.. 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 :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py: ========================================= Advanced Plotting With Partial Dependence ========================================= The :class:`~sklearn.inspection.PartialDependenceDisplay` object 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 15-26 .. code-block:: Python import matplotlib.pyplot as plt import pandas as pd from sklearn.datasets import load_diabetes from sklearn.inspection import PartialDependenceDisplay from sklearn.neural_network import MLPRegressor from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeRegressor .. GENERATED FROM PYTHON SOURCE LINES 27-32 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 32-45 .. code-block:: Python 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

Pipeline(steps=[('standardscaler', StandardScaler()),
                    ('mlpregressor',
                     MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500,
                                  random_state=0, tol=0.01))])

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.. GENERATED FROM PYTHON SOURCE LINES 46-54 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.PartialDependenceDisplay.from_estimator` 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 54-58 .. code-block:: Python fig, ax = plt.subplots(figsize=(12, 6)) ax.set_title("Decision Tree") tree_disp = PartialDependenceDisplay.from_estimator(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 .. GENERATED FROM PYTHON SOURCE LINES 59-63 The partial dependence curves can be plotted for the multi-layer perceptron. In this case, `line_kw` is passed to :func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` to change the color of the curve. .. GENERATED FROM PYTHON SOURCE LINES 63-69 .. code-block:: Python fig, ax = plt.subplots(figsize=(12, 6)) ax.set_title("Multi-layer Perceptron") mlp_disp = PartialDependenceDisplay.from_estimator( mlp, X, ["age", "bmi"], ax=ax, line_kw={"color": "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:: Python 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={"color": "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 .. 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 100-109 .. code-block:: Python 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", "color": "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 .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 111-117 `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 117-127 .. code-block:: Python tree_disp.plot(line_kw={"label": "Decision Tree"}) mlp_disp.plot( line_kw={"label": "Multi-layer Perceptron", "color": "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 128-134 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 134-138 .. code-block:: Python tree_disp = PartialDependenceDisplay.from_estimator(tree, X, ["age"]) mlp_disp = PartialDependenceDisplay.from_estimator( mlp, X, ["age"], ax=tree_disp.axes_, line_kw={"color": "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-timing **Total running time of the script:** (0 minutes 2.729 seconds) .. _sphx_glr_download_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.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/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/miscellaneous/plot_partial_dependence_visualization_api.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_partial_dependence_visualization_api.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_partial_dependence_visualization_api.py ` .. include:: plot_partial_dependence_visualization_api.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_