"""Partial dependence plots for regression and classification models.""" # Authors: Peter Prettenhofer # Trevor Stephens # Nicolas Hug # License: BSD 3 clause from collections.abc import Iterable import numpy as np from scipy import sparse from scipy.stats.mstats import mquantiles from ..base import is_classifier, is_regressor from ..ensemble import RandomForestRegressor from ..ensemble._gb import BaseGradientBoosting from ..ensemble._hist_gradient_boosting.gradient_boosting import ( BaseHistGradientBoosting, ) from ..exceptions import NotFittedError from ..tree import DecisionTreeRegressor from ..utils import ( Bunch, _determine_key_type, _get_column_indices, _safe_assign, _safe_indexing, check_array, check_matplotlib_support, # noqa ) from ..utils._param_validation import ( HasMethods, Integral, Interval, StrOptions, validate_params, ) from ..utils.extmath import cartesian from ..utils.validation import _check_sample_weight, check_is_fitted from ._pd_utils import _check_feature_names, _get_feature_index __all__ = [ "partial_dependence", ] def _grid_from_X(X, percentiles, is_categorical, grid_resolution): """Generate a grid of points based on the percentiles of X. The grid is a cartesian product between the columns of ``values``. The ith column of ``values`` consists in ``grid_resolution`` equally-spaced points between the percentiles of the jth column of X. If ``grid_resolution`` is bigger than the number of unique values in the j-th column of X or if the feature is a categorical feature (by inspecting `is_categorical`) , then those unique values will be used instead. Parameters ---------- X : array-like of shape (n_samples, n_target_features) The data. percentiles : tuple of float The percentiles which are used to construct the extreme values of the grid. Must be in [0, 1]. is_categorical : list of bool For each feature, tells whether it is categorical or not. If a feature is categorical, then the values used will be the unique ones (i.e. categories) instead of the percentiles. grid_resolution : int The number of equally spaced points to be placed on the grid for each feature. Returns ------- grid : ndarray of shape (n_points, n_target_features) A value for each feature at each point in the grid. ``n_points`` is always ``<= grid_resolution ** X.shape[1]``. values : list of 1d ndarrays The values with which the grid has been created. The size of each array ``values[j]`` is either ``grid_resolution``, or the number of unique values in ``X[:, j]``, whichever is smaller. """ if not isinstance(percentiles, Iterable) or len(percentiles) != 2: raise ValueError("'percentiles' must be a sequence of 2 elements.") if not all(0 <= x <= 1 for x in percentiles): raise ValueError("'percentiles' values must be in [0, 1].") if percentiles[0] >= percentiles[1]: raise ValueError("percentiles[0] must be strictly less than percentiles[1].") if grid_resolution <= 1: raise ValueError("'grid_resolution' must be strictly greater than 1.") values = [] # TODO: we should handle missing values (i.e. `np.nan`) specifically and store them # in a different Bunch attribute. for feature, is_cat in enumerate(is_categorical): try: uniques = np.unique(_safe_indexing(X, feature, axis=1)) except TypeError as exc: # `np.unique` will fail in the presence of `np.nan` and `str` categories # due to sorting. Temporary, we reraise an error explaining the problem. raise ValueError( f"The column #{feature} contains mixed data types. Finding unique " "categories fail due to sorting. It usually means that the column " "contains `np.nan` values together with `str` categories. Such use " "case is not yet supported in scikit-learn." ) from exc if is_cat or uniques.shape[0] < grid_resolution: # Use the unique values either because: # - feature has low resolution use unique values # - feature is categorical axis = uniques else: # create axis based on percentiles and grid resolution emp_percentiles = mquantiles( _safe_indexing(X, feature, axis=1), prob=percentiles, axis=0 ) if np.allclose(emp_percentiles[0], emp_percentiles[1]): raise ValueError( "percentiles are too close to each other, " "unable to build the grid. Please choose percentiles " "that are further apart." ) axis = np.linspace( emp_percentiles[0], emp_percentiles[1], num=grid_resolution, endpoint=True, ) values.append(axis) return cartesian(values), values def _partial_dependence_recursion(est, grid, features): """Calculate partial dependence via the recursion method. The recursion method is in particular enabled for tree-based estimators. For each `grid` value, a weighted tree traversal is performed: if a split node involves an input feature of interest, the corresponding left or right branch is followed; otherwise both branches are followed, each branch being weighted by the fraction of training samples that entered that branch. Finally, the partial dependence is given by a weighted average of all the visited leaves values. This method is more efficient in terms of speed than the `'brute'` method (:func:`~sklearn.inspection._partial_dependence._partial_dependence_brute`). However, here, the partial dependence computation is done explicitly with the `X` used during training of `est`. Parameters ---------- est : BaseEstimator A fitted estimator object implementing :term:`predict` or :term:`decision_function`. Multioutput-multiclass classifiers are not supported. Note that `'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`, ). grid : array-like of shape (n_points, n_target_features) The grid of feature values for which the partial dependence is calculated. Note that `n_points` is the number of points in the grid and `n_target_features` is the number of features you are doing partial dependence at. features : array-like of {int, str} The feature (e.g. `[0]`) or pair of interacting features (e.g. `[(0, 1)]`) for which the partial dependency should be computed. Returns ------- averaged_predictions : array-like of shape (n_targets, n_points) The averaged predictions for the given `grid` of features values. Note that `n_targets` is the number of targets (e.g. 1 for binary classification, `n_tasks` for multi-output regression, and `n_classes` for multiclass classification) and `n_points` is the number of points in the `grid`. """ averaged_predictions = est._compute_partial_dependence_recursion(grid, features) if averaged_predictions.ndim == 1: # reshape to (1, n_points) for consistency with # _partial_dependence_brute averaged_predictions = averaged_predictions.reshape(1, -1) return averaged_predictions def _partial_dependence_brute( est, grid, features, X, response_method, sample_weight=None ): """Calculate partial dependence via the brute force method. The brute method explicitly averages the predictions of an estimator over a grid of feature values. For each `grid` value, all the samples from `X` have their variables of interest replaced by that specific `grid` value. The predictions are then made and averaged across the samples. This method is slower than the `'recursion'` (:func:`~sklearn.inspection._partial_dependence._partial_dependence_recursion`) version for estimators with this second option. However, with the `'brute'` force method, the average will be done with the given `X` and not the `X` used during training, as it is done in the `'recursion'` version. Therefore the average can always accept `sample_weight` (even when the estimator was fitted without). Parameters ---------- est : BaseEstimator A fitted estimator object implementing :term:`predict`, :term:`predict_proba`, or :term:`decision_function`. Multioutput-multiclass classifiers are not supported. grid : array-like of shape (n_points, n_target_features) The grid of feature values for which the partial dependence is calculated. Note that `n_points` is the number of points in the grid and `n_target_features` is the number of features you are doing partial dependence at. features : array-like of {int, str} The feature (e.g. `[0]`) or pair of interacting features (e.g. `[(0, 1)]`) for which the partial dependency should be computed. X : array-like of shape (n_samples, n_features) `X` is used to generate values for the complement features. That is, for each value in `grid`, the method will average the prediction of each sample from `X` having that grid value for `features`. 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. sample_weight : array-like of shape (n_samples,), default=None Sample weights are used to calculate weighted means when averaging the model output. If `None`, then samples are equally weighted. Note that `sample_weight` does not change the individual predictions. Returns ------- averaged_predictions : array-like of shape (n_targets, n_points) The averaged predictions for the given `grid` of features values. Note that `n_targets` is the number of targets (e.g. 1 for binary classification, `n_tasks` for multi-output regression, and `n_classes` for multiclass classification) and `n_points` is the number of points in the `grid`. predictions : array-like The predictions for the given `grid` of features values over the samples from `X`. For non-multioutput regression and binary classification the shape is `(n_instances, n_points)` and for multi-output regression and multiclass classification the shape is `(n_targets, n_instances, n_points)`, where `n_targets` is the number of targets (`n_tasks` for multi-output regression, and `n_classes` for multiclass classification), `n_instances` is the number of instances in `X`, and `n_points` is the number of points in the `grid`. """ predictions = [] averaged_predictions = [] # define the prediction_method (predict, predict_proba, decision_function). if is_regressor(est): prediction_method = est.predict else: predict_proba = getattr(est, "predict_proba", None) decision_function = getattr(est, "decision_function", None) if response_method == "auto": # try predict_proba, then decision_function if it doesn't exist prediction_method = predict_proba or decision_function else: prediction_method = ( predict_proba if response_method == "predict_proba" else decision_function ) if prediction_method is None: if response_method == "auto": raise ValueError( "The estimator has no predict_proba and no " "decision_function method." ) elif response_method == "predict_proba": raise ValueError("The estimator has no predict_proba method.") else: raise ValueError("The estimator has no decision_function method.") X_eval = X.copy() for new_values in grid: for i, variable in enumerate(features): _safe_assign(X_eval, new_values[i], column_indexer=variable) try: # Note: predictions is of shape # (n_points,) for non-multioutput regressors # (n_points, n_tasks) for multioutput regressors # (n_points, 1) for the regressors in cross_decomposition (I think) # (n_points, 2) for binary classification # (n_points, n_classes) for multiclass classification pred = prediction_method(X_eval) predictions.append(pred) # average over samples averaged_predictions.append(np.average(pred, axis=0, weights=sample_weight)) except NotFittedError as e: raise ValueError("'estimator' parameter must be a fitted estimator") from e n_samples = X.shape[0] # reshape to (n_targets, n_instances, n_points) where n_targets is: # - 1 for non-multioutput regression and binary classification (shape is # already correct in those cases) # - n_tasks for multi-output regression # - n_classes for multiclass classification. predictions = np.array(predictions).T if is_regressor(est) and predictions.ndim == 2: # non-multioutput regression, shape is (n_instances, n_points,) predictions = predictions.reshape(n_samples, -1) elif is_classifier(est) and predictions.shape[0] == 2: # Binary classification, shape is (2, n_instances, n_points). # we output the effect of **positive** class predictions = predictions[1] predictions = predictions.reshape(n_samples, -1) # reshape averaged_predictions to (n_targets, n_points) where n_targets is: # - 1 for non-multioutput regression and binary classification (shape is # already correct in those cases) # - n_tasks for multi-output regression # - n_classes for multiclass classification. averaged_predictions = np.array(averaged_predictions).T if is_regressor(est) and averaged_predictions.ndim == 1: # non-multioutput regression, shape is (n_points,) averaged_predictions = averaged_predictions.reshape(1, -1) elif is_classifier(est) and averaged_predictions.shape[0] == 2: # Binary classification, shape is (2, n_points). # we output the effect of **positive** class averaged_predictions = averaged_predictions[1] averaged_predictions = averaged_predictions.reshape(1, -1) return averaged_predictions, predictions @validate_params( { "estimator": [ HasMethods(["fit", "predict"]), HasMethods(["fit", "predict_proba"]), HasMethods(["fit", "decision_function"]), ], "X": ["array-like", "sparse matrix"], "features": ["array-like", Integral, str], "sample_weight": ["array-like", None], "categorical_features": ["array-like", None], "feature_names": ["array-like", None], "response_method": [StrOptions({"auto", "predict_proba", "decision_function"})], "percentiles": [tuple], "grid_resolution": [Interval(Integral, 1, None, closed="left")], "method": [StrOptions({"auto", "recursion", "brute"})], "kind": [StrOptions({"average", "individual", "both"})], }, prefer_skip_nested_validation=True, ) def partial_dependence( estimator, X, features, *, sample_weight=None, categorical_features=None, feature_names=None, response_method="auto", percentiles=(0.05, 0.95), grid_resolution=100, method="auto", kind="average", ): """Partial dependence of ``features``. Partial dependence of a feature (or a set of features) corresponds to the average response of an estimator for each possible value of the feature. Read more in the :ref:`User Guide `. .. warning:: For :class:`~sklearn.ensemble.GradientBoostingClassifier` and :class:`~sklearn.ensemble.GradientBoostingRegressor`, the `'recursion'` method (used by default) will not account for the `init` predictor of the boosting process. In practice, this will produce the same values as `'brute'` up to a constant offset in the target response, provided that `init` is a constant estimator (which is the default). However, if `init` is not a constant estimator, the partial dependence values are incorrect for `'recursion'` because the offset will be sample-dependent. It is preferable to use the `'brute'` method. Note that this only applies to :class:`~sklearn.ensemble.GradientBoostingClassifier` and :class:`~sklearn.ensemble.GradientBoostingRegressor`, not to :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and :class:`~sklearn.ensemble.HistGradientBoostingRegressor`. Parameters ---------- estimator : BaseEstimator A fitted estimator object implementing :term:`predict`, :term:`predict_proba`, or :term:`decision_function`. Multioutput-multiclass classifiers are not supported. X : {array-like, sparse matrix or dataframe} of shape (n_samples, n_features) ``X`` is used to generate a grid of values for the target ``features`` (where the partial dependence will be evaluated), and also to generate values for the complement features when the `method` is 'brute'. features : array-like of {int, str, bool} or int or str The feature (e.g. `[0]`) or pair of interacting features (e.g. `[(0, 1)]`) for which the partial dependency should be computed. sample_weight : array-like of shape (n_samples,), default=None Sample weights are used to calculate weighted means when averaging the model output. If `None`, then samples are equally weighted. If `sample_weight` is not `None`, then `method` will be set to `'brute'`. Note that `sample_weight` is ignored for `kind='individual'`. .. versionadded:: 1.3 categorical_features : array-like of shape (n_features,) or shape \ (n_categorical_features,), dtype={bool, int, str}, default=None 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. .. versionadded:: 1.2 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`. percentiles : tuple of float, default=(0.05, 0.95) The lower and upper percentile used to create the extreme values for the grid. Must be in [0, 1]. grid_resolution : int, default=100 The number of equally spaced points on the grid, for each target feature. method : {'auto', 'recursion', 'brute'}, 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`, ) when `kind='average'`. This 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 Individual Conditional Expectation (ICE) 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. If `sample_weight` is not `None`, then `'brute'` is used regardless of the estimator. Please see :ref:`this note ` for differences between the `'brute'` and `'recursion'` method. kind : {'average', 'individual', 'both'}, default='average' Whether to return the partial dependence averaged across all the samples in the dataset or one value per sample or both. See Returns below. Note that the fast `method='recursion'` option is only available for `kind='average'` and `sample_weights=None`. Computing individual dependencies and doing weighted averages requires using the slower `method='brute'`. .. versionadded:: 0.24 Returns ------- predictions : :class:`~sklearn.utils.Bunch` Dictionary-like object, with the following attributes. individual : ndarray of shape (n_outputs, n_instances, \ len(values[0]), len(values[1]), ...) The predictions for all the points in the grid for all samples in X. This is also known as Individual Conditional Expectation (ICE). Only available when `kind='individual'` or `kind='both'`. average : ndarray of shape (n_outputs, len(values[0]), \ len(values[1]), ...) The predictions for all the points in the grid, averaged over all samples in X (or over the training data if `method` is 'recursion'). Only available when `kind='average'` or `kind='both'`. values : seq of 1d ndarrays The values with which the grid has been created. .. deprecated:: 1.3 The key `values` has been deprecated in 1.3 and will be removed in 1.5 in favor of `grid_values`. See `grid_values` for details about the `values` attribute. grid_values : seq of 1d ndarrays The values with which the grid has been created. The generated grid is a cartesian product of the arrays in `grid_values` where `len(grid_values) == len(features)`. The size of each array `grid_values[j]` is either `grid_resolution`, or the number of unique values in `X[:, j]`, whichever is smaller. .. versionadded:: 1.3 `n_outputs` corresponds to the number of classes in a multi-class setting, or to the number of tasks for multi-output regression. For classical regression and binary classification `n_outputs==1`. `n_values_feature_j` corresponds to the size `grid_values[j]`. See Also -------- PartialDependenceDisplay.from_estimator : Plot Partial Dependence. PartialDependenceDisplay : Partial Dependence visualization. Examples -------- >>> X = [[0, 0, 2], [1, 0, 0]] >>> y = [0, 1] >>> from sklearn.ensemble import GradientBoostingClassifier >>> gb = GradientBoostingClassifier(random_state=0).fit(X, y) >>> partial_dependence(gb, features=[0], X=X, percentiles=(0, 1), ... grid_resolution=2) # doctest: +SKIP (array([[-4.52..., 4.52...]]), [array([ 0., 1.])]) """ check_is_fitted(estimator) if not (is_classifier(estimator) or is_regressor(estimator)): raise ValueError("'estimator' must be a fitted regressor or classifier.") if is_classifier(estimator) and isinstance(estimator.classes_[0], np.ndarray): raise ValueError("Multiclass-multioutput estimators are not supported") # 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) if is_regressor(estimator) and response_method != "auto": raise ValueError( "The response_method parameter is ignored for regressors and " "must be 'auto'." ) if kind != "average": if method == "recursion": raise ValueError( "The 'recursion' method only applies when 'kind' is set to 'average'" ) method = "brute" if method == "recursion" and sample_weight is not None: raise ValueError( "The 'recursion' method can only be applied when sample_weight is None." ) if method == "auto": if sample_weight is not None: method = "brute" elif isinstance(estimator, BaseGradientBoosting) and estimator.init is None: method = "recursion" elif isinstance( estimator, (BaseHistGradientBoosting, DecisionTreeRegressor, RandomForestRegressor), ): method = "recursion" else: method = "brute" if method == "recursion": if not isinstance( estimator, ( BaseGradientBoosting, BaseHistGradientBoosting, DecisionTreeRegressor, RandomForestRegressor, ), ): supported_classes_recursion = ( "GradientBoostingClassifier", "GradientBoostingRegressor", "HistGradientBoostingClassifier", "HistGradientBoostingRegressor", "HistGradientBoostingRegressor", "DecisionTreeRegressor", "RandomForestRegressor", ) raise ValueError( "Only the following estimators support the 'recursion' " "method: {}. Try using method='brute'.".format( ", ".join(supported_classes_recursion) ) ) if response_method == "auto": response_method = "decision_function" if response_method != "decision_function": raise ValueError( "With the 'recursion' method, the response_method must be " "'decision_function'. Got {}.".format(response_method) ) if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X) if _determine_key_type(features, accept_slice=False) == "int": # _get_column_indices() supports negative indexing. Here, we limit # the indexing to be positive. The upper bound will be checked # by _get_column_indices() if np.any(np.less(features, 0)): raise ValueError("all features must be in [0, {}]".format(X.shape[1] - 1)) features_indices = np.asarray( _get_column_indices(X, features), dtype=np.int32, order="C" ).ravel() feature_names = _check_feature_names(X, feature_names) n_features = X.shape[1] if categorical_features is None: is_categorical = [False] * len(features_indices) else: categorical_features = np.asarray(categorical_features) 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 = [categorical_features[idx] for idx in features_indices] 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 = [ idx in categorical_features_idx for idx in features_indices ] else: raise ValueError( "Expected `categorical_features` to be an array-like of boolean," f" integer, or string. Got {categorical_features.dtype} instead." ) grid, values = _grid_from_X( _safe_indexing(X, features_indices, axis=1), percentiles, is_categorical, grid_resolution, ) if method == "brute": averaged_predictions, predictions = _partial_dependence_brute( estimator, grid, features_indices, X, response_method, sample_weight ) # reshape predictions to # (n_outputs, n_instances, n_values_feature_0, n_values_feature_1, ...) predictions = predictions.reshape( -1, X.shape[0], *[val.shape[0] for val in values] ) else: averaged_predictions = _partial_dependence_recursion( estimator, grid, features_indices ) # reshape averaged_predictions to # (n_outputs, n_values_feature_0, n_values_feature_1, ...) averaged_predictions = averaged_predictions.reshape( -1, *[val.shape[0] for val in values] ) pdp_results = Bunch() msg = ( "Key: 'values', is deprecated in 1.3 and will be removed in 1.5. " "Please use 'grid_values' instead." ) pdp_results._set_deprecated( values, new_key="grid_values", deprecated_key="values", warning_message=msg ) if kind == "average": pdp_results["average"] = averaged_predictions elif kind == "individual": pdp_results["individual"] = predictions else: # kind='both' pdp_results["average"] = averaged_predictions pdp_results["individual"] = predictions return pdp_results