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<div class="section" id="sklearn-datasets-fetch-lfw-pairs">
<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>.fetch_lfw_pairs<a class="headerlink" href="#sklearn-datasets-fetch-lfw-pairs" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.fetch_lfw_pairs">
<code class="descclassname">sklearn.datasets.</code><code class="descname">fetch_lfw_pairs</code><span class="sig-paren">(</span><em>subset='train'</em>, <em>data_home=None</em>, <em>funneled=True</em>, <em>resize=0.5</em>, <em>color=False</em>, <em>slice_=(slice(70</em>, <em>195</em>, <em>None)</em>, <em>slice(78</em>, <em>172</em>, <em>None))</em>, <em>download_if_missing=True</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/474b5e0/sklearn/datasets/lfw.py#L404"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.fetch_lfw_pairs" title="Permalink to this definition">¶</a></dt>
<dd><p>Loader for the Labeled Faces in the Wild (LFW) pairs dataset</p>
<p>This dataset is a collection of JPEG pictures of famous people
collected on the internet, all details are available on the
official website:</p>
<blockquote>
<div><a class="reference external" href="http://vis-www.cs.umass.edu/lfw/">http://vis-www.cs.umass.edu/lfw/</a></div></blockquote>
<p>Each picture is centered on a single face. Each pixel of each channel
(color in RGB) is encoded by a float in range 0.0 - 1.0.</p>
<p>The task is called Face Verification: given a pair of two pictures,
a binary classifier must predict whether the two images are from
the same person.</p>
<p>In the official <a class="reference external" href="http://vis-www.cs.umass.edu/lfw/README.txt">README.txt</a> this task is described as the
“Restricted” task. As I am not sure as to implement the
“Unrestricted” variant correctly, I left it as unsupported for now.</p>
<blockquote>
<div></div></blockquote>
<p>The original images are 250 x 250 pixels, but the default slice and resize
arguments reduce them to 62 x 47.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/labeled_faces.html#labeled-faces-in-the-wild"><span class="std std-ref">User Guide</span></a>.</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>subset</strong> : optional, default: ‘train’</p>
<blockquote>
<div><p>Select the dataset to load: ‘train’ for the development training
set, ‘test’ for the development test set, and ‘10_folds’ for the
official evaluation set that is meant to be used with a 10-folds
cross validation.</p>
</div></blockquote>
<p><strong>data_home</strong> : optional, default: None</p>
<blockquote>
<div><p>Specify another download and cache folder for the datasets. By
default all scikit-learn data is stored in ‘~/scikit_learn_data’
subfolders.</p>
</div></blockquote>
<p><strong>funneled</strong> : boolean, optional, default: True</p>
<blockquote>
<div><p>Download and use the funneled variant of the dataset.</p>
</div></blockquote>
<p><strong>resize</strong> : float, optional, default 0.5</p>
<blockquote>
<div><p>Ratio used to resize the each face picture.</p>
</div></blockquote>
<p><strong>color</strong> : boolean, optional, default False</p>
<blockquote>
<div><p>Keep the 3 RGB channels instead of averaging them to a single
gray level channel. If color is True the shape of the data has
one more dimension than the shape with color = False.</p>
</div></blockquote>
<p><strong>slice_</strong> : optional</p>
<blockquote>
<div><p>Provide a custom 2D slice (height, width) to extract the
‘interesting’ part of the jpeg files and avoid use statistical
correlation from the background</p>
</div></blockquote>
<p><strong>download_if_missing</strong> : optional, True by default</p>
<blockquote>
<div><p>If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first">The data is returned as a Bunch object with the following attributes:</p>
<p><strong>data</strong> : numpy array of shape (2200, 5828). Shape depends on <code class="docutils literal"><span class="pre">subset</span></code>.</p>
<blockquote>
<div><p>Each row corresponds to 2 ravel’d face images of original size 62 x 47
pixels. Changing the <code class="docutils literal"><span class="pre">slice_</span></code>, <code class="docutils literal"><span class="pre">resize</span></code> or <code class="docutils literal"><span class="pre">subset</span></code> parameters
will change the shape of the output.</p>
</div></blockquote>
<p><strong>pairs</strong> : numpy array of shape (2200, 2, 62, 47). Shape depends on</p>
<blockquote>
<div><blockquote>
<div><p><code class="docutils literal"><span class="pre">subset</span></code>.</p>
</div></blockquote>
<p>Each row has 2 face images corresponding to same or different person
from the dataset containing 5749 people. Changing the <code class="docutils literal"><span class="pre">slice_</span></code>,
<code class="docutils literal"><span class="pre">resize</span></code> or <code class="docutils literal"><span class="pre">subset</span></code> parameters will change the shape of the
output.</p>
</div></blockquote>
<p><strong>target</strong> : numpy array of shape (2200,). Shape depends on <code class="docutils literal"><span class="pre">subset</span></code>.</p>
<blockquote>
<div><p>Labels associated to each pair of images. The two label values being
different persons or the same person.</p>
</div></blockquote>
<p><strong>DESCR</strong> : string</p>
<blockquote class="last">
<div><p>Description of the Labeled Faces in the Wild (LFW) dataset.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
</dd></dl>
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