
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_feature_transformation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_ensemble_plot_feature_transformation.py>`
        to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ensemble_plot_feature_transformation.py:


===============================================
Feature transformations with ensembles of trees
===============================================

Transform your features into a higher dimensional, sparse space. Then train a
linear model on these features.

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.

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.

The resulting transformer has then learned a supervised, sparse,
high-dimensional categorical embedding of the data.

.. GENERATED FROM PYTHON SOURCE LINES 22-28

.. code-block:: default



    # Author: Tim Head <betatim@gmail.com>
    #
    # License: BSD 3 clause








.. GENERATED FROM PYTHON SOURCE LINES 29-38

First, we will create a large dataset and split it into three sets:

- a set to train the ensemble methods which are later used to as a feature
  engineering transformer;
- a set to train the linear model;
- a set to test the linear model.

It is important to split the data in such way to avoid overfitting by leaking
data.

.. GENERATED FROM PYTHON SOURCE LINES 38-51

.. code-block:: default


    from sklearn.datasets import make_classification
    from sklearn.model_selection import train_test_split

    X, y = make_classification(n_samples=80_000, random_state=10)

    X_full_train, X_test, y_full_train, y_test = train_test_split(
        X, y, test_size=0.5, random_state=10
    )
    X_train_ensemble, X_train_linear, y_train_ensemble, y_train_linear = train_test_split(
        X_full_train, y_full_train, test_size=0.5, random_state=10
    )








.. GENERATED FROM PYTHON SOURCE LINES 52-54

For each of the ensemble methods, we will use 10 estimators and a maximum
depth of 3 levels.

.. GENERATED FROM PYTHON SOURCE LINES 54-58

.. code-block:: default


    n_estimators = 10
    max_depth = 3








.. GENERATED FROM PYTHON SOURCE LINES 59-61

First, we will start by training the random forest and gradient boosting on
the separated training set

.. GENERATED FROM PYTHON SOURCE LINES 61-74

.. code-block:: default


    from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier

    random_forest = RandomForestClassifier(
        n_estimators=n_estimators, max_depth=max_depth, random_state=10
    )
    random_forest.fit(X_train_ensemble, y_train_ensemble)

    gradient_boosting = GradientBoostingClassifier(
        n_estimators=n_estimators, max_depth=max_depth, random_state=10
    )
    _ = gradient_boosting.fit(X_train_ensemble, y_train_ensemble)








.. GENERATED FROM PYTHON SOURCE LINES 75-82

Notice that :class:`~sklearn.ensemble.HistGradientBoostingClassifier` is much
faster than :class:`~sklearn.ensemble.GradientBoostingClassifier` starting
with intermediate datasets (`n_samples >= 10_000`), which is not the case of
the present example.

The :class:`~sklearn.ensemble.RandomTreesEmbedding` is an unsupervised method
and thus does not required to be trained independently.

.. GENERATED FROM PYTHON SOURCE LINES 82-89

.. code-block:: default


    from sklearn.ensemble import RandomTreesEmbedding

    random_tree_embedding = RandomTreesEmbedding(
        n_estimators=n_estimators, max_depth=max_depth, random_state=0
    )








.. GENERATED FROM PYTHON SOURCE LINES 90-95

Now, we will create three pipelines that will use the above embedding as
a preprocessing stage.

The random trees embedding can be directly pipelined with the logistic
regression because it is a standard scikit-learn transformer.

.. GENERATED FROM PYTHON SOURCE LINES 95-102

.. code-block:: default


    from sklearn.linear_model import LogisticRegression
    from sklearn.pipeline import make_pipeline

    rt_model = make_pipeline(random_tree_embedding, LogisticRegression(max_iter=1000))
    rt_model.fit(X_train_linear, y_train_linear)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-18 {color: black;}#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=[(&#x27;randomtreesembedding&#x27;,
                     RandomTreesEmbedding(max_depth=3, n_estimators=10,
                                          random_state=0)),
                    (&#x27;logisticregression&#x27;, 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=[(&#x27;randomtreesembedding&#x27;,
                     RandomTreesEmbedding(max_depth=3, n_estimators=10,
                                          random_state=0)),
                    (&#x27;logisticregression&#x27;, 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 />

.. GENERATED FROM PYTHON SOURCE LINES 103-107

Then, we can pipeline random forest or gradient boosting with a logistic
regression. However, the feature transformation will happen by calling the
method `apply`. The pipeline in scikit-learn expects a call to `transform`.
Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.

.. GENERATED FROM PYTHON SOURCE LINES 107-125

.. code-block:: default


    from sklearn.preprocessing import FunctionTransformer, OneHotEncoder


    def rf_apply(X, model):
        return model.apply(X)


    rf_leaves_yielder = FunctionTransformer(rf_apply, kw_args={"model": random_forest})

    rf_model = make_pipeline(
        rf_leaves_yielder,
        OneHotEncoder(handle_unknown="ignore"),
        LogisticRegression(max_iter=1000),
    )
    rf_model.fit(X_train_linear, y_train_linear)







.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-19 {color: black;}#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=[(&#x27;functiontransformer&#x27;,
                     FunctionTransformer(func=&lt;function rf_apply at 0x7fca1f1e6700&gt;,
                                         kw_args={&#x27;model&#x27;: RandomForestClassifier(max_depth=3,
                                                                                  n_estimators=10,
                                                                                  random_state=10)})),
                    (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                    (&#x27;logisticregression&#x27;, 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=[(&#x27;functiontransformer&#x27;,
                     FunctionTransformer(func=&lt;function rf_apply at 0x7fca1f1e6700&gt;,
                                         kw_args={&#x27;model&#x27;: RandomForestClassifier(max_depth=3,
                                                                                  n_estimators=10,
                                                                                  random_state=10)})),
                    (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                    (&#x27;logisticregression&#x27;, 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=&lt;function rf_apply at 0x7fca1f1e6700&gt;,
                        kw_args={&#x27;model&#x27;: 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=&#x27;ignore&#x27;)</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 />

.. GENERATED FROM PYTHON SOURCE LINES 126-141

.. code-block:: default

    def gbdt_apply(X, model):
        return model.apply(X)[:, :, 0]


    gbdt_leaves_yielder = FunctionTransformer(
        gbdt_apply, kw_args={"model": gradient_boosting}
    )

    gbdt_model = make_pipeline(
        gbdt_leaves_yielder,
        OneHotEncoder(handle_unknown="ignore"),
        LogisticRegression(max_iter=1000),
    )
    gbdt_model.fit(X_train_linear, y_train_linear)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-20 {color: black;}#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=[(&#x27;functiontransformer&#x27;,
                     FunctionTransformer(func=&lt;function gbdt_apply at 0x7fca1f1e5a80&gt;,
                                         kw_args={&#x27;model&#x27;: GradientBoostingClassifier(n_estimators=10,
                                                                                      random_state=10)})),
                    (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                    (&#x27;logisticregression&#x27;, 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=[(&#x27;functiontransformer&#x27;,
                     FunctionTransformer(func=&lt;function gbdt_apply at 0x7fca1f1e5a80&gt;,
                                         kw_args={&#x27;model&#x27;: GradientBoostingClassifier(n_estimators=10,
                                                                                      random_state=10)})),
                    (&#x27;onehotencoder&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),
                    (&#x27;logisticregression&#x27;, 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=&lt;function gbdt_apply at 0x7fca1f1e5a80&gt;,
                        kw_args={&#x27;model&#x27;: 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=&#x27;ignore&#x27;)</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 />

.. GENERATED FROM PYTHON SOURCE LINES 142-143

We can finally show the different ROC curves for all the models.

.. GENERATED FROM PYTHON SOURCE LINES 143-165

.. code-block:: default


    import matplotlib.pyplot as plt

    from sklearn.metrics import RocCurveDisplay

    fig, ax = plt.subplots()

    models = [
        ("RT embedding -> LR", rt_model),
        ("RF", random_forest),
        ("RF embedding -> LR", rf_model),
        ("GBDT", gradient_boosting),
        ("GBDT embedding -> LR", gbdt_model),
    ]

    model_displays = {}
    for name, pipeline in models:
        model_displays[name] = RocCurveDisplay.from_estimator(
            pipeline, X_test, y_test, ax=ax, name=name
        )
    _ = ax.set_title("ROC curve")




.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_feature_transformation_001.png
   :alt: ROC curve
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_feature_transformation_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 166-173

.. code-block:: default

    fig, ax = plt.subplots()
    for name, pipeline in models:
        model_displays[name].plot(ax=ax)

    ax.set_xlim(0, 0.2)
    ax.set_ylim(0.8, 1)
    _ = ax.set_title("ROC curve (zoomed in at top left)")



.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_feature_transformation_002.png
   :alt: ROC curve (zoomed in at top left)
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_feature_transformation_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 2.423 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_feature_transformation.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.3.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_feature_transformation.ipynb
        :alt: Launch binder
        :width: 150 px



    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_feature_transformation.py <plot_feature_transformation.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_feature_transformation.ipynb <plot_feature_transformation.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
