""" 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 numbers from warnings import catch_warnings, simplefilter, warn import threading from abc import ABCMeta, abstractmethod import numpy as np from scipy.sparse import issparse from scipy.sparse import hstack as sparse_hstack from joblib import Parallel, delayed from ..base import ClassifierMixin, RegressorMixin, MultiOutputMixin from ..metrics import r2_score from ..preprocessing import OneHotEncoder from ..tree import (DecisionTreeClassifier, DecisionTreeRegressor, ExtraTreeClassifier, ExtraTreeRegressor) from ..tree._tree import DTYPE, DOUBLE from ..utils import check_random_state, check_array, compute_sample_weight from ..exceptions import DataConversionWarning from ._base import BaseEnsemble, _partition_estimators from ..utils.fixes import _joblib_parallel_args from ..utils.multiclass import check_classification_targets from ..utils.validation import check_is_fitted, _check_sample_weight from ..utils.validation import _deprecate_positional_args __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, 1)`; - 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, numbers.Integral): if not (1 <= max_samples <= n_samples): msg = "`max_samples` must be in range 1 to {} but got value {}" raise ValueError(msg.format(n_samples, max_samples)) return max_samples if isinstance(max_samples, numbers.Real): if not (0 < max_samples < 1): msg = "`max_samples` must be in range (0, 1) but got value {}" raise ValueError(msg.format(max_samples)) return int(round(n_samples * max_samples)) msg = "`max_samples` should be int or float, but got type '{}'" raise TypeError(msg.format(type(max_samples))) 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) 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, forest, X, y, sample_weight, tree_idx, n_trees, verbose=0, class_weight=None, n_samples_bootstrap=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 forest.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) else: tree.fit(X, y, sample_weight=sample_weight, check_input=False) 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. """ @abstractmethod def __init__(self, base_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__( base_estimator=base_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, **_joblib_parallel_args(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, **_joblib_parallel_args(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 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 """ # 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) 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() # Remap output self.n_features_ = X.shape[1] 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)) self.n_outputs_ = y.shape[1] 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 # Get bootstrap sample size n_samples_bootstrap = _get_n_samples_bootstrap( n_samples=X.shape[0], max_samples=self.max_samples ) # Check parameters 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, **_joblib_parallel_args(prefer='threads'))( delayed(_parallel_build_trees)( t, self, X, y, sample_weight, i, len(trees), verbose=self.verbose, class_weight=self.class_weight, n_samples_bootstrap=n_samples_bootstrap) for i, t in enumerate(trees)) # Collect newly grown trees self.estimators_.extend(trees) if self.oob_score: self._set_oob_score(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(self, X, y): """ Calculate out of bag predictions and score.""" 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) return self.estimators_[0]._validate_X_predict(X, check_input=True) @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, **_joblib_parallel_args(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_, dtype=np.float64) all_importances = np.mean(all_importances, axis=0, dtype=np.float64) return all_importances / np.sum(all_importances) 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, base_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__( base_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) def _set_oob_score(self, X, y): """ Compute out-of-bag score.""" X = check_array(X, dtype=DTYPE, accept_sparse='csr') n_classes_ = self.n_classes_ n_samples = y.shape[0] oob_decision_function = [] oob_score = 0.0 predictions = [np.zeros((n_samples, n_classes_[k])) for k in range(self.n_outputs_)] 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) p_estimator = estimator.predict_proba(X[unsampled_indices, :], check_input=False) if self.n_outputs_ == 1: p_estimator = [p_estimator] for k in range(self.n_outputs_): predictions[k][unsampled_indices, :] += p_estimator[k] for k in range(self.n_outputs_): if (predictions[k].sum(axis=1) == 0).any(): warn("Some inputs do not have OOB scores. " "This probably means too few trees were used " "to compute any reliable oob estimates.") decision = (predictions[k] / predictions[k].sum(axis=1)[:, np.newaxis]) oob_decision_function.append(decision) oob_score += np.mean(y[:, k] == np.argmax(predictions[k], axis=1), axis=0) if self.n_outputs_ == 1: self.oob_decision_function_ = oob_decision_function[0] else: self.oob_decision_function_ = oob_decision_function self.oob_score_ = oob_score / self.n_outputs_ 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=np.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 n_outputs such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. """ check_is_fitted(self) # 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, **_joblib_parallel_args(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 n_outputs such arrays if n_outputs > 1. The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. """ 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 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, base_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__( base_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, **_joblib_parallel_args(require="sharedmem"))( delayed(_accumulate_prediction)(e.predict, X, [y_hat], lock) for e in self.estimators_) y_hat /= len(self.estimators_) return y_hat def _set_oob_score(self, X, y): """ Compute out-of-bag scores.""" X = check_array(X, dtype=DTYPE, accept_sparse='csr') n_samples = y.shape[0] predictions = np.zeros((n_samples, self.n_outputs_)) n_predictions = np.zeros((n_samples, self.n_outputs_)) 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) p_estimator = estimator.predict( X[unsampled_indices, :], check_input=False) if self.n_outputs_ == 1: p_estimator = p_estimator[:, np.newaxis] predictions[unsampled_indices, :] += p_estimator n_predictions[unsampled_indices, :] += 1 if (n_predictions == 0).any(): warn("Some inputs do not have OOB scores. " "This probably means too few trees were used " "to compute any reliable oob estimates.") n_predictions[n_predictions == 0] = 1 predictions /= n_predictions self.oob_prediction_ = predictions if self.n_outputs_ == 1: self.oob_prediction_ = \ self.oob_prediction_.reshape((n_samples, )) self.oob_score_ = 0.0 for k in range(self.n_outputs_): self.oob_score_ += r2_score(y[:, k], predictions[:, k]) self.oob_score_ /= self.n_outputs_ 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 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. 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. 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"}, default="gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. 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 : {"auto", "sqrt", "log2"}, int or float, default="auto" The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=sqrt(n_features)`. - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto"). - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. 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 min_impurity_split : float, default=None Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. 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, default=False Whether to use out-of-bag samples to estimate the generalization accuracy. 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 or RandomState, 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:`the Glossary `. 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, 1)`. .. versionadded:: 0.22 Attributes ---------- base_estimator_ : DecisionTreeClassifier The child estimator template used to create the collection of fitted sub-estimators. 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_ : int The number of features when ``fit`` is performed. 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) 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. See Also -------- DecisionTreeClassifier, ExtraTreesClassifier 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] """ @_deprecate_positional_args def __init__(self, n_estimators=100, *, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, 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): super().__init__( base_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", "min_impurity_split", "random_state", "ccp_alpha"), 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.min_impurity_split = min_impurity_split self.ccp_alpha = ccp_alpha class RandomForestRegressor(ForestRegressor): """ A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. 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. 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 : {"mse", "mae"}, default="mse" The function to measure the quality of a split. Supported criteria are "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion, and "mae" for the mean absolute 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 : {"auto", "sqrt", "log2"}, int or float, default="auto" The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=n_features`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. 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 min_impurity_split : float, default=None Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. 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, default=False whether to use out-of-bag samples to estimate the R^2 on unseen data. 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 or RandomState, 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:`the Glossary `. 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, 1)`. .. versionadded:: 0.22 Attributes ---------- base_estimator_ : DecisionTreeRegressor The child estimator template used to create the collection of fitted sub-estimators. 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_ : int The number of features when ``fit`` is performed. 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,) Prediction computed with out-of-bag estimate on the training set. This attribute exists only when ``oob_score`` is True. See Also -------- DecisionTreeRegressor, ExtraTreesRegressor 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="auto"`` 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] """ @_deprecate_positional_args def __init__(self, n_estimators=100, *, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None): super().__init__( base_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", "min_impurity_split", "random_state", "ccp_alpha"), 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.min_impurity_split = min_impurity_split self.ccp_alpha = ccp_alpha 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"}, default="gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain. 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 : {"auto", "sqrt", "log2"}, int or float, default="auto" The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=sqrt(n_features)`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. 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 min_impurity_split : float, default=None Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. 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, default=False Whether to use out-of-bag samples to estimate the generalization accuracy. 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, 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:`the Glossary `. 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, 1)`. .. versionadded:: 0.22 Attributes ---------- base_estimator_ : ExtraTreesClassifier The child estimator template used to create the collection of fitted sub-estimators. 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_ : int The number of features when ``fit`` is performed. 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) 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. See Also -------- sklearn.tree.ExtraTreeClassifier : Base classifier for this ensemble. RandomForestClassifier : Ensemble Classifier based on 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]) """ @_deprecate_positional_args def __init__(self, n_estimators=100, *, criterion="gini", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, 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): super().__init__( base_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", "min_impurity_split", "random_state", "ccp_alpha"), 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.min_impurity_split = min_impurity_split self.ccp_alpha = ccp_alpha 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 : {"mse", "mae"}, default="mse" The function to measure the quality of a split. Supported criteria are "mse" for the mean squared error, which is equal to variance reduction as feature selection criterion, and "mae" for the mean absolute 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 : {"auto", "sqrt", "log2"} int or float, default="auto" The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split. - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split. - If "auto", then `max_features=n_features`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features. 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 min_impurity_split : float, default=None Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. 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, default=False Whether to use out-of-bag samples to estimate the R^2 on unseen data. 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 or RandomState, 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:`the Glossary `. 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, 1)`. .. versionadded:: 0.22 Attributes ---------- base_estimator_ : ExtraTreeRegressor The child estimator template used to create the collection of fitted sub-estimators. 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_ : int The number of features. 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,) Prediction computed with out-of-bag estimate on the training set. This attribute exists only when ``oob_score`` is True. See Also -------- sklearn.tree.ExtraTreeRegressor: Base estimator for this ensemble. 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) 0.2708... """ @_deprecate_positional_args def __init__(self, n_estimators=100, *, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ccp_alpha=0.0, max_samples=None): super().__init__( base_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", "min_impurity_split", "random_state", "ccp_alpha"), 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.min_impurity_split = min_impurity_split self.ccp_alpha = ccp_alpha class RandomTreesEmbedding(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 min_impurity_split : float, default=None Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf. .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` has changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead. 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 or RandomState, 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:`the Glossary `. Attributes ---------- estimators_ : list of DecisionTreeClassifier The collection of fitted sub-estimators. 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.]]) """ criterion = 'mse' max_features = 1 @_deprecate_positional_args def __init__(self, n_estimators=100, *, max_depth=5, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0., max_leaf_nodes=None, min_impurity_decrease=0., min_impurity_split=None, sparse_output=True, n_jobs=None, random_state=None, verbose=0, warm_start=False): super().__init__( base_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", "min_impurity_split", "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.min_impurity_split = min_impurity_split self.sparse_output = sparse_output def _set_oob_score(self, X, y): 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 """ self.fit_transform(X, y, sample_weight=sample_weight) return self 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. """ X = check_array(X, accept_sparse=['csc']) if issparse(X): # Pre-sort indices to avoid that each individual tree of the # ensemble sorts the indices. X.sort_indices() rnd = check_random_state(self.random_state) y = rnd.uniform(size=X.shape[0]) super().fit(X, y, sample_weight=sample_weight) self.one_hot_encoder_ = OneHotEncoder(sparse=self.sparse_output) return self.one_hot_encoder_.fit_transform(self.apply(X)) 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))