"""Fast Gradient Boosting decision trees for classification and regression.""" # Author: Nicolas Hug from abc import ABC, abstractmethod from functools import partial import itertools from numbers import Real, Integral import warnings import numpy as np from timeit import default_timer as time from ..._loss.loss import ( _LOSSES, BaseLoss, HalfBinomialLoss, HalfMultinomialLoss, HalfPoissonLoss, PinballLoss, ) from ...base import BaseEstimator, RegressorMixin, ClassifierMixin, is_classifier from ...utils import check_random_state, resample, compute_sample_weight from ...utils.validation import ( check_is_fitted, check_consistent_length, _check_sample_weight, _check_monotonic_cst, ) from ...utils._param_validation import Interval, StrOptions from ...utils._openmp_helpers import _openmp_effective_n_threads from ...utils.multiclass import check_classification_targets from ...metrics import check_scoring from ...model_selection import train_test_split from ...preprocessing import LabelEncoder from ._gradient_boosting import _update_raw_predictions from .common import Y_DTYPE, X_DTYPE, G_H_DTYPE from .binning import _BinMapper from .grower import TreeGrower _LOSSES = _LOSSES.copy() # TODO(1.3): Remove "binary_crossentropy" and "categorical_crossentropy" _LOSSES.update( { "poisson": HalfPoissonLoss, "quantile": PinballLoss, "binary_crossentropy": HalfBinomialLoss, "categorical_crossentropy": HalfMultinomialLoss, } ) def _update_leaves_values(loss, grower, y_true, raw_prediction, sample_weight): """Update the leaf values to be predicted by the tree. Update equals: loss.fit_intercept_only(y_true - raw_prediction) This is only applied if loss.need_update_leaves_values is True. Note: It only works, if the loss is a function of the residual, as is the case for AbsoluteError and PinballLoss. Otherwise, one would need to get the minimum of loss(y_true, raw_prediction + x) in x. A few examples: - AbsoluteError: median(y_true - raw_prediction). - PinballLoss: quantile(y_true - raw_prediction). See also notes about need_update_leaves_values in BaseLoss. """ # TODO: Ideally this should be computed in parallel over the leaves using something # similar to _update_raw_predictions(), but this requires a cython version of # median(). for leaf in grower.finalized_leaves: indices = leaf.sample_indices if sample_weight is None: sw = None else: sw = sample_weight[indices] update = loss.fit_intercept_only( y_true=y_true[indices] - raw_prediction[indices], sample_weight=sw, ) leaf.value = grower.shrinkage * update # Note that the regularization is ignored here class BaseHistGradientBoosting(BaseEstimator, ABC): """Base class for histogram-based gradient boosting estimators.""" _parameter_constraints: dict = { "loss": [BaseLoss], "learning_rate": [Interval(Real, 0, None, closed="neither")], "max_iter": [Interval(Integral, 1, None, closed="left")], "max_leaf_nodes": [Interval(Integral, 2, None, closed="left"), None], "max_depth": [Interval(Integral, 1, None, closed="left"), None], "min_samples_leaf": [Interval(Integral, 1, None, closed="left")], "l2_regularization": [Interval(Real, 0, None, closed="left")], "monotonic_cst": ["array-like", dict, None], "interaction_cst": [ list, tuple, StrOptions({"pairwise", "no_interactions"}), None, ], "n_iter_no_change": [Interval(Integral, 1, None, closed="left")], "validation_fraction": [ Interval(Real, 0, 1, closed="neither"), Interval(Integral, 1, None, closed="left"), None, ], "tol": [Interval(Real, 0, None, closed="left")], "max_bins": [Interval(Integral, 2, 255, closed="both")], "categorical_features": ["array-like", None], "warm_start": ["boolean"], "early_stopping": [StrOptions({"auto"}), "boolean"], "scoring": [str, callable, None], "verbose": ["verbose"], "random_state": ["random_state"], } @abstractmethod def __init__( self, loss, *, learning_rate, max_iter, max_leaf_nodes, max_depth, min_samples_leaf, l2_regularization, max_bins, categorical_features, monotonic_cst, interaction_cst, warm_start, early_stopping, scoring, validation_fraction, n_iter_no_change, tol, verbose, random_state, ): self.loss = loss self.learning_rate = learning_rate self.max_iter = max_iter self.max_leaf_nodes = max_leaf_nodes self.max_depth = max_depth self.min_samples_leaf = min_samples_leaf self.l2_regularization = l2_regularization self.max_bins = max_bins self.monotonic_cst = monotonic_cst self.interaction_cst = interaction_cst self.categorical_features = categorical_features self.warm_start = warm_start self.early_stopping = early_stopping self.scoring = scoring self.validation_fraction = validation_fraction self.n_iter_no_change = n_iter_no_change self.tol = tol self.verbose = verbose self.random_state = random_state def _validate_parameters(self): """Validate parameters passed to __init__. The parameters that are directly passed to the grower are checked in TreeGrower.""" if self.monotonic_cst is not None and self.n_trees_per_iteration_ != 1: raise ValueError( "monotonic constraints are not supported for multiclass classification." ) def _finalize_sample_weight(self, sample_weight, y): """Finalize sample weight. Used by subclasses to adjust sample_weights. This is useful for implementing class weights. """ return sample_weight def _check_categories(self, X): """Check and validate categorical features in X Return ------ is_categorical : ndarray of shape (n_features,) or None, dtype=bool Indicates whether a feature is categorical. If no feature is categorical, this is None. known_categories : list of size n_features or None The list contains, for each feature: - an array of shape (n_categories,) with the unique cat values - None if the feature is not categorical None if no feature is categorical. """ if self.categorical_features is None: return None, None categorical_features = np.asarray(self.categorical_features) if categorical_features.size == 0: return None, None if categorical_features.dtype.kind not in ("i", "b", "U", "O"): raise ValueError( "categorical_features must be an array-like of bool, int or " f"str, got: {categorical_features.dtype.name}." ) if categorical_features.dtype.kind == "O": types = set(type(f) for f in categorical_features) if types != {str}: raise ValueError( "categorical_features must be an array-like of bool, int or " f"str, got: {', '.join(sorted(t.__name__ for t in types))}." ) n_features = X.shape[1] if categorical_features.dtype.kind in ("U", "O"): # check for feature names if not hasattr(self, "feature_names_in_"): raise ValueError( "categorical_features should be passed as an array of " "integers or as a boolean mask when the model is fitted " "on data without feature names." ) is_categorical = np.zeros(n_features, dtype=bool) feature_names = self.feature_names_in_.tolist() for feature_name in categorical_features: try: is_categorical[feature_names.index(feature_name)] = True except ValueError as e: raise ValueError( f"categorical_features has a item value '{feature_name}' " "which is not a valid feature name of the training " f"data. Observed feature names: {feature_names}" ) from e elif categorical_features.dtype.kind == "i": # check for categorical features as indices if ( np.max(categorical_features) >= n_features or np.min(categorical_features) < 0 ): raise ValueError( "categorical_features set as integer " "indices must be in [0, n_features - 1]" ) is_categorical = np.zeros(n_features, dtype=bool) is_categorical[categorical_features] = True else: if categorical_features.shape[0] != n_features: raise ValueError( "categorical_features set as a boolean mask " "must have shape (n_features,), got: " f"{categorical_features.shape}" ) is_categorical = categorical_features if not np.any(is_categorical): return None, None # compute the known categories in the training data. We need to do # that here instead of in the BinMapper because in case of early # stopping, the mapper only gets a fraction of the training data. known_categories = [] for f_idx in range(n_features): if is_categorical[f_idx]: categories = np.unique(X[:, f_idx]) missing = np.isnan(categories) if missing.any(): categories = categories[~missing] if hasattr(self, "feature_names_in_"): feature_name = f"'{self.feature_names_in_[f_idx]}'" else: feature_name = f"at index {f_idx}" if categories.size > self.max_bins: raise ValueError( f"Categorical feature {feature_name} is expected to " f"have a cardinality <= {self.max_bins}" ) if (categories >= self.max_bins).any(): raise ValueError( f"Categorical feature {feature_name} is expected to " f"be encoded with values < {self.max_bins}" ) else: categories = None known_categories.append(categories) return is_categorical, known_categories def _check_interaction_cst(self, n_features): """Check and validation for interaction constraints.""" if self.interaction_cst is None: return None if self.interaction_cst == "no_interactions": interaction_cst = [[i] for i in range(n_features)] elif self.interaction_cst == "pairwise": interaction_cst = itertools.combinations(range(n_features), 2) else: interaction_cst = self.interaction_cst try: constraints = [set(group) for group in interaction_cst] except TypeError: raise ValueError( "Interaction constraints must be a sequence of tuples or lists, got:" f" {self.interaction_cst!r}." ) for group in constraints: for x in group: if not (isinstance(x, Integral) and 0 <= x < n_features): raise ValueError( "Interaction constraints must consist of integer indices in" f" [0, n_features - 1] = [0, {n_features - 1}], specifying the" " position of features, got invalid indices:" f" {group!r}" ) # Add all not listed features as own group by default. rest = set(range(n_features)) - set().union(*constraints) if len(rest) > 0: constraints.append(rest) return constraints def fit(self, X, y, sample_weight=None): """Fit the gradient boosting model. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. y : array-like of shape (n_samples,) Target values. sample_weight : array-like of shape (n_samples,) default=None Weights of training data. .. versionadded:: 0.23 Returns ------- self : object Fitted estimator. """ self._validate_params() fit_start_time = time() acc_find_split_time = 0.0 # time spent finding the best splits acc_apply_split_time = 0.0 # time spent splitting nodes acc_compute_hist_time = 0.0 # time spent computing histograms # time spent predicting X for gradient and hessians update acc_prediction_time = 0.0 X, y = self._validate_data(X, y, dtype=[X_DTYPE], force_all_finite=False) y = self._encode_y(y) check_consistent_length(X, y) # Do not create unit sample weights by default to later skip some # computation if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X, dtype=np.float64) # TODO: remove when PDP supports sample weights self._fitted_with_sw = True sample_weight = self._finalize_sample_weight(sample_weight, y) rng = check_random_state(self.random_state) # When warm starting, we want to re-use the same seed that was used # the first time fit was called (e.g. for subsampling or for the # train/val split). if not (self.warm_start and self._is_fitted()): self._random_seed = rng.randint(np.iinfo(np.uint32).max, dtype="u8") self._validate_parameters() monotonic_cst = _check_monotonic_cst(self, self.monotonic_cst) # used for validation in predict n_samples, self._n_features = X.shape self.is_categorical_, known_categories = self._check_categories(X) # Encode constraints into a list of sets of features indices (integers). interaction_cst = self._check_interaction_cst(self._n_features) # we need this stateful variable to tell raw_predict() that it was # called from fit() (this current method), and that the data it has # received is pre-binned. # predicting is faster on pre-binned data, so we want early stopping # predictions to be made on pre-binned data. Unfortunately the _scorer # can only call predict() or predict_proba(), not raw_predict(), and # there's no way to tell the scorer that it needs to predict binned # data. self._in_fit = True # `_openmp_effective_n_threads` is used to take cgroups CPU quotes # into account when determine the maximum number of threads to use. n_threads = _openmp_effective_n_threads() if isinstance(self.loss, str): self._loss = self._get_loss(sample_weight=sample_weight) elif isinstance(self.loss, BaseLoss): self._loss = self.loss if self.early_stopping == "auto": self.do_early_stopping_ = n_samples > 10000 else: self.do_early_stopping_ = self.early_stopping # create validation data if needed self._use_validation_data = self.validation_fraction is not None if self.do_early_stopping_ and self._use_validation_data: # stratify for classification # instead of checking predict_proba, loss.n_classes >= 2 would also work stratify = y if hasattr(self._loss, "predict_proba") else None # Save the state of the RNG for the training and validation split. # This is needed in order to have the same split when using # warm starting. if sample_weight is None: X_train, X_val, y_train, y_val = train_test_split( X, y, test_size=self.validation_fraction, stratify=stratify, random_state=self._random_seed, ) sample_weight_train = sample_weight_val = None else: # TODO: incorporate sample_weight in sampling here, as well as # stratify ( X_train, X_val, y_train, y_val, sample_weight_train, sample_weight_val, ) = train_test_split( X, y, sample_weight, test_size=self.validation_fraction, stratify=stratify, random_state=self._random_seed, ) else: X_train, y_train, sample_weight_train = X, y, sample_weight X_val = y_val = sample_weight_val = None # Bin the data # For ease of use of the API, the user-facing GBDT classes accept the # parameter max_bins, which doesn't take into account the bin for # missing values (which is always allocated). However, since max_bins # isn't the true maximal number of bins, all other private classes # (binmapper, histbuilder...) accept n_bins instead, which is the # actual total number of bins. Everywhere in the code, the # convention is that n_bins == max_bins + 1 n_bins = self.max_bins + 1 # + 1 for missing values self._bin_mapper = _BinMapper( n_bins=n_bins, is_categorical=self.is_categorical_, known_categories=known_categories, random_state=self._random_seed, n_threads=n_threads, ) X_binned_train = self._bin_data(X_train, is_training_data=True) if X_val is not None: X_binned_val = self._bin_data(X_val, is_training_data=False) else: X_binned_val = None # Uses binned data to check for missing values has_missing_values = ( (X_binned_train == self._bin_mapper.missing_values_bin_idx_) .any(axis=0) .astype(np.uint8) ) if self.verbose: print("Fitting gradient boosted rounds:") n_samples = X_binned_train.shape[0] # First time calling fit, or no warm start if not (self._is_fitted() and self.warm_start): # Clear random state and score attributes self._clear_state() # initialize raw_predictions: those are the accumulated values # predicted by the trees for the training data. raw_predictions has # shape (n_samples, n_trees_per_iteration) where # n_trees_per_iterations is n_classes in multiclass classification, # else 1. # self._baseline_prediction has shape (1, n_trees_per_iteration) self._baseline_prediction = self._loss.fit_intercept_only( y_true=y_train, sample_weight=sample_weight_train ).reshape((1, -1)) raw_predictions = np.zeros( shape=(n_samples, self.n_trees_per_iteration_), dtype=self._baseline_prediction.dtype, order="F", ) raw_predictions += self._baseline_prediction # predictors is a matrix (list of lists) of TreePredictor objects # with shape (n_iter_, n_trees_per_iteration) self._predictors = predictors = [] # Initialize structures and attributes related to early stopping self._scorer = None # set if scoring != loss raw_predictions_val = None # set if scoring == loss and use val self.train_score_ = [] self.validation_score_ = [] if self.do_early_stopping_: # populate train_score and validation_score with the # predictions of the initial model (before the first tree) if self.scoring == "loss": # we're going to compute scoring w.r.t the loss. As losses # take raw predictions as input (unlike the scorers), we # can optimize a bit and avoid repeating computing the # predictions of the previous trees. We'll re-use # raw_predictions (as it's needed for training anyway) for # evaluating the training loss, and create # raw_predictions_val for storing the raw predictions of # the validation data. if self._use_validation_data: raw_predictions_val = np.zeros( shape=(X_binned_val.shape[0], self.n_trees_per_iteration_), dtype=self._baseline_prediction.dtype, order="F", ) raw_predictions_val += self._baseline_prediction self._check_early_stopping_loss( raw_predictions=raw_predictions, y_train=y_train, sample_weight_train=sample_weight_train, raw_predictions_val=raw_predictions_val, y_val=y_val, sample_weight_val=sample_weight_val, n_threads=n_threads, ) else: self._scorer = check_scoring(self, self.scoring) # _scorer is a callable with signature (est, X, y) and # calls est.predict() or est.predict_proba() depending on # its nature. # Unfortunately, each call to _scorer() will compute # the predictions of all the trees. So we use a subset of # the training set to compute train scores. # Compute the subsample set ( X_binned_small_train, y_small_train, sample_weight_small_train, ) = self._get_small_trainset( X_binned_train, y_train, sample_weight_train, self._random_seed ) self._check_early_stopping_scorer( X_binned_small_train, y_small_train, sample_weight_small_train, X_binned_val, y_val, sample_weight_val, ) begin_at_stage = 0 # warm start: this is not the first time fit was called else: # Check that the maximum number of iterations is not smaller # than the number of iterations from the previous fit if self.max_iter < self.n_iter_: raise ValueError( "max_iter=%d must be larger than or equal to " "n_iter_=%d when warm_start==True" % (self.max_iter, self.n_iter_) ) # Convert array attributes to lists self.train_score_ = self.train_score_.tolist() self.validation_score_ = self.validation_score_.tolist() # Compute raw predictions raw_predictions = self._raw_predict(X_binned_train, n_threads=n_threads) if self.do_early_stopping_ and self._use_validation_data: raw_predictions_val = self._raw_predict( X_binned_val, n_threads=n_threads ) else: raw_predictions_val = None if self.do_early_stopping_ and self.scoring != "loss": # Compute the subsample set ( X_binned_small_train, y_small_train, sample_weight_small_train, ) = self._get_small_trainset( X_binned_train, y_train, sample_weight_train, self._random_seed ) # Get the predictors from the previous fit predictors = self._predictors begin_at_stage = self.n_iter_ # initialize gradients and hessians (empty arrays). # shape = (n_samples, n_trees_per_iteration). gradient, hessian = self._loss.init_gradient_and_hessian( n_samples=n_samples, dtype=G_H_DTYPE, order="F" ) for iteration in range(begin_at_stage, self.max_iter): if self.verbose: iteration_start_time = time() print( "[{}/{}] ".format(iteration + 1, self.max_iter), end="", flush=True ) # Update gradients and hessians, inplace # Note that self._loss expects shape (n_samples,) for # n_trees_per_iteration = 1 else shape (n_samples, n_trees_per_iteration). if self._loss.constant_hessian: self._loss.gradient( y_true=y_train, raw_prediction=raw_predictions, sample_weight=sample_weight_train, gradient_out=gradient, n_threads=n_threads, ) else: self._loss.gradient_hessian( y_true=y_train, raw_prediction=raw_predictions, sample_weight=sample_weight_train, gradient_out=gradient, hessian_out=hessian, n_threads=n_threads, ) # Append a list since there may be more than 1 predictor per iter predictors.append([]) # 2-d views of shape (n_samples, n_trees_per_iteration_) or (n_samples, 1) # on gradient and hessian to simplify the loop over n_trees_per_iteration_. if gradient.ndim == 1: g_view = gradient.reshape((-1, 1)) h_view = hessian.reshape((-1, 1)) else: g_view = gradient h_view = hessian # Build `n_trees_per_iteration` trees. for k in range(self.n_trees_per_iteration_): grower = TreeGrower( X_binned=X_binned_train, gradients=g_view[:, k], hessians=h_view[:, k], n_bins=n_bins, n_bins_non_missing=self._bin_mapper.n_bins_non_missing_, has_missing_values=has_missing_values, is_categorical=self.is_categorical_, monotonic_cst=monotonic_cst, interaction_cst=interaction_cst, max_leaf_nodes=self.max_leaf_nodes, max_depth=self.max_depth, min_samples_leaf=self.min_samples_leaf, l2_regularization=self.l2_regularization, shrinkage=self.learning_rate, n_threads=n_threads, ) grower.grow() acc_apply_split_time += grower.total_apply_split_time acc_find_split_time += grower.total_find_split_time acc_compute_hist_time += grower.total_compute_hist_time if self._loss.need_update_leaves_values: _update_leaves_values( loss=self._loss, grower=grower, y_true=y_train, raw_prediction=raw_predictions[:, k], sample_weight=sample_weight_train, ) predictor = grower.make_predictor( binning_thresholds=self._bin_mapper.bin_thresholds_ ) predictors[-1].append(predictor) # Update raw_predictions with the predictions of the newly # created tree. tic_pred = time() _update_raw_predictions(raw_predictions[:, k], grower, n_threads) toc_pred = time() acc_prediction_time += toc_pred - tic_pred should_early_stop = False if self.do_early_stopping_: if self.scoring == "loss": # Update raw_predictions_val with the newest tree(s) if self._use_validation_data: for k, pred in enumerate(self._predictors[-1]): raw_predictions_val[:, k] += pred.predict_binned( X_binned_val, self._bin_mapper.missing_values_bin_idx_, n_threads, ) should_early_stop = self._check_early_stopping_loss( raw_predictions=raw_predictions, y_train=y_train, sample_weight_train=sample_weight_train, raw_predictions_val=raw_predictions_val, y_val=y_val, sample_weight_val=sample_weight_val, n_threads=n_threads, ) else: should_early_stop = self._check_early_stopping_scorer( X_binned_small_train, y_small_train, sample_weight_small_train, X_binned_val, y_val, sample_weight_val, ) if self.verbose: self._print_iteration_stats(iteration_start_time) # maybe we could also early stop if all the trees are stumps? if should_early_stop: break if self.verbose: duration = time() - fit_start_time n_total_leaves = sum( predictor.get_n_leaf_nodes() for predictors_at_ith_iteration in self._predictors for predictor in predictors_at_ith_iteration ) n_predictors = sum( len(predictors_at_ith_iteration) for predictors_at_ith_iteration in self._predictors ) print( "Fit {} trees in {:.3f} s, ({} total leaves)".format( n_predictors, duration, n_total_leaves ) ) print( "{:<32} {:.3f}s".format( "Time spent computing histograms:", acc_compute_hist_time ) ) print( "{:<32} {:.3f}s".format( "Time spent finding best splits:", acc_find_split_time ) ) print( "{:<32} {:.3f}s".format( "Time spent applying splits:", acc_apply_split_time ) ) print( "{:<32} {:.3f}s".format("Time spent predicting:", acc_prediction_time) ) self.train_score_ = np.asarray(self.train_score_) self.validation_score_ = np.asarray(self.validation_score_) del self._in_fit # hard delete so we're sure it can't be used anymore return self def _is_fitted(self): return len(getattr(self, "_predictors", [])) > 0 def _clear_state(self): """Clear the state of the gradient boosting model.""" for var in ("train_score_", "validation_score_"): if hasattr(self, var): delattr(self, var) def _get_small_trainset(self, X_binned_train, y_train, sample_weight_train, seed): """Compute the indices of the subsample set and return this set. For efficiency, we need to subsample the training set to compute scores with scorers. """ # TODO: incorporate sample_weights here in `resample` subsample_size = 10000 if X_binned_train.shape[0] > subsample_size: indices = np.arange(X_binned_train.shape[0]) stratify = y_train if is_classifier(self) else None indices = resample( indices, n_samples=subsample_size, replace=False, random_state=seed, stratify=stratify, ) X_binned_small_train = X_binned_train[indices] y_small_train = y_train[indices] if sample_weight_train is not None: sample_weight_small_train = sample_weight_train[indices] else: sample_weight_small_train = None X_binned_small_train = np.ascontiguousarray(X_binned_small_train) return (X_binned_small_train, y_small_train, sample_weight_small_train) else: return X_binned_train, y_train, sample_weight_train def _check_early_stopping_scorer( self, X_binned_small_train, y_small_train, sample_weight_small_train, X_binned_val, y_val, sample_weight_val, ): """Check if fitting should be early-stopped based on scorer. Scores are computed on validation data or on training data. """ if is_classifier(self): y_small_train = self.classes_[y_small_train.astype(int)] if sample_weight_small_train is None: self.train_score_.append( self._scorer(self, X_binned_small_train, y_small_train) ) else: self.train_score_.append( self._scorer( self, X_binned_small_train, y_small_train, sample_weight=sample_weight_small_train, ) ) if self._use_validation_data: if is_classifier(self): y_val = self.classes_[y_val.astype(int)] if sample_weight_val is None: self.validation_score_.append(self._scorer(self, X_binned_val, y_val)) else: self.validation_score_.append( self._scorer( self, X_binned_val, y_val, sample_weight=sample_weight_val ) ) return self._should_stop(self.validation_score_) else: return self._should_stop(self.train_score_) def _check_early_stopping_loss( self, raw_predictions, y_train, sample_weight_train, raw_predictions_val, y_val, sample_weight_val, n_threads=1, ): """Check if fitting should be early-stopped based on loss. Scores are computed on validation data or on training data. """ self.train_score_.append( -self._loss( y_true=y_train, raw_prediction=raw_predictions, sample_weight=sample_weight_train, n_threads=n_threads, ) ) if self._use_validation_data: self.validation_score_.append( -self._loss( y_true=y_val, raw_prediction=raw_predictions_val, sample_weight=sample_weight_val, n_threads=n_threads, ) ) return self._should_stop(self.validation_score_) else: return self._should_stop(self.train_score_) def _should_stop(self, scores): """ Return True (do early stopping) if the last n scores aren't better than the (n-1)th-to-last score, up to some tolerance. """ reference_position = self.n_iter_no_change + 1 if len(scores) < reference_position: return False # A higher score is always better. Higher tol means that it will be # harder for subsequent iteration to be considered an improvement upon # the reference score, and therefore it is more likely to early stop # because of the lack of significant improvement. reference_score = scores[-reference_position] + self.tol recent_scores = scores[-reference_position + 1 :] recent_improvements = [score > reference_score for score in recent_scores] return not any(recent_improvements) def _bin_data(self, X, is_training_data): """Bin data X. If is_training_data, then fit the _bin_mapper attribute. Else, the binned data is converted to a C-contiguous array. """ description = "training" if is_training_data else "validation" if self.verbose: print( "Binning {:.3f} GB of {} data: ".format(X.nbytes / 1e9, description), end="", flush=True, ) tic = time() if is_training_data: X_binned = self._bin_mapper.fit_transform(X) # F-aligned array else: X_binned = self._bin_mapper.transform(X) # F-aligned array # We convert the array to C-contiguous since predicting is faster # with this layout (training is faster on F-arrays though) X_binned = np.ascontiguousarray(X_binned) toc = time() if self.verbose: duration = toc - tic print("{:.3f} s".format(duration)) return X_binned def _print_iteration_stats(self, iteration_start_time): """Print info about the current fitting iteration.""" log_msg = "" predictors_of_ith_iteration = [ predictors_list for predictors_list in self._predictors[-1] if predictors_list ] n_trees = len(predictors_of_ith_iteration) max_depth = max( predictor.get_max_depth() for predictor in predictors_of_ith_iteration ) n_leaves = sum( predictor.get_n_leaf_nodes() for predictor in predictors_of_ith_iteration ) if n_trees == 1: log_msg += "{} tree, {} leaves, ".format(n_trees, n_leaves) else: log_msg += "{} trees, {} leaves ".format(n_trees, n_leaves) log_msg += "({} on avg), ".format(int(n_leaves / n_trees)) log_msg += "max depth = {}, ".format(max_depth) if self.do_early_stopping_: if self.scoring == "loss": factor = -1 # score_ arrays contain the negative loss name = "loss" else: factor = 1 name = "score" log_msg += "train {}: {:.5f}, ".format(name, factor * self.train_score_[-1]) if self._use_validation_data: log_msg += "val {}: {:.5f}, ".format( name, factor * self.validation_score_[-1] ) iteration_time = time() - iteration_start_time log_msg += "in {:0.3f}s".format(iteration_time) print(log_msg) def _raw_predict(self, X, n_threads=None): """Return the sum of the leaves values over all predictors. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. n_threads : int, default=None Number of OpenMP threads to use. `_openmp_effective_n_threads` is called to determine the effective number of threads use, which takes cgroups CPU quotes into account. See the docstring of `_openmp_effective_n_threads` for details. Returns ------- raw_predictions : array, shape (n_samples, n_trees_per_iteration) The raw predicted values. """ is_binned = getattr(self, "_in_fit", False) if not is_binned: X = self._validate_data( X, dtype=X_DTYPE, force_all_finite=False, reset=False ) check_is_fitted(self) if X.shape[1] != self._n_features: raise ValueError( "X has {} features but this estimator was trained with " "{} features.".format(X.shape[1], self._n_features) ) n_samples = X.shape[0] raw_predictions = np.zeros( shape=(n_samples, self.n_trees_per_iteration_), dtype=self._baseline_prediction.dtype, order="F", ) raw_predictions += self._baseline_prediction # We intentionally decouple the number of threads used at prediction # time from the number of threads used at fit time because the model # can be deployed on a different machine for prediction purposes. n_threads = _openmp_effective_n_threads(n_threads) self._predict_iterations( X, self._predictors, raw_predictions, is_binned, n_threads ) return raw_predictions def _predict_iterations(self, X, predictors, raw_predictions, is_binned, n_threads): """Add the predictions of the predictors to raw_predictions.""" if not is_binned: ( known_cat_bitsets, f_idx_map, ) = self._bin_mapper.make_known_categories_bitsets() for predictors_of_ith_iteration in predictors: for k, predictor in enumerate(predictors_of_ith_iteration): if is_binned: predict = partial( predictor.predict_binned, missing_values_bin_idx=self._bin_mapper.missing_values_bin_idx_, n_threads=n_threads, ) else: predict = partial( predictor.predict, known_cat_bitsets=known_cat_bitsets, f_idx_map=f_idx_map, n_threads=n_threads, ) raw_predictions[:, k] += predict(X) def _staged_raw_predict(self, X): """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 of shape (n_samples, n_features) The input samples. Yields ------ raw_predictions : generator of ndarray of shape \ (n_samples, n_trees_per_iteration) The raw predictions of the input samples. The order of the classes corresponds to that in the attribute :term:`classes_`. """ X = self._validate_data(X, dtype=X_DTYPE, force_all_finite=False, reset=False) check_is_fitted(self) if X.shape[1] != self._n_features: raise ValueError( "X has {} features but this estimator was trained with " "{} features.".format(X.shape[1], self._n_features) ) n_samples = X.shape[0] raw_predictions = np.zeros( shape=(n_samples, self.n_trees_per_iteration_), dtype=self._baseline_prediction.dtype, order="F", ) raw_predictions += self._baseline_prediction # We intentionally decouple the number of threads used at prediction # time from the number of threads used at fit time because the model # can be deployed on a different machine for prediction purposes. n_threads = _openmp_effective_n_threads() for iteration in range(len(self._predictors)): self._predict_iterations( X, self._predictors[iteration : iteration + 1], raw_predictions, is_binned=False, n_threads=n_threads, ) yield raw_predictions.copy() def _compute_partial_dependence_recursion(self, grid, target_features): """Fast partial dependence computation. Parameters ---------- grid : ndarray, shape (n_samples, n_target_features) The grid points on which the partial dependence should be evaluated. target_features : ndarray, shape (n_target_features) The set of target features for which the partial dependence should be evaluated. Returns ------- averaged_predictions : ndarray, shape \ (n_trees_per_iteration, n_samples) The value of the partial dependence function on each grid point. """ if getattr(self, "_fitted_with_sw", False): raise NotImplementedError( "{} does not support partial dependence " "plots with the 'recursion' method when " "sample weights were given during fit " "time.".format(self.__class__.__name__) ) grid = np.asarray(grid, dtype=X_DTYPE, order="C") averaged_predictions = np.zeros( (self.n_trees_per_iteration_, grid.shape[0]), dtype=Y_DTYPE ) for predictors_of_ith_iteration in self._predictors: for k, predictor in enumerate(predictors_of_ith_iteration): predictor.compute_partial_dependence( grid, target_features, averaged_predictions[k] ) # Note that the learning rate is already accounted for in the leaves # values. return averaged_predictions def _more_tags(self): return {"allow_nan": True} @abstractmethod def _get_loss(self, sample_weight): pass @abstractmethod def _encode_y(self, y=None): pass @property def n_iter_(self): """Number of iterations of the boosting process.""" check_is_fitted(self) return len(self._predictors) class HistGradientBoostingRegressor(RegressorMixin, BaseHistGradientBoosting): """Histogram-based Gradient Boosting Regression Tree. This estimator is much faster than :class:`GradientBoostingRegressor` for big datasets (n_samples >= 10 000). This estimator has native support for missing values (NaNs). During training, the tree grower learns at each split point whether samples with missing values should go to the left or right child, based on the potential gain. When predicting, samples with missing values are assigned to the left or right child consequently. If no missing values were encountered for a given feature during training, then samples with missing values are mapped to whichever child has the most samples. This implementation is inspired by `LightGBM `_. Read more in the :ref:`User Guide `. .. versionadded:: 0.21 Parameters ---------- loss : {'squared_error', 'absolute_error', 'poisson', 'quantile'}, \ default='squared_error' The loss function to use in the boosting process. Note that the "squared error" and "poisson" losses actually implement "half least squares loss" and "half poisson deviance" to simplify the computation of the gradient. Furthermore, "poisson" loss internally uses a log-link and requires ``y >= 0``. "quantile" uses the pinball loss. .. versionchanged:: 0.23 Added option 'poisson'. .. versionchanged:: 1.1 Added option 'quantile'. quantile : float, default=None If loss is "quantile", this parameter specifies which quantile to be estimated and must be between 0 and 1. learning_rate : float, default=0.1 The learning rate, also known as *shrinkage*. This is used as a multiplicative factor for the leaves values. Use ``1`` for no shrinkage. max_iter : int, default=100 The maximum number of iterations of the boosting process, i.e. the maximum number of trees. max_leaf_nodes : int or None, default=31 The maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limit. max_depth : int or None, default=None The maximum depth of each tree. The depth of a tree is the number of edges to go from the root to the deepest leaf. Depth isn't constrained by default. min_samples_leaf : int, default=20 The minimum number of samples per leaf. For small datasets with less than a few hundred samples, it is recommended to lower this value since only very shallow trees would be built. l2_regularization : float, default=0 The L2 regularization parameter. Use ``0`` for no regularization (default). max_bins : int, default=255 The maximum number of bins to use for non-missing values. Before training, each feature of the input array `X` is binned into integer-valued bins, which allows for a much faster training stage. Features with a small number of unique values may use less than ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin is always reserved for missing values. Must be no larger than 255. categorical_features : array-like of {bool, int, str} of shape (n_features) \ or shape (n_categorical_features,), default=None Indicates the categorical features. - None : no feature will be considered categorical. - boolean array-like : boolean mask indicating categorical features. - integer array-like : integer indices indicating categorical features. - str array-like: names of categorical features (assuming the training data has feature names). For each categorical feature, there must be at most `max_bins` unique categories, and each categorical value must be in [0, max_bins -1]. During prediction, categories encoded as a negative value are treated as missing values. Read more in the :ref:`User Guide `. .. versionadded:: 0.24 .. versionchanged:: 1.2 Added support for feature names. monotonic_cst : array-like of int of shape (n_features) or dict, default=None Monotonic constraint to enforce on each feature are specified using the following integer values: - 1: monotonic increase - 0: no constraint - -1: monotonic decrease If a dict with str keys, map feature to monotonic constraints by name. If an array, the features are mapped to constraints by position. See :ref:`monotonic_cst_features_names` for a usage example. The constraints are only valid for binary classifications and hold over the probability of the positive class. Read more in the :ref:`User Guide `. .. versionadded:: 0.23 .. versionchanged:: 1.2 Accept dict of constraints with feature names as keys. interaction_cst : {"pairwise", "no_interaction"} or sequence of lists/tuples/sets \ of int, default=None Specify interaction constraints, the sets of features which can interact with each other in child node splits. Each item specifies the set of feature indices that are allowed to interact with each other. If there are more features than specified in these constraints, they are treated as if they were specified as an additional set. The strings "pairwise" and "no_interactions" are shorthands for allowing only pairwise or no interactions, respectively. For instance, with 5 features in total, `interaction_cst=[{0, 1}]` is equivalent to `interaction_cst=[{0, 1}, {2, 3, 4}]`, and specifies that each branch of a tree will either only split on features 0 and 1 or only split on features 2, 3 and 4. .. versionadded:: 1.2 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. For results to be valid, the estimator should be re-trained on the same data only. See :term:`the Glossary `. early_stopping : 'auto' or bool, default='auto' If 'auto', early stopping is enabled if the sample size is larger than 10000. If True, early stopping is enabled, otherwise early stopping is disabled. .. versionadded:: 0.23 scoring : str or callable or None, default='loss' Scoring parameter to use for early stopping. It can be a single string (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`). If None, the estimator's default scorer is used. If ``scoring='loss'``, early stopping is checked w.r.t the loss value. Only used if early stopping is performed. validation_fraction : int or float or None, default=0.1 Proportion (or absolute size) of training data to set aside as validation data for early stopping. If None, early stopping is done on the training data. Only used if early stopping is performed. n_iter_no_change : int, default=10 Used to determine when to "early stop". The fitting process is stopped when none of the last ``n_iter_no_change`` scores are better than the ``n_iter_no_change - 1`` -th-to-last one, up to some tolerance. Only used if early stopping is performed. tol : float, default=1e-7 The absolute tolerance to use when comparing scores during early stopping. The higher the tolerance, the more likely we are to early stop: higher tolerance means that it will be harder for subsequent iterations to be considered an improvement upon the reference score. verbose : int, default=0 The verbosity level. If not zero, print some information about the fitting process. random_state : int, RandomState instance or None, default=None Pseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. Attributes ---------- do_early_stopping_ : bool Indicates whether early stopping is used during training. n_iter_ : int The number of iterations as selected by early stopping, depending on the `early_stopping` parameter. Otherwise it corresponds to max_iter. n_trees_per_iteration_ : int The number of tree that are built at each iteration. For regressors, this is always 1. train_score_ : ndarray, shape (n_iter_+1,) The scores at each iteration on the training data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the ``scoring`` parameter. If ``scoring`` is not 'loss', scores are computed on a subset of at most 10 000 samples. Empty if no early stopping. validation_score_ : ndarray, shape (n_iter_+1,) The scores at each iteration on the held-out validation data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the ``scoring`` parameter. Empty if no early stopping or if ``validation_fraction`` is None. is_categorical_ : ndarray, shape (n_features, ) or None Boolean mask for the categorical features. ``None`` if there are no categorical features. 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 See Also -------- GradientBoostingRegressor : Exact gradient boosting method that does not scale as good on datasets with a large number of samples. sklearn.tree.DecisionTreeRegressor : A decision tree regressor. RandomForestRegressor : A meta-estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the statistical performance and control over-fitting. AdaBoostRegressor : A meta-estimator that begins by fitting a regressor on the original dataset and then fits additional copies of the regressor on the same dataset but where the weights of instances are adjusted according to the error of the current prediction. As such, subsequent regressors focus more on difficult cases. Examples -------- >>> from sklearn.ensemble import HistGradientBoostingRegressor >>> from sklearn.datasets import load_diabetes >>> X, y = load_diabetes(return_X_y=True) >>> est = HistGradientBoostingRegressor().fit(X, y) >>> est.score(X, y) 0.92... """ _parameter_constraints: dict = { **BaseHistGradientBoosting._parameter_constraints, "loss": [ StrOptions({"squared_error", "absolute_error", "poisson", "quantile"}), BaseLoss, ], "quantile": [Interval(Real, 0, 1, closed="both"), None], } def __init__( self, loss="squared_error", *, quantile=None, learning_rate=0.1, max_iter=100, max_leaf_nodes=31, max_depth=None, min_samples_leaf=20, l2_regularization=0.0, max_bins=255, categorical_features=None, monotonic_cst=None, interaction_cst=None, warm_start=False, early_stopping="auto", scoring="loss", validation_fraction=0.1, n_iter_no_change=10, tol=1e-7, verbose=0, random_state=None, ): super(HistGradientBoostingRegressor, self).__init__( loss=loss, learning_rate=learning_rate, max_iter=max_iter, max_leaf_nodes=max_leaf_nodes, max_depth=max_depth, min_samples_leaf=min_samples_leaf, l2_regularization=l2_regularization, max_bins=max_bins, monotonic_cst=monotonic_cst, interaction_cst=interaction_cst, categorical_features=categorical_features, early_stopping=early_stopping, warm_start=warm_start, scoring=scoring, validation_fraction=validation_fraction, n_iter_no_change=n_iter_no_change, tol=tol, verbose=verbose, random_state=random_state, ) self.quantile = quantile def predict(self, X): """Predict values for X. Parameters ---------- X : array-like, shape (n_samples, n_features) The input samples. Returns ------- y : ndarray, shape (n_samples,) The predicted values. """ check_is_fitted(self) # Return inverse link of raw predictions after converting # shape (n_samples, 1) to (n_samples,) return self._loss.link.inverse(self._raw_predict(X).ravel()) def staged_predict(self, X): """Predict regression target for each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. .. versionadded:: 0.24 Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Yields ------ y : generator of ndarray of shape (n_samples,) The predicted values of the input samples, for each iteration. """ for raw_predictions in self._staged_raw_predict(X): yield self._loss.link.inverse(raw_predictions.ravel()) def _encode_y(self, y): # Just convert y to the expected dtype self.n_trees_per_iteration_ = 1 y = y.astype(Y_DTYPE, copy=False) if self.loss == "poisson": # Ensure y >= 0 and sum(y) > 0 if not (np.all(y >= 0) and np.sum(y) > 0): raise ValueError( "loss='poisson' requires non-negative y and sum(y) > 0." ) return y def _get_loss(self, sample_weight): if self.loss == "quantile": return _LOSSES[self.loss]( sample_weight=sample_weight, quantile=self.quantile ) else: return _LOSSES[self.loss](sample_weight=sample_weight) class HistGradientBoostingClassifier(ClassifierMixin, BaseHistGradientBoosting): """Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples >= 10 000). This estimator has native support for missing values (NaNs). During training, the tree grower learns at each split point whether samples with missing values should go to the left or right child, based on the potential gain. When predicting, samples with missing values are assigned to the left or right child consequently. If no missing values were encountered for a given feature during training, then samples with missing values are mapped to whichever child has the most samples. This implementation is inspired by `LightGBM `_. Read more in the :ref:`User Guide `. .. versionadded:: 0.21 Parameters ---------- loss : {'log_loss', 'auto', 'binary_crossentropy', 'categorical_crossentropy'}, \ default='log_loss' The loss function to use in the boosting process. For binary classification problems, 'log_loss' is also known as logistic loss, binomial deviance or binary crossentropy. Internally, the model fits one tree per boosting iteration and uses the logistic sigmoid function (expit) as inverse link function to compute the predicted positive class probability. For multiclass classification problems, 'log_loss' is also known as multinomial deviance or categorical crossentropy. Internally, the model fits one tree per boosting iteration and per class and uses the softmax function as inverse link function to compute the predicted probabilities of the classes. .. deprecated:: 1.1 The loss arguments 'auto', 'binary_crossentropy' and 'categorical_crossentropy' were deprecated in v1.1 and will be removed in version 1.3. Use `loss='log_loss'` which is equivalent. learning_rate : float, default=0.1 The learning rate, also known as *shrinkage*. This is used as a multiplicative factor for the leaves values. Use ``1`` for no shrinkage. max_iter : int, default=100 The maximum number of iterations of the boosting process, i.e. the maximum number of trees for binary classification. For multiclass classification, `n_classes` trees per iteration are built. max_leaf_nodes : int or None, default=31 The maximum number of leaves for each tree. Must be strictly greater than 1. If None, there is no maximum limit. max_depth : int or None, default=None The maximum depth of each tree. The depth of a tree is the number of edges to go from the root to the deepest leaf. Depth isn't constrained by default. min_samples_leaf : int, default=20 The minimum number of samples per leaf. For small datasets with less than a few hundred samples, it is recommended to lower this value since only very shallow trees would be built. l2_regularization : float, default=0 The L2 regularization parameter. Use 0 for no regularization. max_bins : int, default=255 The maximum number of bins to use for non-missing values. Before training, each feature of the input array `X` is binned into integer-valued bins, which allows for a much faster training stage. Features with a small number of unique values may use less than ``max_bins`` bins. In addition to the ``max_bins`` bins, one more bin is always reserved for missing values. Must be no larger than 255. categorical_features : array-like of {bool, int, str} of shape (n_features) \ or shape (n_categorical_features,), default=None Indicates the categorical features. - None : no feature will be considered categorical. - boolean array-like : boolean mask indicating categorical features. - integer array-like : integer indices indicating categorical features. - str array-like: names of categorical features (assuming the training data has feature names). For each categorical feature, there must be at most `max_bins` unique categories, and each categorical value must be in [0, max_bins -1]. During prediction, categories encoded as a negative value are treated as missing values. Read more in the :ref:`User Guide `. .. versionadded:: 0.24 .. versionchanged:: 1.2 Added support for feature names. monotonic_cst : array-like of int of shape (n_features) or dict, default=None Monotonic constraint to enforce on each feature are specified using the following integer values: - 1: monotonic increase - 0: no constraint - -1: monotonic decrease If a dict with str keys, map feature to monotonic constraints by name. If an array, the features are mapped to constraints by position. See :ref:`monotonic_cst_features_names` for a usage example. The constraints are only valid for binary classifications and hold over the probability of the positive class. Read more in the :ref:`User Guide `. .. versionadded:: 0.23 .. versionchanged:: 1.2 Accept dict of constraints with feature names as keys. interaction_cst : {"pairwise", "no_interaction"} or sequence of lists/tuples/sets \ of int, default=None Specify interaction constraints, the sets of features which can interact with each other in child node splits. Each item specifies the set of feature indices that are allowed to interact with each other. If there are more features than specified in these constraints, they are treated as if they were specified as an additional set. The strings "pairwise" and "no_interactions" are shorthands for allowing only pairwise or no interactions, respectively. For instance, with 5 features in total, `interaction_cst=[{0, 1}]` is equivalent to `interaction_cst=[{0, 1}, {2, 3, 4}]`, and specifies that each branch of a tree will either only split on features 0 and 1 or only split on features 2, 3 and 4. .. versionadded:: 1.2 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. For results to be valid, the estimator should be re-trained on the same data only. See :term:`the Glossary `. early_stopping : 'auto' or bool, default='auto' If 'auto', early stopping is enabled if the sample size is larger than 10000. If True, early stopping is enabled, otherwise early stopping is disabled. .. versionadded:: 0.23 scoring : str or callable or None, default='loss' Scoring parameter to use for early stopping. It can be a single string (see :ref:`scoring_parameter`) or a callable (see :ref:`scoring`). If None, the estimator's default scorer is used. If ``scoring='loss'``, early stopping is checked w.r.t the loss value. Only used if early stopping is performed. validation_fraction : int or float or None, default=0.1 Proportion (or absolute size) of training data to set aside as validation data for early stopping. If None, early stopping is done on the training data. Only used if early stopping is performed. n_iter_no_change : int, default=10 Used to determine when to "early stop". The fitting process is stopped when none of the last ``n_iter_no_change`` scores are better than the ``n_iter_no_change - 1`` -th-to-last one, up to some tolerance. Only used if early stopping is performed. tol : float, default=1e-7 The absolute tolerance to use when comparing scores. The higher the tolerance, the more likely we are to early stop: higher tolerance means that it will be harder for subsequent iterations to be considered an improvement upon the reference score. verbose : int, default=0 The verbosity level. If not zero, print some information about the fitting process. random_state : int, RandomState instance or None, default=None Pseudo-random number generator to control the subsampling in the binning process, and the train/validation data split if early stopping is enabled. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. class_weight : dict or 'balanced', default=None Weights associated with classes in the form `{class_label: weight}`. If not given, all classes are supposed to have weight one. 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))`. Note that these weights will be multiplied with sample_weight (passed through the fit method) if `sample_weight` is specified. .. versionadded:: 1.2 Attributes ---------- classes_ : array, shape = (n_classes,) Class labels. do_early_stopping_ : bool Indicates whether early stopping is used during training. n_iter_ : int The number of iterations as selected by early stopping, depending on the `early_stopping` parameter. Otherwise it corresponds to max_iter. n_trees_per_iteration_ : int The number of tree that are built at each iteration. This is equal to 1 for binary classification, and to ``n_classes`` for multiclass classification. train_score_ : ndarray, shape (n_iter_+1,) The scores at each iteration on the training data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the ``scoring`` parameter. If ``scoring`` is not 'loss', scores are computed on a subset of at most 10 000 samples. Empty if no early stopping. validation_score_ : ndarray, shape (n_iter_+1,) The scores at each iteration on the held-out validation data. The first entry is the score of the ensemble before the first iteration. Scores are computed according to the ``scoring`` parameter. Empty if no early stopping or if ``validation_fraction`` is None. is_categorical_ : ndarray, shape (n_features, ) or None Boolean mask for the categorical features. ``None`` if there are no categorical features. 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 See Also -------- GradientBoostingClassifier : Exact gradient boosting method that does not scale as good on datasets with a large number of samples. 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. Examples -------- >>> from sklearn.ensemble import HistGradientBoostingClassifier >>> from sklearn.datasets import load_iris >>> X, y = load_iris(return_X_y=True) >>> clf = HistGradientBoostingClassifier().fit(X, y) >>> clf.score(X, y) 1.0 """ # TODO(1.3): Remove "binary_crossentropy", "categorical_crossentropy", "auto" _parameter_constraints: dict = { **BaseHistGradientBoosting._parameter_constraints, "loss": [ StrOptions( { "log_loss", "binary_crossentropy", "categorical_crossentropy", "auto", }, deprecated={ "auto", "binary_crossentropy", "categorical_crossentropy", }, ), BaseLoss, ], "class_weight": [dict, StrOptions({"balanced"}), None], } def __init__( self, loss="log_loss", *, learning_rate=0.1, max_iter=100, max_leaf_nodes=31, max_depth=None, min_samples_leaf=20, l2_regularization=0.0, max_bins=255, categorical_features=None, monotonic_cst=None, interaction_cst=None, warm_start=False, early_stopping="auto", scoring="loss", validation_fraction=0.1, n_iter_no_change=10, tol=1e-7, verbose=0, random_state=None, class_weight=None, ): super(HistGradientBoostingClassifier, self).__init__( loss=loss, learning_rate=learning_rate, max_iter=max_iter, max_leaf_nodes=max_leaf_nodes, max_depth=max_depth, min_samples_leaf=min_samples_leaf, l2_regularization=l2_regularization, max_bins=max_bins, categorical_features=categorical_features, monotonic_cst=monotonic_cst, interaction_cst=interaction_cst, warm_start=warm_start, early_stopping=early_stopping, scoring=scoring, validation_fraction=validation_fraction, n_iter_no_change=n_iter_no_change, tol=tol, verbose=verbose, random_state=random_state, ) self.class_weight = class_weight def _finalize_sample_weight(self, sample_weight, y): """Adjust sample_weights with class_weights.""" if self.class_weight is None: return sample_weight expanded_class_weight = compute_sample_weight(self.class_weight, y) if sample_weight is not None: return sample_weight * expanded_class_weight else: return expanded_class_weight def predict(self, X): """Predict classes for X. Parameters ---------- X : array-like, shape (n_samples, n_features) The input samples. Returns ------- y : ndarray, shape (n_samples,) The predicted classes. """ # TODO: This could be done in parallel encoded_classes = np.argmax(self.predict_proba(X), axis=1) return self.classes_[encoded_classes] def staged_predict(self, X): """Predict classes at each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. .. versionadded:: 0.24 Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Yields ------ y : generator of ndarray of shape (n_samples,) The predicted classes of the input samples, for each iteration. """ for proba in self.staged_predict_proba(X): encoded_classes = np.argmax(proba, axis=1) yield self.classes_.take(encoded_classes, axis=0) def predict_proba(self, X): """Predict class probabilities for X. Parameters ---------- X : array-like, shape (n_samples, n_features) The input samples. Returns ------- p : ndarray, shape (n_samples, n_classes) The class probabilities of the input samples. """ raw_predictions = self._raw_predict(X) return self._loss.predict_proba(raw_predictions) def staged_predict_proba(self, X): """Predict class probabilities at each iteration. This method allows monitoring (i.e. determine error on testing set) after each stage. Parameters ---------- X : array-like of shape (n_samples, n_features) The input samples. Yields ------ y : generator of ndarray of shape (n_samples,) The predicted class probabilities of the input samples, for each iteration. """ for raw_predictions in self._staged_raw_predict(X): yield self._loss.predict_proba(raw_predictions) def decision_function(self, X): """Compute the decision function of ``X``. Parameters ---------- X : array-like, shape (n_samples, n_features) The input samples. Returns ------- decision : ndarray, shape (n_samples,) or \ (n_samples, n_trees_per_iteration) The raw predicted values (i.e. the sum of the trees leaves) for each sample. n_trees_per_iteration is equal to the number of classes in multiclass classification. """ decision = self._raw_predict(X) if decision.shape[1] == 1: decision = decision.ravel() return decision 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 of shape (n_samples, n_features) The input samples. Yields ------ decision : generator of ndarray of shape (n_samples,) or \ (n_samples, n_trees_per_iteration) 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_`. """ for staged_decision in self._staged_raw_predict(X): if staged_decision.shape[1] == 1: staged_decision = staged_decision.ravel() yield staged_decision def _encode_y(self, y): # 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 = 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.astype(Y_DTYPE, copy=False) return encoded_y def _get_loss(self, sample_weight): # TODO(1.3): Remove "auto", "binary_crossentropy", "categorical_crossentropy" if self.loss in ("auto", "binary_crossentropy", "categorical_crossentropy"): warnings.warn( f"The loss '{self.loss}' was deprecated in v1.1 and will be removed in " "version 1.3. Use 'log_loss' which is equivalent.", FutureWarning, ) if self.loss in ("log_loss", "auto"): if self.n_trees_per_iteration_ == 1: return HalfBinomialLoss(sample_weight=sample_weight) else: return HalfMultinomialLoss( sample_weight=sample_weight, n_classes=self.n_trees_per_iteration_ ) if self.loss == "categorical_crossentropy": if self.n_trees_per_iteration_ == 1: raise ValueError( f"loss='{self.loss}' is not suitable for a binary classification " "problem. Please use loss='log_loss' instead." ) else: return HalfMultinomialLoss( sample_weight=sample_weight, n_classes=self.n_trees_per_iteration_ ) if self.loss == "binary_crossentropy": if self.n_trees_per_iteration_ > 1: raise ValueError( f"loss='{self.loss}' is not defined for multiclass " f"classification with n_classes={self.n_trees_per_iteration_}, " "use loss='log_loss' instead." ) else: return HalfBinomialLoss(sample_weight=sample_weight)