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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.load_svmlight_files</a></li>
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<div class="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"><span class="pre">sklearn.datasets</span></code></a>.load_svmlight_files<a class="headerlink" href="#sklearn-datasets-load-svmlight-files" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.load_svmlight_files">
<code class="descclassname">sklearn.datasets.</code><code class="descname">load_svmlight_files</code><span class="sig-paren">(</span><em>files</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><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/datasets/svmlight_format.py#L175"><span class="viewcode-link">[source]</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>
<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>files</strong> : iterable over {str, file-like, int}</p>
<blockquote>
<div><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></blockquote>
<p><strong>n_features: int or None</strong> :</p>
<blockquote>
<div><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>
</div></blockquote>
<p><strong>multilabel: boolean, optional</strong> :</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: boolean or “auto”, optional</strong> :</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.</p>
</div></blockquote>
<p><strong>query_id: boolean, defaults to False</strong> :</p>
<blockquote>
<div><p>If True, will return the query_id array for each file.</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>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>[X1, y1, ..., Xn, yn]</strong> :</p>
<p><strong>where each (Xi, yi) pair is the result from load_svmlight_file(files[i]).</strong> :</p>
<p><strong>If query_id is set to True, this will return instead [X1, y1, q1,</strong> :</p>
<p><strong>..., Xn, yn, qn] where (Xi, yi, qi) is the result from</strong> :</p>
<p class="last"><strong>load_svmlight_file(files[i])</strong> :</p>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<p class="last"><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"><span class="pre">load_svmlight_file</span></code></a></p>
</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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