.. 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`


.. 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









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

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


.. 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)






.. only:: builder_html

    .. raw:: html

        <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="c9f78637-4038-45c7-bb05-27fbe3773477" type="checkbox" ><label class="sk-toggleable__label" for="c9f78637-4038-45c7-bb05-27fbe3773477">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="425882b2-d6d0-4aca-8e91-860e7b7f0f6c" type="checkbox" ><label class="sk-toggleable__label" for="425882b2-d6d0-4aca-8e91-860e7b7f0f6c">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="5d28cf23-e041-4d24-af4a-4fcb26dc01b7" type="checkbox" ><label class="sk-toggleable__label" for="5d28cf23-e041-4d24-af4a-4fcb26dc01b7">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>
        <br />
        <br />

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` .


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


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

 Out:

 .. code-block:: none

    /build/scikit-learn-BOS8cN/scikit-learn-0.23.2/.pybuild/cpython3_3.8/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)




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.


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





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.


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


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

 Out:

 .. code-block:: none


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



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.


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


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

 Out:

 .. code-block:: none


    <matplotlib.legend.Legend object at 0x7f3b20ae9940>



`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.


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





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.


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


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

 Out:

 .. code-block:: none

    /build/scikit-learn-BOS8cN/scikit-learn-0.23.2/.pybuild/cpython3_3.8/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  10.472 seconds)


.. _sphx_glr_download_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py:


.. only :: html

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



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

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



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


.. only:: html

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

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