diff --git "a/venv/lib/python3.10/site-packages/sklearn/ensemble/_forest.py" "b/venv/lib/python3.10/site-packages/sklearn/ensemble/_forest.py" new file mode 100644--- /dev/null +++ "b/venv/lib/python3.10/site-packages/sklearn/ensemble/_forest.py" @@ -0,0 +1,2982 @@ +""" +Forest of trees-based ensemble methods. + +Those methods include random forests and extremely randomized trees. + +The module structure is the following: + +- The ``BaseForest`` base class implements a common ``fit`` method for all + the estimators in the module. The ``fit`` method of the base ``Forest`` + class calls the ``fit`` method of each sub-estimator on random samples + (with replacement, a.k.a. bootstrap) of the training set. + + The init of the sub-estimator is further delegated to the + ``BaseEnsemble`` constructor. + +- The ``ForestClassifier`` and ``ForestRegressor`` base classes further + implement the prediction logic by computing an average of the predicted + outcomes of the sub-estimators. + +- The ``RandomForestClassifier`` and ``RandomForestRegressor`` derived + classes provide the user with concrete implementations of + the forest ensemble method using classical, deterministic + ``DecisionTreeClassifier`` and ``DecisionTreeRegressor`` as + sub-estimator implementations. + +- The ``ExtraTreesClassifier`` and ``ExtraTreesRegressor`` derived + classes provide the user with concrete implementations of the + forest ensemble method using the extremely randomized trees + ``ExtraTreeClassifier`` and ``ExtraTreeRegressor`` as + sub-estimator implementations. + +Single and multi-output problems are both handled. +""" + +# Authors: Gilles Louppe +# Brian Holt +# Joly Arnaud +# Fares Hedayati +# +# License: BSD 3 clause + + +import threading +from abc import ABCMeta, abstractmethod +from numbers import Integral, Real +from warnings import catch_warnings, simplefilter, warn + +import numpy as np +from scipy.sparse import hstack as sparse_hstack +from scipy.sparse import issparse + +from ..base import ( + ClassifierMixin, + MultiOutputMixin, + RegressorMixin, + TransformerMixin, + _fit_context, + is_classifier, +) +from ..exceptions import DataConversionWarning +from ..metrics import accuracy_score, r2_score +from ..preprocessing import OneHotEncoder +from ..tree import ( + BaseDecisionTree, + DecisionTreeClassifier, + DecisionTreeRegressor, + ExtraTreeClassifier, + ExtraTreeRegressor, +) +from ..tree._tree import DOUBLE, DTYPE +from ..utils import check_random_state, compute_sample_weight +from ..utils._param_validation import Interval, RealNotInt, StrOptions +from ..utils._tags import _safe_tags +from ..utils.multiclass import check_classification_targets, type_of_target +from ..utils.parallel import Parallel, delayed +from ..utils.validation import ( + _check_feature_names_in, + _check_sample_weight, + _num_samples, + check_is_fitted, +) +from ._base import BaseEnsemble, _partition_estimators + +__all__ = [ + "RandomForestClassifier", + "RandomForestRegressor", + "ExtraTreesClassifier", + "ExtraTreesRegressor", + "RandomTreesEmbedding", +] + +MAX_INT = np.iinfo(np.int32).max + + +def _get_n_samples_bootstrap(n_samples, max_samples): + """ + Get the number of samples in a bootstrap sample. + + Parameters + ---------- + n_samples : int + Number of samples in the dataset. + max_samples : int or float + The maximum number of samples to draw from the total available: + - if float, this indicates a fraction of the total and should be + the interval `(0.0, 1.0]`; + - if int, this indicates the exact number of samples; + - if None, this indicates the total number of samples. + + Returns + ------- + n_samples_bootstrap : int + The total number of samples to draw for the bootstrap sample. + """ + if max_samples is None: + return n_samples + + if isinstance(max_samples, Integral): + if max_samples > n_samples: + msg = "`max_samples` must be <= n_samples={} but got value {}" + raise ValueError(msg.format(n_samples, max_samples)) + return max_samples + + if isinstance(max_samples, Real): + return max(round(n_samples * max_samples), 1) + + +def _generate_sample_indices(random_state, n_samples, n_samples_bootstrap): + """ + Private function used to _parallel_build_trees function.""" + + random_instance = check_random_state(random_state) + sample_indices = random_instance.randint( + 0, n_samples, n_samples_bootstrap, dtype=np.int32 + ) + + return sample_indices + + +def _generate_unsampled_indices(random_state, n_samples, n_samples_bootstrap): + """ + Private function used to forest._set_oob_score function.""" + sample_indices = _generate_sample_indices( + random_state, n_samples, n_samples_bootstrap + ) + sample_counts = np.bincount(sample_indices, minlength=n_samples) + unsampled_mask = sample_counts == 0 + indices_range = np.arange(n_samples) + unsampled_indices = indices_range[unsampled_mask] + + return unsampled_indices + + +def _parallel_build_trees( + tree, + bootstrap, + X, + y, + sample_weight, + tree_idx, + n_trees, + verbose=0, + class_weight=None, + n_samples_bootstrap=None, + missing_values_in_feature_mask=None, +): + """ + Private function used to fit a single tree in parallel.""" + if verbose > 1: + print("building tree %d of %d" % (tree_idx + 1, n_trees)) + + if bootstrap: + n_samples = X.shape[0] + if sample_weight is None: + curr_sample_weight = np.ones((n_samples,), dtype=np.float64) + else: + curr_sample_weight = sample_weight.copy() + + indices = _generate_sample_indices( + tree.random_state, n_samples, n_samples_bootstrap + ) + sample_counts = np.bincount(indices, minlength=n_samples) + curr_sample_weight *= sample_counts + + if class_weight == "subsample": + with catch_warnings(): + simplefilter("ignore", DeprecationWarning) + curr_sample_weight *= compute_sample_weight("auto", y, indices=indices) + elif class_weight == "balanced_subsample": + curr_sample_weight *= compute_sample_weight("balanced", y, indices=indices) + + tree._fit( + X, + y, + sample_weight=curr_sample_weight, + check_input=False, + missing_values_in_feature_mask=missing_values_in_feature_mask, + ) + else: + tree._fit( + X, + y, + sample_weight=sample_weight, + check_input=False, + missing_values_in_feature_mask=missing_values_in_feature_mask, + ) + + return tree + + +class BaseForest(MultiOutputMixin, BaseEnsemble, metaclass=ABCMeta): + """ + Base class for forests of trees. + + Warning: This class should not be used directly. Use derived classes + instead. + """ + + _parameter_constraints: dict = { + "n_estimators": [Interval(Integral, 1, None, closed="left")], + "bootstrap": ["boolean"], + "oob_score": ["boolean", callable], + "n_jobs": [Integral, None], + "random_state": ["random_state"], + "verbose": ["verbose"], + "warm_start": ["boolean"], + "max_samples": [ + None, + Interval(RealNotInt, 0.0, 1.0, closed="right"), + Interval(Integral, 1, None, closed="left"), + ], + } + + @abstractmethod + def __init__( + self, + estimator, + n_estimators=100, + *, + estimator_params=tuple(), + bootstrap=False, + oob_score=False, + n_jobs=None, + random_state=None, + verbose=0, + warm_start=False, + class_weight=None, + max_samples=None, + ): + super().__init__( + estimator=estimator, + n_estimators=n_estimators, + estimator_params=estimator_params, + ) + + self.bootstrap = bootstrap + self.oob_score = oob_score + self.n_jobs = n_jobs + self.random_state = random_state + self.verbose = verbose + self.warm_start = warm_start + self.class_weight = class_weight + self.max_samples = max_samples + + def apply(self, X): + """ + Apply trees in the forest to X, return leaf indices. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. Internally, its dtype will be converted to + ``dtype=np.float32``. If a sparse matrix is provided, it will be + converted into a sparse ``csr_matrix``. + + Returns + ------- + X_leaves : ndarray of shape (n_samples, n_estimators) + For each datapoint x in X and for each tree in the forest, + return the index of the leaf x ends up in. + """ + X = self._validate_X_predict(X) + results = Parallel( + n_jobs=self.n_jobs, + verbose=self.verbose, + prefer="threads", + )(delayed(tree.apply)(X, check_input=False) for tree in self.estimators_) + + return np.array(results).T + + def decision_path(self, X): + """ + Return the decision path in the forest. + + .. versionadded:: 0.18 + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. Internally, its dtype will be converted to + ``dtype=np.float32``. If a sparse matrix is provided, it will be + converted into a sparse ``csr_matrix``. + + Returns + ------- + indicator : sparse matrix of shape (n_samples, n_nodes) + Return a node indicator matrix where non zero elements indicates + that the samples goes through the nodes. The matrix is of CSR + format. + + n_nodes_ptr : ndarray of shape (n_estimators + 1,) + The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] + gives the indicator value for the i-th estimator. + """ + X = self._validate_X_predict(X) + indicators = Parallel( + n_jobs=self.n_jobs, + verbose=self.verbose, + prefer="threads", + )( + delayed(tree.decision_path)(X, check_input=False) + for tree in self.estimators_ + ) + + n_nodes = [0] + n_nodes.extend([i.shape[1] for i in indicators]) + n_nodes_ptr = np.array(n_nodes).cumsum() + + return sparse_hstack(indicators).tocsr(), n_nodes_ptr + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y, sample_weight=None): + """ + Build a forest of trees from the training set (X, y). + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The training input samples. Internally, its dtype will be converted + to ``dtype=np.float32``. If a sparse matrix is provided, it will be + converted into a sparse ``csc_matrix``. + + y : array-like of shape (n_samples,) or (n_samples, n_outputs) + The target values (class labels in classification, real numbers in + regression). + + 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. In the case of + classification, splits are also ignored if they would result in any + single class carrying a negative weight in either child node. + + Returns + ------- + self : object + Fitted estimator. + """ + # Validate or convert input data + if issparse(y): + raise ValueError("sparse multilabel-indicator for y is not supported.") + + X, y = self._validate_data( + X, + y, + multi_output=True, + accept_sparse="csc", + dtype=DTYPE, + force_all_finite=False, + ) + # _compute_missing_values_in_feature_mask checks if X has missing values and + # will raise an error if the underlying tree base estimator can't handle missing + # values. Only the criterion is required to determine if the tree supports + # missing values. + estimator = type(self.estimator)(criterion=self.criterion) + missing_values_in_feature_mask = ( + estimator._compute_missing_values_in_feature_mask( + X, estimator_name=self.__class__.__name__ + ) + ) + + if sample_weight is not None: + sample_weight = _check_sample_weight(sample_weight, X) + + if issparse(X): + # Pre-sort indices to avoid that each individual tree of the + # ensemble sorts the indices. + X.sort_indices() + + y = np.atleast_1d(y) + if y.ndim == 2 and y.shape[1] == 1: + warn( + ( + "A column-vector y was passed when a 1d array was" + " expected. Please change the shape of y to " + "(n_samples,), for example using ravel()." + ), + DataConversionWarning, + stacklevel=2, + ) + + 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)) + + if self.criterion == "poisson": + if np.any(y < 0): + raise ValueError( + "Some value(s) of y are negative which is " + "not allowed for Poisson regression." + ) + if np.sum(y) <= 0: + raise ValueError( + "Sum of y is not strictly positive which " + "is necessary for Poisson regression." + ) + + self._n_samples, self.n_outputs_ = y.shape + + y, expanded_class_weight = self._validate_y_class_weight(y) + + if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous: + y = np.ascontiguousarray(y, dtype=DOUBLE) + + if expanded_class_weight is not None: + if sample_weight is not None: + sample_weight = sample_weight * expanded_class_weight + else: + sample_weight = expanded_class_weight + + if not self.bootstrap and self.max_samples is not None: + raise ValueError( + "`max_sample` cannot be set if `bootstrap=False`. " + "Either switch to `bootstrap=True` or set " + "`max_sample=None`." + ) + elif self.bootstrap: + n_samples_bootstrap = _get_n_samples_bootstrap( + n_samples=X.shape[0], max_samples=self.max_samples + ) + else: + n_samples_bootstrap = None + + self._n_samples_bootstrap = n_samples_bootstrap + + self._validate_estimator() + + if not self.bootstrap and self.oob_score: + raise ValueError("Out of bag estimation only available if bootstrap=True") + + random_state = check_random_state(self.random_state) + + if not self.warm_start or not hasattr(self, "estimators_"): + # Free allocated memory, if any + self.estimators_ = [] + + n_more_estimators = self.n_estimators - len(self.estimators_) + + if n_more_estimators < 0: + raise ValueError( + "n_estimators=%d must be larger or equal to " + "len(estimators_)=%d when warm_start==True" + % (self.n_estimators, len(self.estimators_)) + ) + + elif n_more_estimators == 0: + warn( + "Warm-start fitting without increasing n_estimators does not " + "fit new trees." + ) + else: + if self.warm_start and len(self.estimators_) > 0: + # We draw from the random state to get the random state we + # would have got if we hadn't used a warm_start. + random_state.randint(MAX_INT, size=len(self.estimators_)) + + trees = [ + self._make_estimator(append=False, random_state=random_state) + for i in range(n_more_estimators) + ] + + # Parallel loop: we prefer the threading backend as the Cython code + # for fitting the trees is internally releasing the Python GIL + # making threading more efficient than multiprocessing in + # that case. However, for joblib 0.12+ we respect any + # parallel_backend contexts set at a higher level, + # since correctness does not rely on using threads. + trees = Parallel( + n_jobs=self.n_jobs, + verbose=self.verbose, + prefer="threads", + )( + delayed(_parallel_build_trees)( + t, + self.bootstrap, + X, + y, + sample_weight, + i, + len(trees), + verbose=self.verbose, + class_weight=self.class_weight, + n_samples_bootstrap=n_samples_bootstrap, + missing_values_in_feature_mask=missing_values_in_feature_mask, + ) + for i, t in enumerate(trees) + ) + + # Collect newly grown trees + self.estimators_.extend(trees) + + if self.oob_score and ( + n_more_estimators > 0 or not hasattr(self, "oob_score_") + ): + y_type = type_of_target(y) + if y_type == "unknown" or ( + self._estimator_type == "classifier" + and y_type == "multiclass-multioutput" + ): + # FIXME: we could consider to support multiclass-multioutput if + # we introduce or reuse a constructor parameter (e.g. + # oob_score) allowing our user to pass a callable defining the + # scoring strategy on OOB sample. + raise ValueError( + "The type of target cannot be used to compute OOB " + f"estimates. Got {y_type} while only the following are " + "supported: continuous, continuous-multioutput, binary, " + "multiclass, multilabel-indicator." + ) + + if callable(self.oob_score): + self._set_oob_score_and_attributes( + X, y, scoring_function=self.oob_score + ) + else: + self._set_oob_score_and_attributes(X, y) + + # Decapsulate classes_ attributes + if hasattr(self, "classes_") and self.n_outputs_ == 1: + self.n_classes_ = self.n_classes_[0] + self.classes_ = self.classes_[0] + + return self + + @abstractmethod + def _set_oob_score_and_attributes(self, X, y, scoring_function=None): + """Compute and set the OOB score and attributes. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data matrix. + y : ndarray of shape (n_samples, n_outputs) + The target matrix. + scoring_function : callable, default=None + Scoring function for OOB score. Default depends on whether + this is a regression (R2 score) or classification problem + (accuracy score). + """ + + def _compute_oob_predictions(self, X, y): + """Compute and set the OOB score. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data matrix. + y : ndarray of shape (n_samples, n_outputs) + The target matrix. + + Returns + ------- + oob_pred : ndarray of shape (n_samples, n_classes, n_outputs) or \ + (n_samples, 1, n_outputs) + The OOB predictions. + """ + # Prediction requires X to be in CSR format + if issparse(X): + X = X.tocsr() + + n_samples = y.shape[0] + n_outputs = self.n_outputs_ + if is_classifier(self) and hasattr(self, "n_classes_"): + # n_classes_ is a ndarray at this stage + # all the supported type of target will have the same number of + # classes in all outputs + oob_pred_shape = (n_samples, self.n_classes_[0], n_outputs) + else: + # for regression, n_classes_ does not exist and we create an empty + # axis to be consistent with the classification case and make + # the array operations compatible with the 2 settings + oob_pred_shape = (n_samples, 1, n_outputs) + + oob_pred = np.zeros(shape=oob_pred_shape, dtype=np.float64) + n_oob_pred = np.zeros((n_samples, n_outputs), dtype=np.int64) + + n_samples_bootstrap = _get_n_samples_bootstrap( + n_samples, + self.max_samples, + ) + for estimator in self.estimators_: + unsampled_indices = _generate_unsampled_indices( + estimator.random_state, + n_samples, + n_samples_bootstrap, + ) + + y_pred = self._get_oob_predictions(estimator, X[unsampled_indices, :]) + oob_pred[unsampled_indices, ...] += y_pred + n_oob_pred[unsampled_indices, :] += 1 + + for k in range(n_outputs): + if (n_oob_pred == 0).any(): + warn( + ( + "Some inputs do not have OOB scores. This probably means " + "too few trees were used to compute any reliable OOB " + "estimates." + ), + UserWarning, + ) + n_oob_pred[n_oob_pred == 0] = 1 + oob_pred[..., k] /= n_oob_pred[..., [k]] + + return oob_pred + + def _validate_y_class_weight(self, y): + # Default implementation + return y, None + + def _validate_X_predict(self, X): + """ + Validate X whenever one tries to predict, apply, predict_proba.""" + check_is_fitted(self) + if self.estimators_[0]._support_missing_values(X): + force_all_finite = "allow-nan" + else: + force_all_finite = True + + X = self._validate_data( + X, + dtype=DTYPE, + accept_sparse="csr", + reset=False, + force_all_finite=force_all_finite, + ) + if issparse(X) and (X.indices.dtype != np.intc or X.indptr.dtype != np.intc): + raise ValueError("No support for np.int64 index based sparse matrices") + return X + + @property + def feature_importances_(self): + """ + 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. + + Warning: impurity-based feature importances can be misleading for + high cardinality features (many unique values). See + :func:`sklearn.inspection.permutation_importance` as an alternative. + + Returns + ------- + feature_importances_ : ndarray of shape (n_features,) + The values of this array sum to 1, unless all trees are single node + trees consisting of only the root node, in which case it will be an + array of zeros. + """ + check_is_fitted(self) + + all_importances = Parallel(n_jobs=self.n_jobs, prefer="threads")( + delayed(getattr)(tree, "feature_importances_") + for tree in self.estimators_ + if tree.tree_.node_count > 1 + ) + + if not all_importances: + return np.zeros(self.n_features_in_, dtype=np.float64) + + all_importances = np.mean(all_importances, axis=0, dtype=np.float64) + return all_importances / np.sum(all_importances) + + def _get_estimators_indices(self): + # Get drawn indices along both sample and feature axes + for tree in self.estimators_: + if not self.bootstrap: + yield np.arange(self._n_samples, dtype=np.int32) + else: + # tree.random_state is actually an immutable integer seed rather + # than a mutable RandomState instance, so it's safe to use it + # repeatedly when calling this property. + seed = tree.random_state + # Operations accessing random_state must be performed identically + # to those in `_parallel_build_trees()` + yield _generate_sample_indices( + seed, self._n_samples, self._n_samples_bootstrap + ) + + @property + def estimators_samples_(self): + """The subset of drawn samples for each base estimator. + + Returns a dynamically generated list of indices identifying + the samples used for fitting each member of the ensemble, i.e., + the in-bag samples. + + Note: the list is re-created at each call to the property in order + to reduce the object memory footprint by not storing the sampling + data. Thus fetching the property may be slower than expected. + """ + return [sample_indices for sample_indices in self._get_estimators_indices()] + + def _more_tags(self): + # Only the criterion is required to determine if the tree supports + # missing values + estimator = type(self.estimator)(criterion=self.criterion) + return {"allow_nan": _safe_tags(estimator, key="allow_nan")} + + +def _accumulate_prediction(predict, X, out, lock): + """ + This is a utility function for joblib's Parallel. + + It can't go locally in ForestClassifier or ForestRegressor, because joblib + complains that it cannot pickle it when placed there. + """ + prediction = predict(X, check_input=False) + with lock: + if len(out) == 1: + out[0] += prediction + else: + for i in range(len(out)): + out[i] += prediction[i] + + +class ForestClassifier(ClassifierMixin, BaseForest, metaclass=ABCMeta): + """ + Base class for forest of trees-based classifiers. + + Warning: This class should not be used directly. Use derived classes + instead. + """ + + @abstractmethod + def __init__( + self, + estimator, + n_estimators=100, + *, + estimator_params=tuple(), + bootstrap=False, + oob_score=False, + n_jobs=None, + random_state=None, + verbose=0, + warm_start=False, + class_weight=None, + max_samples=None, + ): + super().__init__( + estimator=estimator, + n_estimators=n_estimators, + estimator_params=estimator_params, + bootstrap=bootstrap, + oob_score=oob_score, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start, + class_weight=class_weight, + max_samples=max_samples, + ) + + @staticmethod + def _get_oob_predictions(tree, X): + """Compute the OOB predictions for an individual tree. + + Parameters + ---------- + tree : DecisionTreeClassifier object + A single decision tree classifier. + X : ndarray of shape (n_samples, n_features) + The OOB samples. + + Returns + ------- + y_pred : ndarray of shape (n_samples, n_classes, n_outputs) + The OOB associated predictions. + """ + y_pred = tree.predict_proba(X, check_input=False) + y_pred = np.asarray(y_pred) + if y_pred.ndim == 2: + # binary and multiclass + y_pred = y_pred[..., np.newaxis] + else: + # Roll the first `n_outputs` axis to the last axis. We will reshape + # from a shape of (n_outputs, n_samples, n_classes) to a shape of + # (n_samples, n_classes, n_outputs). + y_pred = np.rollaxis(y_pred, axis=0, start=3) + return y_pred + + def _set_oob_score_and_attributes(self, X, y, scoring_function=None): + """Compute and set the OOB score and attributes. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data matrix. + y : ndarray of shape (n_samples, n_outputs) + The target matrix. + scoring_function : callable, default=None + Scoring function for OOB score. Defaults to `accuracy_score`. + """ + self.oob_decision_function_ = super()._compute_oob_predictions(X, y) + if self.oob_decision_function_.shape[-1] == 1: + # drop the n_outputs axis if there is a single output + self.oob_decision_function_ = self.oob_decision_function_.squeeze(axis=-1) + + if scoring_function is None: + scoring_function = accuracy_score + + self.oob_score_ = scoring_function( + y, np.argmax(self.oob_decision_function_, axis=1) + ) + + def _validate_y_class_weight(self, y): + check_classification_targets(y) + + y = np.copy(y) + expanded_class_weight = None + + if self.class_weight is not None: + y_original = np.copy(y) + + self.classes_ = [] + self.n_classes_ = [] + + y_store_unique_indices = np.zeros(y.shape, dtype=int) + for k in range(self.n_outputs_): + classes_k, y_store_unique_indices[:, k] = np.unique( + y[:, k], return_inverse=True + ) + self.classes_.append(classes_k) + self.n_classes_.append(classes_k.shape[0]) + y = y_store_unique_indices + + if self.class_weight is not None: + valid_presets = ("balanced", "balanced_subsample") + if isinstance(self.class_weight, str): + if self.class_weight not in valid_presets: + raise ValueError( + "Valid presets for class_weight include " + '"balanced" and "balanced_subsample".' + 'Given "%s".' + % self.class_weight + ) + if self.warm_start: + warn( + 'class_weight presets "balanced" or ' + '"balanced_subsample" are ' + "not recommended for warm_start if the fitted data " + "differs from the full dataset. In order to use " + '"balanced" weights, use compute_class_weight ' + '("balanced", classes, y). In place of y you can use ' + "a large enough sample of the full training set " + "target to properly estimate the class frequency " + "distributions. Pass the resulting weights as the " + "class_weight parameter." + ) + + if self.class_weight != "balanced_subsample" or not self.bootstrap: + if self.class_weight == "balanced_subsample": + class_weight = "balanced" + else: + class_weight = self.class_weight + expanded_class_weight = compute_sample_weight(class_weight, y_original) + + return y, expanded_class_weight + + def predict(self, X): + """ + Predict class for X. + + The predicted class of an input sample is a vote by the trees in + the forest, weighted by their probability estimates. That is, + the predicted class is the one with highest mean probability + estimate across the trees. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. Internally, its dtype will be converted to + ``dtype=np.float32``. If a sparse matrix is provided, it will be + converted into a sparse ``csr_matrix``. + + Returns + ------- + y : ndarray of shape (n_samples,) or (n_samples, n_outputs) + The predicted classes. + """ + proba = self.predict_proba(X) + + if self.n_outputs_ == 1: + return self.classes_.take(np.argmax(proba, axis=1), axis=0) + + else: + n_samples = proba[0].shape[0] + # all dtypes should be the same, so just take the first + class_type = self.classes_[0].dtype + predictions = np.empty((n_samples, self.n_outputs_), dtype=class_type) + + for k in range(self.n_outputs_): + predictions[:, k] = self.classes_[k].take( + np.argmax(proba[k], axis=1), axis=0 + ) + + return predictions + + def predict_proba(self, X): + """ + Predict class probabilities for X. + + The predicted class probabilities of an input sample are computed as + the mean predicted class probabilities of the trees in the forest. + The class probability of a single tree 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, its dtype will be converted to + ``dtype=np.float32``. If a sparse matrix is provided, it will be + converted into a sparse ``csr_matrix``. + + Returns + ------- + p : ndarray of shape (n_samples, n_classes), or a list of such arrays + The class probabilities of the input samples. The order of the + classes corresponds to that in the attribute :term:`classes_`. + """ + check_is_fitted(self) + # Check data + X = self._validate_X_predict(X) + + # Assign chunk of trees to jobs + n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs) + + # avoid storing the output of every estimator by summing them here + all_proba = [ + np.zeros((X.shape[0], j), dtype=np.float64) + for j in np.atleast_1d(self.n_classes_) + ] + lock = threading.Lock() + Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")( + delayed(_accumulate_prediction)(e.predict_proba, X, all_proba, lock) + for e in self.estimators_ + ) + + for proba in all_proba: + proba /= len(self.estimators_) + + if len(all_proba) == 1: + return all_proba[0] + else: + return all_proba + + def predict_log_proba(self, X): + """ + Predict class log-probabilities for X. + + The predicted class log-probabilities of an input sample is computed as + the log of the mean predicted class probabilities of the trees in the + forest. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. Internally, its dtype will be converted to + ``dtype=np.float32``. If a sparse matrix is provided, it will be + converted into a sparse ``csr_matrix``. + + Returns + ------- + p : ndarray of shape (n_samples, n_classes), or a list of such arrays + The class probabilities of the input samples. The order of the + classes corresponds to that in the attribute :term:`classes_`. + """ + proba = self.predict_proba(X) + + if self.n_outputs_ == 1: + return np.log(proba) + + else: + for k in range(self.n_outputs_): + proba[k] = np.log(proba[k]) + + return proba + + def _more_tags(self): + return {"multilabel": True} + + +class ForestRegressor(RegressorMixin, BaseForest, metaclass=ABCMeta): + """ + Base class for forest of trees-based regressors. + + Warning: This class should not be used directly. Use derived classes + instead. + """ + + @abstractmethod + def __init__( + self, + estimator, + n_estimators=100, + *, + estimator_params=tuple(), + bootstrap=False, + oob_score=False, + n_jobs=None, + random_state=None, + verbose=0, + warm_start=False, + max_samples=None, + ): + super().__init__( + estimator, + n_estimators=n_estimators, + estimator_params=estimator_params, + bootstrap=bootstrap, + oob_score=oob_score, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start, + max_samples=max_samples, + ) + + def predict(self, X): + """ + Predict regression target for X. + + The predicted regression target of an input sample is computed as the + mean predicted regression targets of the trees in the forest. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. Internally, its dtype will be converted to + ``dtype=np.float32``. If a sparse matrix is provided, it will be + converted into a sparse ``csr_matrix``. + + Returns + ------- + y : ndarray of shape (n_samples,) or (n_samples, n_outputs) + The predicted values. + """ + check_is_fitted(self) + # Check data + X = self._validate_X_predict(X) + + # Assign chunk of trees to jobs + n_jobs, _, _ = _partition_estimators(self.n_estimators, self.n_jobs) + + # avoid storing the output of every estimator by summing them here + if self.n_outputs_ > 1: + y_hat = np.zeros((X.shape[0], self.n_outputs_), dtype=np.float64) + else: + y_hat = np.zeros((X.shape[0]), dtype=np.float64) + + # Parallel loop + lock = threading.Lock() + Parallel(n_jobs=n_jobs, verbose=self.verbose, require="sharedmem")( + delayed(_accumulate_prediction)(e.predict, X, [y_hat], lock) + for e in self.estimators_ + ) + + y_hat /= len(self.estimators_) + + return y_hat + + @staticmethod + def _get_oob_predictions(tree, X): + """Compute the OOB predictions for an individual tree. + + Parameters + ---------- + tree : DecisionTreeRegressor object + A single decision tree regressor. + X : ndarray of shape (n_samples, n_features) + The OOB samples. + + Returns + ------- + y_pred : ndarray of shape (n_samples, 1, n_outputs) + The OOB associated predictions. + """ + y_pred = tree.predict(X, check_input=False) + if y_pred.ndim == 1: + # single output regression + y_pred = y_pred[:, np.newaxis, np.newaxis] + else: + # multioutput regression + y_pred = y_pred[:, np.newaxis, :] + return y_pred + + def _set_oob_score_and_attributes(self, X, y, scoring_function=None): + """Compute and set the OOB score and attributes. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data matrix. + y : ndarray of shape (n_samples, n_outputs) + The target matrix. + scoring_function : callable, default=None + Scoring function for OOB score. Defaults to `r2_score`. + """ + self.oob_prediction_ = super()._compute_oob_predictions(X, y).squeeze(axis=1) + if self.oob_prediction_.shape[-1] == 1: + # drop the n_outputs axis if there is a single output + self.oob_prediction_ = self.oob_prediction_.squeeze(axis=-1) + + if scoring_function is None: + scoring_function = r2_score + + self.oob_score_ = scoring_function(y, self.oob_prediction_) + + def _compute_partial_dependence_recursion(self, grid, target_features): + """Fast partial dependence computation. + + Parameters + ---------- + grid : ndarray of shape (n_samples, n_target_features) + The grid points on which the partial dependence should be + evaluated. + target_features : ndarray of shape (n_target_features) + The set of target features for which the partial dependence + should be evaluated. + + Returns + ------- + averaged_predictions : ndarray of shape (n_samples,) + The value of the partial dependence function on each grid point. + """ + grid = np.asarray(grid, dtype=DTYPE, order="C") + averaged_predictions = np.zeros( + shape=grid.shape[0], dtype=np.float64, order="C" + ) + + for tree in self.estimators_: + # Note: we don't sum in parallel because the GIL isn't released in + # the fast method. + tree.tree_.compute_partial_dependence( + grid, target_features, averaged_predictions + ) + # Average over the forest + averaged_predictions /= len(self.estimators_) + + return averaged_predictions + + def _more_tags(self): + return {"multilabel": True} + + +class RandomForestClassifier(ForestClassifier): + """ + A random forest classifier. + + A random forest is a meta estimator that fits a number of decision tree + classifiers on various sub-samples of the dataset and uses averaging to + improve the predictive accuracy and control over-fitting. + Trees in the forest use the best split strategy, i.e. equivalent to passing + `splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`. + The sub-sample size is controlled with the `max_samples` parameter if + `bootstrap=True` (default), otherwise the whole dataset is used to build + each tree. + + For a comparison between tree-based ensemble models see the example + :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_estimators : int, default=100 + The number of trees in the forest. + + .. versionchanged:: 0.22 + The default value of ``n_estimators`` changed from 10 to 100 + in 0.22. + + criterion : {"gini", "entropy", "log_loss"}, default="gini" + The function to measure the quality of a split. Supported criteria are + "gini" for the Gini impurity and "log_loss" and "entropy" both for the + Shannon information gain, see :ref:`tree_mathematical_formulation`. + Note: This parameter is tree-specific. + + 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. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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 : {"sqrt", "log2", None}, int or float, default="sqrt" + 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 + `max(1, int(max_features * n_features_in_))` features are considered at each + split. + - If "sqrt", then `max_features=sqrt(n_features)`. + - If "log2", then `max_features=log2(n_features)`. + - If None, then `max_features=n_features`. + + .. versionchanged:: 1.1 + The default of `max_features` changed from `"auto"` to `"sqrt"`. + + 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. + + max_leaf_nodes : int, default=None + Grow trees 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. + + .. versionadded:: 0.19 + + bootstrap : bool, default=True + Whether bootstrap samples are used when building trees. If False, the + whole dataset is used to build each tree. + + oob_score : bool or callable, default=False + Whether to use out-of-bag samples to estimate the generalization score. + By default, :func:`~sklearn.metrics.accuracy_score` is used. + Provide a callable with signature `metric(y_true, y_pred)` to use a + custom metric. Only available if `bootstrap=True`. + + n_jobs : int, default=None + The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, + :meth:`decision_path` and :meth:`apply` are all parallelized over the + trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See :term:`Glossary + ` for more details. + + random_state : int, RandomState instance or None, default=None + Controls both the randomness of the bootstrapping of the samples used + when building trees (if ``bootstrap=True``) and the sampling of the + features to consider when looking for the best split at each node + (if ``max_features < n_features``). + See :term:`Glossary ` for details. + + verbose : int, default=0 + Controls the verbosity when fitting and predicting. + + warm_start : bool, default=False + When set to ``True``, reuse the solution of the previous call to fit + and add more estimators to the ensemble, otherwise, just fit a whole + new forest. See :term:`Glossary ` and + :ref:`gradient_boosting_warm_start` for details. + + class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \ + default=None + Weights associated with classes in the form ``{class_label: weight}``. + If not given, 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))`` + + The "balanced_subsample" mode is the same as "balanced" except that + weights are computed based on the bootstrap sample for every tree + grown. + + 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. + + .. versionadded:: 0.22 + + max_samples : int or float, default=None + If bootstrap is True, the number of samples to draw from X + to train each base estimator. + + - If None (default), then draw `X.shape[0]` samples. + - If int, then draw `max_samples` samples. + - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus, + `max_samples` should be in the interval `(0.0, 1.0]`. + + .. versionadded:: 0.22 + + 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 `. + + .. versionadded:: 1.4 + + Attributes + ---------- + estimator_ : :class:`~sklearn.tree.DecisionTreeClassifier` + The child estimator template used to create the collection of fitted + sub-estimators. + + .. versionadded:: 1.2 + `base_estimator_` was renamed to `estimator_`. + + estimators_ : list of DecisionTreeClassifier + The collection of fitted sub-estimators. + + classes_ : ndarray of shape (n_classes,) or a list of such arrays + The classes labels (single output problem), or a list of arrays of + class labels (multi-output problem). + + n_classes_ : int or list + The number of classes (single output problem), or a list containing the + number of classes for each output (multi-output problem). + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + 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. + + .. versionadded:: 1.0 + + n_outputs_ : int + The number of outputs when ``fit`` is performed. + + 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. + + Warning: impurity-based feature importances can be misleading for + high cardinality features (many unique values). See + :func:`sklearn.inspection.permutation_importance` as an alternative. + + oob_score_ : float + Score of the training dataset obtained using an out-of-bag estimate. + This attribute exists only when ``oob_score`` is True. + + oob_decision_function_ : ndarray of shape (n_samples, n_classes) or \ + (n_samples, n_classes, n_outputs) + Decision function computed with out-of-bag estimate on the training + set. If n_estimators is small it might be possible that a data point + was never left out during the bootstrap. In this case, + `oob_decision_function_` might contain NaN. This attribute exists + only when ``oob_score`` is True. + + estimators_samples_ : list of arrays + The subset of drawn samples (i.e., the in-bag samples) for each base + estimator. Each subset is defined by an array of the indices selected. + + .. versionadded:: 1.4 + + See Also + -------- + sklearn.tree.DecisionTreeClassifier : A decision tree classifier. + sklearn.ensemble.ExtraTreesClassifier : Ensemble of extremely randomized + tree classifiers. + sklearn.ensemble.HistGradientBoostingClassifier : A Histogram-based Gradient + Boosting Classification Tree, very fast for big datasets (n_samples >= + 10_000). + + 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 features are always randomly permuted at each split. Therefore, + the best found split may vary, even with the same training data, + ``max_features=n_features`` and ``bootstrap=False``, if the improvement + of the criterion is identical for several splits enumerated during the + search of the best split. To obtain a deterministic behaviour during + fitting, ``random_state`` has to be fixed. + + References + ---------- + .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001. + + Examples + -------- + >>> from sklearn.ensemble import RandomForestClassifier + >>> from sklearn.datasets import make_classification + >>> X, y = make_classification(n_samples=1000, n_features=4, + ... n_informative=2, n_redundant=0, + ... random_state=0, shuffle=False) + >>> clf = RandomForestClassifier(max_depth=2, random_state=0) + >>> clf.fit(X, y) + RandomForestClassifier(...) + >>> print(clf.predict([[0, 0, 0, 0]])) + [1] + """ + + _parameter_constraints: dict = { + **ForestClassifier._parameter_constraints, + **DecisionTreeClassifier._parameter_constraints, + "class_weight": [ + StrOptions({"balanced_subsample", "balanced"}), + dict, + list, + None, + ], + } + _parameter_constraints.pop("splitter") + + def __init__( + self, + n_estimators=100, + *, + criterion="gini", + max_depth=None, + min_samples_split=2, + min_samples_leaf=1, + min_weight_fraction_leaf=0.0, + max_features="sqrt", + max_leaf_nodes=None, + min_impurity_decrease=0.0, + bootstrap=True, + oob_score=False, + n_jobs=None, + random_state=None, + verbose=0, + warm_start=False, + class_weight=None, + ccp_alpha=0.0, + max_samples=None, + monotonic_cst=None, + ): + super().__init__( + estimator=DecisionTreeClassifier(), + n_estimators=n_estimators, + estimator_params=( + "criterion", + "max_depth", + "min_samples_split", + "min_samples_leaf", + "min_weight_fraction_leaf", + "max_features", + "max_leaf_nodes", + "min_impurity_decrease", + "random_state", + "ccp_alpha", + "monotonic_cst", + ), + bootstrap=bootstrap, + oob_score=oob_score, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start, + class_weight=class_weight, + max_samples=max_samples, + ) + + self.criterion = criterion + 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.min_impurity_decrease = min_impurity_decrease + self.monotonic_cst = monotonic_cst + self.ccp_alpha = ccp_alpha + + +class RandomForestRegressor(ForestRegressor): + """ + A random forest regressor. + + A random forest is a meta estimator that fits a number of decision tree + regressors on various sub-samples of the dataset and uses averaging to + improve the predictive accuracy and control over-fitting. + Trees in the forest use the best split strategy, i.e. equivalent to passing + `splitter="best"` to the underlying :class:`~sklearn.tree.DecisionTreeRegressor`. + The sub-sample size is controlled with the `max_samples` parameter if + `bootstrap=True` (default), otherwise the whole dataset is used to build + each tree. + + For a comparison between tree-based ensemble models see the example + :ref:`sphx_glr_auto_examples_ensemble_plot_forest_hist_grad_boosting_comparison.py`. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_estimators : int, default=100 + The number of trees in the forest. + + .. versionchanged:: 0.22 + The default value of ``n_estimators`` changed from 10 to 100 + in 0.22. + + criterion : {"squared_error", "absolute_error", "friedman_mse", "poisson"}, \ + default="squared_error" + The function to measure the quality of a split. Supported criteria + are "squared_error" for the mean squared error, which is equal to + variance reduction as feature selection criterion and minimizes the L2 + loss using the mean of each terminal node, "friedman_mse", which uses + mean squared error with Friedman's improvement score for potential + splits, "absolute_error" for the mean absolute error, which minimizes + the L1 loss using the median of each terminal node, and "poisson" which + uses reduction in Poisson deviance to find splits. + Training using "absolute_error" is significantly slower + than when using "squared_error". + + .. versionadded:: 0.18 + Mean Absolute Error (MAE) criterion. + + .. versionadded:: 1.0 + Poisson criterion. + + 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. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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 : {"sqrt", "log2", None}, int or float, default=1.0 + 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 + `max(1, int(max_features * n_features_in_))` features are considered at each + split. + - If "sqrt", then `max_features=sqrt(n_features)`. + - If "log2", then `max_features=log2(n_features)`. + - If None or 1.0, then `max_features=n_features`. + + .. note:: + The default of 1.0 is equivalent to bagged trees and more + randomness can be achieved by setting smaller values, e.g. 0.3. + + .. versionchanged:: 1.1 + The default of `max_features` changed from `"auto"` to 1.0. + + 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. + + max_leaf_nodes : int, default=None + Grow trees 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. + + .. versionadded:: 0.19 + + bootstrap : bool, default=True + Whether bootstrap samples are used when building trees. If False, the + whole dataset is used to build each tree. + + oob_score : bool or callable, default=False + Whether to use out-of-bag samples to estimate the generalization score. + By default, :func:`~sklearn.metrics.r2_score` is used. + Provide a callable with signature `metric(y_true, y_pred)` to use a + custom metric. Only available if `bootstrap=True`. + + n_jobs : int, default=None + The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, + :meth:`decision_path` and :meth:`apply` are all parallelized over the + trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See :term:`Glossary + ` for more details. + + random_state : int, RandomState instance or None, default=None + Controls both the randomness of the bootstrapping of the samples used + when building trees (if ``bootstrap=True``) and the sampling of the + features to consider when looking for the best split at each node + (if ``max_features < n_features``). + See :term:`Glossary ` for details. + + verbose : int, default=0 + Controls the verbosity when fitting and predicting. + + warm_start : bool, default=False + When set to ``True``, reuse the solution of the previous call to fit + and add more estimators to the ensemble, otherwise, just fit a whole + new forest. See :term:`Glossary ` and + :ref:`gradient_boosting_warm_start` for details. + + 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. + + .. versionadded:: 0.22 + + max_samples : int or float, default=None + If bootstrap is True, the number of samples to draw from X + to train each base estimator. + + - If None (default), then draw `X.shape[0]` samples. + - If int, then draw `max_samples` samples. + - If float, then draw `max(round(n_samples * max_samples), 1)` samples. Thus, + `max_samples` should be in the interval `(0.0, 1.0]`. + + .. versionadded:: 0.22 + + monotonic_cst : array-like of int of shape (n_features), default=None + Indicates the monotonicity constraint to enforce on each feature. + - 1: monotonically increasing + - 0: no constraint + - -1: monotonically decreasing + + If monotonic_cst is None, no constraints are applied. + + Monotonicity constraints are not supported for: + - multioutput regressions (i.e. when `n_outputs_ > 1`), + - regressions trained on data with missing values. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 1.4 + + Attributes + ---------- + estimator_ : :class:`~sklearn.tree.DecisionTreeRegressor` + The child estimator template used to create the collection of fitted + sub-estimators. + + .. versionadded:: 1.2 + `base_estimator_` was renamed to `estimator_`. + + estimators_ : list of DecisionTreeRegressor + The collection of fitted sub-estimators. + + 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. + + Warning: impurity-based feature importances can be misleading for + high cardinality features (many unique values). See + :func:`sklearn.inspection.permutation_importance` as an alternative. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + 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. + + .. versionadded:: 1.0 + + n_outputs_ : int + The number of outputs when ``fit`` is performed. + + oob_score_ : float + Score of the training dataset obtained using an out-of-bag estimate. + This attribute exists only when ``oob_score`` is True. + + oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs) + Prediction computed with out-of-bag estimate on the training set. + This attribute exists only when ``oob_score`` is True. + + estimators_samples_ : list of arrays + The subset of drawn samples (i.e., the in-bag samples) for each base + estimator. Each subset is defined by an array of the indices selected. + + .. versionadded:: 1.4 + + See Also + -------- + sklearn.tree.DecisionTreeRegressor : A decision tree regressor. + sklearn.ensemble.ExtraTreesRegressor : Ensemble of extremely randomized + tree regressors. + sklearn.ensemble.HistGradientBoostingRegressor : A Histogram-based Gradient + Boosting Regression Tree, very fast for big datasets (n_samples >= + 10_000). + + 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 features are always randomly permuted at each split. Therefore, + the best found split may vary, even with the same training data, + ``max_features=n_features`` and ``bootstrap=False``, if the improvement + of the criterion is identical for several splits enumerated during the + search of the best split. To obtain a deterministic behaviour during + fitting, ``random_state`` has to be fixed. + + The default value ``max_features=1.0`` uses ``n_features`` + rather than ``n_features / 3``. The latter was originally suggested in + [1], whereas the former was more recently justified empirically in [2]. + + References + ---------- + .. [1] L. Breiman, "Random Forests", Machine Learning, 45(1), 5-32, 2001. + + .. [2] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized + trees", Machine Learning, 63(1), 3-42, 2006. + + Examples + -------- + >>> from sklearn.ensemble import RandomForestRegressor + >>> from sklearn.datasets import make_regression + >>> X, y = make_regression(n_features=4, n_informative=2, + ... random_state=0, shuffle=False) + >>> regr = RandomForestRegressor(max_depth=2, random_state=0) + >>> regr.fit(X, y) + RandomForestRegressor(...) + >>> print(regr.predict([[0, 0, 0, 0]])) + [-8.32987858] + """ + + _parameter_constraints: dict = { + **ForestRegressor._parameter_constraints, + **DecisionTreeRegressor._parameter_constraints, + } + _parameter_constraints.pop("splitter") + + def __init__( + self, + n_estimators=100, + *, + criterion="squared_error", + max_depth=None, + min_samples_split=2, + min_samples_leaf=1, + min_weight_fraction_leaf=0.0, + max_features=1.0, + max_leaf_nodes=None, + min_impurity_decrease=0.0, + bootstrap=True, + oob_score=False, + n_jobs=None, + random_state=None, + verbose=0, + warm_start=False, + ccp_alpha=0.0, + max_samples=None, + monotonic_cst=None, + ): + super().__init__( + estimator=DecisionTreeRegressor(), + n_estimators=n_estimators, + estimator_params=( + "criterion", + "max_depth", + "min_samples_split", + "min_samples_leaf", + "min_weight_fraction_leaf", + "max_features", + "max_leaf_nodes", + "min_impurity_decrease", + "random_state", + "ccp_alpha", + "monotonic_cst", + ), + bootstrap=bootstrap, + oob_score=oob_score, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start, + max_samples=max_samples, + ) + + self.criterion = criterion + 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.min_impurity_decrease = min_impurity_decrease + self.ccp_alpha = ccp_alpha + self.monotonic_cst = monotonic_cst + + +class ExtraTreesClassifier(ForestClassifier): + """ + An extra-trees classifier. + + This class implements a meta estimator that fits a number of + randomized decision trees (a.k.a. extra-trees) on various sub-samples + of the dataset and uses averaging to improve the predictive accuracy + and control over-fitting. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_estimators : int, default=100 + The number of trees in the forest. + + .. versionchanged:: 0.22 + The default value of ``n_estimators`` changed from 10 to 100 + in 0.22. + + criterion : {"gini", "entropy", "log_loss"}, default="gini" + The function to measure the quality of a split. Supported criteria are + "gini" for the Gini impurity and "log_loss" and "entropy" both for the + Shannon information gain, see :ref:`tree_mathematical_formulation`. + Note: This parameter is tree-specific. + + 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. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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 : {"sqrt", "log2", None}, int or float, default="sqrt" + 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 + `max(1, int(max_features * n_features_in_))` features are considered at each + split. + - If "sqrt", then `max_features=sqrt(n_features)`. + - If "log2", then `max_features=log2(n_features)`. + - If None, then `max_features=n_features`. + + .. versionchanged:: 1.1 + The default of `max_features` changed from `"auto"` to `"sqrt"`. + + 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. + + max_leaf_nodes : int, default=None + Grow trees 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. + + .. versionadded:: 0.19 + + bootstrap : bool, default=False + Whether bootstrap samples are used when building trees. If False, the + whole dataset is used to build each tree. + + oob_score : bool or callable, default=False + Whether to use out-of-bag samples to estimate the generalization score. + By default, :func:`~sklearn.metrics.accuracy_score` is used. + Provide a callable with signature `metric(y_true, y_pred)` to use a + custom metric. Only available if `bootstrap=True`. + + n_jobs : int, default=None + The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, + :meth:`decision_path` and :meth:`apply` are all parallelized over the + trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See :term:`Glossary + ` for more details. + + random_state : int, RandomState instance or None, default=None + Controls 3 sources of randomness: + + - the bootstrapping of the samples used when building trees + (if ``bootstrap=True``) + - the sampling of the features to consider when looking for the best + split at each node (if ``max_features < n_features``) + - the draw of the splits for each of the `max_features` + + See :term:`Glossary ` for details. + + verbose : int, default=0 + Controls the verbosity when fitting and predicting. + + warm_start : bool, default=False + When set to ``True``, reuse the solution of the previous call to fit + and add more estimators to the ensemble, otherwise, just fit a whole + new forest. See :term:`Glossary ` and + :ref:`gradient_boosting_warm_start` for details. + + class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, \ + default=None + Weights associated with classes in the form ``{class_label: weight}``. + If not given, 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))`` + + The "balanced_subsample" mode is the same as "balanced" except that + weights are computed based on the bootstrap sample for every tree + grown. + + 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. + + .. versionadded:: 0.22 + + max_samples : int or float, default=None + If bootstrap is True, the number of samples to draw from X + to train each base estimator. + + - If None (default), then draw `X.shape[0]` samples. + - If int, then draw `max_samples` samples. + - If float, then draw `max_samples * X.shape[0]` samples. Thus, + `max_samples` should be in the interval `(0.0, 1.0]`. + + .. versionadded:: 0.22 + + monotonic_cst : array-like of int of shape (n_features), default=None + Indicates the monotonicity constraint to enforce on each feature. + - 1: monotonically increasing + - 0: no constraint + - -1: monotonically decreasing + + 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 `. + + .. versionadded:: 1.4 + + Attributes + ---------- + estimator_ : :class:`~sklearn.tree.ExtraTreeClassifier` + The child estimator template used to create the collection of fitted + sub-estimators. + + .. versionadded:: 1.2 + `base_estimator_` was renamed to `estimator_`. + + estimators_ : list of DecisionTreeClassifier + The collection of fitted sub-estimators. + + classes_ : ndarray of shape (n_classes,) or a list of such arrays + The classes labels (single output problem), or a list of arrays of + class labels (multi-output problem). + + n_classes_ : int or list + The number of classes (single output problem), or a list containing the + number of classes for each output (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. + + Warning: impurity-based feature importances can be misleading for + high cardinality features (many unique values). See + :func:`sklearn.inspection.permutation_importance` as an alternative. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + 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. + + .. versionadded:: 1.0 + + n_outputs_ : int + The number of outputs when ``fit`` is performed. + + oob_score_ : float + Score of the training dataset obtained using an out-of-bag estimate. + This attribute exists only when ``oob_score`` is True. + + oob_decision_function_ : ndarray of shape (n_samples, n_classes) or \ + (n_samples, n_classes, n_outputs) + Decision function computed with out-of-bag estimate on the training + set. If n_estimators is small it might be possible that a data point + was never left out during the bootstrap. In this case, + `oob_decision_function_` might contain NaN. This attribute exists + only when ``oob_score`` is True. + + estimators_samples_ : list of arrays + The subset of drawn samples (i.e., the in-bag samples) for each base + estimator. Each subset is defined by an array of the indices selected. + + .. versionadded:: 1.4 + + See Also + -------- + ExtraTreesRegressor : An extra-trees regressor with random splits. + RandomForestClassifier : A random forest classifier with optimal splits. + RandomForestRegressor : Ensemble regressor using trees with optimal splits. + + 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. + + References + ---------- + .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized + trees", Machine Learning, 63(1), 3-42, 2006. + + Examples + -------- + >>> from sklearn.ensemble import ExtraTreesClassifier + >>> from sklearn.datasets import make_classification + >>> X, y = make_classification(n_features=4, random_state=0) + >>> clf = ExtraTreesClassifier(n_estimators=100, random_state=0) + >>> clf.fit(X, y) + ExtraTreesClassifier(random_state=0) + >>> clf.predict([[0, 0, 0, 0]]) + array([1]) + """ + + _parameter_constraints: dict = { + **ForestClassifier._parameter_constraints, + **DecisionTreeClassifier._parameter_constraints, + "class_weight": [ + StrOptions({"balanced_subsample", "balanced"}), + dict, + list, + None, + ], + } + _parameter_constraints.pop("splitter") + + def __init__( + self, + n_estimators=100, + *, + criterion="gini", + max_depth=None, + min_samples_split=2, + min_samples_leaf=1, + min_weight_fraction_leaf=0.0, + max_features="sqrt", + max_leaf_nodes=None, + min_impurity_decrease=0.0, + bootstrap=False, + oob_score=False, + n_jobs=None, + random_state=None, + verbose=0, + warm_start=False, + class_weight=None, + ccp_alpha=0.0, + max_samples=None, + monotonic_cst=None, + ): + super().__init__( + estimator=ExtraTreeClassifier(), + n_estimators=n_estimators, + estimator_params=( + "criterion", + "max_depth", + "min_samples_split", + "min_samples_leaf", + "min_weight_fraction_leaf", + "max_features", + "max_leaf_nodes", + "min_impurity_decrease", + "random_state", + "ccp_alpha", + "monotonic_cst", + ), + bootstrap=bootstrap, + oob_score=oob_score, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start, + class_weight=class_weight, + max_samples=max_samples, + ) + + self.criterion = criterion + 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.min_impurity_decrease = min_impurity_decrease + self.ccp_alpha = ccp_alpha + self.monotonic_cst = monotonic_cst + + +class ExtraTreesRegressor(ForestRegressor): + """ + An extra-trees regressor. + + This class implements a meta estimator that fits a number of + randomized decision trees (a.k.a. extra-trees) on various sub-samples + of the dataset and uses averaging to improve the predictive accuracy + and control over-fitting. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_estimators : int, default=100 + The number of trees in the forest. + + .. versionchanged:: 0.22 + The default value of ``n_estimators`` changed from 10 to 100 + in 0.22. + + criterion : {"squared_error", "absolute_error", "friedman_mse", "poisson"}, \ + default="squared_error" + The function to measure the quality of a split. Supported criteria + are "squared_error" for the mean squared error, which is equal to + variance reduction as feature selection criterion and minimizes the L2 + loss using the mean of each terminal node, "friedman_mse", which uses + mean squared error with Friedman's improvement score for potential + splits, "absolute_error" for the mean absolute error, which minimizes + the L1 loss using the median of each terminal node, and "poisson" which + uses reduction in Poisson deviance to find splits. + Training using "absolute_error" is significantly slower + than when using "squared_error". + + .. versionadded:: 0.18 + Mean Absolute Error (MAE) criterion. + + 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. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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 : {"sqrt", "log2", None}, int or float, default=1.0 + 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 + `max(1, int(max_features * n_features_in_))` features are considered at each + split. + - If "sqrt", then `max_features=sqrt(n_features)`. + - If "log2", then `max_features=log2(n_features)`. + - If None or 1.0, then `max_features=n_features`. + + .. note:: + The default of 1.0 is equivalent to bagged trees and more + randomness can be achieved by setting smaller values, e.g. 0.3. + + .. versionchanged:: 1.1 + The default of `max_features` changed from `"auto"` to 1.0. + + 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. + + max_leaf_nodes : int, default=None + Grow trees 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. + + .. versionadded:: 0.19 + + bootstrap : bool, default=False + Whether bootstrap samples are used when building trees. If False, the + whole dataset is used to build each tree. + + oob_score : bool or callable, default=False + Whether to use out-of-bag samples to estimate the generalization score. + By default, :func:`~sklearn.metrics.r2_score` is used. + Provide a callable with signature `metric(y_true, y_pred)` to use a + custom metric. Only available if `bootstrap=True`. + + n_jobs : int, default=None + The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, + :meth:`decision_path` and :meth:`apply` are all parallelized over the + trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See :term:`Glossary + ` for more details. + + random_state : int, RandomState instance or None, default=None + Controls 3 sources of randomness: + + - the bootstrapping of the samples used when building trees + (if ``bootstrap=True``) + - the sampling of the features to consider when looking for the best + split at each node (if ``max_features < n_features``) + - the draw of the splits for each of the `max_features` + + See :term:`Glossary ` for details. + + verbose : int, default=0 + Controls the verbosity when fitting and predicting. + + warm_start : bool, default=False + When set to ``True``, reuse the solution of the previous call to fit + and add more estimators to the ensemble, otherwise, just fit a whole + new forest. See :term:`Glossary ` and + :ref:`gradient_boosting_warm_start` for details. + + 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. + + .. versionadded:: 0.22 + + max_samples : int or float, default=None + If bootstrap is True, the number of samples to draw from X + to train each base estimator. + + - If None (default), then draw `X.shape[0]` samples. + - If int, then draw `max_samples` samples. + - If float, then draw `max_samples * X.shape[0]` samples. Thus, + `max_samples` should be in the interval `(0.0, 1.0]`. + + .. versionadded:: 0.22 + + monotonic_cst : array-like of int of shape (n_features), default=None + Indicates the monotonicity constraint to enforce on each feature. + - 1: monotonically increasing + - 0: no constraint + - -1: monotonically decreasing + + If monotonic_cst is None, no constraints are applied. + + Monotonicity constraints are not supported for: + - multioutput regressions (i.e. when `n_outputs_ > 1`), + - regressions trained on data with missing values. + + Read more in the :ref:`User Guide `. + + .. versionadded:: 1.4 + + Attributes + ---------- + estimator_ : :class:`~sklearn.tree.ExtraTreeRegressor` + The child estimator template used to create the collection of fitted + sub-estimators. + + .. versionadded:: 1.2 + `base_estimator_` was renamed to `estimator_`. + + estimators_ : list of DecisionTreeRegressor + The collection of fitted sub-estimators. + + 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. + + Warning: impurity-based feature importances can be misleading for + high cardinality features (many unique values). See + :func:`sklearn.inspection.permutation_importance` as an alternative. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + 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. + + .. versionadded:: 1.0 + + n_outputs_ : int + The number of outputs. + + oob_score_ : float + Score of the training dataset obtained using an out-of-bag estimate. + This attribute exists only when ``oob_score`` is True. + + oob_prediction_ : ndarray of shape (n_samples,) or (n_samples, n_outputs) + Prediction computed with out-of-bag estimate on the training set. + This attribute exists only when ``oob_score`` is True. + + estimators_samples_ : list of arrays + The subset of drawn samples (i.e., the in-bag samples) for each base + estimator. Each subset is defined by an array of the indices selected. + + .. versionadded:: 1.4 + + See Also + -------- + ExtraTreesClassifier : An extra-trees classifier with random splits. + RandomForestClassifier : A random forest classifier with optimal splits. + RandomForestRegressor : Ensemble regressor using trees with optimal splits. + + 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. + + References + ---------- + .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", + Machine Learning, 63(1), 3-42, 2006. + + Examples + -------- + >>> from sklearn.datasets import load_diabetes + >>> from sklearn.model_selection import train_test_split + >>> from sklearn.ensemble import ExtraTreesRegressor + >>> X, y = load_diabetes(return_X_y=True) + >>> X_train, X_test, y_train, y_test = train_test_split( + ... X, y, random_state=0) + >>> reg = ExtraTreesRegressor(n_estimators=100, random_state=0).fit( + ... X_train, y_train) + >>> reg.score(X_test, y_test) + 0.2727... + """ + + _parameter_constraints: dict = { + **ForestRegressor._parameter_constraints, + **DecisionTreeRegressor._parameter_constraints, + } + _parameter_constraints.pop("splitter") + + def __init__( + self, + n_estimators=100, + *, + criterion="squared_error", + max_depth=None, + min_samples_split=2, + min_samples_leaf=1, + min_weight_fraction_leaf=0.0, + max_features=1.0, + max_leaf_nodes=None, + min_impurity_decrease=0.0, + bootstrap=False, + oob_score=False, + n_jobs=None, + random_state=None, + verbose=0, + warm_start=False, + ccp_alpha=0.0, + max_samples=None, + monotonic_cst=None, + ): + super().__init__( + estimator=ExtraTreeRegressor(), + n_estimators=n_estimators, + estimator_params=( + "criterion", + "max_depth", + "min_samples_split", + "min_samples_leaf", + "min_weight_fraction_leaf", + "max_features", + "max_leaf_nodes", + "min_impurity_decrease", + "random_state", + "ccp_alpha", + "monotonic_cst", + ), + bootstrap=bootstrap, + oob_score=oob_score, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start, + max_samples=max_samples, + ) + + self.criterion = criterion + 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.min_impurity_decrease = min_impurity_decrease + self.ccp_alpha = ccp_alpha + self.monotonic_cst = monotonic_cst + + +class RandomTreesEmbedding(TransformerMixin, BaseForest): + """ + An ensemble of totally random trees. + + An unsupervised transformation of a dataset to a high-dimensional + sparse representation. A datapoint is coded according to which leaf of + each tree it is sorted into. Using a one-hot encoding of the leaves, + this leads to a binary coding with as many ones as there are trees in + the forest. + + The dimensionality of the resulting representation is + ``n_out <= n_estimators * max_leaf_nodes``. If ``max_leaf_nodes == None``, + the number of leaf nodes is at most ``n_estimators * 2 ** max_depth``. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_estimators : int, default=100 + Number of trees in the forest. + + .. versionchanged:: 0.22 + The default value of ``n_estimators`` changed from 10 to 100 + in 0.22. + + max_depth : int, default=5 + The maximum depth of each 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)` is the minimum + number of samples for each split. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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)` is the minimum + number of samples for each node. + + .. versionchanged:: 0.18 + Added float values for fractions. + + 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_leaf_nodes : int, default=None + Grow trees 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. + + .. versionadded:: 0.19 + + sparse_output : bool, default=True + Whether or not to return a sparse CSR matrix, as default behavior, + or to return a dense array compatible with dense pipeline operators. + + n_jobs : int, default=None + The number of jobs to run in parallel. :meth:`fit`, :meth:`transform`, + :meth:`decision_path` and :meth:`apply` are all parallelized over the + trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` + context. ``-1`` means using all processors. See :term:`Glossary + ` for more details. + + random_state : int, RandomState instance or None, default=None + Controls the generation of the random `y` used to fit the trees + and the draw of the splits for each feature at the trees' nodes. + See :term:`Glossary ` for details. + + verbose : int, default=0 + Controls the verbosity when fitting and predicting. + + warm_start : bool, default=False + When set to ``True``, reuse the solution of the previous call to fit + and add more estimators to the ensemble, otherwise, just fit a whole + new forest. See :term:`Glossary ` and + :ref:`gradient_boosting_warm_start` for details. + + Attributes + ---------- + estimator_ : :class:`~sklearn.tree.ExtraTreeRegressor` instance + The child estimator template used to create the collection of fitted + sub-estimators. + + .. versionadded:: 1.2 + `base_estimator_` was renamed to `estimator_`. + + estimators_ : list of :class:`~sklearn.tree.ExtraTreeRegressor` instances + The collection of fitted sub-estimators. + + feature_importances_ : ndarray of shape (n_features,) + The feature importances (the higher, the more important the feature). + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + 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. + + .. versionadded:: 1.0 + + n_outputs_ : int + The number of outputs when ``fit`` is performed. + + one_hot_encoder_ : OneHotEncoder instance + One-hot encoder used to create the sparse embedding. + + estimators_samples_ : list of arrays + The subset of drawn samples (i.e., the in-bag samples) for each base + estimator. Each subset is defined by an array of the indices selected. + + .. versionadded:: 1.4 + + See Also + -------- + ExtraTreesClassifier : An extra-trees classifier. + ExtraTreesRegressor : An extra-trees regressor. + RandomForestClassifier : A random forest classifier. + RandomForestRegressor : A random forest regressor. + sklearn.tree.ExtraTreeClassifier: An extremely randomized + tree classifier. + sklearn.tree.ExtraTreeRegressor : An extremely randomized + tree regressor. + + References + ---------- + .. [1] P. Geurts, D. Ernst., and L. Wehenkel, "Extremely randomized trees", + Machine Learning, 63(1), 3-42, 2006. + .. [2] Moosmann, F. and Triggs, B. and Jurie, F. "Fast discriminative + visual codebooks using randomized clustering forests" + NIPS 2007 + + Examples + -------- + >>> from sklearn.ensemble import RandomTreesEmbedding + >>> X = [[0,0], [1,0], [0,1], [-1,0], [0,-1]] + >>> random_trees = RandomTreesEmbedding( + ... n_estimators=5, random_state=0, max_depth=1).fit(X) + >>> X_sparse_embedding = random_trees.transform(X) + >>> X_sparse_embedding.toarray() + array([[0., 1., 1., 0., 1., 0., 0., 1., 1., 0.], + [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.], + [0., 1., 0., 1., 0., 1., 0., 1., 0., 1.], + [1., 0., 1., 0., 1., 0., 1., 0., 1., 0.], + [0., 1., 1., 0., 1., 0., 0., 1., 1., 0.]]) + """ + + _parameter_constraints: dict = { + "n_estimators": [Interval(Integral, 1, None, closed="left")], + "n_jobs": [Integral, None], + "verbose": ["verbose"], + "warm_start": ["boolean"], + **BaseDecisionTree._parameter_constraints, + "sparse_output": ["boolean"], + } + for param in ("max_features", "ccp_alpha", "splitter", "monotonic_cst"): + _parameter_constraints.pop(param) + + criterion = "squared_error" + max_features = 1 + + def __init__( + self, + n_estimators=100, + *, + max_depth=5, + min_samples_split=2, + min_samples_leaf=1, + min_weight_fraction_leaf=0.0, + max_leaf_nodes=None, + min_impurity_decrease=0.0, + sparse_output=True, + n_jobs=None, + random_state=None, + verbose=0, + warm_start=False, + ): + super().__init__( + estimator=ExtraTreeRegressor(), + n_estimators=n_estimators, + estimator_params=( + "criterion", + "max_depth", + "min_samples_split", + "min_samples_leaf", + "min_weight_fraction_leaf", + "max_features", + "max_leaf_nodes", + "min_impurity_decrease", + "random_state", + ), + bootstrap=False, + oob_score=False, + n_jobs=n_jobs, + random_state=random_state, + verbose=verbose, + warm_start=warm_start, + max_samples=None, + ) + + 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_leaf_nodes = max_leaf_nodes + self.min_impurity_decrease = min_impurity_decrease + self.sparse_output = sparse_output + + def _set_oob_score_and_attributes(self, X, y, scoring_function=None): + raise NotImplementedError("OOB score not supported by tree embedding") + + def fit(self, X, y=None, sample_weight=None): + """ + Fit estimator. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. Use ``dtype=np.float32`` for maximum + efficiency. Sparse matrices are also supported, use sparse + ``csc_matrix`` for maximum efficiency. + + y : Ignored + Not used, present for API consistency by convention. + + 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. In the case of + classification, splits are also ignored if they would result in any + single class carrying a negative weight in either child node. + + Returns + ------- + self : object + Returns the instance itself. + """ + # Parameters are validated in fit_transform + self.fit_transform(X, y, sample_weight=sample_weight) + return self + + @_fit_context(prefer_skip_nested_validation=True) + def fit_transform(self, X, y=None, sample_weight=None): + """ + Fit estimator and transform dataset. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Input data used to build forests. Use ``dtype=np.float32`` for + maximum efficiency. + + y : Ignored + Not used, present for API consistency by convention. + + 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. In the case of + classification, splits are also ignored if they would result in any + single class carrying a negative weight in either child node. + + Returns + ------- + X_transformed : sparse matrix of shape (n_samples, n_out) + Transformed dataset. + """ + rnd = check_random_state(self.random_state) + y = rnd.uniform(size=_num_samples(X)) + super().fit(X, y, sample_weight=sample_weight) + + self.one_hot_encoder_ = OneHotEncoder(sparse_output=self.sparse_output) + output = self.one_hot_encoder_.fit_transform(self.apply(X)) + self._n_features_out = output.shape[1] + return output + + def get_feature_names_out(self, input_features=None): + """Get output feature names for transformation. + + Parameters + ---------- + input_features : array-like of str or None, default=None + Only used to validate feature names with the names seen in :meth:`fit`. + + Returns + ------- + feature_names_out : ndarray of str objects + Transformed feature names, in the format of + `randomtreesembedding_{tree}_{leaf}`, where `tree` is the tree used + to generate the leaf and `leaf` is the index of a leaf node + in that tree. Note that the node indexing scheme is used to + index both nodes with children (split nodes) and leaf nodes. + Only the latter can be present as output features. + As a consequence, there are missing indices in the output + feature names. + """ + check_is_fitted(self, "_n_features_out") + _check_feature_names_in( + self, input_features=input_features, generate_names=False + ) + + feature_names = [ + f"randomtreesembedding_{tree}_{leaf}" + for tree in range(self.n_estimators) + for leaf in self.one_hot_encoder_.categories_[tree] + ] + return np.asarray(feature_names, dtype=object) + + def transform(self, X): + """ + Transform dataset. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Input data to be transformed. Use ``dtype=np.float32`` for maximum + efficiency. Sparse matrices are also supported, use sparse + ``csr_matrix`` for maximum efficiency. + + Returns + ------- + X_transformed : sparse matrix of shape (n_samples, n_out) + Transformed dataset. + """ + check_is_fitted(self) + return self.one_hot_encoder_.transform(self.apply(X))