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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py:


=========================================
Advanced Plotting With Partial Dependence
=========================================
The :func:`~sklearn.inspection.plot_partial_dependence` function returns a
:class:`~sklearn.inspection.PartialDependenceDisplay` object that can be used
for plotting without needing to recalculate the partial dependence. In this
example, we show how to plot partial dependence plots and how to quickly
customize the plot with the visualization API.

.. note::

    See also :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`

.. GENERATED FROM PYTHON SOURCE LINES 16-28

.. code-block:: default

    print(__doc__)

    import pandas as pd
    import matplotlib.pyplot as plt
    from sklearn.datasets import load_diabetes
    from sklearn.neural_network import MLPRegressor
    from sklearn.preprocessing import StandardScaler
    from sklearn.pipeline import make_pipeline
    from sklearn.tree import DecisionTreeRegressor
    from sklearn.inspection import plot_partial_dependence









.. GENERATED FROM PYTHON SOURCE LINES 29-34

Train models on the diabetes dataset
================================================

First, we train a decision tree and a multi-layer perceptron on the diabetes
dataset.

.. GENERATED FROM PYTHON SOURCE LINES 34-46

.. code-block:: default


    diabetes = load_diabetes()
    X = pd.DataFrame(diabetes.data, columns=diabetes.feature_names)
    y = diabetes.target

    tree = DecisionTreeRegressor()
    mlp = make_pipeline(StandardScaler(),
                        MLPRegressor(hidden_layer_sizes=(100, 100),
                                     tol=1e-2, max_iter=500, random_state=0))
    tree.fit(X, y)
    mlp.fit(X, y)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}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;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class="sk-top-container"><div class="sk-container"><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="8b38f849-1eea-42f3-9d9b-7201c527cfbd" type="checkbox" ><label class="sk-toggleable__label" for="8b38f849-1eea-42f3-9d9b-7201c527cfbd">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('standardscaler', StandardScaler()),
                    ('mlpregressor',
                     MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500,
                                  random_state=0, tol=0.01))])</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="78447707-36c9-42ae-83f2-9e0f8d811a06" type="checkbox" ><label class="sk-toggleable__label" for="78447707-36c9-42ae-83f2-9e0f8d811a06">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="a76c80c2-1cd6-421f-87b0-ee2e891022ad" type="checkbox" ><label class="sk-toggleable__label" for="a76c80c2-1cd6-421f-87b0-ee2e891022ad">MLPRegressor</label><div class="sk-toggleable__content"><pre>MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, random_state=0,
                 tol=0.01)</pre></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 47-55

Plotting partial dependence for two features
============================================

We plot partial dependence curves for features "age" and "bmi" (body mass
index) for the decision tree. With two features,
:func:`~sklearn.inspection.plot_partial_dependence` expects to plot two
curves. Here the plot function place a grid of two plots using the space
defined by `ax` .

.. GENERATED FROM PYTHON SOURCE LINES 55-59

.. code-block:: default

    fig, ax = plt.subplots(figsize=(12, 6))
    ax.set_title("Decision Tree")
    tree_disp = plot_partial_dependence(tree, X, ["age", "bmi"], ax=ax)




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_001.png
   :alt: Decision Tree
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /build/scikit-learn-ZSX7SD/scikit-learn-0.23.2/.pybuild/cpython3_3.10/build/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.
      warnings.warn(msg, FutureWarning)




.. GENERATED FROM PYTHON SOURCE LINES 60-64

The partial depdendence curves can be plotted for the multi-layer perceptron.
In this case, `line_kw` is passed to
:func:`~sklearn.inspection.plot_partial_dependence` to change the color of
the curve.

.. GENERATED FROM PYTHON SOURCE LINES 64-69

.. code-block:: default

    fig, ax = plt.subplots(figsize=(12, 6))
    ax.set_title("Multi-layer Perceptron")
    mlp_disp = plot_partial_dependence(mlp, X, ["age", "bmi"], ax=ax,
                                       line_kw={"c": "red"})




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_002.png
   :alt: Multi-layer Perceptron
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 70-87

Plotting partial dependence of the two models together
======================================================

The `tree_disp` and `mlp_disp`
:class:`~sklearn.inspection.PartialDependenceDisplay` objects contain all the
computed information needed to recreate the partial dependence curves. This
means we can easily create additional plots without needing to recompute the
curves.

One way to plot the curves is to place them in the same figure, with the
curves of each model on each row. First, we create a figure with two axes
within two rows and one column. The two axes are passed to the
:func:`~sklearn.inspection.PartialDependenceDisplay.plot` functions of
`tree_disp` and `mlp_disp`. The given axes will be used by the plotting
function to draw the partial dependence. The resulting plot places the
decision tree partial dependence curves in the first row of the
multi-layer perceptron in the second row.

.. GENERATED FROM PYTHON SOURCE LINES 87-94

.. code-block:: default


    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 10))
    tree_disp.plot(ax=ax1)
    ax1.set_title("Decision Tree")
    mlp_disp.plot(ax=ax2, line_kw={"c": "red"})
    ax2.set_title("Multi-layer Perceptron")




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_003.png
   :alt: Decision Tree, Multi-layer Perceptron
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_003.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Text(0.5, 1.0, 'Multi-layer Perceptron')



.. GENERATED FROM PYTHON SOURCE LINES 95-100

Another way to compare the curves is to plot them on top of each other. Here,
we create a figure with one row and two columns. The axes are passed into the
:func:`~sklearn.inspection.PartialDependenceDisplay.plot` function as a list,
which will plot the partial dependence curves of each model on the same axes.
The length of the axes list must be equal to the number of plots drawn.

.. GENERATED FROM PYTHON SOURCE LINES 100-108

.. code-block:: default


    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6))
    tree_disp.plot(ax=[ax1, ax2], line_kw={"label": "Decision Tree"})
    mlp_disp.plot(ax=[ax1, ax2], line_kw={"label": "Multi-layer Perceptron",
                                          "c": "red"})
    ax1.legend()
    ax2.legend()




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_004.png
   :alt: plot partial dependence visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_004.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    <matplotlib.legend.Legend object at 0x7fb4ae1b34f0>



.. GENERATED FROM PYTHON SOURCE LINES 110-116

`tree_disp.axes_` is a numpy array container the axes used to draw the
partial dependence plots. This can be passed to `mlp_disp` to have the same
affect of drawing the plots on top of each other. Furthermore, the
`mlp_disp.figure_` stores the figure, which allows for resizing the figure
after calling `plot`. In this case `tree_disp.axes_` has two dimensions, thus
`plot` will only show the y label and y ticks on the left most plot.

.. GENERATED FROM PYTHON SOURCE LINES 116-125

.. code-block:: default


    tree_disp.plot(line_kw={"label": "Decision Tree"})
    mlp_disp.plot(line_kw={"label": "Multi-layer Perceptron", "c": "red"},
                  ax=tree_disp.axes_)
    tree_disp.figure_.set_size_inches(10, 6)
    tree_disp.axes_[0, 0].legend()
    tree_disp.axes_[0, 1].legend()
    plt.show()




.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_005.png
   :alt: plot partial dependence visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_005.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 126-132

Plotting partial dependence for one feature
===========================================

Here, we plot the partial dependence curves for a single feature, "age", on
the same axes. In this case, `tree_disp.axes_` is passed into the second
plot function.

.. GENERATED FROM PYTHON SOURCE LINES 132-135

.. code-block:: default

    tree_disp = plot_partial_dependence(tree, X, ["age"])
    mlp_disp = plot_partial_dependence(mlp, X, ["age"],
                                       ax=tree_disp.axes_, line_kw={"c": "red"})



.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_006.png
   :alt: plot partial dependence visualization api
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_partial_dependence_visualization_api_006.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /build/scikit-learn-ZSX7SD/scikit-learn-0.23.2/.pybuild/cpython3_3.10/build/sklearn/tree/_classes.py:1254: FutureWarning: the classes_ attribute is to be deprecated from version 0.22 and will be removed in 0.24.
      warnings.warn(msg, FutureWarning)





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

   **Total running time of the script:** ( 0 minutes  2.808 seconds)


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



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