Source code for sktree.tree._honest_tree

# Authors: Ronan Perry, Sambit Panda, Haoyin Xu
# Adopted from: https://github.com/neurodata/honest-forests

import numpy as np
from sklearn.base import ClassifierMixin, MetaEstimatorMixin, _fit_context, clone
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
from sklearn.utils.multiclass import _check_partial_fit_first_call, check_classification_targets
from sklearn.utils.validation import check_is_fitted, check_X_y

from .._lib.sklearn.tree import DecisionTreeClassifier
from .._lib.sklearn.tree._classes import BaseDecisionTree


[docs] class HonestTreeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseDecisionTree): """ A decision tree classifier with honest predictions. Parameters ---------- tree_estimator : object, default=None Instantiated tree of type BaseDecisionTree from sktree. If None, then sklearn's DecisionTreeClassifier with default parameters will be used. Note that none of the parameters in ``tree_estimator`` need to be set. The parameters of the ``tree_estimator`` can be set using the ``tree_estimator_params`` keyword argument. criterion : {"gini", "entropy"}, default="gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. splitter : {"best", "random"}, default="best" The strategy used to choose the split at each node. Supported strategies are "best" to choose the best split and "random" to choose the best random split. max_depth : int, default=None The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number. - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split. min_samples_leaf : int or float, default=1 The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression. - If int, then consider `min_samples_leaf` as the minimum number. - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node. min_weight_fraction_leaf : float, default=0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. max_features : int, float or {"auto", "sqrt", "log2"}, default=None The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=sqrt(n_features)`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. random_state : int, RandomState instance or None, default=None Controls the randomness of the tree estimator. The features are always randomly permuted at each split, even if ``splitter`` is set to ``"best"``. When ``max_features < n_features``, the algorithm will select ``max_features`` at random at each split before finding the best split among them. But the best found split may vary across different runs, even if ``max_features=n_features``. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, ``random_state`` has to be fixed to an integer. See :term:`Glossary <random_state>` for details. max_leaf_nodes : int, default=None Grow a tree with ``max_leaf_nodes`` in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes. min_impurity_decrease : float, default=0.0 A node will be split if this split induces a decrease of the impurity greater than or equal to this value. The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child. ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed. class_weight : dict, list of dict or "balanced", default=None Weights associated with classes in the form ``{class_label: weight}``. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y. Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}]. The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` For multi-output, the weights of each column of y will be multiplied. Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified. ccp_alpha : non-negative float, default=0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details. monotonic_cst : array-like of int of shape (n_features), default=None Indicates the monotonicity constraint to enforce on each feature. - 1: monotonic increase - 0: no constraint - -1: monotonic decrease If monotonic_cst is None, no constraints are applied. Monotonicity constraints are not supported for: - multiclass classifications (i.e. when `n_classes > 2`), - multioutput classifications (i.e. when `n_outputs_ > 1`), - classifications trained on data with missing values. The constraints hold over the probability of the positive class. Read more in the :ref:`User Guide <monotonic_cst_gbdt>`. honest_fraction : float, default=0.5 Fraction of training samples used for estimates in the leaves. The remaining samples will be used to learn the tree structure. A larger fraction creates shallower trees with lower variance estimates. honest_prior : {"ignore", "uniform", "empirical"}, default="empirical" Method for dealing with empty leaves during evaluation of a test sample. If "ignore", returns numpy.nan. If "uniform", the prior tree posterior is 1/(number of classes). If "empirical", the prior tree posterior is the relative class frequency in the voting subsample. stratify : bool Whether or not to stratify sample when considering structure and leaf indices. By default False. **tree_estimator_params : dict Parameters to pass to the underlying base tree estimators. These must be parameters for ``tree_estimator``. Attributes ---------- estimator_ : object The child tree estimator template used to create the collection of fitted sub-estimators. classes_ : ndarray of shape (n_classes,) or list of ndarray The classes labels (single output problem), or a list of arrays of class labels (multi-output problem). feature_importances_ : ndarray of shape (n_features,) The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance [4]_. Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See :func:`sklearn.inspection.permutation_importance` as an alternative. max_features_ : int The inferred value of max_features. n_classes_ : int or list of int The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems). n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. n_outputs_ : int The number of outputs when ``fit`` is performed. tree_ : Tree instance The underlying Tree object. Please refer to ``help(sklearn.tree._tree.Tree)`` for attributes of Tree object and :ref:`sphx_glr_auto_examples_tree_plot_unveil_tree_structure.py` for basic usage of these attributes. empirical_prior_ : float Proportion of each class in the training labels y structure_indices_ : numpy.ndarray, shape=(n_structure,) Indices of training samples used to learn the structure honest_indices_ : numpy.ndarray, shape=(n_honest,) Indices of training samples used to learn leaf estimates Notes ----- The default values for the parameters controlling the size of the trees (e.g. ``max_depth``, ``min_samples_leaf``, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values. The :meth:`predict` method operates using the :func:`numpy.argmax` function on the outputs of :meth:`predict_proba`. This means that in case the highest predicted probabilities are tied, the classifier will predict the tied class with the lowest index in :term:`classes_`. References ---------- .. [1] https://en.wikipedia.org/wiki/Decision_tree_learning .. [2] L. Breiman, J. Friedman, R. Olshen, and C. Stone, "Classification and Regression Trees", Wadsworth, Belmont, CA, 1984. .. [3] T. Hastie, R. Tibshirani and J. Friedman. "Elements of Statistical Learning", Springer, 2009. .. [4] L. Breiman, and A. Cutler, "Random Forests", https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm .. [5] S. Athey, J. Tibshirani, and S. Wager. "Generalized Random Forests", Annals of Statistics, 2019. Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.model_selection import cross_val_score >>> from honest_forests import HonestTreeClassifier >>> clf = HonestTreeClassifier(random_state=0) >>> iris = load_iris() >>> cross_val_score(clf, iris.data, iris.target, cv=10) ... # doctest: +SKIP ... array([0.93333333, 0.93333333, 1. , 1. , 0.93333333, 0.8 , 0.8 , 0.93333333, 1. , 1. ]) """ _parameter_constraints: dict = { **BaseDecisionTree._parameter_constraints, "tree_estimator": [ HasMethods(["fit", "predict", "predict_proba", "apply"]), None, ], "honest_fraction": [Interval(RealNotInt, 0.0, 1.0, closed="neither")], "honest_prior": [StrOptions({"empirical", "uniform", "ignore"})], "stratify": ["boolean"], } def __init__( self, tree_estimator=None, criterion="gini", splitter="best", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0, monotonic_cst=None, honest_fraction=0.5, honest_prior="empirical", stratify=False, **tree_estimator_params, ): self.tree_estimator = tree_estimator self.criterion = criterion self.splitter = splitter self.max_depth = max_depth self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.max_features = max_features self.max_leaf_nodes = max_leaf_nodes self.class_weight = class_weight self.random_state = random_state self.min_impurity_decrease = min_impurity_decrease self.ccp_alpha = ccp_alpha self.monotonic_cst = monotonic_cst self.honest_fraction = honest_fraction self.honest_prior = honest_prior self.stratify = stratify # XXX: to enable this, we need to also reset the leaf node samples during `_set_leaf_nodes` self.store_leaf_values = False self._tree_estimator_params = tree_estimator_params
[docs] @_fit_context(prefer_skip_nested_validation=True) def fit( self, X, y, sample_weight=None, check_input=True, classes=None, ): """Build a decision tree classifier from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you're doing. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. Returns ------- self : HonestTreeClassifier Fitted estimator. """ self._fit( X, y, sample_weight=sample_weight, check_input=check_input, classes=classes, ) return self
[docs] def partial_fit(self, X, y, sample_weight=None, check_input=True, classes=None): """Update a decision tree classifier from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls. Returns ------- self : HonestTreeClassifier Fitted estimator. """ self._validate_params() # validate input parameters first_call = _check_partial_fit_first_call(self, classes=classes) # Fit if no tree exists yet if first_call: self._fit( X, y, sample_weight=sample_weight, check_input=check_input, classes=classes, ) return self rng = np.random.default_rng(self.random_state) if sample_weight is None: _sample_weight = np.ones((X.shape[0],), dtype=np.float64) else: _sample_weight = np.array(sample_weight) nonzero_indices = np.where(_sample_weight > 0)[0] self.structure_indices_ = rng.choice( nonzero_indices, int((1 - self.honest_fraction) * len(nonzero_indices)), replace=False, ) self.honest_indices_ = np.setdiff1d(nonzero_indices, self.structure_indices_) _sample_weight[self.honest_indices_] = 0 self.estimator_.partial_fit( X, y, sample_weight=_sample_weight, check_input=check_input, classes=classes, ) self._inherit_estimator_attributes() # update the number of classes, unsplit if y.ndim == 1: # reshape is necessary to preserve the data contiguity against vs # [:, np.newaxis] that does not. y = np.reshape(y, (-1, 1)) check_classification_targets(y) y = np.copy(y) # .astype(int) # Normally called by super X = self.estimator_._validate_X_predict(X, True) # Fit leaves using other subsample honest_leaves = self.tree_.apply(X[self.honest_indices_]) # preserve from underlying tree self._tree_classes_ = self.classes_ self._tree_n_classes_ = self.n_classes_ self.classes_ = [] self.n_classes_ = [] self.empirical_prior_ = [] y_encoded = np.zeros(y.shape, dtype=int) for k in range(self.n_outputs_): classes_k, y_encoded[:, k] = np.unique(y[:, k], return_inverse=True) self.classes_.append(classes_k) self.n_classes_.append(classes_k.shape[0]) self.empirical_prior_.append( np.bincount(y_encoded[:, k], minlength=classes_k.shape[0]) / y.shape[0] ) y = y_encoded # y-encoded ensures that y values match the indices of the classes self._set_leaf_nodes(honest_leaves, y) self.n_classes_ = np.array(self.n_classes_, dtype=np.intp) if self.n_outputs_ == 1: self.n_classes_ = self.n_classes_[0] self.classes_ = self.classes_[0] self.empirical_prior_ = self.empirical_prior_[0] y = y[:, 0] return self
def _partition_honest_indices(self, y, sample_weight): rng = np.random.default_rng(self.random_state) # Account for bootstrapping too if sample_weight is None: _sample_weight = np.ones((len(y),), dtype=np.float64) else: _sample_weight = np.array(sample_weight) nonzero_indices = np.where(_sample_weight > 0)[0] # sample the structure indices if self.stratify: ss = StratifiedShuffleSplit( n_splits=1, test_size=self.honest_fraction, random_state=self.random_state ) for structure_idx, _ in ss.split( np.zeros((len(nonzero_indices), 1)), y[nonzero_indices] ): self.structure_indices_ = nonzero_indices[structure_idx] else: self.structure_indices_ = rng.choice( nonzero_indices, int((1 - self.honest_fraction) * len(nonzero_indices)), replace=False, ) self.honest_indices_ = np.setdiff1d(nonzero_indices, self.structure_indices_) _sample_weight[self.honest_indices_] = 0 return _sample_weight def _get_estimator(self): """Resolve which estimator to return (default is DecisionTreeClassifier)""" if self.tree_estimator is None: self.estimator_ = DecisionTreeClassifier(random_state=self.random_state) else: # XXX: maybe error out if the base tree estimator is already fitted self.estimator_ = clone(self.tree_estimator) return self.estimator_ def _fit( self, X, y, sample_weight=None, check_input=True, missing_values_in_feature_mask=None, classes=None, ): """Build an honest tree classifier from the training set (X, y). Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like of shape (n_samples,) or (n_samples, n_outputs) The target values (class labels) as integers or strings. sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do. classes : array-like of shape (n_classes,), default=None List of all the classes that can possibly appear in the y vector. Returns ------- self : HonestTreeClassifier Fitted tree estimator. """ if check_input: X, y = check_X_y(X, y, multi_output=True) self.estimator_ = self._get_estimator() # check that all of tree_estimator_params are valid init_params = self.estimator_.__init__.__code__.co_varnames[1:] # exclude 'self' honest_tree_init_params = self.__init__.__code__.co_varnames[1:] # exclude 'self' invalid_params = [] for param in self._tree_estimator_params.keys(): if param not in init_params or param in honest_tree_init_params: invalid_params.append(param) if invalid_params: raise ValueError( f"Invalid parameter(s) for estimator {self.estimator_.__class__.__name__}: " f'{", ".join(invalid_params)}' ) self.estimator_.set_params( **dict( criterion=self.criterion, splitter=self.splitter, max_depth=self.max_depth, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, min_weight_fraction_leaf=self.min_weight_fraction_leaf, max_features=self.max_features, max_leaf_nodes=self.max_leaf_nodes, class_weight=self.class_weight, min_impurity_decrease=self.min_impurity_decrease, ccp_alpha=self.ccp_alpha, random_state=self.random_state, ) ) try: self.estimator_.set_params(**dict(monotonic_cst=self.monotonic_cst)) self.estimator_.set_params( **dict( store_leaf_values=self.store_leaf_values, ) ) except Exception: from warnings import warn warn("Using sklearn tree so store_leaf_values cannot be set.") # obtain the structure sample weights sample_weights_structure = self._partition_honest_indices(y, sample_weight) # Learn structure on subsample # XXX: this allows us to use BaseDecisionTree without partial_fit API try: self.estimator_._fit( X, y, sample_weight=sample_weights_structure, check_input=check_input, missing_values_in_feature_mask=missing_values_in_feature_mask, classes=classes, ) except Exception: self.estimator_._fit( X, y, sample_weight=sample_weights_structure, check_input=check_input, missing_values_in_feature_mask=missing_values_in_feature_mask, ) self._inherit_estimator_attributes() # fit the leaves on the non-structure indices not_honest_mask = np.ones(len(y), dtype=bool) not_honest_mask[self.honest_indices_] = False if sample_weight is None: sample_weight_leaves = np.ones((len(y),), dtype=np.float64) else: sample_weight_leaves = np.array(sample_weight) sample_weight_leaves[not_honest_mask] = 0 self._fit_leaves(X, y, sample_weight=sample_weight_leaves) return self def _fit_leaves(self, X, y, sample_weight): nonzero_indices = np.where(sample_weight > 0)[0] # sample the structure indices self.honest_indices_ = nonzero_indices # update the number of classes, unsplit if y.ndim == 1: # reshape is necessary to preserve the data contiguity against vs # [:, np.newaxis] that does not. y = np.reshape(y, (-1, 1)) check_classification_targets(y) y = np.copy(y) # .astype(int) # Normally called by super X = self.estimator_._validate_X_predict(X, True) # Fit leaves using other subsample honest_leaves = self.tree_.apply(X[self.honest_indices_]) # preserve from underlying tree # https://github.com/scikit-learn/scikit-learn/blob/1.0.X/sklearn/tree/_classes.py#L202 self._tree_classes_ = self.classes_ self._tree_n_classes_ = self.n_classes_ self.classes_ = [] self.n_classes_ = [] self.empirical_prior_ = [] y_encoded = np.zeros(y.shape, dtype=int) for k in range(self.n_outputs_): classes_k, y_encoded[:, k] = np.unique(y[:, k], return_inverse=True) self.classes_.append(classes_k) self.n_classes_.append(classes_k.shape[0]) self.empirical_prior_.append( np.bincount(y_encoded[:, k], minlength=classes_k.shape[0]) / y.shape[0] ) y = y_encoded # y-encoded ensures that y values match the indices of the classes self._set_leaf_nodes(honest_leaves, y) self.n_classes_ = np.array(self.n_classes_, dtype=np.intp) if self.n_outputs_ == 1: self.n_classes_ = self.n_classes_[0] self.classes_ = self.classes_[0] self.empirical_prior_ = self.empirical_prior_[0] y = y[:, 0] def _set_leaf_nodes(self, leaf_ids, y): """Traverse the already built tree with X and set leaf nodes with y. tree_.value has shape (n_nodes, n_outputs, max_n_classes), where n_nodes are the number of nodes in the tree (each node is either a split, or leaf node), n_outputs is the number of outputs (1 for classification, n for regression), and max_n_classes is the maximum number of classes across all outputs. For classification with n_classes classes, the classes are ordered by their index in the tree_.value array. """ self.tree_.value[:, :, :] = 0 for leaf_id, yval in zip(leaf_ids, y[self.honest_indices_, :]): self.tree_.value[leaf_id][:, yval] += 1 def _inherit_estimator_attributes(self): """Initialize necessary attributes from the provided tree estimator""" if hasattr(self.estimator_, "_inheritable_fitted_attribute"): for attr in self.estimator_._inheritable_fitted_attribute: setattr(self, attr, getattr(self.estimator_, attr)) self.classes_ = self.estimator_.classes_ self.max_features_ = self.estimator_.max_features_ self.n_classes_ = self.estimator_.n_classes_ self.n_features_in_ = self.estimator_.n_features_in_ self.n_outputs_ = self.estimator_.n_outputs_ self.tree_ = self.estimator_.tree_ # XXX: scikit-learn trees do not store their builder, or min_samples_split_ self.builder_ = getattr(self.estimator_, "builder_", None) self.min_samples_split_ = getattr(self.estimator_, "min_samples_split_", None) def _empty_leaf_correction(self, proba, pos=0): """Leaves with empty posteriors are assigned values. The posteriors are corrected according to the honest prior. In multi-output cases, the posterior corrections only correspond to the respective y dimension, indicated by the position param pos. """ zero_mask = proba.sum(axis=1) == 0.0 # For multi-output cases if self.n_outputs_ > 1: if self.honest_prior == "empirical": proba[zero_mask] = self.empirical_prior_[pos] elif self.honest_prior == "uniform": proba[zero_mask] = 1 / self.n_classes_[pos] elif self.honest_prior == "ignore": proba[zero_mask] = np.nan else: raise ValueError(f"honest_prior {self.honest_prior} not a valid input.") else: if self.honest_prior == "empirical": proba[zero_mask] = self.empirical_prior_ elif self.honest_prior == "uniform": proba[zero_mask] = 1 / self.n_classes_ elif self.honest_prior == "ignore": proba[zero_mask] = np.nan else: raise ValueError(f"honest_prior {self.honest_prior} not a valid input.") return proba
[docs] def predict_proba(self, X, check_input=True): """Predict class probabilities of the input samples X. The predicted class probability is the fraction of samples of the same class in a leaf. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- proba : ndarray of shape (n_samples, n_classes) or list of n_outputs \ such arrays if n_outputs > 1 The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. """ check_is_fitted(self) X = self.estimator_._validate_X_predict(X, check_input) proba = self.tree_.predict(X) if self.n_outputs_ == 1: proba = proba[:, : self._tree_n_classes_] normalizer = proba.sum(axis=1)[:, np.newaxis] normalizer[normalizer == 0.0] = 1.0 proba /= normalizer proba = self._empty_leaf_correction(proba) return proba else: all_proba = [] for k in range(self.n_outputs_): proba_k = proba[:, k, : self._tree_n_classes_[k]] normalizer = proba_k.sum(axis=1)[:, np.newaxis] normalizer[normalizer == 0.0] = 1.0 proba_k /= normalizer proba_k = self._empty_leaf_correction(proba_k, k) all_proba.append(proba_k) return all_proba
[docs] def predict(self, X, check_input=True): """Predict class for X. For a classification model, the predicted class for each sample in X is returned. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. check_input : bool, default=True Allow to bypass several input checking. Don't use this parameter unless you know what you're doing. Returns ------- y : array-like of shape (n_samples,) or (n_samples, n_outputs) The predicted classes, or the predict values. """ check_is_fitted(self) X = self._validate_X_predict(X, check_input) return self.estimator_.predict(X, False)