"""Fast Gradient Boosting decision trees for classification and regression."""

# Author: Nicolas Hug

import itertools
import warnings
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 ..._loss.loss import (
    _LOSSES,
    BaseLoss,
    HalfBinomialLoss,
    HalfGammaLoss,
    HalfMultinomialLoss,
    HalfPoissonLoss,
    PinballLoss,
)
from ...base import (
    BaseEstimator,
    ClassifierMixin,
    RegressorMixin,
    _fit_context,
    is_classifier,
)
from ...compose import ColumnTransformer
from ...metrics import check_scoring
from ...metrics._scorer import _SCORERS
from ...model_selection import train_test_split
from ...preprocessing import FunctionTransformer, LabelEncoder, OrdinalEncoder
from ...utils import check_random_state, compute_sample_weight, is_scalar_nan, resample
from ...utils._openmp_helpers import _openmp_effective_n_threads
from ...utils._param_validation import Hidden, Interval, RealNotInt, StrOptions
from ...utils.multiclass import check_classification_targets
from ...utils.validation import (
    _check_monotonic_cst,
    _check_sample_weight,
    _check_y,
    _is_pandas_df,
    check_array,
    check_consistent_length,
    check_is_fitted,
)
from ._gradient_boosting import _update_raw_predictions
from .binning import _BinMapper
from .common import G_H_DTYPE, X_DTYPE, Y_DTYPE
from .grower import TreeGrower

_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"}),
            Hidden(StrOptions({"warn"})),
            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], force_all_finite=False)
        if not reset:
            if self._preprocessor is None:
                return self._validate_data(X, reset=False, **check_X_kwargs)
            return self._preprocessor.transform(X)

        # At this point, reset is False, which runs during `fit`.
        self.is_categorical_ = self._check_categorical_features(X)

        if self.is_categorical_ is None:
            self._preprocessor = None
            self._is_categorical_remapped = None

            X = self._validate_data(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_categorical_features(self, X):
        """Check and validate categorical features in X

        Parameters
        ----------
        X : {array-like, pandas DataFrame} of shape (n_samples, n_features)
            Input data.

        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.
        """
        # Special code for pandas because of a bug in recent pandas, which is
        # fixed in main and maybe included in 2.2.1, see
        # https://github.com/pandas-dev/pandas/pull/57173.
        # Also pandas versions < 1.5.1 do not support the dataframe interchange
        if _is_pandas_df(X):
            X_is_dataframe = True
            categorical_columns_mask = np.asarray(X.dtypes == "category")
            X_has_categorical_columns = categorical_columns_mask.any()
        elif hasattr(X, "__dataframe__"):
            X_is_dataframe = True
            categorical_columns_mask = np.asarray(
                [
                    c.dtype[0].name == "CATEGORICAL"
                    for c in X.__dataframe__().get_columns()
                ]
            )
            X_has_categorical_columns = categorical_columns_mask.any()
        else:
            X_is_dataframe = False
            categorical_columns_mask = None
            X_has_categorical_columns = False

        # TODO(1.6): Remove warning and change default to "from_dtype" in v1.6
        if (
            isinstance(self.categorical_features, str)
            and self.categorical_features == "warn"
        ):
            if X_has_categorical_columns:
                warnings.warn(
                    (
                        "The categorical_features parameter will change to 'from_dtype'"
                        " in v1.6. The 'from_dtype' option automatically treats"
                        " categorical dtypes in a DataFrame as categorical features."
                    ),
                    FutureWarning,
                )
            categorical_features = None
        else:
            categorical_features = self.categorical_features

        categorical_by_dtype = (
            isinstance(categorical_features, str)
            and categorical_features == "from_dtype"
        )
        no_categorical_dtype = categorical_features is None or (
            categorical_by_dtype and not X_is_dataframe
        )

        if no_categorical_dtype:
            return None

        use_pandas_categorical = categorical_by_dtype and X_is_dataframe
        if use_pandas_categorical:
            categorical_features = categorical_columns_mask
        else:
            categorical_features = np.asarray(categorical_features)

        if categorical_features.size == 0:
            return 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]
        # At this point `_validate_data` was not called yet because we want to use the
        # dtypes are used to discover the categorical features. Thus `feature_names_in_`
        # is not defined yet.
        feature_names_in_ = getattr(X, "columns", None)

        if categorical_features.dtype.kind in ("U", "O"):
            # check for feature names
            if feature_names_in_ is None:
                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 = list(feature_names_in_)
            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
        return is_categorical

    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):
        """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.
        """
        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)

        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)

        # 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 > 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_remapped,
            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]
        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 re-using 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:
                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,
                    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 re-using 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:
                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, 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.
        """
        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)
            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<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.

    This implementation is inspired by
    `LightGBM <https://github.com/Microsoft/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. 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=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).
        - `"from_dtype"`: dataframe columns with dtype "category" are
          considered to be categorical features. The input must be an object
          exposing a ``__dataframe__`` method such as pandas or polars
          DataFrames to use this feature.

        For each categorical feature, there must be at most `max_bins` unique
        categories. Negative values for categorical features encoded as numeric
        dtypes are treated as missing values. All categorical values are
        converted to floating point numbers. This means that categorical values
        of 1.0 and 1 are treated as the same category.

        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. The default will change to `"from_dtype"` in
           v1.6.

    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.

        .. 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. 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 <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)
    0.92...
    """

    _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="warn",
        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_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 _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/Microsoft/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. 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=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).
        - `"from_dtype"`: dataframe columns with dtype "category" are
          considered to be categorical features. The input must be an object
          exposing a ``__dataframe__`` method such as pandas or polars
          DataFrames to use this feature.

        For each categorical feature, there must be at most `max_bins` unique
        categories. Negative values for categorical features encoded as numeric
        dtypes are treated as missing values. All categorical values are
        converted to floating point numbers. This means that categorical values
        of 1.0 and 1 are treated as the same category.

        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. The default will change to `"from_dtype"` in
           v1.6.

    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.

        .. 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. 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 <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="warn",
        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_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
        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):
        # 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_
            )