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<li><a class="reference internal" href="#">Feature transformations with ensembles of trees</a></li>
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<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-ensemble-plot-feature-transformation-py"><span class="std std-ref">here</span></a>
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<section class="sphx-glr-example-title" id="feature-transformations-with-ensembles-of-trees">
<span id="sphx-glr-auto-examples-ensemble-plot-feature-transformation-py"></span><h1>Feature transformations with ensembles of trees<a class="headerlink" href="#feature-transformations-with-ensembles-of-trees" title="Permalink to this heading">¶</a></h1>
<p>Transform your features into a higher dimensional, sparse space. Then train a
linear model on these features.</p>
<p>First fit an ensemble of trees (totally random trees, a random forest, or
gradient boosted trees) on the training set. Then each leaf of each tree in the
ensemble is assigned a fixed arbitrary feature index in a new feature space.
These leaf indices are then encoded in a one-hot fashion.</p>
<p>Each sample goes through the decisions of each tree of the ensemble and ends up
in one leaf per tree. The sample is encoded by setting feature values for these
leaves to 1 and the other feature values to 0.</p>
<p>The resulting transformer has then learned a supervised, sparse,
high-dimensional categorical embedding of the data.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Tim Head <[email protected]></span>
<span class="c1">#</span>
<span class="c1"># License: BSD 3 clause</span>
</pre></div>
</div>
<p>First, we will create a large dataset and split it into three sets:</p>
<ul class="simple">
<li><p>a set to train the ensemble methods which are later used to as a feature
engineering transformer;</p></li>
<li><p>a set to train the linear model;</p></li>
<li><p>a set to test the linear model.</p></li>
</ul>
<p>It is important to split the data in such way to avoid overfitting by leaking
data.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification" class="sphx-glr-backref-module-sklearn-datasets sphx-glr-backref-type-py-function"><span class="n">make_classification</span></a><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">80_000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">X_full_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_full_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><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">test_size</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span>
<span class="p">)</span>
<span class="n">X_train_ensemble</span><span class="p">,</span> <span class="n">X_train_linear</span><span class="p">,</span> <span class="n">y_train_ensemble</span><span class="p">,</span> <span class="n">y_train_linear</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split" title="sklearn.model_selection.train_test_split" class="sphx-glr-backref-module-sklearn-model_selection sphx-glr-backref-type-py-function"><span class="n">train_test_split</span></a><span class="p">(</span>
<span class="n">X_full_train</span><span class="p">,</span> <span class="n">y_full_train</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span>
<span class="p">)</span>
</pre></div>
</div>
<p>For each of the ensemble methods, we will use 10 estimators and a maximum
depth of 3 levels.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">n_estimators</span> <span class="o">=</span> <span class="mi">10</span>
<span class="n">max_depth</span> <span class="o">=</span> <span class="mi">3</span>
</pre></div>
</div>
<p>First, we will start by training the random forest and gradient boosting on
the separated training set</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">,</span> <a href="../../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GradientBoostingClassifier</span></a>
<span class="n">random_forest</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier" title="sklearn.ensemble.RandomForestClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomForestClassifier</span></a><span class="p">(</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="n">n_estimators</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="n">max_depth</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span>
<span class="p">)</span>
<span class="n">random_forest</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_ensemble</span><span class="p">,</span> <span class="n">y_train_ensemble</span><span class="p">)</span>
<span class="n">gradient_boosting</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">GradientBoostingClassifier</span></a><span class="p">(</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="n">n_estimators</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="n">max_depth</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">10</span>
<span class="p">)</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">gradient_boosting</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_ensemble</span><span class="p">,</span> <span class="n">y_train_ensemble</span><span class="p">)</span>
</pre></div>
</div>
<p>Notice that <a class="reference internal" href="../../modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html#sklearn.ensemble.HistGradientBoostingClassifier" title="sklearn.ensemble.HistGradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">HistGradientBoostingClassifier</span></code></a> is much
faster than <a class="reference internal" href="../../modules/generated/sklearn.ensemble.GradientBoostingClassifier.html#sklearn.ensemble.GradientBoostingClassifier" title="sklearn.ensemble.GradientBoostingClassifier"><code class="xref py py-class docutils literal notranslate"><span class="pre">GradientBoostingClassifier</span></code></a> starting
with intermediate datasets (<code class="docutils literal notranslate"><span class="pre">n_samples</span> <span class="pre">>=</span> <span class="pre">10_000</span></code>), which is not the case of
the present example.</p>
<p>The <a class="reference internal" href="../../modules/generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble.RandomTreesEmbedding"><code class="xref py py-class docutils literal notranslate"><span class="pre">RandomTreesEmbedding</span></code></a> is an unsupervised method
and thus does not required to be trained independently.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble.RandomTreesEmbedding" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomTreesEmbedding</span></a>
<span class="n">random_tree_embedding</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.ensemble.RandomTreesEmbedding.html#sklearn.ensemble.RandomTreesEmbedding" title="sklearn.ensemble.RandomTreesEmbedding" class="sphx-glr-backref-module-sklearn-ensemble sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">RandomTreesEmbedding</span></a><span class="p">(</span>
<span class="n">n_estimators</span><span class="o">=</span><span class="n">n_estimators</span><span class="p">,</span> <span class="n">max_depth</span><span class="o">=</span><span class="n">max_depth</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span>
<span class="p">)</span>
</pre></div>
</div>
<p>Now, we will create three pipelines that will use the above embedding as
a preprocessing stage.</p>
<p>The random trees embedding can be directly pipelined with the logistic
regression because it is a standard scikit-learn transformer.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></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.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a>
<span class="n">rt_model</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span><span class="n">random_tree_embedding</span><span class="p">,</span> <a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">))</span>
<span class="n">rt_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_linear</span><span class="p">,</span> <span class="n">y_train_linear</span><span class="p">)</span>
</pre></div>
</div>
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 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-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 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-18 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-18" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('randomtreesembedding',
RandomTreesEmbedding(max_depth=3, n_estimators=10,
random_state=0)),
('logisticregression', LogisticRegression(max_iter=1000))])</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-90" type="checkbox" ><label for="sk-estimator-id-90" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('randomtreesembedding',
RandomTreesEmbedding(max_depth=3, n_estimators=10,
random_state=0)),
('logisticregression', LogisticRegression(max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-91" type="checkbox" ><label for="sk-estimator-id-91" class="sk-toggleable__label sk-toggleable__label-arrow">RandomTreesEmbedding</label><div class="sk-toggleable__content"><pre>RandomTreesEmbedding(max_depth=3, n_estimators=10, random_state=0)</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-92" type="checkbox" ><label for="sk-estimator-id-92" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div>
</div>
<br />
<br /><p>Then, we can pipeline random forest or gradient boosting with a logistic
regression. However, the feature transformation will happen by calling the
method <code class="docutils literal notranslate"><span class="pre">apply</span></code>. The pipeline in scikit-learn expects a call to <code class="docutils literal notranslate"><span class="pre">transform</span></code>.
Therefore, we wrapped the call to <code class="docutils literal notranslate"><span class="pre">apply</span></code> within a <code class="docutils literal notranslate"><span class="pre">FunctionTransformer</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FunctionTransformer</span></a>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <a href="../../modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a>
<span class="k">def</span> <span class="nf">rf_apply</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">rf_leaves_yielder</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FunctionTransformer</span></a><span class="p">(</span><span class="n">rf_apply</span><span class="p">,</span> <span class="n">kw_args</span><span class="o">=</span><span class="p">{</span><span class="s2">"model"</span><span class="p">:</span> <span class="n">random_forest</span><span class="p">})</span>
<span class="n">rf_model</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
<span class="n">rf_leaves_yielder</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">rf_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_linear</span><span class="p">,</span> <span class="n">y_train_linear</span><span class="p">)</span>
</pre></div>
</div>
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 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-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 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-19 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-19" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('functiontransformer',
FunctionTransformer(func=<function rf_apply at 0x7fd05f49d550>,
kw_args={'model': RandomForestClassifier(max_depth=3,
n_estimators=10,
random_state=10)})),
('onehotencoder', OneHotEncoder(handle_unknown='ignore')),
('logisticregression', LogisticRegression(max_iter=1000))])</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-93" type="checkbox" ><label for="sk-estimator-id-93" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('functiontransformer',
FunctionTransformer(func=<function rf_apply at 0x7fd05f49d550>,
kw_args={'model': RandomForestClassifier(max_depth=3,
n_estimators=10,
random_state=10)})),
('onehotencoder', OneHotEncoder(handle_unknown='ignore')),
('logisticregression', LogisticRegression(max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-94" type="checkbox" ><label for="sk-estimator-id-94" class="sk-toggleable__label sk-toggleable__label-arrow">FunctionTransformer</label><div class="sk-toggleable__content"><pre>FunctionTransformer(func=<function rf_apply at 0x7fd05f49d550>,
kw_args={'model': RandomForestClassifier(max_depth=3,
n_estimators=10,
random_state=10)})</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-95" type="checkbox" ><label for="sk-estimator-id-95" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-96" type="checkbox" ><label for="sk-estimator-id-96" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div>
</div>
<br />
<br /><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">gbdt_apply</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
<span class="k">return</span> <span class="n">model</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">X</span><span class="p">)[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span>
<span class="n">gbdt_leaves_yielder</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.preprocessing.FunctionTransformer.html#sklearn.preprocessing.FunctionTransformer" title="sklearn.preprocessing.FunctionTransformer" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">FunctionTransformer</span></a><span class="p">(</span>
<span class="n">gbdt_apply</span><span class="p">,</span> <span class="n">kw_args</span><span class="o">=</span><span class="p">{</span><span class="s2">"model"</span><span class="p">:</span> <span class="n">gradient_boosting</span><span class="p">}</span>
<span class="p">)</span>
<span class="n">gbdt_model</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline" title="sklearn.pipeline.make_pipeline" class="sphx-glr-backref-module-sklearn-pipeline sphx-glr-backref-type-py-function"><span class="n">make_pipeline</span></a><span class="p">(</span>
<span class="n">gbdt_leaves_yielder</span><span class="p">,</span>
<a href="../../modules/generated/sklearn.preprocessing.OneHotEncoder.html#sklearn.preprocessing.OneHotEncoder" title="sklearn.preprocessing.OneHotEncoder" class="sphx-glr-backref-module-sklearn-preprocessing sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">OneHotEncoder</span></a><span class="p">(</span><span class="n">handle_unknown</span><span class="o">=</span><span class="s2">"ignore"</span><span class="p">),</span>
<a href="../../modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" title="sklearn.linear_model.LogisticRegression" class="sphx-glr-backref-module-sklearn-linear_model sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">LogisticRegression</span></a><span class="p">(</span><span class="n">max_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">),</span>
<span class="p">)</span>
<span class="n">gbdt_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_linear</span><span class="p">,</span> <span class="n">y_train_linear</span><span class="p">)</span>
</pre></div>
</div>
<div class="output_subarea output_html rendered_html output_result">
<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 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-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 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-20 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-20" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('functiontransformer',
FunctionTransformer(func=<function gbdt_apply at 0x7fd05ecc5790>,
kw_args={'model': GradientBoostingClassifier(n_estimators=10,
random_state=10)})),
('onehotencoder', OneHotEncoder(handle_unknown='ignore')),
('logisticregression', LogisticRegression(max_iter=1000))])</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 sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-97" type="checkbox" ><label for="sk-estimator-id-97" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('functiontransformer',
FunctionTransformer(func=<function gbdt_apply at 0x7fd05ecc5790>,
kw_args={'model': GradientBoostingClassifier(n_estimators=10,
random_state=10)})),
('onehotencoder', OneHotEncoder(handle_unknown='ignore')),
('logisticregression', LogisticRegression(max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-98" type="checkbox" ><label for="sk-estimator-id-98" class="sk-toggleable__label sk-toggleable__label-arrow">FunctionTransformer</label><div class="sk-toggleable__content"><pre>FunctionTransformer(func=<function gbdt_apply at 0x7fd05ecc5790>,
kw_args={'model': GradientBoostingClassifier(n_estimators=10,
random_state=10)})</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-99" type="checkbox" ><label for="sk-estimator-id-99" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-100" type="checkbox" ><label for="sk-estimator-id-100" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div>
</div>
<br />
<br /><p>We can finally show the different ROC curves for all the models.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">RocCurveDisplay</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
<span class="n">models</span> <span class="o">=</span> <span class="p">[</span>
<span class="p">(</span><span class="s2">"RT embedding -> LR"</span><span class="p">,</span> <span class="n">rt_model</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"RF"</span><span class="p">,</span> <span class="n">random_forest</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"RF embedding -> LR"</span><span class="p">,</span> <span class="n">rf_model</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"GBDT"</span><span class="p">,</span> <span class="n">gradient_boosting</span><span class="p">),</span>
<span class="p">(</span><span class="s2">"GBDT embedding -> LR"</span><span class="p">,</span> <span class="n">gbdt_model</span><span class="p">),</span>
<span class="p">]</span>
<span class="n">model_displays</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">pipeline</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
<span class="n">model_displays</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <a href="../../modules/generated/sklearn.metrics.RocCurveDisplay.html#sklearn.metrics.RocCurveDisplay.from_estimator" title="sklearn.metrics.RocCurveDisplay.from_estimator" class="sphx-glr-backref-module-sklearn-metrics-RocCurveDisplay sphx-glr-backref-type-py-method"><span class="n">RocCurveDisplay</span><span class="o">.</span><span class="n">from_estimator</span></a><span class="p">(</span>
<span class="n">pipeline</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">name</span>
<span class="p">)</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"ROC curve"</span><span class="p">)</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_plot_feature_transformation_001.png" srcset="../../_images/sphx_glr_plot_feature_transformation_001.png" alt="ROC curve" class = "sphx-glr-single-img"/><div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html#matplotlib.pyplot.subplots" title="matplotlib.pyplot.subplots" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">subplots</span></a><span class="p">()</span>
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">pipeline</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
<span class="n">model_displays</span><span class="p">[</span><span class="n">name</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="mf">0.8</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">"ROC curve (zoomed in at top left)"</span><span class="p">)</span>
</pre></div>
</div>
<img src="../../_images/sphx_glr_plot_feature_transformation_002.png" srcset="../../_images/sphx_glr_plot_feature_transformation_002.png" alt="ROC curve (zoomed in at top left)" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 2.689 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-ensemble-plot-feature-transformation-py">
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<p><a class="reference download internal" download="" href="../../_downloads/3a10dcfbc1a4bf1349c7101a429aa47b/plot_feature_transformation.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_feature_transformation.py</span></code></a></p>
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