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<section id="resample">
<h1>resample<a class="headerlink" href="#resample" title="Link to this heading">#</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.utils.resample">
<span class="sig-prename descclassname"><span class="pre">sklearn.utils.</span></span><span class="sig-name descname"><span class="pre">resample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">arrays</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stratify</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/b54e4deea/sklearn/utils/_indexing.py#L412"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.utils.resample" title="Link to this definition">#</a></dt>
<dd><p>Resample arrays or sparse matrices in a consistent way.</p>
<p>The default strategy implements one step of the bootstrapping
procedure.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>*arrays</strong><span class="classifier">sequence of array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>Indexable data-structures can be arrays, lists, dataframes or scipy
sparse matrices with consistent first dimension.</p>
</dd>
<dt><strong>replace</strong><span class="classifier">bool, default=True</span></dt><dd><p>Implements resampling with replacement. It must be set to True
whenever sampling with non-uniform weights: a few data points with very large
weights are expected to be sampled several times with probability to preserve
the distribution induced by the weights. If False, this will implement
(sliced) random permutations.</p>
</dd>
<dt><strong>n_samples</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of samples to generate. If left to None this is
automatically set to the first dimension of the arrays.
If replace is False it should not be larger than the length of
arrays.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Determines random number generation for shuffling
the data.
Pass an int for reproducible results across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>stratify</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_outputs), default=None</span></dt><dd><p>If not None, data is split in a stratified fashion, using this as
the class labels.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>Contains weight values to be associated with each sample. Values are
normalized to sum to one and interpreted as probability for sampling
each data point.</p>
<div class="versionadded">
<p><span class="versionmodified added">Added in version 1.7.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>resampled_arrays</strong><span class="classifier">sequence of array-like of shape (n_samples,) or (n_samples, n_outputs)</span></dt><dd><p>Sequence of resampled copies of the collections. The original arrays
are not impacted.</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.utils.shuffle.html#sklearn.utils.shuffle" title="sklearn.utils.shuffle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">shuffle</span></code></a></dt><dd><p>Shuffle arrays or sparse matrices in a consistent way.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<p>It is possible to mix sparse and dense arrays in the same run:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</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="mf">1.</span><span class="p">,</span> <span class="mf">0.</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">0.</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">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">coo_matrix</span>
<span class="gp">>>> </span><span class="n">X_sparse</span> <span class="o">=</span> <span class="n">coo_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">resample</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">X_sparse</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">resample</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">X_sparse</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span>
<span class="go">array([[1., 0.],</span>
<span class="go"> [2., 1.],</span>
<span class="go"> [1., 0.]])</span>
<span class="gp">>>> </span><span class="n">X_sparse</span>
<span class="go"><Compressed Sparse Row sparse matrix of dtype 'float64'</span>
<span class="go"> with 4 stored elements and shape (3, 2)></span>
<span class="gp">>>> </span><span class="n">X_sparse</span><span class="o">.</span><span class="n">toarray</span><span class="p">()</span>
<span class="go">array([[1., 0.],</span>
<span class="go"> [2., 1.],</span>
<span class="go"> [1., 0.]])</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">array([0, 1, 0])</span>
<span class="gp">>>> </span><span class="n">resample</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([0, 1])</span>
</pre></div>
</div>
<p>Example using stratification:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">y</span> <span class="o">=</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="mi">1</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">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="gp">>>> </span><span class="n">resample</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">[1, 1, 1, 0, 1]</span>
</pre></div>
</div>
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