-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathloading_other_datasets.html
635 lines (553 loc) · 45.3 KB
/
loading_other_datasets.html
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
<!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" />
<meta property="og:title" content="7.4. Loading other datasets" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://scikit-learn/stable/datasets/loading_other_datasets.html" />
<meta property="og:site_name" content="scikit-learn" />
<meta property="og:description" content="Sample images: Scikit-learn also embeds a couple of sample JPEG images published under Creative Commons license by their authors. Those images can be useful to test algorithms and pipelines on 2D d..." />
<meta property="og:image" content="https://scikit-learn/stable/_images/sphx_glr_plot_color_quantization_001.png" />
<meta property="og:image:alt" content="scikit-learn" />
<meta name="description" content="Sample images: Scikit-learn also embeds a couple of sample JPEG images published under Creative Commons license by their authors. Those images can be useful to test algorithms and pipelines on 2D d..." />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>7.4. Loading other datasets — scikit-learn 1.4.dev0 documentation</title>
<link rel="canonical" href="http://scikit-learn.org/stable/datasets/loading_other_datasets.html" />
<link rel="shortcut icon" href="../_static/favicon.ico"/>
<link rel="stylesheet" href="../_static/css/vendor/bootstrap.min.css" type="text/css" />
<link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
<link rel="stylesheet" href="../_static/copybutton.css" type="text/css" />
<link rel="stylesheet" href="../_static/plot_directive.css" type="text/css" />
<link rel="stylesheet" href="../https://fonts.googleapis.com/css?family=Vibur" type="text/css" />
<link rel="stylesheet" href="../_static/jupyterlite_sphinx.css" type="text/css" />
<link rel="stylesheet" href="../_static/sg_gallery.css" type="text/css" />
<link rel="stylesheet" href="../_static/sg_gallery-binder.css" type="text/css" />
<link rel="stylesheet" href="../_static/sg_gallery-dataframe.css" type="text/css" />
<link rel="stylesheet" href="../_static/sg_gallery-rendered-html.css" type="text/css" />
<link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
<script id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
<script src="../_static/js/vendor/jquery-3.6.3.slim.min.js"></script>
<script src="../_static/js/details-permalink.js"></script>
</head>
<body>
<nav id="navbar" class="sk-docs-navbar navbar navbar-expand-md navbar-light bg-light py-0">
<div class="container-fluid sk-docs-container px-0">
<a class="navbar-brand py-0" href="../index.html">
<img
class="sk-brand-img"
src="../_static/scikit-learn-logo-small.png"
alt="logo"/>
</a>
<button
id="sk-navbar-toggler"
class="navbar-toggler"
type="button"
data-toggle="collapse"
data-target="#navbarSupportedContent"
aria-controls="navbarSupportedContent"
aria-expanded="false"
aria-label="Toggle navigation"
>
<span class="navbar-toggler-icon"></span>
</button>
<div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
<ul class="navbar-nav mr-auto">
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../install.html">Install</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../user_guide.html">User Guide</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../modules/classes.html">API</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" href="../auto_examples/index.html">Examples</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link" target="_blank" rel="noopener noreferrer" href="https://blog.scikit-learn.org/">Community</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../getting_started.html" >Getting Started</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../tutorial/index.html" >Tutorial</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../whats_new/v1.4.html" >What's new</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../glossary.html" >Glossary</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../developers/index.html" >Development</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../faq.html" >FAQ</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../support.html" >Support</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../related_projects.html" >Related packages</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../roadmap.html" >Roadmap</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../governance.html" >Governance</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../about.html" >About us</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://github.com/scikit-learn/scikit-learn" >GitHub</a>
</li>
<li class="nav-item">
<a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-learn.org/dev/versions.html" >Other Versions and Download</a>
</li>
<li class="nav-item dropdown nav-more-item-dropdown">
<a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
<div class="dropdown-menu" aria-labelledby="navbarDropdown">
<a class="sk-nav-dropdown-item dropdown-item" href="../getting_started.html" >Getting Started</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../tutorial/index.html" >Tutorial</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../whats_new/v1.4.html" >What's new</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../glossary.html" >Glossary</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../developers/index.html" >Development</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../faq.html" >FAQ</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../support.html" >Support</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../related_projects.html" >Related packages</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../roadmap.html" >Roadmap</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../governance.html" >Governance</a>
<a class="sk-nav-dropdown-item dropdown-item" href="../about.html" >About us</a>
<a class="sk-nav-dropdown-item dropdown-item" href="https://github.com/scikit-learn/scikit-learn" >GitHub</a>
<a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-learn.org/dev/versions.html" >Other Versions and Download</a>
</div>
</li>
</ul>
<div id="searchbox" role="search">
<div class="searchformwrapper">
<form class="search" action="../search.html" method="get">
<input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
<input class="sk-search-text-btn" type="submit" value="Go" />
</form>
</div>
</div>
</div>
</div>
</nav>
<div class="d-flex" id="sk-doc-wrapper">
<input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
<label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
<div id="sk-sidebar-wrapper" class="border-right">
<div class="sk-sidebar-toc-wrapper">
<div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
<a href="sample_generators.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="7.3. Generated datasets">Prev</a><a href="../datasets.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="7. Dataset loading utilities">Up</a>
<a href="../computing.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="8. Computing with scikit-learn">Next</a>
</div>
<div class="alert alert-danger p-1 mb-2" role="alert">
<p class="text-center mb-0">
<strong>scikit-learn 1.4.dev0</strong><br/>
<a href="http://scikit-learn.org/dev/versions.html">Other versions</a>
</p>
</div>
<div class="alert alert-warning p-1 mb-2" role="alert">
<p class="text-center mb-0">
Please <a class="font-weight-bold" href="../about.html#citing-scikit-learn"><string>cite us</string></a> if you use the software.
</p>
</div>
<div class="sk-sidebar-toc">
<ul>
<li>
<a href="../user_guide.html" class="sk-toc-active">User Guide</a>
</li>
<ul>
<li>
<a href="../supervised_learning.html" class="">1. Supervised learning</a>
</li>
<li>
<a href="../unsupervised_learning.html" class="">2. Unsupervised learning</a>
</li>
<li>
<a href="../model_selection.html" class="">3. Model selection and evaluation</a>
</li>
<li>
<a href="../inspection.html" class="">4. Inspection</a>
</li>
<li>
<a href="../visualizations.html" class="">5. Visualizations</a>
</li>
<li>
<a href="../data_transforms.html" class="">6. Dataset transformations</a>
</li>
<li>
<a href="../datasets.html" class="sk-toc-active">7. Dataset loading utilities</a>
<ul>
<li class="sk-toctree-l3">
<a href="toy_dataset.html">7.1. Toy datasets</a>
</li>
<li class="sk-toctree-l3">
<a href="real_world.html">7.2. Real world datasets</a>
</li>
<li class="sk-toctree-l3">
<a href="sample_generators.html">7.3. Generated datasets</a>
</li>
<li class="sk-toctree-l3">
<a href="">7.4. Loading other datasets</a>
</li>
</ul>
</li>
<li>
<a href="../computing.html" class="">8. Computing with scikit-learn</a>
</li>
<li>
<a href="../model_persistence.html" class="">9. Model persistence</a>
</li>
<li>
<a href="../common_pitfalls.html" class="">10. Common pitfalls and recommended practices</a>
</li>
<li>
<a href="../dispatching.html" class="">11. Dispatching</a>
</li>
</ul>
</ul>
</div>
</div>
</div>
<div id="sk-page-content-wrapper">
<div class="sk-page-content container-fluid body px-md-3" role="main">
<section id="loading-other-datasets">
<span id="id1"></span><h1><span class="section-number">7.4. </span>Loading other datasets<a class="headerlink" href="#loading-other-datasets" title="Link to this heading">¶</a></h1>
<section id="sample-images">
<span id="id2"></span><h2><span class="section-number">7.4.1. </span>Sample images<a class="headerlink" href="#sample-images" title="Link to this heading">¶</a></h2>
<p>Scikit-learn also embeds a couple of sample JPEG images published under Creative
Commons license by their authors. Those images can be useful to test algorithms
and pipelines on 2D data.</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_sample_images.html#sklearn.datasets.load_sample_images" title="sklearn.datasets.load_sample_images"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_sample_images</span></code></a>()</p></td>
<td><p>Load sample images for image manipulation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="../modules/generated/sklearn.datasets.load_sample_image.html#sklearn.datasets.load_sample_image" title="sklearn.datasets.load_sample_image"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_sample_image</span></code></a>(image_name)</p></td>
<td><p>Load the numpy array of a single sample image.</p></td>
</tr>
</tbody>
</table>
<a class="reference external image-reference" href="../auto_examples/cluster/plot_color_quantization.html"><img alt="../_images/sphx_glr_plot_color_quantization_001.png" class="align-right" src="../_images/sphx_glr_plot_color_quantization_001.png" style="width: 192.0px; height: 144.0px;" /></a>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>The default coding of images is based on the <code class="docutils literal notranslate"><span class="pre">uint8</span></code> dtype to
spare memory. Often machine learning algorithms work best if the
input is converted to a floating point representation first. Also,
if you plan to use <code class="docutils literal notranslate"><span class="pre">matplotlib.pyplpt.imshow</span></code>, don’t forget to scale to the range
0 - 1 as done in the following example.</p>
</div>
<aside class="topic">
<p class="topic-title">Examples:</p>
<ul class="simple">
<li><p><a class="reference internal" href="../auto_examples/cluster/plot_color_quantization.html#sphx-glr-auto-examples-cluster-plot-color-quantization-py"><span class="std std-ref">Color Quantization using K-Means</span></a></p></li>
</ul>
</aside>
</section>
<section id="datasets-in-svmlight-libsvm-format">
<span id="libsvm-loader"></span><h2><span class="section-number">7.4.2. </span>Datasets in svmlight / libsvm format<a class="headerlink" href="#datasets-in-svmlight-libsvm-format" title="Link to this heading">¶</a></h2>
<p>scikit-learn includes utility functions for loading
datasets in the svmlight / libsvm format. In this format, each line
takes the form <code class="docutils literal notranslate"><span class="pre"><label></span> <span class="pre"><feature-id>:<feature-value></span>
<span class="pre"><feature-id>:<feature-value></span> <span class="pre">...</span></code>. This format is especially suitable for sparse datasets.
In this module, scipy sparse CSR matrices are used for <code class="docutils literal notranslate"><span class="pre">X</span></code> and numpy arrays are used for <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
<p>You may load a dataset like as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_svmlight_file</span>
<span class="gp">>>> </span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">load_svmlight_file</span><span class="p">(</span><span class="s2">"/path/to/train_dataset.txt"</span><span class="p">)</span>
<span class="gp">... </span>
</pre></div>
</div>
<p>You may also load two (or more) datasets at once:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">load_svmlight_files</span><span class="p">(</span>
<span class="gp">... </span> <span class="p">(</span><span class="s2">"/path/to/train_dataset.txt"</span><span class="p">,</span> <span class="s2">"/path/to/test_dataset.txt"</span><span class="p">))</span>
<span class="gp">... </span>
</pre></div>
</div>
<p>In this case, <code class="docutils literal notranslate"><span class="pre">X_train</span></code> and <code class="docutils literal notranslate"><span class="pre">X_test</span></code> are guaranteed to have the same number
of features. Another way to achieve the same result is to fix the number of
features:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">load_svmlight_file</span><span class="p">(</span>
<span class="gp">... </span> <span class="s2">"/path/to/test_dataset.txt"</span><span class="p">,</span> <span class="n">n_features</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="gp">... </span>
</pre></div>
</div>
<aside class="topic">
<p class="topic-title">Related links:</p>
<p><span class="target" id="public-datasets-in-svmlight-libsvm-format">Public datasets in svmlight / libsvm format</span>: <a class="reference external" href="https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets">https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets</a></p>
<p><span class="target" id="faster-api-compatible-implementation">Faster API-compatible implementation</span>: <a class="reference external" href="https://github.com/mblondel/svmlight-loader">https://github.com/mblondel/svmlight-loader</a></p>
</aside>
</section>
<section id="downloading-datasets-from-the-openml-org-repository">
<span id="openml"></span><h2><span class="section-number">7.4.3. </span>Downloading datasets from the openml.org repository<a class="headerlink" href="#downloading-datasets-from-the-openml-org-repository" title="Link to this heading">¶</a></h2>
<p><a class="reference external" href="https://openml.org">openml.org</a> is a public repository for machine learning
data and experiments, that allows everybody to upload open datasets.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">sklearn.datasets</span></code> package is able to download datasets
from the repository using the function
<a class="reference internal" href="../modules/generated/sklearn.datasets.fetch_openml.html#sklearn.datasets.fetch_openml" title="sklearn.datasets.fetch_openml"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_openml</span></code></a>.</p>
<p>For example, to download a dataset of gene expressions in mice brains:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_openml</span>
<span class="gp">>>> </span><span class="n">mice</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'miceprotein'</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">parser</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)</span>
</pre></div>
</div>
<p>To fully specify a dataset, you need to provide a name and a version, though
the version is optional, see <a class="reference internal" href="#openml-versions"><span class="std std-ref">Dataset Versions</span></a> below.
The dataset contains a total of 1080 examples belonging to 8 different
classes:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mice</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(1080, 77)</span>
<span class="gp">>>> </span><span class="n">mice</span><span class="o">.</span><span class="n">target</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(1080,)</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">mice</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="go">array(['c-CS-m', 'c-CS-s', 'c-SC-m', 'c-SC-s', 't-CS-m', 't-CS-s', 't-SC-m', 't-SC-s'], dtype=object)</span>
</pre></div>
</div>
<p>You can get more information on the dataset by looking at the <code class="docutils literal notranslate"><span class="pre">DESCR</span></code>
and <code class="docutils literal notranslate"><span class="pre">details</span></code> attributes:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">mice</span><span class="o">.</span><span class="n">DESCR</span><span class="p">)</span>
<span class="go">**Author**: Clara Higuera, Katheleen J. Gardiner, Krzysztof J. Cios</span>
<span class="go">**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression) - 2015</span>
<span class="go">**Please cite**: Higuera C, Gardiner KJ, Cios KJ (2015) Self-Organizing</span>
<span class="go">Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down</span>
<span class="go">Syndrome. PLoS ONE 10(6): e0129126...</span>
<span class="gp">>>> </span><span class="n">mice</span><span class="o">.</span><span class="n">details</span>
<span class="go">{'id': '40966', 'name': 'MiceProtein', 'version': '4', 'format': 'ARFF',</span>
<span class="go">'upload_date': '2017-11-08T16:00:15', 'licence': 'Public',</span>
<span class="go">'url': 'https://www.openml.org/data/v1/download/17928620/MiceProtein.arff',</span>
<span class="go">'file_id': '17928620', 'default_target_attribute': 'class',</span>
<span class="go">'row_id_attribute': 'MouseID',</span>
<span class="go">'ignore_attribute': ['Genotype', 'Treatment', 'Behavior'],</span>
<span class="go">'tag': ['OpenML-CC18', 'study_135', 'study_98', 'study_99'],</span>
<span class="go">'visibility': 'public', 'status': 'active',</span>
<span class="go">'md5_checksum': '3c479a6885bfa0438971388283a1ce32'}</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">DESCR</span></code> contains a free-text description of the data, while <code class="docutils literal notranslate"><span class="pre">details</span></code>
contains a dictionary of meta-data stored by openml, like the dataset id.
For more details, see the <a class="reference external" href="https://docs.openml.org/#data">OpenML documentation</a> The <code class="docutils literal notranslate"><span class="pre">data_id</span></code> of the mice protein dataset
is 40966, and you can use this (or the name) to get more information on the
dataset on the openml website:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mice</span><span class="o">.</span><span class="n">url</span>
<span class="go">'https://www.openml.org/d/40966'</span>
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">data_id</span></code> also uniquely identifies a dataset from OpenML:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mice</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">data_id</span><span class="o">=</span><span class="mi">40966</span><span class="p">,</span> <span class="n">parser</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mice</span><span class="o">.</span><span class="n">details</span>
<span class="go">{'id': '4550', 'name': 'MiceProtein', 'version': '1', 'format': 'ARFF',</span>
<span class="go">'creator': ...,</span>
<span class="go">'upload_date': '2016-02-17T14:32:49', 'licence': 'Public', 'url':</span>
<span class="go">'https://www.openml.org/data/v1/download/1804243/MiceProtein.ARFF', 'file_id':</span>
<span class="go">'1804243', 'default_target_attribute': 'class', 'citation': 'Higuera C,</span>
<span class="go">Gardiner KJ, Cios KJ (2015) Self-Organizing Feature Maps Identify Proteins</span>
<span class="go">Critical to Learning in a Mouse Model of Down Syndrome. PLoS ONE 10(6):</span>
<span class="go">e0129126. [Web Link] journal.pone.0129126', 'tag': ['OpenML100', 'study_14',</span>
<span class="go">'study_34'], 'visibility': 'public', 'status': 'active', 'md5_checksum':</span>
<span class="go">'3c479a6885bfa0438971388283a1ce32'}</span>
</pre></div>
</div>
<section id="dataset-versions">
<span id="openml-versions"></span><h3><span class="section-number">7.4.3.1. </span>Dataset Versions<a class="headerlink" href="#dataset-versions" title="Link to this heading">¶</a></h3>
<p>A dataset is uniquely specified by its <code class="docutils literal notranslate"><span class="pre">data_id</span></code>, but not necessarily by its
name. Several different “versions” of a dataset with the same name can exist
which can contain entirely different datasets.
If a particular version of a dataset has been found to contain significant
issues, it might be deactivated. Using a name to specify a dataset will yield
the earliest version of a dataset that is still active. That means that
<code class="docutils literal notranslate"><span class="pre">fetch_openml(name="miceprotein",</span> <span class="pre">parser="auto")</span></code> can yield different results
at different times if earlier versions become inactive.
You can see that the dataset with <code class="docutils literal notranslate"><span class="pre">data_id</span></code> 40966 that we fetched above is
the first version of the “miceprotein” dataset:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">mice</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'version'</span><span class="p">]</span>
<span class="go">'1'</span>
</pre></div>
</div>
<p>In fact, this dataset only has one version. The iris dataset on the other hand
has multiple versions:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">iris</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"iris"</span><span class="p">,</span> <span class="n">parser</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'version'</span><span class="p">]</span>
<span class="go">'1'</span>
<span class="gp">>>> </span><span class="n">iris</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'id'</span><span class="p">]</span>
<span class="go">'61'</span>
<span class="gp">>>> </span><span class="n">iris_61</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">data_id</span><span class="o">=</span><span class="mi">61</span><span class="p">,</span> <span class="n">parser</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris_61</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'version'</span><span class="p">]</span>
<span class="go">'1'</span>
<span class="gp">>>> </span><span class="n">iris_61</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'id'</span><span class="p">]</span>
<span class="go">'61'</span>
<span class="gp">>>> </span><span class="n">iris_969</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">data_id</span><span class="o">=</span><span class="mi">969</span><span class="p">,</span> <span class="n">parser</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris_969</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'version'</span><span class="p">]</span>
<span class="go">'3'</span>
<span class="gp">>>> </span><span class="n">iris_969</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'id'</span><span class="p">]</span>
<span class="go">'969'</span>
</pre></div>
</div>
<p>Specifying the dataset by the name “iris” yields the lowest version, version 1,
with the <code class="docutils literal notranslate"><span class="pre">data_id</span></code> 61. To make sure you always get this exact dataset, it is
safest to specify it by the dataset <code class="docutils literal notranslate"><span class="pre">data_id</span></code>. The other dataset, with
<code class="docutils literal notranslate"><span class="pre">data_id</span></code> 969, is version 3 (version 2 has become inactive), and contains a
binarized version of the data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">iris_969</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
<span class="go">array(['N', 'P'], dtype=object)</span>
</pre></div>
</div>
<p>You can also specify both the name and the version, which also uniquely
identifies the dataset:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">iris_version_3</span> <span class="o">=</span> <span class="n">fetch_openml</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"iris"</span><span class="p">,</span> <span class="n">version</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">parser</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">iris_version_3</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'version'</span><span class="p">]</span>
<span class="go">'3'</span>
<span class="gp">>>> </span><span class="n">iris_version_3</span><span class="o">.</span><span class="n">details</span><span class="p">[</span><span class="s1">'id'</span><span class="p">]</span>
<span class="go">'969'</span>
</pre></div>
</div>
<aside class="topic">
<p class="topic-title">References:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://arxiv.org/abs/1407.7722">Vanschoren, van Rijn, Bischl and Torgo. “OpenML: networked science in
machine learning” ACM SIGKDD Explorations Newsletter, 15(2), 49-60, 2014.</a></p></li>
</ul>
</aside>
</section>
<section id="arff-parser">
<span id="openml-parser"></span><h3><span class="section-number">7.4.3.2. </span>ARFF parser<a class="headerlink" href="#arff-parser" title="Link to this heading">¶</a></h3>
<p>From version 1.2, scikit-learn provides a new keyword argument <code class="docutils literal notranslate"><span class="pre">parser</span></code> that
provides several options to parse the ARFF files provided by OpenML. The legacy
parser (i.e. <code class="docutils literal notranslate"><span class="pre">parser="liac-arff"</span></code>) is based on the project
<a class="reference external" href="https://github.com/renatopp/liac-arff">LIAC-ARFF</a>. This parser is however
slow and consume more memory than required. A new parser based on pandas
(i.e. <code class="docutils literal notranslate"><span class="pre">parser="pandas"</span></code>) is both faster and more memory efficient.
However, this parser does not support sparse data.
Therefore, we recommend using <code class="docutils literal notranslate"><span class="pre">parser="auto"</span></code> which will use the best parser
available for the requested dataset.</p>
<p>The <code class="docutils literal notranslate"><span class="pre">"pandas"</span></code> and <code class="docutils literal notranslate"><span class="pre">"liac-arff"</span></code> parsers can lead to different data types in
the output. The notable differences are the following:</p>
<ul class="simple">
<li><p>The <code class="docutils literal notranslate"><span class="pre">"liac-arff"</span></code> parser always encodes categorical features as <code class="docutils literal notranslate"><span class="pre">str</span></code>
objects. To the contrary, the <code class="docutils literal notranslate"><span class="pre">"pandas"</span></code> parser instead infers the type while
reading and numerical categories will be casted into integers whenever
possible.</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">"liac-arff"</span></code> parser uses float64 to encode numerical features tagged as
‘REAL’ and ‘NUMERICAL’ in the metadata. The <code class="docutils literal notranslate"><span class="pre">"pandas"</span></code> parser instead infers
if these numerical features corresponds to integers and uses panda’s Integer
extension dtype.</p></li>
<li><p>In particular, classification datasets with integer categories are typically
loaded as such <code class="docutils literal notranslate"><span class="pre">(0,</span> <span class="pre">1,</span> <span class="pre">...)</span></code> with the <code class="docutils literal notranslate"><span class="pre">"pandas"</span></code> parser while <code class="docutils literal notranslate"><span class="pre">"liac-arff"</span></code>
will force the use of string encoded class labels such as <code class="docutils literal notranslate"><span class="pre">"0"</span></code>, <code class="docutils literal notranslate"><span class="pre">"1"</span></code> and so
on.</p></li>
<li><p>The <code class="docutils literal notranslate"><span class="pre">"pandas"</span></code> parser will not strip single quotes - i.e. <code class="docutils literal notranslate"><span class="pre">'</span></code> - from string
columns. For instance, a string <code class="docutils literal notranslate"><span class="pre">'my</span> <span class="pre">string'</span></code> will be kept as is while the
<code class="docutils literal notranslate"><span class="pre">"liac-arff"</span></code> parser will strip the single quotes. For categorical columns,
the single quotes are stripped from the values.</p></li>
</ul>
<p>In addition, when <code class="docutils literal notranslate"><span class="pre">as_frame=False</span></code> is used, the <code class="docutils literal notranslate"><span class="pre">"liac-arff"</span></code> parser returns
ordinally encoded data where the categories are provided in the attribute
<code class="docutils literal notranslate"><span class="pre">categories</span></code> of the <code class="docutils literal notranslate"><span class="pre">Bunch</span></code> instance. Instead, <code class="docutils literal notranslate"><span class="pre">"pandas"</span></code> returns a NumPy array
were the categories. Then it’s up to the user to design a feature
engineering pipeline with an instance of <code class="docutils literal notranslate"><span class="pre">OneHotEncoder</span></code> or
<code class="docutils literal notranslate"><span class="pre">OrdinalEncoder</span></code> typically wrapped in a <code class="docutils literal notranslate"><span class="pre">ColumnTransformer</span></code> to
preprocess the categorical columns explicitly. See for instance: <a class="reference internal" href="../auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py"><span class="std std-ref">Column Transformer with Mixed Types</span></a>.</p>
</section>
</section>
<section id="loading-from-external-datasets">
<span id="external-datasets"></span><h2><span class="section-number">7.4.4. </span>Loading from external datasets<a class="headerlink" href="#loading-from-external-datasets" title="Link to this heading">¶</a></h2>
<p>scikit-learn works on any numeric data stored as numpy arrays or scipy sparse
matrices. Other types that are convertible to numeric arrays such as pandas
DataFrame are also acceptable.</p>
<p>Here are some recommended ways to load standard columnar data into a
format usable by scikit-learn:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/io.html">pandas.io</a>
provides tools to read data from common formats including CSV, Excel, JSON
and SQL. DataFrames may also be constructed from lists of tuples or dicts.
Pandas handles heterogeneous data smoothly and provides tools for
manipulation and conversion into a numeric array suitable for scikit-learn.</p></li>
<li><p><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/io.html">scipy.io</a>
specializes in binary formats often used in scientific computing
context such as .mat and .arff</p></li>
<li><p><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/routines.io.html">numpy/routines.io</a>
for standard loading of columnar data into numpy arrays</p></li>
<li><p>scikit-learn’s <a class="reference internal" href="../modules/generated/sklearn.datasets.load_svmlight_file.html#sklearn.datasets.load_svmlight_file" title="sklearn.datasets.load_svmlight_file"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_svmlight_file</span></code></a> for the svmlight or libSVM
sparse format</p></li>
<li><p>scikit-learn’s <a class="reference internal" href="../modules/generated/sklearn.datasets.load_files.html#sklearn.datasets.load_files" title="sklearn.datasets.load_files"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_files</span></code></a> for directories of text files where
the name of each directory is the name of each category and each file inside
of each directory corresponds to one sample from that category</p></li>
</ul>
<p>For some miscellaneous data such as images, videos, and audio, you may wish to
refer to:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://scikit-image.org/docs/dev/api/skimage.io.html">skimage.io</a> or
<a class="reference external" href="https://imageio.readthedocs.io/en/stable/reference/core_v3.html">Imageio</a>
for loading images and videos into numpy arrays</p></li>
<li><p><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.wavfile.read.html">scipy.io.wavfile.read</a>
for reading WAV files into a numpy array</p></li>
</ul>
<p>Categorical (or nominal) features stored as strings (common in pandas DataFrames)
will need converting to numerical features using <a class="reference internal" href="../modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OneHotEncoder</span></code></a>
or <a class="reference internal" href="../modules/generated/sklearn.preprocessing.OrdinalEncoder.html#sklearn.preprocessing.OrdinalEncoder" title="sklearn.preprocessing.OrdinalEncoder"><code class="xref py py-class docutils literal notranslate"><span class="pre">OrdinalEncoder</span></code></a> or similar.
See <a class="reference internal" href="../modules/preprocessing.html#preprocessing"><span class="std std-ref">Preprocessing data</span></a>.</p>
<p>Note: if you manage your own numerical data it is recommended to use an
optimized file format such as HDF5 to reduce data load times. Various libraries
such as H5Py, PyTables and pandas provides a Python interface for reading and
writing data in that format.</p>
</section>
</section>
</div>
<div class="container">
<footer class="sk-content-footer">
© 2007 - 2023, scikit-learn developers (BSD License).
<a href="../_sources/datasets/loading_other_datasets.rst.txt" rel="nofollow">Show this page source</a>
</footer>
</div>
</div>
</div>
<script src="../_static/js/vendor/bootstrap.min.js"></script>
<script>
window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
ga('create', 'UA-22606712-2', 'auto');
ga('set', 'anonymizeIp', true);
ga('send', 'pageview');
</script>
<script async src='https://www.google-analytics.com/analytics.js'></script>
<script defer data-domain="scikit-learn.org" src="https://views.scientific-python.org/js/script.js">
</script>
<script src="../_static/clipboard.min.js"></script>
<script src="../_static/copybutton.js"></script>
<script>
$(document).ready(function() {
/* Add a [>>>] button on the top-right corner of code samples to hide
* the >>> and ... prompts and the output and thus make the code
* copyable. */
var div = $('.highlight-python .highlight,' +
'.highlight-python3 .highlight,' +
'.highlight-pycon .highlight,' +
'.highlight-default .highlight')
var pre = div.find('pre');
// get the styles from the current theme
pre.parent().parent().css('position', 'relative');
// create and add the button to all the code blocks that contain >>>
div.each(function(index) {
var jthis = $(this);
// tracebacks (.gt) contain bare text elements that need to be
// wrapped in a span to work with .nextUntil() (see later)
jthis.find('pre:has(.gt)').contents().filter(function() {
return ((this.nodeType == 3) && (this.data.trim().length > 0));
}).wrap('<span>');
});
/*** Add permalink buttons next to glossary terms ***/
$('dl.glossary > dt[id]').append(function() {
return ('<a class="headerlink" href="#' +
this.getAttribute('id') +
'" title="Permalink to this term">¶</a>');
});
});
</script>
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
</body>
</html>