421 lines
17 KiB
Python
421 lines
17 KiB
Python
"""Partial dependence plots for regression and classification models."""
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# Authors: Peter Prettenhofer
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# Trevor Stephens
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# Nicolas Hug
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# License: BSD 3 clause
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from collections.abc import Iterable
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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 ..base import is_classifier, is_regressor
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from ..pipeline import Pipeline
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from ..utils.extmath import cartesian
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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 _safe_indexing
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from ..utils import _determine_key_type
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from ..utils import _get_column_indices
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from ..utils.validation import check_is_fitted
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from ..utils.validation import _deprecate_positional_args
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from ..tree import DecisionTreeRegressor
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from ..ensemble import RandomForestRegressor
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from ..exceptions import NotFittedError
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from ..ensemble._gb import BaseGradientBoosting
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from sklearn.ensemble._hist_gradient_boosting.gradient_boosting import (
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BaseHistGradientBoosting)
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__all__ = [
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'partial_dependence',
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]
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def _grid_from_X(X, percentiles, grid_resolution):
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"""Generate a grid of points based on the percentiles of X.
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The grid is a cartesian product between the columns of ``values``. The
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ith column of ``values`` consists in ``grid_resolution`` equally-spaced
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points between the percentiles of the jth column of X.
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If ``grid_resolution`` is bigger than the number of unique values in the
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jth column of X, then those unique values will be used instead.
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Parameters
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----------
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X : ndarray, shape (n_samples, n_target_features)
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The data
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percentiles : tuple of floats
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The percentiles which are used to construct the extreme values of
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the grid. Must be in [0, 1].
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grid_resolution : int
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The number of equally spaced points to be placed on the grid for each
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feature.
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Returns
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-------
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grid : ndarray, shape (n_points, n_target_features)
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A value for each feature at each point in the grid. ``n_points`` is
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always ``<= grid_resolution ** X.shape[1]``.
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values : list of 1d ndarrays
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The values with which the grid has been created. The size of each
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array ``values[j]`` is either ``grid_resolution``, or the number of
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unique values in ``X[:, j]``, whichever is smaller.
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"""
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if not isinstance(percentiles, Iterable) or len(percentiles) != 2:
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raise ValueError("'percentiles' must be a sequence of 2 elements.")
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if not all(0 <= x <= 1 for x in percentiles):
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raise ValueError("'percentiles' values must be in [0, 1].")
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if percentiles[0] >= percentiles[1]:
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raise ValueError('percentiles[0] must be strictly less '
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'than percentiles[1].')
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if grid_resolution <= 1:
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raise ValueError("'grid_resolution' must be strictly greater than 1.")
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values = []
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for feature in range(X.shape[1]):
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uniques = np.unique(_safe_indexing(X, feature, axis=1))
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if uniques.shape[0] < grid_resolution:
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# feature has low resolution use unique vals
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axis = uniques
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else:
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# create axis based on percentiles and grid resolution
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emp_percentiles = mquantiles(
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_safe_indexing(X, feature, axis=1), prob=percentiles, axis=0
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)
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if np.allclose(emp_percentiles[0], emp_percentiles[1]):
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raise ValueError(
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'percentiles are too close to each other, '
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'unable to build the grid. Please choose percentiles '
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'that are further apart.')
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axis = np.linspace(emp_percentiles[0],
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emp_percentiles[1],
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num=grid_resolution, endpoint=True)
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values.append(axis)
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return cartesian(values), values
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def _partial_dependence_recursion(est, grid, features):
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averaged_predictions = est._compute_partial_dependence_recursion(grid,
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features)
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if averaged_predictions.ndim == 1:
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# reshape to (1, n_points) for consistency with
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# _partial_dependence_brute
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averaged_predictions = averaged_predictions.reshape(1, -1)
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return averaged_predictions
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def _partial_dependence_brute(est, grid, features, X, response_method):
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averaged_predictions = []
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# define the prediction_method (predict, predict_proba, decision_function).
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if is_regressor(est):
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prediction_method = est.predict
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else:
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predict_proba = getattr(est, 'predict_proba', None)
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decision_function = getattr(est, 'decision_function', None)
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if response_method == 'auto':
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# try predict_proba, then decision_function if it doesn't exist
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prediction_method = predict_proba or decision_function
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else:
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prediction_method = (predict_proba if response_method ==
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'predict_proba' else decision_function)
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if prediction_method is None:
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if response_method == 'auto':
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raise ValueError(
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'The estimator has no predict_proba and no '
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'decision_function method.'
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)
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elif response_method == 'predict_proba':
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raise ValueError('The estimator has no predict_proba method.')
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else:
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raise ValueError(
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'The estimator has no decision_function method.')
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for new_values in grid:
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X_eval = X.copy()
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for i, variable in enumerate(features):
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if hasattr(X_eval, 'iloc'):
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X_eval.iloc[:, variable] = new_values[i]
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else:
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X_eval[:, variable] = new_values[i]
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try:
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predictions = prediction_method(X_eval)
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except NotFittedError:
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raise ValueError(
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"'estimator' parameter must be a fitted estimator")
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# Note: predictions is of shape
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# (n_points,) for non-multioutput regressors
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# (n_points, n_tasks) for multioutput regressors
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# (n_points, 1) for the regressors in cross_decomposition (I think)
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# (n_points, 2) for binary classification
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# (n_points, n_classes) for multiclass classification
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# average over samples
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averaged_predictions.append(np.mean(predictions, axis=0))
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# reshape to (n_targets, n_points) where n_targets is:
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# - 1 for non-multioutput regression and binary classification (shape is
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# already correct in those cases)
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# - n_tasks for multi-output regression
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# - n_classes for multiclass classification.
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averaged_predictions = np.array(averaged_predictions).T
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if is_regressor(est) and averaged_predictions.ndim == 1:
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# non-multioutput regression, shape is (n_points,)
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averaged_predictions = averaged_predictions.reshape(1, -1)
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elif is_classifier(est) and averaged_predictions.shape[0] == 2:
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# Binary classification, shape is (2, n_points).
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# we output the effect of **positive** class
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averaged_predictions = averaged_predictions[1]
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averaged_predictions = averaged_predictions.reshape(1, -1)
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return averaged_predictions
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@_deprecate_positional_args
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def partial_dependence(estimator, X, features, *, response_method='auto',
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percentiles=(0.05, 0.95), grid_resolution=100,
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method='auto'):
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"""Partial dependence of ``features``.
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Partial dependence of a feature (or a set of features) corresponds to
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the average response of an estimator for each possible value of the
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feature.
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Read more in the :ref:`User Guide <partial_dependence>`.
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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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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 or 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 : array-like of {int, str}
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The feature (e.g. `[0]`) or pair of interacting features
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(e.g. `[(0, 1)]`) for which the partial dependency should be computed.
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response_method : 'auto', 'predict_proba' or 'decision_function', \
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optional (default='auto')
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Specifies whether to use :term:`predict_proba` or
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:term:`decision_function` as the target response. For regressors
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this parameter is ignored and the response is always the output of
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:term:`predict`. By default, :term:`predict_proba` is tried first
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and we revert to :term:`decision_function` if it doesn't exist. If
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``method`` is 'recursion', the response is always the output of
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:term:`decision_function`.
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percentiles : tuple of float, optional (default=(0.05, 0.95))
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The lower and upper percentile used to create the extreme values
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for the grid. Must be in [0, 1].
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grid_resolution : int, optional (default=100)
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The number of equally spaced points on the grid, for each target
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feature.
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method : str, optional (default='auto')
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The method used to calculate the averaged predictions:
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- 'recursion' is only supported for some tree-based estimators (namely
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:class:`~sklearn.ensemble.GradientBoostingClassifier`,
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:class:`~sklearn.ensemble.GradientBoostingRegressor`,
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:class:`~sklearn.ensemble.HistGradientBoostingClassifier`,
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:class:`~sklearn.ensemble.HistGradientBoostingRegressor`,
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:class:`~sklearn.tree.DecisionTreeRegressor`,
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:class:`~sklearn.ensemble.RandomForestRegressor`,
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)
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but is more efficient in terms of speed.
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With this method, the target response of a
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classifier is always the decision function, not the predicted
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probabilities.
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- 'brute' is supported for any estimator, but is more
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computationally intensive.
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- 'auto': the 'recursion' is used for estimators that support it,
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and 'brute' is used otherwise.
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Please see :ref:`this note <pdp_method_differences>` for
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differences between the 'brute' and 'recursion' method.
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Returns
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-------
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averaged_predictions : ndarray, \
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shape (n_outputs, len(values[0]), len(values[1]), ...)
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The predictions for all the points in the grid, averaged over all
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samples in X (or over the training data if ``method`` is
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'recursion'). ``n_outputs`` corresponds to the number of classes in
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a multi-class setting, or to the number of tasks for multi-output
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regression. For classical regression and binary classification
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``n_outputs==1``. ``n_values_feature_j`` corresponds to the size
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``values[j]``.
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values : seq of 1d ndarrays
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The values with which the grid has been created. The generated grid
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is a cartesian product of the arrays in ``values``. ``len(values) ==
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len(features)``. The size of each array ``values[j]`` is either
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``grid_resolution``, or the number of unique values in ``X[:, j]``,
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whichever is smaller.
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Examples
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--------
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>>> X = [[0, 0, 2], [1, 0, 0]]
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>>> y = [0, 1]
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>>> from sklearn.ensemble import GradientBoostingClassifier
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>>> gb = GradientBoostingClassifier(random_state=0).fit(X, y)
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>>> partial_dependence(gb, features=[0], X=X, percentiles=(0, 1),
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... grid_resolution=2) # doctest: +SKIP
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(array([[-4.52..., 4.52...]]), [array([ 0., 1.])])
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See also
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--------
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sklearn.inspection.plot_partial_dependence: Plot partial dependence
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"""
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if not (is_classifier(estimator) or is_regressor(estimator)):
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raise ValueError(
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"'estimator' must be a fitted regressor or classifier."
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)
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if isinstance(estimator, Pipeline):
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# TODO: to be removed if/when pipeline get a `steps_` attributes
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# assuming Pipeline is the only estimator that does not store a new
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# attribute
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for est in estimator:
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# FIXME: remove the None option when it will be deprecated
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if est not in (None, 'drop'):
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check_is_fitted(est)
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else:
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check_is_fitted(estimator)
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if (is_classifier(estimator) and
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isinstance(estimator.classes_[0], np.ndarray)):
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raise ValueError(
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'Multiclass-multioutput estimators are not supported'
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)
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# Use check_array only on lists and other non-array-likes / sparse. Do not
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# convert DataFrame into a NumPy array.
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if not(hasattr(X, '__array__') or sparse.issparse(X)):
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X = check_array(X, force_all_finite='allow-nan', dtype=np.object)
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accepted_responses = ('auto', 'predict_proba', 'decision_function')
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if response_method not in accepted_responses:
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raise ValueError(
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'response_method {} is invalid. Accepted response_method names '
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'are {}.'.format(response_method, ', '.join(accepted_responses)))
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if is_regressor(estimator) and response_method != 'auto':
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raise ValueError(
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"The response_method parameter is ignored for regressors and "
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"must be 'auto'."
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)
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accepted_methods = ('brute', 'recursion', 'auto')
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if method not in accepted_methods:
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raise ValueError(
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'method {} is invalid. Accepted method names are {}.'.format(
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method, ', '.join(accepted_methods)))
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if method == 'auto':
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if (isinstance(estimator, BaseGradientBoosting) and
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estimator.init is None):
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method = 'recursion'
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elif isinstance(estimator, (BaseHistGradientBoosting,
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DecisionTreeRegressor,
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RandomForestRegressor)):
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method = 'recursion'
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else:
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method = 'brute'
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if method == 'recursion':
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if not isinstance(estimator,
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(BaseGradientBoosting, BaseHistGradientBoosting,
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DecisionTreeRegressor, RandomForestRegressor)):
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supported_classes_recursion = (
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'GradientBoostingClassifier',
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'GradientBoostingRegressor',
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'HistGradientBoostingClassifier',
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'HistGradientBoostingRegressor',
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'HistGradientBoostingRegressor',
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'DecisionTreeRegressor',
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'RandomForestRegressor',
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)
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raise ValueError(
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"Only the following estimators support the 'recursion' "
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"method: {}. Try using method='brute'."
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.format(', '.join(supported_classes_recursion)))
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if response_method == 'auto':
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response_method = 'decision_function'
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if response_method != 'decision_function':
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raise ValueError(
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"With the 'recursion' method, the response_method must be "
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"'decision_function'. Got {}.".format(response_method)
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)
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if _determine_key_type(features, accept_slice=False) == 'int':
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# _get_column_indices() supports negative indexing. Here, we limit
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# the indexing to be positive. The upper bound will be checked
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# by _get_column_indices()
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if np.any(np.less(features, 0)):
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raise ValueError(
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'all features must be in [0, {}]'.format(X.shape[1] - 1)
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)
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features_indices = np.asarray(
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_get_column_indices(X, features), dtype=np.int32, order='C'
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).ravel()
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grid, values = _grid_from_X(
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_safe_indexing(X, features_indices, axis=1), percentiles,
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grid_resolution
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)
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if method == 'brute':
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averaged_predictions = _partial_dependence_brute(
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estimator, grid, features_indices, X, response_method
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)
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else:
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averaged_predictions = _partial_dependence_recursion(
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estimator, grid, features_indices
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)
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# reshape averaged_predictions to
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# (n_outputs, n_values_feature_0, n_values_feature_1, ...)
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averaged_predictions = averaged_predictions.reshape(
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-1, *[val.shape[0] for val in values])
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return averaged_predictions, values
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