.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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_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_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_boston 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 boston housing price dataset ================================================ First, we train a decision tree and a multi-layer perceptron on the boston housing price dataset. .. code-block:: default boston = load_boston() X = pd.DataFrame(boston.data, columns=boston.feature_names) y = boston.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) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Pipeline(steps=[('standardscaler', StandardScaler()), ('mlpregressor', MLPRegressor(hidden_layer_sizes=(100, 100), max_iter=500, random_state=0, tol=0.01))]) Plotting partial dependence for two features ============================================ We plot partial dependence curves for features "LSTAT" and "RM" 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, ["LSTAT", "RM"], ax=ax) .. image:: /auto_examples/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 /home/circleci/project/sklearn/tree/_classes.py:1233: 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, ["LSTAT", "RM"], ax=ax, line_kw={"c": "red"}) .. image:: /auto_examples/images/sphx_glr_plot_partial_dependence_visualization_api_002.png :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/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') 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 # Sets this image as the thumbnail for sphinx gallery # sphinx_gallery_thumbnail_number = 4 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/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 `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/images/sphx_glr_plot_partial_dependence_visualization_api_005.png :class: sphx-glr-single-img Plotting partial dependence for one feature =========================================== Here, we plot the partial dependence curves for a single feature, "LSTAT", 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, ["LSTAT"]) mlp_disp = plot_partial_dependence(mlp, X, ["LSTAT"], ax=tree_disp.axes_, line_kw={"c": "red"}) .. image:: /auto_examples/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 /home/circleci/project/sklearn/tree/_classes.py:1233: 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 3.668 seconds) **Estimated memory usage:** 47 MB .. _sphx_glr_download_auto_examples_plot_partial_dependence_visualization_api.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/plot_partial_dependence_visualization_api.ipynb :width: 150 px .. container:: sphx-glr-download :download:`Download Python source code: plot_partial_dependence_visualization_api.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_partial_dependence_visualization_api.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_