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<li><a class="reference internal" href="#">Metadata Routing</a><ul>
<li><a class="reference internal" href="#estimators">Estimators</a></li>
<li><a class="reference internal" href="#router-and-consumer">Router and Consumer</a></li>
<li><a class="reference internal" href="#simple-pipeline">Simple Pipeline</a></li>
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<p><a class="reference internal" href="#sphx-glr-download-auto-examples-miscellaneous-plot-metadata-routing-py"><span class="std std-ref">Go to the end</span></a>
to download the full example code or to run this example in your browser via JupyterLite or Binder</p>
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<section class="sphx-glr-example-title" id="metadata-routing">
<span id="sphx-glr-auto-examples-miscellaneous-plot-metadata-routing-py"></span><h1>Metadata Routing<a class="headerlink" href="#metadata-routing" title="Link to this heading">¶</a></h1>
<p>This document shows how you can use the <a class="reference internal" href="../../metadata_routing.html#metadata-routing"><span class="std std-ref">metadata routing mechanism</span></a> in scikit-learn to route metadata through meta-estimators
to the estimators consuming them. To better understand the rest of the
document, we need to introduce two concepts: routers and consumers. A router is
an object, in most cases a meta-estimator, which forwards given data and
metadata to other objects and estimators. A consumer, on the other hand, is an
object which accepts and uses a certain given metadata. For instance, an
estimator taking into account <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in its <a class="reference internal" href="../../glossary.html#term-fit"><span class="xref std std-term">fit</span></a> method is a
consumer of <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>. It is possible for an object to be both a router
and a consumer. For instance, a meta-estimator may take into account
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in certain calculations, but it may also route it to the
underlying estimator.</p>
<p>First a few imports and some random data for the rest of the script.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <a href="https://docs.python.org/3/library/pprint.html#pprint.pprint" title="pprint.pprint" class="sphx-glr-backref-module-pprint sphx-glr-backref-type-py-function"><span class="n">pprint</span></a>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config" class="sphx-glr-backref-module-sklearn sphx-glr-backref-type-py-function"><span class="n">set_config</span></a>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="p">(</span>
<a href="../../modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BaseEstimator</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin" title="sklearn.base.ClassifierMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ClassifierMixin</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.base.MetaEstimatorMixin.html#sklearn.base.MetaEstimatorMixin" title="sklearn.base.MetaEstimatorMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MetaEstimatorMixin</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.base.RegressorMixin.html#sklearn.base.RegressorMixin" title="sklearn.base.RegressorMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RegressorMixin</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.base.TransformerMixin.html#sklearn.base.TransformerMixin" title="sklearn.base.TransformerMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">TransformerMixin</span></a><span class="p">,</span>
<span class="n">clone</span><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression" title="sklearn.linear_model.LinearRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LinearRegression</span></a>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="kn">import</span> <span class="n">metadata_routing</span>
<span class="kn">from</span> <span class="nn">sklearn.utils.metadata_routing</span> <span class="kn">import</span> <span class="p">(</span>
<a href="../../modules/generated/sklearn.utils.metadata_routing.MetadataRouter.html#sklearn.utils.metadata_routing.MetadataRouter" title="sklearn.utils.metadata_routing.MetadataRouter" class="sphx-glr-backref-module-sklearn-utils-metadata_routing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MetadataRouter</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.utils.metadata_routing.MethodMapping.html#sklearn.utils.metadata_routing.MethodMapping" title="sklearn.utils.metadata_routing.MethodMapping" class="sphx-glr-backref-module-sklearn-utils-metadata_routing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MethodMapping</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html#sklearn.utils.metadata_routing.get_routing_for_object" title="sklearn.utils.metadata_routing.get_routing_for_object" class="sphx-glr-backref-module-sklearn-utils-metadata_routing sphx-glr-backref-type-py-function"><span class="n">get_routing_for_object</span></a><span class="p">,</span>
<a href="../../modules/generated/sklearn.utils.metadata_routing.process_routing.html#sklearn.utils.metadata_routing.process_routing" title="sklearn.utils.metadata_routing.process_routing" class="sphx-glr-backref-module-sklearn-utils-metadata_routing sphx-glr-backref-type-py-function"><span class="n">process_routing</span></a><span class="p">,</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.utils.validation</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.utils.validation.check_is_fitted.html#sklearn.utils.validation.check_is_fitted" title="sklearn.utils.validation.check_is_fitted" class="sphx-glr-backref-module-sklearn-utils-validation sphx-glr-backref-type-py-function"><span class="n">check_is_fitted</span></a>
<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="n">rng</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="numpy.random.RandomState" class="sphx-glr-backref-module-numpy-random sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span></a><span class="p">(</span><span class="mi">42</span><span class="p">)</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="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="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="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="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>
<p>This feature is only available if explicitly enabled:</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><a href="../../modules/generated/sklearn.set_config.html#sklearn.set_config" title="sklearn.set_config" class="sphx-glr-backref-module-sklearn sphx-glr-backref-type-py-function"><span class="n">set_config</span></a><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>
<p>This utility function is a dummy to check if a metadata is passed.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">check_metadata</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">value</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span>
<span class="sa">f</span><span class="s2">"Received </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2"> of length = </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="si">}</span><span class="s2"> in </span><span class="si">{</span><span class="n">obj</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">."</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2"> is None in </span><span class="si">{</span><span class="n">obj</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">."</span><span class="p">)</span>
</pre></div>
</div>
<p>A utility function to nicely print the routing information of an object</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">print_routing</span><span class="p">(</span><span class="n">obj</span><span class="p">):</span>
<a href="https://docs.python.org/3/library/pprint.html#pprint.pprint" title="pprint.pprint" class="sphx-glr-backref-module-pprint sphx-glr-backref-type-py-function"><span class="n">pprint</span></a><span class="p">(</span><span class="n">obj</span><span class="o">.</span><span class="n">get_metadata_routing</span><span class="p">()</span><span class="o">.</span><span class="n">_serialize</span><span class="p">())</span>
</pre></div>
</div>
<section id="estimators">
<h2>Estimators<a class="headerlink" href="#estimators" title="Link to this heading">¶</a></h2>
<p>Here we demonstrate how an estimator can expose the required API to support
metadata routing as a consumer. Imagine a simple classifier accepting
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> as a metadata on its <code class="docutils literal notranslate"><span class="pre">fit</span></code> and <code class="docutils literal notranslate"><span class="pre">groups</span></code> in its
<code class="docutils literal notranslate"><span class="pre">predict</span></code> method:</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">ExampleClassifier</span><span class="p">(</span><a href="../../modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin" title="sklearn.base.ClassifierMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ClassifierMixin</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BaseEstimator</span></a><span class="p">):</span>
<span class="k">def</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="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">check_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
<span class="c1"># all classifiers need to expose a classes_ attribute once they're fit.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.array.html#numpy.array" title="numpy.array" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">array</span></a><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">predict</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">groups</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">check_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">)</span>
<span class="c1"># return a constant value of 1, not a very smart classifier!</span>
<span class="k">return</span> <a href="https://numpy.org/doc/stable/reference/generated/numpy.ones.html#numpy.ones" title="numpy.ones" class="sphx-glr-backref-module-numpy sphx-glr-backref-type-py-function"><span class="n">np</span><span class="o">.</span><span class="n">ones</span></a><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
</pre></div>
</div>
<p>The above estimator now has all it needs to consume metadata. This is
accomplished by some magic done in <a class="reference internal" href="../../modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator"><code class="xref py py-class docutils literal notranslate"><span class="pre">BaseEstimator</span></code></a>. There are
now three methods exposed by the above class: <code class="docutils literal notranslate"><span class="pre">set_fit_request</span></code>,
<code class="docutils literal notranslate"><span class="pre">set_predict_request</span></code>, and <code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code>. There is also a
<code class="docutils literal notranslate"><span class="pre">set_score_request</span></code> for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> which is present since
<a class="reference internal" href="../../modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin" title="sklearn.base.ClassifierMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">ClassifierMixin</span></code></a> implements a <code class="docutils literal notranslate"><span class="pre">score</span></code> method accepting
<code class="docutils literal notranslate"><span class="pre">sample_weight</span></code>. The same applies to regressors which inherit from
<a class="reference internal" href="../../modules/generated/sklearn.base.RegressorMixin.html#sklearn.base.RegressorMixin" title="sklearn.base.RegressorMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">RegressorMixin</span></code></a>.</p>
<p>By default, no metadata is requested, which we can see as:</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">print_routing</span><span class="p">(</span><span class="n">ExampleClassifier</span><span class="p">())</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'fit': {'sample_weight': None},
'predict': {'groups': None},
'score': {'sample_weight': None}}
</pre></div>
</div>
<p>The above output means that <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> and <code class="docutils literal notranslate"><span class="pre">groups</span></code> are not
requested, but if a router is given those metadata, it should raise an error,
since the user has not explicitly set whether they are required or not. The
same is true for <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> in the <code class="docutils literal notranslate"><span class="pre">score</span></code> method, which is
inherited from <a class="reference internal" href="../../modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin" title="sklearn.base.ClassifierMixin"><code class="xref py py-class docutils literal notranslate"><span class="pre">ClassifierMixin</span></code></a>. In order to explicitly set
request values for those metadata, we can use these methods:</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">est</span> <span class="o">=</span> <span class="p">(</span>
<span class="n">ExampleClassifier</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="o">.</span><span class="n">set_predict_request</span><span class="p">(</span><span class="n">groups</span><span class="o">=</span><span class="kc">True</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>
<span class="p">)</span>
<span class="n">print_routing</span><span class="p">(</span><span class="n">est</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>{'fit': {'sample_weight': False},
'predict': {'groups': True},
'score': {'sample_weight': False}}
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Please note that as long as the above estimator is not used in another
meta-estimator, the user does not need to set any requests for the
metadata and the set values are ignored, since a consumer does not
validate or route given metadata. A simple usage of the above estimator
would work as expected.</p>
</div>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">est</span> <span class="o">=</span> <span class="n">ExampleClassifier</span><span class="p">()</span>
<span class="n">est</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="n">est</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">[:</span><span class="mi">3</span><span class="p">,</span> <span class="p">:],</span> <span class="n">groups</span><span class="o">=</span><span class="n">my_groups</span><span class="p">)</span>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Received sample_weight of length = 100 in ExampleClassifier.
Received groups of length = 100 in ExampleClassifier.
array([1., 1., 1.])
</pre></div>
</div>
<p>Now let’s have a meta-estimator, which doesn’t do much other than routing the
metadata.</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MetaClassifier</span><span class="p">(</span><a href="../../modules/generated/sklearn.base.MetaEstimatorMixin.html#sklearn.base.MetaEstimatorMixin" title="sklearn.base.MetaEstimatorMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MetaEstimatorMixin</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.base.ClassifierMixin.html#sklearn.base.ClassifierMixin" title="sklearn.base.ClassifierMixin" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">ClassifierMixin</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.base.BaseEstimator.html#sklearn.base.BaseEstimator" title="sklearn.base.BaseEstimator" class="sphx-glr-backref-module-sklearn-base sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">BaseEstimator</span></a><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">estimator</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">=</span> <span class="n">estimator</span>
<span class="k">def</span> <span class="nf">get_metadata_routing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># This method defines the routing for this meta-estimator.</span>
<span class="c1"># In order to do so, a `MetadataRouter` instance is created, and the</span>
<span class="c1"># right routing is added to it. More explanations follow.</span>
<span class="n">router</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.utils.metadata_routing.MetadataRouter.html#sklearn.utils.metadata_routing.MetadataRouter" title="sklearn.utils.metadata_routing.MetadataRouter" class="sphx-glr-backref-module-sklearn-utils-metadata_routing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">MetadataRouter</span></a><span class="p">(</span><span class="n">owner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">add</span><span class="p">(</span>
<span class="n">estimator</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="p">,</span> <span class="n">method_mapping</span><span class="o">=</span><span class="s2">"one-to-one"</span>
<span class="p">)</span>
<span class="k">return</span> <span class="n">router</span>
<span class="k">def</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="p">,</span> <span class="o">**</span><span class="n">fit_params</span><span class="p">):</span>
<span class="c1"># meta-estimators are responsible for validating the given metadata.</span>
<span class="c1"># `get_routing_for_object` is a safe way to construct a</span>
<span class="c1"># `MetadataRouter` or a `MetadataRequest` from the given object.</span>
<span class="n">request_router</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html#sklearn.utils.metadata_routing.get_routing_for_object" title="sklearn.utils.metadata_routing.get_routing_for_object" class="sphx-glr-backref-module-sklearn-utils-metadata_routing sphx-glr-backref-type-py-function"><span class="n">get_routing_for_object</span></a><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="n">request_router</span><span class="o">.</span><span class="n">validate_metadata</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="n">fit_params</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"fit"</span><span class="p">)</span>
<span class="c1"># we can use provided utility methods to map the given metadata to what</span>
<span class="c1"># is required by the underlying estimator. Here `method` refers to the</span>
<span class="c1"># parent's method, i.e. `fit` in this example.</span>
<span class="n">routed_params</span> <span class="o">=</span> <span class="n">request_router</span><span class="o">.</span><span class="n">route_params</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="n">fit_params</span><span class="p">,</span> <span class="n">caller</span><span class="o">=</span><span class="s2">"fit"</span><span class="p">)</span>
<span class="c1"># the output has a key for each object's method which is used here,</span>
<span class="c1"># i.e. parent's `fit` method, containing the metadata which should be</span>
<span class="c1"># routed to them, based on the information provided in</span>
<span class="c1"># `get_metadata_routing`.</span>
<span class="bp">self</span><span class="o">.</span><span class="n">estimator_</span> <span class="o">=</span> <span class="n">clone</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">estimator</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="o">**</span><span class="n">routed_params</span><span class="o">.</span><span class="n">estimator</span><span class="o">.</span><span class="n">fit</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator_</span><span class="o">.</span><span class="n">classes_</span>
<span class="k">return</span> <span class="bp">self</span>
<span class="k">def</span> <span class="nf">predict</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="o">**</span><span class="n">predict_params</span><span class="p">):</span>
<a href="../../modules/generated/sklearn.utils.validation.check_is_fitted.html#sklearn.utils.validation.check_is_fitted" title="sklearn.utils.validation.check_is_fitted" class="sphx-glr-backref-module-sklearn-utils-validation sphx-glr-backref-type-py-function"><span class="n">check_is_fitted</span></a><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="c1"># same as in `fit`, we validate the given metadata</span>
<span class="n">request_router</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html#sklearn.utils.metadata_routing.get_routing_for_object" title="sklearn.utils.metadata_routing.get_routing_for_object" class="sphx-glr-backref-module-sklearn-utils-metadata_routing sphx-glr-backref-type-py-function"><span class="n">get_routing_for_object</span></a><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
<span class="n">request_router</span><span class="o">.</span><span class="n">validate_metadata</span><span class="p">(</span><span class="n">params</span><span class="o">=</span><span class="n">predict_params</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">"predict"</span><span class="p">)</span>
<span class="c1"># and then prepare the input to the underlying `predict` method.</span>
<span class="n">routed_params</span> <span class="o">=</span> <span class="n">request_router</span><span class="o">.</span><span class="n">route_params</span><span class="p">(</span>
<span class="n">params</span><span class="o">=</span><span class="n">predict_params</span><span class="p">,</span> <span class="n">caller</span><span class="o">=</span><span class="s2">"predict"</span>
<span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator_</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="o">**</span><span class="n">routed_params</span><span class="o">.</span><span class="n">estimator</span><span class="o">.</span><span class="n">predict</span><span class="p">)</span>
</pre></div>
</div>
<p>Let’s break down different parts of the above code.</p>
<p>First, the <a class="reference internal" href="../../modules/generated/sklearn.utils.metadata_routing.get_routing_for_object.html#sklearn.utils.metadata_routing.get_routing_for_object" title="sklearn.utils.metadata_routing.get_routing_for_object"><code class="xref py py-meth docutils literal notranslate"><span class="pre">get_routing_for_object</span></code></a> takes an
estimator (<code class="docutils literal notranslate"><span class="pre">self</span></code>) and returns a
<a class="reference internal" href="../../modules/generated/sklearn.utils.metadata_routing.MetadataRouter.html#sklearn.utils.metadata_routing.MetadataRouter" title="sklearn.utils.metadata_routing.MetadataRouter"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRouter</span></code></a> or a
<a class="reference internal" href="../../modules/generated/sklearn.utils.metadata_routing.MetadataRequest.html#sklearn.utils.metadata_routing.MetadataRequest" title="sklearn.utils.metadata_routing.MetadataRequest"><code class="xref py py-class docutils literal notranslate"><span class="pre">MetadataRequest</span></code></a> based on the output of the
estimator’s <code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code> method.</p>
<p>Then in each method, we use the <code class="docutils literal notranslate"><span class="pre">route_params</span></code> method to construct a
dictionary of the form <code class="docutils literal notranslate"><span class="pre">{"object_name":</span> <span class="pre">{"method_name":</span> <span class="pre">{"metadata":</span>
<span class="pre">value}}}</span></code> to pass to the underlying estimator’s method. The <code class="docutils literal notranslate"><span class="pre">object_name</span></code>
(<code class="docutils literal notranslate"><span class="pre">estimator</span></code> in the above <code class="docutils literal notranslate"><span class="pre">routed_params.estimator.fit</span></code> example) is the
same as the one added in the <code class="docutils literal notranslate"><span class="pre">get_metadata_routing</span></code>. <code class="docutils literal notranslate"><span class="pre">validate_metadata</span></code>
makes sure all given metadata are requested to avoid silent bugs. Now, we
illustrate the different behaviors and notably the type of errors raised:</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">est</span> <span class="o">=</span> <span class="n">MetaClassifier</span><span class="p">(</span><span class="n">estimator</span><span class="o">=</span><span class="n">ExampleClassifier</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="n">est</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>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Received sample_weight of length = 100 in ExampleClassifier.
</pre></div>
</div>
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-48 {
/* Definition of color scheme common for light and dark mode */
--sklearn-color-text: black;
--sklearn-color-line: gray;
/* Definition of color scheme for unfitted estimators */
--sklearn-color-unfitted-level-0: #fff5e6;
--sklearn-color-unfitted-level-1: #f6e4d2;
--sklearn-color-unfitted-level-2: #ffe0b3;
--sklearn-color-unfitted-level-3: chocolate;
/* Definition of color scheme for fitted estimators */
--sklearn-color-fitted-level-0: #f0f8ff;
--sklearn-color-fitted-level-1: #d4ebff;
--sklearn-color-fitted-level-2: #b3dbfd;
--sklearn-color-fitted-level-3: cornflowerblue;
/* Specific color for light theme */
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
--sklearn-color-icon: #696969;
@media (prefers-color-scheme: dark) {
/* Redefinition of color scheme for dark theme */
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
--sklearn-color-icon: #878787;
}
}
#sk-container-id-48 {
color: var(--sklearn-color-text);
}
#sk-container-id-48 pre {
padding: 0;
}
#sk-container-id-48 input.sk-hidden--visually {
border: 0;
clip: rect(1px 1px 1px 1px);
clip: rect(1px, 1px, 1px, 1px);
height: 1px;
margin: -1px;
overflow: hidden;
padding: 0;
position: absolute;
width: 1px;
}
#sk-container-id-48 div.sk-dashed-wrapped {
border: 1px dashed var(--sklearn-color-line);
margin: 0 0.4em 0.5em 0.4em;
box-sizing: border-box;
padding-bottom: 0.4em;
background-color: var(--sklearn-color-background);
}
#sk-container-id-48 div.sk-container {
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
but bootstrap.min.css set `[hidden] { display: none !important; }`
so we also need the `!important` here to be able to override the
default hidden behavior on the sphinx rendered scikit-learn.org.
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
display: inline-block !important;
position: relative;
}
#sk-container-id-48 div.sk-text-repr-fallback {
display: none;
}
div.sk-parallel-item,
div.sk-serial,
div.sk-item {
/* draw centered vertical line to link estimators */
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
background-size: 2px 100%;
background-repeat: no-repeat;
background-position: center center;
}
/* Parallel-specific style estimator block */
#sk-container-id-48 div.sk-parallel-item::after {
content: "";
width: 100%;
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
flex-grow: 1;
}
#sk-container-id-48 div.sk-parallel {
display: flex;
align-items: stretch;
justify-content: center;
background-color: var(--sklearn-color-background);
position: relative;
}
#sk-container-id-48 div.sk-parallel-item {
display: flex;
flex-direction: column;
}
#sk-container-id-48 div.sk-parallel-item:first-child::after {
align-self: flex-end;
width: 50%;
}
#sk-container-id-48 div.sk-parallel-item:last-child::after {
align-self: flex-start;
width: 50%;
}
#sk-container-id-48 div.sk-parallel-item:only-child::after {
width: 0;
}
/* Serial-specific style estimator block */
#sk-container-id-48 div.sk-serial {
display: flex;
flex-direction: column;
align-items: center;
background-color: var(--sklearn-color-background);
padding-right: 1em;
padding-left: 1em;
}
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*/
/* Pipeline and ColumnTransformer style (default) */
#sk-container-id-48 div.sk-toggleable {
/* Default theme specific background. It is overwritten whether we have a
specific estimator or a Pipeline/ColumnTransformer */
background-color: var(--sklearn-color-background);
}
/* Toggleable label */
#sk-container-id-48 label.sk-toggleable__label {
cursor: pointer;
display: block;
width: 100%;
margin-bottom: 0;
padding: 0.5em;
box-sizing: border-box;
text-align: center;
}
#sk-container-id-48 label.sk-toggleable__label-arrow:before {
/* Arrow on the left of the label */
content: "▸";
float: left;
margin-right: 0.25em;
color: var(--sklearn-color-icon);
}
#sk-container-id-48 label.sk-toggleable__label-arrow:hover:before {
color: var(--sklearn-color-text);
}
/* Toggleable content - dropdown */
#sk-container-id-48 div.sk-toggleable__content {
max-height: 0;
max-width: 0;
overflow: hidden;
text-align: left;
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-48 div.sk-toggleable__content.fitted {
/* fitted */
background-color: var(--sklearn-color-fitted-level-0);
}
#sk-container-id-48 div.sk-toggleable__content pre {
margin: 0.2em;
border-radius: 0.25em;
color: var(--sklearn-color-text);
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-48 div.sk-toggleable__content.fitted pre {
/* unfitted */
background-color: var(--sklearn-color-fitted-level-0);
}
#sk-container-id-48 input.sk-toggleable__control:checked~div.sk-toggleable__content {
/* Expand drop-down */
max-height: 200px;
max-width: 100%;
overflow: auto;
}
#sk-container-id-48 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
content: "▾";
}
/* Pipeline/ColumnTransformer-specific style */
#sk-container-id-48 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-48 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
background-color: var(--sklearn-color-fitted-level-2);
}
/* Estimator-specific style */
/* Colorize estimator box */
#sk-container-id-48 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-48 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* fitted */
background-color: var(--sklearn-color-fitted-level-2);
}
#sk-container-id-48 div.sk-label label.sk-toggleable__label,
#sk-container-id-48 div.sk-label label {
/* The background is the default theme color */
color: var(--sklearn-color-text-on-default-background);
}
/* On hover, darken the color of the background */
#sk-container-id-48 div.sk-label:hover label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-unfitted-level-2);
}
/* Label box, darken color on hover, fitted */
#sk-container-id-48 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-fitted-level-2);
}
/* Estimator label */
#sk-container-id-48 div.sk-label label {
font-family: monospace;
font-weight: bold;
display: inline-block;
line-height: 1.2em;
}
#sk-container-id-48 div.sk-label-container {
text-align: center;
}
/* Estimator-specific */
#sk-container-id-48 div.sk-estimator {
font-family: monospace;
border: 1px dotted var(--sklearn-color-border-box);
border-radius: 0.25em;
box-sizing: border-box;
margin-bottom: 0.5em;
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-48 div.sk-estimator.fitted {
/* fitted */
background-color: var(--sklearn-color-fitted-level-0);
}
/* on hover */
#sk-container-id-48 div.sk-estimator:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-48 div.sk-estimator.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-2);
}
/* Specification for estimator info (e.g. "i" and "?") */
/* Common style for "i" and "?" */
.sk-estimator-doc-link,
a:link.sk-estimator-doc-link,
a:visited.sk-estimator-doc-link {
float: right;
font-size: smaller;
line-height: 1em;
font-family: monospace;
background-color: var(--sklearn-color-background);
border-radius: 1em;
height: 1em;
width: 1em;
text-decoration: none !important;
margin-left: 1ex;
/* unfitted */
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
color: var(--sklearn-color-unfitted-level-1);
}
.sk-estimator-doc-link.fitted,
a:link.sk-estimator-doc-link.fitted,
a:visited.sk-estimator-doc-link.fitted {
/* fitted */
border: var(--sklearn-color-fitted-level-1) 1pt solid;
color: var(--sklearn-color-fitted-level-1);
}
/* On hover */
div.sk-estimator:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover,
div.sk-label-container:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover,
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
/* Span, style for the box shown on hovering the info icon */
.sk-estimator-doc-link span {
display: none;
z-index: 9999;
position: relative;
font-weight: normal;
right: .2ex;
padding: .5ex;
margin: .5ex;
width: min-content;
min-width: 20ex;
max-width: 50ex;
color: var(--sklearn-color-text);
box-shadow: 2pt 2pt 4pt #999;
/* unfitted */
background: var(--sklearn-color-unfitted-level-0);
border: .5pt solid var(--sklearn-color-unfitted-level-3);
}
.sk-estimator-doc-link.fitted span {
/* fitted */
background: var(--sklearn-color-fitted-level-0);
border: var(--sklearn-color-fitted-level-3);
}
.sk-estimator-doc-link:hover span {
display: block;
}
/* "?"-specific style due to the `<a>` HTML tag */
#sk-container-id-48 a.estimator_doc_link {
float: right;
font-size: 1rem;
line-height: 1em;
font-family: monospace;
background-color: var(--sklearn-color-background);
border-radius: 1rem;
height: 1rem;
width: 1rem;
text-decoration: none;
/* unfitted */
color: var(--sklearn-color-unfitted-level-1);
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
}
#sk-container-id-48 a.estimator_doc_link.fitted {
/* fitted */
border: var(--sklearn-color-fitted-level-1) 1pt solid;
color: var(--sklearn-color-fitted-level-1);
}
/* On hover */
#sk-container-id-48 a.estimator_doc_link:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
#sk-container-id-48 a.estimator_doc_link.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-3);
}
</style><div id="sk-container-id-48" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MetaClassifier(estimator=ExampleClassifier())</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-212" type="checkbox" ><label for="sk-estimator-id-212" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> MetaClassifier<span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>MetaClassifier(estimator=ExampleClassifier())</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-213" type="checkbox" ><label for="sk-estimator-id-213" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">estimator: ExampleClassifier</label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-214" type="checkbox" ><label for="sk-estimator-id-214" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">ExampleClassifier</label><div class="sk-toggleable__content fitted"><pre>ExampleClassifier()</pre></div> </div></div></div></div></div></div></div></div></div>
</div>
<br />
<br /><p>Note that the above example checks that <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is correctly passed
to <code class="docutils literal notranslate"><span class="pre">ExampleClassifier</span></code>, or else it would print that <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> is
<code class="docutils literal notranslate"><span class="pre">None</span></code>:</p>
<div class="highlight-Python notranslate"><div class="highlight"><pre><span></span><span class="n">est</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>
</pre></div>
</div>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>sample_weight is None in ExampleClassifier.
</pre></div>
</div>
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-49 {
/* Definition of color scheme common for light and dark mode */
--sklearn-color-text: black;
--sklearn-color-line: gray;
/* Definition of color scheme for unfitted estimators */
--sklearn-color-unfitted-level-0: #fff5e6;
--sklearn-color-unfitted-level-1: #f6e4d2;
--sklearn-color-unfitted-level-2: #ffe0b3;
--sklearn-color-unfitted-level-3: chocolate;
/* Definition of color scheme for fitted estimators */
--sklearn-color-fitted-level-0: #f0f8ff;
--sklearn-color-fitted-level-1: #d4ebff;
--sklearn-color-fitted-level-2: #b3dbfd;
--sklearn-color-fitted-level-3: cornflowerblue;
/* Specific color for light theme */
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
--sklearn-color-icon: #696969;
@media (prefers-color-scheme: dark) {
/* Redefinition of color scheme for dark theme */
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
--sklearn-color-icon: #878787;
}
}
#sk-container-id-49 {
color: var(--sklearn-color-text);
}
#sk-container-id-49 pre {
padding: 0;
}
#sk-container-id-49 input.sk-hidden--visually {
border: 0;
clip: rect(1px 1px 1px 1px);
clip: rect(1px, 1px, 1px, 1px);
height: 1px;
margin: -1px;
overflow: hidden;
padding: 0;
position: absolute;
width: 1px;
}
#sk-container-id-49 div.sk-dashed-wrapped {
border: 1px dashed var(--sklearn-color-line);
margin: 0 0.4em 0.5em 0.4em;
box-sizing: border-box;
padding-bottom: 0.4em;
background-color: var(--sklearn-color-background);
}
#sk-container-id-49 div.sk-container {
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
but bootstrap.min.css set `[hidden] { display: none !important; }`
so we also need the `!important` here to be able to override the
default hidden behavior on the sphinx rendered scikit-learn.org.
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
display: inline-block !important;
position: relative;
}
#sk-container-id-49 div.sk-text-repr-fallback {
display: none;
}
div.sk-parallel-item,
div.sk-serial,
div.sk-item {
/* draw centered vertical line to link estimators */
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
background-size: 2px 100%;
background-repeat: no-repeat;
background-position: center center;
}
/* Parallel-specific style estimator block */
#sk-container-id-49 div.sk-parallel-item::after {
content: "";
width: 100%;
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
flex-grow: 1;
}
#sk-container-id-49 div.sk-parallel {
display: flex;
align-items: stretch;
justify-content: center;
background-color: var(--sklearn-color-background);
position: relative;
}
#sk-container-id-49 div.sk-parallel-item {
display: flex;
flex-direction: column;
}
#sk-container-id-49 div.sk-parallel-item:first-child::after {
align-self: flex-end;
width: 50%;
}
#sk-container-id-49 div.sk-parallel-item:last-child::after {
align-self: flex-start;
width: 50%;
}
#sk-container-id-49 div.sk-parallel-item:only-child::after {
width: 0;
}
/* Serial-specific style estimator block */
#sk-container-id-49 div.sk-serial {
display: flex;
flex-direction: column;
align-items: center;
background-color: var(--sklearn-color-background);
padding-right: 1em;
padding-left: 1em;
}
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*/
/* Pipeline and ColumnTransformer style (default) */
#sk-container-id-49 div.sk-toggleable {
/* Default theme specific background. It is overwritten whether we have a
specific estimator or a Pipeline/ColumnTransformer */
background-color: var(--sklearn-color-background);
}
/* Toggleable label */
#sk-container-id-49 label.sk-toggleable__label {
cursor: pointer;
display: block;
width: 100%;
margin-bottom: 0;
padding: 0.5em;
box-sizing: border-box;
text-align: center;
}
#sk-container-id-49 label.sk-toggleable__label-arrow:before {
/* Arrow on the left of the label */
content: "▸";
float: left;
margin-right: 0.25em;
color: var(--sklearn-color-icon);
}
#sk-container-id-49 label.sk-toggleable__label-arrow:hover:before {
color: var(--sklearn-color-text);
}
/* Toggleable content - dropdown */
#sk-container-id-49 div.sk-toggleable__content {
max-height: 0;
max-width: 0;