n">String</span> <span class="nf">getText</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">text</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setText</span><span class="o">(</span><span class="n">String</span> <span class="n">text</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">text</span> <span class="o">=</span> <span class="n">text</span><span class="o">;</span> <span class="o">}</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kd">class</span> <span class="nc">LabeledDocument</span> <span class="kd">extends</span> <span class="n">Document</span> <span class="kd">implements</span> <span class="n">Serializable</span> <span class="o">{</span>
<span class="kd">private</span> <span class="kt">double</span> <span class="n">label</span><span class="o">;</span>
<span class="kd">public</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="kt">long</span> <span class="n">id</span><span class="o">,</span> <span class="n">String</span> <span class="n">text</span><span class="o">,</span> <span class="kt">double</span> <span class="n">label</span><span class="o">)</span> <span class="o">{</span>
<span class="kd">super</span><span class="o">(</span><span class="n">id</span><span class="o">,</span> <span class="n">text</span><span class="o">);</span>
<span class="k">this</span><span class="o">.</span><span class="na">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">;</span>
<span class="o">}</span>
<span class="kd">public</span> <span class="kt">double</span> <span class="nf">getLabel</span><span class="o">()</span> <span class="o">{</span> <span class="k">return</span> <span class="k">this</span><span class="o">.</span><span class="na">label</span><span class="o">;</span> <span class="o">}</span>
<span class="kd">public</span> <span class="kt">void</span> <span class="nf">setLabel</span><span class="o">(</span><span class="kt">double</span> <span class="n">label</span><span class="o">)</span> <span class="o">{</span> <span class="k">this</span><span class="o">.</span><span class="na">label</span> <span class="o">=</span> <span class="n">label</span><span class="o">;</span> <span class="o">}</span>
<span class="o">}</span>
<span class="n">SparkConf</span> <span class="n">conf</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SparkConf</span><span class="o">().</span><span class="na">setAppName</span><span class="o">(</span><span class="s">"JavaCrossValidatorExample"</span><span class="o">);</span>
<span class="n">JavaSparkContext</span> <span class="n">jsc</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">JavaSparkContext</span><span class="o">(</span><span class="n">conf</span><span class="o">);</span>
<span class="n">SQLContext</span> <span class="n">jsql</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">SQLContext</span><span class="o">(</span><span class="n">jsc</span><span class="o">);</span>
<span class="c1">// Prepare training documents, which are labeled.</span>
<span class="n">List</span><span class="o"><</span><span class="n">LabeledDocument</span><span class="o">></span> <span class="n">localTraining</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">0L</span><span class="o">,</span> <span class="s">"a b c d e spark"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">1L</span><span class="o">,</span> <span class="s">"b d"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">2L</span><span class="o">,</span> <span class="s">"spark f g h"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">3L</span><span class="o">,</span> <span class="s">"hadoop mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"b spark who"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"g d a y"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"spark fly"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"was mapreduce"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">8L</span><span class="o">,</span> <span class="s">"e spark program"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">9L</span><span class="o">,</span> <span class="s">"a e c l"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">10L</span><span class="o">,</span> <span class="s">"spark compile"</span><span class="o">,</span> <span class="mf">1.0</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">LabeledDocument</span><span class="o">(</span><span class="mi">11L</span><span class="o">,</span> <span class="s">"hadoop software"</span><span class="o">,</span> <span class="mf">0.0</span><span class="o">));</span>
<span class="n">DataFrame</span> <span class="n">training</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTraining</span><span class="o">),</span> <span class="n">LabeledDocument</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Configure an ML pipeline, which consists of three stages: tokenizer, hashingTF, and lr.</span>
<span class="n">Tokenizer</span> <span class="n">tokenizer</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Tokenizer</span><span class="o">()</span>
<span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="s">"text"</span><span class="o">)</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"words"</span><span class="o">);</span>
<span class="n">HashingTF</span> <span class="n">hashingTF</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">HashingTF</span><span class="o">()</span>
<span class="o">.</span><span class="na">setNumFeatures</span><span class="o">(</span><span class="mi">1000</span><span class="o">)</span>
<span class="o">.</span><span class="na">setInputCol</span><span class="o">(</span><span class="n">tokenizer</span><span class="o">.</span><span class="na">getOutputCol</span><span class="o">())</span>
<span class="o">.</span><span class="na">setOutputCol</span><span class="o">(</span><span class="s">"features"</span><span class="o">);</span>
<span class="n">LogisticRegression</span> <span class="n">lr</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">LogisticRegression</span><span class="o">()</span>
<span class="o">.</span><span class="na">setMaxIter</span><span class="o">(</span><span class="mi">10</span><span class="o">)</span>
<span class="o">.</span><span class="na">setRegParam</span><span class="o">(</span><span class="mf">0.01</span><span class="o">);</span>
<span class="n">Pipeline</span> <span class="n">pipeline</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">Pipeline</span><span class="o">()</span>
<span class="o">.</span><span class="na">setStages</span><span class="o">(</span><span class="k">new</span> <span class="n">PipelineStage</span><span class="o">[]</span> <span class="o">{</span><span class="n">tokenizer</span><span class="o">,</span> <span class="n">hashingTF</span><span class="o">,</span> <span class="n">lr</span><span class="o">});</span>
<span class="c1">// We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance.</span>
<span class="c1">// This will allow us to jointly choose parameters for all Pipeline stages.</span>
<span class="c1">// A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator.</span>
<span class="n">CrossValidator</span> <span class="n">crossval</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">CrossValidator</span><span class="o">()</span>
<span class="o">.</span><span class="na">setEstimator</span><span class="o">(</span><span class="n">pipeline</span><span class="o">)</span>
<span class="o">.</span><span class="na">setEvaluator</span><span class="o">(</span><span class="k">new</span> <span class="nf">BinaryClassificationEvaluator</span><span class="o">());</span>
<span class="c1">// We use a ParamGridBuilder to construct a grid of parameters to search over.</span>
<span class="c1">// With 3 values for hashingTF.numFeatures and 2 values for lr.regParam,</span>
<span class="c1">// this grid will have 3 x 2 = 6 parameter settings for CrossValidator to choose from.</span>
<span class="n">ParamMap</span><span class="o">[]</span> <span class="n">paramGrid</span> <span class="o">=</span> <span class="k">new</span> <span class="nf">ParamGridBuilder</span><span class="o">()</span>
<span class="o">.</span><span class="na">addGrid</span><span class="o">(</span><span class="n">hashingTF</span><span class="o">.</span><span class="na">numFeatures</span><span class="o">(),</span> <span class="k">new</span> <span class="kt">int</span><span class="o">[]{</span><span class="mi">10</span><span class="o">,</span> <span class="mi">100</span><span class="o">,</span> <span class="mi">1000</span><span class="o">})</span>
<span class="o">.</span><span class="na">addGrid</span><span class="o">(</span><span class="n">lr</span><span class="o">.</span><span class="na">regParam</span><span class="o">(),</span> <span class="k">new</span> <span class="kt">double</span><span class="o">[]{</span><span class="mf">0.1</span><span class="o">,</span> <span class="mf">0.01</span><span class="o">})</span>
<span class="o">.</span><span class="na">build</span><span class="o">();</span>
<span class="n">crossval</span><span class="o">.</span><span class="na">setEstimatorParamMaps</span><span class="o">(</span><span class="n">paramGrid</span><span class="o">);</span>
<span class="n">crossval</span><span class="o">.</span><span class="na">setNumFolds</span><span class="o">(</span><span class="mi">2</span><span class="o">);</span> <span class="c1">// Use 3+ in practice</span>
<span class="c1">// Run cross-validation, and choose the best set of parameters.</span>
<span class="n">CrossValidatorModel</span> <span class="n">cvModel</span> <span class="o">=</span> <span class="n">crossval</span><span class="o">.</span><span class="na">fit</span><span class="o">(</span><span class="n">training</span><span class="o">);</span>
<span class="c1">// Prepare test documents, which are unlabeled.</span>
<span class="n">List</span><span class="o"><</span><span class="n">Document</span><span class="o">></span> <span class="n">localTest</span> <span class="o">=</span> <span class="n">Lists</span><span class="o">.</span><span class="na">newArrayList</span><span class="o">(</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">4L</span><span class="o">,</span> <span class="s">"spark i j k"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">5L</span><span class="o">,</span> <span class="s">"l m n"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">6L</span><span class="o">,</span> <span class="s">"mapreduce spark"</span><span class="o">),</span>
<span class="k">new</span> <span class="nf">Document</span><span class="o">(</span><span class="mi">7L</span><span class="o">,</span> <span class="s">"apache hadoop"</span><span class="o">));</span>
<span class="n">DataFrame</span> <span class="n">test</span> <span class="o">=</span> <span class="n">jsql</span><span class="o">.</span><span class="na">createDataFrame</span><span class="o">(</span><span class="n">jsc</span><span class="o">.</span><span class="na">parallelize</span><span class="o">(</span><span class="n">localTest</span><span class="o">),</span> <span class="n">Document</span><span class="o">.</span><span class="na">class</span><span class="o">);</span>
<span class="c1">// Make predictions on test documents. cvModel uses the best model found (lrModel).</span>
<span class="n">DataFrame</span> <span class="n">predictions</span> <span class="o">=</span> <span class="n">cvModel</span><span class="o">.</span><span class="na">transform</span><span class="o">(</span><span class="n">test</span><span class="o">);</span>
<span class="k">for</span> <span class="o">(</span><span class="n">Row</span> <span class="nl">r:</span> <span class="n">predictions</span><span class="o">.</span><span class="na">select</span><span class="o">(</span><span class="s">"id"</span><span class="o">,</span> <span class="s">"text"</span><span class="o">,</span> <span class="s">"probability"</span><span class="o">,</span> <span class="s">"prediction"</span><span class="o">).</span><span class="na">collect</span><span class="o">())</span> <span class="o">{</span>
<span class="n">System</span><span class="o">.</span><span class="na">out</span><span class="o">.</span><span class="na">println</span><span class="o">(</span><span class="s">"("</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">0</span><span class="o">)</span> <span class="o">+</span> <span class="s">", "</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">1</span><span class="o">)</span> <span class="o">+</span> <span class="s">") --> prob="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">2</span><span class="o">)</span>
<span class="o">+</span> <span class="s">", prediction="</span> <span class="o">+</span> <span class="n">r</span><span class="o">.</span><span class="na">get</span><span class="o">(</span><span class="mi">3</span><span class="o">));</span>
<span class="o">}</span>
<span class="n">jsc</span><span class="o">.</span><span class="na">stop</span><span class="o">();</span></code></pre></div>
</div>
</div>
<h1 id="dependencies">Dependencies</h1>
<p>Spark ML currently depends on MLlib and has the same dependencies.
Please see the <a href="mllib-guide.html#dependencies">MLlib Dependencies guide</a> for more info.</p>
<p>Spark ML also depends upon Spark SQL, but the relevant parts of Spark SQL do not bring additional dependencies.</p>
<h1 id="migration-guide">Migration Guide</h1>
<h2 id="from-12-to-13">From 1.2 to 1.3</h2>
<p>The main API changes are from Spark SQL. We list the most important changes here:</p>
<ul>
<li>The old <a href="http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD">SchemaRDD</a> has been replaced with <a href="api/scala/index.html#org.apache.spark.sql.DataFrame">DataFrame</a> with a somewhat modified API. All algorithms in Spark ML which used to use SchemaRDD now use DataFrame.</li>
<li>In Spark 1.2, we used implicit conversions from <code>RDD</code>s of <code>LabeledPoint</code> into <code>SchemaRDD</code>s by calling <code>import sqlContext._</code> where <code>sqlContext</code> was an instance of <code>SQLContext</code>. These implicits have been moved, so we now call <code>import sqlContext.implicits._</code>.</li>
<li>Java APIs for SQL have also changed accordingly. Please see the examples above and the <a href="sql-programming-guide.html">Spark SQL Programming Guide</a> for details.</li>
</ul>
<p>Other changes were in <code>LogisticRegression</code>:</p>
<ul>
<li>The <code>scoreCol</code> output column (with default value “score”) was renamed to be <code>probabilityCol</code> (with default value “probability”). The type was originally <code>Double</code> (for the probability of class 1.0), but it is now <code>Vector</code> (for the probability of each class, to support multiclass classification in the future).</li>
<li>In Spark 1.2, <code>LogisticRegressionModel</code> did not include an intercept. In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for <a href="api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS">spark.mllib.LogisticRegressionWithLBFGS</a>. The option to use an intercept will be added in the future.</li>
</ul>
</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>
// 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>
|