Traktor/myenv/Lib/site-packages/sklearn/tree/_criterion.pyx

# Authors: Gilles Louppe <g.louppe@gmail.com>
#          Peter Prettenhofer <peter.prettenhofer@gmail.com>
#          Brian Holt <bdholt1@gmail.com>
#          Noel Dawe <noel@dawe.me>
#          Satrajit Gosh <satrajit.ghosh@gmail.com>
#          Lars Buitinck
#          Arnaud Joly <arnaud.v.joly@gmail.com>
#          Joel Nothman <joel.nothman@gmail.com>
#          Fares Hedayati <fares.hedayati@gmail.com>
#          Jacob Schreiber <jmschreiber91@gmail.com>
#          Nelson Liu <nelson@nelsonliu.me>
#
# License: BSD 3 clause

from libc.string cimport memcpy
from libc.string cimport memset
from libc.math cimport fabs, INFINITY

import numpy as np
cimport numpy as cnp
cnp.import_array()

from scipy.special.cython_special cimport xlogy

from ._utils cimport log
from ._utils cimport WeightedMedianCalculator

# EPSILON is used in the Poisson criterion
cdef float64_t EPSILON = 10 * np.finfo('double').eps

cdef class Criterion:
    """Interface for impurity criteria.

    This object stores methods on how to calculate how good a split is using
    different metrics.
    """
    def __getstate__(self):
        return {}

    def __setstate__(self, d):
        pass

    cdef int init(
        self,
        const float64_t[:, ::1] y,
        const float64_t[:] sample_weight,
        float64_t weighted_n_samples,
        const intp_t[:] sample_indices,
        intp_t start,
        intp_t end,
    ) except -1 nogil:
        """Placeholder for a method which will initialize the criterion.

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.

        Parameters
        ----------
        y : ndarray, dtype=float64_t
            y is a buffer that can store values for n_outputs target variables
            stored as a Cython memoryview.
        sample_weight : ndarray, dtype=float64_t
            The weight of each sample stored as a Cython memoryview.
        weighted_n_samples : float64_t
            The total weight of the samples being considered
        sample_indices : ndarray, dtype=intp_t
            A mask on the samples. Indices of the samples in X and y we want to use,
            where sample_indices[start:end] correspond to the samples in this node.
        start : intp_t
            The first sample to be used on this node
        end : intp_t
            The last sample used on this node

        """
        pass

    cdef void init_missing(self, intp_t n_missing) noexcept nogil:
        """Initialize sum_missing if there are missing values.

        This method assumes that caller placed the missing samples in
        self.sample_indices[-n_missing:]

        Parameters
        ----------
        n_missing: intp_t
            Number of missing values for specific feature.
        """
        pass

    cdef int reset(self) except -1 nogil:
        """Reset the criterion at pos=start.

        This method must be implemented by the subclass.
        """
        pass

    cdef int reverse_reset(self) except -1 nogil:
        """Reset the criterion at pos=end.

        This method must be implemented by the subclass.
        """
        pass

    cdef int update(self, intp_t new_pos) except -1 nogil:
        """Updated statistics by moving sample_indices[pos:new_pos] to the left child.

        This updates the collected statistics by moving sample_indices[pos:new_pos]
        from the right child to the left child. It must be implemented by
        the subclass.

        Parameters
        ----------
        new_pos : intp_t
            New starting index position of the sample_indices in the right child
        """
        pass

    cdef float64_t node_impurity(self) noexcept nogil:
        """Placeholder for calculating the impurity of the node.

        Placeholder for a method which will evaluate the impurity of
        the current node, i.e. the impurity of sample_indices[start:end]. This is the
        primary function of the criterion class. The smaller the impurity the
        better.
        """
        pass

    cdef void children_impurity(self, float64_t* impurity_left,
                                float64_t* impurity_right) noexcept nogil:
        """Placeholder for calculating the impurity of children.

        Placeholder for a method which evaluates the impurity in
        children nodes, i.e. the impurity of sample_indices[start:pos] + the impurity
        of sample_indices[pos:end].

        Parameters
        ----------
        impurity_left : float64_t pointer
            The memory address where the impurity of the left child should be
            stored.
        impurity_right : float64_t pointer
            The memory address where the impurity of the right child should be
            stored
        """
        pass

    cdef void node_value(self, float64_t* dest) noexcept nogil:
        """Placeholder for storing the node value.

        Placeholder for a method which will compute the node value
        of sample_indices[start:end] and save the value into dest.

        Parameters
        ----------
        dest : float64_t pointer
            The memory address where the node value should be stored.
        """
        pass

    cdef void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
        pass

    cdef float64_t middle_value(self) noexcept nogil:
        """Compute the middle value of a split for monotonicity constraints

        This method is implemented in ClassificationCriterion and RegressionCriterion.
        """
        pass

    cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
        """Compute a proxy of the impurity reduction.

        This method is used to speed up the search for the best split.
        It is a proxy quantity such that the split that maximizes this value
        also maximizes the impurity improvement. It neglects all constant terms
        of the impurity decrease for a given split.

        The absolute impurity improvement is only computed by the
        impurity_improvement method once the best split has been found.
        """
        cdef float64_t impurity_left
        cdef float64_t impurity_right
        self.children_impurity(&impurity_left, &impurity_right)

        return (- self.weighted_n_right * impurity_right
                - self.weighted_n_left * impurity_left)

    cdef float64_t impurity_improvement(self, float64_t impurity_parent,
                                        float64_t impurity_left,
                                        float64_t impurity_right) noexcept nogil:
        """Compute the improvement in impurity.

        This method computes the improvement in impurity when a split occurs.
        The weighted impurity improvement equation is the following:

            N_t / N * (impurity - N_t_R / N_t * right_impurity
                                - N_t_L / N_t * left_impurity)

        where N is the total number of samples, N_t is the number of samples
        at the current node, N_t_L is the number of samples in the left child,
        and N_t_R is the number of samples in the right child,

        Parameters
        ----------
        impurity_parent : float64_t
            The initial impurity of the parent node before the split

        impurity_left : float64_t
            The impurity of the left child

        impurity_right : float64_t
            The impurity of the right child

        Return
        ------
        float64_t : improvement in impurity after the split occurs
        """
        return ((self.weighted_n_node_samples / self.weighted_n_samples) *
                (impurity_parent - (self.weighted_n_right /
                                    self.weighted_n_node_samples * impurity_right)
                                 - (self.weighted_n_left /
                                    self.weighted_n_node_samples * impurity_left)))

    cdef bint check_monotonicity(
        self,
        cnp.int8_t monotonic_cst,
        float64_t lower_bound,
        float64_t upper_bound,
    ) noexcept nogil:
        pass

    cdef inline bint _check_monotonicity(
        self,
        cnp.int8_t monotonic_cst,
        float64_t lower_bound,
        float64_t upper_bound,
        float64_t value_left,
        float64_t value_right,
    ) noexcept nogil:
        cdef:
            bint check_lower_bound = (
                (value_left >= lower_bound) &
                (value_right >= lower_bound)
            )
            bint check_upper_bound = (
                (value_left <= upper_bound) &
                (value_right <= upper_bound)
            )
            bint check_monotonic_cst = (
                (value_left - value_right) * monotonic_cst <= 0
            )
        return check_lower_bound & check_upper_bound & check_monotonic_cst

    cdef void init_sum_missing(self):
        """Init sum_missing to hold sums for missing values."""

cdef inline void _move_sums_classification(
    ClassificationCriterion criterion,
    float64_t[:, ::1] sum_1,
    float64_t[:, ::1] sum_2,
    float64_t* weighted_n_1,
    float64_t* weighted_n_2,
    bint put_missing_in_1,
) noexcept nogil:
    """Distribute sum_total and sum_missing into sum_1 and sum_2.

    If there are missing values and:
    - put_missing_in_1 is True, then missing values to go sum_1. Specifically:
        sum_1 = sum_missing
        sum_2 = sum_total - sum_missing

    - put_missing_in_1 is False, then missing values go to sum_2. Specifically:
        sum_1 = 0
        sum_2 = sum_total
    """
    cdef intp_t k, c, n_bytes
    if criterion.n_missing != 0 and put_missing_in_1:
        for k in range(criterion.n_outputs):
            n_bytes = criterion.n_classes[k] * sizeof(float64_t)
            memcpy(&sum_1[k, 0], &criterion.sum_missing[k, 0], n_bytes)

        for k in range(criterion.n_outputs):
            for c in range(criterion.n_classes[k]):
                sum_2[k, c] = criterion.sum_total[k, c] - criterion.sum_missing[k, c]

        weighted_n_1[0] = criterion.weighted_n_missing
        weighted_n_2[0] = criterion.weighted_n_node_samples - criterion.weighted_n_missing
    else:
        # Assigning sum_2 = sum_total for all outputs.
        for k in range(criterion.n_outputs):
            n_bytes = criterion.n_classes[k] * sizeof(float64_t)
            memset(&sum_1[k, 0], 0, n_bytes)
            memcpy(&sum_2[k, 0], &criterion.sum_total[k, 0], n_bytes)

        weighted_n_1[0] = 0.0
        weighted_n_2[0] = criterion.weighted_n_node_samples


cdef class ClassificationCriterion(Criterion):
    """Abstract criterion for classification."""

    def __cinit__(self, intp_t n_outputs,
                  cnp.ndarray[intp_t, ndim=1] n_classes):
        """Initialize attributes for this criterion.

        Parameters
        ----------
        n_outputs : intp_t
            The number of targets, the dimensionality of the prediction
        n_classes : numpy.ndarray, dtype=intp_t
            The number of unique classes in each target
        """
        self.start = 0
        self.pos = 0
        self.end = 0
        self.missing_go_to_left = 0

        self.n_outputs = n_outputs
        self.n_samples = 0
        self.n_node_samples = 0
        self.weighted_n_node_samples = 0.0
        self.weighted_n_left = 0.0
        self.weighted_n_right = 0.0
        self.weighted_n_missing = 0.0

        self.n_classes = np.empty(n_outputs, dtype=np.intp)

        cdef intp_t k = 0
        cdef intp_t max_n_classes = 0

        # For each target, set the number of unique classes in that target,
        # and also compute the maximal stride of all targets
        for k in range(n_outputs):
            self.n_classes[k] = n_classes[k]

            if n_classes[k] > max_n_classes:
                max_n_classes = n_classes[k]

        self.max_n_classes = max_n_classes

        # Count labels for each output
        self.sum_total = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
        self.sum_left = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
        self.sum_right = np.zeros((n_outputs, max_n_classes), dtype=np.float64)

    def __reduce__(self):
        return (type(self),
                (self.n_outputs, np.asarray(self.n_classes)), self.__getstate__())

    cdef int init(
        self,
        const float64_t[:, ::1] y,
        const float64_t[:] sample_weight,
        float64_t weighted_n_samples,
        const intp_t[:] sample_indices,
        intp_t start,
        intp_t end
    ) except -1 nogil:
        """Initialize the criterion.

        This initializes the criterion at node sample_indices[start:end] and children
        sample_indices[start:start] and sample_indices[start:end].

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.

        Parameters
        ----------
        y : ndarray, dtype=float64_t
            The target stored as a buffer for memory efficiency.
        sample_weight : ndarray, dtype=float64_t
            The weight of each sample stored as a Cython memoryview.
        weighted_n_samples : float64_t
            The total weight of all samples
        sample_indices : ndarray, dtype=intp_t
            A mask on the samples. Indices of the samples in X and y we want to use,
            where sample_indices[start:end] correspond to the samples in this node.
        start : intp_t
            The first sample to use in the mask
        end : intp_t
            The last sample to use in the mask
        """
        self.y = y
        self.sample_weight = sample_weight
        self.sample_indices = sample_indices
        self.start = start
        self.end = end
        self.n_node_samples = end - start
        self.weighted_n_samples = weighted_n_samples
        self.weighted_n_node_samples = 0.0

        cdef intp_t i
        cdef intp_t p
        cdef intp_t k
        cdef intp_t c
        cdef float64_t w = 1.0

        for k in range(self.n_outputs):
            memset(&self.sum_total[k, 0], 0, self.n_classes[k] * sizeof(float64_t))

        for p in range(start, end):
            i = sample_indices[p]

            # w is originally set to be 1.0, meaning that if no sample weights
            # are given, the default weight of each sample is 1.0.
            if sample_weight is not None:
                w = sample_weight[i]

            # Count weighted class frequency for each target
            for k in range(self.n_outputs):
                c = <intp_t> self.y[i, k]
                self.sum_total[k, c] += w

            self.weighted_n_node_samples += w

        # Reset to pos=start
        self.reset()
        return 0

    cdef void init_sum_missing(self):
        """Init sum_missing to hold sums for missing values."""
        self.sum_missing = np.zeros((self.n_outputs, self.max_n_classes), dtype=np.float64)

    cdef void init_missing(self, intp_t n_missing) noexcept nogil:
        """Initialize sum_missing if there are missing values.

        This method assumes that caller placed the missing samples in
        self.sample_indices[-n_missing:]
        """
        cdef intp_t i, p, k, c
        cdef float64_t w = 1.0

        self.n_missing = n_missing
        if n_missing == 0:
            return

        memset(&self.sum_missing[0, 0], 0, self.max_n_classes * self.n_outputs * sizeof(float64_t))

        self.weighted_n_missing = 0.0

        # The missing samples are assumed to be in self.sample_indices[-n_missing:]
        for p in range(self.end - n_missing, self.end):
            i = self.sample_indices[p]
            if self.sample_weight is not None:
                w = self.sample_weight[i]

            for k in range(self.n_outputs):
                c = <intp_t> self.y[i, k]
                self.sum_missing[k, c] += w

            self.weighted_n_missing += w

    cdef int reset(self) except -1 nogil:
        """Reset the criterion at pos=start.

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.
        """
        self.pos = self.start
        _move_sums_classification(
            self,
            self.sum_left,
            self.sum_right,
            &self.weighted_n_left,
            &self.weighted_n_right,
            self.missing_go_to_left,
        )
        return 0

    cdef int reverse_reset(self) except -1 nogil:
        """Reset the criterion at pos=end.

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.
        """
        self.pos = self.end
        _move_sums_classification(
            self,
            self.sum_right,
            self.sum_left,
            &self.weighted_n_right,
            &self.weighted_n_left,
            not self.missing_go_to_left
        )
        return 0

    cdef int update(self, intp_t new_pos) except -1 nogil:
        """Updated statistics by moving sample_indices[pos:new_pos] to the left child.

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.

        Parameters
        ----------
        new_pos : intp_t
            The new ending position for which to move sample_indices from the right
            child to the left child.
        """
        cdef intp_t pos = self.pos
        # The missing samples are assumed to be in
        # self.sample_indices[-self.n_missing:] that is
        # self.sample_indices[end_non_missing:self.end].
        cdef intp_t end_non_missing = self.end - self.n_missing

        cdef const intp_t[:] sample_indices = self.sample_indices
        cdef const float64_t[:] sample_weight = self.sample_weight

        cdef intp_t i
        cdef intp_t p
        cdef intp_t k
        cdef intp_t c
        cdef float64_t w = 1.0

        # Update statistics up to new_pos
        #
        # Given that
        #   sum_left[x] +  sum_right[x] = sum_total[x]
        # and that sum_total is known, we are going to update
        # sum_left from the direction that require the least amount
        # of computations, i.e. from pos to new_pos or from end to new_po.
        if (new_pos - pos) <= (end_non_missing - new_pos):
            for p in range(pos, new_pos):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                for k in range(self.n_outputs):
                    self.sum_left[k, <intp_t> self.y[i, k]] += w

                self.weighted_n_left += w

        else:
            self.reverse_reset()

            for p in range(end_non_missing - 1, new_pos - 1, -1):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                for k in range(self.n_outputs):
                    self.sum_left[k, <intp_t> self.y[i, k]] -= w

                self.weighted_n_left -= w

        # Update right part statistics
        self.weighted_n_right = self.weighted_n_node_samples - self.weighted_n_left
        for k in range(self.n_outputs):
            for c in range(self.n_classes[k]):
                self.sum_right[k, c] = self.sum_total[k, c] - self.sum_left[k, c]

        self.pos = new_pos
        return 0

    cdef float64_t node_impurity(self) noexcept nogil:
        pass

    cdef void children_impurity(self, float64_t* impurity_left,
                                float64_t* impurity_right) noexcept nogil:
        pass

    cdef void node_value(self, float64_t* dest) noexcept nogil:
        """Compute the node value of sample_indices[start:end] and save it into dest.

        Parameters
        ----------
        dest : float64_t pointer
            The memory address which we will save the node value into.
        """
        cdef intp_t k, c

        for k in range(self.n_outputs):
            for c in range(self.n_classes[k]):
                dest[c] = self.sum_total[k, c] / self.weighted_n_node_samples
            dest += self.max_n_classes

    cdef inline void clip_node_value(
        self, float64_t * dest, float64_t lower_bound, float64_t upper_bound
    ) noexcept nogil:
        """Clip the values in dest such that predicted probabilities stay between
        `lower_bound` and `upper_bound` when monotonic constraints are enforced.
        Note that monotonicity constraints are only supported for:
        - single-output trees and
        - binary classifications.
        """
        if dest[0] < lower_bound:
            dest[0] = lower_bound
        elif dest[0] > upper_bound:
            dest[0] = upper_bound

        # Values for binary classification must sum to 1.
        dest[1] = 1 - dest[0]

    cdef inline float64_t middle_value(self) noexcept nogil:
        """Compute the middle value of a split for monotonicity constraints as the simple average
        of the left and right children values.

        Note that monotonicity constraints are only supported for:
        - single-output trees and
        - binary classifications.
        """
        return (
            (self.sum_left[0, 0] / (2 * self.weighted_n_left)) +
            (self.sum_right[0, 0] / (2 * self.weighted_n_right))
        )

    cdef inline bint check_monotonicity(
        self,
        cnp.int8_t monotonic_cst,
        float64_t lower_bound,
        float64_t upper_bound,
    ) noexcept nogil:
        """Check monotonicity constraint is satisfied at the current classification split"""
        cdef:
            float64_t value_left = self.sum_left[0][0] / self.weighted_n_left
            float64_t value_right = self.sum_right[0][0] / self.weighted_n_right

        return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)


cdef class Entropy(ClassificationCriterion):
    r"""Cross Entropy impurity criterion.

    This handles cases where the target is a classification taking values
    0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
    then let

        count_k = 1 / Nm \sum_{x_i in Rm} I(yi = k)

    be the proportion of class k observations in node m.

    The cross-entropy is then defined as

        cross-entropy = -\sum_{k=0}^{K-1} count_k log(count_k)
    """

    cdef float64_t node_impurity(self) noexcept nogil:
        """Evaluate the impurity of the current node.

        Evaluate the cross-entropy criterion as impurity of the current node,
        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
        better.
        """
        cdef float64_t entropy = 0.0
        cdef float64_t count_k
        cdef intp_t k
        cdef intp_t c

        for k in range(self.n_outputs):
            for c in range(self.n_classes[k]):
                count_k = self.sum_total[k, c]
                if count_k > 0.0:
                    count_k /= self.weighted_n_node_samples
                    entropy -= count_k * log(count_k)

        return entropy / self.n_outputs

    cdef void children_impurity(self, float64_t* impurity_left,
                                float64_t* impurity_right) noexcept nogil:
        """Evaluate the impurity in children nodes.

        i.e. the impurity of the left child (sample_indices[start:pos]) and the
        impurity the right child (sample_indices[pos:end]).

        Parameters
        ----------
        impurity_left : float64_t pointer
            The memory address to save the impurity of the left node
        impurity_right : float64_t pointer
            The memory address to save the impurity of the right node
        """
        cdef float64_t entropy_left = 0.0
        cdef float64_t entropy_right = 0.0
        cdef float64_t count_k
        cdef intp_t k
        cdef intp_t c

        for k in range(self.n_outputs):
            for c in range(self.n_classes[k]):
                count_k = self.sum_left[k, c]
                if count_k > 0.0:
                    count_k /= self.weighted_n_left
                    entropy_left -= count_k * log(count_k)

                count_k = self.sum_right[k, c]
                if count_k > 0.0:
                    count_k /= self.weighted_n_right
                    entropy_right -= count_k * log(count_k)

        impurity_left[0] = entropy_left / self.n_outputs
        impurity_right[0] = entropy_right / self.n_outputs


cdef class Gini(ClassificationCriterion):
    r"""Gini Index impurity criterion.

    This handles cases where the target is a classification taking values
    0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
    then let

        count_k = 1/ Nm \sum_{x_i in Rm} I(yi = k)

    be the proportion of class k observations in node m.

    The Gini Index is then defined as:

        index = \sum_{k=0}^{K-1} count_k (1 - count_k)
              = 1 - \sum_{k=0}^{K-1} count_k ** 2
    """

    cdef float64_t node_impurity(self) noexcept nogil:
        """Evaluate the impurity of the current node.

        Evaluate the Gini criterion as impurity of the current node,
        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
        better.
        """
        cdef float64_t gini = 0.0
        cdef float64_t sq_count
        cdef float64_t count_k
        cdef intp_t k
        cdef intp_t c

        for k in range(self.n_outputs):
            sq_count = 0.0

            for c in range(self.n_classes[k]):
                count_k = self.sum_total[k, c]
                sq_count += count_k * count_k

            gini += 1.0 - sq_count / (self.weighted_n_node_samples *
                                      self.weighted_n_node_samples)

        return gini / self.n_outputs

    cdef void children_impurity(self, float64_t* impurity_left,
                                float64_t* impurity_right) noexcept nogil:
        """Evaluate the impurity in children nodes.

        i.e. the impurity of the left child (sample_indices[start:pos]) and the
        impurity the right child (sample_indices[pos:end]) using the Gini index.

        Parameters
        ----------
        impurity_left : float64_t pointer
            The memory address to save the impurity of the left node to
        impurity_right : float64_t pointer
            The memory address to save the impurity of the right node to
        """
        cdef float64_t gini_left = 0.0
        cdef float64_t gini_right = 0.0
        cdef float64_t sq_count_left
        cdef float64_t sq_count_right
        cdef float64_t count_k
        cdef intp_t k
        cdef intp_t c

        for k in range(self.n_outputs):
            sq_count_left = 0.0
            sq_count_right = 0.0

            for c in range(self.n_classes[k]):
                count_k = self.sum_left[k, c]
                sq_count_left += count_k * count_k

                count_k = self.sum_right[k, c]
                sq_count_right += count_k * count_k

            gini_left += 1.0 - sq_count_left / (self.weighted_n_left *
                                                self.weighted_n_left)

            gini_right += 1.0 - sq_count_right / (self.weighted_n_right *
                                                  self.weighted_n_right)

        impurity_left[0] = gini_left / self.n_outputs
        impurity_right[0] = gini_right / self.n_outputs


cdef inline void _move_sums_regression(
    RegressionCriterion criterion,
    float64_t[::1] sum_1,
    float64_t[::1] sum_2,
    float64_t* weighted_n_1,
    float64_t* weighted_n_2,
    bint put_missing_in_1,
) noexcept nogil:
    """Distribute sum_total and sum_missing into sum_1 and sum_2.

    If there are missing values and:
    - put_missing_in_1 is True, then missing values to go sum_1. Specifically:
        sum_1 = sum_missing
        sum_2 = sum_total - sum_missing

    - put_missing_in_1 is False, then missing values go to sum_2. Specifically:
        sum_1 = 0
        sum_2 = sum_total
    """
    cdef:
        intp_t i
        intp_t n_bytes = criterion.n_outputs * sizeof(float64_t)
        bint has_missing = criterion.n_missing != 0

    if has_missing and put_missing_in_1:
        memcpy(&sum_1[0], &criterion.sum_missing[0], n_bytes)
        for i in range(criterion.n_outputs):
            sum_2[i] = criterion.sum_total[i] - criterion.sum_missing[i]
        weighted_n_1[0] = criterion.weighted_n_missing
        weighted_n_2[0] = criterion.weighted_n_node_samples - criterion.weighted_n_missing
    else:
        memset(&sum_1[0], 0, n_bytes)
        # Assigning sum_2 = sum_total for all outputs.
        memcpy(&sum_2[0], &criterion.sum_total[0], n_bytes)
        weighted_n_1[0] = 0.0
        weighted_n_2[0] = criterion.weighted_n_node_samples


cdef class RegressionCriterion(Criterion):
    r"""Abstract regression criterion.

    This handles cases where the target is a continuous value, and is
    evaluated by computing the variance of the target values left and right
    of the split point. The computation takes linear time with `n_samples`
    by using ::

        var = \sum_i^n (y_i - y_bar) ** 2
            = (\sum_i^n y_i ** 2) - n_samples * y_bar ** 2
    """

    def __cinit__(self, intp_t n_outputs, intp_t n_samples):
        """Initialize parameters for this criterion.

        Parameters
        ----------
        n_outputs : intp_t
            The number of targets to be predicted

        n_samples : intp_t
            The total number of samples to fit on
        """
        # Default values
        self.start = 0
        self.pos = 0
        self.end = 0

        self.n_outputs = n_outputs
        self.n_samples = n_samples
        self.n_node_samples = 0
        self.weighted_n_node_samples = 0.0
        self.weighted_n_left = 0.0
        self.weighted_n_right = 0.0
        self.weighted_n_missing = 0.0

        self.sq_sum_total = 0.0

        self.sum_total = np.zeros(n_outputs, dtype=np.float64)
        self.sum_left = np.zeros(n_outputs, dtype=np.float64)
        self.sum_right = np.zeros(n_outputs, dtype=np.float64)

    def __reduce__(self):
        return (type(self), (self.n_outputs, self.n_samples), self.__getstate__())

    cdef int init(
        self,
        const float64_t[:, ::1] y,
        const float64_t[:] sample_weight,
        float64_t weighted_n_samples,
        const intp_t[:] sample_indices,
        intp_t start,
        intp_t end,
    ) except -1 nogil:
        """Initialize the criterion.

        This initializes the criterion at node sample_indices[start:end] and children
        sample_indices[start:start] and sample_indices[start:end].
        """
        # Initialize fields
        self.y = y
        self.sample_weight = sample_weight
        self.sample_indices = sample_indices
        self.start = start
        self.end = end
        self.n_node_samples = end - start
        self.weighted_n_samples = weighted_n_samples
        self.weighted_n_node_samples = 0.

        cdef intp_t i
        cdef intp_t p
        cdef intp_t k
        cdef float64_t y_ik
        cdef float64_t w_y_ik
        cdef float64_t w = 1.0
        self.sq_sum_total = 0.0
        memset(&self.sum_total[0], 0, self.n_outputs * sizeof(float64_t))

        for p in range(start, end):
            i = sample_indices[p]

            if sample_weight is not None:
                w = sample_weight[i]

            for k in range(self.n_outputs):
                y_ik = self.y[i, k]
                w_y_ik = w * y_ik
                self.sum_total[k] += w_y_ik
                self.sq_sum_total += w_y_ik * y_ik

            self.weighted_n_node_samples += w

        # Reset to pos=start
        self.reset()
        return 0

    cdef void init_sum_missing(self):
        """Init sum_missing to hold sums for missing values."""
        self.sum_missing = np.zeros(self.n_outputs, dtype=np.float64)

    cdef void init_missing(self, intp_t n_missing) noexcept nogil:
        """Initialize sum_missing if there are missing values.

        This method assumes that caller placed the missing samples in
        self.sample_indices[-n_missing:]
        """
        cdef intp_t i, p, k
        cdef float64_t y_ik
        cdef float64_t w_y_ik
        cdef float64_t w = 1.0

        self.n_missing = n_missing
        if n_missing == 0:
            return

        memset(&self.sum_missing[0], 0, self.n_outputs * sizeof(float64_t))

        self.weighted_n_missing = 0.0

        # The missing samples are assumed to be in self.sample_indices[-n_missing:]
        for p in range(self.end - n_missing, self.end):
            i = self.sample_indices[p]
            if self.sample_weight is not None:
                w = self.sample_weight[i]

            for k in range(self.n_outputs):
                y_ik = self.y[i, k]
                w_y_ik = w * y_ik
                self.sum_missing[k] += w_y_ik

            self.weighted_n_missing += w

    cdef int reset(self) except -1 nogil:
        """Reset the criterion at pos=start."""
        self.pos = self.start
        _move_sums_regression(
            self,
            self.sum_left,
            self.sum_right,
            &self.weighted_n_left,
            &self.weighted_n_right,
            self.missing_go_to_left
        )
        return 0

    cdef int reverse_reset(self) except -1 nogil:
        """Reset the criterion at pos=end."""
        self.pos = self.end
        _move_sums_regression(
            self,
            self.sum_right,
            self.sum_left,
            &self.weighted_n_right,
            &self.weighted_n_left,
            not self.missing_go_to_left
        )
        return 0

    cdef int update(self, intp_t new_pos) except -1 nogil:
        """Updated statistics by moving sample_indices[pos:new_pos] to the left."""
        cdef const float64_t[:] sample_weight = self.sample_weight
        cdef const intp_t[:] sample_indices = self.sample_indices

        cdef intp_t pos = self.pos

        # The missing samples are assumed to be in
        # self.sample_indices[-self.n_missing:] that is
        # self.sample_indices[end_non_missing:self.end].
        cdef intp_t end_non_missing = self.end - self.n_missing
        cdef intp_t i
        cdef intp_t p
        cdef intp_t k
        cdef float64_t w = 1.0

        # Update statistics up to new_pos
        #
        # Given that
        #           sum_left[x] +  sum_right[x] = sum_total[x]
        # and that sum_total is known, we are going to update
        # sum_left from the direction that require the least amount
        # of computations, i.e. from pos to new_pos or from end to new_pos.
        if (new_pos - pos) <= (end_non_missing - new_pos):
            for p in range(pos, new_pos):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                for k in range(self.n_outputs):
                    self.sum_left[k] += w * self.y[i, k]

                self.weighted_n_left += w
        else:
            self.reverse_reset()

            for p in range(end_non_missing - 1, new_pos - 1, -1):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                for k in range(self.n_outputs):
                    self.sum_left[k] -= w * self.y[i, k]

                self.weighted_n_left -= w

        self.weighted_n_right = (self.weighted_n_node_samples -
                                 self.weighted_n_left)
        for k in range(self.n_outputs):
            self.sum_right[k] = self.sum_total[k] - self.sum_left[k]

        self.pos = new_pos
        return 0

    cdef float64_t node_impurity(self) noexcept nogil:
        pass

    cdef void children_impurity(self, float64_t* impurity_left,
                                float64_t* impurity_right) noexcept nogil:
        pass

    cdef void node_value(self, float64_t* dest) noexcept nogil:
        """Compute the node value of sample_indices[start:end] into dest."""
        cdef intp_t k

        for k in range(self.n_outputs):
            dest[k] = self.sum_total[k] / self.weighted_n_node_samples

    cdef inline void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
        """Clip the value in dest between lower_bound and upper_bound for monotonic constraints."""
        if dest[0] < lower_bound:
            dest[0] = lower_bound
        elif dest[0] > upper_bound:
            dest[0] = upper_bound

    cdef float64_t middle_value(self) noexcept nogil:
        """Compute the middle value of a split for monotonicity constraints as the simple average
        of the left and right children values.

        Monotonicity constraints are only supported for single-output trees we can safely assume
        n_outputs == 1.
        """
        return (
            (self.sum_left[0] / (2 * self.weighted_n_left)) +
            (self.sum_right[0] / (2 * self.weighted_n_right))
        )

    cdef bint check_monotonicity(
        self,
        cnp.int8_t monotonic_cst,
        float64_t lower_bound,
        float64_t upper_bound,
    ) noexcept nogil:
        """Check monotonicity constraint is satisfied at the current regression split"""
        cdef:
            float64_t value_left = self.sum_left[0] / self.weighted_n_left
            float64_t value_right = self.sum_right[0] / self.weighted_n_right

        return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)

cdef class MSE(RegressionCriterion):
    """Mean squared error impurity criterion.

        MSE = var_left + var_right
    """

    cdef float64_t node_impurity(self) noexcept nogil:
        """Evaluate the impurity of the current node.

        Evaluate the MSE criterion as impurity of the current node,
        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
        better.
        """
        cdef float64_t impurity
        cdef intp_t k

        impurity = self.sq_sum_total / self.weighted_n_node_samples
        for k in range(self.n_outputs):
            impurity -= (self.sum_total[k] / self.weighted_n_node_samples)**2.0

        return impurity / self.n_outputs

    cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
        """Compute a proxy of the impurity reduction.

        This method is used to speed up the search for the best split.
        It is a proxy quantity such that the split that maximizes this value
        also maximizes the impurity improvement. It neglects all constant terms
        of the impurity decrease for a given split.

        The absolute impurity improvement is only computed by the
        impurity_improvement method once the best split has been found.

        The MSE proxy is derived from

            sum_{i left}(y_i - y_pred_L)^2 + sum_{i right}(y_i - y_pred_R)^2
            = sum(y_i^2) - n_L * mean_{i left}(y_i)^2 - n_R * mean_{i right}(y_i)^2

        Neglecting constant terms, this gives:

            - 1/n_L * sum_{i left}(y_i)^2 - 1/n_R * sum_{i right}(y_i)^2
        """
        cdef intp_t k
        cdef float64_t proxy_impurity_left = 0.0
        cdef float64_t proxy_impurity_right = 0.0

        for k in range(self.n_outputs):
            proxy_impurity_left += self.sum_left[k] * self.sum_left[k]
            proxy_impurity_right += self.sum_right[k] * self.sum_right[k]

        return (proxy_impurity_left / self.weighted_n_left +
                proxy_impurity_right / self.weighted_n_right)

    cdef void children_impurity(self, float64_t* impurity_left,
                                float64_t* impurity_right) noexcept nogil:
        """Evaluate the impurity in children nodes.

        i.e. the impurity of the left child (sample_indices[start:pos]) and the
        impurity the right child (sample_indices[pos:end]).
        """
        cdef const float64_t[:] sample_weight = self.sample_weight
        cdef const intp_t[:] sample_indices = self.sample_indices
        cdef intp_t pos = self.pos
        cdef intp_t start = self.start

        cdef float64_t y_ik

        cdef float64_t sq_sum_left = 0.0
        cdef float64_t sq_sum_right

        cdef intp_t i
        cdef intp_t p
        cdef intp_t k
        cdef float64_t w = 1.0

        cdef intp_t end_non_missing

        for p in range(start, pos):
            i = sample_indices[p]

            if sample_weight is not None:
                w = sample_weight[i]

            for k in range(self.n_outputs):
                y_ik = self.y[i, k]
                sq_sum_left += w * y_ik * y_ik

        if self.missing_go_to_left:
            # add up the impact of these missing values on the left child
            # statistics.
            # Note: this only impacts the square sum as the sum
            # is modified elsewhere.
            end_non_missing = self.end - self.n_missing

            for p in range(end_non_missing, self.end):
                i = sample_indices[p]
                if sample_weight is not None:
                    w = sample_weight[i]

                for k in range(self.n_outputs):
                    y_ik = self.y[i, k]
                    sq_sum_left += w * y_ik * y_ik

        sq_sum_right = self.sq_sum_total - sq_sum_left

        impurity_left[0] = sq_sum_left / self.weighted_n_left
        impurity_right[0] = sq_sum_right / self.weighted_n_right

        for k in range(self.n_outputs):
            impurity_left[0] -= (self.sum_left[k] / self.weighted_n_left) ** 2.0
            impurity_right[0] -= (self.sum_right[k] / self.weighted_n_right) ** 2.0

        impurity_left[0] /= self.n_outputs
        impurity_right[0] /= self.n_outputs


cdef class MAE(RegressionCriterion):
    r"""Mean absolute error impurity criterion.

       MAE = (1 / n)*(\sum_i |y_i - f_i|), where y_i is the true
       value and f_i is the predicted value."""

    cdef cnp.ndarray left_child
    cdef cnp.ndarray right_child
    cdef void** left_child_ptr
    cdef void** right_child_ptr
    cdef float64_t[::1] node_medians

    def __cinit__(self, intp_t n_outputs, intp_t n_samples):
        """Initialize parameters for this criterion.

        Parameters
        ----------
        n_outputs : intp_t
            The number of targets to be predicted

        n_samples : intp_t
            The total number of samples to fit on
        """
        # Default values
        self.start = 0
        self.pos = 0
        self.end = 0

        self.n_outputs = n_outputs
        self.n_samples = n_samples
        self.n_node_samples = 0
        self.weighted_n_node_samples = 0.0
        self.weighted_n_left = 0.0
        self.weighted_n_right = 0.0

        self.node_medians = np.zeros(n_outputs, dtype=np.float64)

        self.left_child = np.empty(n_outputs, dtype='object')
        self.right_child = np.empty(n_outputs, dtype='object')
        # initialize WeightedMedianCalculators
        for k in range(n_outputs):
            self.left_child[k] = WeightedMedianCalculator(n_samples)
            self.right_child[k] = WeightedMedianCalculator(n_samples)

        self.left_child_ptr = <void**> cnp.PyArray_DATA(self.left_child)
        self.right_child_ptr = <void**> cnp.PyArray_DATA(self.right_child)

    cdef int init(
        self,
        const float64_t[:, ::1] y,
        const float64_t[:] sample_weight,
        float64_t weighted_n_samples,
        const intp_t[:] sample_indices,
        intp_t start,
        intp_t end,
    ) except -1 nogil:
        """Initialize the criterion.

        This initializes the criterion at node sample_indices[start:end] and children
        sample_indices[start:start] and sample_indices[start:end].
        """
        cdef intp_t i, p, k
        cdef float64_t w = 1.0

        # Initialize fields
        self.y = y
        self.sample_weight = sample_weight
        self.sample_indices = sample_indices
        self.start = start
        self.end = end
        self.n_node_samples = end - start
        self.weighted_n_samples = weighted_n_samples
        self.weighted_n_node_samples = 0.

        cdef void** left_child = self.left_child_ptr
        cdef void** right_child = self.right_child_ptr

        for k in range(self.n_outputs):
            (<WeightedMedianCalculator> left_child[k]).reset()
            (<WeightedMedianCalculator> right_child[k]).reset()

        for p in range(start, end):
            i = sample_indices[p]

            if sample_weight is not None:
                w = sample_weight[i]

            for k in range(self.n_outputs):
                # push method ends up calling safe_realloc, hence `except -1`
                # push all values to the right side,
                # since pos = start initially anyway
                (<WeightedMedianCalculator> right_child[k]).push(self.y[i, k], w)

            self.weighted_n_node_samples += w
        # calculate the node medians
        for k in range(self.n_outputs):
            self.node_medians[k] = (<WeightedMedianCalculator> right_child[k]).get_median()

        # Reset to pos=start
        self.reset()
        return 0

    cdef void init_missing(self, intp_t n_missing) noexcept nogil:
        """Raise error if n_missing != 0."""
        if n_missing == 0:
            return
        with gil:
            raise ValueError("missing values is not supported for MAE.")

    cdef int reset(self) except -1 nogil:
        """Reset the criterion at pos=start.

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.
        """
        cdef intp_t i, k
        cdef float64_t value
        cdef float64_t weight

        cdef void** left_child = self.left_child_ptr
        cdef void** right_child = self.right_child_ptr

        self.weighted_n_left = 0.0
        self.weighted_n_right = self.weighted_n_node_samples
        self.pos = self.start

        # reset the WeightedMedianCalculators, left should have no
        # elements and right should have all elements.

        for k in range(self.n_outputs):
            # if left has no elements, it's already reset
            for i in range((<WeightedMedianCalculator> left_child[k]).size()):
                # remove everything from left and put it into right
                (<WeightedMedianCalculator> left_child[k]).pop(&value,
                                                               &weight)
                # push method ends up calling safe_realloc, hence `except -1`
                (<WeightedMedianCalculator> right_child[k]).push(value,
                                                                 weight)
        return 0

    cdef int reverse_reset(self) except -1 nogil:
        """Reset the criterion at pos=end.

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.
        """
        self.weighted_n_right = 0.0
        self.weighted_n_left = self.weighted_n_node_samples
        self.pos = self.end

        cdef float64_t value
        cdef float64_t weight
        cdef void** left_child = self.left_child_ptr
        cdef void** right_child = self.right_child_ptr

        # reverse reset the WeightedMedianCalculators, right should have no
        # elements and left should have all elements.
        for k in range(self.n_outputs):
            # if right has no elements, it's already reset
            for i in range((<WeightedMedianCalculator> right_child[k]).size()):
                # remove everything from right and put it into left
                (<WeightedMedianCalculator> right_child[k]).pop(&value,
                                                                &weight)
                # push method ends up calling safe_realloc, hence `except -1`
                (<WeightedMedianCalculator> left_child[k]).push(value,
                                                                weight)
        return 0

    cdef int update(self, intp_t new_pos) except -1 nogil:
        """Updated statistics by moving sample_indices[pos:new_pos] to the left.

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.
        """
        cdef const float64_t[:] sample_weight = self.sample_weight
        cdef const intp_t[:] sample_indices = self.sample_indices

        cdef void** left_child = self.left_child_ptr
        cdef void** right_child = self.right_child_ptr

        cdef intp_t pos = self.pos
        cdef intp_t end = self.end
        cdef intp_t i, p, k
        cdef float64_t w = 1.0

        # Update statistics up to new_pos
        #
        # We are going to update right_child and left_child
        # from the direction that require the least amount of
        # computations, i.e. from pos to new_pos or from end to new_pos.
        if (new_pos - pos) <= (end - new_pos):
            for p in range(pos, new_pos):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                for k in range(self.n_outputs):
                    # remove y_ik and its weight w from right and add to left
                    (<WeightedMedianCalculator> right_child[k]).remove(self.y[i, k], w)
                    # push method ends up calling safe_realloc, hence except -1
                    (<WeightedMedianCalculator> left_child[k]).push(self.y[i, k], w)

                self.weighted_n_left += w
        else:
            self.reverse_reset()

            for p in range(end - 1, new_pos - 1, -1):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                for k in range(self.n_outputs):
                    # remove y_ik and its weight w from left and add to right
                    (<WeightedMedianCalculator> left_child[k]).remove(self.y[i, k], w)
                    (<WeightedMedianCalculator> right_child[k]).push(self.y[i, k], w)

                self.weighted_n_left -= w

        self.weighted_n_right = (self.weighted_n_node_samples -
                                 self.weighted_n_left)
        self.pos = new_pos
        return 0

    cdef void node_value(self, float64_t* dest) noexcept nogil:
        """Computes the node value of sample_indices[start:end] into dest."""
        cdef intp_t k
        for k in range(self.n_outputs):
            dest[k] = <float64_t> self.node_medians[k]

    cdef inline float64_t middle_value(self) noexcept nogil:
        """Compute the middle value of a split for monotonicity constraints as the simple average
        of the left and right children values.

        Monotonicity constraints are only supported for single-output trees we can safely assume
        n_outputs == 1.
        """
        return (
                (<WeightedMedianCalculator> self.left_child_ptr[0]).get_median() +
                (<WeightedMedianCalculator> self.right_child_ptr[0]).get_median()
        ) / 2

    cdef inline bint check_monotonicity(
        self,
        cnp.int8_t monotonic_cst,
        float64_t lower_bound,
        float64_t upper_bound,
    ) noexcept nogil:
        """Check monotonicity constraint is satisfied at the current regression split"""
        cdef:
            float64_t value_left = (<WeightedMedianCalculator> self.left_child_ptr[0]).get_median()
            float64_t value_right = (<WeightedMedianCalculator> self.right_child_ptr[0]).get_median()

        return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)

    cdef float64_t node_impurity(self) noexcept nogil:
        """Evaluate the impurity of the current node.

        Evaluate the MAE criterion as impurity of the current node,
        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
        better.
        """
        cdef const float64_t[:] sample_weight = self.sample_weight
        cdef const intp_t[:] sample_indices = self.sample_indices
        cdef intp_t i, p, k
        cdef float64_t w = 1.0
        cdef float64_t impurity = 0.0

        for k in range(self.n_outputs):
            for p in range(self.start, self.end):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                impurity += fabs(self.y[i, k] - self.node_medians[k]) * w

        return impurity / (self.weighted_n_node_samples * self.n_outputs)

    cdef void children_impurity(self, float64_t* p_impurity_left,
                                float64_t* p_impurity_right) noexcept nogil:
        """Evaluate the impurity in children nodes.

        i.e. the impurity of the left child (sample_indices[start:pos]) and the
        impurity the right child (sample_indices[pos:end]).
        """
        cdef const float64_t[:] sample_weight = self.sample_weight
        cdef const intp_t[:] sample_indices = self.sample_indices

        cdef intp_t start = self.start
        cdef intp_t pos = self.pos
        cdef intp_t end = self.end

        cdef intp_t i, p, k
        cdef float64_t median
        cdef float64_t w = 1.0
        cdef float64_t impurity_left = 0.0
        cdef float64_t impurity_right = 0.0

        cdef void** left_child = self.left_child_ptr
        cdef void** right_child = self.right_child_ptr

        for k in range(self.n_outputs):
            median = (<WeightedMedianCalculator> left_child[k]).get_median()
            for p in range(start, pos):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                impurity_left += fabs(self.y[i, k] - median) * w
        p_impurity_left[0] = impurity_left / (self.weighted_n_left *
                                              self.n_outputs)

        for k in range(self.n_outputs):
            median = (<WeightedMedianCalculator> right_child[k]).get_median()
            for p in range(pos, end):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                impurity_right += fabs(self.y[i, k] - median) * w
        p_impurity_right[0] = impurity_right / (self.weighted_n_right *
                                                self.n_outputs)


cdef class FriedmanMSE(MSE):
    """Mean squared error impurity criterion with improvement score by Friedman.

    Uses the formula (35) in Friedman's original Gradient Boosting paper:

        diff = mean_left - mean_right
        improvement = n_left * n_right * diff^2 / (n_left + n_right)
    """

    cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
        """Compute a proxy of the impurity reduction.

        This method is used to speed up the search for the best split.
        It is a proxy quantity such that the split that maximizes this value
        also maximizes the impurity improvement. It neglects all constant terms
        of the impurity decrease for a given split.

        The absolute impurity improvement is only computed by the
        impurity_improvement method once the best split has been found.
        """
        cdef float64_t total_sum_left = 0.0
        cdef float64_t total_sum_right = 0.0

        cdef intp_t k
        cdef float64_t diff = 0.0

        for k in range(self.n_outputs):
            total_sum_left += self.sum_left[k]
            total_sum_right += self.sum_right[k]

        diff = (self.weighted_n_right * total_sum_left -
                self.weighted_n_left * total_sum_right)

        return diff * diff / (self.weighted_n_left * self.weighted_n_right)

    cdef float64_t impurity_improvement(self, float64_t impurity_parent, float64_t
                                        impurity_left, float64_t impurity_right) noexcept nogil:
        # Note: none of the arguments are used here
        cdef float64_t total_sum_left = 0.0
        cdef float64_t total_sum_right = 0.0

        cdef intp_t k
        cdef float64_t diff = 0.0

        for k in range(self.n_outputs):
            total_sum_left += self.sum_left[k]
            total_sum_right += self.sum_right[k]

        diff = (self.weighted_n_right * total_sum_left -
                self.weighted_n_left * total_sum_right) / self.n_outputs

        return (diff * diff / (self.weighted_n_left * self.weighted_n_right *
                               self.weighted_n_node_samples))


cdef class Poisson(RegressionCriterion):
    """Half Poisson deviance as impurity criterion.

    Poisson deviance = 2/n * sum(y_true * log(y_true/y_pred) + y_pred - y_true)

    Note that the deviance is >= 0, and since we have `y_pred = mean(y_true)`
    at the leaves, one always has `sum(y_pred - y_true) = 0`. It remains the
    implemented impurity (factor 2 is skipped):
        1/n * sum(y_true * log(y_true/y_pred)
    """
    # FIXME in 1.0:
    # min_impurity_split with default = 0 forces us to use a non-negative
    # impurity like the Poisson deviance. Without this restriction, one could
    # throw away the 'constant' term sum(y_true * log(y_true)) and just use
    # Poisson loss = - 1/n * sum(y_true * log(y_pred))
    #              = - 1/n * sum(y_true * log(mean(y_true))
    #              = - mean(y_true) * log(mean(y_true))
    # With this trick (used in proxy_impurity_improvement()), as for MSE,
    # children_impurity would only need to go over left xor right split, not
    # both. This could be faster.

    cdef float64_t node_impurity(self) noexcept nogil:
        """Evaluate the impurity of the current node.

        Evaluate the Poisson criterion as impurity of the current node,
        i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
        better.
        """
        return self.poisson_loss(self.start, self.end, self.sum_total,
                                 self.weighted_n_node_samples)

    cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
        """Compute a proxy of the impurity reduction.

        This method is used to speed up the search for the best split.
        It is a proxy quantity such that the split that maximizes this value
        also maximizes the impurity improvement. It neglects all constant terms
        of the impurity decrease for a given split.

        The absolute impurity improvement is only computed by the
        impurity_improvement method once the best split has been found.

        The Poisson proxy is derived from:

              sum_{i left }(y_i * log(y_i / y_pred_L))
            + sum_{i right}(y_i * log(y_i / y_pred_R))
            = sum(y_i * log(y_i) - n_L * mean_{i left}(y_i) * log(mean_{i left}(y_i))
                                 - n_R * mean_{i right}(y_i) * log(mean_{i right}(y_i))

        Neglecting constant terms, this gives

            - sum{i left }(y_i) * log(mean{i left}(y_i))
            - sum{i right}(y_i) * log(mean{i right}(y_i))
        """
        cdef intp_t k
        cdef float64_t proxy_impurity_left = 0.0
        cdef float64_t proxy_impurity_right = 0.0
        cdef float64_t y_mean_left = 0.
        cdef float64_t y_mean_right = 0.

        for k in range(self.n_outputs):
            if (self.sum_left[k] <= EPSILON) or (self.sum_right[k] <= EPSILON):
                # Poisson loss does not allow non-positive predictions. We
                # therefore forbid splits that have child nodes with
                # sum(y_i) <= 0.
                # Since sum_right = sum_total - sum_left, it can lead to
                # floating point rounding error and will not give zero. Thus,
                # we relax the above comparison to sum(y_i) <= EPSILON.
                return -INFINITY
            else:
                y_mean_left = self.sum_left[k] / self.weighted_n_left
                y_mean_right = self.sum_right[k] / self.weighted_n_right
                proxy_impurity_left -= self.sum_left[k] * log(y_mean_left)
                proxy_impurity_right -= self.sum_right[k] * log(y_mean_right)

        return - proxy_impurity_left - proxy_impurity_right

    cdef void children_impurity(self, float64_t* impurity_left,
                                float64_t* impurity_right) noexcept nogil:
        """Evaluate the impurity in children nodes.

        i.e. the impurity of the left child (sample_indices[start:pos]) and the
        impurity of the right child (sample_indices[pos:end]) for Poisson.
        """
        cdef intp_t start = self.start
        cdef intp_t pos = self.pos
        cdef intp_t end = self.end

        impurity_left[0] = self.poisson_loss(start, pos, self.sum_left,
                                             self.weighted_n_left)

        impurity_right[0] = self.poisson_loss(pos, end, self.sum_right,
                                              self.weighted_n_right)

    cdef inline float64_t poisson_loss(
        self,
        intp_t start,
        intp_t end,
        const float64_t[::1] y_sum,
        float64_t weight_sum
    ) noexcept nogil:
        """Helper function to compute Poisson loss (~deviance) of a given node.
        """
        cdef const float64_t[:, ::1] y = self.y
        cdef const float64_t[:] sample_weight = self.sample_weight
        cdef const intp_t[:] sample_indices = self.sample_indices

        cdef float64_t y_mean = 0.
        cdef float64_t poisson_loss = 0.
        cdef float64_t w = 1.0
        cdef intp_t i, k, p
        cdef intp_t n_outputs = self.n_outputs

        for k in range(n_outputs):
            if y_sum[k] <= EPSILON:
                # y_sum could be computed from the subtraction
                # sum_right = sum_total - sum_left leading to a potential
                # floating point rounding error.
                # Thus, we relax the comparison y_sum <= 0 to
                # y_sum <= EPSILON.
                return INFINITY

            y_mean = y_sum[k] / weight_sum

            for p in range(start, end):
                i = sample_indices[p]

                if sample_weight is not None:
                    w = sample_weight[i]

                poisson_loss += w * xlogy(y[i, k], y[i, k] / y_mean)
        return poisson_loss / (weight_sum * n_outputs)