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  <section id="onetoonefeaturemixin">
<h1>OneToOneFeatureMixin<a class="headerlink" href="#onetoonefeaturemixin" title="Link to this heading">#</a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="sklearn.base.OneToOneFeatureMixin">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">sklearn.base.</span></span><span class="sig-name descname"><span class="pre">OneToOneFeatureMixin</span></span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/86d099ec1/sklearn/base.py#L865"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OneToOneFeatureMixin" title="Link to this definition">#</a></dt>
<dd><p>Provides <code class="docutils literal notranslate"><span class="pre">get_feature_names_out</span></code> for simple transformers.</p>
<p>This mixin assumes there’s a 1-to-1 correspondence between input features
and output features, such as <a class="reference internal" href="sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">StandardScaler</span></code></a>.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </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">&gt;&gt;&gt; </span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">OneToOneFeatureMixin</span><span class="p">,</span> <span class="n">BaseEstimator</span>
<span class="gp">&gt;&gt;&gt; </span><span class="k">class</span><span class="w"> </span><span class="nc">MyEstimator</span><span class="p">(</span><span class="n">OneToOneFeatureMixin</span><span class="p">,</span> <span class="n">BaseEstimator</span><span class="p">):</span>
<span class="gp">... </span>    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="gp">... </span>        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="gp">... </span>        <span class="k">return</span> <span class="bp">self</span>
<span class="gp">&gt;&gt;&gt; </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="gp">&gt;&gt;&gt; </span><span class="n">MyEstimator</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">get_feature_names_out</span><span class="p">()</span>
<span class="go">array([&#39;x0&#39;, &#39;x1&#39;], dtype=object)</span>
</pre></div>
</div>
<dl class="py method">
<dt class="sig sig-object py" id="sklearn.base.OneToOneFeatureMixin.get_feature_names_out">
<span class="sig-name descname"><span class="pre">get_feature_names_out</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_features</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/86d099ec1/sklearn/base.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.base.OneToOneFeatureMixin.get_feature_names_out" title="Link to this definition">#</a></dt>
<dd><p>Get output feature names for transformation.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>input_features</strong><span class="classifier">array-like of str or None, default=None</span></dt><dd><p>Input features.</p>
<ul class="simple">
<li><p>If <code class="docutils literal notranslate"><span class="pre">input_features</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code>, then <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is
used as feature names in. If <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is not defined,
then the following input feature names are generated:
<code class="docutils literal notranslate"><span class="pre">[&quot;x0&quot;,</span> <span class="pre">&quot;x1&quot;,</span> <span class="pre">...,</span> <span class="pre">&quot;x(n_features_in_</span> <span class="pre">-</span> <span class="pre">1)&quot;]</span></code>.</p></li>
<li><p>If <code class="docutils literal notranslate"><span class="pre">input_features</span></code> is an array-like, then <code class="docutils literal notranslate"><span class="pre">input_features</span></code> must
match <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> if <code class="docutils literal notranslate"><span class="pre">feature_names_in_</span></code> is defined.</p></li>
</ul>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>feature_names_out</strong><span class="classifier">ndarray of str objects</span></dt><dd><p>Same as input features.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>


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