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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cross_validation</span></code>.LeaveOneLabelOut</a></li>
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<div class="section" id="sklearn-cross-validation-leaveonelabelout">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cross_validation" title="sklearn.cross_validation"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.cross_validation</span></code></a>.LeaveOneLabelOut<a class="headerlink" href="#sklearn-cross-validation-leaveonelabelout" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cross_validation.LeaveOneLabelOut">
<em class="property">class </em><code class="descclassname">sklearn.cross_validation.</code><code class="descname">LeaveOneLabelOut</code><span class="sig-paren">(</span><em>labels</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cross_validation.py#L565"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_validation.LeaveOneLabelOut" title="Permalink to this definition">¶</a></dt>
<dd><p>Leave-One-Label_Out cross-validation iterator</p>
<p>Provides train/test indices to split data according to a third-party
provided label. This label information can be used to encode arbitrary
domain specific stratifications of the samples as integers.</p>
<p>For instance the labels could be the year of collection of the samples
and thus allow for cross-validation against time-based splits.</p>
<p>Read more in the <a class="reference internal" href="../cross_validation.html#cross-validation"><span>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>labels</strong> : array-like of int with shape (n_samples,)</p>
<blockquote class="last">
<div><p>Arbitrary domain-specific stratification of the data to be used
to draw the splits.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="sklearn.cross_validation.LabelKFold.html#sklearn.cross_validation.LabelKFold" title="sklearn.cross_validation.LabelKFold"><code class="xref py py-obj docutils literal"><span class="pre">LabelKFold</span></code></a></dt>
<dd>K-fold iterator variant with non-overlapping labels.</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">cross_validation</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="gp">>>> </span><span class="n">lol</span> <span class="o">=</span> <span class="n">cross_validation</span><span class="o">.</span><span class="n">LeaveOneLabelOut</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">lol</span><span class="p">)</span>
<span class="go">2</span>
<span class="gp">>>> </span><span class="k">print</span><span class="p">(</span><span class="n">lol</span><span class="p">)</span>
<span class="go">sklearn.cross_validation.LeaveOneLabelOut(labels=[1 1 2 2])</span>
<span class="gp">>>> </span><span class="k">for</span> <span class="n">train_index</span><span class="p">,</span> <span class="n">test_index</span> <span class="ow">in</span> <span class="n">lol</span><span class="p">:</span>
<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="s2">"TRAIN:"</span><span class="p">,</span> <span class="n">train_index</span><span class="p">,</span> <span class="s2">"TEST:"</span><span class="p">,</span> <span class="n">test_index</span><span class="p">)</span>
<span class="gp">... </span> <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">X</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="gp">... </span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">y</span><span class="p">[</span><span class="n">train_index</span><span class="p">],</span> <span class="n">y</span><span class="p">[</span><span class="n">test_index</span><span class="p">]</span>
<span class="gp">... </span> <span class="k">print</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>
<span class="go">TRAIN: [2 3] TEST: [0 1]</span>
<span class="go">[[5 6]</span>
<span class="go"> [7 8]] [[1 2]</span>
<span class="go"> [3 4]] [1 2] [1 2]</span>
<span class="go">TRAIN: [0 1] TEST: [2 3]</span>
<span class="go">[[1 2]</span>
<span class="go"> [3 4]] [[5 6]</span>
<span class="go"> [7 8]] [1 2] [1 2]</span>
<span class="go">.. automethod:: __init__</span>
</pre></div>
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