-
Notifications
You must be signed in to change notification settings - Fork 81
/
Copy pathmlcomp_sparse_document_classification.html
358 lines (295 loc) · 27.3 KB
/
mlcomp_sparse_document_classification.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
<!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>Classification of text documents: using a MLComp dataset — scikit-learn 0.17.1 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: '0.17.1',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
HAS_SOURCE: true
};
</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>
<link rel="shortcut icon" href="../../_static/favicon.ico"/>
<link rel="author" title="About these documents" href="../../about.html" />
<link rel="top" title="scikit-learn 0.17.1 documentation" href="../../index.html" />
<link rel="up" title="Examples" href="../index.html" />
<link rel="next" title="Clustering text documents using k-means" href="document_clustering.html" />
<link rel="prev" title="FeatureHasher and DictVectorizer Comparison" href="hashing_vs_dict_vectorizer.html" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<script src="../../_static/js/bootstrap.min.js" type="text/javascript"></script>
<link rel="canonical" href="https://scikit-learn.org/stable/auto_examples/text/mlcomp_sparse_document_classification.html" />
<script type="text/javascript">
$("div.buttonNext, div.buttonPrevious").hover(
function () {
$(this).css('background-color', '#FF9C34');
},
function () {
$(this).css('background-color', '#A7D6E2');
}
);
</script>
</head>
<body role="document">
<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">
<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 0.17 (stable)</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="../../faq.html">FAQ</a></li>
<li><a href="../../developers/contributing.html">Contributing</a></li>
<li class="divider"></li>
<li><a href="http://scikit-learn.org/dev/documentation.html">Scikit-learn 0.18 (development)</a></li>
<li><a href="http://scikit-learn.org/0.16/documentation.html">Scikit-learn 0.16</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>
<div class="search_form">
<div 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">
<!-- rellinks[1:] is an ugly hack to avoid link to module
index -->
<div class="rellink">
<a href="hashing_vs_dict_vectorizer.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
FeatureHasher...
</span>
<span class="hiddenrellink">
FeatureHasher and DictVectorizer Comparison
</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">This documentation is for scikit-learn <strong>version 0.17.1</strong> — <a href="http://scikit-learn.org/stable/support.html#documentation-resources">Other versions</a></p>
<p class="citing">If you use the software, please consider <a href="../../about.html#citing-scikit-learn">citing scikit-learn</a>.</p>
<ul>
<li><a class="reference internal" href="#">Classification of text documents: using a MLComp dataset</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="section" id="classification-of-text-documents-using-a-mlcomp-dataset">
<span id="example-text-mlcomp-sparse-document-classification-py"></span><h1>Classification of text documents: using a MLComp dataset<a class="headerlink" href="#classification-of-text-documents-using-a-mlcomp-dataset" title="Permalink to this headline">¶</a></h1>
<p>This is an example showing how the scikit-learn can be used to classify
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of standard numpy arrays.</p>
<p>The dataset used in this example is the 20 newsgroups dataset and should be
downloaded from the <a class="reference external" href="http://mlcomp.org">http://mlcomp.org</a> (free registration required):</p>
<blockquote>
<div><a class="reference external" href="http://mlcomp.org/datasets/379">http://mlcomp.org/datasets/379</a></div></blockquote>
<p>Once downloaded unzip the archive somewhere on your filesystem.
For instance in:</p>
<div class="highlight-python"><div class="highlight"><pre>% mkdir -p ~/data/mlcomp
% cd ~/data/mlcomp
% unzip /path/to/dataset-379-20news-18828_XXXXX.zip
</pre></div>
</div>
<p>You should get a folder <code class="docutils literal"><span class="pre">~/data/mlcomp/379</span></code> with a file named <code class="docutils literal"><span class="pre">metadata</span></code>
and subfolders <code class="docutils literal"><span class="pre">raw</span></code>, <code class="docutils literal"><span class="pre">train</span></code> and <code class="docutils literal"><span class="pre">test</span></code> holding the text documents
organized by newsgroups.</p>
<p>Then set the <code class="docutils literal"><span class="pre">MLCOMP_DATASETS_HOME</span></code> environment variable pointing to
the root folder holding the uncompressed archive:</p>
<div class="highlight-python"><div class="highlight"><pre>% export MLCOMP_DATASETS_HOME="~/data/mlcomp"
</pre></div>
</div>
<p>Then you are ready to run this example using your favorite python shell:</p>
<div class="highlight-python"><div class="highlight"><pre>% ipython examples/mlcomp_sparse_document_classification.py
</pre></div>
</div>
<p><strong>Python source code:</strong> <a class="reference download internal" href="../../_downloads/mlcomp_sparse_document_classification.py"><code class="xref download docutils literal"><span class="pre">mlcomp_sparse_document_classification.py</span></code></a></p>
<div class="highlight-python"><div class="highlight"><pre>
<span class="c1"># Author: Olivier Grisel <[email protected]></span>
<span class="c1"># License: BSD 3 clause</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">sys</span>
<span class="kn">import</span> <span class="nn">os</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">scipy.sparse</span> <span class="kn">as</span> <span class="nn">sp</span>
<span class="kn">import</span> <span class="nn">pylab</span> <span class="kn">as</span> <span class="nn">pl</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.load_mlcomp.html#sklearn.datasets.load_mlcomp"><span class="n">load_mlcomp</span></a>
<span class="kn">from</span> <span class="nn">sklearn.feature_extraction.text</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier"><span class="n">SGDClassifier</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"><span class="n">confusion_matrix</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"><span class="n">classification_report</span></a>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB"><span class="n">MultinomialNB</span></a>
<span class="k">print</span><span class="p">(</span><span class="n">__doc__</span><span class="p">)</span>
<span class="k">if</span> <span class="s1">'MLCOMP_DATASETS_HOME'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">:</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"MLCOMP_DATASETS_HOME not set; please follow the above instructions"</span><span class="p">)</span>
<span class="n">sys</span><span class="o">.</span><span class="n">exit</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># Load the training set</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Loading 20 newsgroups training set... "</span><span class="p">)</span>
<span class="n">news_train</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_mlcomp.html#sklearn.datasets.load_mlcomp"><span class="n">load_mlcomp</span></a><span class="p">(</span><span class="s1">'20news-18828'</span><span class="p">,</span> <span class="s1">'train'</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">news_train</span><span class="o">.</span><span class="n">DESCR</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"</span><span class="si">%d</span><span class="s2"> documents"</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">news_train</span><span class="o">.</span><span class="n">filenames</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"</span><span class="si">%d</span><span class="s2"> categories"</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">news_train</span><span class="o">.</span><span class="n">target_names</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Extracting features from the dataset using a sparse vectorizer"</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">vectorizer</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer"><span class="n">TfidfVectorizer</span></a><span class="p">(</span><span class="n">encoding</span><span class="o">=</span><span class="s1">'latin1'</span><span class="p">)</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">((</span><span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">news_train</span><span class="o">.</span><span class="n">filenames</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"done in </span><span class="si">%f</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">"n_samples: </span><span class="si">%d</span><span class="s2">, n_features: </span><span class="si">%d</span><span class="s2">"</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="k">assert</span> <span class="n">sp</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">news_train</span><span class="o">.</span><span class="n">target</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Loading 20 newsgroups test set... "</span><span class="p">)</span>
<span class="n">news_test</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.load_mlcomp.html#sklearn.datasets.load_mlcomp"><span class="n">load_mlcomp</span></a><span class="p">(</span><span class="s1">'20news-18828'</span><span class="p">,</span> <span class="s1">'test'</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="k">print</span><span class="p">(</span><span class="s2">"done in </span><span class="si">%f</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">"Predicting the labels of the test set..."</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"</span><span class="si">%d</span><span class="s2"> documents"</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">news_test</span><span class="o">.</span><span class="n">filenames</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"</span><span class="si">%d</span><span class="s2"> categories"</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">news_test</span><span class="o">.</span><span class="n">target_names</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Extracting features from the dataset using the same vectorizer"</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_test</span> <span class="o">=</span> <span class="n">vectorizer</span><span class="o">.</span><span class="n">transform</span><span class="p">((</span><span class="nb">open</span><span class="p">(</span><span class="n">f</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">news_test</span><span class="o">.</span><span class="n">filenames</span><span class="p">))</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">news_test</span><span class="o">.</span><span class="n">target</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"done in </span><span class="si">%f</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">"n_samples: </span><span class="si">%d</span><span class="s2">, n_features: </span><span class="si">%d</span><span class="s2">"</span> <span class="o">%</span> <span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="c1">###############################################################################</span>
<span class="c1"># Benchmark classifiers</span>
<span class="k">def</span> <span class="nf">benchmark</span><span class="p">(</span><span class="n">clf_class</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"parameters:"</span><span class="p">,</span> <span class="n">params</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">clf</span> <span class="o">=</span> <span class="n">clf_class</span><span class="p">(</span><span class="o">**</span><span class="n">params</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="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">%f</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">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="s1">'coef_'</span><span class="p">):</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Percentage of non zeros coef: </span><span class="si">%f</span><span class="s2">"</span>
<span class="o">%</span> <span class="p">(</span><a href="http://docs.scipy.org/doc/numpy-1.6.0/reference/generated/numpy.mean.html#numpy.mean"><span class="n">np</span><span class="o">.</span><span class="n">mean</span></a><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">coef_</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Predicting the outcomes of the testing 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">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</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"done in </span><span class="si">%f</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">"Classification report on test set for classifier:"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">clf</span><span class="p">)</span>
<span class="k">print</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"><span class="n">classification_report</span></a><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred</span><span class="p">,</span>
<span class="n">target_names</span><span class="o">=</span><span class="n">news_test</span><span class="o">.</span><span class="n">target_names</span><span class="p">))</span>
<span class="n">cm</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.metrics.confusion_matrix.html#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">pred</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Confusion matrix:"</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span>
<span class="c1"># Show confusion matrix</span>
<span class="n">pl</span><span class="o">.</span><span class="n">matshow</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Confusion matrix of the </span><span class="si">%s</span><span class="s1"> classifier'</span> <span class="o">%</span> <span class="n">name</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Testbenching a linear classifier..."</span><span class="p">)</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">'loss'</span><span class="p">:</span> <span class="s1">'hinge'</span><span class="p">,</span>
<span class="s1">'penalty'</span><span class="p">:</span> <span class="s1">'l2'</span><span class="p">,</span>
<span class="s1">'n_iter'</span><span class="p">:</span> <span class="mi">50</span><span class="p">,</span>
<span class="s1">'alpha'</span><span class="p">:</span> <span class="mf">0.00001</span><span class="p">,</span>
<span class="s1">'fit_intercept'</span><span class="p">:</span> <span class="bp">True</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">benchmark</span><span class="p">(</span><a href="../../modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier"><span class="n">SGDClassifier</span></a><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'SGD'</span><span class="p">)</span>
<span class="k">print</span><span class="p">(</span><span class="s2">"Testbenching a MultinomialNB classifier..."</span><span class="p">)</span>
<span class="n">parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'alpha'</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">}</span>
<span class="n">benchmark</span><span class="p">(</span><a href="../../modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB"><span class="n">MultinomialNB</span></a><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="s1">'MultinomialNB'</span><span class="p">)</span>
<span class="n">pl</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2010 - 2014, scikit-learn developers (BSD License).
<a href="../../_sources/auto_examples/text/mlcomp_sparse_document_classification.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="hashing_vs_dict_vectorizer.html">Previous
</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 src="http://www.google.com/jsapi" type="text/javascript"></script>
<script type="text/javascript"> google.load('search', '1',
{language : 'en'}); google.setOnLoadCallback(function() {
var customSearchControl = new
google.search.CustomSearchControl('016639176250731907682:tjtqbvtvij0');
customSearchControl.setResultSetSize(google.search.Search.FILTERED_CSE_RESULTSET);
var options = new google.search.DrawOptions();
options.setAutoComplete(true);
customSearchControl.draw('cse', options); }, true);
</script>
<script src="https://scikit-learn.org/versionwarning.js"></script>
</body>
</html>