forked from scikit-learn/scikit-learn.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathsklearn.datasets.load_svmlight_file.html
377 lines (340 loc) · 18.1 KB
/
sklearn.datasets.load_svmlight_file.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
<!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>sklearn.datasets.load_svmlight_file — 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="sklearn.datasets.load_svmlight_files" href="sklearn.datasets.load_svmlight_files.html" />
<link rel="prev" title="sklearn.datasets.load_sample_images" href="sklearn.datasets.load_sample_images.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/modules/generated/sklearn.datasets.load_svmlight_file.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="../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="../../auto_examples/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="sklearn.datasets.load_sample_images.html"
accesskey="P">Previous
<br/>
<span class="smallrellink">
sklearn.datas...
</span>
<span class="hiddenrellink">
sklearn.datasets.load_sample_images
</span>
</a>
</div>
<div class="spacer">
</div>
<div class="rellink">
<a href="sklearn.datasets.load_svmlight_files.html"
accesskey="N">Next
<br/>
<span class="smallrellink">
sklearn.datas...
</span>
<span class="hiddenrellink">
sklearn.datasets.load_svmlight_files
</span>
</a>
</div>
<!-- Ad a link to the 'up' page -->
<div class="spacer">
</div>
<div class="rellink">
<a href="../classes.html">
Up
<br/>
<span class="smallrellink">
API Reference
</span>
<span class="hiddenrellink">
API Reference
</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="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.load_svmlight_file</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="sklearn-datasets-load-svmlight-file">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a>.load_svmlight_file<a class="headerlink" href="#sklearn-datasets-load-svmlight-file" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.load_svmlight_file">
<code class="descclassname">sklearn.datasets.</code><code class="descname">load_svmlight_file</code><span class="sig-paren">(</span><em>f</em>, <em>n_features=None</em>, <em>dtype=<class 'numpy.float64'></em>, <em>multilabel=False</em>, <em>zero_based='auto'</em>, <em>query_id=False</em>, <em>offset=0</em>, <em>length=-1</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/datasets/svmlight_format.py#L33"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.load_svmlight_file" title="Permalink to this definition">¶</a></dt>
<dd><p>Load datasets in the svmlight / libsvm format into sparse CSR matrix</p>
<p>This format is a text-based format, with one sample per line. It does
not store zero valued features hence is suitable for sparse dataset.</p>
<p>The first element of each line can be used to store a target variable
to predict.</p>
<p>This format is used as the default format for both svmlight and the
libsvm command line programs.</p>
<p>Parsing a text based source can be expensive. When working on
repeatedly on the same dataset, it is recommended to wrap this
loader with joblib.Memory.cache to store a memmapped backup of the
CSR results of the first call and benefit from the near instantaneous
loading of memmapped structures for the subsequent calls.</p>
<p>In case the file contains a pairwise preference constraint (known
as “qid” in the svmlight format) these are ignored unless the
query_id parameter is set to True. These pairwise preference
constraints can be used to constraint the combination of samples
when using pairwise loss functions (as is the case in some
learning to rank problems) so that only pairs with the same
query_id value are considered.</p>
<p>This implementation is written in Cython and is reasonably fast.
However, a faster API-compatible loader is also available at:</p>
<blockquote>
<div><a class="reference external" href="https://github.com/mblondel/svmlight-loader">https://github.com/mblondel/svmlight-loader</a></div></blockquote>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>f</strong> : {str, file-like, int}</p>
<blockquote>
<div><p>(Path to) a file to load. If a path ends in “.gz” or “.bz2”, it will
be uncompressed on the fly. If an integer is passed, it is assumed to
be a file descriptor. A file-like or file descriptor will not be closed
by this function. A file-like object must be opened in binary mode.</p>
</div></blockquote>
<p><strong>n_features</strong> : int or None</p>
<blockquote>
<div><p>The number of features to use. If None, it will be inferred. This
argument is useful to load several files that are subsets of a
bigger sliced dataset: each subset might not have examples of
every feature, hence the inferred shape might vary from one
slice to another.
n_features is only required if <code class="docutils literal"><span class="pre">offset</span></code> or <code class="docutils literal"><span class="pre">length</span></code> are passed a
non-default value.</p>
</div></blockquote>
<p><strong>dtype</strong> : numpy data type, default np.float64</p>
<blockquote>
<div><p>Data type of dataset to be loaded. This will be the data type of the
output numpy arrays <code class="docutils literal"><span class="pre">X</span></code> and <code class="docutils literal"><span class="pre">y</span></code>.</p>
</div></blockquote>
<p><strong>multilabel</strong> : boolean, optional, default False</p>
<blockquote>
<div><p>Samples may have several labels each (see
<a class="reference external" href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html">http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html</a>)</p>
</div></blockquote>
<p><strong>zero_based</strong> : boolean or “auto”, optional, default “auto”</p>
<blockquote>
<div><p>Whether column indices in f are zero-based (True) or one-based
(False). If column indices are one-based, they are transformed to
zero-based to match Python/NumPy conventions.
If set to “auto”, a heuristic check is applied to determine this from
the file contents. Both kinds of files occur “in the wild”, but they
are unfortunately not self-identifying. Using “auto” or True should
always be safe when no <code class="docutils literal"><span class="pre">offset</span></code> or <code class="docutils literal"><span class="pre">length</span></code> is passed.
If <code class="docutils literal"><span class="pre">offset</span></code> or <code class="docutils literal"><span class="pre">length</span></code> are passed, the “auto” mode falls back
to <code class="docutils literal"><span class="pre">zero_based=True</span></code> to avoid having the heuristic check yield
inconsistent results on different segments of the file.</p>
</div></blockquote>
<p><strong>query_id</strong> : boolean, default False</p>
<blockquote>
<div><p>If True, will return the query_id array for each file.</p>
</div></blockquote>
<p><strong>offset</strong> : integer, optional, default 0</p>
<blockquote>
<div><p>Ignore the offset first bytes by seeking forward, then
discarding the following bytes up until the next new line
character.</p>
</div></blockquote>
<p><strong>length</strong> : integer, optional, default -1</p>
<blockquote>
<div><p>If strictly positive, stop reading any new line of data once the
position in the file has reached the (offset + length) bytes threshold.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X</strong> : scipy.sparse matrix of shape (n_samples, n_features)</p>
<p><strong>y</strong> : ndarray of shape (n_samples,), or, in the multilabel a list of</p>
<blockquote>
<div><p>tuples of length n_samples.</p>
</div></blockquote>
<p><strong>query_id</strong> : array of shape (n_samples,)</p>
<blockquote class="last">
<div><p>query_id for each sample. Only returned when query_id is set to
True.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="docutils">
<dt><a class="reference internal" href="sklearn.datasets.load_svmlight_files.html#sklearn.datasets.load_svmlight_files" title="sklearn.datasets.load_svmlight_files"><code class="xref py py-obj docutils literal"><span class="pre">load_svmlight_files</span></code></a></dt>
<dd>similar function for loading multiple files in this</dd>
</dl>
<p class="last"><a class="reference external" href="https://docs.python.org/3/library/functions.html#format" title="(in Python v3.6)"><code class="xref py py-obj docutils literal"><span class="pre">format</span></code></a>, <code class="xref py py-obj docutils literal"><span class="pre">enforcing</span></code></p>
</div>
<p class="rubric">Examples</p>
<p>To use joblib.Memory to cache the svmlight file:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.externals.joblib</span> <span class="k">import</span> <span class="n">Memory</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="k">import</span> <span class="n">load_svmlight_file</span>
<span class="n">mem</span> <span class="o">=</span> <span class="n">Memory</span><span class="p">(</span><span class="s2">"./mycache"</span><span class="p">)</span>
<span class="nd">@mem</span><span class="o">.</span><span class="n">cache</span>
<span class="k">def</span> <span class="nf">get_data</span><span class="p">():</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">load_svmlight_file</span><span class="p">(</span><span class="s2">"mysvmlightfile"</span><span class="p">)</span>
<span class="k">return</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">get_data</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<div class="clearer"></div></div>
</div>
</div>
</div>
<div class="clearer"></div>
</div>
</div>
<div class="footer">
© 2007 - 2018, scikit-learn developers (BSD License).
<a href="../../_sources/modules/generated/sklearn.datasets.load_svmlight_file.rst.txt" rel="nofollow">Show this page source</a>
</div>
<div class="rel">
<div class="buttonPrevious">
<a href="sklearn.datasets.load_sample_images.html">Previous
</a>
</div>
<div class="buttonNext">
<a href="sklearn.datasets.load_svmlight_files.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>