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<li><a class="reference internal" href="#">1. Metadata Routing</a><ul>
<li><a class="reference internal" href="#usage-examples">1.1. Usage Examples</a><ul>
<li><a class="reference internal" href="#weighted-scoring-and-fitting">1.1.1. Weighted scoring and fitting</a></li>
<li><a class="reference internal" href="#weighted-scoring-and-unweighted-fitting">1.1.2. Weighted scoring and unweighted fitting</a></li>
<li><a class="reference internal" href="#unweighted-feature-selection">1.1.3. Unweighted feature selection</a></li>
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<span class="target" id="metadata-routing"></span><section id="id1">
<h1><span class="section-number">1. </span>Metadata Routing<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The Metadata Routing API is experimental, and is not implemented yet for many
estimators. It may change without the usual deprecation cycle. By default
this feature is not enabled. You can enable this feature by setting the
<code class="docutils literal notranslate"><span class="pre">enable_metadata_routing</span></code> flag to <code class="docutils literal notranslate"><span class="pre">True</span></code>:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">sklearn</span>
<span class="gp">>>> </span><span class="n">sklearn</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="n">enable_metadata_routing</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
</div>
<p>This guide demonstrates how metadata such as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> can be routed
and passed along to estimators, scorers, and CV splitters through
meta-estimators such as <a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> and
<a class="reference internal" href="modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV" title="sklearn.model_selection.GridSearchCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">GridSearchCV</span></code></a>. In order to pass metadata to a method
such as <code class="docutils literal notranslate"><span class="pre">fit</span></code> or <code class="docutils literal notranslate"><span class="pre">score</span></code>, the object consuming the metadata, must <em>request</em>
it. For estimators and splitters, this is done via <code class="docutils literal notranslate"><span class="pre">set_*_request</span></code> methods,
e.g. <code class="docutils literal notranslate"><span class="pre">set_fit_request(...)</span></code>, and for scorers this is done via the
<code class="docutils literal notranslate"><span class="pre">set_score_request</span></code> method. For grouped splitters such as
<a class="reference internal" href="modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a>, a <code class="docutils literal notranslate"><span class="pre">groups</span></code> parameter is requested by
default. This is best demonstrated by the following examples.</p>
<p>If you are developing a scikit-learn compatible estimator or meta-estimator,
you can check our related developer guide:
<a class="reference internal" href="auto_examples/miscellaneous/plot_metadata_routing.html#sphx-glr-auto-examples-miscellaneous-plot-metadata-routing-py"><span class="std std-ref">Metadata Routing</span></a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Note that the methods and requirements introduced in this document are only
relevant if you want to pass <a class="reference internal" href="glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> (e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>) to a method.
If you’re only passing <code class="docutils literal notranslate"><span class="pre">X</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code> and no other parameter / metadata to
methods such as <a class="reference internal" href="glossary.html#term-fit"><span class="xref std std-term">fit</span></a>, <a class="reference internal" href="glossary.html#term-transform"><span class="xref std std-term">transform</span></a>, etc, then you don’t need to set
anything.</p>
</div>
<section id="usage-examples">
<h2><span class="section-number">1.1. </span>Usage Examples<a class="headerlink" href="#usage-examples" title="Permalink to this heading">¶</a></h2>
<p>Here we present a few examples to show different common use-cases. The examples
in this section require the following imports and data:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span><span class="p">,</span> <span class="n">accuracy_score</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegressionCV</span><span class="p">,</span> <span class="n">LogisticRegression</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_validate</span><span class="p">,</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">GroupKFold</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.feature_selection</span> <span class="kn">import</span> <span class="n">SelectKBest</span>
<span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="gp">>>> </span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">4</span>
<span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">42</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">,</span> <span class="n">n_features</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_groups</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_weights</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">my_other_weights</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n_samples</span><span class="p">)</span>
</pre></div>
</div>
<section id="weighted-scoring-and-fitting">
<h3><span class="section-number">1.1.1. </span>Weighted scoring and fitting<a class="headerlink" href="#weighted-scoring-and-fitting" title="Permalink to this heading">¶</a></h3>
<p>Here <a class="reference internal" href="modules/generated/sklearn.model_selection.GroupKFold.html#sklearn.model_selection.GroupKFold" title="sklearn.model_selection.GroupKFold"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupKFold</span></code></a> requests <code class="docutils literal notranslate"><span class="pre">groups</span></code> by default. However, we
need to explicitly request weights for our scorer and the internal cross validation of
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>. Both of these <em>consumers</em> know how to use
metadata called <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">props</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>Note that in this example, <code class="docutils literal notranslate"><span class="pre">my_weights</span></code> is passed to both the scorer and
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>.</p>
<p>Error handling: if <code class="docutils literal notranslate"><span class="pre">props={"sample_weigh":</span> <span class="pre">my_weights,</span> <span class="pre">...}</span></code> were passed
(note the typo), <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> would raise an error,
since <code class="docutils literal notranslate"><span class="pre">sample_weigh</span></code> was not requested by any of its underlying objects.</p>
</section>
<section id="weighted-scoring-and-unweighted-fitting">
<h3><span class="section-number">1.1.2. </span>Weighted scoring and unweighted fitting<a class="headerlink" href="#weighted-scoring-and-unweighted-fitting" title="Permalink to this heading">¶</a></h3>
<p>When passing metadata such as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> around, all <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>
<a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumers</span></a> require weights to be either explicitly requested
or not requested (i.e. <code class="docutils literal notranslate"><span class="pre">True</span></code> or <code class="docutils literal notranslate"><span class="pre">False</span></code>) when used in another
<a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">router</span></a> such as a <a class="reference internal" href="modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a> or a <code class="docutils literal notranslate"><span class="pre">*GridSearchCV</span></code>. To
perform an unweighted fit, we need to configure
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> to not request sample weights, so
that <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> does not pass the weights along:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">props</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
<p>If <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV.set_fit_request" title="sklearn.linear_model.LogisticRegressionCV.set_fit_request"><code class="xref py py-meth docutils literal notranslate"><span class="pre">linear_model.LogisticRegressionCV.set_fit_request</span></code></a> has not
been called, <a class="reference internal" href="modules/generated/sklearn.model_selection.cross_validate.html#sklearn.model_selection.cross_validate" title="sklearn.model_selection.cross_validate"><code class="xref py py-func docutils literal notranslate"><span class="pre">cross_validate</span></code></a> will raise an
error because <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is passed in but
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> would not be explicitly configured
to recognize the weights.</p>
</section>
<section id="unweighted-feature-selection">
<h3><span class="section-number">1.1.3. </span>Unweighted feature selection<a class="headerlink" href="#unweighted-feature-selection" title="Permalink to this heading">¶</a></h3>
<p>Setting request values for metadata are only required if the object, e.g. estimator,
scorer, etc., is a consumer of that metadata Unlike
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>, <a class="reference internal" href="modules/generated/sklearn.feature_selection.SelectKBest.html#sklearn.feature_selection.SelectKBest" title="sklearn.feature_selection.SelectKBest"><code class="xref py py-class docutils literal notranslate"><span class="pre">SelectKBest</span></code></a>
doesn’t consume weights and therefore no request value for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> on its
instance is set and <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is not routed to it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">sel</span> <span class="o">=</span> <span class="n">SelectKBest</span><span class="p">(</span><span class="n">k</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">pipe</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">sel</span><span class="p">,</span> <span class="n">lr</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">pipe</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">props</span><span class="o">=</span><span class="p">{</span><span class="s2">"sample_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
</section>
<section id="advanced-different-scoring-and-fitting-weights">
<h3><span class="section-number">1.1.4. </span>Advanced: Different scoring and fitting weights<a class="headerlink" href="#advanced-different-scoring-and-fitting-weights" title="Permalink to this heading">¶</a></h3>
<p>Despite <a class="reference internal" href="modules/generated/sklearn.metrics.make_scorer.html#sklearn.metrics.make_scorer" title="sklearn.metrics.make_scorer"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_scorer</span></code></a> and
<a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a> both expecting the key
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>, we can use aliases to pass different weights to different
consumers. In this example, we pass <code class="docutils literal notranslate"><span class="pre">scoring_weight</span></code> to the scorer, and
<code class="docutils literal notranslate"><span class="pre">fitting_weight</span></code> to <a class="reference internal" href="modules/generated/sklearn.linear_model.LogisticRegressionCV.html#sklearn.linear_model.LogisticRegressionCV" title="sklearn.linear_model.LogisticRegressionCV"><code class="xref py py-class docutils literal notranslate"><span class="pre">LogisticRegressionCV</span></code></a>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">weighted_acc</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="s2">"scoring_weight"</span>
<span class="gp">... </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegressionCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="s2">"fitting_weight"</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">cv_results</span> <span class="o">=</span> <span class="n">cross_validate</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">lr</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">X</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">y</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">cv</span><span class="o">=</span><span class="n">GroupKFold</span><span class="p">(),</span>
<span class="gp">... </span> <span class="n">props</span><span class="o">=</span><span class="p">{</span>
<span class="gp">... </span> <span class="s2">"scoring_weight"</span><span class="p">:</span> <span class="n">my_weights</span><span class="p">,</span>
<span class="gp">... </span> <span class="s2">"fitting_weight"</span><span class="p">:</span> <span class="n">my_other_weights</span><span class="p">,</span>
<span class="gp">... </span> <span class="s2">"groups"</span><span class="p">:</span> <span class="n">my_groups</span><span class="p">,</span>
<span class="gp">... </span> <span class="p">},</span>
<span class="gp">... </span> <span class="n">scoring</span><span class="o">=</span><span class="n">weighted_acc</span><span class="p">,</span>
<span class="gp">... </span><span class="p">)</span>
</pre></div>
</div>
</section>
</section>
<section id="api-interface">
<h2><span class="section-number">1.2. </span>API Interface<a class="headerlink" href="#api-interface" title="Permalink to this heading">¶</a></h2>
<p>A <a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a> is an object (estimator, meta-estimator, scorer, splitter)
which accepts and uses some <a class="reference internal" href="glossary.html#term-metadata"><span class="xref std std-term">metadata</span></a> in at least one of its methods
(<code class="docutils literal notranslate"><span class="pre">fit</span></code>, <code class="docutils literal notranslate"><span class="pre">predict</span></code>, <code class="docutils literal notranslate"><span class="pre">inverse_transform</span></code>, <code class="docutils literal notranslate"><span class="pre">transform</span></code>, <code class="docutils literal notranslate"><span class="pre">score</span></code>,
<code class="docutils literal notranslate"><span class="pre">split</span></code>). Meta-estimators which only forward the metadata to other objects
(the child estimator, scorers, or splitters) and don’t use the metadata
themselves are not consumers. (Meta-)Estimators which route metadata to other
objects are <a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">routers</span></a>. A(n) (meta-)estimator can be a
<a class="reference internal" href="glossary.html#term-consumer"><span class="xref std std-term">consumer</span></a> and a <a class="reference internal" href="glossary.html#term-router"><span class="xref std std-term">router</span></a> at the same time. (Meta-)Estimators and
splitters expose a <code class="docutils literal notranslate"><span class="pre">set_*_request</span></code> method for each method which accepts at
least one metadata. For instance, if an estimator supports <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in
<code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">score</span></code>, it exposes
<code class="docutils literal notranslate"><span class="pre">estimator.set_fit_request(sample_weight=value)</span></code> and
<code class="docutils literal notranslate"><span class="pre">estimator.set_score_request(sample_weight=value)</span></code>. Here <code class="docutils literal notranslate"><span class="pre">value</span></code> can be:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">True</span></code>: method requests a <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>. This means if the metadata is
provided, it will be used, otherwise no error is raised.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">False</span></code>: method does not request a <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">None</span></code>: router will raise an error if <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is passed. This is
in almost all cases the default value when an object is instantiated and
ensures the user sets the metadata requests explicitly when a metadata is
passed. The only exception are <code class="docutils literal notranslate"><span class="pre">Group*Fold</span></code> splitters.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">"param_name"</span></code>: if this estimator is used in a meta-estimator, the
meta-estimator should forward <code class="docutils literal notranslate"><span class="pre">"param_name"</span></code> as <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> to this
estimator. This means the mapping between the metadata required by the
object, e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> and what is provided by the user, e.g.
<code class="docutils literal notranslate"><span class="pre">my_weights</span></code> is done at the router level, and not by the object, e.g.
estimator, itself.</p></li>
</ul>
<p>Metadata are requested in the same way for scorers using <code class="docutils literal notranslate"><span class="pre">set_score_request</span></code>.</p>
<p>If a metadata, e.g. <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>, is passed by the user, the metadata
request for all objects which potentially can consume <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> should
be set by the user, otherwise an error is raised by the router object. For
example, the following code raises an error, since it hasn’t been explicitly
specified whether <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> should be passed to the estimator’s scorer
or not:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"C"</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]}</span>
<span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">try</span><span class="p">:</span>
<span class="gp">... </span> <span class="n">GridSearchCV</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">estimator</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span>
<span class="gp">... </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="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">my_weights</span><span class="p">)</span>
<span class="gp">... </span><span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
<span class="gp">... </span> <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="go">[sample_weight] are passed but are not explicitly set as requested or not for</span>
<span class="go">LogisticRegression.score</span>
</pre></div>
</div>
<p>The issue can be fixed by explicitly setting the request value:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span><span class="o">.</span><span class="n">set_fit_request</span><span class="p">(</span>
<span class="gp">... </span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">True</span>
<span class="gp">... </span><span class="p">)</span><span class="o">.</span><span class="n">set_score_request</span><span class="p">(</span><span class="n">sample_weight</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
</pre></div>
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