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sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py
2 258 строк
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Arthur Lacote
DOC: clarify categorical missing docs (HGB & trees) (#34553)
24 июл 2026, 06:51
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24 июл 2026, 06:51
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"""Fast Gradient Boosting decision trees for classification and regression.""" # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import itertools from abc import ABC, abstractmethod from contextlib import contextmanager, nullcontext, suppress from functools import partial from numbers import Integral, Real from time import time import numpy as np from sklearn._loss.loss import ( _LOSSES, BaseLoss, HalfBinomialLoss, HalfGammaLoss, HalfMultinomialLoss, HalfPoissonLoss, PinballLoss, ) from sklearn.base import ( BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier, ) from sklearn.compose import ColumnTransformer from sklearn.ensemble._hist_gradient_boosting._gradient_boosting import ( _update_raw_predictions, ) from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE, X_DTYPE, Y_DTYPE from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower from sklearn.metrics import check_scoring from sklearn.metrics._scorer import _SCORERS from sklearn.model_selection import train_test_split from sklearn.preprocessing import FunctionTransformer, LabelEncoder, OrdinalEncoder from sklearn.utils import check_random_state, compute_sample_weight, resample from sklearn.utils._missing import is_scalar_nan from sklearn.utils._openmp_helpers import _openmp_effective_n_threads from sklearn.utils._param_validation import Interval, RealNotInt, StrOptions from sklearn.utils.multiclass import check_classification_targets from sklearn.utils.validation import ( _check_categorical_features, _check_monotonic_cst, _check_sample_weight, _check_y, check_array, check_consistent_length, check_is_fitted, validate_data, ) _LOSSES = _LOSSES.copy() _LOSSES.update( { "poisson": HalfPoissonLoss, "gamma": HalfGammaLoss, "quantile": PinballLoss, } ) 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.differentiable is False. 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). More background: For the standard gradient descent method according to "Greedy Function Approximation: A Gradient Boosting Machine" by Friedman, all loss functions but the squared loss need a line search step. BaseHistGradientBoosting, however, implements a so called Newton boosting where the trees are fitted to a 2nd order approximations of the loss in terms of gradients and hessians. In this case, the line search step is only necessary if the loss is not smooth, i.e. not differentiable, which renders the 2nd order approximation invalid. In fact, non-smooth losses arbitrarily set hessians to 1 and effectively use the standard gradient descent method with line search. """ # 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 @contextmanager def _patch_raw_predict(estimator, raw_predictions): """Context manager that patches _raw_predict to return raw_predictions. `raw_predictions` is typically a precomputed array to avoid redundant state-wise computations fitting with early stopping enabled: in this case `raw_predictions` is incrementally updated whenever we add a tree to the boosted ensemble. Note: this makes fitting HistGradientBoosting* models inherently non thread safe at fit time. However thread-safety at fit time was never guaranteed nor enforced for scikit-learn estimators in general. Thread-safety at prediction/transform time is another matter as those operations are typically side-effect free and therefore often thread-safe by default for most scikit-learn models and would like to keep it that way. Therefore this context manager should only be used at fit time. TODO: in the future, we could explore the possibility to extend the scorer public API to expose a way to compute vales from raw predictions. That would probably require also making the scorer aware of the inverse link function used by the estimator which is typically private API for now, hence the need for this patching mechanism. """ orig_raw_predict = estimator._raw_predict def _patched_raw_predicts(*args, **kwargs): return raw_predictions estimator._raw_predict = _patched_raw_predicts yield estimator estimator._raw_predict = orig_raw_predict 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")], "max_features": [Interval(RealNotInt, 0, 1, closed="right")], "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(RealNotInt, 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", StrOptions({"from_dtype"}), 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_features, 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_features = max_features 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 _preprocess_X(self, X, *, reset): """Preprocess and validate X. Parameters ---------- X : {array-like, pandas DataFrame} of shape (n_samples, n_features) Input data. reset : bool Whether to reset the `n_features_in_` and `feature_names_in_ attributes. Returns ------- X : ndarray of shape (n_samples, n_features) Validated input data. known_categories : list of ndarray of shape (n_categories,) List of known categories for each categorical feature. """ # If there is a preprocessor, we let the preprocessor handle the validation. # Otherwise, we validate the data ourselves. check_X_kwargs = dict(dtype=[X_DTYPE], ensure_all_finite=False) if not reset: if self._preprocessor is None: return validate_data(self, X, reset=False, **check_X_kwargs) return self._preprocessor.transform(X) # At this point, reset is False, which runs during `fit`. self.is_categorical_ = _check_categorical_features(X, self.categorical_features) if self.is_categorical_ is None: self._preprocessor = None self._is_categorical_remapped = None X = validate_data(self, X, **check_X_kwargs) return X, None n_features = X.shape[1] ordinal_encoder = OrdinalEncoder( categories="auto", handle_unknown="use_encoded_value", unknown_value=np.nan, encoded_missing_value=np.nan, dtype=X_DTYPE, ) check_X = partial(check_array, **check_X_kwargs) numerical_preprocessor = FunctionTransformer(check_X) self._preprocessor = ColumnTransformer( [ ("encoder", ordinal_encoder, self.is_categorical_), ("numerical", numerical_preprocessor, ~self.is_categorical_), ] ) self._preprocessor.set_output(transform="default") X = self._preprocessor.fit_transform(X) # check categories found by the OrdinalEncoder and get their encoded values known_categories = self._check_categories() self.n_features_in_ = self._preprocessor.n_features_in_ with suppress(AttributeError): self.feature_names_in_ = self._preprocessor.feature_names_in_ # The ColumnTransformer's output places the categorical features at the # beginning categorical_remapped = np.zeros(n_features, dtype=bool) categorical_remapped[self._preprocessor.output_indices_["encoder"]] = True self._is_categorical_remapped = categorical_remapped return X, known_categories def _check_categories(self): """Check categories found by the preprocessor and return their encoded values. Returns a list of length ``self.n_features_in_``, with one entry per input feature. For non-categorical features, the corresponding entry is ``None``. For categorical features, the corresponding entry is an array containing the categories as encoded by the preprocessor (an ``OrdinalEncoder``), excluding missing values. The entry is therefore ``np.arange(n_categories)`` where ``n_categories`` is the number of unique values in the considered feature column, after removing missing values. If ``n_categories > self.max_bins`` for any feature, a ``ValueError`` is raised. """ encoder = self._preprocessor.named_transformers_["encoder"] known_categories = [None] * self._preprocessor.n_features_in_ categorical_column_indices = np.arange(self._preprocessor.n_features_in_)[ self._preprocessor.output_indices_["encoder"] ] for feature_idx, categories in zip( categorical_column_indices, encoder.categories_ ): # OrdinalEncoder always puts np.nan as the last category if the # training data has missing values. Here we remove it because it is # already added by the _BinMapper. if len(categories) and is_scalar_nan(categories[-1]): categories = categories[:-1] if categories.size > self.max_bins: try: feature_name = repr(encoder.feature_names_in_[feature_idx]) except AttributeError: feature_name = f"at index {feature_idx}" raise ValueError( f"Categorical feature {feature_name} is expected to " f"have a cardinality <= {self.max_bins} but actually " f"has a cardinality of {categories.size}." ) known_categories[feature_idx] = np.arange(len(categories), dtype=X_DTYPE) return 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 @_fit_context(prefer_skip_nested_validation=True) def fit( self, X, y, sample_weight=None, *, X_val=None, y_val=None, sample_weight_val=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 X_val : array-like of shape (n_val, n_features) Additional sample of features for validation used in early stopping. In a `Pipeline`, `X_val` can be transformed the same way as `X` with `Pipeline(..., transform_input=["X_val"])`. .. versionadded:: 1.7 y_val : array-like of shape (n_samples,) Additional sample of target values for validation used in early stopping. .. versionadded:: 1.7 sample_weight_val : array-like of shape (n_samples,) default=None Additional weights for validation used in early stopping. .. versionadded:: 1.7 Returns ------- self : object Fitted estimator. """ 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, known_categories = self._preprocess_X(X, reset=True) y = _check_y(y, estimator=self) 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) validation_data_provided = X_val is not None or y_val is not None if validation_data_provided: if y_val is None: raise ValueError("X_val is provided, but y_val was not provided.") if X_val is None: raise ValueError("y_val is provided, but X_val was not provided.") X_val = self._preprocess_X(X_val, reset=False) y_val = _check_y(y_val, estimator=self) y_val = self._encode_y_val(y_val) check_consistent_length(X_val, y_val) if sample_weight_val is not None: sample_weight_val = _check_sample_weight( sample_weight_val, X_val, dtype=np.float64 ) if self.early_stopping is False: raise ValueError( "X_val and y_val are passed to fit while at the same time " "early_stopping is False. When passing X_val and y_val to fit," "early_stopping should be set to either 'auto' or True." ) # Note: At this point, we could delete self._label_encoder if it exists. # But we don't to keep the code even simpler. rng = check_random_state(self.random_state) # When warm starting, we want to reuse the same seed that was used # the first time fit was called (e.g. train/val split). # For feature subsampling, we want to continue with the rng we started with. if not self.warm_start or not self._is_fitted(): self._random_seed = rng.randint(np.iinfo(np.uint32).max, dtype="u8") feature_subsample_seed = rng.randint(np.iinfo(np.uint32).max, dtype="u8") self._feature_subsample_rng = np.random.default_rng(feature_subsample_seed) self._validate_parameters() monotonic_cst = _check_monotonic_cst(self, self.monotonic_cst) # _preprocess_X places the categorical features at the beginning, # change the order of monotonic_cst accordingly if self.is_categorical_ is not None: monotonic_cst_remapped = np.concatenate( ( monotonic_cst[self.is_categorical_], monotonic_cst[~self.is_categorical_], ) ) else: monotonic_cst_remapped = monotonic_cst # used for validation in predict n_samples, self._n_features = X.shape # 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 > 10_000 else: self.do_early_stopping_ = self.early_stopping # create validation data if needed self._use_validation_data = ( self.validation_fraction is not None or validation_data_provided ) if ( self.do_early_stopping_ and self._use_validation_data and not validation_data_provided ): # 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 if not validation_data_provided: 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_remapped, known_categories=known_categories, random_state=self._random_seed, n_threads=n_threads, ) X_binned_train = self._bin_data( X_train, sample_weight_train, is_training_data=True ) if X_val is not None: X_binned_val = self._bin_data( X_val, sample_weight_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] scoring_is_predefined_string = self.scoring in _SCORERS need_raw_predictions_val = X_binned_val is not None and ( scoring_is_predefined_string or self.scoring == "loss" ) # 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 use val and scoring is a string 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) # Create raw_predictions_val for storing the raw predictions of # the validation data. if need_raw_predictions_val: 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 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 reuse # raw_predictions (as it's needed for training anyway) for # evaluating the training loss. 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, indices_small_train, ) = self._get_small_trainset( X_binned_train, y_train, sample_weight_train, self._random_seed, ) # If the scorer is a predefined string, then we optimize # the evaluation by reusing the incrementally updated raw # predictions. if scoring_is_predefined_string: raw_predictions_small_train = raw_predictions[ indices_small_train ] else: raw_predictions_small_train = None self._check_early_stopping_scorer( X_binned_small_train, y_small_train, sample_weight_small_train, X_binned_val, y_val, sample_weight_val, raw_predictions_small_train=raw_predictions_small_train, raw_predictions_val=raw_predictions_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 need_raw_predictions_val: 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, indices_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 >= 2: 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_remapped, monotonic_cst=monotonic_cst_remapped, 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, feature_fraction_per_split=self.max_features, rng=self._feature_subsample_rng, 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 not self._loss.differentiable: _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_: # Update raw_predictions_val with the newest tree(s) if need_raw_predictions_val: 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, ) if self.scoring == "loss": 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: # If the scorer is a predefined string, then we optimize the # evaluation by reusing the incrementally computed raw predictions. if scoring_is_predefined_string: raw_predictions_small_train = raw_predictions[ indices_small_train ] else: raw_predictions_small_train = None 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, raw_predictions_small_train=raw_predictions_small_train, raw_predictions_val=raw_predictions_val, ) if self.verbose >= 2: 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, indices, ) else: return X_binned_train, y_train, sample_weight_train, slice(None) 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, raw_predictions_small_train=None, raw_predictions_val=None, ): """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)] self.train_score_.append( self._score_with_raw_predictions( X_binned_small_train, y_small_train, sample_weight_small_train, raw_predictions_small_train, ) ) if self._use_validation_data: if is_classifier(self): y_val = self.classes_[y_val.astype(int)] self.validation_score_.append( self._score_with_raw_predictions( X_binned_val, y_val, sample_weight_val, raw_predictions_val ) ) return self._should_stop(self.validation_score_) else: return self._should_stop(self.train_score_) def _score_with_raw_predictions(self, X, y, sample_weight, raw_predictions=None): if raw_predictions is None: patcher_raw_predict = nullcontext() else: patcher_raw_predict = _patch_raw_predict(self, raw_predictions) with patcher_raw_predict: if sample_weight is None: return self._scorer(self, X, y) else: return self._scorer(self, X, y, sample_weight=sample_weight) 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, sample_weight, 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, sample_weight=sample_weight ) # 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. """ check_is_fitted(self) is_binned = getattr(self, "_in_fit", False) if not is_binned: X = self._preprocess_X(X, reset=False) 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_`. """ check_is_fitted(self) X = self._preprocess_X(X, reset=False) 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), dtype=np.float32 The grid points on which the partial dependence should be evaluated. target_features : ndarray, shape (n_target_features), dtype=np.intp 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 ) target_features = np.asarray(target_features, dtype=np.intp, order="C") 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 __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.allow_nan = True return tags @abstractmethod def _get_loss(self, sample_weight): pass @abstractmethod def _encode_y(self, y=None): pass # pragma: no cover @abstractmethod def _encode_y_val(self, y=None): pass # pragma: no cover @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<sklearn.ensemble.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. See :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for a usecase example of this feature. This implementation is inspired by `LightGBM <https://github.com/lightgbm-org/LightGBM>`_. Read more in the :ref:`User Guide <histogram_based_gradient_boosting>`. .. versionadded:: 0.21 Parameters ---------- loss : {'squared_error', 'absolute_error', 'gamma', 'poisson', 'quantile'}, \ default='squared_error' The loss function to use in the boosting process. Note that the "squared error", "gamma" and "poisson" losses actually implement "half least squares loss", "half gamma deviance" and "half poisson deviance" to simplify the computation of the gradient. Furthermore, "gamma" and "poisson" losses internally use a log-link, "gamma" requires ``y > 0`` and "poisson" requires ``y >= 0``. "quantile" uses the pinball loss. .. versionchanged:: 0.23 Added option 'poisson'. .. versionchanged:: 1.1 Added option 'quantile'. .. versionchanged:: 1.3 Added option 'gamma'. 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 penalizing leaves with small hessians. Use ``0`` for no regularization (default). max_features : float, default=1.0 Proportion of randomly chosen features in each and every node split. This is a form of regularization, smaller values make the trees weaker learners and might prevent overfitting. If interaction constraints from `interaction_cst` are present, only allowed features are taken into account for the subsampling. .. versionadded:: 1.4 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='from_dtype' 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). - `"from_dtype"`: dataframe columns with dtype "Categorical" and "Enum" are considered to be categorical features. The input must be a dataframe that is supported by narwhals (or supports it): :func:`narwhals.from_native` must work. This is the case, for instance, for pandas and polars DataFrames. For each categorical feature, there must be at most `max_bins` unique categories. Missing values for categorical features should be represented by ``np.nan``; unknown categories at prediction time are also treated as missing values. Read more in the :ref:`User Guide <categorical_support_gbdt>` and :ref:`sphx_glr_auto_examples_ensemble_plot_gradient_boosting_categorical.py`. .. versionadded:: 0.24 .. versionchanged:: 1.2 Added support for feature names. .. versionchanged:: 1.4 Added `"from_dtype"` option. .. versionchanged:: 1.6 The default value changed from `None` to `"from_dtype"`. 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. Read more in the :ref:`User Guide <monotonic_cst_gbdt>`. .. versionadded:: 0.23 .. versionchanged:: 1.2 Accept dict of constraints with feature names as keys. interaction_cst : {"pairwise", "no_interactions"} 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. See :ref:`this example<ice-vs-pdp>` on how to use `interaction_cst`. .. 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 <warm_start>`. early_stopping : 'auto' or bool, default='auto' If 'auto', early stopping is enabled if the sample size is larger than 10000 or if `X_val` and `y_val` are passed to `fit`. If True, early stopping is enabled, otherwise early stopping is disabled. .. versionadded:: 0.23 scoring : str or callable or None, default='loss' Scoring method to use for early stopping. Only used if `early_stopping` is enabled. Options: - str: see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details. - `None`: the :ref:`coefficient of determination <r2_score>` (:math:`R^2`) is used. - 'loss': early stopping is checked w.r.t the loss value. 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. The value is ignored if either early stopping is not performed, e.g. `early_stopping=False`, or if `X_val` and `y_val` are passed to fit. 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. ``1`` prints only summary info, ``2`` prints info per iteration. 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 <random_state>`. 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) 0.93... """ _parameter_constraints: dict = { **BaseHistGradientBoosting._parameter_constraints, "loss": [ StrOptions( { "squared_error", "absolute_error", "poisson", "gamma", "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_features=1.0, max_bins=255, categorical_features="from_dtype", 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().__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_features=max_features, 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 == "gamma": # Ensure y > 0 if not np.all(y > 0): raise ValueError("loss='gamma' requires strictly positive y.") elif 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 _encode_y_val(self, y=None): return self._encode_y(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<sklearn.ensemble.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 <https://github.com/lightgbm-org/LightGBM>`_. Read more in the :ref:`User Guide <histogram_based_gradient_boosting>`. .. versionadded:: 0.21 Parameters ---------- loss : {'log_loss'}, 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. 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 penalizing leaves with small hessians. Use ``0`` for no regularization (default). max_features : float, default=1.0 Proportion of randomly chosen features in each and every node split. This is a form of regularization, smaller values make the trees weaker learners and might prevent overfitting. If interaction constraints from `interaction_cst` are present, only allowed features are taken into account for the subsampling. .. versionadded:: 1.4 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='from_dtype' 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). - `"from_dtype"`: dataframe columns with dtype "Categorical" and "Enum" are considered to be categorical features. The input must be a dataframe that is supported by narwhals (or supports it): :func:`narwhals.from_native` must work. This is the case, for instance, for pandas and polars DataFrames. For each categorical feature, there must be at most `max_bins` unique categories. Missing values for categorical features should be represented by ``np.nan``; unknown categories at prediction time are also treated as missing values. Read more in the :ref:`User Guide <categorical_support_gbdt>`. .. versionadded:: 0.24 .. versionchanged:: 1.2 Added support for feature names. .. versionchanged:: 1.4 Added `"from_dtype"` option. .. versionchanged:: 1.6 The default value changed from `None` to `"from_dtype"`. 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 <monotonic_cst_gbdt>`. .. versionadded:: 0.23 .. versionchanged:: 1.2 Accept dict of constraints with feature names as keys. interaction_cst : {"pairwise", "no_interactions"} 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. See :ref:`this example<ice-vs-pdp>` on how to use `interaction_cst`. .. 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 <warm_start>`. early_stopping : 'auto' or bool, default='auto' If 'auto', early stopping is enabled if the sample size is larger than 10000 or if `X_val` and `y_val` are passed to `fit`. If True, early stopping is enabled, otherwise early stopping is disabled. .. versionadded:: 0.23 scoring : str or callable or None, default='loss' Scoring method to use for early stopping. Only used if `early_stopping` is enabled. Options: - str: see :ref:`scoring_string_names` for options. - callable: a scorer callable object (e.g., function) with signature ``scorer(estimator, X, y)``. See :ref:`scoring_callable` for details. - `None`: :ref:`accuracy <accuracy_score>` is used. - 'loss': early stopping is checked w.r.t the loss value. 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. The value is ignored if either early stopping is not performed, e.g. `early_stopping=False`, or if `X_val` and `y_val` are passed to fit. 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. ``1`` prints only summary info, ``2`` prints info per iteration. 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 <random_state>`. 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 """ _parameter_constraints: dict = { **BaseHistGradientBoosting._parameter_constraints, "loss": [StrOptions({"log_loss"}), 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_features=1.0, max_bins=255, categorical_features="from_dtype", 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().__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_features=max_features, 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 raw_predictions = self._raw_predict(X) if raw_predictions.shape[1] == 1: # np.argmax([0.5, 0.5]) is 0, not 1. Therefore "> 0" not ">= 0" to be # consistent with the multiclass case. encoded_classes = (raw_predictions.ravel() > 0).astype(int) else: encoded_classes = np.argmax(raw_predictions, 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 raw_predictions in self._staged_raw_predict(X): if raw_predictions.shape[1] == 1: # np.argmax([0, 0]) is 0, not 1, therefore "> 0" not ">= 0" encoded_classes = (raw_predictions.ravel() > 0).astype(int) else: 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, 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): """Create self._label_encoder and encode y correspondingly.""" # encode classes into 0 ... n_classes - 1 and sets attributes classes_ # and n_trees_per_iteration_ check_classification_targets(y) # We need to store the label encoder in case y_val needs to be label encoded, # too. self._label_encoder = LabelEncoder() encoded_y = self._label_encoder.fit_transform(y) self.classes_ = self._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 _encode_y_val(self, y): encoded_y = self._label_encoder.transform(y) return encoded_y.astype(Y_DTYPE, copy=False) def _get_loss(self, sample_weight): # At this point self.loss == "log_loss" 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_ )