# cython: cdivision=True
# cython: boundscheck=False
# cython: wraparound=False

# 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 <L.J.Buitinck@uva.nl>
#          Arnaud Joly <arnaud.v.joly@gmail.com>
#          Joel Nothman <joel.nothman@gmail.com>
#          Fares Hedayati <fares.hedayati@gmail.com>
#          Jacob Schreiber <jmschreiber91@gmail.com>
#
# Licence: BSD 3 clause

from ._criterion cimport Criterion

from libc.stdlib cimport free
from libc.stdlib cimport qsort
from libc.string cimport memcpy
from libc.string cimport memset

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

from scipy.sparse import csc_matrix

from ._utils cimport log
from ._utils cimport rand_int
from ._utils cimport rand_uniform
from ._utils cimport RAND_R_MAX
from ._utils cimport safe_realloc

cdef double INFINITY = np.inf

# Mitigate precision differences between 32 bit and 64 bit
cdef DTYPE_t FEATURE_THRESHOLD = 1e-7

# Constant to switch between algorithm non zero value extract algorithm
# in SparseSplitter
cdef DTYPE_t EXTRACT_NNZ_SWITCH = 0.1

cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil:
    self.impurity_left = INFINITY
    self.impurity_right = INFINITY
    self.pos = start_pos
    self.feature = 0
    self.threshold = 0.
    self.improvement = -INFINITY

cdef class Splitter:
    """Abstract splitter class.

    Splitters are called by tree builders to find the best splits on both
    sparse and dense data, one split at a time.
    """

    def __cinit__(self, Criterion criterion, SIZE_t max_features,
                  SIZE_t min_samples_leaf, double min_weight_leaf,
                  object random_state, bint presort):
        """
        Parameters
        ----------
        criterion: Criterion
            The criterion to measure the quality of a split.

        max_features: SIZE_t
            The maximal number of randomly selected features which can be
            considered for a split.

        min_samples_leaf: SIZE_t
            The minimal number of samples each leaf can have, where splits
            which would result in having less samples in a leaf are not
            considered.

        min_weight_leaf: double
            The minimal weight each leaf can have, where the weight is the sum
            of the weights of each sample in it.

        random_state: object
            The user inputted random state to be used for pseudo-randomness
        """

        self.criterion = criterion

        self.samples = NULL
        self.n_samples = 0
        self.features = NULL
        self.n_features = 0
        self.feature_values = NULL

        self.y = NULL
        self.y_stride = 0
        self.sample_weight = NULL

        self.max_features = max_features
        self.min_samples_leaf = min_samples_leaf
        self.min_weight_leaf = min_weight_leaf
        self.random_state = random_state
        self.presort = presort

    def __dealloc__(self):
        """Destructor."""

        free(self.samples)
        free(self.features)
        free(self.constant_features)
        free(self.feature_values)

    def __getstate__(self):
        return {}

    def __setstate__(self, d):
        pass

    cdef void init(self,
                   object X,
                   np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
                   DOUBLE_t* sample_weight,
                   np.ndarray X_idx_sorted=None) except *:
        """Initialize the splitter.

        Take in the input data X, the target Y, and optional sample weights.

        Parameters
        ----------
        X: object
            This contains the inputs. Usually it is a 2d numpy array.

        y: numpy.ndarray, dtype=DOUBLE_t
            This is the vector of targets, or true labels, for the samples

        sample_weight: numpy.ndarray, dtype=DOUBLE_t (optional)
            The weights of the samples, where higher weighted samples are fit
            closer than lower weight samples. If not provided, all samples
            are assumed to have uniform weight.
        """

        self.rand_r_state = self.random_state.randint(0, RAND_R_MAX)
        cdef SIZE_t n_samples = X.shape[0]

        # Create a new array which will be used to store nonzero
        # samples from the feature of interest
        cdef SIZE_t* samples = safe_realloc(&self.samples, n_samples)

        cdef SIZE_t i, j
        cdef double weighted_n_samples = 0.0
        j = 0

        for i in range(n_samples):
            # Only work with positively weighted samples
            if sample_weight == NULL or sample_weight[i] != 0.0:
                samples[j] = i
                j += 1

            if sample_weight != NULL:
                weighted_n_samples += sample_weight[i]
            else:
                weighted_n_samples += 1.0

        # Number of samples is number of positively weighted samples
        self.n_samples = j
        self.weighted_n_samples = weighted_n_samples

        cdef SIZE_t n_features = X.shape[1]
        cdef SIZE_t* features = safe_realloc(&self.features, n_features)

        for i in range(n_features):
            features[i] = i

        self.n_features = n_features

        safe_realloc(&self.feature_values, n_samples)
        safe_realloc(&self.constant_features, n_features)

        self.y = <DOUBLE_t*> y.data
        self.y_stride = <SIZE_t> y.strides[0] / <SIZE_t> y.itemsize

        self.sample_weight = sample_weight

    cdef void node_reset(self, SIZE_t start, SIZE_t end,
                         double* weighted_n_node_samples) nogil:
        """Reset splitter on node samples[start:end].

        Parameters
        ----------
        start: SIZE_t
            The index of the first sample to consider
        end: SIZE_t
            The index of the last sample to consider
        weighted_n_node_samples: numpy.ndarray, dtype=double pointer
            The total weight of those samples
        """

        self.start = start
        self.end = end

        self.criterion.init(self.y,
                            self.y_stride,
                            self.sample_weight,
                            self.weighted_n_samples,
                            self.samples,
                            start,
                            end)

        weighted_n_node_samples[0] = self.criterion.weighted_n_node_samples

    cdef void node_split(self, double impurity, SplitRecord* split,
                         SIZE_t* n_constant_features) nogil:
        """Find the best split on node samples[start:end].

        This is a placeholder method. The majority of computation will be done
        here.
        """

        pass

    cdef void node_value(self, double* dest) nogil:
        """Copy the value of node samples[start:end] into dest."""

        self.criterion.node_value(dest)

    cdef double node_impurity(self) nogil:
        """Return the impurity of the current node."""

        return self.criterion.node_impurity()


cdef class BaseDenseSplitter(Splitter):
    cdef DTYPE_t* X
    cdef SIZE_t X_sample_stride
    cdef SIZE_t X_feature_stride

    cdef np.ndarray X_idx_sorted
    cdef INT32_t* X_idx_sorted_ptr
    cdef SIZE_t X_idx_sorted_stride
    cdef SIZE_t n_total_samples
    cdef SIZE_t* sample_mask

    def __cinit__(self, Criterion criterion, SIZE_t max_features,
                  SIZE_t min_samples_leaf, double min_weight_leaf,
                  object random_state, bint presort):

        self.X = NULL
        self.X_sample_stride = 0
        self.X_feature_stride = 0
        self.X_idx_sorted_ptr = NULL
        self.X_idx_sorted_stride = 0
        self.sample_mask = NULL
        self.presort = presort

    def __dealloc__(self):
        """Destructor."""
        if self.presort == 1:
            free(self.sample_mask)

    cdef void init(self,
                   object X,
                   np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
                   DOUBLE_t* sample_weight,
                   np.ndarray X_idx_sorted=None) except *:
        """Initialize the splitter."""

        # Call parent init
        Splitter.init(self, X, y, sample_weight)

        # Initialize X
        cdef np.ndarray X_ndarray = X

        self.X = <DTYPE_t*> X_ndarray.data
        self.X_sample_stride = <SIZE_t> X.strides[0] / <SIZE_t> X.itemsize
        self.X_feature_stride = <SIZE_t> X.strides[1] / <SIZE_t> X.itemsize

        if self.presort == 1:
            self.X_idx_sorted = X_idx_sorted
            self.X_idx_sorted_ptr = <INT32_t*> self.X_idx_sorted.data
            self.X_idx_sorted_stride = (<SIZE_t> self.X_idx_sorted.strides[1] /
                                        <SIZE_t> self.X_idx_sorted.itemsize)

            self.n_total_samples = X.shape[0]
            safe_realloc(&self.sample_mask, self.n_total_samples)
            memset(self.sample_mask, 0, self.n_total_samples*sizeof(SIZE_t))


cdef class BestSplitter(BaseDenseSplitter):
    """Splitter for finding the best split."""
    def __reduce__(self):
        return (BestSplitter, (self.criterion,
                               self.max_features,
                               self.min_samples_leaf,
                               self.min_weight_leaf,
                               self.random_state,
                               self.presort), self.__getstate__())

    cdef void node_split(self, double impurity, SplitRecord* split,
                         SIZE_t* n_constant_features) nogil:
        """Find the best split on node samples[start:end]."""
        # Find the best split
        cdef SIZE_t* samples = self.samples
        cdef SIZE_t start = self.start
        cdef SIZE_t end = self.end

        cdef SIZE_t* features = self.features
        cdef SIZE_t* constant_features = self.constant_features
        cdef SIZE_t n_features = self.n_features

        cdef DTYPE_t* X = self.X
        cdef DTYPE_t* Xf = self.feature_values
        cdef SIZE_t X_sample_stride = self.X_sample_stride
        cdef SIZE_t X_feature_stride = self.X_feature_stride
        cdef SIZE_t max_features = self.max_features
        cdef SIZE_t min_samples_leaf = self.min_samples_leaf
        cdef double min_weight_leaf = self.min_weight_leaf
        cdef UINT32_t* random_state = &self.rand_r_state

        cdef INT32_t* X_idx_sorted = self.X_idx_sorted_ptr
        cdef SIZE_t* sample_mask = self.sample_mask

        cdef SplitRecord best, current
        cdef double current_proxy_improvement = -INFINITY
        cdef double best_proxy_improvement = -INFINITY

        cdef SIZE_t f_i = n_features
        cdef SIZE_t f_j
        cdef SIZE_t tmp
        cdef SIZE_t p
        cdef SIZE_t feature_idx_offset
        cdef SIZE_t feature_offset
        cdef SIZE_t i
        cdef SIZE_t j

        cdef SIZE_t n_visited_features = 0
        # Number of features discovered to be constant during the split search
        cdef SIZE_t n_found_constants = 0
        # Number of features known to be constant and drawn without replacement
        cdef SIZE_t n_drawn_constants = 0
        cdef SIZE_t n_known_constants = n_constant_features[0]
        # n_total_constants = n_known_constants + n_found_constants
        cdef SIZE_t n_total_constants = n_known_constants
        cdef DTYPE_t current_feature_value
        cdef SIZE_t partition_end

        _init_split(&best, end)

        if self.presort == 1:
            for p in range(start, end):
                sample_mask[samples[p]] = 1

        # Sample up to max_features without replacement using a
        # Fisher-Yates-based algorithm (using the local variables `f_i` and
        # `f_j` to compute a permutation of the `features` array).
        #
        # Skip the CPU intensive evaluation of the impurity criterion for
        # features that were already detected as constant (hence not suitable
        # for good splitting) by ancestor nodes and save the information on
        # newly discovered constant features to spare computation on descendant
        # nodes.
        while (f_i > n_total_constants and  # Stop early if remaining features
                                            # are constant
                (n_visited_features < max_features or
                 # At least one drawn features must be non constant
                 n_visited_features <= n_found_constants + n_drawn_constants)):

            n_visited_features += 1

            # Loop invariant: elements of features in
            # - [:n_drawn_constant[ holds drawn and known constant features;
            # - [n_drawn_constant:n_known_constant[ holds known constant
            #   features that haven't been drawn yet;
            # - [n_known_constant:n_total_constant[ holds newly found constant
            #   features;
            # - [n_total_constant:f_i[ holds features that haven't been drawn
            #   yet and aren't constant apriori.
            # - [f_i:n_features[ holds features that have been drawn
            #   and aren't constant.

            # Draw a feature at random
            f_j = rand_int(n_drawn_constants, f_i - n_found_constants,
                           random_state)

            if f_j < n_known_constants:
                # f_j in the interval [n_drawn_constants, n_known_constants[
                tmp = features[f_j]
                features[f_j] = features[n_drawn_constants]
                features[n_drawn_constants] = tmp

                n_drawn_constants += 1

            else:
                # f_j in the interval [n_known_constants, f_i - n_found_constants[
                f_j += n_found_constants
                # f_j in the interval [n_total_constants, f_i[
                current.feature = features[f_j]
                feature_offset = self.X_feature_stride * current.feature

                # Sort samples along that feature; either by utilizing
                # presorting, or by copying the values into an array and
                # sorting the array in a manner which utilizes the cache more
                # effectively.
                if self.presort == 1:
                    p = start
                    feature_idx_offset = self.X_idx_sorted_stride * current.feature

                    for i in range(self.n_total_samples): 
                        j = X_idx_sorted[i + feature_idx_offset]
                        if sample_mask[j] == 1:
                            samples[p] = j
                            Xf[p] = X[self.X_sample_stride * j + feature_offset]
                            p += 1
                else:
                    for i in range(start, end):
                        Xf[i] = X[self.X_sample_stride * samples[i] + feature_offset]

                    sort(Xf + start, samples + start, end - start)

                if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
                    features[f_j] = features[n_total_constants]
                    features[n_total_constants] = current.feature

                    n_found_constants += 1
                    n_total_constants += 1

                else:
                    f_i -= 1
                    features[f_i], features[f_j] = features[f_j], features[f_i]

                    # Evaluate all splits
                    self.criterion.reset()
                    p = start

                    while p < end:
                        while (p + 1 < end and
                               Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
                            p += 1

                        # (p + 1 >= end) or (X[samples[p + 1], current.feature] >
                        #                    X[samples[p], current.feature])
                        p += 1
                        # (p >= end) or (X[samples[p], current.feature] >
                        #                X[samples[p - 1], current.feature])

                        if p < end:
                            current.pos = p

                            # Reject if min_samples_leaf is not guaranteed
                            if (((current.pos - start) < min_samples_leaf) or
                                    ((end - current.pos) < min_samples_leaf)):
                                continue

                            self.criterion.update(current.pos)

                            # Reject if min_weight_leaf is not satisfied
                            if ((self.criterion.weighted_n_left < min_weight_leaf) or
                                    (self.criterion.weighted_n_right < min_weight_leaf)):
                                continue

                            current_proxy_improvement = self.criterion.proxy_impurity_improvement()

                            if current_proxy_improvement > best_proxy_improvement:
                                best_proxy_improvement = current_proxy_improvement
                                current.threshold = (Xf[p - 1] + Xf[p]) / 2.0

                                if current.threshold == Xf[p]:
                                    current.threshold = Xf[p - 1]

                                best = current  # copy

        # Reorganize into samples[start:best.pos] + samples[best.pos:end]
        if best.pos < end:
            feature_offset = X_feature_stride * best.feature
            partition_end = end
            p = start

            while p < partition_end:
                if X[X_sample_stride * samples[p] + feature_offset] <= best.threshold:
                    p += 1

                else:
                    partition_end -= 1

                    tmp = samples[partition_end]
                    samples[partition_end] = samples[p]
                    samples[p] = tmp

            self.criterion.reset()
            self.criterion.update(best.pos)
            best.improvement = self.criterion.impurity_improvement(impurity)
            self.criterion.children_impurity(&best.impurity_left,
                                             &best.impurity_right)

        # Reset sample mask
        if self.presort == 1:
            for p in range(start, end):
                sample_mask[samples[p]] = 0

        # Respect invariant for constant features: the original order of
        # element in features[:n_known_constants] must be preserved for sibling
        # and child nodes
        memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants)

        # Copy newly found constant features
        memcpy(constant_features + n_known_constants,
               features + n_known_constants,
               sizeof(SIZE_t) * n_found_constants)

        # Return values
        split[0] = best
        n_constant_features[0] = n_total_constants


# Sort n-element arrays pointed to by Xf and samples, simultaneously,
# by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997).
cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:
    cdef int maxd = 2 * <int>log(n)
    introsort(Xf, samples, n, maxd)


cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples, SIZE_t i, SIZE_t j) nogil:
    # Helper for sort
    Xf[i], Xf[j] = Xf[j], Xf[i]
    samples[i], samples[j] = samples[j], samples[i]


cdef inline DTYPE_t median3(DTYPE_t* Xf, SIZE_t n) nogil:
    # Median of three pivot selection, after Bentley and McIlroy (1993).
    # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
    cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1]
    if a < b:
        if b < c:
            return b
        elif a < c:
            return c
        else:
            return a
    elif b < c:
        if a < c:
            return a
        else:
            return c
    else:
        return b


# Introsort with median of 3 pivot selection and 3-way partition function
# (robust to repeated elements, e.g. lots of zero features).
cdef void introsort(DTYPE_t* Xf, SIZE_t *samples, SIZE_t n, int maxd) nogil:
    cdef DTYPE_t pivot
    cdef SIZE_t i, l, r

    while n > 1:
        if maxd <= 0:   # max depth limit exceeded ("gone quadratic")
            heapsort(Xf, samples, n)
            return
        maxd -= 1

        pivot = median3(Xf, n)

        # Three-way partition.
        i = l = 0
        r = n
        while i < r:
            if Xf[i] < pivot:
                swap(Xf, samples, i, l)
                i += 1
                l += 1
            elif Xf[i] > pivot:
                r -= 1
                swap(Xf, samples, i, r)
            else:
                i += 1

        introsort(Xf, samples, l, maxd)
        Xf += r
        samples += r
        n -= r


cdef inline void sift_down(DTYPE_t* Xf, SIZE_t* samples,
                           SIZE_t start, SIZE_t end) nogil:
    # Restore heap order in Xf[start:end] by moving the max element to start.
    cdef SIZE_t child, maxind, root

    root = start
    while True:
        child = root * 2 + 1

        # find max of root, left child, right child
        maxind = root
        if child < end and Xf[maxind] < Xf[child]:
            maxind = child
        if child + 1 < end and Xf[maxind] < Xf[child + 1]:
            maxind = child + 1

        if maxind == root:
            break
        else:
            swap(Xf, samples, root, maxind)
            root = maxind


cdef void heapsort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:
    cdef SIZE_t start, end

    # heapify
    start = (n - 2) / 2
    end = n
    while True:
        sift_down(Xf, samples, start, end)
        if start == 0:
            break
        start -= 1

    # sort by shrinking the heap, putting the max element immediately after it
    end = n - 1
    while end > 0:
        swap(Xf, samples, 0, end)
        sift_down(Xf, samples, 0, end)
        end = end - 1


cdef class RandomSplitter(BaseDenseSplitter):
    """Splitter for finding the best random split."""
    def __reduce__(self):
        return (RandomSplitter, (self.criterion,
                                 self.max_features,
                                 self.min_samples_leaf,
                                 self.min_weight_leaf,
                                 self.random_state,
                                 self.presort), self.__getstate__())

    cdef void node_split(self, double impurity, SplitRecord* split,
                         SIZE_t* n_constant_features) nogil:
        """Find the best random split on node samples[start:end]."""
        # Draw random splits and pick the best
        cdef SIZE_t* samples = self.samples
        cdef SIZE_t start = self.start
        cdef SIZE_t end = self.end

        cdef SIZE_t* features = self.features
        cdef SIZE_t* constant_features = self.constant_features
        cdef SIZE_t n_features = self.n_features

        cdef DTYPE_t* X = self.X
        cdef DTYPE_t* Xf = self.feature_values
        cdef SIZE_t X_sample_stride = self.X_sample_stride
        cdef SIZE_t X_feature_stride = self.X_feature_stride
        cdef SIZE_t max_features = self.max_features
        cdef SIZE_t min_samples_leaf = self.min_samples_leaf
        cdef double min_weight_leaf = self.min_weight_leaf
        cdef UINT32_t* random_state = &self.rand_r_state

        cdef SplitRecord best, current
        cdef double current_proxy_improvement = - INFINITY
        cdef double best_proxy_improvement = - INFINITY

        cdef SIZE_t f_i = n_features
        cdef SIZE_t f_j
        cdef SIZE_t p
        cdef SIZE_t tmp
        cdef SIZE_t feature_stride
        # Number of features discovered to be constant during the split search
        cdef SIZE_t n_found_constants = 0
        # Number of features known to be constant and drawn without replacement
        cdef SIZE_t n_drawn_constants = 0
        cdef SIZE_t n_known_constants = n_constant_features[0]
        # n_total_constants = n_known_constants + n_found_constants
        cdef SIZE_t n_total_constants = n_known_constants
        cdef SIZE_t n_visited_features = 0
        cdef DTYPE_t min_feature_value
        cdef DTYPE_t max_feature_value
        cdef DTYPE_t current_feature_value
        cdef SIZE_t partition_end

        _init_split(&best, end)

        # Sample up to max_features without replacement using a
        # Fisher-Yates-based algorithm (using the local variables `f_i` and
        # `f_j` to compute a permutation of the `features` array).
        #
        # Skip the CPU intensive evaluation of the impurity criterion for
        # features that were already detected as constant (hence not suitable
        # for good splitting) by ancestor nodes and save the information on
        # newly discovered constant features to spare computation on descendant
        # nodes.
        while (f_i > n_total_constants and  # Stop early if remaining features
                                            # are constant
                (n_visited_features < max_features or
                 # At least one drawn features must be non constant
                 n_visited_features <= n_found_constants + n_drawn_constants)):
            n_visited_features += 1

            # Loop invariant: elements of features in
            # - [:n_drawn_constant[ holds drawn and known constant features;
            # - [n_drawn_constant:n_known_constant[ holds known constant
            #   features that haven't been drawn yet;
            # - [n_known_constant:n_total_constant[ holds newly found constant
            #   features;
            # - [n_total_constant:f_i[ holds features that haven't been drawn
            #   yet and aren't constant apriori.
            # - [f_i:n_features[ holds features that have been drawn
            #   and aren't constant.

            # Draw a feature at random
            f_j = rand_int(n_drawn_constants, f_i - n_found_constants,
                           random_state)

            if f_j < n_known_constants:
                # f_j in the interval [n_drawn_constants, n_known_constants[
                tmp = features[f_j]
                features[f_j] = features[n_drawn_constants]
                features[n_drawn_constants] = tmp

                n_drawn_constants += 1

            else:
                # f_j in the interval [n_known_constants, f_i - n_found_constants[
                f_j += n_found_constants
                # f_j in the interval [n_total_constants, f_i[

                current.feature = features[f_j]
                feature_stride = X_feature_stride * current.feature

                # Find min, max
                min_feature_value = X[X_sample_stride * samples[start] + feature_stride]
                max_feature_value = min_feature_value
                Xf[start] = min_feature_value

                for p in range(start + 1, end):
                    current_feature_value = X[X_sample_stride * samples[p] + feature_stride]
                    Xf[p] = current_feature_value

                    if current_feature_value < min_feature_value:
                        min_feature_value = current_feature_value
                    elif current_feature_value > max_feature_value:
                        max_feature_value = current_feature_value

                if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
                    features[f_j] = features[n_total_constants]
                    features[n_total_constants] = current.feature

                    n_found_constants += 1
                    n_total_constants += 1

                else:
                    f_i -= 1
                    features[f_i], features[f_j] = features[f_j], features[f_i]

                    # Draw a random threshold
                    current.threshold = rand_uniform(min_feature_value,
                                                     max_feature_value,
                                                     random_state)

                    if current.threshold == max_feature_value:
                        current.threshold = min_feature_value

                    # Partition
                    partition_end = end
                    p = start
                    while p < partition_end:
                        current_feature_value = Xf[p]
                        if current_feature_value <= current.threshold:
                            p += 1
                        else:
                            partition_end -= 1

                            Xf[p] = Xf[partition_end]
                            Xf[partition_end] = current_feature_value

                            tmp = samples[partition_end]
                            samples[partition_end] = samples[p]
                            samples[p] = tmp

                    current.pos = partition_end

                    # Reject if min_samples_leaf is not guaranteed
                    if (((current.pos - start) < min_samples_leaf) or
                            ((end - current.pos) < min_samples_leaf)):
                        continue

                    # Evaluate split
                    self.criterion.reset()
                    self.criterion.update(current.pos)

                    # Reject if min_weight_leaf is not satisfied
                    if ((self.criterion.weighted_n_left < min_weight_leaf) or
                            (self.criterion.weighted_n_right < min_weight_leaf)):
                        continue

                    current_proxy_improvement = self.criterion.proxy_impurity_improvement()

                    if current_proxy_improvement > best_proxy_improvement:
                        best_proxy_improvement = current_proxy_improvement
                        best = current  # copy

        # Reorganize into samples[start:best.pos] + samples[best.pos:end]
        feature_stride = X_feature_stride * best.feature
        if best.pos < end:
            if current.feature != best.feature:
                partition_end = end
                p = start

                while p < partition_end:
                    if X[X_sample_stride * samples[p] + feature_stride] <= best.threshold:
                        p += 1

                    else:
                        partition_end -= 1

                        tmp = samples[partition_end]
                        samples[partition_end] = samples[p]
                        samples[p] = tmp


            self.criterion.reset()
            self.criterion.update(best.pos)
            best.improvement = self.criterion.impurity_improvement(impurity)
            self.criterion.children_impurity(&best.impurity_left,
                                             &best.impurity_right)

        # Respect invariant for constant features: the original order of
        # element in features[:n_known_constants] must be preserved for sibling
        # and child nodes
        memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants)

        # Copy newly found constant features
        memcpy(constant_features + n_known_constants,
               features + n_known_constants,
               sizeof(SIZE_t) * n_found_constants)

        # Return values
        split[0] = best
        n_constant_features[0] = n_total_constants


cdef class BaseSparseSplitter(Splitter):
    # The sparse splitter works only with csc sparse matrix format
    cdef DTYPE_t* X_data
    cdef INT32_t* X_indices
    cdef INT32_t* X_indptr

    cdef SIZE_t n_total_samples

    cdef SIZE_t* index_to_samples
    cdef SIZE_t* sorted_samples

    def __cinit__(self, Criterion criterion, SIZE_t max_features,
                  SIZE_t min_samples_leaf, double min_weight_leaf,
                  object random_state, bint presort):
        # Parent __cinit__ is automatically called

        self.X_data = NULL
        self.X_indices = NULL
        self.X_indptr = NULL

        self.n_total_samples = 0

        self.index_to_samples = NULL
        self.sorted_samples = NULL

    def __dealloc__(self):
        """Deallocate memory."""
        free(self.index_to_samples)
        free(self.sorted_samples)

    cdef void init(self,
                   object X,
                   np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
                   DOUBLE_t* sample_weight,
                   np.ndarray X_idx_sorted=None) except *:
        """Initialize the splitter."""

        # Call parent init
        Splitter.init(self, X, y, sample_weight)

        if not isinstance(X, csc_matrix):
            raise ValueError("X should be in csc format")

        cdef SIZE_t* samples = self.samples
        cdef SIZE_t n_samples = self.n_samples

        # Initialize X
        cdef np.ndarray[dtype=DTYPE_t, ndim=1] data = X.data
        cdef np.ndarray[dtype=INT32_t, ndim=1] indices = X.indices
        cdef np.ndarray[dtype=INT32_t, ndim=1] indptr = X.indptr
        cdef SIZE_t n_total_samples = X.shape[0]

        self.X_data = <DTYPE_t*> data.data
        self.X_indices = <INT32_t*> indices.data
        self.X_indptr = <INT32_t*> indptr.data
        self.n_total_samples = n_total_samples

        # Initialize auxiliary array used to perform split
        safe_realloc(&self.index_to_samples, n_total_samples)
        safe_realloc(&self.sorted_samples, n_samples)

        cdef SIZE_t* index_to_samples = self.index_to_samples
        cdef SIZE_t p
        for p in range(n_total_samples):
            index_to_samples[p] = -1

        for p in range(n_samples):
            index_to_samples[samples[p]] = p

    cdef inline SIZE_t _partition(self, double threshold,
                                  SIZE_t end_negative, SIZE_t start_positive,
                                  SIZE_t zero_pos) nogil:
        """Partition samples[start:end] based on threshold."""

        cdef double value
        cdef SIZE_t partition_end
        cdef SIZE_t p

        cdef DTYPE_t* Xf = self.feature_values
        cdef SIZE_t* samples = self.samples
        cdef SIZE_t* index_to_samples = self.index_to_samples

        if threshold < 0.:
            p = self.start
            partition_end = end_negative
        elif threshold > 0.:
            p = start_positive
            partition_end = self.end
        else:
            # Data are already split
            return zero_pos

        while p < partition_end:
            value = Xf[p]

            if value <= threshold:
                p += 1

            else:
                partition_end -= 1

                Xf[p] = Xf[partition_end]
                Xf[partition_end] = value
                sparse_swap(index_to_samples, samples, p, partition_end)

        return partition_end

    cdef inline void extract_nnz(self, SIZE_t feature,
                                 SIZE_t* end_negative, SIZE_t* start_positive,
                                 bint* is_samples_sorted) nogil:
        """Extract and partition values for a given feature.

        The extracted values are partitioned between negative values
        Xf[start:end_negative[0]] and positive values Xf[start_positive[0]:end].
        The samples and index_to_samples are modified according to this
        partition.

        The extraction corresponds to the intersection between the arrays
        X_indices[indptr_start:indptr_end] and samples[start:end].
        This is done efficiently using either an index_to_samples based approach
        or binary search based approach.

        Parameters
        ----------
        feature : SIZE_t,
            Index of the feature we want to extract non zero value.


        end_negative, start_positive : SIZE_t*, SIZE_t*,
            Return extracted non zero values in self.samples[start:end] where
            negative values are in self.feature_values[start:end_negative[0]]
            and positive values are in
            self.feature_values[start_positive[0]:end].

        is_samples_sorted : bint*,
            If is_samples_sorted, then self.sorted_samples[start:end] will be
            the sorted version of self.samples[start:end].

        """
        cdef SIZE_t indptr_start = self.X_indptr[feature],
        cdef SIZE_t indptr_end = self.X_indptr[feature + 1]
        cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start)
        cdef SIZE_t n_samples = self.end - self.start

        # Use binary search if n_samples * log(n_indices) <
        # n_indices and index_to_samples approach otherwise.
        # O(n_samples * log(n_indices)) is the running time of binary
        # search and O(n_indices) is the running time of index_to_samples
        # approach.
        if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +
                n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices):
            extract_nnz_binary_search(self.X_indices, self.X_data,
                                      indptr_start, indptr_end,
                                      self.samples, self.start, self.end,
                                      self.index_to_samples,
                                      self.feature_values,
                                      end_negative, start_positive,
                                      self.sorted_samples, is_samples_sorted)

        # Using an index to samples  technique to extract non zero values
        # index_to_samples is a mapping from X_indices to samples
        else:
            extract_nnz_index_to_samples(self.X_indices, self.X_data,
                                         indptr_start, indptr_end,
                                         self.samples, self.start, self.end,
                                         self.index_to_samples,
                                         self.feature_values,
                                         end_negative, start_positive)


cdef int compare_SIZE_t(const void* a, const void* b) nogil:
    """Comparison function for sort."""
    return <int>((<SIZE_t*>a)[0] - (<SIZE_t*>b)[0])


cdef inline void binary_search(INT32_t* sorted_array,
                               INT32_t start, INT32_t end,
                               SIZE_t value, SIZE_t* index,
                               INT32_t* new_start) nogil:
    """Return the index of value in the sorted array.

    If not found, return -1. new_start is the last pivot + 1
    """
    cdef INT32_t pivot
    index[0] = -1
    while start < end:
        pivot = start + (end - start) / 2

        if sorted_array[pivot] == value:
            index[0] = pivot
            start = pivot + 1
            break

        if sorted_array[pivot] < value:
            start = pivot + 1
        else:
            end = pivot
    new_start[0] = start


cdef inline void extract_nnz_index_to_samples(INT32_t* X_indices,
                                              DTYPE_t* X_data,
                                              INT32_t indptr_start,
                                              INT32_t indptr_end,
                                              SIZE_t* samples,
                                              SIZE_t start,
                                              SIZE_t end,
                                              SIZE_t* index_to_samples,
                                              DTYPE_t* Xf,
                                              SIZE_t* end_negative,
                                              SIZE_t* start_positive) nogil:
    """Extract and partition values for a feature using index_to_samples.

    Complexity is O(indptr_end - indptr_start).
    """
    cdef INT32_t k
    cdef SIZE_t index
    cdef SIZE_t end_negative_ = start
    cdef SIZE_t start_positive_ = end

    for k in range(indptr_start, indptr_end):
        if start <= index_to_samples[X_indices[k]] < end:
            if X_data[k] > 0:
                start_positive_ -= 1
                Xf[start_positive_] = X_data[k]
                index = index_to_samples[X_indices[k]]
                sparse_swap(index_to_samples, samples, index, start_positive_)


            elif X_data[k] < 0:
                Xf[end_negative_] = X_data[k]
                index = index_to_samples[X_indices[k]]
                sparse_swap(index_to_samples, samples, index, end_negative_)
                end_negative_ += 1

    # Returned values
    end_negative[0] = end_negative_
    start_positive[0] = start_positive_


cdef inline void extract_nnz_binary_search(INT32_t* X_indices,
                                           DTYPE_t* X_data,
                                           INT32_t indptr_start,
                                           INT32_t indptr_end,
                                           SIZE_t* samples,
                                           SIZE_t start,
                                           SIZE_t end,
                                           SIZE_t* index_to_samples,
                                           DTYPE_t* Xf,
                                           SIZE_t* end_negative,
                                           SIZE_t* start_positive,
                                           SIZE_t* sorted_samples,
                                           bint* is_samples_sorted) nogil:
    """Extract and partition values for a given feature using binary search.

    If n_samples = end - start and n_indices = indptr_end - indptr_start,
    the complexity is

        O((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +
          n_samples * log(n_indices)).
    """
    cdef SIZE_t n_samples

    if not is_samples_sorted[0]:
        n_samples = end - start
        memcpy(sorted_samples + start, samples + start,
               n_samples * sizeof(SIZE_t))
        qsort(sorted_samples + start, n_samples, sizeof(SIZE_t),
              compare_SIZE_t)
        is_samples_sorted[0] = 1

    while (indptr_start < indptr_end and
           sorted_samples[start] > X_indices[indptr_start]):
        indptr_start += 1

    while (indptr_start < indptr_end and
           sorted_samples[end - 1] < X_indices[indptr_end - 1]):
        indptr_end -= 1

    cdef SIZE_t p = start
    cdef SIZE_t index
    cdef SIZE_t k
    cdef SIZE_t end_negative_ = start
    cdef SIZE_t start_positive_ = end

    while (p < end and indptr_start < indptr_end):
        # Find index of sorted_samples[p] in X_indices
        binary_search(X_indices, indptr_start, indptr_end,
                      sorted_samples[p], &k, &indptr_start)

        if k != -1:
             # If k != -1, we have found a non zero value

            if X_data[k] > 0:
                start_positive_ -= 1
                Xf[start_positive_] = X_data[k]
                index = index_to_samples[X_indices[k]]
                sparse_swap(index_to_samples, samples, index, start_positive_)


            elif X_data[k] < 0:
                Xf[end_negative_] = X_data[k]
                index = index_to_samples[X_indices[k]]
                sparse_swap(index_to_samples, samples, index, end_negative_)
                end_negative_ += 1
        p += 1

    # Returned values
    end_negative[0] = end_negative_
    start_positive[0] = start_positive_


cdef inline void sparse_swap(SIZE_t* index_to_samples, SIZE_t* samples,
                             SIZE_t pos_1, SIZE_t pos_2) nogil  :
    """Swap sample pos_1 and pos_2 preserving sparse invariant."""
    samples[pos_1], samples[pos_2] =  samples[pos_2], samples[pos_1]
    index_to_samples[samples[pos_1]] = pos_1
    index_to_samples[samples[pos_2]] = pos_2


cdef class BestSparseSplitter(BaseSparseSplitter):
    """Splitter for finding the best split, using the sparse data."""

    def __reduce__(self):
        return (BestSparseSplitter, (self.criterion,
                                     self.max_features,
                                     self.min_samples_leaf,
                                     self.min_weight_leaf,
                                     self.random_state,
                                     self.presort), self.__getstate__())

    cdef void node_split(self, double impurity, SplitRecord* split,
                         SIZE_t* n_constant_features) nogil:
        """Find the best split on node samples[start:end], using sparse
           features.
        """
        # Find the best split
        cdef SIZE_t* samples = self.samples
        cdef SIZE_t start = self.start
        cdef SIZE_t end = self.end

        cdef INT32_t* X_indices = self.X_indices
        cdef INT32_t* X_indptr = self.X_indptr
        cdef DTYPE_t* X_data = self.X_data

        cdef SIZE_t* features = self.features
        cdef SIZE_t* constant_features = self.constant_features
        cdef SIZE_t n_features = self.n_features

        cdef DTYPE_t* Xf = self.feature_values
        cdef SIZE_t* sorted_samples = self.sorted_samples
        cdef SIZE_t* index_to_samples = self.index_to_samples
        cdef SIZE_t max_features = self.max_features
        cdef SIZE_t min_samples_leaf = self.min_samples_leaf
        cdef double min_weight_leaf = self.min_weight_leaf
        cdef UINT32_t* random_state = &self.rand_r_state

        cdef SplitRecord best, current
        _init_split(&best, end)
        cdef double current_proxy_improvement = - INFINITY
        cdef double best_proxy_improvement = - INFINITY

        cdef SIZE_t f_i = n_features
        cdef SIZE_t f_j, p, tmp
        cdef SIZE_t n_visited_features = 0
        # Number of features discovered to be constant during the split search
        cdef SIZE_t n_found_constants = 0
        # Number of features known to be constant and drawn without replacement
        cdef SIZE_t n_drawn_constants = 0
        cdef SIZE_t n_known_constants = n_constant_features[0]
        # n_total_constants = n_known_constants + n_found_constants
        cdef SIZE_t n_total_constants = n_known_constants
        cdef DTYPE_t current_feature_value

        cdef SIZE_t p_next
        cdef SIZE_t p_prev
        cdef bint is_samples_sorted = 0  # indicate is sorted_samples is
                                         # inititialized

        # We assume implicitely that end_positive = end and
        # start_negative = start
        cdef SIZE_t start_positive
        cdef SIZE_t end_negative

        # Sample up to max_features without replacement using a
        # Fisher-Yates-based algorithm (using the local variables `f_i` and
        # `f_j` to compute a permutation of the `features` array).
        #
        # Skip the CPU intensive evaluation of the impurity criterion for
        # features that were already detected as constant (hence not suitable
        # for good splitting) by ancestor nodes and save the information on
        # newly discovered constant features to spare computation on descendant
        # nodes.
        while (f_i > n_total_constants and  # Stop early if remaining features
                                            # are constant
                (n_visited_features < max_features or
                 # At least one drawn features must be non constant
                 n_visited_features <= n_found_constants + n_drawn_constants)):

            n_visited_features += 1

            # Loop invariant: elements of features in
            # - [:n_drawn_constant[ holds drawn and known constant features;
            # - [n_drawn_constant:n_known_constant[ holds known constant
            #   features that haven't been drawn yet;
            # - [n_known_constant:n_total_constant[ holds newly found constant
            #   features;
            # - [n_total_constant:f_i[ holds features that haven't been drawn
            #   yet and aren't constant apriori.
            # - [f_i:n_features[ holds features that have been drawn
            #   and aren't constant.

            # Draw a feature at random
            f_j = rand_int(n_drawn_constants, f_i - n_found_constants,
                           random_state)

            if f_j < n_known_constants:
                # f_j in the interval [n_drawn_constants, n_known_constants[
                tmp = features[f_j]
                features[f_j] = features[n_drawn_constants]
                features[n_drawn_constants] = tmp

                n_drawn_constants += 1

            else:
                # f_j in the interval [n_known_constants, f_i - n_found_constants[
                f_j += n_found_constants
                # f_j in the interval [n_total_constants, f_i[

                current.feature = features[f_j]
                self.extract_nnz(current.feature,
                                 &end_negative, &start_positive,
                                 &is_samples_sorted)

                # Sort the positive and negative parts of `Xf`
                sort(Xf + start, samples + start, end_negative - start)
                sort(Xf + start_positive, samples + start_positive,
                     end - start_positive)

                # Update index_to_samples to take into account the sort
                for p in range(start, end_negative):
                    index_to_samples[samples[p]] = p
                for p in range(start_positive, end):
                    index_to_samples[samples[p]] = p

                # Add one or two zeros in Xf, if there is any
                if end_negative < start_positive:
                    start_positive -= 1
                    Xf[start_positive] = 0.

                    if end_negative != start_positive:
                        Xf[end_negative] = 0.
                        end_negative += 1

                if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
                    features[f_j] = features[n_total_constants]
                    features[n_total_constants] = current.feature

                    n_found_constants += 1
                    n_total_constants += 1

                else:
                    f_i -= 1
                    features[f_i], features[f_j] = features[f_j], features[f_i]

                    # Evaluate all splits
                    self.criterion.reset()
                    p = start

                    while p < end:
                        if p + 1 != end_negative:
                            p_next = p + 1
                        else:
                            p_next = start_positive

                        while (p_next < end and
                               Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
                            p = p_next
                            if p + 1 != end_negative:
                                p_next = p + 1
                            else:
                                p_next = start_positive


                        # (p_next >= end) or (X[samples[p_next], current.feature] >
                        #                     X[samples[p], current.feature])
                        p_prev = p
                        p = p_next
                        # (p >= end) or (X[samples[p], current.feature] >
                        #                X[samples[p_prev], current.feature])


                        if p < end:
                            current.pos = p

                            # Reject if min_samples_leaf is not guaranteed
                            if (((current.pos - start) < min_samples_leaf) or
                                    ((end - current.pos) < min_samples_leaf)):
                                continue

                            self.criterion.update(current.pos)

                            # Reject if min_weight_leaf is not satisfied
                            if ((self.criterion.weighted_n_left < min_weight_leaf) or
                                    (self.criterion.weighted_n_right < min_weight_leaf)):
                                continue

                            current_proxy_improvement = self.criterion.proxy_impurity_improvement()

                            if current_proxy_improvement > best_proxy_improvement:
                                best_proxy_improvement = current_proxy_improvement

                                current.threshold = (Xf[p_prev] + Xf[p]) / 2.0
                                if current.threshold == Xf[p]:
                                    current.threshold = Xf[p_prev]

                                best = current

        # Reorganize into samples[start:best.pos] + samples[best.pos:end]
        if best.pos < end:
            self.extract_nnz(best.feature, &end_negative, &start_positive,
                             &is_samples_sorted)

            self._partition(best.threshold, end_negative, start_positive,
                            best.pos)

            self.criterion.reset()
            self.criterion.update(best.pos)
            best.improvement = self.criterion.impurity_improvement(impurity)
            self.criterion.children_impurity(&best.impurity_left,
                                             &best.impurity_right)

        # Respect invariant for constant features: the original order of
        # element in features[:n_known_constants] must be preserved for sibling
        # and child nodes
        memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants)

        # Copy newly found constant features
        memcpy(constant_features + n_known_constants,
               features + n_known_constants,
               sizeof(SIZE_t) * n_found_constants)

        # Return values
        split[0] = best
        n_constant_features[0] = n_total_constants


cdef class RandomSparseSplitter(BaseSparseSplitter):
    """Splitter for finding a random split, using the sparse data."""

    def __reduce__(self):
        return (RandomSparseSplitter, (self.criterion,
                                       self.max_features,
                                       self.min_samples_leaf,
                                       self.min_weight_leaf,
                                       self.random_state,
                                       self.presort), self.__getstate__())

    cdef void node_split(self, double impurity, SplitRecord* split,
                         SIZE_t* n_constant_features) nogil:
        """Find a random split on node samples[start:end], using sparse
           features.
        """
        # Find the best split
        cdef SIZE_t* samples = self.samples
        cdef SIZE_t start = self.start
        cdef SIZE_t end = self.end

        cdef INT32_t* X_indices = self.X_indices
        cdef INT32_t* X_indptr = self.X_indptr
        cdef DTYPE_t* X_data = self.X_data

        cdef SIZE_t* features = self.features
        cdef SIZE_t* constant_features = self.constant_features
        cdef SIZE_t n_features = self.n_features

        cdef DTYPE_t* Xf = self.feature_values
        cdef SIZE_t* sorted_samples = self.sorted_samples
        cdef SIZE_t* index_to_samples = self.index_to_samples
        cdef SIZE_t max_features = self.max_features
        cdef SIZE_t min_samples_leaf = self.min_samples_leaf
        cdef double min_weight_leaf = self.min_weight_leaf
        cdef UINT32_t* random_state = &self.rand_r_state

        cdef SplitRecord best, current
        _init_split(&best, end)
        cdef double current_proxy_improvement = - INFINITY
        cdef double best_proxy_improvement = - INFINITY

        cdef DTYPE_t current_feature_value

        cdef SIZE_t f_i = n_features
        cdef SIZE_t f_j, p, tmp
        cdef SIZE_t n_visited_features = 0
        # Number of features discovered to be constant during the split search
        cdef SIZE_t n_found_constants = 0
        # Number of features known to be constant and drawn without replacement
        cdef SIZE_t n_drawn_constants = 0
        cdef SIZE_t n_known_constants = n_constant_features[0]
        # n_total_constants = n_known_constants + n_found_constants
        cdef SIZE_t n_total_constants = n_known_constants
        cdef SIZE_t partition_end

        cdef DTYPE_t min_feature_value
        cdef DTYPE_t max_feature_value

        cdef bint is_samples_sorted = 0  # indicate that sorted_samples is
                                         # inititialized

        # We assume implicitely that end_positive = end and
        # start_negative = start
        cdef SIZE_t start_positive
        cdef SIZE_t end_negative

        # Sample up to max_features without replacement using a
        # Fisher-Yates-based algorithm (using the local variables `f_i` and
        # `f_j` to compute a permutation of the `features` array).
        #
        # Skip the CPU intensive evaluation of the impurity criterion for
        # features that were already detected as constant (hence not suitable
        # for good splitting) by ancestor nodes and save the information on
        # newly discovered constant features to spare computation on descendant
        # nodes.
        while (f_i > n_total_constants and  # Stop early if remaining features
                                            # are constant
                (n_visited_features < max_features or
                 # At least one drawn features must be non constant
                 n_visited_features <= n_found_constants + n_drawn_constants)):

            n_visited_features += 1

            # Loop invariant: elements of features in
            # - [:n_drawn_constant[ holds drawn and known constant features;
            # - [n_drawn_constant:n_known_constant[ holds known constant
            #   features that haven't been drawn yet;
            # - [n_known_constant:n_total_constant[ holds newly found constant
            #   features;
            # - [n_total_constant:f_i[ holds features that haven't been drawn
            #   yet and aren't constant apriori.
            # - [f_i:n_features[ holds features that have been drawn
            #   and aren't constant.

            # Draw a feature at random
            f_j = rand_int(n_drawn_constants, f_i - n_found_constants,
                           random_state)

            if f_j < n_known_constants:
                # f_j in the interval [n_drawn_constants, n_known_constants[
                tmp = features[f_j]
                features[f_j] = features[n_drawn_constants]
                features[n_drawn_constants] = tmp

                n_drawn_constants += 1

            else:
                # f_j in the interval [n_known_constants, f_i - n_found_constants[
                f_j += n_found_constants
                # f_j in the interval [n_total_constants, f_i[

                current.feature = features[f_j]

                self.extract_nnz(current.feature,
                                 &end_negative, &start_positive,
                                 &is_samples_sorted)

                # Add one or two zeros in Xf, if there is any
                if end_negative < start_positive:
                    start_positive -= 1
                    Xf[start_positive] = 0.

                    if end_negative != start_positive:
                        Xf[end_negative] = 0.
                        end_negative += 1

                # Find min, max in Xf[start:end_negative]
                min_feature_value = Xf[start]
                max_feature_value = min_feature_value

                for p in range(start, end_negative):
                    current_feature_value = Xf[p]

                    if current_feature_value < min_feature_value:
                        min_feature_value = current_feature_value
                    elif current_feature_value > max_feature_value:
                        max_feature_value = current_feature_value

                # Update min, max given Xf[start_positive:end]
                for p in range(start_positive, end):
                    current_feature_value = Xf[p]

                    if current_feature_value < min_feature_value:
                        min_feature_value = current_feature_value
                    elif current_feature_value > max_feature_value:
                        max_feature_value = current_feature_value

                if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
                    features[f_j] = features[n_total_constants]
                    features[n_total_constants] = current.feature

                    n_found_constants += 1
                    n_total_constants += 1

                else:
                    f_i -= 1
                    features[f_i], features[f_j] = features[f_j], features[f_i]

                    # Draw a random threshold
                    current.threshold = rand_uniform(min_feature_value,
                                                     max_feature_value,
                                                     random_state)

                    if current.threshold == max_feature_value:
                        current.threshold = min_feature_value

                    # Partition
                    current.pos = self._partition(current.threshold,
                                                  end_negative,
                                                  start_positive,
                                                  start_positive +
                                                  (Xf[start_positive] == 0.))

                    # Reject if min_samples_leaf is not guaranteed
                    if (((current.pos - start) < min_samples_leaf) or
                            ((end - current.pos) < min_samples_leaf)):
                        continue

                    # Evaluate split
                    self.criterion.reset()
                    self.criterion.update(current.pos)

                    # Reject if min_weight_leaf is not satisfied
                    if ((self.criterion.weighted_n_left < min_weight_leaf) or
                            (self.criterion.weighted_n_right < min_weight_leaf)):
                        continue

                    current_proxy_improvement = self.criterion.proxy_impurity_improvement()

                    if current_proxy_improvement > best_proxy_improvement:
                        best_proxy_improvement = current_proxy_improvement
                        current.improvement = self.criterion.impurity_improvement(impurity)

                        self.criterion.children_impurity(&current.impurity_left,
                                                         &current.impurity_right)
                        best = current

        # Reorganize into samples[start:best.pos] + samples[best.pos:end]
        if best.pos < end:
            if current.feature != best.feature:
                self.extract_nnz(best.feature, &end_negative, &start_positive,
                                 &is_samples_sorted)

                self._partition(best.threshold, end_negative, start_positive,
                                best.pos)

            self.criterion.reset()
            self.criterion.update(best.pos)
            best.improvement = self.criterion.impurity_improvement(impurity)
            self.criterion.children_impurity(&best.impurity_left,
                                             &best.impurity_right)

        # Respect invariant for constant features: the original order of
        # element in features[:n_known_constants] must be preserved for sibling
        # and child nodes
        memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants)

        # Copy newly found constant features
        memcpy(constant_features + n_known_constants,
               features + n_known_constants,
               sizeof(SIZE_t) * n_found_constants)

        # Return values
        split[0] = best
        n_constant_features[0] = n_total_constants
