forked from scikit-learn/scikit-learn.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathplot_face_recognition.html
522 lines (447 loc) · 39.1 KB
/
plot_face_recognition.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
<!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>Faces recognition example using eigenfaces and SVMs — scikit-learn 0.20.dev0 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: '0.20.dev0',
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="Model Complexity Influence" href="plot_model_complexity_influence.html" />
<link rel="prev" title="Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation" href="plot_topics_extraction_with_nmf_lda.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="http://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.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/contributing.html">Contributing</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_topics_extraction_with_nmf_lda.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
Topic extract...
</span>
<span class="hiddenrellink">
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
</span>
</a>
</div>
<div class="spacer">
</div>
<div class="rellink">
<a href="plot_model_complexity_influence.html"
accesskey="N">Next
<br/>
<span class="smallrellink">
Model Complex...
</span>
<span class="hiddenrellink">
Model Complexity Influence
</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 v0.20.dev0</b><br/>
<a href="http://scikit-learn.org/stable/support.html#documentation-resources">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="#">Faces recognition example using eigenfaces and SVMs</a></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-applications-plot-face-recognition-py"><span class="std std-ref">here</span></a> to download the full example code or run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="faces-recognition-example-using-eigenfaces-and-svms">
<span id="sphx-glr-auto-examples-applications-plot-face-recognition-py"></span><h1>Faces recognition example using eigenfaces and SVMs<a class="headerlink" href="#faces-recognition-example-using-eigenfaces-and-svms" title="Permalink to this headline">¶</a></h1>
<p>The dataset used in this example is a preprocessed excerpt of the
“Labeled Faces in the Wild”, aka <a class="reference external" href="http://vis-www.cs.umass.edu/lfw/">LFW</a>:</p>
<blockquote>
<div><a class="reference external" href="http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz">http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz</a> (233MB)</div></blockquote>
<p>Expected results for the top 5 most represented people in the dataset:</p>
<table border="1" class="docutils">
<colgroup>
<col width="33%" />
<col width="22%" />
<col width="13%" />
<col width="19%" />
<col width="13%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head"> </th>
<th class="head"> </th>
<th class="head"> </th>
<th class="head"> </th>
<th class="head"> </th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>Ariel Sharon</td>
<td>0.67</td>
<td>0.92</td>
<td>0.77</td>
<td>13</td>
</tr>
<tr class="row-odd"><td>Colin Powell</td>
<td>0.75</td>
<td>0.78</td>
<td>0.76</td>
<td>60</td>
</tr>
<tr class="row-even"><td>Donald Rumsfeld</td>
<td>0.78</td>
<td>0.67</td>
<td>0.72</td>
<td>27</td>
</tr>
<tr class="row-odd"><td>George W Bush</td>
<td>0.86</td>
<td>0.86</td>
<td>0.86</td>
<td>146</td>
</tr>
<tr class="row-even"><td>Gerhard Schroeder</td>
<td>0.76</td>
<td>0.76</td>
<td>0.76</td>
<td>25</td>
</tr>
<tr class="row-odd"><td>Hugo Chavez</td>
<td>0.67</td>
<td>0.67</td>
<td>0.67</td>
<td>15</td>
</tr>
<tr class="row-even"><td>Tony Blair</td>
<td>0.81</td>
<td>0.69</td>
<td>0.75</td>
<td>36</td>
</tr>
<tr class="row-odd"><td>avg / total</td>
<td>0.80</td>
<td>0.80</td>
<td>0.80</td>
<td>322</td>
</tr>
</tbody>
</table>
<ul class="sphx-glr-horizontal">
<li><img alt="../../_images/sphx_glr_plot_face_recognition_001.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_face_recognition_001.png" />
</li>
<li><img alt="../../_images/sphx_glr_plot_face_recognition_002.png" class="sphx-glr-multi-img first" src="../../_images/sphx_glr_plot_face_recognition_002.png" />
</li>
</ul>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none"><div class="highlight"><pre><span></span>Total dataset size:
n_samples: 1288
n_features: 1850
n_classes: 7
Extracting the top 150 eigenfaces from 966 faces
done in 0.123s
Projecting the input data on the eigenfaces orthonormal basis
done in 0.014s
Fitting the classifier to the training set
done in 21.812s
Best estimator found by grid search:
SVC(C=1000.0, cache_size=200, class_weight='balanced', coef0=0.0,
decision_function_shape='ovr', degree=3, gamma=0.005, kernel='rbf',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
Predicting people's names on the test set
done in 0.054s
precision recall f1-score support
Ariel Sharon 0.75 0.46 0.57 13
Colin Powell 0.80 0.87 0.83 60
Donald Rumsfeld 0.86 0.67 0.75 27
George W Bush 0.84 0.98 0.91 146
Gerhard Schroeder 0.95 0.80 0.87 25
Hugo Chavez 1.00 0.53 0.70 15
Tony Blair 0.97 0.78 0.86 36
avg / total 0.86 0.85 0.85 322
[[ 6 2 0 5 0 0 0]
[ 1 52 2 5 0 0 0]
[ 1 2 18 6 0 0 0]
[ 0 3 0 143 0 0 0]
[ 0 1 0 3 20 0 1]
[ 0 4 0 2 1 8 0]
[ 0 1 1 6 0 0 28]]
</pre></div>
</div>
<div class="line-block">
<div class="line"><br /></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">import</span> <span class="nn">logging</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">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="View documentation for sklearn.model_selection.train_test_split"><span class="n">train_test_split</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="View documentation for sklearn.model_selection.GridSearchCV"><span class="n">GridSearchCV</span></a>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" title="View documentation for sklearn.datasets.fetch_lfw_people"><span class="n">fetch_lfw_people</span></a>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="View documentation for sklearn.metrics.classification_report"><span class="n">classification_report</span></a>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="View documentation for sklearn.metrics.confusion_matrix"><span class="n">confusion_matrix</span></a>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="View documentation for sklearn.svm.SVC"><span class="n">SVC</span></a>
<span class="k">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>
<span class="c1"># Display progress logs on stdout</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span> <span class="n">format</span><span class="o">=</span><span class="s1">'</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(message)s</span><span class="s1">'</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Download the data, if not already on disk and load it as numpy arrays</span>
<span class="n">lfw_people</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.fetch_lfw_people.html#sklearn.datasets.fetch_lfw_people" title="View documentation for sklearn.datasets.fetch_lfw_people"><span class="n">fetch_lfw_people</span></a><span class="p">(</span><span class="n">min_faces_per_person</span><span class="o">=</span><span class="mi">70</span><span class="p">,</span> <span class="n">resize</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>
<span class="c1"># introspect the images arrays to find the shapes (for plotting)</span>
<span class="n">n_samples</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">images</span><span class="o">.</span><span class="n">shape</span>
<span class="c1"># for machine learning we use the 2 data directly (as relative pixel</span>
<span class="c1"># positions info is ignored by this model)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">data</span>
<span class="n">n_features</span> <span class="o">=</span> <span class="n">X</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="c1"># the label to predict is the id of the person</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">target</span>
<span class="n">target_names</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">target_names</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">target_names</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Total dataset size:"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"n_samples: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">n_samples</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"n_features: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">n_features</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"n_classes: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">n_classes</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Split into a training set and a test set using a stratified k fold</span>
<span class="c1"># split into a training and testing set</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="View documentation for sklearn.model_selection.train_test_split"><span class="n">train_test_split</span></a><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">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled</span>
<span class="c1"># dataset): unsupervised feature extraction / dimensionality reduction</span>
<span class="n">n_components</span> <span class="o">=</span> <span class="mi">150</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Extracting the top </span><span class="si">%d</span><span class="s2"> eigenfaces from </span><span class="si">%d</span><span class="s2"> faces"</span>
<span class="o">%</span> <span class="p">(</span><span class="n">n_components</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">pca</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA" title="View documentation for sklearn.decomposition.PCA"><span class="n">PCA</span></a><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_components</span><span class="p">,</span> <span class="n">svd_solver</span><span class="o">=</span><span class="s1">'randomized'</span><span class="p">,</span>
<span class="n">whiten</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"done in </span><span class="si">%0.3f</span><span class="s2">s"</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="n">eigenfaces</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">n_components</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Projecting the input data on the eigenfaces orthonormal basis"</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">X_train_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"done in </span><span class="si">%0.3f</span><span class="s2">s"</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Train a SVM classification model</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Fitting the classifier to the training set"</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'C'</span><span class="p">:</span> <span class="p">[</span><span class="mf">1e3</span><span class="p">,</span> <span class="mf">5e3</span><span class="p">,</span> <span class="mf">1e4</span><span class="p">,</span> <span class="mf">5e4</span><span class="p">,</span> <span class="mf">1e5</span><span class="p">],</span>
<span class="s1">'gamma'</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.0001</span><span class="p">,</span> <span class="mf">0.0005</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">}</span>
<span class="n">clf</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="View documentation for sklearn.model_selection.GridSearchCV"><span class="n">GridSearchCV</span></a><span class="p">(</span><a href="../../modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC" title="View documentation for sklearn.svm.SVC"><span class="n">SVC</span></a><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'rbf'</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">'balanced'</span><span class="p">),</span> <span class="n">param_grid</span><span class="p">)</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_pca</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"done in </span><span class="si">%0.3f</span><span class="s2">s"</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Best estimator found by grid search:"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">best_estimator_</span><span class="p">)</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Quantitative evaluation of the model quality on the test set</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Predicting people's names on the test set"</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_pca</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"done in </span><span class="si">%0.3f</span><span class="s2">s"</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><a href="../../modules/generated/sklearn.metrics.classification_report.html#sklearn.metrics.classification_report" title="View documentation for sklearn.metrics.classification_report"><span class="n">classification_report</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">target_names</span><span class="o">=</span><span class="n">target_names</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><a href="../../modules/generated/sklearn.metrics.confusion_matrix.html#sklearn.metrics.confusion_matrix" title="View documentation for sklearn.metrics.confusion_matrix"><span class="n">confusion_matrix</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)))</span>
<span class="c1"># #############################################################################</span>
<span class="c1"># Qualitative evaluation of the predictions using matplotlib</span>
<span class="k">def</span> <span class="nf">plot_gallery</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">n_row</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_col</span><span class="o">=</span><span class="mi">4</span><span class="p">):</span>
<span class="sd">"""Helper function to plot a gallery of portraits"""</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure" title="View documentation for matplotlib.pyplot.figure"><span class="n">plt</span><span class="o">.</span><span class="n">figure</span></a><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">1.8</span> <span class="o">*</span> <span class="n">n_col</span><span class="p">,</span> <span class="mf">2.4</span> <span class="o">*</span> <span class="n">n_row</span><span class="p">))</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots_adjust.html#matplotlib.pyplot.subplots_adjust" title="View documentation for matplotlib.pyplot.subplots_adjust"><span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span></a><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">left</span><span class="o">=.</span><span class="mo">01</span><span class="p">,</span> <span class="n">right</span><span class="o">=.</span><span class="mi">99</span><span class="p">,</span> <span class="n">top</span><span class="o">=.</span><span class="mi">90</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=.</span><span class="mi">35</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_row</span> <span class="o">*</span> <span class="n">n_col</span><span class="p">):</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html#matplotlib.pyplot.subplot" title="View documentation for matplotlib.pyplot.subplot"><span class="n">plt</span><span class="o">.</span><span class="n">subplot</span></a><span class="p">(</span><span class="n">n_row</span><span class="p">,</span> <span class="n">n_col</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.imshow.html#matplotlib.pyplot.imshow" title="View documentation for matplotlib.pyplot.imshow"><span class="n">plt</span><span class="o">.</span><span class="n">imshow</span></a><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)),</span> <span class="n">cmap</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">gray</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.title.html#matplotlib.pyplot.title" title="View documentation for matplotlib.pyplot.title"><span class="n">plt</span><span class="o">.</span><span class="n">title</span></a><span class="p">(</span><span class="n">titles</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.xticks.html#matplotlib.pyplot.xticks" title="View documentation for matplotlib.pyplot.xticks"><span class="n">plt</span><span class="o">.</span><span class="n">xticks</span></a><span class="p">(())</span>
<a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.yticks.html#matplotlib.pyplot.yticks" title="View documentation for matplotlib.pyplot.yticks"><span class="n">plt</span><span class="o">.</span><span class="n">yticks</span></a><span class="p">(())</span>
<span class="c1"># plot the result of the prediction on a portion of the test set</span>
<span class="k">def</span> <span class="nf">title</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">target_names</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
<span class="n">pred_name</span> <span class="o">=</span> <span class="n">target_names</span><span class="p">[</span><span class="n">y_pred</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">rsplit</span><span class="p">(</span><span class="s1">' '</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">true_name</span> <span class="o">=</span> <span class="n">target_names</span><span class="p">[</span><span class="n">y_test</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">rsplit</span><span class="p">(</span><span class="s1">' '</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="k">return</span> <span class="s1">'predicted: </span><span class="si">%s</span><span class="se">\n</span><span class="s1">true: </span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span> <span class="p">(</span><span class="n">pred_name</span><span class="p">,</span> <span class="n">true_name</span><span class="p">)</span>
<span class="n">prediction_titles</span> <span class="o">=</span> <span class="p">[</span><span class="n">title</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">target_names</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y_pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
<span class="n">plot_gallery</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">prediction_titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
<span class="c1"># plot the gallery of the most significative eigenfaces</span>
<span class="n">eigenface_titles</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"eigenface </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">eigenfaces</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
<span class="n">plot_gallery</span><span class="p">(</span><span class="n">eigenfaces</span><span class="p">,</span> <span class="n">eigenface_titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</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>
<p><strong>Total running time of the script:</strong> ( 0 minutes 22.283 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-applications-plot-face-recognition-py">
<div class="binder-badge docutils container">
<a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn.github.io/master?urlpath=lab/tree/notebooks/auto_examples/applications/plot_face_recognition.ipynb"><img alt="https://static.mybinder.org/badge.svg" src="https://static.mybinder.org/badge.svg" width="150px" /></a>
</div>
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../../_downloads/plot_face_recognition.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_face_recognition.py</span></code></a></div>
<div class="sphx-glr-download docutils container">
<a class="reference download internal" href="../../_downloads/plot_face_recognition.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_face_recognition.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 class="clearer"></div>
</div>
</div>
<div class="footer">
© 2007 - 2018, scikit-learn developers (BSD License).
<a href="../../_sources/auto_examples/applications/plot_face_recognition.rst.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="plot_topics_extraction_with_nmf_lda.html">Previous
</a>
</div>
<div class="buttonNext">
<a href="plot_model_complexity_influence.html">Next
</a>
</div>
</div>
<script type="text/javascript">
var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-22606712-2']);
_gaq.push(['_trackPageview']);
(function() {
var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
})();
</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>
</body>
</html>