-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathloading_other_datasets.html
1080 lines (847 loc) · 60.8 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
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html lang="en" data-content_root="../" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<meta property="og:title" content="8.4. 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/loading_other_datasets-1.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..." />
<title>8.4. Loading other datasets — scikit-learn 1.7.dev0 documentation</title>
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
</script>
<!--
this give us a css class that will be invisible only if js is disabled
-->
<noscript>
<style>
.pst-js-only { display: none !important; }
</style>
</noscript>
<!-- Loaded before other Sphinx assets -->
<link href="../_static/styles/theme.css?digest=8878045cc6db502f8baf" rel="stylesheet" />
<link href="../_static/styles/pydata-sphinx-theme.css?digest=8878045cc6db502f8baf" rel="stylesheet" />
<link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=8f2a1f02" />
<link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
<link rel="stylesheet" type="text/css" href="../_static/plot_directive.css" />
<link rel="stylesheet" type="text/css" href="https://fonts.googleapis.com/css?family=Vibur" />
<link rel="stylesheet" type="text/css" href="../_static/jupyterlite_sphinx.css?v=2c9f8f05" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=d2d258e8" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
<link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
<link rel="stylesheet" type="text/css" href="../_static/sphinx-design.min.css?v=95c83b7e" />
<link rel="stylesheet" type="text/css" href="../_static/styles/colors.css?v=cc94ab7d" />
<link rel="stylesheet" type="text/css" href="../_static/styles/custom.css?v=8f525996" />
<!-- So that users can add custom icons -->
<script src="../_static/scripts/fontawesome.js?digest=8878045cc6db502f8baf"></script>
<!-- Pre-loaded scripts that we'll load fully later -->
<link rel="preload" as="script" href="../_static/scripts/bootstrap.js?digest=8878045cc6db502f8baf" />
<link rel="preload" as="script" href="../_static/scripts/pydata-sphinx-theme.js?digest=8878045cc6db502f8baf" />
<script src="../_static/documentation_options.js?v=473747f4"></script>
<script src="../_static/doctools.js?v=9bcbadda"></script>
<script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="../_static/clipboard.min.js?v=a7894cd8"></script>
<script src="../_static/copybutton.js?v=97f0b27d"></script>
<script src="../_static/jupyterlite_sphinx.js?v=96e329c5"></script>
<script src="../_static/design-tabs.js?v=f930bc37"></script>
<script data-domain="scikit-learn.org" defer="defer" src="https://views.scientific-python.org/js/script.js"></script>
<script>DOCUMENTATION_OPTIONS.pagename = 'datasets/loading_other_datasets';</script>
<script>
DOCUMENTATION_OPTIONS.theme_version = '0.16.1';
DOCUMENTATION_OPTIONS.theme_switcher_json_url = 'https://scikit-learn.org/dev/_static/versions.json';
DOCUMENTATION_OPTIONS.theme_switcher_version_match = '1.7.dev0';
DOCUMENTATION_OPTIONS.show_version_warning_banner =
true;
</script>
<script src="../_static/scripts/dropdown.js?v=d6825577"></script>
<script src="../_static/scripts/version-switcher.js?v=a6dd8357"></script>
<script src="../_static/scripts/sg_plotly_resize.js?v=2167d4db"></script>
<link rel="canonical" href="https://scikit-learn.org/stable/datasets/loading_other_datasets.html" />
<link rel="icon" href="../_static/favicon.ico"/>
<link rel="author" title="About these documents" href="../about.html" />
<link rel="search" title="Search" href="../search.html" />
<link rel="next" title="9. Computing with scikit-learn" href="../computing.html" />
<link rel="prev" title="8.3. Generated datasets" href="sample_generators.html" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
<meta name="docsearch:version" content="1.7" />
</head>
<body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
<div id="pst-scroll-pixel-helper"></div>
<button type="button" class="btn rounded-pill" id="pst-back-to-top">
<i class="fa-solid fa-arrow-up"></i>Back to top</button>
<dialog id="pst-search-dialog">
<form class="bd-search d-flex align-items-center"
action="../search.html"
method="get">
<i class="fa-solid fa-magnifying-glass"></i>
<input type="search"
class="form-control"
name="q"
placeholder="Search the docs ..."
aria-label="Search the docs ..."
autocomplete="off"
autocorrect="off"
autocapitalize="off"
spellcheck="false"/>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form>
</dialog>
<div class="pst-async-banner-revealer d-none">
<aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>
<header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
<div class="bd-header__inner bd-page-width">
<button class="pst-navbar-icon sidebar-toggle primary-toggle" aria-label="Site navigation">
<span class="fa-solid fa-bars"></span>
</button>
<div class=" navbar-header-items__start">
<div class="navbar-item">
<a class="navbar-brand logo" href="../index.html">
<img src="../_static/scikit-learn-logo-small.png" class="logo__image only-light" alt="scikit-learn homepage"/>
<img src="../_static/scikit-learn-logo-small.png" class="logo__image only-dark pst-js-only" alt="scikit-learn homepage"/>
</a></div>
</div>
<div class=" navbar-header-items">
<div class="me-auto navbar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item ">
<a class="nav-link nav-internal" href="../install.html">
Install
</a>
</li>
<li class="nav-item current active">
<a class="nav-link nav-internal" href="../user_guide.html">
User Guide
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../api/index.html">
API
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../auto_examples/index.html">
Examples
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://blog.scikit-learn.org/">
Community
</a>
</li>
<li class="nav-item dropdown">
<button class="btn dropdown-toggle nav-item" type="button"
data-bs-toggle="dropdown" aria-expanded="false"
aria-controls="pst-nav-more-links">
More
</button>
<ul id="pst-nav-more-links" class="dropdown-menu">
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../getting_started.html">
Getting Started
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../whats_new.html">
Release History
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../glossary.html">
Glossary
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../developers/index.html">
Development
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../faq.html">
FAQ
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../support.html">
Support
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../related_projects.html">
Related Projects
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../roadmap.html">
Roadmap
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../governance.html">
Governance
</a>
</li>
<li class=" ">
<a class="nav-link dropdown-item nav-internal" href="../about.html">
About us
</a>
</li>
</ul>
</li>
</ul>
</nav></div>
</div>
<div class="navbar-header-items__end">
<div class="navbar-item navbar-persistent--container">
<button class="btn btn-sm pst-navbar-icon search-button search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass fa-lg"></i>
</button>
</div>
<div class="navbar-item">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto" title="System Settings"></i>
</button></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/scikit-learn/scikit-learn" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
<div class="navbar-item">
<div class="version-switcher__container dropdown pst-js-only">
<button id="pst-version-switcher-button-2"
type="button"
class="version-switcher__button btn btn-sm dropdown-toggle"
data-bs-toggle="dropdown"
aria-haspopup="listbox"
aria-controls="pst-version-switcher-list-2"
aria-label="Version switcher list"
>
Choose version <!-- this text may get changed later by javascript -->
<span class="caret"></span>
</button>
<div id="pst-version-switcher-list-2"
class="version-switcher__menu dropdown-menu list-group-flush py-0"
role="listbox" aria-labelledby="pst-version-switcher-button-2">
<!-- dropdown will be populated by javascript on page load -->
</div>
</div></div>
</div>
</div>
<div class="navbar-persistent--mobile">
<button class="btn btn-sm pst-navbar-icon search-button search-button__button pst-js-only" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass fa-lg"></i>
</button>
</div>
<button class="pst-navbar-icon sidebar-toggle secondary-toggle" aria-label="On this page">
<span class="fa-solid fa-outdent"></span>
</button>
</div>
</header>
<div class="bd-container">
<div class="bd-container__inner bd-page-width">
<dialog id="pst-primary-sidebar-modal"></dialog>
<div id="pst-primary-sidebar" class="bd-sidebar-primary bd-sidebar">
<div class="sidebar-header-items sidebar-primary__section">
<div class="sidebar-header-items__center">
<div class="navbar-item">
<nav>
<ul class="bd-navbar-elements navbar-nav">
<li class="nav-item ">
<a class="nav-link nav-internal" href="../install.html">
Install
</a>
</li>
<li class="nav-item current active">
<a class="nav-link nav-internal" href="../user_guide.html">
User Guide
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../api/index.html">
API
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../auto_examples/index.html">
Examples
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-external" href="https://blog.scikit-learn.org/">
Community
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../getting_started.html">
Getting Started
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../whats_new.html">
Release History
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../glossary.html">
Glossary
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../developers/index.html">
Development
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../faq.html">
FAQ
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../support.html">
Support
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../related_projects.html">
Related Projects
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../roadmap.html">
Roadmap
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../governance.html">
Governance
</a>
</li>
<li class="nav-item ">
<a class="nav-link nav-internal" href="../about.html">
About us
</a>
</li>
</ul>
</nav></div>
</div>
<div class="sidebar-header-items__end">
<div class="navbar-item">
<button class="btn btn-sm nav-link pst-navbar-icon theme-switch-button pst-js-only" aria-label="Color mode" data-bs-title="Color mode" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="theme-switch fa-solid fa-sun fa-lg" data-mode="light" title="Light"></i>
<i class="theme-switch fa-solid fa-moon fa-lg" data-mode="dark" title="Dark"></i>
<i class="theme-switch fa-solid fa-circle-half-stroke fa-lg" data-mode="auto" title="System Settings"></i>
</button></div>
<div class="navbar-item"><ul class="navbar-icon-links"
aria-label="Icon Links">
<li class="nav-item">
<a href="https://github.com/scikit-learn/scikit-learn" title="GitHub" class="nav-link pst-navbar-icon" rel="noopener" target="_blank" data-bs-toggle="tooltip" data-bs-placement="bottom"><i class="fa-brands fa-square-github fa-lg" aria-hidden="true"></i>
<span class="sr-only">GitHub</span></a>
</li>
</ul></div>
<div class="navbar-item">
<div class="version-switcher__container dropdown pst-js-only">
<button id="pst-version-switcher-button-3"
type="button"
class="version-switcher__button btn btn-sm dropdown-toggle"
data-bs-toggle="dropdown"
aria-haspopup="listbox"
aria-controls="pst-version-switcher-list-3"
aria-label="Version switcher list"
>
Choose version <!-- this text may get changed later by javascript -->
<span class="caret"></span>
</button>
<div id="pst-version-switcher-list-3"
class="version-switcher__menu dropdown-menu list-group-flush py-0"
role="listbox" aria-labelledby="pst-version-switcher-button-3">
<!-- dropdown will be populated by javascript on page load -->
</div>
</div></div>
</div>
</div>
<div class="sidebar-primary-items__start sidebar-primary__section">
<div class="sidebar-primary-item">
<nav class="bd-docs-nav bd-links"
aria-label="Section Navigation">
<p class="bd-links__title" role="heading" aria-level="1">Section Navigation</p>
<div class="bd-toc-item navbar-nav"><ul class="current nav bd-sidenav">
<li class="toctree-l1 has-children"><a class="reference internal" href="../supervised_learning.html">1. Supervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/linear_model.html">1.1. Linear Models</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/lda_qda.html">1.2. Linear and Quadratic Discriminant Analysis</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/kernel_ridge.html">1.3. Kernel ridge regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/svm.html">1.4. Support Vector Machines</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/sgd.html">1.5. Stochastic Gradient Descent</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/neighbors.html">1.6. Nearest Neighbors</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/gaussian_process.html">1.7. Gaussian Processes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/cross_decomposition.html">1.8. Cross decomposition</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/naive_bayes.html">1.9. Naive Bayes</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/tree.html">1.10. Decision Trees</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/ensemble.html">1.11. Ensembles: Gradient boosting, random forests, bagging, voting, stacking</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/multiclass.html">1.12. Multiclass and multioutput algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/feature_selection.html">1.13. Feature selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/semi_supervised.html">1.14. Semi-supervised learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/isotonic.html">1.15. Isotonic regression</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/calibration.html">1.16. Probability calibration</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_supervised.html">1.17. Neural network models (supervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../unsupervised_learning.html">2. Unsupervised learning</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/mixture.html">2.1. Gaussian mixture models</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/manifold.html">2.2. Manifold learning</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/clustering.html">2.3. Clustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/biclustering.html">2.4. Biclustering</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/decomposition.html">2.5. Decomposing signals in components (matrix factorization problems)</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/covariance.html">2.6. Covariance estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/outlier_detection.html">2.7. Novelty and Outlier Detection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/density.html">2.8. Density Estimation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/neural_networks_unsupervised.html">2.9. Neural network models (unsupervised)</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../model_selection.html">3. Model selection and evaluation</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/classification_threshold.html">3.3. Tuning the decision threshold for class prediction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/model_evaluation.html">3.4. Metrics and scoring: quantifying the quality of predictions</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/learning_curve.html">3.5. Validation curves: plotting scores to evaluate models</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../metadata_routing.html">4. Metadata Routing</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../inspection.html">5. Inspection</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/partial_dependence.html">5.1. Partial Dependence and Individual Conditional Expectation plots</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/permutation_importance.html">5.2. Permutation feature importance</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../visualizations.html">6. Visualizations</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../data_transforms.html">7. Dataset transformations</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/compose.html">7.1. Pipelines and composite estimators</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/feature_extraction.html">7.2. Feature extraction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/preprocessing.html">7.3. Preprocessing data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/impute.html">7.4. Imputation of missing values</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/unsupervised_reduction.html">7.5. Unsupervised dimensionality reduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/random_projection.html">7.6. Random Projection</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/kernel_approximation.html">7.7. Kernel Approximation</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/metrics.html">7.8. Pairwise metrics, Affinities and Kernels</a></li>
<li class="toctree-l2"><a class="reference internal" href="../modules/preprocessing_targets.html">7.9. Transforming the prediction target (<code class="docutils literal notranslate"><span class="pre">y</span></code>)</a></li>
</ul>
</details></li>
<li class="toctree-l1 current active has-children"><a class="reference internal" href="../datasets.html">8. Dataset loading utilities</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="toy_dataset.html">8.1. Toy datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="real_world.html">8.2. Real world datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="sample_generators.html">8.3. Generated datasets</a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">8.4. Loading other datasets</a></li>
</ul>
</details></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../computing.html">9. Computing with scikit-learn</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../computing/scaling_strategies.html">9.1. Strategies to scale computationally: bigger data</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/computational_performance.html">9.2. Computational Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../computing/parallelism.html">9.3. Parallelism, resource management, and configuration</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../model_persistence.html">10. Model persistence</a></li>
<li class="toctree-l1"><a class="reference internal" href="../common_pitfalls.html">11. Common pitfalls and recommended practices</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../dispatching.html">12. Dispatching</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
<li class="toctree-l2"><a class="reference internal" href="../modules/array_api.html">12.1. Array API support (experimental)</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../machine_learning_map.html">13. Choosing the right estimator</a></li>
<li class="toctree-l1"><a class="reference internal" href="../presentations.html">14. External Resources, Videos and Talks</a></li>
</ul>
</div>
</nav></div>
</div>
<div class="sidebar-primary-items__end sidebar-primary__section">
</div>
</div>
<main id="main-content" class="bd-main" role="main">
<div class="bd-content">
<div class="bd-article-container">
<div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
<div class="header-article-items__start">
<div class="header-article-item">
<nav aria-label="Breadcrumb" class="d-print-none">
<ul class="bd-breadcrumbs">
<li class="breadcrumb-item breadcrumb-home">
<a href="../index.html" class="nav-link" aria-label="Home">
<i class="fa-solid fa-home"></i>
</a>
</li>
<li class="breadcrumb-item"><a href="../user_guide.html" class="nav-link">User Guide</a></li>
<li class="breadcrumb-item"><a href="../datasets.html" class="nav-link"><span class="section-number">8. </span>Dataset loading utilities</a></li>
<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis"><span class="section-number">8.4. </span>Loading other datasets</span></li>
</ul>
</nav>
</div>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article">
<section id="loading-other-datasets">
<span id="id1"></span><h1><span class="section-number">8.4. </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">8.4.1. </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>
<div class="pst-scrollable-table-container"><table class="autosummary longtable table autosummary">
<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>
</div>
<figure class="align-right">
<a class="plot-directive reference internal image-reference" href="../_images/loading_other_datasets-1.png"><img alt="../_images/loading_other_datasets-1.png" class="plot-directive" src="../_images/loading_other_datasets-1.png" style="width: 153.6px; height: 115.19999999999999px;" />
</a>
</figure>
<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.pyplot.imshow</span></code>, don’t forget to scale to the range
0 - 1 as done in the following example.</p>
</div>
</section>
<section id="datasets-in-svmlight-libsvm-format">
<span id="libsvm-loader"></span><h2><span class="section-number">8.4.2. </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 this 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="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </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>
<p class="rubric">Related links</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">Public</span> <span class="pre">datasets</span> <span class="pre">in</span> <span class="pre">svmlight</span> <span class="pre">/</span> <span class="pre">libsvm</span> <span class="pre">format</span></code>: <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></li>
<li><p><code class="docutils literal notranslate"><span class="pre">Faster</span> <span class="pre">API-compatible</span> <span class="pre">implementation</span></code>: <a class="github reference external" href="https://github.com/mblondel/svmlight-loader">mblondel/svmlight-loader</a></p></li>
</ul>
</section>
<section id="downloading-datasets-from-the-openml-org-repository">
<span id="openml"></span><h2><span class="section-number">8.4.3. </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="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </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>
</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="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">8.4.3.1. </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></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="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="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="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="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>
<p class="rubric">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>
</section>
<section id="arff-parser">
<span id="openml-parser"></span><h3><span class="section-number">8.4.3.2. </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 consumes 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 correspond to integers and uses pandas’ 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">8.4.4. </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
contexts 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 provide a Python interface for reading and
writing data in that format.</p>
</section>
</section>
</article>
<footer class="bd-footer-article">
<div class="footer-article-items footer-article__inner">
<div class="footer-article-item">
<div class="prev-next-area">
<a class="left-prev"
href="sample_generators.html"
title="previous page">
<i class="fa-solid fa-angle-left"></i>
<div class="prev-next-info">
<p class="prev-next-subtitle">previous</p>
<p class="prev-next-title"><span class="section-number">8.3. </span>Generated datasets</p>
</div>
</a>
<a class="right-next"
href="../computing.html"
title="next page">
<div class="prev-next-info">
<p class="prev-next-subtitle">next</p>
<p class="prev-next-title"><span class="section-number">9. </span>Computing with scikit-learn</p>
</div>
<i class="fa-solid fa-angle-right"></i>
</a>
</div></div>
</div>