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<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.cross_validation</span></code>.PredefinedSplit</a></li>
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<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>.PredefinedSplit<a class="headerlink" href="#sklearn-cross-validation-predefinedsplit" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="sklearn.cross_validation.PredefinedSplit">
<em class="property">class </em><code class="descclassname">sklearn.cross_validation.</code><code class="descname">PredefinedSplit</code><span class="sig-paren">(</span><em>test_fold</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/cross_validation.py#L1026"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cross_validation.PredefinedSplit" title="Permalink to this definition">¶</a></dt>
<dd><p>Predefined split cross validation iterator</p>
<p>Splits the data into training/test set folds according to a predefined
scheme. Each sample can be assigned to at most one test set fold, as
specified by the user through the <code class="docutils literal"><span class="pre">test_fold</span></code> parameter.</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>test_fold</strong> : “array-like, shape (n_samples,)</p>
<blockquote class="last">
<div><p>test_fold[i] gives the test set fold of sample i. A value of -1
indicates that the corresponding sample is not part of any test set
folds, but will instead always be put into the training fold.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<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.cross_validation</span> <span class="kn">import</span> <span class="n">PredefinedSplit</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">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="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">0</span><span class="p">,</span> <span class="mi">0</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="gp">>>> </span><span class="n">ps</span> <span class="o">=</span> <span class="n">PredefinedSplit</span><span class="p">(</span><span class="n">test_fold</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="gp">>>> </span><span class="nb">len</span><span class="p">(</span><span class="n">ps</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">ps</span><span class="p">)</span>
<span class="go">sklearn.cross_validation.PredefinedSplit(test_fold=[ 0 1 -1 1])</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">ps</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="go">TRAIN: [1 2 3] TEST: [0]</span>
<span class="go">TRAIN: [0 2] TEST: [1 3]</span>
<span class="go">.. automethod:: __init__</span>
</pre></div>
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