-
Notifications
You must be signed in to change notification settings - Fork 1
Expand file tree
/
Copy pathpysparkapidocumentation.html
More file actions
752 lines (619 loc) · 47.7 KB
/
pysparkapidocumentation.html
File metadata and controls
752 lines (619 loc) · 47.7 KB
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
<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>snap-ml-spark API — Snap Machine Learning documentation</title>
<link rel="shortcut icon" href="_static/favicon.ico"/>
<script type="text/javascript" src="_static/js/modernizr.min.js"></script>
<script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
<script type="text/javascript" src="_static/jquery.js"></script>
<script type="text/javascript" src="_static/underscore.js"></script>
<script type="text/javascript" src="_static/doctools.js"></script>
<script type="text/javascript" src="_static/language_data.js"></script>
<script type="text/javascript" src="_static/js/theme.js"></script>
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
</head>
<body class="wy-body-for-nav">
<div class="wy-grid-for-nav">
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
<div class="wy-side-scroll">
<div class="wy-side-nav-search" >
<a href="index.html" class="icon icon-home"> Snap Machine Learning
</a>
<div class="version">
1.3.0
</div>
<div role="search">
<form id="rtd-search-form" class="wy-form" action="search.html" method="get">
<input type="text" name="q" placeholder="Search docs" />
<input type="hidden" name="check_keywords" value="yes" />
<input type="hidden" name="area" value="default" />
</form>
</div>
</div>
<div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
<p class="caption"><span class="caption-text">Overview</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="manual.html">Manual</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials.html">Tutorials</a></li>
<li class="toctree-l1"><a class="reference internal" href="frequentlyaskedquestions.html">FAQ</a></li>
</ul>
<p class="caption"><span class="caption-text">pai4sk ML APIs</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="ridgedoc.html">linear_model.Ridge</a></li>
<li class="toctree-l1"><a class="reference internal" href="lassodoc.html">linear_model.Lasso</a></li>
<li class="toctree-l1"><a class="reference internal" href="sklogregdoc.html">linear_model.LogisticRegression</a></li>
<li class="toctree-l1"><a class="reference internal" href="svcdoc.html">svm.LinearSVC</a></li>
<li class="toctree-l1"><a class="reference internal" href="kmeansdoc.html">cluster.KMeans</a></li>
<li class="toctree-l1"><a class="reference internal" href="dbscandoc.html">cluster.DBSCAN</a></li>
<li class="toctree-l1"><a class="reference internal" href="pcadoc.html">decomposition.PCA</a></li>
<li class="toctree-l1"><a class="reference internal" href="svddoc.html">decomposition.TruncatedSVD</a></li>
</ul>
<p class="caption"><span class="caption-text">pai4sk Loaders APIs</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="sksvmloaderfiledoc.html">load_svmlight_file</a></li>
</ul>
<p class="caption"><span class="caption-text">pai4sk Metrics APIs</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="sklogdoc.html">log_loss</a></li>
<li class="toctree-l1"><a class="reference internal" href="skaccdoc.html">accuracy_score</a></li>
<li class="toctree-l1"><a class="reference internal" href="skhingedoc.html">hinge_loss</a></li>
<li class="toctree-l1"><a class="reference internal" href="skmsedoc.html">mean_squared_error</a></li>
</ul>
<p class="caption"><span class="caption-text">snapML APIs</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="linregapidoc.html">LinearRegression</a></li>
<li class="toctree-l1"><a class="reference internal" href="logregapidoc.html">LogisticRegression</a></li>
<li class="toctree-l1"><a class="reference internal" href="svmapidoc.html">SVM</a></li>
<li class="toctree-l1"><a class="reference internal" href="dectreeapidoc.html">DecisionTreeClassifier</a></li>
<li class="toctree-l1"><a class="reference internal" href="ranforapidoc.html">RandomForestClassifier</a></li>
<li class="toctree-l1"><a class="reference internal" href="logdoc.html">log_loss</a></li>
<li class="toctree-l1"><a class="reference internal" href="accdoc.html">accuracy_score</a></li>
<li class="toctree-l1"><a class="reference internal" href="hingedoc.html">hinge_loss</a></li>
<li class="toctree-l1"><a class="reference internal" href="msedoc.html">mean_squared_error</a></li>
</ul>
<p class="caption"><span class="caption-text">snapML Loaders APIs</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="svmloaderdoc.html">load_from_svmlight_format</a></li>
<li class="toctree-l1"><a class="reference internal" href="snaploaderdoc.html">load_from_snap_format</a></li>
<li class="toctree-l1"><a class="reference internal" href="snaploaderfiledoc.html">load_snap_file</a></li>
<li class="toctree-l1"><a class="reference internal" href="snapwritedoc.html">write_to_snap_format</a></li>
</ul>
<p class="caption"><span class="caption-text">snapML Spark APIs</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="splinregdoc.html">LinearRegression</a></li>
<li class="toctree-l1"><a class="reference internal" href="splogregdoc.html">LogisticRegression</a></li>
<li class="toctree-l1"><a class="reference internal" href="spsvmdoc.html">SupportVectorMachine</a></li>
<li class="toctree-l1"><a class="reference internal" href="spreaddoc.html">DatasetReader</a></li>
<li class="toctree-l1"><a class="reference internal" href="spmetdoc.html">Metrics</a></li>
<li class="toctree-l1"><a class="reference internal" href="sputildoc.html">Utils</a></li>
</ul>
</div>
</div>
</nav>
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
<nav class="wy-nav-top" aria-label="top navigation">
<i data-toggle="wy-nav-top" class="fa fa-bars"></i>
<a href="index.html">Snap Machine Learning</a>
</nav>
<div class="wy-nav-content">
<div class="rst-content">
<div role="navigation" aria-label="breadcrumbs navigation">
<ul class="wy-breadcrumbs">
<li><a href="index.html">Docs</a> »</li>
<li>snap-ml-spark API</li>
<li class="wy-breadcrumbs-aside">
</li>
</ul>
<hr/>
</div>
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
<div class="section" id="snap-ml-spark-api">
<span id="pyspark-api-documentation"></span><h1>snap-ml-spark API<a class="headerlink" href="#snap-ml-spark-api" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="snap_ml_spark.LinearRegression.LinearRegression">
<em class="property">class </em><code class="descclassname">snap_ml_spark.LinearRegression.</code><code class="descname">LinearRegression</code><span class="sig-paren">(</span><em>max_iter=1000</em>, <em>dual=True</em>, <em>regularizer=1.0</em>, <em>verbose=False</em>, <em>use_gpu=False</em>, <em>class_weights=None</em>, <em>gpu_mem_limit=0</em>, <em>n_threads=-1</em>, <em>penalty='l2'</em>, <em>tol=0.001</em>, <em>return_training_history=None</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LinearRegression.LinearRegression" title="Permalink to this definition">¶</a></dt>
<dd><p>Linear Regression classifier</p>
<p>This class implements Regularized Linear regression using the IBM Snap ML distributed solver. It can handle sparse and dense dataset formats. Please use libsvm, snap or csv format for the Dual algorithm, or snap.t (transposed) format for the primal algorithm.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>max_iter</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 1000</em>) – Maximum number of iterations used by the solver to converge.</li>
<li><strong>dual</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>default : True</em>) – Dual or primal formulation.
Recommendation: if n_samples > n_features use dual=True, else dual=False.</li>
<li><strong>regularizer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>default : 1.0</em>) – Regularization strength. It must be a positive float.
Larger regularization values imply stronger regularization.</li>
<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>default : False</em>) – Flag for indicating if the training loss will be printed at each epoch.</li>
<li><strong>use_gpu</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>default : False</em>) – Flag for indicating the hardware platform used for training. If True, the training
is performed using the GPU. If False, the training is performed using the CPU.</li>
<li><strong>class_weights</strong> (<em>'balanced'/True</em><em> or </em><em>None/False</em><em>, </em><em>optional</em>) – If set to ‘None’, all classes will have weight = 1.</li>
<li><strong>gpu_mem_limit</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 0</em>) – Limit of the GPU memory. If set to the default value 0,
the maximum possible memory is used.</li>
<li><strong>n_threads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : -1 meaning that n_threads=256 if GPU is enabled</em><em>, </em><em>else 1</em>) – Number of threads to be used.</li>
<li><strong>penalty</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em>, </em><em>default : "l2"</em>) – The regularization / penalty type. Possible values are “l2” for L2 regularization (RidgeRegression)
or “l1” for L1 regularization (LassoRegression). L1 regularization is possible only for the primal
optimization problem (dual=False).</li>
<li><strong>tol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>default : 0.001</em>) – The tolerance parameter. Training will finish when maximum change in model coefficients is less than tol.</li>
<li><strong>return_training_history</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>, </em><em>default : None</em>) – How much information about the training should be collected and returned by the fit function. By
default no information is returned (None), but this parameter can be set to “summary”, to obtain
summary statistics at the end of training, or “full” to obtain a complete set of statistics
for the entire training procedure. Note, enabling either option will result in slower training.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Variables:</th><td class="field-body"><ul class="first last simple">
<li><strong>coef</strong> (<em>ndarray</em><em>, </em><em>shape</em><em> (</em><em>n_features</em><em>,</em><em>)</em>) – Coefficients of the features in the trained model.</li>
<li><strong>pred_array</strong> (<em>ndarray</em><em>, </em><em>shape</em><em>(</em><em>number_of_test_examples</em><em>,</em><em>)</em>) – linear predictions written by the predict() function of this class</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="snap_ml_spark.LinearRegression.LinearRegression.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LinearRegression.LinearRegression.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>learn model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. The data cannot be accessed by python as a python array.</em>) – data to fit model</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">double – final training loss of the last epoch</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.LinearRegression.LinearRegression.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LinearRegression.LinearRegression.get_params" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">all the initialized parameters of the Linear Regression model as a python dictionary</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.LinearRegression.LinearRegression.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>data</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LinearRegression.LinearRegression.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict regression values</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. Cannot be accessed by python as an array but only can passed as a parameter to this function in order to get the predictions</em>) – data to make predictions</li>
<li><strong>num_threads</strong> – the number of threads to use for inference (default 0 means use all avaliable threads)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a pointer which points to a com.ibm.snap.ml.DatasetWithPredictions java object. This pointer cannot be accessed by python but the user can access the predictions from the <a href="#id1"><span class="problematic" id="id2">pred_array_</span></a> field which is a python array.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="snap_ml_spark.LogisticRegression.LogisticRegression">
<em class="property">class </em><code class="descclassname">snap_ml_spark.LogisticRegression.</code><code class="descname">LogisticRegression</code><span class="sig-paren">(</span><em>max_iter=1000</em>, <em>dual=True</em>, <em>regularizer=1.0</em>, <em>verbose=False</em>, <em>use_gpu=False</em>, <em>class_weights=None</em>, <em>gpu_mem_limit=0</em>, <em>n_threads=-1</em>, <em>penalty='l2'</em>, <em>tol=0.001</em>, <em>return_training_history=None</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LogisticRegression.LogisticRegression" title="Permalink to this definition">¶</a></dt>
<dd><p>Logistic Regression classifier</p>
<p>This class implements regularized Logistic Regression using the IBM Snap ML solver. It can handle sparse and dense dataset formats. Use libsvm, snap or csv format for the Dual algorithm, or snap.t (transposed) format for the primal algorithm.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>max_iter</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 1000</em>) – Maximum number of iterations used by the solver to converge.</li>
<li><strong>dual</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>default : True</em>) – Dual or Primal formulation.
Recommendation: if n_samples > n_features use dual=True.</li>
<li><strong>regularizer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>default : 1.0</em>) – Regularization strength. It must be a positive float.
Larger regularization values imply stronger regularization.</li>
<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>default : False</em>) – Flag for indicating if the training loss will be printed at each epoch.</li>
<li><strong>use_gpu</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>default : False</em>) – Flag for indicating the hardware platform used for training. If True, the training
is performed using the GPU. If False, the training is performed using the CPU.</li>
<li><strong>class_weights</strong> (<em>'balanced'/True</em><em> or </em><em>None/False</em><em>, </em><em>optional</em>) – If set to ‘None’, all classes will have weight 1.</li>
<li><strong>gpu_mem_limit</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 0</em>) – Limit of the GPU memory. If set to the default value 0,
the maximum possible memory is used.</li>
<li><strong>n_threads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : -1 meaning that n_threads=256 if GPU is enabled</em><em>, </em><em>else 1</em>) – Number of threads to be used.</li>
<li><strong>penalty</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em>, </em><em>default : "l2"</em>) – The regularization / penalty type. Possible values are “l2” for L2 regularization
or “l1” for L1 regularization. L1 regularization is possible only for the primal
optimization problem (dual=False).</li>
<li><strong>tol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>default : 0.001</em>) – The tolerance parameter. Training will finish when maximum change in model coefficients is less than tol.</li>
<li><strong>return_training_history</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>, </em><em>default : None</em>) – How much information about the training should be collected and returned by the fit function. By
default no information is returned (None), but this parameter can be set to “summary”, to obtain
summary statistics at the end of training, or “full” to obtain a complete set of statistics
for the entire training procedure. Note, enabling either option will result in slower training.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Variables:</th><td class="field-body"><ul class="first last simple">
<li><strong>coef</strong> (<em>ndarray</em><em>, </em><em>shape</em><em> (</em><em>n_features</em><em>,</em><em>)</em>) – Coefficients of the features in the trained model.</li>
<li><strong>pred_array</strong> (<em>ndarray</em><em>, </em><em>shape</em><em>(</em><em>number_of_test_examples</em><em>,</em><em>)</em>) – binary predictions written by the predict() function</li>
<li><strong>proba_array</strong> (<em>ndarray</em><em>, </em><em>shape</em><em>(</em><em>number_of_test_examples</em><em>,</em><em>)</em>) – predicted probabilities written by the predict_proba() function</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="snap_ml_spark.LogisticRegression.LogisticRegression.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LogisticRegression.LogisticRegression.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>learn model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. The data cannot be accessed by python as a python array.</em>) – data to fit model</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">double – final training loss of the last epoch</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.LogisticRegression.LogisticRegression.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LogisticRegression.LogisticRegression.get_params" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">all the initialized parameters of the Logistic Regression model as a python dictionary</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.LogisticRegression.LogisticRegression.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>data</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LogisticRegression.LogisticRegression.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict label</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. Cannot be accessed by python as an array but only can passed as a parameter to this function in order to get the predictions</em>) – data to make predictions</li>
<li><strong>num_threads</strong> – the number of threads to use for inference (default 0 means use all avaliable threads)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a pointer which points to a com.ibm.snap.ml.DatasetWithPredictions java object. This pointer cannot be accessed by python but the user can access the predictions from the <a href="#id3"><span class="problematic" id="id4">pred_array_</span></a> field which is a python array.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.LogisticRegression.LogisticRegression.predict_proba">
<code class="descname">predict_proba</code><span class="sig-paren">(</span><em>data</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.LogisticRegression.LogisticRegression.predict_proba" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict probabilities</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. Cannot be accessed by python as an array but only can passed as a parameter to this function in order to get the predictions</em>) – data to make predictions</li>
<li><strong>num_threads</strong> – the number of threads to use for inference (default 0 means use all avaliable threads)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a pointer which points to a com.ibm.snap.ml.DatasetWithPredictions java object. This pointer cannot be accessed by python but the user can access the predictions from the <a href="#id5"><span class="problematic" id="id6">proba_array_</span></a> field which is a python array.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="snap_ml_spark.SupportVectorMachine.SupportVectorMachine">
<em class="property">class </em><code class="descclassname">snap_ml_spark.SupportVectorMachine.</code><code class="descname">SupportVectorMachine</code><span class="sig-paren">(</span><em>max_iter=1000</em>, <em>regularizer=1.0</em>, <em>verbose=False</em>, <em>use_gpu=False</em>, <em>class_weights=None</em>, <em>gpu_mem_limit=0</em>, <em>n_threads=-1</em>, <em>tol=0.001</em>, <em>return_training_history=None</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.SupportVectorMachine.SupportVectorMachine" title="Permalink to this definition">¶</a></dt>
<dd><p>Support Vector Machine classifier</p>
<p>This class implements regularized support vector machine using the IBM Snap ML solver. It can handle sparse and dense dataset formats. Use libsvm, snap or csv format for the Dual algorithm, or snap.t (transposed) format for the primal algorithm.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>max_iter</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 1000</em>) – Maximum number of iterations used by the solver to converge.</li>
<li><strong>regularizer</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>default : 1.0</em>) – Regularization strength. It must be a positive float.
Larger regularization values imply stronger regularization.</li>
<li><strong>verbose</strong> (<em>boolean</em><em>, </em><em>default : False</em>) – Flag for indicating if the training loss will be printed at each epoch.</li>
<li><strong>use_gpu</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>default : False</em>) – Flag for indicating the hardware platform used for training. If True, the training
is performed using the GPU. If False, the training is performed using the CPU.</li>
<li><strong>class_weights</strong> (<em>'balanced'/True</em><em> or </em><em>None/False</em><em>, </em><em>optional</em>) – If set to ‘None’, all classes will have weight 1.</li>
<li><strong>gpu_mem_limit</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : 0</em>) – Limit of the GPU memory. If set to the default value 0,
the maximum possible memory is used.</li>
<li><strong>n_threads</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>default : -1 meaning that n_threads=256 if GPU is enabled</em><em>, </em><em>else 1</em>) – Number of threads to be used.</li>
<li><strong>tol</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.7)"><em>float</em></a><em>, </em><em>default : 0.001</em>) – The tolerance parameter. Training will finish when maximum change in model coefficients is less than tol.</li>
<li><strong>return_training_history</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.7)"><em>None</em></a><em>, </em><em>default : None</em>) – How much information about the training should be collected and returned by the fit function. By
default no information is returned (None), but this parameter can be set to “summary”, to obtain
summary statistics at the end of training, or “full” to obtain a complete set of statistics
for the entire training procedure. Note, enabling either option will result in slower training.</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Variables:</th><td class="field-body"><ul class="first last simple">
<li><strong>coef</strong> (<em>ndarray</em><em>, </em><em>shape</em><em> (</em><em>n_features</em><em>,</em><em>)</em>) – Coefficients of the features in the trained model.</li>
<li><strong>pred_array</strong> (<em>ndarray</em><em>, </em><em>shape</em><em>(</em><em>number_of_test_examples</em><em>,</em><em>)</em>) – binary predictions written by the predict() function</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="snap_ml_spark.SupportVectorMachine.SupportVectorMachine.fit">
<code class="descname">fit</code><span class="sig-paren">(</span><em>data</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.SupportVectorMachine.SupportVectorMachine.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>learn model</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. The data cannot be accessed by python as a python array.</em>) – data to fit model</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">double – final training loss of the last epoch</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.SupportVectorMachine.SupportVectorMachine.get_params">
<code class="descname">get_params</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.SupportVectorMachine.SupportVectorMachine.get_params" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Returns:</th><td class="field-body">all the initialized parameters of the Support Vector Machine Model as a python dictionary</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.SupportVectorMachine.SupportVectorMachine.predict">
<code class="descname">predict</code><span class="sig-paren">(</span><em>data</em>, <em>num_threads=0</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.SupportVectorMachine.SupportVectorMachine.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict label</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first simple">
<li><strong>data</strong> (<em>py4j.java_gateway.JavaObject</em><em>, </em><em>pointer which points to a memory address where the actual data is stored. Cannot be accessed by python as an array but only can passed as a parameter to this function in order to get the predictions</em>) – data to make predictions</li>
<li><strong>num_threads</strong> – the number of threads to use for inference (default 0 means use all avaliable threads)</li>
</ul>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first last">a pointer which points to a com.ibm.snap.ml.DatasetWithPredictions java object. This pointer cannot be accessed by python but the user can access the predictions from the <a href="#id7"><span class="problematic" id="id8">pred_array_</span></a> field which is a python array.</p>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="snap_ml_spark.DatasetReader.DatasetReader">
<em class="property">class </em><code class="descclassname">snap_ml_spark.DatasetReader.</code><code class="descname">DatasetReader</code><a class="headerlink" href="#snap_ml_spark.DatasetReader.DatasetReader" title="Permalink to this definition">¶</a></dt>
<dd><p>Load distributed dataset from file.</p>
<dl class="method">
<dt id="snap_ml_spark.DatasetReader.DatasetReader.load">
<code class="descname">load</code><span class="sig-paren">(</span><em>file</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.DatasetReader.DatasetReader.load" title="Permalink to this definition">¶</a></dt>
<dd><p>Load training data in memory</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>file</strong> (<em>string</em>) – filename</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.DatasetReader.DatasetReader.setFormat">
<code class="descname">setFormat</code><span class="sig-paren">(</span><em>format</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.DatasetReader.DatasetReader.setFormat" title="Permalink to this definition">¶</a></dt>
<dd><p>Specify the dataformat of the file. Format values: “snap” or “libsvm” or “csv”</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>format</strong> (<em>string</em>) – data format</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.DatasetReader.DatasetReader.setNumFt">
<code class="descname">setNumFt</code><span class="sig-paren">(</span><em>x</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.DatasetReader.DatasetReader.setNumFt" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the number of features</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>x</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – index</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="snap_ml_spark.DatasetReader.DatasetReader.takeRange">
<code class="descname">takeRange</code><span class="sig-paren">(</span><em>idx_start</em>, <em>idx_end</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.DatasetReader.DatasetReader.takeRange" title="Permalink to this definition">¶</a></dt>
<dd><p>If not the whole dataset should be loaded specify start and end index.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>idx_start</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – first sample to load</li>
<li><strong>idx_end</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – last sample to load</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
<span class="target" id="module-snap_ml_spark.Metrics"></span><dl class="function">
<dt id="snap_ml_spark.Metrics.accuracy">
<code class="descclassname">snap_ml_spark.Metrics.</code><code class="descname">accuracy</code><span class="sig-paren">(</span><em>dataWithPredictions</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.Metrics.accuracy" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dataWithPredictions</strong> – binary predictions computed by the LogisticRegression or SupportVectorMachine predict() function</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">accuracy computed based on the binary predictions of a classifier (LogisticRegression, SupportVectorMachines)</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">double</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="snap_ml_spark.Metrics.f1score">
<code class="descclassname">snap_ml_spark.Metrics.</code><code class="descname">f1score</code><span class="sig-paren">(</span><em>dataWithPredictions</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.Metrics.f1score" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dataWithPredictions</strong> – binary predictions computed by the LogisticRegression or SupportVectorMachine predict() function</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">f1score metric (2*(precision*recall)/(precision+recall)), computed based on the binary predictions of a classifier (LogisticRegression, SupportVectorMachines)</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">double</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="snap_ml_spark.Metrics.logisticLoss">
<code class="descclassname">snap_ml_spark.Metrics.</code><code class="descname">logisticLoss</code><span class="sig-paren">(</span><em>dataWithPredictions</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.Metrics.logisticLoss" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dataWithPredictions</strong> – probabilities computed by the LogisticRegression predict_proba() function</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">logistic loss computed by the logistic regression predicted probabilities</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">double</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="snap_ml_spark.Metrics.meanSquaredError">
<code class="descclassname">snap_ml_spark.Metrics.</code><code class="descname">meanSquaredError</code><span class="sig-paren">(</span><em>dataWithPredictions</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.Metrics.meanSquaredError" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dataWithPredictions</strong> – linear regression predictions, predicted by the RidgeRegression predict() function</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">mean squared error computed based on the provided dataWithPredictions parameter</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">double</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="snap_ml_spark.Metrics.precision">
<code class="descclassname">snap_ml_spark.Metrics.</code><code class="descname">precision</code><span class="sig-paren">(</span><em>dataWithPredictions</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.Metrics.precision" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dataWithPredictions</strong> – binary predictions computed by the LogisticRegression or SupportVectorMachine predict() function</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">precision metric (TP/(TP+FP)), computed based on the binary predictions of a classifier (LogisticRegression, SupportVectorMachines)</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">double</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="snap_ml_spark.Metrics.recall">
<code class="descclassname">snap_ml_spark.Metrics.</code><code class="descname">recall</code><span class="sig-paren">(</span><em>dataWithPredictions</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.Metrics.recall" title="Permalink to this definition">¶</a></dt>
<dd><table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>dataWithPredictions</strong> – binary predictions computed by the LogisticRegression or SupportVectorMachine predict() function</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">recall metric (TP/(TP+FN)), computed based on the binary predictions of a classifier (LogisticRegression, SupportVectorMachines)</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">double</td>
</tr>
</tbody>
</table>
</dd></dl>
<span class="target" id="module-snap_ml_spark.Utils"></span><dl class="function">
<dt id="snap_ml_spark.Utils.dump_to_snap_format">
<code class="descclassname">snap_ml_spark.Utils.</code><code class="descname">dump_to_snap_format</code><span class="sig-paren">(</span><em>X</em>, <em>y</em>, <em>filename</em>, <em>transpose=False</em>, <em>implicit_vals=False</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.Utils.dump_to_snap_format" title="Permalink to this definition">¶</a></dt>
<dd><p>Non-distributed data writing to snap format</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>X</strong> (<em>numpy array</em><em> or </em><em>sparse matrix</em>) – The data used for training or inference.</li>
<li><strong>y</strong> (<em>numpy array</em>) – The labels of the samples in X.</li>
<li><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – The file where X and y will be stored in snap format.</li>
<li><strong>transpose</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em> , </em><em>default : False</em>) – If transpose is True, X will be stored in transposed format.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="function">
<dt id="snap_ml_spark.Utils.read_from_snap_format">
<code class="descclassname">snap_ml_spark.Utils.</code><code class="descname">read_from_snap_format</code><span class="sig-paren">(</span><em>filename</em><span class="sig-paren">)</span><a class="headerlink" href="#snap_ml_spark.Utils.read_from_snap_format" title="Permalink to this definition">¶</a></dt>
<dd><p>Non-distributed data loading from snap format</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.7)"><em>str</em></a>) – The file where the data resides.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><strong>X, y</strong> – Returns two datasets.
X : the data used for training or inference
y : the labels of the samples in X.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">numpy array or sparse matrix, numpy array</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
</div>
</div>
<footer>
<hr/>
<div role="contentinfo">
<p>
© Copyright IBM Corporation 2018, 2019
</p>
</div>
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
</footer>
</div>
</div>
</section>
</div>
<script type="text/javascript">
jQuery(function () {
SphinxRtdTheme.Navigation.enable(true);
});
</script>
</body>
</html>