"""Gradient Boosted Regression Trees. This module contains methods for fitting gradient boosted regression trees for both classification and regression. The module structure is the following: - The ``BaseGradientBoosting`` base class implements a common ``fit`` method for all the estimators in the module. Regression and classification only differ in the concrete ``LossFunction`` used. - ``GradientBoostingClassifier`` implements gradient boosting for classification problems. - ``GradientBoostingRegressor`` implements gradient boosting for regression problems. """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import math import warnings from abc import ABCMeta, abstractmethod from numbers import Integral, Real from time import time import numpy as np from scipy.sparse import csc_matrix, csr_matrix, issparse from .._loss.loss import ( _LOSSES, AbsoluteError, ExponentialLoss, HalfBinomialLoss, HalfMultinomialLoss, HalfSquaredError, HuberLoss, PinballLoss, ) from ..base import ClassifierMixin, RegressorMixin, _fit_context, is_classifier from ..dummy import DummyClassifier, DummyRegressor from ..exceptions import NotFittedError from ..model_selection import train_test_split from ..preprocessing import LabelEncoder from ..tree import DecisionTreeRegressor from ..tree._tree import DOUBLE, DTYPE, TREE_LEAF from ..utils import check_array, check_random_state, column_or_1d from ..utils._param_validation import HasMethods, Interval, StrOptions from ..utils.multiclass import check_classification_targets from ..utils.stats import _weighted_percentile from ..utils.validation import _check_sample_weight, check_is_fitted, validate_data from ._base import BaseEnsemble from ._gradient_boosting import _random_sample_mask, predict_stage, predict_stages _LOSSES = _LOSSES.copy() _LOSSES.update( { "quantile": PinballLoss, "huber": HuberLoss, } ) def _safe_divide(numerator, denominator): """Prevents overflow and division by zero.""" # This is used for classifiers where the denominator might become zero exatly. # For instance for log loss, HalfBinomialLoss, if proba=0 or proba=1 exactly, then # denominator = hessian = 0, and we should set the node value in the line search to # zero as there is no improvement of the loss possible. # For numerical safety, we do this already for extremely tiny values. if abs(denominator) < 1e-150: return 0.0 else: # Cast to Python float to trigger Python errors, e.g. ZeroDivisionError, # without relying on `np.errstate` that is not supported by Pyodide. result = float(numerator) / float(denominator) # Cast to Python float to trigger a ZeroDivisionError without relying # on `np.errstate` that is not supported by Pyodide. result = float(numerator) / float(denominator) if math.isinf(result): warnings.warn("overflow encountered in _safe_divide", RuntimeWarning) return result def _init_raw_predictions(X, estimator, loss, use_predict_proba): """Return the initial raw predictions. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data array. estimator : object The estimator to use to compute the predictions. loss : BaseLoss An instance of a loss function class. use_predict_proba : bool Whether estimator.predict_proba is used instead of estimator.predict. Returns ------- raw_predictions : ndarray of shape (n_samples, K) The initial raw predictions. K is equal to 1 for binary classification and regression, and equal to the number of classes for multiclass classification. ``raw_predictions`` is casted into float64. """ # TODO: Use loss.fit_intercept_only where appropriate instead of # DummyRegressor which is the default given by the `init` parameter, # see also _init_state. if use_predict_proba: # Our parameter validation, set via _fit_context and _parameter_constraints # already guarantees that estimator has a predict_proba method. predictions = estimator.predict_proba(X) if not loss.is_multiclass: predictions = predictions[:, 1] # probability of positive class eps = np.finfo(np.float32).eps # FIXME: This is quite large! predictions = np.clip(predictions, eps, 1 - eps, dtype=np.float64) else: predictions = estimator.predict(X).astype(np.float64) if predictions.ndim == 1: return loss.link.link(predictions).reshape(-1, 1) else: return loss.link.link(predictions) def _update_terminal_regions( loss, tree, X, y, neg_gradient, raw_prediction, sample_weight, sample_mask, learning_rate=0.1, k=0, ): """Update the leaf values to be predicted by the tree and raw_prediction. The current raw predictions of the model (of this stage) are updated. Additionally, the terminal regions (=leaves) of the given tree are updated as well. This corresponds to the line search step in "Greedy Function Approximation" by Friedman, Algorithm 1 step 5. Update equals: argmin_{x} loss(y_true, raw_prediction_old + x * tree.value) For non-trivial cases like the Binomial loss, the update has no closed formula and is an approximation, again, see the Friedman paper. Also note that the update formula for the SquaredError is the identity. Therefore, in this case, the leaf values don't need an update and only the raw_predictions are updated (with the learning rate included). Parameters ---------- loss : BaseLoss tree : tree.Tree The tree object. X : ndarray of shape (n_samples, n_features) The data array. y : ndarray of shape (n_samples,) The target labels. neg_gradient : ndarray of shape (n_samples,) The negative gradient. raw_prediction : ndarray of shape (n_samples, n_trees_per_iteration) The raw predictions (i.e. values from the tree leaves) of the tree ensemble at iteration ``i - 1``. sample_weight : ndarray of shape (n_samples,) The weight of each sample. sample_mask : ndarray of shape (n_samples,) The sample mask to be used. learning_rate : float, default=0.1 Learning rate shrinks the contribution of each tree by ``learning_rate``. k : int, default=0 The index of the estimator being updated. """ # compute leaf for each sample in ``X``. terminal_regions = tree.apply(X) if not isinstance(loss, HalfSquaredError): # mask all which are not in sample mask. masked_terminal_regions = terminal_regions.copy() masked_terminal_regions[~sample_mask] = -1 if isinstance(loss, HalfBinomialLoss): def compute_update(y_, indices, neg_gradient, raw_prediction, k): # Make a single Newton-Raphson step, see "Additive Logistic Regression: # A Statistical View of Boosting" FHT00 and note that we use a slightly # different version (factor 2) of "F" with proba=expit(raw_prediction). # Our node estimate is given by: # sum(w * (y - prob)) / sum(w * prob * (1 - prob)) # we take advantage that: y - prob = neg_gradient neg_g = neg_gradient.take(indices, axis=0) prob = y_ - neg_g # numerator = negative gradient = y - prob numerator = np.average(neg_g, weights=sw) # denominator = hessian = prob * (1 - prob) denominator = np.average(prob * (1 - prob), weights=sw) return _safe_divide(numerator, denominator) elif isinstance(loss, HalfMultinomialLoss): def compute_update(y_, indices, neg_gradient, raw_prediction, k): # we take advantage that: y - prob = neg_gradient neg_g = neg_gradient.take(indices, axis=0) prob = y_ - neg_g K = loss.n_classes # numerator = negative gradient * (k - 1) / k # Note: The factor (k - 1)/k appears in the original papers "Greedy # Function Approximation" by Friedman and "Additive Logistic # Regression" by Friedman, Hastie, Tibshirani. This factor is, however, # wrong or at least arbitrary as it directly multiplies the # learning_rate. We keep it for backward compatibility. numerator = np.average(neg_g, weights=sw) numerator *= (K - 1) / K # denominator = (diagonal) hessian = prob * (1 - prob) denominator = np.average(prob * (1 - prob), weights=sw) return _safe_divide(numerator, denominator) elif isinstance(loss, ExponentialLoss): def compute_update(y_, indices, neg_gradient, raw_prediction, k): neg_g = neg_gradient.take(indices, axis=0) # numerator = negative gradient = y * exp(-raw) - (1-y) * exp(raw) numerator = np.average(neg_g, weights=sw) # denominator = hessian = y * exp(-raw) + (1-y) * exp(raw) # if y=0: hessian = exp(raw) = -neg_g # y=1: hessian = exp(-raw) = neg_g hessian = neg_g.copy() hessian[y_ == 0] *= -1 denominator = np.average(hessian, weights=sw) return _safe_divide(numerator, denominator) else: def compute_update(y_, indices, neg_gradient, raw_prediction, k): return loss.fit_intercept_only( y_true=y_ - raw_prediction[indices, k], sample_weight=sw, ) # update each leaf (= perform line search) for leaf in np.nonzero(tree.children_left == TREE_LEAF)[0]: indices = np.nonzero(masked_terminal_regions == leaf)[ 0 ] # of terminal regions y_ = y.take(indices, axis=0) sw = None if sample_weight is None else sample_weight[indices] update = compute_update(y_, indices, neg_gradient, raw_prediction, k) # TODO: Multiply here by learning rate instead of everywhere else. tree.value[leaf, 0, 0] = update # update predictions (both in-bag and out-of-bag) raw_prediction[:, k] += learning_rate * tree.value[:, 0, 0].take( terminal_regions, axis=0 ) def set_huber_delta(loss, y_true, raw_prediction, sample_weight=None): """Calculate and set self.closs.delta based on self.quantile.""" abserr = np.abs(y_true - raw_prediction.squeeze()) # sample_weight is always a ndarray, never None. delta = _weighted_percentile(abserr, sample_weight, 100 * loss.quantile) loss.closs.delta = float(delta) class VerboseReporter: """Reports verbose output to stdout. Parameters ---------- verbose : int Verbosity level. If ``verbose==1`` output is printed once in a while (when iteration mod verbose_mod is zero).; if larger than 1 then output is printed for each update. """ def __init__(self, verbose): self.verbose = verbose def init(self, est, begin_at_stage=0): """Initialize reporter Parameters ---------- est : Estimator The estimator begin_at_stage : int, default=0 stage at which to begin reporting """ # header fields and line format str header_fields = ["Iter", "Train Loss"] verbose_fmt = ["{iter:>10d}", "{train_score:>16.4f}"] # do oob? if est.subsample < 1: header_fields.append("OOB Improve") verbose_fmt.append("{oob_impr:>16.4f}") header_fields.append("Remaining Time") verbose_fmt.append("{remaining_time:>16s}") # print the header line print(("%10s " + "%16s " * (len(header_fields) - 1)) % tuple(header_fields)) self.verbose_fmt = " ".join(verbose_fmt) # plot verbose info each time i % verbose_mod == 0 self.verbose_mod = 1 self.start_time = time() self.begin_at_stage = begin_at_stage def update(self, j, est): """Update reporter with new iteration. Parameters ---------- j : int The new iteration. est : Estimator The estimator. """ do_oob = est.subsample < 1 # we need to take into account if we fit additional estimators. i = j - self.begin_at_stage # iteration relative to the start iter if (i + 1) % self.verbose_mod == 0: oob_impr = est.oob_improvement_[j] if do_oob else 0 remaining_time = ( (est.n_estimators - (j + 1)) * (time() - self.start_time) / float(i + 1) ) if remaining_time > 60: remaining_time = "{0:.2f}m".format(remaining_time / 60.0) else: remaining_time = "{0:.2f}s".format(remaining_time) print( self.verbose_fmt.format( iter=j + 1, train_score=est.train_score_[j], oob_impr=oob_impr, remaining_time=remaining_time, ) ) if self.verbose == 1 and ((i + 1) // (self.verbose_mod * 10) > 0): # adjust verbose frequency (powers of 10) self.verbose_mod *= 10 class BaseGradientBoosting(BaseEnsemble, metaclass=ABCMeta): """Abstract base class for Gradient Boosting.""" _parameter_constraints: dict = { **DecisionTreeRegressor._parameter_constraints, "learning_rate": [Interval(Real, 0.0, None, closed="left")], "n_estimators": [Interval(Integral, 1, None, closed="left")], "criterion": [StrOptions({"friedman_mse", "squared_error"})], "subsample": [Interval(Real, 0.0, 1.0, closed="right")], "verbose": ["verbose"], "warm_start": ["boolean"], "validation_fraction": [Interval(Real, 0.0, 1.0, closed="neither")], "n_iter_no_change": [Interval(Integral, 1, None, closed="left"), None], "tol": [Interval(Real, 0.0, None, closed="left")], } _parameter_constraints.pop("splitter") _parameter_constraints.pop("monotonic_cst") @abstractmethod def __init__( self, *, loss, learning_rate, n_estimators, criterion, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_depth, min_impurity_decrease, init, subsample, max_features, ccp_alpha, random_state, alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=1e-4, ): self.n_estimators = n_estimators self.learning_rate = learning_rate self.loss = loss self.criterion = criterion self.min_samples_split = min_samples_split self.min_samples_leaf = min_samples_leaf self.min_weight_fraction_leaf = min_weight_fraction_leaf self.subsample = subsample self.max_features = max_features self.max_depth = max_depth self.min_impurity_decrease = min_impurity_decrease self.ccp_alpha = ccp_alpha self.init = init self.random_state = random_state self.alpha = alpha self.verbose = verbose self.max_leaf_nodes = max_leaf_nodes self.warm_start = warm_start self.validation_fraction = validation_fraction self.n_iter_no_change = n_iter_no_change self.tol = tol @abstractmethod def _encode_y(self, y=None, sample_weight=None): """Called by fit to validate and encode y.""" @abstractmethod def _get_loss(self, sample_weight): """Get loss object from sklearn._loss.loss.""" def _fit_stage( self, i, X, y, raw_predictions, sample_weight, sample_mask, random_state, X_csc=None, X_csr=None, ): """Fit another stage of ``n_trees_per_iteration_`` trees.""" original_y = y if isinstance(self._loss, HuberLoss): set_huber_delta( loss=self._loss, y_true=y, raw_prediction=raw_predictions, sample_weight=sample_weight, ) # TODO: Without oob, i.e. with self.subsample = 1.0, we could call # self._loss.loss_gradient and use it to set train_score_. # But note that train_score_[i] is the score AFTER fitting the i-th tree. # Note: We need the negative gradient! neg_gradient = -self._loss.gradient( y_true=y, raw_prediction=raw_predictions, sample_weight=None, # We pass sample_weights to the tree directly. ) # 2-d views of shape (n_samples, n_trees_per_iteration_) or (n_samples, 1) # on neg_gradient to simplify the loop over n_trees_per_iteration_. if neg_gradient.ndim == 1: neg_g_view = neg_gradient.reshape((-1, 1)) else: neg_g_view = neg_gradient for k in range(self.n_trees_per_iteration_): if self._loss.is_multiclass: y = np.array(original_y == k, dtype=np.float64) # induce regression tree on the negative gradient tree = DecisionTreeRegressor( criterion=self.criterion, splitter="best", max_depth=self.max_depth, min_samples_split=self.min_samples_split, min_samples_leaf=self.min_samples_leaf, min_weight_fraction_leaf=self.min_weight_fraction_leaf, min_impurity_decrease=self.min_impurity_decrease, max_features=self.max_features, max_leaf_nodes=self.max_leaf_nodes, random_state=random_state, ccp_alpha=self.ccp_alpha, ) if self.subsample < 1.0: # no inplace multiplication! sample_weight = sample_weight * sample_mask.astype(np.float64) X = X_csc if X_csc is not None else X tree.fit( X, neg_g_view[:, k], sample_weight=sample_weight, check_input=False ) # update tree leaves X_for_tree_update = X_csr if X_csr is not None else X _update_terminal_regions( self._loss, tree.tree_, X_for_tree_update, y, neg_g_view[:, k], raw_predictions, sample_weight, sample_mask, learning_rate=self.learning_rate, k=k, ) # add tree to ensemble self.estimators_[i, k] = tree return raw_predictions def _set_max_features(self): """Set self.max_features_.""" if isinstance(self.max_features, str): if self.max_features == "auto": if is_classifier(self): max_features = max(1, int(np.sqrt(self.n_features_in_))) else: max_features = self.n_features_in_ elif self.max_features == "sqrt": max_features = max(1, int(np.sqrt(self.n_features_in_))) else: # self.max_features == "log2" max_features = max(1, int(np.log2(self.n_features_in_))) elif self.max_features is None: max_features = self.n_features_in_ elif isinstance(self.max_features, Integral): max_features = self.max_features else: # float max_features = max(1, int(self.max_features * self.n_features_in_)) self.max_features_ = max_features def _init_state(self): """Initialize model state and allocate model state data structures.""" self.init_ = self.init if self.init_ is None: if is_classifier(self): self.init_ = DummyClassifier(strategy="prior") elif isinstance(self._loss, (AbsoluteError, HuberLoss)): self.init_ = DummyRegressor(strategy="quantile", quantile=0.5) elif isinstance(self._loss, PinballLoss): self.init_ = DummyRegressor(strategy="quantile", quantile=self.alpha) else: self.init_ = DummyRegressor(strategy="mean") self.estimators_ = np.empty( (self.n_estimators, self.n_trees_per_iteration_), dtype=object ) self.train_score_ = np.zeros((self.n_estimators,), dtype=np.float64) # do oob? if self.subsample < 1.0: self.oob_improvement_ = np.zeros((self.n_estimators), dtype=np.float64) self.oob_scores_ = np.zeros((self.n_estimators), dtype=np.float64) self.oob_score_ = np.nan def _clear_state(self): """Clear the state of the gradient boosting model.""" if hasattr(self, "estimators_"): self.estimators_ = np.empty((0, 0), dtype=object) if hasattr(self, "train_score_"): del self.train_score_ if hasattr(self, "oob_improvement_"): del self.oob_improvement_ if hasattr(self, "oob_scores_"): del self.oob_scores_ if hasattr(self, "oob_score_"): del self.oob_score_ if hasattr(self, "init_"): del self.init_ if hasattr(self, "_rng"): del self._rng def _resize_state(self): """Add additional ``n_estimators`` entries to all attributes.""" # self.n_estimators is the number of additional est to fit total_n_estimators = self.n_estimators if total_n_estimators < self.estimators_.shape[0]: raise ValueError( "resize with smaller n_estimators %d < %d" % (total_n_estimators, self.estimators_[0]) ) self.estimators_ = np.resize( self.estimators_, (total_n_estimators, self.n_trees_per_iteration_) ) self.train_score_ = np.resize(self.train_score_, total_n_estimators) if self.subsample < 1 or hasattr(self, "oob_improvement_"): # if do oob resize arrays or create new if not available if hasattr(self, "oob_improvement_"): self.oob_improvement_ = np.resize( self.oob_improvement_, total_n_estimators ) self.oob_scores_ = np.resize(self.oob_scores_, total_n_estimators) self.oob_score_ = np.nan else: self.oob_improvement_ = np.zeros( (total_n_estimators,), dtype=np.float64 ) self.oob_scores_ = np.zeros((total_n_estimators,), dtype=np.float64) self.oob_score_ = np.nan def _is_fitted(self): return len(getattr(self, "estimators_", [])) > 0 def _check_initialized(self): """Check that the estimator is initialized, raising an error if not.""" check_is_fitted(self) @_fit_context( # GradientBoosting*.init is not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y, sample_weight=None, monitor=None): """Fit the gradient boosting model. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. y : array-like of shape (n_samples,) Target values (strings or integers in classification, real numbers in regression) For classification, labels must correspond to classes. 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. monitor : callable, default=None The monitor is called after each iteration with the current iteration, a reference to the estimator and the local variables of ``_fit_stages`` as keyword arguments ``callable(i, self, locals())``. If the callable returns ``True`` the fitting procedure is stopped. The monitor can be used for various things such as computing held-out estimates, early stopping, model introspect, and snapshotting. Returns ------- self : object Fitted estimator. """ if not self.warm_start: self._clear_state() # Check input # Since check_array converts both X and y to the same dtype, but the # trees use different types for X and y, checking them separately. X, y = validate_data( self, X, y, accept_sparse=["csr", "csc", "coo"], dtype=DTYPE, multi_output=True, ) sample_weight_is_none = sample_weight is None sample_weight = _check_sample_weight(sample_weight, X) if sample_weight_is_none: y = self._encode_y(y=y, sample_weight=None) else: y = self._encode_y(y=y, sample_weight=sample_weight) y = column_or_1d(y, warn=True) # TODO: Is this still required? self._set_max_features() # self.loss is guaranteed to be a string self._loss = self._get_loss(sample_weight=sample_weight) if self.n_iter_no_change is not None: stratify = y if is_classifier(self) else None ( X_train, X_val, y_train, y_val, sample_weight_train, sample_weight_val, ) = train_test_split( X, y, sample_weight, random_state=self.random_state, test_size=self.validation_fraction, stratify=stratify, ) if is_classifier(self): if self.n_classes_ != np.unique(y_train).shape[0]: # We choose to error here. The problem is that the init # estimator would be trained on y, which has some missing # classes now, so its predictions would not have the # correct shape. raise ValueError( "The training data after the early stopping split " "is missing some classes. Try using another random " "seed." ) else: X_train, y_train, sample_weight_train = X, y, sample_weight X_val = y_val = sample_weight_val = None n_samples = X_train.shape[0] # First time calling fit. if not self._is_fitted(): # init state self._init_state() # fit initial model and initialize raw predictions if self.init_ == "zero": raw_predictions = np.zeros( shape=(n_samples, self.n_trees_per_iteration_), dtype=np.float64, ) else: # XXX clean this once we have a support_sample_weight tag if sample_weight_is_none: self.init_.fit(X_train, y_train) else: msg = ( "The initial estimator {} does not support sample " "weights.".format(self.init_.__class__.__name__) ) try: self.init_.fit( X_train, y_train, sample_weight=sample_weight_train ) except TypeError as e: if "unexpected keyword argument 'sample_weight'" in str(e): # regular estimator without SW support raise ValueError(msg) from e else: # regular estimator whose input checking failed raise except ValueError as e: if ( "pass parameters to specific steps of " "your pipeline using the " "stepname__parameter" in str(e) ): # pipeline raise ValueError(msg) from e else: # regular estimator whose input checking failed raise raw_predictions = _init_raw_predictions( X_train, self.init_, self._loss, is_classifier(self) ) begin_at_stage = 0 # The rng state must be preserved if warm_start is True self._rng = check_random_state(self.random_state) # warm start: this is not the first time fit was called else: # add more estimators to fitted model # invariant: warm_start = True if self.n_estimators < self.estimators_.shape[0]: raise ValueError( "n_estimators=%d must be larger or equal to " "estimators_.shape[0]=%d when " "warm_start==True" % (self.n_estimators, self.estimators_.shape[0]) ) begin_at_stage = self.estimators_.shape[0] # The requirements of _raw_predict # are more constrained than fit. It accepts only CSR # matrices. Finite values have already been checked in _validate_data. X_train = check_array( X_train, dtype=DTYPE, order="C", accept_sparse="csr", ensure_all_finite=False, ) raw_predictions = self._raw_predict(X_train) self._resize_state() # fit the boosting stages n_stages = self._fit_stages( X_train, y_train, raw_predictions, sample_weight_train, self._rng, X_val, y_val, sample_weight_val, begin_at_stage, monitor, ) # change shape of arrays after fit (early-stopping or additional ests) if n_stages != self.estimators_.shape[0]: self.estimators_ = self.estimators_[:n_stages] self.train_score_ = self.train_score_[:n_stages] if hasattr(self, "oob_improvement_"): # OOB scores were computed self.oob_improvement_ = self.oob_improvement_[:n_stages] self.oob_scores_ = self.oob_scores_[:n_stages] self.oob_score_ = self.oob_scores_[-1] self.n_estimators_ = n_stages return self def _fit_stages( self, X, y, raw_predictions, sample_weight, random_state, X_val, y_val, sample_weight_val, begin_at_stage=0, monitor=None, ): """Iteratively fits the stages. For each stage it computes the progress (OOB, train score) and delegates to ``_fit_stage``. Returns the number of stages fit; might differ from ``n_estimators`` due to early stopping. """ n_samples = X.shape[0] do_oob = self.subsample < 1.0 sample_mask = np.ones((n_samples,), dtype=bool) n_inbag = max(1, int(self.subsample * n_samples)) if self.verbose: verbose_reporter = VerboseReporter(verbose=self.verbose) verbose_reporter.init(self, begin_at_stage) X_csc = csc_matrix(X) if issparse(X) else None X_csr = csr_matrix(X) if issparse(X) else None if self.n_iter_no_change is not None: loss_history = np.full(self.n_iter_no_change, np.inf) # We create a generator to get the predictions for X_val after # the addition of each successive stage y_val_pred_iter = self._staged_raw_predict(X_val, check_input=False) # Older versions of GBT had its own loss functions. With the new common # private loss function submodule _loss, we often are a factor of 2 # away from the old version. Here we keep backward compatibility for # oob_scores_ and oob_improvement_, even if the old way is quite # inconsistent (sometimes the gradient is half the gradient, sometimes # not). if isinstance( self._loss, ( HalfSquaredError, HalfBinomialLoss, ), ): factor = 2 else: factor = 1 # perform boosting iterations i = begin_at_stage for i in range(begin_at_stage, self.n_estimators): # subsampling if do_oob: sample_mask = _random_sample_mask(n_samples, n_inbag, random_state) y_oob_masked = y[~sample_mask] sample_weight_oob_masked = sample_weight[~sample_mask] if i == 0: # store the initial loss to compute the OOB score initial_loss = factor * self._loss( y_true=y_oob_masked, raw_prediction=raw_predictions[~sample_mask], sample_weight=sample_weight_oob_masked, ) # fit next stage of trees raw_predictions = self._fit_stage( i, X, y, raw_predictions, sample_weight, sample_mask, random_state, X_csc=X_csc, X_csr=X_csr, ) # track loss if do_oob: self.train_score_[i] = factor * self._loss( y_true=y[sample_mask], raw_prediction=raw_predictions[sample_mask], sample_weight=sample_weight[sample_mask], ) self.oob_scores_[i] = factor * self._loss( y_true=y_oob_masked, raw_prediction=raw_predictions[~sample_mask], sample_weight=sample_weight_oob_masked, ) previous_loss = initial_loss if i == 0 else self.oob_scores_[i - 1] self.oob_improvement_[i] = previous_loss - self.oob_scores_[i] self.oob_score_ = self.oob_scores_[-1] else: # no need to fancy index w/ no subsampling self.train_score_[i] = factor * self._loss( y_true=y, raw_prediction=raw_predictions, sample_weight=sample_weight, ) if self.verbose > 0: verbose_reporter.update(i, self) if monitor is not None: early_stopping = monitor(i, self, locals()) if early_stopping: break # We also provide an early stopping based on the score from # validation set (X_val, y_val), if n_iter_no_change is set if self.n_iter_no_change is not None: # By calling next(y_val_pred_iter), we get the predictions # for X_val after the addition of the current stage validation_loss = factor * self._loss( y_val, next(y_val_pred_iter), sample_weight_val ) # Require validation_score to be better (less) than at least # one of the last n_iter_no_change evaluations if np.any(validation_loss + self.tol < loss_history): loss_history[i % len(loss_history)] = validation_loss else: break return i + 1 def _make_estimator(self, append=True): # we don't need _make_estimator raise NotImplementedError() def _raw_predict_init(self, X): """Check input and compute raw predictions of the init estimator.""" self._check_initialized() X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True) if self.init_ == "zero": raw_predictions = np.zeros( shape=(X.shape[0], self.n_trees_per_iteration_), dtype=np.float64 ) else: raw_predictions = _init_raw_predictions( X, self.init_, self._loss, is_classifier(self) ) return raw_predictions def _raw_predict(self, X): """Return the sum of the trees raw predictions (+ init estimator).""" check_is_fitted(self) raw_predictions = self._raw_predict_init(X) predict_stages(self.estimators_, X, self.learning_rate, raw_predictions) return raw_predictions def _staged_raw_predict(self, X, check_input=True): """Compute raw predictions of ``X`` for each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. check_input : bool, default=True If False, the input arrays X will not be checked. Returns ------- raw_predictions : generator of ndarray of shape (n_samples, k) The raw predictions of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. """ if check_input: X = validate_data( self, X, dtype=DTYPE, order="C", accept_sparse="csr", reset=False ) raw_predictions = self._raw_predict_init(X) for i in range(self.estimators_.shape[0]): predict_stage(self.estimators_, i, X, self.learning_rate, raw_predictions) yield raw_predictions.copy() @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. """ self._check_initialized() relevant_trees = [ tree for stage in self.estimators_ for tree in stage if tree.tree_.node_count > 1 ] if not relevant_trees: # degenerate case where all trees have only one node return np.zeros(shape=self.n_features_in_, dtype=np.float64) relevant_feature_importances = [ tree.tree_.compute_feature_importances(normalize=False) for tree in relevant_trees ] avg_feature_importances = np.mean( relevant_feature_importances, axis=0, dtype=np.float64 ) return avg_feature_importances / np.sum(avg_feature_importances) def _compute_partial_dependence_recursion(self, grid, target_features): """Fast partial dependence computation. Parameters ---------- grid : ndarray of shape (n_samples, n_target_features), dtype=np.float32 The grid points on which the partial dependence should be evaluated. target_features : ndarray of shape (n_target_features,), dtype=np.intp The set of target features for which the partial dependence should be evaluated. Returns ------- averaged_predictions : ndarray of shape \ (n_trees_per_iteration_, n_samples) The value of the partial dependence function on each grid point. """ if self.init is not None: warnings.warn( "Using recursion method with a non-constant init predictor " "will lead to incorrect partial dependence values. " "Got init=%s." % self.init, UserWarning, ) grid = np.asarray(grid, dtype=DTYPE, order="C") n_estimators, n_trees_per_stage = self.estimators_.shape averaged_predictions = np.zeros( (n_trees_per_stage, grid.shape[0]), dtype=np.float64, order="C" ) target_features = np.asarray(target_features, dtype=np.intp, order="C") for stage in range(n_estimators): for k in range(n_trees_per_stage): tree = self.estimators_[stage, k].tree_ tree.compute_partial_dependence( grid, target_features, averaged_predictions[k] ) averaged_predictions *= self.learning_rate return averaged_predictions def apply(self, X): """Apply trees in the ensemble to X, return leaf indices. .. versionadded:: 0.17 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 to a sparse ``csr_matrix``. Returns ------- X_leaves : array-like of shape (n_samples, n_estimators, n_classes) For each datapoint x in X and for each tree in the ensemble, return the index of the leaf x ends up in each estimator. In the case of binary classification n_classes is 1. """ self._check_initialized() X = self.estimators_[0, 0]._validate_X_predict(X, check_input=True) # n_classes will be equal to 1 in the binary classification or the # regression case. n_estimators, n_classes = self.estimators_.shape leaves = np.zeros((X.shape[0], n_estimators, n_classes)) for i in range(n_estimators): for j in range(n_classes): estimator = self.estimators_[i, j] leaves[:, i, j] = estimator.apply(X, check_input=False) return leaves def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.sparse = True return tags class GradientBoostingClassifier(ClassifierMixin, BaseGradientBoosting): """Gradient Boosting for classification. This algorithm builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage ``n_classes_`` regression trees are fit on the negative gradient of the loss function, e.g. binary or multiclass log loss. Binary classification is a special case where only a single regression tree is induced. :class:`~sklearn.ensemble.HistGradientBoostingClassifier` is a much faster variant of this algorithm for intermediate and large datasets (`n_samples >= 10_000`) and supports monotonic constraints. Read more in the :ref:`User Guide `. Parameters ---------- loss : {'log_loss', 'exponential'}, default='log_loss' The loss function to be optimized. 'log_loss' refers to binomial and multinomial deviance, the same as used in logistic regression. It is a good choice for classification with probabilistic outputs. For loss 'exponential', gradient boosting recovers the AdaBoost algorithm. learning_rate : float, default=0.1 Learning rate shrinks the contribution of each tree by `learning_rate`. There is a trade-off between learning_rate and n_estimators. Values must be in the range `[0.0, inf)`. n_estimators : int, default=100 The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance. Values must be in the range `[1, inf)`. subsample : float, default=1.0 The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias. Values must be in the range `(0.0, 1.0]`. criterion : {'friedman_mse', 'squared_error'}, default='friedman_mse' The function to measure the quality of a split. Supported criteria are 'friedman_mse' for the mean squared error with improvement score by Friedman, 'squared_error' for mean squared error. The default value of 'friedman_mse' is generally the best as it can provide a better approximation in some cases. .. versionadded:: 0.18 min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node: - If int, values must be in the range `[2, inf)`. - If float, values must be in the range `(0.0, 1.0]` and `min_samples_split` will be `ceil(min_samples_split * n_samples)`. .. 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, values must be in the range `[1, inf)`. - If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf` will be `ceil(min_samples_leaf * n_samples)`. .. 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. Values must be in the range `[0.0, 0.5]`. max_depth : int or None, default=3 Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. If int, values must be in the range `[1, inf)`. 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. Values must be in the range `[0.0, inf)`. 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 init : estimator or 'zero', default=None An estimator object that is used to compute the initial predictions. ``init`` has to provide :term:`fit` and :term:`predict_proba`. If 'zero', the initial raw predictions are set to zero. By default, a ``DummyEstimator`` predicting the classes priors is used. random_state : int, RandomState instance or None, default=None Controls the random seed given to each Tree estimator at each boosting iteration. In addition, it controls the random permutation of the features at each split (see Notes for more details). It also controls the random splitting of the training data to obtain a validation set if `n_iter_no_change` is not None. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. max_features : {'sqrt', 'log2'}, int or float, default=None The number of features to consider when looking for the best split: - If int, values must be in the range `[1, inf)`. - If float, values must be in the range `(0.0, 1.0]` and the features considered at each split will be `max(1, int(max_features * n_features_in_))`. - If 'sqrt', then `max_features=sqrt(n_features)`. - If 'log2', then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias. 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. verbose : int, default=0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. Values must be in the range `[0, inf)`. 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. Values must be in the range `[2, inf)`. If `None`, then unlimited number of leaf nodes. 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 erase the previous solution. See :term:`the Glossary `. validation_fraction : float, default=0.1 The proportion of training data to set aside as validation set for early stopping. Values must be in the range `(0.0, 1.0)`. Only used if ``n_iter_no_change`` is set to an integer. .. versionadded:: 0.20 n_iter_no_change : int, default=None ``n_iter_no_change`` is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to None to disable early stopping. If set to a number, it will set aside ``validation_fraction`` size of the training data as validation and terminate training when validation score is not improving in all of the previous ``n_iter_no_change`` numbers of iterations. The split is stratified. Values must be in the range `[1, inf)`. See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`. .. versionadded:: 0.20 tol : float, default=1e-4 Tolerance for the early stopping. When the loss is not improving by at least tol for ``n_iter_no_change`` iterations (if set to a number), the training stops. Values must be in the range `[0.0, inf)`. .. versionadded:: 0.20 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. Values must be in the range `[0.0, inf)`. See :ref:`minimal_cost_complexity_pruning` for details. See :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py` for an example of such pruning. .. versionadded:: 0.22 Attributes ---------- n_estimators_ : int The number of estimators as selected by early stopping (if ``n_iter_no_change`` is specified). Otherwise it is set to ``n_estimators``. .. versionadded:: 0.20 n_trees_per_iteration_ : int The number of trees that are built at each iteration. For binary classifiers, this is always 1. .. versionadded:: 1.4.0 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_improvement_ : ndarray of shape (n_estimators,) The improvement in loss on the out-of-bag samples relative to the previous iteration. ``oob_improvement_[0]`` is the improvement in loss of the first stage over the ``init`` estimator. Only available if ``subsample < 1.0``. oob_scores_ : ndarray of shape (n_estimators,) The full history of the loss values on the out-of-bag samples. Only available if `subsample < 1.0`. .. versionadded:: 1.3 oob_score_ : float The last value of the loss on the out-of-bag samples. It is the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`. .. versionadded:: 1.3 train_score_ : ndarray of shape (n_estimators,) The i-th score ``train_score_[i]`` is the loss of the model at iteration ``i`` on the in-bag sample. If ``subsample == 1`` this is the loss on the training data. init_ : estimator The estimator that provides the initial predictions. Set via the ``init`` argument. estimators_ : ndarray of DecisionTreeRegressor of \ shape (n_estimators, ``n_trees_per_iteration_``) The collection of fitted sub-estimators. ``n_trees_per_iteration_`` is 1 for binary classification, otherwise ``n_classes``. classes_ : ndarray of shape (n_classes,) The classes labels. 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_classes_ : int The number of classes. max_features_ : int The inferred value of max_features. See Also -------- HistGradientBoostingClassifier : Histogram-based Gradient Boosting Classification Tree. sklearn.tree.DecisionTreeClassifier : A decision tree classifier. RandomForestClassifier : 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. AdaBoostClassifier : A meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases. Notes ----- The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, 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 ---------- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. J. Friedman, Stochastic Gradient Boosting, 1999 T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009. Examples -------- The following example shows how to fit a gradient boosting classifier with 100 decision stumps as weak learners. >>> from sklearn.datasets import make_hastie_10_2 >>> from sklearn.ensemble import GradientBoostingClassifier >>> X, y = make_hastie_10_2(random_state=0) >>> X_train, X_test = X[:2000], X[2000:] >>> y_train, y_test = y[:2000], y[2000:] >>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, ... max_depth=1, random_state=0).fit(X_train, y_train) >>> clf.score(X_test, y_test) 0.913... """ _parameter_constraints: dict = { **BaseGradientBoosting._parameter_constraints, "loss": [StrOptions({"log_loss", "exponential"})], "init": [StrOptions({"zero"}), None, HasMethods(["fit", "predict_proba"])], } def __init__( self, *, loss="log_loss", learning_rate=0.1, n_estimators=100, subsample=1.0, criterion="friedman_mse", min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=1e-4, ccp_alpha=0.0, ): super().__init__( loss=loss, learning_rate=learning_rate, n_estimators=n_estimators, criterion=criterion, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_depth=max_depth, init=init, subsample=subsample, max_features=max_features, random_state=random_state, verbose=verbose, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, warm_start=warm_start, validation_fraction=validation_fraction, n_iter_no_change=n_iter_no_change, tol=tol, ccp_alpha=ccp_alpha, ) def _encode_y(self, y, sample_weight): # encode classes into 0 ... n_classes - 1 and sets attributes classes_ # and n_trees_per_iteration_ check_classification_targets(y) label_encoder = LabelEncoder() encoded_y_int = label_encoder.fit_transform(y) self.classes_ = label_encoder.classes_ n_classes = self.classes_.shape[0] # only 1 tree for binary classification. For multiclass classification, # we build 1 tree per class. self.n_trees_per_iteration_ = 1 if n_classes <= 2 else n_classes encoded_y = encoded_y_int.astype(float, copy=False) # From here on, it is additional to the HGBT case. # expose n_classes_ attribute self.n_classes_ = n_classes if sample_weight is None: n_trim_classes = n_classes else: n_trim_classes = np.count_nonzero(np.bincount(encoded_y_int, sample_weight)) if n_trim_classes < 2: raise ValueError( "y contains %d class after sample_weight " "trimmed classes with zero weights, while a " "minimum of 2 classes are required." % n_trim_classes ) return encoded_y def _get_loss(self, sample_weight): if self.loss == "log_loss": if self.n_classes_ == 2: return HalfBinomialLoss(sample_weight=sample_weight) else: return HalfMultinomialLoss( sample_weight=sample_weight, n_classes=self.n_classes_ ) elif self.loss == "exponential": if self.n_classes_ > 2: raise ValueError( f"loss='{self.loss}' is only suitable for a binary classification " f"problem, you have n_classes={self.n_classes_}. " "Please use loss='log_loss' instead." ) else: return ExponentialLoss(sample_weight=sample_weight) def decision_function(self, X): """Compute the decision function of ``X``. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- score : ndarray of shape (n_samples, n_classes) or (n_samples,) The decision function of the input samples, which corresponds to the raw values predicted from the trees of the ensemble . The order of the classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification produce an array of shape (n_samples,). """ X = validate_data( self, X, dtype=DTYPE, order="C", accept_sparse="csr", reset=False ) raw_predictions = self._raw_predict(X) if raw_predictions.shape[1] == 1: return raw_predictions.ravel() return raw_predictions def staged_decision_function(self, X): """Compute decision function of ``X`` for each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Yields ------ score : generator of ndarray of shape (n_samples, k) The decision function of the input samples, which corresponds to the raw values predicted from the trees of the ensemble . The classes corresponds to that in the attribute :term:`classes_`. Regression and binary classification are special cases with ``k == 1``, otherwise ``k==n_classes``. """ yield from self._staged_raw_predict(X) def predict(self, X): """Predict class for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : ndarray of shape (n_samples,) The predicted values. """ raw_predictions = self.decision_function(X) if raw_predictions.ndim == 1: # decision_function already squeezed it encoded_classes = (raw_predictions >= 0).astype(int) else: encoded_classes = np.argmax(raw_predictions, axis=1) return self.classes_[encoded_classes] def staged_predict(self, X): """Predict class at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Yields ------ y : generator of ndarray of shape (n_samples,) The predicted value of the input samples. """ if self.n_classes_ == 2: # n_trees_per_iteration_ = 1 for raw_predictions in self._staged_raw_predict(X): encoded_classes = (raw_predictions.squeeze() >= 0).astype(int) yield self.classes_.take(encoded_classes, axis=0) else: for raw_predictions in self._staged_raw_predict(X): encoded_classes = np.argmax(raw_predictions, axis=1) yield self.classes_.take(encoded_classes, axis=0) def predict_proba(self, X): """Predict class probabilities for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- p : ndarray of shape (n_samples, n_classes) The class probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. Raises ------ AttributeError If the ``loss`` does not support probabilities. """ raw_predictions = self.decision_function(X) return self._loss.predict_proba(raw_predictions) def predict_log_proba(self, X): """Predict class log-probabilities for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- p : ndarray of shape (n_samples, n_classes) The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. Raises ------ AttributeError If the ``loss`` does not support probabilities. """ proba = self.predict_proba(X) return np.log(proba) def staged_predict_proba(self, X): """Predict class probabilities at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Yields ------ y : generator of ndarray of shape (n_samples,) The predicted value of the input samples. """ try: for raw_predictions in self._staged_raw_predict(X): yield self._loss.predict_proba(raw_predictions) except NotFittedError: raise except AttributeError as e: raise AttributeError( "loss=%r does not support predict_proba" % self.loss ) from e class GradientBoostingRegressor(RegressorMixin, BaseGradientBoosting): """Gradient Boosting for regression. This estimator builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function. :class:`~sklearn.ensemble.HistGradientBoostingRegressor` is a much faster variant of this algorithm for intermediate and large datasets (`n_samples >= 10_000`) and supports monotonic constraints. Read more in the :ref:`User Guide `. Parameters ---------- loss : {'squared_error', 'absolute_error', 'huber', 'quantile'}, \ default='squared_error' Loss function to be optimized. 'squared_error' refers to the squared error for regression. 'absolute_error' refers to the absolute error of regression and is a robust loss function. 'huber' is a combination of the two. 'quantile' allows quantile regression (use `alpha` to specify the quantile). See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_quantile.py` for an example that demonstrates quantile regression for creating prediction intervals with `loss='quantile'`. learning_rate : float, default=0.1 Learning rate shrinks the contribution of each tree by `learning_rate`. There is a trade-off between learning_rate and n_estimators. Values must be in the range `[0.0, inf)`. n_estimators : int, default=100 The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance. Values must be in the range `[1, inf)`. subsample : float, default=1.0 The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. `subsample` interacts with the parameter `n_estimators`. Choosing `subsample < 1.0` leads to a reduction of variance and an increase in bias. Values must be in the range `(0.0, 1.0]`. criterion : {'friedman_mse', 'squared_error'}, default='friedman_mse' The function to measure the quality of a split. Supported criteria are "friedman_mse" for the mean squared error with improvement score by Friedman, "squared_error" for mean squared error. The default value of "friedman_mse" is generally the best as it can provide a better approximation in some cases. .. versionadded:: 0.18 min_samples_split : int or float, default=2 The minimum number of samples required to split an internal node: - If int, values must be in the range `[2, inf)`. - If float, values must be in the range `(0.0, 1.0]` and `min_samples_split` will be `ceil(min_samples_split * n_samples)`. .. 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, values must be in the range `[1, inf)`. - If float, values must be in the range `(0.0, 1.0)` and `min_samples_leaf` will be `ceil(min_samples_leaf * n_samples)`. .. 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. Values must be in the range `[0.0, 0.5]`. max_depth : int or None, default=3 Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. If int, values must be in the range `[1, inf)`. 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. Values must be in the range `[0.0, inf)`. 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 init : estimator or 'zero', default=None An estimator object that is used to compute the initial predictions. ``init`` has to provide :term:`fit` and :term:`predict`. If 'zero', the initial raw predictions are set to zero. By default a ``DummyEstimator`` is used, predicting either the average target value (for loss='squared_error'), or a quantile for the other losses. random_state : int, RandomState instance or None, default=None Controls the random seed given to each Tree estimator at each boosting iteration. In addition, it controls the random permutation of the features at each split (see Notes for more details). It also controls the random splitting of the training data to obtain a validation set if `n_iter_no_change` is not None. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. max_features : {'sqrt', 'log2'}, int or float, default=None The number of features to consider when looking for the best split: - If int, values must be in the range `[1, inf)`. - If float, values must be in the range `(0.0, 1.0]` and the features considered at each split will be `max(1, int(max_features * n_features_in_))`. - If "sqrt", then `max_features=sqrt(n_features)`. - If "log2", then `max_features=log2(n_features)`. - If None, then `max_features=n_features`. Choosing `max_features < n_features` leads to a reduction of variance and an increase in bias. 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. alpha : float, default=0.9 The alpha-quantile of the huber loss function and the quantile loss function. Only if ``loss='huber'`` or ``loss='quantile'``. Values must be in the range `(0.0, 1.0)`. verbose : int, default=0 Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. Values must be in the range `[0, inf)`. 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. Values must be in the range `[2, inf)`. If None, then unlimited number of leaf nodes. 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 erase the previous solution. See :term:`the Glossary `. validation_fraction : float, default=0.1 The proportion of training data to set aside as validation set for early stopping. Values must be in the range `(0.0, 1.0)`. Only used if ``n_iter_no_change`` is set to an integer. .. versionadded:: 0.20 n_iter_no_change : int, default=None ``n_iter_no_change`` is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to None to disable early stopping. If set to a number, it will set aside ``validation_fraction`` size of the training data as validation and terminate training when validation score is not improving in all of the previous ``n_iter_no_change`` numbers of iterations. Values must be in the range `[1, inf)`. See :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_early_stopping.py`. .. versionadded:: 0.20 tol : float, default=1e-4 Tolerance for the early stopping. When the loss is not improving by at least tol for ``n_iter_no_change`` iterations (if set to a number), the training stops. Values must be in the range `[0.0, inf)`. .. versionadded:: 0.20 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. Values must be in the range `[0.0, inf)`. See :ref:`minimal_cost_complexity_pruning` for details. See :ref:`sphx_glr_auto_examples_tree_plot_cost_complexity_pruning.py` for an example of such pruning. .. versionadded:: 0.22 Attributes ---------- n_estimators_ : int The number of estimators as selected by early stopping (if ``n_iter_no_change`` is specified). Otherwise it is set to ``n_estimators``. n_trees_per_iteration_ : int The number of trees that are built at each iteration. For regressors, this is always 1. .. versionadded:: 1.4.0 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_improvement_ : ndarray of shape (n_estimators,) The improvement in loss on the out-of-bag samples relative to the previous iteration. ``oob_improvement_[0]`` is the improvement in loss of the first stage over the ``init`` estimator. Only available if ``subsample < 1.0``. oob_scores_ : ndarray of shape (n_estimators,) The full history of the loss values on the out-of-bag samples. Only available if `subsample < 1.0`. .. versionadded:: 1.3 oob_score_ : float The last value of the loss on the out-of-bag samples. It is the same as `oob_scores_[-1]`. Only available if `subsample < 1.0`. .. versionadded:: 1.3 train_score_ : ndarray of shape (n_estimators,) The i-th score ``train_score_[i]`` is the loss of the model at iteration ``i`` on the in-bag sample. If ``subsample == 1`` this is the loss on the training data. init_ : estimator The estimator that provides the initial predictions. Set via the ``init`` argument. estimators_ : ndarray of DecisionTreeRegressor of shape (n_estimators, 1) The collection of fitted sub-estimators. 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 max_features_ : int The inferred value of max_features. See Also -------- HistGradientBoostingRegressor : Histogram-based Gradient Boosting Classification Tree. sklearn.tree.DecisionTreeRegressor : A decision tree regressor. sklearn.ensemble.RandomForestRegressor : A random forest regressor. Notes ----- The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data and ``max_features=n_features``, 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 ---------- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, Vol. 29, No. 5, 2001. J. Friedman, Stochastic Gradient Boosting, 1999 T. Hastie, R. Tibshirani and J. Friedman. Elements of Statistical Learning Ed. 2, Springer, 2009. Examples -------- >>> from sklearn.datasets import make_regression >>> from sklearn.ensemble import GradientBoostingRegressor >>> from sklearn.model_selection import train_test_split >>> X, y = make_regression(random_state=0) >>> X_train, X_test, y_train, y_test = train_test_split( ... X, y, random_state=0) >>> reg = GradientBoostingRegressor(random_state=0) >>> reg.fit(X_train, y_train) GradientBoostingRegressor(random_state=0) >>> reg.predict(X_test[1:2]) array([-61...]) >>> reg.score(X_test, y_test) 0.4... For a detailed example of utilizing :class:`~sklearn.ensemble.GradientBoostingRegressor` to fit an ensemble of weak predictive models, please refer to :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_regression.py`. """ _parameter_constraints: dict = { **BaseGradientBoosting._parameter_constraints, "loss": [StrOptions({"squared_error", "absolute_error", "huber", "quantile"})], "init": [StrOptions({"zero"}), None, HasMethods(["fit", "predict"])], "alpha": [Interval(Real, 0.0, 1.0, closed="neither")], } def __init__( self, *, loss="squared_error", learning_rate=0.1, n_estimators=100, subsample=1.0, criterion="friedman_mse", min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, init=None, random_state=None, max_features=None, alpha=0.9, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=1e-4, ccp_alpha=0.0, ): super().__init__( loss=loss, learning_rate=learning_rate, n_estimators=n_estimators, criterion=criterion, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_depth=max_depth, init=init, subsample=subsample, max_features=max_features, min_impurity_decrease=min_impurity_decrease, random_state=random_state, alpha=alpha, verbose=verbose, max_leaf_nodes=max_leaf_nodes, warm_start=warm_start, validation_fraction=validation_fraction, n_iter_no_change=n_iter_no_change, tol=tol, ccp_alpha=ccp_alpha, ) def _encode_y(self, y=None, sample_weight=None): # Just convert y to the expected dtype self.n_trees_per_iteration_ = 1 y = y.astype(DOUBLE, copy=False) return y def _get_loss(self, sample_weight): if self.loss in ("quantile", "huber"): return _LOSSES[self.loss](sample_weight=sample_weight, quantile=self.alpha) else: return _LOSSES[self.loss](sample_weight=sample_weight) def predict(self, X): """Predict regression target for X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Returns ------- y : ndarray of shape (n_samples,) The predicted values. """ X = validate_data( self, X, dtype=DTYPE, order="C", accept_sparse="csr", reset=False ) # In regression we can directly return the raw value from the trees. return self._raw_predict(X).ravel() def staged_predict(self, X): """Predict regression target at each stage for X. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. Yields ------ y : generator of ndarray of shape (n_samples,) The predicted value of the input samples. """ for raw_predictions in self._staged_raw_predict(X): yield raw_predictions.ravel() def apply(self, X): """Apply trees in the ensemble to X, return leaf indices. .. versionadded:: 0.17 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 to a sparse ``csr_matrix``. Returns ------- X_leaves : array-like of shape (n_samples, n_estimators) For each datapoint x in X and for each tree in the ensemble, return the index of the leaf x ends up in each estimator. """ leaves = super().apply(X) leaves = leaves.reshape(X.shape[0], self.estimators_.shape[0]) return leaves