-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathplot_cv_indices.html
424 lines (374 loc) · 40.2 KB
/
plot_cv_indices.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
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<title>Visualizing cross-validation behavior in scikit-learn — scikit-learn 0.21.3 documentation</title>
<!-- htmltitle is before nature.css - we use this hack to load bootstrap first -->
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="stylesheet" href="../../_static/css/bootstrap.min.css" media="screen" />
<link rel="stylesheet" href="../../_static/css/bootstrap-responsive.css"/>
<link rel="stylesheet" href="../../_static/nature.css" type="text/css" />
<link rel="stylesheet" href="../../_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
<script type="text/javascript">
var DOCUMENTATION_OPTIONS = {
URL_ROOT: '../../',
VERSION: '0.21.3',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true,
SOURCELINK_SUFFIX: '.txt'
};
</script>
<script type="text/javascript" src="../../_static/jquery.js"></script>
<script type="text/javascript" src="../../_static/underscore.js"></script>
<script type="text/javascript" src="../../_static/doctools.js"></script>
<script type="text/javascript" src="../../_static/js/copybutton.js"></script>
<script type="text/javascript" src="../../_static/js/extra.js"></script>
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS_SVG"></script>
<link rel="shortcut 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="Receiver Operating Characteristic (ROC)" href="plot_roc.html" />
<link rel="prev" title="Confusion matrix" href="plot_confusion_matrix.html" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="../../_static/js/bootstrap.min.js" type="text/javascript"></script>
<script>
VERSION_SUBDIR = (function(groups) {
return groups ? groups[1] : null;
})(location.href.match(/^https?:\/\/scikit-learn.org\/([^\/]+)/));
</script>
<link rel="canonical" href="https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html" />
<script type="text/javascript">
$("div.buttonNext, div.buttonPrevious").hover(
function () {
$(this).css('background-color', '#FF9C34');
},
function () {
$(this).css('background-color', '#A7D6E2');
}
);
function showMenu() {
var topNav = document.getElementById("scikit-navbar");
if (topNav.className === "navbar") {
topNav.className += " responsive";
} else {
topNav.className = "navbar";
}
};
</script>
</head>
<body>
<div class="header-wrapper">
<div class="header">
<p class="logo"><a href="../../index.html">
<img src="../../_static/scikit-learn-logo-small.png" alt="Logo"/>
</a>
</p><div class="navbar" id="scikit-navbar">
<ul>
<li><a href="../../index.html">Home</a></li>
<li><a href="../../install.html">Installation</a></li>
<li class="btn-li"><div class="btn-group">
<a href="../../documentation.html">Documentation</a>
<a class="btn dropdown-toggle" data-toggle="dropdown">
<span class="caret"></span>
</a>
<ul class="dropdown-menu">
<li class="link-title">Scikit-learn <script>document.write(DOCUMENTATION_OPTIONS.VERSION + (VERSION_SUBDIR ? " (" + VERSION_SUBDIR + ")" : ""));</script></li>
<li><a href="../../tutorial/index.html">Tutorials</a></li>
<li><a href="../../user_guide.html">User guide</a></li>
<li><a href="../../modules/classes.html">API</a></li>
<li><a href="../../glossary.html">Glossary</a></li>
<li><a href="../../faq.html">FAQ</a></li>
<li><a href="../../developers/index.html">Development</a></li>
<li><a href="../../roadmap.html">Roadmap</a></li>
<li><a href="../../about.html">About us</a></li>
<li class="divider"></li>
<script>if (VERSION_SUBDIR != "stable") document.write('<li><a href="http://scikit-learn.org/stable/documentation.html">Stable version</a></li>')</script>
<script>if (VERSION_SUBDIR != "dev") document.write('<li><a href="http://scikit-learn.org/dev/documentation.html">Development version</a></li>')</script>
<li><a href="http://scikit-learn.org/dev/versions.html">All available versions</a></li>
<li><a href="../../_downloads/scikit-learn-docs.pdf">PDF documentation</a></li>
</ul>
</div>
</li>
<li><a href="../index.html">Examples</a></li>
</ul>
<a href="javascript:void(0);" onclick="showMenu()">
<div class="nav-icon">
<div class="hamburger-line"></div>
<div class="hamburger-line"></div>
<div class="hamburger-line"></div>
</div>
</a>
<div class="search_form">
<div class="gcse-search" id="cse" style="width: 100%;"></div>
</div>
</div> <!-- end navbar --></div>
</div>
<!-- GitHub "fork me" ribbon -->
<a href="https://github.com/scikit-learn/scikit-learn">
<img class="fork-me"
style="position: absolute; top: 0; right: 0; border: 0;"
src="../../_static/img/forkme.png"
alt="Fork me on GitHub" />
</a>
<div class="content-wrapper">
<div class="sphinxsidebar">
<div class="sphinxsidebarwrapper">
<div class="rel">
<div class="rellink">
<a href="plot_confusion_matrix.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
Confusion matrix
</span>
<span class="hiddenrellink">
Confusion matrix
</span>
</a>
</div>
<div class="spacer">
</div>
<div class="rellink">
<a href="plot_roc.html"
accesskey="N">Next
<br/>
<span class="smallrellink">
Receiver Oper...
</span>
<span class="hiddenrellink">
Receiver Operating Characteristic (ROC)
</span>
</a>
</div>
<!-- Ad a link to the 'up' page -->
<div class="spacer">
</div>
<div class="rellink">
<a href="../index.html">
Up
<br/>
<span class="smallrellink">
Examples
</span>
<span class="hiddenrellink">
Examples
</span>
</a>
</div>
</div>
<p class="doc-version"><b>scikit-learn v0.21.3</b><br/>
<a href="http://scikit-learn.org/dev/versions.html">Other versions</a></p>
<p class="citing">Please <b><a href="../../about.html#citing-scikit-learn" style="font-size: 110%;">cite us </a></b>if you use the software.</p>
<ul>
<li><a class="reference internal" href="#">Visualizing cross-validation behavior in scikit-learn</a><ul>
<li><a class="reference internal" href="#visualize-our-data">Visualize our data</a></li>
<li><a class="reference internal" href="#define-a-function-to-visualize-cross-validation-behavior">Define a function to visualize cross-validation behavior</a></li>
<li><a class="reference internal" href="#visualize-cross-validation-indices-for-many-cv-objects">Visualize cross-validation indices for many CV objects</a></li>
</ul>
</li>
</ul>
</div>
</div>
<input type="checkbox" id="nav-trigger" class="nav-trigger" checked />
<label for="nav-trigger"></label>
<div class="content">
<div class="documentwrapper">
<div class="bodywrapper">
<div class="body" role="main">
<div class="sphx-glr-download-link-note admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Click <a class="reference internal" href="#sphx-glr-download-auto-examples-model-selection-plot-cv-indices-py"><span class="std std-ref">here</span></a> to download the full example code</p>
</div>
<div class="sphx-glr-example-title section" id="visualizing-cross-validation-behavior-in-scikit-learn">
<span id="sphx-glr-auto-examples-model-selection-plot-cv-indices-py"></span><h1>Visualizing cross-validation behavior in scikit-learn<a class="headerlink" href="#visualizing-cross-validation-behavior-in-scikit-learn" title="Permalink to this headline">¶</a></h1>
<p>Choosing the right cross-validation object is a crucial part of fitting a
model properly. There are many ways to split data into training and test
sets in order to avoid model overfitting, to standardize the number of
groups in test sets, etc.</p>
<p>This example visualizes the behavior of several common scikit-learn objects
for comparison.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="p">(</span><a href="../../modules/generated/sklearn.model_selection.TimeSeriesSplit.html#sklearn.model_selection.TimeSeriesSplit" title="View documentation for sklearn.model_selection.TimeSeriesSplit"><span class="n">TimeSeriesSplit</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="View documentation for sklearn.model_selection.KFold"><span class="n">KFold</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit" title="View documentation for sklearn.model_selection.ShuffleSplit"><span class="n">ShuffleSplit</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="View documentation for sklearn.model_selection.StratifiedKFold"><span class="n">StratifiedKFold</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.GroupShuffleSplit.html#sklearn.model_selection.GroupShuffleSplit" title="View documentation for sklearn.model_selection.GroupShuffleSplit"><span class="n">GroupShuffleSplit</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="View documentation for sklearn.model_selection.GroupKFold"><span class="n">GroupKFold</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit" title="View documentation for sklearn.model_selection.StratifiedShuffleSplit"><span class="n">StratifiedShuffleSplit</span></a><span class="p">)</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="kn">import</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="View documentation for matplotlib.patches.Patch"><span class="n">Patch</span></a>
<span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">1338</span><span class="p">)</span>
<span class="n">cmap_data</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">Paired</span>
<span class="n">cmap_cv</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">coolwarm</span>
<span class="n">n_splits</span> <span class="o">=</span> <span class="mi">4</span>
</pre></div>
</div>
<div class="section" id="visualize-our-data">
<h2>Visualize our data<a class="headerlink" href="#visualize-our-data" title="Permalink to this headline">¶</a></h2>
<p>First, we must understand the structure of our data. It has 100 randomly
generated input datapoints, 3 classes split unevenly across datapoints,
and 10 “groups” split evenly across datapoints.</p>
<p>As we’ll see, some cross-validation objects do specific things with
labeled data, others behave differently with grouped data, and others
do not use this information.</p>
<p>To begin, we’ll visualize our data.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># Generate the class/group data</span>
<span class="n">n_points</span> <span class="o">=</span> <span class="mi">100</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">percentiles_classes</span> <span class="o">=</span> <span class="p">[</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="o">.</span><span class="mi">3</span><span class="p">,</span> <span class="o">.</span><span class="mi">6</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html#numpy.hstack" title="View documentation for numpy.hstack"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">([[</span><span class="n">ii</span><span class="p">]</span> <span class="o">*</span> <span class="nb">int</span><span class="p">(</span><span class="mi">100</span> <span class="o">*</span> <span class="n">perc</span><span class="p">)</span>
<span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="n">perc</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">percentiles_classes</span><span class="p">)])</span>
<span class="c1"># Evenly spaced groups repeated once</span>
<span class="n">groups</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.hstack.html#numpy.hstack" title="View documentation for numpy.hstack"><span class="n">np</span><span class="o">.</span><span class="n">hstack</span></a><span class="p">([[</span><span class="n">ii</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span> <span class="k">for</span> <span class="n">ii</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)])</span>
<span class="k">def</span> <span class="nf">visualize_groups</span><span class="p">(</span><span class="n">classes</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="c1"># Visualize dataset groups</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">)),</span> <span class="p">[</span><span class="o">.</span><span class="mi">5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'_'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">)),</span> <span class="p">[</span><span class="mf">3.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">groups</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="n">classes</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'_'</span><span class="p">,</span>
<span class="n">lw</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">yticks</span><span class="o">=</span><span class="p">[</span><span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="mf">3.5</span><span class="p">],</span>
<span class="n">yticklabels</span><span class="o">=</span><span class="p">[</span><span class="s1">'Data</span><span class="se">\n</span><span class="s1">group'</span><span class="p">,</span> <span class="s1">'Data</span><span class="se">\n</span><span class="s1">class'</span><span class="p">],</span> <span class="n">xlabel</span><span class="o">=</span><span class="s2">"Sample index"</span><span class="p">)</span>
<span class="n">visualize_groups</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="s1">'no groups'</span><span class="p">)</span>
</pre></div>
</div>
<img alt="../../_images/sphx_glr_plot_cv_indices_001.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_plot_cv_indices_001.png" />
</div>
<div class="section" id="define-a-function-to-visualize-cross-validation-behavior">
<h2>Define a function to visualize cross-validation behavior<a class="headerlink" href="#define-a-function-to-visualize-cross-validation-behavior" title="Permalink to this headline">¶</a></h2>
<p>We’ll define a function that lets us visualize the behavior of each
cross-validation object. We’ll perform 4 splits of the data. On each
split, we’ll visualize the indices chosen for the training set
(in blue) and the test set (in red).</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">group</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
<span class="sd">"""Create a sample plot for indices of a cross-validation object."""</span>
<span class="c1"># Generate the training/testing visualizations for each CV split</span>
<span class="k">for</span> <span class="n">ii</span><span class="p">,</span> <span class="p">(</span><span class="n">tr</span><span class="p">,</span> <span class="n">tt</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cv</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">X</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">group</span><span class="p">)):</span>
<span class="c1"># Fill in indices with the training/test groups</span>
<span class="n">indices</span> <span class="o">=</span> <a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html#numpy.array" title="View documentation for numpy.array"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><a href="https://docs.scipy.org/doc/numpy/reference/constants.html#numpy.nan" title="View documentation for numpy.nan"><span class="n">np</span><span class="o">.</span><span class="n">nan</span></a><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
<span class="n">indices</span><span class="p">[</span><span class="n">tt</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">indices</span><span class="p">[</span><span class="n">tr</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Visualize the results</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)),</span> <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="o">.</span><span class="mi">5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">),</span>
<span class="n">c</span><span class="o">=</span><span class="n">indices</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'_'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">,</span>
<span class="n">vmin</span><span class="o">=-.</span><span class="mi">2</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mf">1.2</span><span class="p">)</span>
<span class="c1"># Plot the data classes and groups at the end</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)),</span> <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">1.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">),</span>
<span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'_'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">)),</span> <span class="p">[</span><span class="n">ii</span> <span class="o">+</span> <span class="mf">2.5</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">),</span>
<span class="n">c</span><span class="o">=</span><span class="n">group</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'_'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="n">lw</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_data</span><span class="p">)</span>
<span class="c1"># Formatting</span>
<span class="n">yticklabels</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n_splits</span><span class="p">))</span> <span class="o">+</span> <span class="p">[</span><span class="s1">'class'</span><span class="p">,</span> <span class="s1">'group'</span><span class="p">]</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">yticks</span><span class="o">=</span><a href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html#numpy.arange" title="View documentation for numpy.arange"><span class="n">np</span><span class="o">.</span><span class="n">arange</span></a><span class="p">(</span><span class="n">n_splits</span><span class="o">+</span><span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="n">yticklabels</span><span class="o">=</span><span class="n">yticklabels</span><span class="p">,</span>
<span class="n">xlabel</span><span class="o">=</span><span class="s1">'Sample index'</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s2">"CV iteration"</span><span class="p">,</span>
<span class="n">ylim</span><span class="o">=</span><span class="p">[</span><span class="n">n_splits</span><span class="o">+</span><span class="mf">2.2</span><span class="p">,</span> <span class="o">-.</span><span class="mi">2</span><span class="p">],</span> <span class="n">xlim</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">])</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'{}'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">cv</span><span class="p">)</span><span class="o">.</span><span class="vm">__name__</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">15</span><span class="p">)</span>
<span class="k">return</span> <span class="n">ax</span>
</pre></div>
</div>
<p>Let’s see how it looks for the <a class="reference internal" href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="sklearn.model_selection.KFold"><code class="xref any py py-class docutils literal"><span class="pre">KFold</span></code></a> cross-validation object:</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
<span class="n">cv</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="View documentation for sklearn.model_selection.KFold"><span class="n">KFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="p">)</span>
<span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>
</pre></div>
</div>
<img alt="../../_images/sphx_glr_plot_cv_indices_002.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_plot_cv_indices_002.png" />
<p>As you can see, by default the KFold cross-validation iterator does not
take either datapoint class or group into consideration. We can change this
by using the <code class="docutils literal"><span class="pre">StratifiedKFold</span></code> like so.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
<span class="n">cv</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="View documentation for sklearn.model_selection.StratifiedKFold"><span class="n">StratifiedKFold</span></a><span class="p">(</span><span class="n">n_splits</span><span class="p">)</span>
<span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>
</pre></div>
</div>
<img alt="../../_images/sphx_glr_plot_cv_indices_003.png" class="sphx-glr-single-img" src="../../_images/sphx_glr_plot_cv_indices_003.png" />
<p>In this case, the cross-validation retained the same ratio of classes across
each CV split. Next we’ll visualize this behavior for a number of CV
iterators.</p>
</div>
<div class="section" id="visualize-cross-validation-indices-for-many-cv-objects">
<h2>Visualize cross-validation indices for many CV objects<a class="headerlink" href="#visualize-cross-validation-indices-for-many-cv-objects" title="Permalink to this headline">¶</a></h2>
<p>Let’s visually compare the cross validation behavior for many
scikit-learn cross-validation objects. Below we will loop through several
common cross-validation objects, visualizing the behavior of each.</p>
<p>Note how some use the group/class information while others do not.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">cvs</span> <span class="o">=</span> <span class="p">[</span><a href="../../modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold" title="View documentation for sklearn.model_selection.KFold"><span class="n">KFold</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="View documentation for sklearn.model_selection.GroupKFold"><span class="n">GroupKFold</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.ShuffleSplit.html#sklearn.model_selection.ShuffleSplit" title="View documentation for sklearn.model_selection.ShuffleSplit"><span class="n">ShuffleSplit</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.StratifiedKFold.html#sklearn.model_selection.StratifiedKFold" title="View documentation for sklearn.model_selection.StratifiedKFold"><span class="n">StratifiedKFold</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.model_selection.GroupShuffleSplit.html#sklearn.model_selection.GroupShuffleSplit" title="View documentation for sklearn.model_selection.GroupShuffleSplit"><span class="n">GroupShuffleSplit</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.StratifiedShuffleSplit.html#sklearn.model_selection.StratifiedShuffleSplit" title="View documentation for sklearn.model_selection.StratifiedShuffleSplit"><span class="n">StratifiedShuffleSplit</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.model_selection.TimeSeriesSplit.html#sklearn.model_selection.TimeSeriesSplit" title="View documentation for sklearn.model_selection.TimeSeriesSplit"><span class="n">TimeSeriesSplit</span></a><span class="p">]</span>
<span class="k">for</span> <span class="n">cv</span> <span class="ow">in</span> <span class="n">cvs</span><span class="p">:</span>
<span class="n">this_cv</span> <span class="o">=</span> <span class="n">cv</span><span class="p">(</span><span class="n">n_splits</span><span class="o">=</span><span class="n">n_splits</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="View documentation for matplotlib.pyplot.subplots"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">plot_cv_indices</span><span class="p">(</span><span class="n">this_cv</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">groups</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">n_splits</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><a href="https://matplotlib.org/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="View documentation for matplotlib.patches.Patch"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">(</span><span class="o">.</span><span class="mi">8</span><span class="p">)),</span> <a href="https://matplotlib.org/api/_as_gen/matplotlib.patches.Patch.html#matplotlib.patches.Patch" title="View documentation for matplotlib.patches.Patch"><span class="n">Patch</span></a><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="n">cmap_cv</span><span class="p">(</span><span class="o">.</span><span class="mo">02</span><span class="p">))],</span>
<span class="p">[</span><span class="s1">'Testing set'</span><span class="p">,</span> <span class="s1">'Training set'</span><span class="p">],</span> <span class="n">loc</span><span class="o">=</span><span class="p">(</span><span class="mf">1.02</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">))</span>
<span class="c1"># Make the legend fit</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.tight_layout.html#matplotlib.pyplot.tight_layout" title="View documentation for matplotlib.pyplot.tight_layout"><span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span></a><span class="p">()</span>
<span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">right</span><span class="o">=.</span><span class="mi">7</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="View documentation for matplotlib.pyplot.show"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
<ul class="sphx-glr-horizontal">
<li><img alt="../../_images/sphx_glr_plot_cv_indices_004.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_cv_indices_004.png" />
</li>
<li><img alt="../../_images/sphx_glr_plot_cv_indices_005.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_cv_indices_005.png" />
</li>
<li><img alt="../../_images/sphx_glr_plot_cv_indices_006.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_cv_indices_006.png" />
</li>
<li><img alt="../../_images/sphx_glr_plot_cv_indices_007.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_cv_indices_007.png" />
</li>
<li><img alt="../../_images/sphx_glr_plot_cv_indices_008.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_cv_indices_008.png" />
</li>
<li><img alt="../../_images/sphx_glr_plot_cv_indices_009.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_cv_indices_009.png" />
</li>
<li><img alt="../../_images/sphx_glr_plot_cv_indices_010.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_cv_indices_010.png" />
</li>
</ul>
<p><strong>Total running time of the script:</strong> ( 0 minutes 0.401 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-model-selection-plot-cv-indices-py">
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../../_downloads/plot_cv_indices.py" download=""><code class="xref download docutils literal"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_cv_indices.py</span></code></a></div>
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../../_downloads/plot_cv_indices.ipynb" download=""><code class="xref download docutils literal"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_cv_indices.ipynb</span></code></a></div>
</div>
<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.readthedocs.io">Gallery generated by Sphinx-Gallery</a></p>
</div>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2007 - 2019, scikit-learn developers (BSD License).
<a href="../../_sources/auto_examples/model_selection/plot_cv_indices.rst.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="plot_confusion_matrix.html">Previous
</a>
</div>
<div class="buttonNext">
<a href="plot_roc.html">Next
</a>
</div>
</div>
<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>
(function() {
var cx = '016639176250731907682:tjtqbvtvij0';
var gcse = document.createElement('script'); gcse.type = 'text/javascript'; gcse.async = true;
gcse.src = 'https://cse.google.com/cse.js?cx=' + cx;
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(gcse, s);
})();
</script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
</body>
</html>