1491 lines
59 KiB
Python
1491 lines
59 KiB
Python
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import numbers
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import warnings
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from itertools import chain
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from math import ceil
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import numpy as np
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from scipy import sparse
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from scipy.stats.mstats import mquantiles
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from .. import partial_dependence
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from .._pd_utils import _check_feature_names, _get_feature_index
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from ...base import is_regressor
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from ...utils import Bunch
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from ...utils import check_array
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from ...utils import check_matplotlib_support # noqa
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from ...utils import check_random_state
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from ...utils import _safe_indexing
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from ...utils.parallel import delayed, Parallel
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from ...utils._encode import _unique
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class PartialDependenceDisplay:
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"""Partial Dependence Plot (PDP).
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This can also display individual partial dependencies which are often
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referred to as: Individual Condition Expectation (ICE).
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It is recommended to use
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:func:`~sklearn.inspection.PartialDependenceDisplay.from_estimator` to create a
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:class:`~sklearn.inspection.PartialDependenceDisplay`. All parameters are
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stored as attributes.
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Read more in
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:ref:`sphx_glr_auto_examples_miscellaneous_plot_partial_dependence_visualization_api.py`
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and the :ref:`User Guide <partial_dependence>`.
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.. versionadded:: 0.22
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Parameters
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----------
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pd_results : list of Bunch
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Results of :func:`~sklearn.inspection.partial_dependence` for
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``features``.
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features : list of (int,) or list of (int, int)
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Indices of features for a given plot. A tuple of one integer will plot
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a partial dependence curve of one feature. A tuple of two integers will
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plot a two-way partial dependence curve as a contour plot.
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feature_names : list of str
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Feature names corresponding to the indices in ``features``.
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target_idx : int
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- In a multiclass setting, specifies the class for which the PDPs
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should be computed. Note that for binary classification, the
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positive class (index 1) is always used.
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- In a multioutput setting, specifies the task for which the PDPs
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should be computed.
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Ignored in binary classification or classical regression settings.
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deciles : dict
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Deciles for feature indices in ``features``.
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pdp_lim : dict or None
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Global min and max average predictions, such that all plots will have
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the same scale and y limits. `pdp_lim[1]` is the global min and max for
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single partial dependence curves. `pdp_lim[2]` is the global min and
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max for two-way partial dependence curves. If `None`, the limit will be
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inferred from the global minimum and maximum of all predictions.
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.. deprecated:: 1.1
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Pass the parameter `pdp_lim` to
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:meth:`~sklearn.inspection.PartialDependenceDisplay.plot` instead.
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It will be removed in 1.3.
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kind : {'average', 'individual', 'both'} or list of such str, \
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default='average'
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Whether to plot the partial dependence averaged across all the samples
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in the dataset or one line per sample or both.
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- ``kind='average'`` results in the traditional PD plot;
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- ``kind='individual'`` results in the ICE plot;
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- ``kind='both'`` results in plotting both the ICE and PD on the same
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plot.
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A list of such strings can be provided to specify `kind` on a per-plot
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basis. The length of the list should be the same as the number of
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interaction requested in `features`.
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.. note::
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ICE ('individual' or 'both') is not a valid option for 2-ways
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interactions plot. As a result, an error will be raised.
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2-ways interaction plots should always be configured to
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use the 'average' kind instead.
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.. note::
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The fast ``method='recursion'`` option is only available for
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``kind='average'``. Plotting individual dependencies requires using
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the slower ``method='brute'`` option.
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.. versionadded:: 0.24
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Add `kind` parameter with `'average'`, `'individual'`, and `'both'`
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options.
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.. versionadded:: 1.1
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Add the possibility to pass a list of string specifying `kind`
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for each plot.
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subsample : float, int or None, default=1000
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Sampling for ICE curves when `kind` is 'individual' or 'both'.
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If float, should be between 0.0 and 1.0 and represent the proportion
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of the dataset to be used to plot ICE curves. If int, represents the
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maximum absolute number of samples to use.
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Note that the full dataset is still used to calculate partial
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dependence when `kind='both'`.
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.. versionadded:: 0.24
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random_state : int, RandomState instance or None, default=None
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Controls the randomness of the selected samples when subsamples is not
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`None`. See :term:`Glossary <random_state>` for details.
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.. versionadded:: 0.24
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is_categorical : list of (bool,) or list of (bool, bool), default=None
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Whether each target feature in `features` is categorical or not.
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The list should be same size as `features`. If `None`, all features
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are assumed to be continuous.
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.. versionadded:: 1.2
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Attributes
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----------
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bounding_ax_ : matplotlib Axes or None
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If `ax` is an axes or None, the `bounding_ax_` is the axes where the
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grid of partial dependence plots are drawn. If `ax` is a list of axes
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or a numpy array of axes, `bounding_ax_` is None.
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axes_ : ndarray of matplotlib Axes
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If `ax` is an axes or None, `axes_[i, j]` is the axes on the i-th row
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and j-th column. If `ax` is a list of axes, `axes_[i]` is the i-th item
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in `ax`. Elements that are None correspond to a nonexisting axes in
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that position.
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lines_ : ndarray of matplotlib Artists
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If `ax` is an axes or None, `lines_[i, j]` is the partial dependence
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curve on the i-th row and j-th column. If `ax` is a list of axes,
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`lines_[i]` is the partial dependence curve corresponding to the i-th
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item in `ax`. Elements that are None correspond to a nonexisting axes
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or an axes that does not include a line plot.
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deciles_vlines_ : ndarray of matplotlib LineCollection
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If `ax` is an axes or None, `vlines_[i, j]` is the line collection
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representing the x axis deciles of the i-th row and j-th column. If
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`ax` is a list of axes, `vlines_[i]` corresponds to the i-th item in
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`ax`. Elements that are None correspond to a nonexisting axes or an
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axes that does not include a PDP plot.
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.. versionadded:: 0.23
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deciles_hlines_ : ndarray of matplotlib LineCollection
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If `ax` is an axes or None, `vlines_[i, j]` is the line collection
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representing the y axis deciles of the i-th row and j-th column. If
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`ax` is a list of axes, `vlines_[i]` corresponds to the i-th item in
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`ax`. Elements that are None correspond to a nonexisting axes or an
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axes that does not include a 2-way plot.
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.. versionadded:: 0.23
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contours_ : ndarray of matplotlib Artists
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If `ax` is an axes or None, `contours_[i, j]` is the partial dependence
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plot on the i-th row and j-th column. If `ax` is a list of axes,
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`contours_[i]` is the partial dependence plot corresponding to the i-th
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item in `ax`. Elements that are None correspond to a nonexisting axes
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or an axes that does not include a contour plot.
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bars_ : ndarray of matplotlib Artists
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If `ax` is an axes or None, `bars_[i, j]` is the partial dependence bar
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plot on the i-th row and j-th column (for a categorical feature).
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If `ax` is a list of axes, `bars_[i]` is the partial dependence bar
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plot corresponding to the i-th item in `ax`. Elements that are None
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correspond to a nonexisting axes or an axes that does not include a
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bar plot.
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.. versionadded:: 1.2
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heatmaps_ : ndarray of matplotlib Artists
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If `ax` is an axes or None, `heatmaps_[i, j]` is the partial dependence
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heatmap on the i-th row and j-th column (for a pair of categorical
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features) . If `ax` is a list of axes, `heatmaps_[i]` is the partial
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dependence heatmap corresponding to the i-th item in `ax`. Elements
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that are None correspond to a nonexisting axes or an axes that does not
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include a heatmap.
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.. versionadded:: 1.2
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figure_ : matplotlib Figure
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Figure containing partial dependence plots.
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See Also
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--------
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partial_dependence : Compute Partial Dependence values.
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PartialDependenceDisplay.from_estimator : Plot Partial Dependence.
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Examples
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--------
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>>> import numpy as np
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>>> import matplotlib.pyplot as plt
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>>> from sklearn.datasets import make_friedman1
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>>> from sklearn.ensemble import GradientBoostingRegressor
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>>> from sklearn.inspection import PartialDependenceDisplay
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>>> from sklearn.inspection import partial_dependence
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>>> X, y = make_friedman1()
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>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
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>>> features, feature_names = [(0,)], [f"Features #{i}" for i in range(X.shape[1])]
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>>> deciles = {0: np.linspace(0, 1, num=5)}
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>>> pd_results = partial_dependence(
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... clf, X, features=0, kind="average", grid_resolution=5)
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>>> display = PartialDependenceDisplay(
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... [pd_results], features=features, feature_names=feature_names,
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... target_idx=0, deciles=deciles
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... )
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>>> display.plot(pdp_lim={1: (-1.38, 0.66)})
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<...>
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>>> plt.show()
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"""
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def __init__(
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self,
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pd_results,
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*,
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features,
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feature_names,
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target_idx,
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deciles,
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pdp_lim="deprecated",
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kind="average",
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subsample=1000,
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random_state=None,
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is_categorical=None,
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):
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self.pd_results = pd_results
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self.features = features
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self.feature_names = feature_names
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self.target_idx = target_idx
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self.pdp_lim = pdp_lim
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self.deciles = deciles
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self.kind = kind
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self.subsample = subsample
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self.random_state = random_state
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self.is_categorical = is_categorical
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@classmethod
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def from_estimator(
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cls,
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estimator,
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X,
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features,
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*,
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categorical_features=None,
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feature_names=None,
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target=None,
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response_method="auto",
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n_cols=3,
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grid_resolution=100,
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percentiles=(0.05, 0.95),
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method="auto",
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n_jobs=None,
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verbose=0,
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line_kw=None,
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ice_lines_kw=None,
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pd_line_kw=None,
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contour_kw=None,
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ax=None,
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kind="average",
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centered=False,
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subsample=1000,
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random_state=None,
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):
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"""Partial dependence (PD) and individual conditional expectation (ICE) plots.
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Partial dependence plots, individual conditional expectation plots or an
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overlay of both of them can be plotted by setting the ``kind``
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parameter. The ``len(features)`` plots are arranged in a grid with
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``n_cols`` columns. Two-way partial dependence plots are plotted as
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contour plots. The deciles of the feature values will be shown with tick
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marks on the x-axes for one-way plots, and on both axes for two-way
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plots.
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Read more in the :ref:`User Guide <partial_dependence>`.
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.. note::
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:func:`PartialDependenceDisplay.from_estimator` does not support using the
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same axes with multiple calls. To plot the partial dependence for
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multiple estimators, please pass the axes created by the first call to the
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second call::
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>>> from sklearn.inspection import PartialDependenceDisplay
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>>> from sklearn.datasets import make_friedman1
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>>> from sklearn.linear_model import LinearRegression
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>>> from sklearn.ensemble import RandomForestRegressor
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>>> X, y = make_friedman1()
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>>> est1 = LinearRegression().fit(X, y)
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>>> est2 = RandomForestRegressor().fit(X, y)
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>>> disp1 = PartialDependenceDisplay.from_estimator(est1, X,
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... [1, 2])
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>>> disp2 = PartialDependenceDisplay.from_estimator(est2, X, [1, 2],
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... ax=disp1.axes_)
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.. warning::
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For :class:`~sklearn.ensemble.GradientBoostingClassifier` and
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:class:`~sklearn.ensemble.GradientBoostingRegressor`, the
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`'recursion'` method (used by default) will not account for the `init`
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predictor of the boosting process. In practice, this will produce
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the same values as `'brute'` up to a constant offset in the target
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response, provided that `init` is a constant estimator (which is the
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default). However, if `init` is not a constant estimator, the
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partial dependence values are incorrect for `'recursion'` because the
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offset will be sample-dependent. It is preferable to use the `'brute'`
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method. Note that this only applies to
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:class:`~sklearn.ensemble.GradientBoostingClassifier` and
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:class:`~sklearn.ensemble.GradientBoostingRegressor`, not to
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:class:`~sklearn.ensemble.HistGradientBoostingClassifier` and
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:class:`~sklearn.ensemble.HistGradientBoostingRegressor`.
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.. versionadded:: 1.0
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Parameters
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----------
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estimator : BaseEstimator
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A fitted estimator object implementing :term:`predict`,
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:term:`predict_proba`, or :term:`decision_function`.
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Multioutput-multiclass classifiers are not supported.
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X : {array-like, dataframe} of shape (n_samples, n_features)
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``X`` is used to generate a grid of values for the target
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``features`` (where the partial dependence will be evaluated), and
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also to generate values for the complement features when the
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`method` is `'brute'`.
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features : list of {int, str, pair of int, pair of str}
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The target features for which to create the PDPs.
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If `features[i]` is an integer or a string, a one-way PDP is created;
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if `features[i]` is a tuple, a two-way PDP is created (only supported
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with `kind='average'`). Each tuple must be of size 2.
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If any entry is a string, then it must be in ``feature_names``.
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categorical_features : array-like of shape (n_features,) or shape \
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(n_categorical_features,), dtype={bool, int, str}, default=None
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|
|
Indicates the categorical features.
|
||
|
|
|
||
|
|
- `None`: no feature will be considered categorical;
|
||
|
|
- boolean array-like: boolean mask of shape `(n_features,)`
|
||
|
|
indicating which features are categorical. Thus, this array has
|
||
|
|
the same shape has `X.shape[1]`;
|
||
|
|
- integer or string array-like: integer indices or strings
|
||
|
|
indicating categorical features.
|
||
|
|
|
||
|
|
.. versionadded:: 1.2
|
||
|
|
|
||
|
|
feature_names : array-like of shape (n_features,), dtype=str, default=None
|
||
|
|
Name of each feature; `feature_names[i]` holds the name of the feature
|
||
|
|
with index `i`.
|
||
|
|
By default, the name of the feature corresponds to their numerical
|
||
|
|
index for NumPy array and their column name for pandas dataframe.
|
||
|
|
|
||
|
|
target : int, default=None
|
||
|
|
- In a multiclass setting, specifies the class for which the PDPs
|
||
|
|
should be computed. Note that for binary classification, the
|
||
|
|
positive class (index 1) is always used.
|
||
|
|
- In a multioutput setting, specifies the task for which the PDPs
|
||
|
|
should be computed.
|
||
|
|
|
||
|
|
Ignored in binary classification or classical regression settings.
|
||
|
|
|
||
|
|
response_method : {'auto', 'predict_proba', 'decision_function'}, \
|
||
|
|
default='auto'
|
||
|
|
Specifies whether to use :term:`predict_proba` or
|
||
|
|
:term:`decision_function` as the target response. For regressors
|
||
|
|
this parameter is ignored and the response is always the output of
|
||
|
|
:term:`predict`. By default, :term:`predict_proba` is tried first
|
||
|
|
and we revert to :term:`decision_function` if it doesn't exist. If
|
||
|
|
``method`` is `'recursion'`, the response is always the output of
|
||
|
|
:term:`decision_function`.
|
||
|
|
|
||
|
|
n_cols : int, default=3
|
||
|
|
The maximum number of columns in the grid plot. Only active when `ax`
|
||
|
|
is a single axis or `None`.
|
||
|
|
|
||
|
|
grid_resolution : int, default=100
|
||
|
|
The number of equally spaced points on the axes of the plots, for each
|
||
|
|
target feature.
|
||
|
|
|
||
|
|
percentiles : tuple of float, default=(0.05, 0.95)
|
||
|
|
The lower and upper percentile used to create the extreme values
|
||
|
|
for the PDP axes. Must be in [0, 1].
|
||
|
|
|
||
|
|
method : str, default='auto'
|
||
|
|
The method used to calculate the averaged predictions:
|
||
|
|
|
||
|
|
- `'recursion'` is only supported for some tree-based estimators
|
||
|
|
(namely
|
||
|
|
:class:`~sklearn.ensemble.GradientBoostingClassifier`,
|
||
|
|
:class:`~sklearn.ensemble.GradientBoostingRegressor`,
|
||
|
|
:class:`~sklearn.ensemble.HistGradientBoostingClassifier`,
|
||
|
|
:class:`~sklearn.ensemble.HistGradientBoostingRegressor`,
|
||
|
|
:class:`~sklearn.tree.DecisionTreeRegressor`,
|
||
|
|
:class:`~sklearn.ensemble.RandomForestRegressor`
|
||
|
|
but is more efficient in terms of speed.
|
||
|
|
With this method, the target response of a
|
||
|
|
classifier is always the decision function, not the predicted
|
||
|
|
probabilities. Since the `'recursion'` method implicitly computes
|
||
|
|
the average of the ICEs by design, it is not compatible with ICE and
|
||
|
|
thus `kind` must be `'average'`.
|
||
|
|
|
||
|
|
- `'brute'` is supported for any estimator, but is more
|
||
|
|
computationally intensive.
|
||
|
|
|
||
|
|
- `'auto'`: the `'recursion'` is used for estimators that support it,
|
||
|
|
and `'brute'` is used otherwise.
|
||
|
|
|
||
|
|
Please see :ref:`this note <pdp_method_differences>` for
|
||
|
|
differences between the `'brute'` and `'recursion'` method.
|
||
|
|
|
||
|
|
n_jobs : int, default=None
|
||
|
|
The number of CPUs to use to compute the partial dependences.
|
||
|
|
Computation is parallelized over features specified by the `features`
|
||
|
|
parameter.
|
||
|
|
|
||
|
|
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
|
||
|
|
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
|
||
|
|
for more details.
|
||
|
|
|
||
|
|
verbose : int, default=0
|
||
|
|
Verbose output during PD computations.
|
||
|
|
|
||
|
|
line_kw : dict, default=None
|
||
|
|
Dict with keywords passed to the ``matplotlib.pyplot.plot`` call.
|
||
|
|
For one-way partial dependence plots. It can be used to define common
|
||
|
|
properties for both `ice_lines_kw` and `pdp_line_kw`.
|
||
|
|
|
||
|
|
ice_lines_kw : dict, default=None
|
||
|
|
Dictionary with keywords passed to the `matplotlib.pyplot.plot` call.
|
||
|
|
For ICE lines in the one-way partial dependence plots.
|
||
|
|
The key value pairs defined in `ice_lines_kw` takes priority over
|
||
|
|
`line_kw`.
|
||
|
|
|
||
|
|
pd_line_kw : dict, default=None
|
||
|
|
Dictionary with keywords passed to the `matplotlib.pyplot.plot` call.
|
||
|
|
For partial dependence in one-way partial dependence plots.
|
||
|
|
The key value pairs defined in `pd_line_kw` takes priority over
|
||
|
|
`line_kw`.
|
||
|
|
|
||
|
|
contour_kw : dict, default=None
|
||
|
|
Dict with keywords passed to the ``matplotlib.pyplot.contourf`` call.
|
||
|
|
For two-way partial dependence plots.
|
||
|
|
|
||
|
|
ax : Matplotlib axes or array-like of Matplotlib axes, default=None
|
||
|
|
- If a single axis is passed in, it is treated as a bounding axes
|
||
|
|
and a grid of partial dependence plots will be drawn within
|
||
|
|
these bounds. The `n_cols` parameter controls the number of
|
||
|
|
columns in the grid.
|
||
|
|
- If an array-like of axes are passed in, the partial dependence
|
||
|
|
plots will be drawn directly into these axes.
|
||
|
|
- If `None`, a figure and a bounding axes is created and treated
|
||
|
|
as the single axes case.
|
||
|
|
|
||
|
|
kind : {'average', 'individual', 'both'}, default='average'
|
||
|
|
Whether to plot the partial dependence averaged across all the samples
|
||
|
|
in the dataset or one line per sample or both.
|
||
|
|
|
||
|
|
- ``kind='average'`` results in the traditional PD plot;
|
||
|
|
- ``kind='individual'`` results in the ICE plot.
|
||
|
|
|
||
|
|
Note that the fast ``method='recursion'`` option is only available for
|
||
|
|
``kind='average'``. Plotting individual dependencies requires using the
|
||
|
|
slower ``method='brute'`` option.
|
||
|
|
|
||
|
|
centered : bool, default=False
|
||
|
|
If `True`, the ICE and PD lines will start at the origin of the
|
||
|
|
y-axis. By default, no centering is done.
|
||
|
|
|
||
|
|
.. versionadded:: 1.1
|
||
|
|
|
||
|
|
subsample : float, int or None, default=1000
|
||
|
|
Sampling for ICE curves when `kind` is 'individual' or 'both'.
|
||
|
|
If `float`, should be between 0.0 and 1.0 and represent the proportion
|
||
|
|
of the dataset to be used to plot ICE curves. If `int`, represents the
|
||
|
|
absolute number samples to use.
|
||
|
|
|
||
|
|
Note that the full dataset is still used to calculate averaged partial
|
||
|
|
dependence when `kind='both'`.
|
||
|
|
|
||
|
|
random_state : int, RandomState instance or None, default=None
|
||
|
|
Controls the randomness of the selected samples when subsamples is not
|
||
|
|
`None` and `kind` is either `'both'` or `'individual'`.
|
||
|
|
See :term:`Glossary <random_state>` for details.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
display : :class:`~sklearn.inspection.PartialDependenceDisplay`
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
partial_dependence : Compute Partial Dependence values.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import matplotlib.pyplot as plt
|
||
|
|
>>> from sklearn.datasets import make_friedman1
|
||
|
|
>>> from sklearn.ensemble import GradientBoostingRegressor
|
||
|
|
>>> from sklearn.inspection import PartialDependenceDisplay
|
||
|
|
>>> X, y = make_friedman1()
|
||
|
|
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
|
||
|
|
>>> PartialDependenceDisplay.from_estimator(clf, X, [0, (0, 1)])
|
||
|
|
<...>
|
||
|
|
>>> plt.show()
|
||
|
|
"""
|
||
|
|
check_matplotlib_support(f"{cls.__name__}.from_estimator") # noqa
|
||
|
|
import matplotlib.pyplot as plt # noqa
|
||
|
|
|
||
|
|
# set target_idx for multi-class estimators
|
||
|
|
if hasattr(estimator, "classes_") and np.size(estimator.classes_) > 2:
|
||
|
|
if target is None:
|
||
|
|
raise ValueError("target must be specified for multi-class")
|
||
|
|
target_idx = np.searchsorted(estimator.classes_, target)
|
||
|
|
if (
|
||
|
|
not (0 <= target_idx < len(estimator.classes_))
|
||
|
|
or estimator.classes_[target_idx] != target
|
||
|
|
):
|
||
|
|
raise ValueError("target not in est.classes_, got {}".format(target))
|
||
|
|
else:
|
||
|
|
# regression and binary classification
|
||
|
|
target_idx = 0
|
||
|
|
|
||
|
|
# Use check_array only on lists and other non-array-likes / sparse. Do not
|
||
|
|
# convert DataFrame into a NumPy array.
|
||
|
|
if not (hasattr(X, "__array__") or sparse.issparse(X)):
|
||
|
|
X = check_array(X, force_all_finite="allow-nan", dtype=object)
|
||
|
|
n_features = X.shape[1]
|
||
|
|
|
||
|
|
feature_names = _check_feature_names(X, feature_names)
|
||
|
|
# expand kind to always be a list of str
|
||
|
|
kind_ = [kind] * len(features) if isinstance(kind, str) else kind
|
||
|
|
if len(kind_) != len(features):
|
||
|
|
raise ValueError(
|
||
|
|
"When `kind` is provided as a list of strings, it should contain "
|
||
|
|
f"as many elements as `features`. `kind` contains {len(kind_)} "
|
||
|
|
f"element(s) and `features` contains {len(features)} element(s)."
|
||
|
|
)
|
||
|
|
|
||
|
|
# convert features into a seq of int tuples
|
||
|
|
tmp_features, ice_for_two_way_pd = [], []
|
||
|
|
for kind_plot, fxs in zip(kind_, features):
|
||
|
|
if isinstance(fxs, (numbers.Integral, str)):
|
||
|
|
fxs = (fxs,)
|
||
|
|
try:
|
||
|
|
fxs = tuple(
|
||
|
|
_get_feature_index(fx, feature_names=feature_names) for fx in fxs
|
||
|
|
)
|
||
|
|
except TypeError as e:
|
||
|
|
raise ValueError(
|
||
|
|
"Each entry in features must be either an int, "
|
||
|
|
"a string, or an iterable of size at most 2."
|
||
|
|
) from e
|
||
|
|
if not 1 <= np.size(fxs) <= 2:
|
||
|
|
raise ValueError(
|
||
|
|
"Each entry in features must be either an int, "
|
||
|
|
"a string, or an iterable of size at most 2."
|
||
|
|
)
|
||
|
|
# store the information if 2-way PD was requested with ICE to later
|
||
|
|
# raise a ValueError with an exhaustive list of problematic
|
||
|
|
# settings.
|
||
|
|
ice_for_two_way_pd.append(kind_plot != "average" and np.size(fxs) > 1)
|
||
|
|
|
||
|
|
tmp_features.append(fxs)
|
||
|
|
|
||
|
|
if any(ice_for_two_way_pd):
|
||
|
|
# raise an error and be specific regarding the parameter values
|
||
|
|
# when 1- and 2-way PD were requested
|
||
|
|
kind_ = [
|
||
|
|
"average" if forcing_average else kind_plot
|
||
|
|
for forcing_average, kind_plot in zip(ice_for_two_way_pd, kind_)
|
||
|
|
]
|
||
|
|
raise ValueError(
|
||
|
|
"ICE plot cannot be rendered for 2-way feature interactions. "
|
||
|
|
"2-way feature interactions mandates PD plots using the "
|
||
|
|
"'average' kind: "
|
||
|
|
f"features={features!r} should be configured to use "
|
||
|
|
f"kind={kind_!r} explicitly."
|
||
|
|
)
|
||
|
|
features = tmp_features
|
||
|
|
|
||
|
|
if categorical_features is None:
|
||
|
|
is_categorical = [
|
||
|
|
(False,) if len(fxs) == 1 else (False, False) for fxs in features
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
# we need to create a boolean indicator of which features are
|
||
|
|
# categorical from the categorical_features list.
|
||
|
|
categorical_features = np.array(categorical_features, copy=False)
|
||
|
|
if categorical_features.dtype.kind == "b":
|
||
|
|
# categorical features provided as a list of boolean
|
||
|
|
if categorical_features.size != n_features:
|
||
|
|
raise ValueError(
|
||
|
|
"When `categorical_features` is a boolean array-like, "
|
||
|
|
"the array should be of shape (n_features,). Got "
|
||
|
|
f"{categorical_features.size} elements while `X` contains "
|
||
|
|
f"{n_features} features."
|
||
|
|
)
|
||
|
|
is_categorical = [
|
||
|
|
tuple(categorical_features[fx] for fx in fxs) for fxs in features
|
||
|
|
]
|
||
|
|
elif categorical_features.dtype.kind in ("i", "O", "U"):
|
||
|
|
# categorical features provided as a list of indices or feature names
|
||
|
|
categorical_features_idx = [
|
||
|
|
_get_feature_index(cat, feature_names=feature_names)
|
||
|
|
for cat in categorical_features
|
||
|
|
]
|
||
|
|
is_categorical = [
|
||
|
|
tuple([idx in categorical_features_idx for idx in fxs])
|
||
|
|
for fxs in features
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"Expected `categorical_features` to be an array-like of boolean,"
|
||
|
|
f" integer, or string. Got {categorical_features.dtype} instead."
|
||
|
|
)
|
||
|
|
|
||
|
|
for cats in is_categorical:
|
||
|
|
if np.size(cats) == 2 and (cats[0] != cats[1]):
|
||
|
|
raise ValueError(
|
||
|
|
"Two-way partial dependence plots are not supported for pairs"
|
||
|
|
" of continuous and categorical features."
|
||
|
|
)
|
||
|
|
|
||
|
|
# collect the indices of the categorical features targeted by the partial
|
||
|
|
# dependence computation
|
||
|
|
categorical_features_targeted = set(
|
||
|
|
[
|
||
|
|
fx
|
||
|
|
for fxs, cats in zip(features, is_categorical)
|
||
|
|
for fx in fxs
|
||
|
|
if any(cats)
|
||
|
|
]
|
||
|
|
)
|
||
|
|
if categorical_features_targeted:
|
||
|
|
min_n_cats = min(
|
||
|
|
[
|
||
|
|
len(_unique(_safe_indexing(X, idx, axis=1)))
|
||
|
|
for idx in categorical_features_targeted
|
||
|
|
]
|
||
|
|
)
|
||
|
|
if grid_resolution < min_n_cats:
|
||
|
|
raise ValueError(
|
||
|
|
"The resolution of the computed grid is less than the "
|
||
|
|
"minimum number of categories in the targeted categorical "
|
||
|
|
"features. Expect the `grid_resolution` to be greater than "
|
||
|
|
f"{min_n_cats}. Got {grid_resolution} instead."
|
||
|
|
)
|
||
|
|
|
||
|
|
for is_cat, kind_plot in zip(is_categorical, kind_):
|
||
|
|
if any(is_cat) and kind_plot != "average":
|
||
|
|
raise ValueError(
|
||
|
|
"It is not possible to display individual effects for"
|
||
|
|
" categorical features."
|
||
|
|
)
|
||
|
|
|
||
|
|
# Early exit if the axes does not have the correct number of axes
|
||
|
|
if ax is not None and not isinstance(ax, plt.Axes):
|
||
|
|
axes = np.asarray(ax, dtype=object)
|
||
|
|
if axes.size != len(features):
|
||
|
|
raise ValueError(
|
||
|
|
"Expected ax to have {} axes, got {}".format(
|
||
|
|
len(features), axes.size
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
for i in chain.from_iterable(features):
|
||
|
|
if i >= len(feature_names):
|
||
|
|
raise ValueError(
|
||
|
|
"All entries of features must be less than "
|
||
|
|
"len(feature_names) = {0}, got {1}.".format(len(feature_names), i)
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(subsample, numbers.Integral):
|
||
|
|
if subsample <= 0:
|
||
|
|
raise ValueError(
|
||
|
|
f"When an integer, subsample={subsample} should be positive."
|
||
|
|
)
|
||
|
|
elif isinstance(subsample, numbers.Real):
|
||
|
|
if subsample <= 0 or subsample >= 1:
|
||
|
|
raise ValueError(
|
||
|
|
f"When a floating-point, subsample={subsample} should be in "
|
||
|
|
"the (0, 1) range."
|
||
|
|
)
|
||
|
|
|
||
|
|
# compute predictions and/or averaged predictions
|
||
|
|
pd_results = Parallel(n_jobs=n_jobs, verbose=verbose)(
|
||
|
|
delayed(partial_dependence)(
|
||
|
|
estimator,
|
||
|
|
X,
|
||
|
|
fxs,
|
||
|
|
feature_names=feature_names,
|
||
|
|
categorical_features=categorical_features,
|
||
|
|
response_method=response_method,
|
||
|
|
method=method,
|
||
|
|
grid_resolution=grid_resolution,
|
||
|
|
percentiles=percentiles,
|
||
|
|
kind=kind_plot,
|
||
|
|
)
|
||
|
|
for kind_plot, fxs in zip(kind_, features)
|
||
|
|
)
|
||
|
|
|
||
|
|
# For multioutput regression, we can only check the validity of target
|
||
|
|
# now that we have the predictions.
|
||
|
|
# Also note: as multiclass-multioutput classifiers are not supported,
|
||
|
|
# multiclass and multioutput scenario are mutually exclusive. So there is
|
||
|
|
# no risk of overwriting target_idx here.
|
||
|
|
pd_result = pd_results[0] # checking the first result is enough
|
||
|
|
n_tasks = (
|
||
|
|
pd_result.average.shape[0]
|
||
|
|
if kind_[0] == "average"
|
||
|
|
else pd_result.individual.shape[0]
|
||
|
|
)
|
||
|
|
if is_regressor(estimator) and n_tasks > 1:
|
||
|
|
if target is None:
|
||
|
|
raise ValueError("target must be specified for multi-output regressors")
|
||
|
|
if not 0 <= target <= n_tasks:
|
||
|
|
raise ValueError(
|
||
|
|
"target must be in [0, n_tasks], got {}.".format(target)
|
||
|
|
)
|
||
|
|
target_idx = target
|
||
|
|
|
||
|
|
deciles = {}
|
||
|
|
for fxs, cats in zip(features, is_categorical):
|
||
|
|
for fx, cat in zip(fxs, cats):
|
||
|
|
if not cat and fx not in deciles:
|
||
|
|
X_col = _safe_indexing(X, fx, axis=1)
|
||
|
|
deciles[fx] = mquantiles(X_col, prob=np.arange(0.1, 1.0, 0.1))
|
||
|
|
|
||
|
|
display = PartialDependenceDisplay(
|
||
|
|
pd_results=pd_results,
|
||
|
|
features=features,
|
||
|
|
feature_names=feature_names,
|
||
|
|
target_idx=target_idx,
|
||
|
|
deciles=deciles,
|
||
|
|
kind=kind,
|
||
|
|
subsample=subsample,
|
||
|
|
random_state=random_state,
|
||
|
|
is_categorical=is_categorical,
|
||
|
|
)
|
||
|
|
return display.plot(
|
||
|
|
ax=ax,
|
||
|
|
n_cols=n_cols,
|
||
|
|
line_kw=line_kw,
|
||
|
|
ice_lines_kw=ice_lines_kw,
|
||
|
|
pd_line_kw=pd_line_kw,
|
||
|
|
contour_kw=contour_kw,
|
||
|
|
centered=centered,
|
||
|
|
)
|
||
|
|
|
||
|
|
def _get_sample_count(self, n_samples):
|
||
|
|
"""Compute the number of samples as an integer."""
|
||
|
|
if isinstance(self.subsample, numbers.Integral):
|
||
|
|
if self.subsample < n_samples:
|
||
|
|
return self.subsample
|
||
|
|
return n_samples
|
||
|
|
elif isinstance(self.subsample, numbers.Real):
|
||
|
|
return ceil(n_samples * self.subsample)
|
||
|
|
return n_samples
|
||
|
|
|
||
|
|
def _plot_ice_lines(
|
||
|
|
self,
|
||
|
|
preds,
|
||
|
|
feature_values,
|
||
|
|
n_ice_to_plot,
|
||
|
|
ax,
|
||
|
|
pd_plot_idx,
|
||
|
|
n_total_lines_by_plot,
|
||
|
|
individual_line_kw,
|
||
|
|
):
|
||
|
|
"""Plot the ICE lines.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
preds : ndarray of shape \
|
||
|
|
(n_instances, n_grid_points)
|
||
|
|
The predictions computed for all points of `feature_values` for a
|
||
|
|
given feature for all samples in `X`.
|
||
|
|
feature_values : ndarray of shape (n_grid_points,)
|
||
|
|
The feature values for which the predictions have been computed.
|
||
|
|
n_ice_to_plot : int
|
||
|
|
The number of ICE lines to plot.
|
||
|
|
ax : Matplotlib axes
|
||
|
|
The axis on which to plot the ICE lines.
|
||
|
|
pd_plot_idx : int
|
||
|
|
The sequential index of the plot. It will be unraveled to find the
|
||
|
|
matching 2D position in the grid layout.
|
||
|
|
n_total_lines_by_plot : int
|
||
|
|
The total number of lines expected to be plot on the axis.
|
||
|
|
individual_line_kw : dict
|
||
|
|
Dict with keywords passed when plotting the ICE lines.
|
||
|
|
"""
|
||
|
|
rng = check_random_state(self.random_state)
|
||
|
|
# subsample ice
|
||
|
|
ice_lines_idx = rng.choice(
|
||
|
|
preds.shape[0],
|
||
|
|
n_ice_to_plot,
|
||
|
|
replace=False,
|
||
|
|
)
|
||
|
|
ice_lines_subsampled = preds[ice_lines_idx, :]
|
||
|
|
# plot the subsampled ice
|
||
|
|
for ice_idx, ice in enumerate(ice_lines_subsampled):
|
||
|
|
line_idx = np.unravel_index(
|
||
|
|
pd_plot_idx * n_total_lines_by_plot + ice_idx, self.lines_.shape
|
||
|
|
)
|
||
|
|
self.lines_[line_idx] = ax.plot(
|
||
|
|
feature_values, ice.ravel(), **individual_line_kw
|
||
|
|
)[0]
|
||
|
|
|
||
|
|
def _plot_average_dependence(
|
||
|
|
self,
|
||
|
|
avg_preds,
|
||
|
|
feature_values,
|
||
|
|
ax,
|
||
|
|
pd_line_idx,
|
||
|
|
line_kw,
|
||
|
|
categorical,
|
||
|
|
bar_kw,
|
||
|
|
):
|
||
|
|
"""Plot the average partial dependence.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
avg_preds : ndarray of shape (n_grid_points,)
|
||
|
|
The average predictions for all points of `feature_values` for a
|
||
|
|
given feature for all samples in `X`.
|
||
|
|
feature_values : ndarray of shape (n_grid_points,)
|
||
|
|
The feature values for which the predictions have been computed.
|
||
|
|
ax : Matplotlib axes
|
||
|
|
The axis on which to plot the average PD.
|
||
|
|
pd_line_idx : int
|
||
|
|
The sequential index of the plot. It will be unraveled to find the
|
||
|
|
matching 2D position in the grid layout.
|
||
|
|
line_kw : dict
|
||
|
|
Dict with keywords passed when plotting the PD plot.
|
||
|
|
categorical : bool
|
||
|
|
Whether feature is categorical.
|
||
|
|
bar_kw: dict
|
||
|
|
Dict with keywords passed when plotting the PD bars (categorical).
|
||
|
|
"""
|
||
|
|
if categorical:
|
||
|
|
bar_idx = np.unravel_index(pd_line_idx, self.bars_.shape)
|
||
|
|
self.bars_[bar_idx] = ax.bar(feature_values, avg_preds, **bar_kw)[0]
|
||
|
|
ax.tick_params(axis="x", rotation=90)
|
||
|
|
else:
|
||
|
|
line_idx = np.unravel_index(pd_line_idx, self.lines_.shape)
|
||
|
|
self.lines_[line_idx] = ax.plot(
|
||
|
|
feature_values,
|
||
|
|
avg_preds,
|
||
|
|
**line_kw,
|
||
|
|
)[0]
|
||
|
|
|
||
|
|
def _plot_one_way_partial_dependence(
|
||
|
|
self,
|
||
|
|
kind,
|
||
|
|
preds,
|
||
|
|
avg_preds,
|
||
|
|
feature_values,
|
||
|
|
feature_idx,
|
||
|
|
n_ice_lines,
|
||
|
|
ax,
|
||
|
|
n_cols,
|
||
|
|
pd_plot_idx,
|
||
|
|
n_lines,
|
||
|
|
ice_lines_kw,
|
||
|
|
pd_line_kw,
|
||
|
|
categorical,
|
||
|
|
bar_kw,
|
||
|
|
pdp_lim,
|
||
|
|
):
|
||
|
|
"""Plot 1-way partial dependence: ICE and PDP.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
kind : str
|
||
|
|
The kind of partial plot to draw.
|
||
|
|
preds : ndarray of shape \
|
||
|
|
(n_instances, n_grid_points) or None
|
||
|
|
The predictions computed for all points of `feature_values` for a
|
||
|
|
given feature for all samples in `X`.
|
||
|
|
avg_preds : ndarray of shape (n_grid_points,)
|
||
|
|
The average predictions for all points of `feature_values` for a
|
||
|
|
given feature for all samples in `X`.
|
||
|
|
feature_values : ndarray of shape (n_grid_points,)
|
||
|
|
The feature values for which the predictions have been computed.
|
||
|
|
feature_idx : int
|
||
|
|
The index corresponding to the target feature.
|
||
|
|
n_ice_lines : int
|
||
|
|
The number of ICE lines to plot.
|
||
|
|
ax : Matplotlib axes
|
||
|
|
The axis on which to plot the ICE and PDP lines.
|
||
|
|
n_cols : int or None
|
||
|
|
The number of column in the axis.
|
||
|
|
pd_plot_idx : int
|
||
|
|
The sequential index of the plot. It will be unraveled to find the
|
||
|
|
matching 2D position in the grid layout.
|
||
|
|
n_lines : int
|
||
|
|
The total number of lines expected to be plot on the axis.
|
||
|
|
ice_lines_kw : dict
|
||
|
|
Dict with keywords passed when plotting the ICE lines.
|
||
|
|
pd_line_kw : dict
|
||
|
|
Dict with keywords passed when plotting the PD plot.
|
||
|
|
categorical : bool
|
||
|
|
Whether feature is categorical.
|
||
|
|
bar_kw: dict
|
||
|
|
Dict with keywords passed when plotting the PD bars (categorical).
|
||
|
|
pdp_lim : dict
|
||
|
|
Global min and max average predictions, such that all plots will
|
||
|
|
have the same scale and y limits. `pdp_lim[1]` is the global min
|
||
|
|
and max for single partial dependence curves.
|
||
|
|
"""
|
||
|
|
from matplotlib import transforms # noqa
|
||
|
|
|
||
|
|
if kind in ("individual", "both"):
|
||
|
|
self._plot_ice_lines(
|
||
|
|
preds[self.target_idx],
|
||
|
|
feature_values,
|
||
|
|
n_ice_lines,
|
||
|
|
ax,
|
||
|
|
pd_plot_idx,
|
||
|
|
n_lines,
|
||
|
|
ice_lines_kw,
|
||
|
|
)
|
||
|
|
|
||
|
|
if kind in ("average", "both"):
|
||
|
|
# the average is stored as the last line
|
||
|
|
if kind == "average":
|
||
|
|
pd_line_idx = pd_plot_idx
|
||
|
|
else:
|
||
|
|
pd_line_idx = pd_plot_idx * n_lines + n_ice_lines
|
||
|
|
self._plot_average_dependence(
|
||
|
|
avg_preds[self.target_idx].ravel(),
|
||
|
|
feature_values,
|
||
|
|
ax,
|
||
|
|
pd_line_idx,
|
||
|
|
pd_line_kw,
|
||
|
|
categorical,
|
||
|
|
bar_kw,
|
||
|
|
)
|
||
|
|
|
||
|
|
trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
|
||
|
|
# create the decile line for the vertical axis
|
||
|
|
vlines_idx = np.unravel_index(pd_plot_idx, self.deciles_vlines_.shape)
|
||
|
|
if self.deciles.get(feature_idx[0], None) is not None:
|
||
|
|
self.deciles_vlines_[vlines_idx] = ax.vlines(
|
||
|
|
self.deciles[feature_idx[0]],
|
||
|
|
0,
|
||
|
|
0.05,
|
||
|
|
transform=trans,
|
||
|
|
color="k",
|
||
|
|
)
|
||
|
|
# reset ylim which was overwritten by vlines
|
||
|
|
min_val = min(val[0] for val in pdp_lim.values())
|
||
|
|
max_val = max(val[1] for val in pdp_lim.values())
|
||
|
|
ax.set_ylim([min_val, max_val])
|
||
|
|
|
||
|
|
# Set xlabel if it is not already set
|
||
|
|
if not ax.get_xlabel():
|
||
|
|
ax.set_xlabel(self.feature_names[feature_idx[0]])
|
||
|
|
|
||
|
|
if n_cols is None or pd_plot_idx % n_cols == 0:
|
||
|
|
if not ax.get_ylabel():
|
||
|
|
ax.set_ylabel("Partial dependence")
|
||
|
|
else:
|
||
|
|
ax.set_yticklabels([])
|
||
|
|
|
||
|
|
if pd_line_kw.get("label", None) and kind != "individual" and not categorical:
|
||
|
|
ax.legend()
|
||
|
|
|
||
|
|
def _plot_two_way_partial_dependence(
|
||
|
|
self,
|
||
|
|
avg_preds,
|
||
|
|
feature_values,
|
||
|
|
feature_idx,
|
||
|
|
ax,
|
||
|
|
pd_plot_idx,
|
||
|
|
Z_level,
|
||
|
|
contour_kw,
|
||
|
|
categorical,
|
||
|
|
heatmap_kw,
|
||
|
|
):
|
||
|
|
"""Plot 2-way partial dependence.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
avg_preds : ndarray of shape \
|
||
|
|
(n_instances, n_grid_points, n_grid_points)
|
||
|
|
The average predictions for all points of `feature_values[0]` and
|
||
|
|
`feature_values[1]` for some given features for all samples in `X`.
|
||
|
|
feature_values : seq of 1d array
|
||
|
|
A sequence of array of the feature values for which the predictions
|
||
|
|
have been computed.
|
||
|
|
feature_idx : tuple of int
|
||
|
|
The indices of the target features
|
||
|
|
ax : Matplotlib axes
|
||
|
|
The axis on which to plot the ICE and PDP lines.
|
||
|
|
pd_plot_idx : int
|
||
|
|
The sequential index of the plot. It will be unraveled to find the
|
||
|
|
matching 2D position in the grid layout.
|
||
|
|
Z_level : ndarray of shape (8, 8)
|
||
|
|
The Z-level used to encode the average predictions.
|
||
|
|
contour_kw : dict
|
||
|
|
Dict with keywords passed when plotting the contours.
|
||
|
|
categorical : bool
|
||
|
|
Whether features are categorical.
|
||
|
|
heatmap_kw: dict
|
||
|
|
Dict with keywords passed when plotting the PD heatmap
|
||
|
|
(categorical).
|
||
|
|
"""
|
||
|
|
if categorical:
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
default_im_kw = dict(interpolation="nearest", cmap="viridis")
|
||
|
|
im_kw = {**default_im_kw, **heatmap_kw}
|
||
|
|
|
||
|
|
data = avg_preds[self.target_idx]
|
||
|
|
im = ax.imshow(data, **im_kw)
|
||
|
|
text = None
|
||
|
|
cmap_min, cmap_max = im.cmap(0), im.cmap(1.0)
|
||
|
|
|
||
|
|
text = np.empty_like(data, dtype=object)
|
||
|
|
# print text with appropriate color depending on background
|
||
|
|
thresh = (data.max() + data.min()) / 2.0
|
||
|
|
|
||
|
|
for flat_index in range(data.size):
|
||
|
|
row, col = np.unravel_index(flat_index, data.shape)
|
||
|
|
color = cmap_max if data[row, col] < thresh else cmap_min
|
||
|
|
|
||
|
|
values_format = ".2f"
|
||
|
|
text_data = format(data[row, col], values_format)
|
||
|
|
|
||
|
|
text_kwargs = dict(ha="center", va="center", color=color)
|
||
|
|
text[row, col] = ax.text(col, row, text_data, **text_kwargs)
|
||
|
|
|
||
|
|
fig = ax.figure
|
||
|
|
fig.colorbar(im, ax=ax)
|
||
|
|
ax.set(
|
||
|
|
xticks=np.arange(len(feature_values[1])),
|
||
|
|
yticks=np.arange(len(feature_values[0])),
|
||
|
|
xticklabels=feature_values[1],
|
||
|
|
yticklabels=feature_values[0],
|
||
|
|
xlabel=self.feature_names[feature_idx[1]],
|
||
|
|
ylabel=self.feature_names[feature_idx[0]],
|
||
|
|
)
|
||
|
|
|
||
|
|
plt.setp(ax.get_xticklabels(), rotation="vertical")
|
||
|
|
|
||
|
|
heatmap_idx = np.unravel_index(pd_plot_idx, self.heatmaps_.shape)
|
||
|
|
self.heatmaps_[heatmap_idx] = im
|
||
|
|
else:
|
||
|
|
from matplotlib import transforms # noqa
|
||
|
|
|
||
|
|
XX, YY = np.meshgrid(feature_values[0], feature_values[1])
|
||
|
|
Z = avg_preds[self.target_idx].T
|
||
|
|
CS = ax.contour(XX, YY, Z, levels=Z_level, linewidths=0.5, colors="k")
|
||
|
|
contour_idx = np.unravel_index(pd_plot_idx, self.contours_.shape)
|
||
|
|
self.contours_[contour_idx] = ax.contourf(
|
||
|
|
XX,
|
||
|
|
YY,
|
||
|
|
Z,
|
||
|
|
levels=Z_level,
|
||
|
|
vmax=Z_level[-1],
|
||
|
|
vmin=Z_level[0],
|
||
|
|
**contour_kw,
|
||
|
|
)
|
||
|
|
ax.clabel(CS, fmt="%2.2f", colors="k", fontsize=10, inline=True)
|
||
|
|
|
||
|
|
trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
|
||
|
|
# create the decile line for the vertical axis
|
||
|
|
xlim, ylim = ax.get_xlim(), ax.get_ylim()
|
||
|
|
vlines_idx = np.unravel_index(pd_plot_idx, self.deciles_vlines_.shape)
|
||
|
|
self.deciles_vlines_[vlines_idx] = ax.vlines(
|
||
|
|
self.deciles[feature_idx[0]],
|
||
|
|
0,
|
||
|
|
0.05,
|
||
|
|
transform=trans,
|
||
|
|
color="k",
|
||
|
|
)
|
||
|
|
# create the decile line for the horizontal axis
|
||
|
|
hlines_idx = np.unravel_index(pd_plot_idx, self.deciles_hlines_.shape)
|
||
|
|
self.deciles_hlines_[hlines_idx] = ax.hlines(
|
||
|
|
self.deciles[feature_idx[1]],
|
||
|
|
0,
|
||
|
|
0.05,
|
||
|
|
transform=trans,
|
||
|
|
color="k",
|
||
|
|
)
|
||
|
|
# reset xlim and ylim since they are overwritten by hlines and
|
||
|
|
# vlines
|
||
|
|
ax.set_xlim(xlim)
|
||
|
|
ax.set_ylim(ylim)
|
||
|
|
|
||
|
|
# set xlabel if it is not already set
|
||
|
|
if not ax.get_xlabel():
|
||
|
|
ax.set_xlabel(self.feature_names[feature_idx[0]])
|
||
|
|
ax.set_ylabel(self.feature_names[feature_idx[1]])
|
||
|
|
|
||
|
|
def plot(
|
||
|
|
self,
|
||
|
|
*,
|
||
|
|
ax=None,
|
||
|
|
n_cols=3,
|
||
|
|
line_kw=None,
|
||
|
|
ice_lines_kw=None,
|
||
|
|
pd_line_kw=None,
|
||
|
|
contour_kw=None,
|
||
|
|
bar_kw=None,
|
||
|
|
heatmap_kw=None,
|
||
|
|
pdp_lim=None,
|
||
|
|
centered=False,
|
||
|
|
):
|
||
|
|
"""Plot partial dependence plots.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
ax : Matplotlib axes or array-like of Matplotlib axes, default=None
|
||
|
|
- If a single axis is passed in, it is treated as a bounding axes
|
||
|
|
and a grid of partial dependence plots will be drawn within
|
||
|
|
these bounds. The `n_cols` parameter controls the number of
|
||
|
|
columns in the grid.
|
||
|
|
- If an array-like of axes are passed in, the partial dependence
|
||
|
|
plots will be drawn directly into these axes.
|
||
|
|
- If `None`, a figure and a bounding axes is created and treated
|
||
|
|
as the single axes case.
|
||
|
|
|
||
|
|
n_cols : int, default=3
|
||
|
|
The maximum number of columns in the grid plot. Only active when
|
||
|
|
`ax` is a single axes or `None`.
|
||
|
|
|
||
|
|
line_kw : dict, default=None
|
||
|
|
Dict with keywords passed to the `matplotlib.pyplot.plot` call.
|
||
|
|
For one-way partial dependence plots.
|
||
|
|
|
||
|
|
ice_lines_kw : dict, default=None
|
||
|
|
Dictionary with keywords passed to the `matplotlib.pyplot.plot` call.
|
||
|
|
For ICE lines in the one-way partial dependence plots.
|
||
|
|
The key value pairs defined in `ice_lines_kw` takes priority over
|
||
|
|
`line_kw`.
|
||
|
|
|
||
|
|
.. versionadded:: 1.0
|
||
|
|
|
||
|
|
pd_line_kw : dict, default=None
|
||
|
|
Dictionary with keywords passed to the `matplotlib.pyplot.plot` call.
|
||
|
|
For partial dependence in one-way partial dependence plots.
|
||
|
|
The key value pairs defined in `pd_line_kw` takes priority over
|
||
|
|
`line_kw`.
|
||
|
|
|
||
|
|
.. versionadded:: 1.0
|
||
|
|
|
||
|
|
contour_kw : dict, default=None
|
||
|
|
Dict with keywords passed to the `matplotlib.pyplot.contourf`
|
||
|
|
call for two-way partial dependence plots.
|
||
|
|
|
||
|
|
bar_kw : dict, default=None
|
||
|
|
Dict with keywords passed to the `matplotlib.pyplot.bar`
|
||
|
|
call for one-way categorical partial dependence plots.
|
||
|
|
|
||
|
|
.. versionadded:: 1.2
|
||
|
|
|
||
|
|
heatmap_kw : dict, default=None
|
||
|
|
Dict with keywords passed to the `matplotlib.pyplot.imshow`
|
||
|
|
call for two-way categorical partial dependence plots.
|
||
|
|
|
||
|
|
.. versionadded:: 1.2
|
||
|
|
|
||
|
|
pdp_lim : dict, default=None
|
||
|
|
Global min and max average predictions, such that all plots will have the
|
||
|
|
same scale and y limits. `pdp_lim[1]` is the global min and max for single
|
||
|
|
partial dependence curves. `pdp_lim[2]` is the global min and max for
|
||
|
|
two-way partial dependence curves. If `None` (default), the limit will be
|
||
|
|
inferred from the global minimum and maximum of all predictions.
|
||
|
|
|
||
|
|
.. versionadded:: 1.1
|
||
|
|
|
||
|
|
centered : bool, default=False
|
||
|
|
If `True`, the ICE and PD lines will start at the origin of the
|
||
|
|
y-axis. By default, no centering is done.
|
||
|
|
|
||
|
|
.. versionadded:: 1.1
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
display : :class:`~sklearn.inspection.PartialDependenceDisplay`
|
||
|
|
Returns a :class:`~sklearn.inspection.PartialDependenceDisplay`
|
||
|
|
object that contains the partial dependence plots.
|
||
|
|
"""
|
||
|
|
|
||
|
|
check_matplotlib_support("plot_partial_dependence")
|
||
|
|
import matplotlib.pyplot as plt # noqa
|
||
|
|
from matplotlib.gridspec import GridSpecFromSubplotSpec # noqa
|
||
|
|
|
||
|
|
if isinstance(self.kind, str):
|
||
|
|
kind = [self.kind] * len(self.features)
|
||
|
|
else:
|
||
|
|
kind = self.kind
|
||
|
|
|
||
|
|
if self.is_categorical is None:
|
||
|
|
is_categorical = [
|
||
|
|
(False,) if len(fx) == 1 else (False, False) for fx in self.features
|
||
|
|
]
|
||
|
|
else:
|
||
|
|
is_categorical = self.is_categorical
|
||
|
|
|
||
|
|
if len(kind) != len(self.features):
|
||
|
|
raise ValueError(
|
||
|
|
"When `kind` is provided as a list of strings, it should "
|
||
|
|
"contain as many elements as `features`. `kind` contains "
|
||
|
|
f"{len(kind)} element(s) and `features` contains "
|
||
|
|
f"{len(self.features)} element(s)."
|
||
|
|
)
|
||
|
|
|
||
|
|
valid_kinds = {"average", "individual", "both"}
|
||
|
|
if any([k not in valid_kinds for k in kind]):
|
||
|
|
raise ValueError(
|
||
|
|
f"Values provided to `kind` must be one of: {valid_kinds!r} or a list"
|
||
|
|
f" of such values. Currently, kind={self.kind!r}"
|
||
|
|
)
|
||
|
|
|
||
|
|
# FIXME: remove in 1.3
|
||
|
|
if self.pdp_lim != "deprecated":
|
||
|
|
warnings.warn(
|
||
|
|
"The `pdp_lim` parameter is deprecated in version 1.1 and will be "
|
||
|
|
"removed in version 1.3. Provide `pdp_lim` to the `plot` method."
|
||
|
|
"instead.",
|
||
|
|
FutureWarning,
|
||
|
|
)
|
||
|
|
if pdp_lim is not None and self.pdp_lim != pdp_lim:
|
||
|
|
warnings.warn(
|
||
|
|
"`pdp_lim` has been passed in both the constructor and the `plot` "
|
||
|
|
"method. For backward compatibility, the parameter from the "
|
||
|
|
"constructor will be used.",
|
||
|
|
UserWarning,
|
||
|
|
)
|
||
|
|
pdp_lim = self.pdp_lim
|
||
|
|
|
||
|
|
# Center results before plotting
|
||
|
|
if not centered:
|
||
|
|
pd_results_ = self.pd_results
|
||
|
|
else:
|
||
|
|
pd_results_ = []
|
||
|
|
for kind_plot, pd_result in zip(kind, self.pd_results):
|
||
|
|
current_results = {"values": pd_result["values"]}
|
||
|
|
|
||
|
|
if kind_plot in ("individual", "both"):
|
||
|
|
preds = pd_result.individual
|
||
|
|
preds = preds - preds[self.target_idx, :, 0, None]
|
||
|
|
current_results["individual"] = preds
|
||
|
|
|
||
|
|
if kind_plot in ("average", "both"):
|
||
|
|
avg_preds = pd_result.average
|
||
|
|
avg_preds = avg_preds - avg_preds[self.target_idx, 0, None]
|
||
|
|
current_results["average"] = avg_preds
|
||
|
|
|
||
|
|
pd_results_.append(Bunch(**current_results))
|
||
|
|
|
||
|
|
if pdp_lim is None:
|
||
|
|
# get global min and max average predictions of PD grouped by plot type
|
||
|
|
pdp_lim = {}
|
||
|
|
for kind_plot, pdp in zip(kind, pd_results_):
|
||
|
|
values = pdp["values"]
|
||
|
|
preds = pdp.average if kind_plot == "average" else pdp.individual
|
||
|
|
min_pd = preds[self.target_idx].min()
|
||
|
|
max_pd = preds[self.target_idx].max()
|
||
|
|
|
||
|
|
# expand the limits to account so that the plotted lines do not touch
|
||
|
|
# the edges of the plot
|
||
|
|
span = max_pd - min_pd
|
||
|
|
min_pd -= 0.05 * span
|
||
|
|
max_pd += 0.05 * span
|
||
|
|
|
||
|
|
n_fx = len(values)
|
||
|
|
old_min_pd, old_max_pd = pdp_lim.get(n_fx, (min_pd, max_pd))
|
||
|
|
min_pd = min(min_pd, old_min_pd)
|
||
|
|
max_pd = max(max_pd, old_max_pd)
|
||
|
|
pdp_lim[n_fx] = (min_pd, max_pd)
|
||
|
|
|
||
|
|
if line_kw is None:
|
||
|
|
line_kw = {}
|
||
|
|
if ice_lines_kw is None:
|
||
|
|
ice_lines_kw = {}
|
||
|
|
if pd_line_kw is None:
|
||
|
|
pd_line_kw = {}
|
||
|
|
if bar_kw is None:
|
||
|
|
bar_kw = {}
|
||
|
|
if heatmap_kw is None:
|
||
|
|
heatmap_kw = {}
|
||
|
|
|
||
|
|
if ax is None:
|
||
|
|
_, ax = plt.subplots()
|
||
|
|
|
||
|
|
if contour_kw is None:
|
||
|
|
contour_kw = {}
|
||
|
|
default_contour_kws = {"alpha": 0.75}
|
||
|
|
contour_kw = {**default_contour_kws, **contour_kw}
|
||
|
|
|
||
|
|
n_features = len(self.features)
|
||
|
|
is_average_plot = [kind_plot == "average" for kind_plot in kind]
|
||
|
|
if all(is_average_plot):
|
||
|
|
# only average plots are requested
|
||
|
|
n_ice_lines = 0
|
||
|
|
n_lines = 1
|
||
|
|
else:
|
||
|
|
# we need to determine the number of ICE samples computed
|
||
|
|
ice_plot_idx = is_average_plot.index(False)
|
||
|
|
n_ice_lines = self._get_sample_count(
|
||
|
|
len(pd_results_[ice_plot_idx].individual[0])
|
||
|
|
)
|
||
|
|
if any([kind_plot == "both" for kind_plot in kind]):
|
||
|
|
n_lines = n_ice_lines + 1 # account for the average line
|
||
|
|
else:
|
||
|
|
n_lines = n_ice_lines
|
||
|
|
|
||
|
|
if isinstance(ax, plt.Axes):
|
||
|
|
# If ax was set off, it has most likely been set to off
|
||
|
|
# by a previous call to plot.
|
||
|
|
if not ax.axison:
|
||
|
|
raise ValueError(
|
||
|
|
"The ax was already used in another plot "
|
||
|
|
"function, please set ax=display.axes_ "
|
||
|
|
"instead"
|
||
|
|
)
|
||
|
|
|
||
|
|
ax.set_axis_off()
|
||
|
|
self.bounding_ax_ = ax
|
||
|
|
self.figure_ = ax.figure
|
||
|
|
|
||
|
|
n_cols = min(n_cols, n_features)
|
||
|
|
n_rows = int(np.ceil(n_features / float(n_cols)))
|
||
|
|
|
||
|
|
self.axes_ = np.empty((n_rows, n_cols), dtype=object)
|
||
|
|
if all(is_average_plot):
|
||
|
|
self.lines_ = np.empty((n_rows, n_cols), dtype=object)
|
||
|
|
else:
|
||
|
|
self.lines_ = np.empty((n_rows, n_cols, n_lines), dtype=object)
|
||
|
|
self.contours_ = np.empty((n_rows, n_cols), dtype=object)
|
||
|
|
self.bars_ = np.empty((n_rows, n_cols), dtype=object)
|
||
|
|
self.heatmaps_ = np.empty((n_rows, n_cols), dtype=object)
|
||
|
|
|
||
|
|
axes_ravel = self.axes_.ravel()
|
||
|
|
|
||
|
|
gs = GridSpecFromSubplotSpec(
|
||
|
|
n_rows, n_cols, subplot_spec=ax.get_subplotspec()
|
||
|
|
)
|
||
|
|
for i, spec in zip(range(n_features), gs):
|
||
|
|
axes_ravel[i] = self.figure_.add_subplot(spec)
|
||
|
|
|
||
|
|
else: # array-like
|
||
|
|
ax = np.asarray(ax, dtype=object)
|
||
|
|
if ax.size != n_features:
|
||
|
|
raise ValueError(
|
||
|
|
"Expected ax to have {} axes, got {}".format(n_features, ax.size)
|
||
|
|
)
|
||
|
|
|
||
|
|
if ax.ndim == 2:
|
||
|
|
n_cols = ax.shape[1]
|
||
|
|
else:
|
||
|
|
n_cols = None
|
||
|
|
|
||
|
|
self.bounding_ax_ = None
|
||
|
|
self.figure_ = ax.ravel()[0].figure
|
||
|
|
self.axes_ = ax
|
||
|
|
if all(is_average_plot):
|
||
|
|
self.lines_ = np.empty_like(ax, dtype=object)
|
||
|
|
else:
|
||
|
|
self.lines_ = np.empty(ax.shape + (n_lines,), dtype=object)
|
||
|
|
self.contours_ = np.empty_like(ax, dtype=object)
|
||
|
|
self.bars_ = np.empty_like(ax, dtype=object)
|
||
|
|
self.heatmaps_ = np.empty_like(ax, dtype=object)
|
||
|
|
|
||
|
|
# create contour levels for two-way plots
|
||
|
|
if 2 in pdp_lim:
|
||
|
|
Z_level = np.linspace(*pdp_lim[2], num=8)
|
||
|
|
|
||
|
|
self.deciles_vlines_ = np.empty_like(self.axes_, dtype=object)
|
||
|
|
self.deciles_hlines_ = np.empty_like(self.axes_, dtype=object)
|
||
|
|
|
||
|
|
for pd_plot_idx, (axi, feature_idx, cat, pd_result, kind_plot) in enumerate(
|
||
|
|
zip(
|
||
|
|
self.axes_.ravel(),
|
||
|
|
self.features,
|
||
|
|
is_categorical,
|
||
|
|
pd_results_,
|
||
|
|
kind,
|
||
|
|
)
|
||
|
|
):
|
||
|
|
avg_preds = None
|
||
|
|
preds = None
|
||
|
|
feature_values = pd_result["values"]
|
||
|
|
if kind_plot == "individual":
|
||
|
|
preds = pd_result.individual
|
||
|
|
elif kind_plot == "average":
|
||
|
|
avg_preds = pd_result.average
|
||
|
|
else: # kind_plot == 'both'
|
||
|
|
avg_preds = pd_result.average
|
||
|
|
preds = pd_result.individual
|
||
|
|
|
||
|
|
if len(feature_values) == 1:
|
||
|
|
# define the line-style for the current plot
|
||
|
|
default_line_kws = {
|
||
|
|
"color": "C0",
|
||
|
|
"label": "average" if kind_plot == "both" else None,
|
||
|
|
}
|
||
|
|
if kind_plot == "individual":
|
||
|
|
default_ice_lines_kws = {"alpha": 0.3, "linewidth": 0.5}
|
||
|
|
default_pd_lines_kws = {}
|
||
|
|
elif kind_plot == "both":
|
||
|
|
# by default, we need to distinguish the average line from
|
||
|
|
# the individual lines via color and line style
|
||
|
|
default_ice_lines_kws = {
|
||
|
|
"alpha": 0.3,
|
||
|
|
"linewidth": 0.5,
|
||
|
|
"color": "tab:blue",
|
||
|
|
}
|
||
|
|
default_pd_lines_kws = {
|
||
|
|
"color": "tab:orange",
|
||
|
|
"linestyle": "--",
|
||
|
|
}
|
||
|
|
else:
|
||
|
|
default_ice_lines_kws = {}
|
||
|
|
default_pd_lines_kws = {}
|
||
|
|
|
||
|
|
ice_lines_kw = {
|
||
|
|
**default_line_kws,
|
||
|
|
**default_ice_lines_kws,
|
||
|
|
**line_kw,
|
||
|
|
**ice_lines_kw,
|
||
|
|
}
|
||
|
|
del ice_lines_kw["label"]
|
||
|
|
|
||
|
|
pd_line_kw = {
|
||
|
|
**default_line_kws,
|
||
|
|
**default_pd_lines_kws,
|
||
|
|
**line_kw,
|
||
|
|
**pd_line_kw,
|
||
|
|
}
|
||
|
|
|
||
|
|
default_bar_kws = {"color": "C0"}
|
||
|
|
bar_kw = {**default_bar_kws, **bar_kw}
|
||
|
|
|
||
|
|
default_heatmap_kw = {}
|
||
|
|
heatmap_kw = {**default_heatmap_kw, **heatmap_kw}
|
||
|
|
|
||
|
|
self._plot_one_way_partial_dependence(
|
||
|
|
kind_plot,
|
||
|
|
preds,
|
||
|
|
avg_preds,
|
||
|
|
feature_values[0],
|
||
|
|
feature_idx,
|
||
|
|
n_ice_lines,
|
||
|
|
axi,
|
||
|
|
n_cols,
|
||
|
|
pd_plot_idx,
|
||
|
|
n_lines,
|
||
|
|
ice_lines_kw,
|
||
|
|
pd_line_kw,
|
||
|
|
cat[0],
|
||
|
|
bar_kw,
|
||
|
|
pdp_lim,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
self._plot_two_way_partial_dependence(
|
||
|
|
avg_preds,
|
||
|
|
feature_values,
|
||
|
|
feature_idx,
|
||
|
|
axi,
|
||
|
|
pd_plot_idx,
|
||
|
|
Z_level,
|
||
|
|
contour_kw,
|
||
|
|
cat[0] and cat[1],
|
||
|
|
heatmap_kw,
|
||
|
|
)
|
||
|
|
|
||
|
|
return self
|