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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.load_svmlight_files</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.load_svmlight_files"><code class="docutils literal notranslate"><span class="pre">load_svmlight_files</span></code></a></li>
</ul>
</li>
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<section id="sklearn-datasets-load-svmlight-files">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.load_svmlight_files<a class="headerlink" href="#sklearn-datasets-load-svmlight-files" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.load_svmlight_files">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">load_svmlight_files</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">files</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_features=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=<class</span> <span class="pre">'numpy.float64'></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multilabel=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_based='auto'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query_id=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length=-1</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/datasets/_svmlight_format_io.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.load_svmlight_files" title="Permalink to this definition">¶</a></dt>
<dd><p>Load dataset from multiple files in SVMlight format.</p>
<p>This function is equivalent to mapping load_svmlight_file over a list of
files, except that the results are concatenated into a single, flat list
and the samples vectors are constrained to all have the same number of
features.</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>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>files</strong><span class="classifier">array-like, dtype=str, path-like, file-like or int</span></dt><dd><p>(Paths of) files 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. File-likes and file descriptors will not be
closed by this function. File-like objects must be opened in binary
mode.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 1.2: </span>Path-like objects are now accepted.</p>
</div>
</dd>
<dt><strong>n_features</strong><span class="classifier">int, default=None</span></dt><dd><p>The number of features to use. If None, it will be inferred from the
maximum column index occurring in any of the files.</p>
<p>This can be set to a higher value than the actual number of features
in any of the input files, but setting it to a lower value will cause
an exception to be raised.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">numpy data type, default=np.float64</span></dt><dd><p>Data type of dataset to be loaded. This will be the data type of the
output numpy arrays <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code>.</p>
</dd>
<dt><strong>multilabel</strong><span class="classifier">bool, default=False</span></dt><dd><p>Samples may have several labels each (see
<a class="reference external" href="https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html">https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html</a>).</p>
</dd>
<dt><strong>zero_based</strong><span class="classifier">bool or “auto”, default=”auto”</span></dt><dd><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 offset or length is passed.
If offset or length are passed, the “auto” mode falls back
to zero_based=True to avoid having the heuristic check yield
inconsistent results on different segments of the file.</p>
</dd>
<dt><strong>query_id</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, will return the query_id array for each file.</p>
</dd>
<dt><strong>offset</strong><span class="classifier">int, default=0</span></dt><dd><p>Ignore the offset first bytes by seeking forward, then
discarding the following bytes up until the next new line
character.</p>
</dd>
<dt><strong>length</strong><span class="classifier">int, default=-1</span></dt><dd><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>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt>[X1, y1, …, Xn, yn] or [X1, y1, q1, …, Xn, yn, qn]: list of arrays</dt><dd><p>Each (Xi, yi) pair is the result from load_svmlight_file(files[i]).
If query_id is set to True, this will return instead (Xi, yi, qi)
triplets.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.datasets.load_svmlight_file.html#sklearn.datasets.load_svmlight_file" title="sklearn.datasets.load_svmlight_file"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_svmlight_file</span></code></a></dt><dd><p>Similar function for loading a single file in this format.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>When fitting a model to a matrix X_train and evaluating it against a
matrix X_test, it is essential that X_train and X_test have the same
number of features (X_train.shape[1] == X_test.shape[1]). This may not
be the case if you load the files individually with load_svmlight_file.</p>
</dd></dl>
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