# 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
#          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 cpython cimport Py_INCREF, PyObject

from libc.math cimport exp
from libc.math cimport fmax
from libc.math cimport sqrt
from libc.stdlib cimport free
from libc.stdlib cimport malloc
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 issparse
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix

from ._utils cimport Stack
from ._utils cimport StackRecord
from ._utils cimport safe_realloc
from ._utils cimport sizet_ptr_to_ndarray

cdef extern from "numpy/arrayobject.h":
    object PyArray_NewFromDescr(object subtype, np.dtype descr,
                                int nd, np.npy_intp* dims,
                                np.npy_intp* strides,
                                void* data, int flags, object obj)

# =============================================================================
# Types and constants
# =============================================================================

from numpy import float32 as DTYPE
from numpy import float64 as DOUBLE

cdef double INFINITY = np.inf

# Some handy constants (BestFirstTreeBuilder)
cdef int IS_FIRST = 1
cdef int IS_NOT_FIRST = 0
cdef int IS_LEFT = 1
cdef int IS_NOT_LEFT = 0

TREE_LEAF = -1
TREE_UNDEFINED = -2
cdef SIZE_t _TREE_LEAF = TREE_LEAF
cdef SIZE_t _TREE_UNDEFINED = TREE_UNDEFINED
cdef SIZE_t INITIAL_STACK_SIZE = 10

# Repeat struct definition for numpy
NODE_DTYPE = np.dtype({
    'names': ['left_child', 'right_child', 'feature', 'threshold', 'impurity',
              'n_node_samples', 'weighted_n_node_samples', 'tau', 'variance'],
    'formats': [np.intp, np.intp, np.intp, np.float64, np.float64, np.intp,
                np.float64, np.float32, np.float64],
    'offsets': [
        <Py_ssize_t> &(<Node*> NULL).left_child,
        <Py_ssize_t> &(<Node*> NULL).right_child,
        <Py_ssize_t> &(<Node*> NULL).feature,
        <Py_ssize_t> &(<Node*> NULL).threshold,
        <Py_ssize_t> &(<Node*> NULL).impurity,
        <Py_ssize_t> &(<Node*> NULL).n_node_samples,
        <Py_ssize_t> &(<Node*> NULL).weighted_n_node_samples,
        <Py_ssize_t> &(<Node*> NULL).tau,
        <Py_ssize_t> &(<Node*> NULL).variance,
    ]
})

# =============================================================================
# TreeBuilder
# =============================================================================

cdef class TreeBuilder:
    """Interface for different tree building strategies."""

    cpdef build(self, Tree tree, object X, np.ndarray y,
                np.ndarray sample_weight=None,
                np.ndarray X_idx_sorted=None):
        """Build a decision tree from the training set (X, y)."""
        pass

    cdef inline _check_input(self, object X, np.ndarray y,
                             np.ndarray sample_weight):
        """Check input dtype, layout and format"""
        if issparse(X):
            X = X.tocsc()
            X.sort_indices()

            if X.data.dtype != DTYPE:
                X.data = np.ascontiguousarray(X.data, dtype=DTYPE)

            if X.indices.dtype != np.int32 or X.indptr.dtype != np.int32:
                raise ValueError("No support for np.int64 index based "
                                 "sparse matrices")

        elif X.dtype != DTYPE:
            # since we have to copy we will make it fortran for efficiency
            X = np.asfortranarray(X, dtype=DTYPE)

        if y.dtype != DOUBLE or not y.flags.contiguous:
            y = np.ascontiguousarray(y, dtype=DOUBLE)

        if (sample_weight is not None and
            (sample_weight.dtype != DOUBLE or
            not sample_weight.flags.contiguous)):
                sample_weight = np.asarray(sample_weight, dtype=DOUBLE,
                                           order="C")

        return X, y, sample_weight

# Depth first builder ---------------------------------------------------------

cdef class DepthFirstTreeBuilder(TreeBuilder):
    """Build a decision tree in depth-first fashion."""

    def __cinit__(self, Splitter splitter, SIZE_t min_samples_split,
                  SIZE_t max_depth):
        self.splitter = splitter
        self.min_samples_split = min_samples_split
        self.max_depth = max_depth

    cpdef build(self, Tree tree, object X, np.ndarray y,
                np.ndarray sample_weight=None,
                np.ndarray X_idx_sorted=None):
        """Build a decision tree from the training set (X, y)."""

        # check input
        X, y, sample_weight = self._check_input(X, y, sample_weight)

        cdef DOUBLE_t* sample_weight_ptr = NULL
        if sample_weight is not None:
            sample_weight_ptr = <DOUBLE_t*> sample_weight.data

        # Initial capacity
        cdef int init_capacity

        if tree.max_depth <= 10:
            init_capacity = (2 ** (tree.max_depth + 1)) - 1
        else:
            init_capacity = 2047

        tree._resize(init_capacity)

        # Parameters
        cdef Splitter splitter = self.splitter
        cdef SIZE_t max_depth = self.max_depth
        cdef SIZE_t min_samples_split = self.min_samples_split

        # Recursive partition (without actual recursion)
        splitter.init(X, y, sample_weight_ptr, X_idx_sorted)

        cdef SIZE_t start
        cdef SIZE_t end
        cdef SIZE_t depth
        cdef SIZE_t parent
        cdef bint is_left
        cdef SIZE_t n_node_samples = splitter.n_samples
        cdef double weighted_n_samples = splitter.weighted_n_samples
        cdef double weighted_n_node_samples
        cdef SplitRecord split
        cdef SIZE_t node_id

        cdef double threshold
        cdef double impurity = INFINITY
        cdef SIZE_t n_constant_features
        cdef bint is_leaf
        cdef bint first = 1
        cdef SIZE_t max_depth_seen = -1
        cdef int rc = 0

        cdef Stack stack = Stack(INITIAL_STACK_SIZE)
        cdef StackRecord stack_record

        with nogil:
            # push root node onto stack
            rc = stack.push(0, n_node_samples, 0, _TREE_UNDEFINED, 0, INFINITY, 0)
            if rc == -1:
                # got return code -1 - out-of-memory
                with gil:
                    raise MemoryError()

            while not stack.is_empty():
                stack.pop(&stack_record)

                start = stack_record.start
                end = stack_record.end
                depth = stack_record.depth
                parent = stack_record.parent
                is_left = stack_record.is_left
                impurity = stack_record.impurity
                n_constant_features = stack_record.n_constant_features

                n_node_samples = end - start
                splitter.node_reset(start, end, &weighted_n_node_samples)

                is_leaf = (depth >= max_depth or
                           n_node_samples < min_samples_split)

                if first:
                    impurity = splitter.node_impurity()
                    first = 0

                if not is_leaf:
                    is_leaf = splitter.node_split(impurity, &split, &n_constant_features)
                    is_leaf = is_leaf or (split.pos >= end)
                else:
                    splitter.set_bounds()

                node_id = tree._add_node(parent, is_left, is_leaf, split.feature,
                                         split.threshold, impurity, n_node_samples,
                                         weighted_n_node_samples,
                                         splitter.lower_bounds,
                                         splitter.upper_bounds,
                                         split.E)

                if node_id == <SIZE_t>(-1):
                    rc = -1
                    break

                # Store value for all nodes, to facilitate tree/model
                # inspection and interpretation
                splitter.node_value(tree.value + node_id * tree.value_stride)

                if not is_leaf:
                    # Push right child on stack
                    rc = stack.push(split.pos, end, depth + 1, node_id, 0,
                                    split.impurity_right, n_constant_features)
                    if rc == -1:
                        break

                    # Push left child on stack
                    rc = stack.push(start, split.pos, depth + 1, node_id, 1,
                                    split.impurity_left, n_constant_features)
                    if rc == -1:
                        break

                if depth > max_depth_seen:
                    max_depth_seen = depth

            if rc >= 0:
                rc = tree._resize_c(tree.node_count)

            if rc >= 0:
                tree.max_depth = max_depth_seen
        if rc == -1:
            raise MemoryError()

# =============================================================================
# Tree
# =============================================================================

cdef class Tree:
    """Array-based representation of a binary decision tree.

    The binary tree is represented as a number of parallel arrays. The i-th
    element of each array holds information about the node `i`. Node 0 is the
    tree's root. You can find a detailed description of all arrays in
    `_tree.pxd`. NOTE: Some of the arrays only apply to either leaves or split
    nodes, resp. In this case the values of nodes of the other type are
    arbitrary!

    Attributes
    ----------
    node_count : int
        The number of nodes (internal nodes + leaves) in the tree.

    capacity : int
        The current capacity (i.e., size) of the arrays, which is at least as
        great as `node_count`.

    max_depth : int
        The maximal depth of the tree.

    children_left : array of int, shape [node_count]
        children_left[i] holds the node id of the left child of node i.
        For leaves, children_left[i] == TREE_LEAF. Otherwise,
        children_left[i] > i. This child handles the case where
        X[:, feature[i]] <= threshold[i].

    children_right : array of int, shape [node_count]
        children_right[i] holds the node id of the right child of node i.
        For leaves, children_right[i] == TREE_LEAF. Otherwise,
        children_right[i] > i. This child handles the case where
        X[:, feature[i]] > threshold[i].

    feature : array of int, shape [node_count]
        feature[i] holds the feature to split on, for the internal node i.

    threshold : array of double, shape [node_count]
        threshold[i] holds the threshold for the internal node i.

    value : array of double, shape [node_count, n_outputs, max_n_classes]
        Contains the constant prediction value of each node.

    impurity : array of double, shape [node_count]
        impurity[i] holds the impurity (i.e., the value of the splitting
        criterion) at node i.

    n_node_samples : array of int, shape [node_count]
        n_node_samples[i] holds the number of training samples reaching node i.

    weighted_n_node_samples : array of int, shape [node_count]
        weighted_n_node_samples[i] holds the weighted number of training samples
        reaching node i.
    """
    # Wrap for outside world.
    # WARNING: these reference the current `nodes` and `value` buffers, which
    # must not be freed by a subsequent memory allocation.
    # (i.e. through `_resize` or `__setstate__`)
    property n_classes:
        def __get__(self):
            return sizet_ptr_to_ndarray(self.n_classes, self.n_outputs)

    property children_left:
        def __get__(self):
            return self._get_node_ndarray()['left_child'][:self.node_count]

    property children_right:
        def __get__(self):
            return self._get_node_ndarray()['right_child'][:self.node_count]

    property feature:
        def __get__(self):
            return self._get_node_ndarray()['feature'][:self.node_count]

    property threshold:
        def __get__(self):
            return self._get_node_ndarray()['threshold'][:self.node_count]

    property impurity:
        def __get__(self):
            return self._get_node_ndarray()['impurity'][:self.node_count]

    property n_node_samples:
        def __get__(self):
            return self._get_node_ndarray()['n_node_samples'][:self.node_count]

    property weighted_n_node_samples:
        def __get__(self):
            return self._get_node_ndarray()['weighted_n_node_samples'][:self.node_count]

    property value:
        def __get__(self):
            return self._get_value_ndarray()[:self.node_count]

    property tau:
        def __get__(self):
            return self._get_node_ndarray()["tau"][:self.node_count]

    property mean:
        def __get__(self):
            return self._get_value_ndarray()[:self.node_count].ravel()

    property variance:
        def __get__(self):
            return self._get_node_ndarray()["variance"][:self.node_count]

    def __cinit__(self, int n_features, np.ndarray[SIZE_t, ndim=1] n_classes,
                  int n_outputs):
        """Constructor."""
        # Input/Output layout
        self.n_features = n_features
        self.n_outputs = n_outputs
        self.n_classes = NULL
        safe_realloc(&self.n_classes, n_outputs)

        self.max_n_classes = np.max(n_classes)
        self.value_stride = n_outputs * self.max_n_classes

        cdef SIZE_t k
        for k in range(n_outputs):
            self.n_classes[k] = n_classes[k]

        # Inner structures
        self.max_depth = 0
        self.node_count = 0
        self.capacity = 0
        self.value = NULL
        self.nodes = NULL

    def __dealloc__(self):
        """Destructor."""
        # Free all inner structures
        free(self.n_classes)
        free(self.value)
        free(self.nodes)

    def __reduce__(self):
        """Reduce re-implementation, for pickling."""
        return (Tree, (self.n_features,
                       sizet_ptr_to_ndarray(self.n_classes, self.n_outputs),
                       self.n_outputs), self.__getstate__())

    def __getstate__(self):
        """Getstate re-implementation, for pickling."""
        d = {}
        # capacity is infered during the __setstate__ using nodes
        d["max_depth"] = self.max_depth
        d["node_count"] = self.node_count
        d["nodes"] = self._get_node_ndarray()
        d["values"] = self._get_value_ndarray()
        return d

    def __setstate__(self, d):
        """Setstate re-implementation, for unpickling."""
        self.max_depth = d["max_depth"]
        self.node_count = d["node_count"]

        if 'nodes' not in d:
            raise ValueError('You have loaded Tree version which '
                             'cannot be imported')

        node_ndarray = d['nodes']
        value_ndarray = d['values']

        value_shape = (node_ndarray.shape[0], self.n_outputs,
                       self.max_n_classes)
        if (node_ndarray.ndim != 1 or
                node_ndarray.dtype != NODE_DTYPE or
                not node_ndarray.flags.c_contiguous or
                value_ndarray.shape != value_shape or
                not value_ndarray.flags.c_contiguous or
                value_ndarray.dtype != np.float64):
            raise ValueError('Did not recognise loaded array layout')

        self.capacity = node_ndarray.shape[0]
        if self._resize_c(self.capacity) != 0:
            raise MemoryError("resizing tree to %d" % self.capacity)
        nodes = memcpy(self.nodes, (<np.ndarray> node_ndarray).data,
                       self.capacity * sizeof(Node))
        value = memcpy(self.value, (<np.ndarray> value_ndarray).data,
                       self.capacity * self.value_stride * sizeof(double))

    cdef int _resize(self, SIZE_t capacity) nogil except -1:
        """Resize all inner arrays to `capacity`, if `capacity` == -1, then
           double the size of the inner arrays.

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.
        """
        if self._resize_c(capacity) != 0:
            # Acquire gil only if we need to raise
            with gil:
                raise MemoryError()

    # XXX using (size_t)(-1) is ugly, but SIZE_MAX is not available in C89
    # (i.e., older MSVC).
    cdef int _resize_c(self, SIZE_t capacity=<SIZE_t>(-1)) nogil except -1:
        """Guts of _resize

        Returns -1 in case of failure to allocate memory (and raise MemoryError)
        or 0 otherwise.
        """
        if capacity == self.capacity and self.nodes != NULL:
            return 0

        if capacity == <SIZE_t>(-1):
            if self.capacity == 0:
                capacity = 3  # default initial value
            else:
                capacity = 2 * self.capacity

        safe_realloc(&self.nodes, capacity)
        safe_realloc(&self.value, capacity * self.value_stride)

        # value memory is initialised to 0 to enable classifier argmax
        if capacity > self.capacity:
            memset(<void*>(self.value + self.capacity * self.value_stride), 0,
                   (capacity - self.capacity) * self.value_stride *
                   sizeof(double))

        # if capacity smaller than node_count, adjust the counter
        if capacity < self.node_count:
            self.node_count = capacity

        self.capacity = capacity
        return 0

    cdef SIZE_t _add_node(self, SIZE_t parent, bint is_left, bint is_leaf,
                          SIZE_t feature, double threshold, double impurity,
                          SIZE_t n_node_samples,
                          double weighted_n_node_samples,
                          DTYPE_t* lower_bounds,
                          DTYPE_t* upper_bounds,
                          double E) nogil except -1:
        """Add a node to the tree.

        The new node registers itself as the child of its parent.

        Returns (size_t)(-1) on error.
        """
        cdef SIZE_t node_id = self.node_count

        if node_id >= self.capacity:
            if self._resize_c() != 0:
                return <SIZE_t>(-1)

        cdef Node* node = &self.nodes[node_id]
        node.impurity = impurity
        node.n_node_samples = n_node_samples
        node.weighted_n_node_samples = weighted_n_node_samples

        if parent != _TREE_UNDEFINED:
            if is_left:
                self.nodes[parent].left_child = node_id
            else:
                self.nodes[parent].right_child = node_id

        if parent == _TREE_UNDEFINED:
            node.tau = E
        elif is_leaf:
            node.tau = INFINITY
        else:
            node.tau = E + self.nodes[parent].tau

        node.lower_bounds = <DTYPE_t*> malloc(self.n_features * sizeof(DTYPE_t))
        node.upper_bounds = <DTYPE_t*> malloc(self.n_features * sizeof(DTYPE_t))
        memcpy(node.lower_bounds, lower_bounds, self.n_features*sizeof(DTYPE_t))
        memcpy(node.upper_bounds, upper_bounds, self.n_features*sizeof(DTYPE_t))
        node.variance = impurity

        if is_leaf:
            node.left_child = _TREE_LEAF
            node.right_child = _TREE_LEAF
            node.feature = _TREE_UNDEFINED
            node.threshold = _TREE_UNDEFINED
        else:
            # left_child and right_child will be set later
            node.feature = feature
            node.threshold = threshold
        self.node_count += 1
        return node_id

    cpdef np.ndarray apply(self, object X):
        """Finds the terminal region (=leaf node) for each sample in X."""
        if issparse(X):
            return self._apply_sparse_csr(X)
        else:
            return self._apply_dense(X)

    cpdef tuple predict(self, object X, bint return_std=False, bint is_regression=True):
        """Predicts the regressor and standard deviation for all samples."""

        # Check input
        if not isinstance(X, np.ndarray):
            raise ValueError("X should be in np.ndarray format, got %s"
                             % type(X))

        if X.dtype != DTYPE:
            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)

        # Extract input
        cdef np.ndarray X_ndarray = X
        cdef DTYPE_t* X_ptr = <DTYPE_t*> X_ndarray.data
        cdef SIZE_t X_sample_stride = <SIZE_t> X.strides[0] / <SIZE_t> X.itemsize
        cdef SIZE_t X_fx_stride = <SIZE_t> X.strides[1] / <SIZE_t> X.itemsize
        cdef SIZE_t n_samples = X.shape[0]
        cdef SIZE_t n_features = X.shape[1]
        cdef SIZE_t f_ind

        # We currently support only single-output y.
        # These node values are the means in case of regression.
        # For classification these are the class counts.
        cdef np.ndarray[DOUBLE_t, ndim=2] node_values = self._get_value_ndarray()[:, 0, :]

        # Initialize output
        cdef np.ndarray[DTYPE_t, ndim=1] mean = np.zeros(n_samples, dtype=DTYPE)
        cdef np.ndarray[DTYPE_t, ndim=1] std = np.zeros(n_samples, dtype=DTYPE)

        cdef SIZE_t n_classes = node_values.shape[1]
        cdef np.ndarray[DTYPE_t, ndim=2] proba = np.zeros((n_samples, n_classes), dtype=DTYPE)
        cdef np.ndarray[SIZE_t, ndim=1] n_node_samples = self.n_node_samples

        # Initialize auxiliary data-structure
        cdef Node* node = NULL
        cdef SIZE_t i = 0
        cdef SIZE_t j
        cdef SIZE_t node_id = 0
        cdef DOUBLE_t Delta = 0.0
        cdef DOUBLE_t parent_tau
        cdef DOUBLE_t p_js
        cdef DOUBLE_t X_val
        cdef DOUBLE_t w_j

        # Algorithm 6.5
        cdef DOUBLE_t p_nsy
        cdef SIZE_t sample_ind
        cdef SIZE_t class_ind

        with nogil:
            for i in range(n_samples):
                # Step 3
                parent_tau = 0.0
                p_nsy = 1.0
                node_id = 0

                while True:
                    node = &self.nodes[node_id]

                    # Step 5: First part.
                    # Calculate Delta
                    Delta = node.tau - parent_tau
                    parent_tau = node.tau

                    # Step 5: Second part.
                    # Calculate eta
                    eta = 0.0
                    for f_ind in range(n_features):
                        X_val = X_ptr[X_sample_stride*i + X_fx_stride*f_ind]

                        eta += (fmax(X_val - node.upper_bounds[f_ind], 0) +
                                fmax(node.lower_bounds[f_ind] - X_val, 0))

                    # Step 6: Calculate p_j
                    # Step 7-11
                    if node.left_child == _TREE_LEAF:
                        w_j = p_nsy
                    else:
                        p_js = 1 - exp(-Delta * eta)
                        w_j = p_nsy * p_js

                    if is_regression:
                        mean[i] += w_j * node_values[node_id, 0]
                    else:
                        for class_ind in range(n_classes):
                            proba[i, class_ind] += w_j * (node_values[node_id, class_ind] / n_node_samples[node_id])

                    if return_std:
                        std[i] += w_j * (node_values[node_id, 0]**2 + node.variance)

                    if node.left_child == _TREE_LEAF:
                        break
                    p_nsy = p_nsy * (1 - p_js)

                    # Step 12-14
                    if X_ptr[X_sample_stride * i +
                             X_fx_stride * node.feature] <= node.threshold:
                        node_id = node.left_child
                    else:
                        node_id = node.right_child

                if return_std:
                    std[i] -= mean[i]**2
                    if std[i] <= 0:
                        std[i] = 0.0
                    std[i] = sqrt(std[i])

        if is_regression:
            if return_std:
                return mean, std
            return mean,
        else:
            return proba,


    cdef inline np.ndarray _apply_dense(self, object X):
        """Finds the terminal region (=leaf node) for each sample in X."""

        # Check input
        if not isinstance(X, np.ndarray):
            raise ValueError("X should be in np.ndarray format, got %s"
                             % type(X))

        if X.dtype != DTYPE:
            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)

        # Extract input
        cdef np.ndarray X_ndarray = X
        cdef DTYPE_t* X_ptr = <DTYPE_t*> X_ndarray.data
        cdef SIZE_t X_sample_stride = <SIZE_t> X.strides[0] / <SIZE_t> X.itemsize
        cdef SIZE_t X_fx_stride = <SIZE_t> X.strides[1] / <SIZE_t> X.itemsize
        cdef SIZE_t n_samples = X.shape[0]
        cdef SIZE_t n_features = X.shape[1]
        cdef SIZE_t f_ind

        # Initialize output
        cdef np.ndarray[SIZE_t] out = np.zeros((n_samples,), dtype=np.intp)
        cdef SIZE_t* out_ptr = <SIZE_t*> out.data

        # Initialize auxiliary data-structure
        cdef Node* node = NULL
        cdef SIZE_t i = 0

        with nogil:
            for i in range(n_samples):
                node = self.nodes
                # While node not a leaf
                while node.left_child != _TREE_LEAF:
                    # ... and node.right_child != _TREE_LEAF:
                    if X_ptr[X_sample_stride * i +
                             X_fx_stride * node.feature] <= node.threshold:
                        node = &self.nodes[node.left_child]
                    else:
                        node = &self.nodes[node.right_child]

                out_ptr[i] = <SIZE_t>(node - self.nodes)  # node offset

        return out

    cdef inline np.ndarray _apply_sparse_csr(self, object X):
        """Finds the terminal region (=leaf node) for each sample in sparse X.
        """
        # Check input
        if not isinstance(X, csr_matrix):
            raise ValueError("X should be in csr_matrix format, got %s"
                             % type(X))

        if X.dtype != DTYPE:
            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)

        # Extract input
        cdef np.ndarray[ndim=1, dtype=DTYPE_t] X_data_ndarray = X.data
        cdef np.ndarray[ndim=1, dtype=INT32_t] X_indices_ndarray  = X.indices
        cdef np.ndarray[ndim=1, dtype=INT32_t] X_indptr_ndarray  = X.indptr

        cdef DTYPE_t* X_data = <DTYPE_t*>X_data_ndarray.data
        cdef INT32_t* X_indices = <INT32_t*>X_indices_ndarray.data
        cdef INT32_t* X_indptr = <INT32_t*>X_indptr_ndarray.data

        cdef SIZE_t n_samples = X.shape[0]
        cdef SIZE_t n_features = X.shape[1]

        # Initialize output
        cdef np.ndarray[SIZE_t, ndim=1] out = np.zeros((n_samples,),
                                                       dtype=np.intp)
        cdef SIZE_t* out_ptr = <SIZE_t*> out.data

        # Initialize auxiliary data-structure
        cdef DTYPE_t feature_value = 0.
        cdef Node* node = NULL
        cdef DTYPE_t* X_sample = NULL
        cdef SIZE_t i = 0
        cdef INT32_t k = 0

        # feature_to_sample as a data structure records the last seen sample
        # for each feature; functionally, it is an efficient way to identify
        # which features are nonzero in the present sample.
        cdef SIZE_t* feature_to_sample = NULL

        safe_realloc(&X_sample, n_features)
        safe_realloc(&feature_to_sample, n_features)

        with nogil:
            memset(feature_to_sample, -1, n_features * sizeof(SIZE_t))

            for i in range(n_samples):
                node = self.nodes

                for k in range(X_indptr[i], X_indptr[i + 1]):
                    feature_to_sample[X_indices[k]] = i
                    X_sample[X_indices[k]] = X_data[k]

                # While node not a leaf
                while node.left_child != _TREE_LEAF:
                    # ... and node.right_child != _TREE_LEAF:
                    if feature_to_sample[node.feature] == i:
                        feature_value = X_sample[node.feature]

                    else:
                        feature_value = 0.

                    if feature_value <= node.threshold:
                        node = &self.nodes[node.left_child]
                    else:
                        node = &self.nodes[node.right_child]

                out_ptr[i] = <SIZE_t>(node - self.nodes)  # node offset

            # Free auxiliary arrays
            free(X_sample)
            free(feature_to_sample)

        return out

    cpdef object decision_path(self, object X):
        """Finds the decision path (=node) for each sample in X."""
        if issparse(X):
            return self._decision_path_sparse_csr(X)
        else:
            return self._decision_path_dense(X)

    cpdef object weighted_decision_path(self, object X):
        """Returns the weight at each node for each sample in X."""
        if not isinstance(X, np.ndarray):
            raise ValueError("X should be in np.ndarray format, got %s"
                             % type(X))

        if X.dtype != DTYPE:
            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)

        # Extract input
        cdef np.ndarray X_ndarray = X
        cdef DTYPE_t* X_ptr = <DTYPE_t*> X_ndarray.data
        cdef SIZE_t X_sample_stride = <SIZE_t> X.strides[0] / <SIZE_t> X.itemsize
        cdef SIZE_t X_fx_stride = <SIZE_t> X.strides[1] / <SIZE_t> X.itemsize
        cdef SIZE_t n_samples = X.shape[0]
        cdef SIZE_t n_features = X.shape[1]

        # Initialize output
        cdef np.ndarray[SIZE_t] indptr = np.zeros(n_samples + 1, dtype=np.intp)
        cdef SIZE_t* indptr_ptr = <SIZE_t*> indptr.data

        cdef np.ndarray[SIZE_t] indices = np.zeros(n_samples *
                                                   (1 + self.max_depth),
                                                   dtype=np.intp)
        cdef SIZE_t* indices_ptr = <SIZE_t*> indices.data

        cdef np.ndarray[DTYPE_t] values = np.zeros(n_samples *
                                                (1 + self.max_depth),
                                                 dtype=DTYPE)
        cdef DTYPE_t* values_ptr = <DTYPE_t*> values.data

        # Initialize auxiliary data-structure
        cdef Node* node = NULL
        cdef SIZE_t i = 0

        cdef DTYPE_t parent_tau
        cdef DTYPE_t delta
        cdef DTYPE_t eta
        cdef SIZE_t f_ind
        cdef DTYPE_t X_val
        cdef DTYPE_t p_s
        cdef DTYPE_t p_nsy

        with nogil:
            for i in range(n_samples):
                p_nsy = 1.0
                node = self.nodes
                parent_tau = 0.0

                indptr_ptr[i + 1] = indptr_ptr[i]

                # Add all external nodes
                while node.left_child != _TREE_LEAF:

                    delta = node.tau - parent_tau
                    parent_tau = node.tau

                    eta = 0.0
                    for f_ind in range(n_features):
                        X_val = X_ptr[X_sample_stride * i + X_fx_stride * f_ind]
                        eta += (fmax(X_val - node.upper_bounds[f_ind], 0.0) +
                                fmax(node.lower_bounds[f_ind] - X_val, 0.0))
                    p_s = 1 - exp(-delta*eta)

                    if p_s > 0:
                        # ... and node.right_child != _TREE_LEAF:
                        indices_ptr[indptr_ptr[i + 1]] = <SIZE_t>(node - self.nodes)
                        values_ptr[indptr_ptr[i + 1]] = p_s * p_nsy
                        indptr_ptr[i + 1] += 1

                    if X_ptr[X_sample_stride * i +
                             X_fx_stride * node.feature] <= node.threshold:
                        node = &self.nodes[node.left_child]
                    else:
                        node = &self.nodes[node.right_child]
                    p_nsy *= (1 - p_s)

                # Add the leave node
                indices_ptr[indptr_ptr[i + 1]] = <SIZE_t>(node - self.nodes)
                values_ptr[indptr_ptr[i + 1]] = p_nsy
                indptr_ptr[i + 1] += 1

        indices = indices[:indptr[n_samples]]
        values = values[:indptr[n_samples]]
        out = csr_matrix((values, indices, indptr),
                         shape=(n_samples, self.node_count))

        return out



    cdef inline object _decision_path_dense(self, object X):
        """Finds the decision path (=node) for each sample in X."""

        # Check input
        if not isinstance(X, np.ndarray):
            raise ValueError("X should be in np.ndarray format, got %s"
                             % type(X))

        if X.dtype != DTYPE:
            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)

        # Extract input
        cdef np.ndarray X_ndarray = X
        cdef DTYPE_t* X_ptr = <DTYPE_t*> X_ndarray.data
        cdef SIZE_t X_sample_stride = <SIZE_t> X.strides[0] / <SIZE_t> X.itemsize
        cdef SIZE_t X_fx_stride = <SIZE_t> X.strides[1] / <SIZE_t> X.itemsize
        cdef SIZE_t n_samples = X.shape[0]

        # Initialize output
        cdef np.ndarray[SIZE_t] indptr = np.zeros(n_samples + 1, dtype=np.intp)
        cdef SIZE_t* indptr_ptr = <SIZE_t*> indptr.data

        cdef np.ndarray[SIZE_t] indices = np.zeros(n_samples *
                                                   (1 + self.max_depth),
                                                   dtype=np.intp)
        cdef SIZE_t* indices_ptr = <SIZE_t*> indices.data

        # Initialize auxiliary data-structure
        cdef Node* node = NULL
        cdef SIZE_t i = 0

        with nogil:
            for i in range(n_samples):
                node = self.nodes
                indptr_ptr[i + 1] = indptr_ptr[i]

                # Add all external nodes
                while node.left_child != _TREE_LEAF:
                    # ... and node.right_child != _TREE_LEAF:
                    indices_ptr[indptr_ptr[i + 1]] = <SIZE_t>(node - self.nodes)
                    indptr_ptr[i + 1] += 1

                    if X_ptr[X_sample_stride * i +
                             X_fx_stride * node.feature] <= node.threshold:
                        node = &self.nodes[node.left_child]
                    else:
                        node = &self.nodes[node.right_child]

                # Add the leave node
                indices_ptr[indptr_ptr[i + 1]] = <SIZE_t>(node - self.nodes)
                indptr_ptr[i + 1] += 1

        indices = indices[:indptr[n_samples]]
        cdef np.ndarray[SIZE_t] data = np.ones(shape=len(indices),
                                               dtype=np.intp)
        out = csr_matrix((data, indices, indptr),
                         shape=(n_samples, self.node_count))

        return out

    cdef inline object _decision_path_sparse_csr(self, object X):
        """Finds the decision path (=node) for each sample in X."""

        # Check input
        if not isinstance(X, csr_matrix):
            raise ValueError("X should be in csr_matrix format, got %s"
                             % type(X))

        if X.dtype != DTYPE:
            raise ValueError("X.dtype should be np.float32, got %s" % X.dtype)

        # Extract input
        cdef np.ndarray[ndim=1, dtype=DTYPE_t] X_data_ndarray = X.data
        cdef np.ndarray[ndim=1, dtype=INT32_t] X_indices_ndarray  = X.indices
        cdef np.ndarray[ndim=1, dtype=INT32_t] X_indptr_ndarray  = X.indptr

        cdef DTYPE_t* X_data = <DTYPE_t*>X_data_ndarray.data
        cdef INT32_t* X_indices = <INT32_t*>X_indices_ndarray.data
        cdef INT32_t* X_indptr = <INT32_t*>X_indptr_ndarray.data

        cdef SIZE_t n_samples = X.shape[0]
        cdef SIZE_t n_features = X.shape[1]

        # Initialize output
        cdef np.ndarray[SIZE_t] indptr = np.zeros(n_samples + 1, dtype=np.intp)
        cdef SIZE_t* indptr_ptr = <SIZE_t*> indptr.data

        cdef np.ndarray[SIZE_t] indices = np.zeros(n_samples *
                                                   (1 + self.max_depth),
                                                   dtype=np.intp)
        cdef SIZE_t* indices_ptr = <SIZE_t*> indices.data

        # Initialize auxiliary data-structure
        cdef DTYPE_t feature_value = 0.
        cdef Node* node = NULL
        cdef DTYPE_t* X_sample = NULL
        cdef SIZE_t i = 0
        cdef INT32_t k = 0

        # feature_to_sample as a data structure records the last seen sample
        # for each feature; functionally, it is an efficient way to identify
        # which features are nonzero in the present sample.
        cdef SIZE_t* feature_to_sample = NULL

        safe_realloc(&X_sample, n_features)
        safe_realloc(&feature_to_sample, n_features)

        with nogil:
            memset(feature_to_sample, -1, n_features * sizeof(SIZE_t))

            for i in range(n_samples):
                node = self.nodes
                indptr_ptr[i + 1] = indptr_ptr[i]

                for k in range(X_indptr[i], X_indptr[i + 1]):
                    feature_to_sample[X_indices[k]] = i
                    X_sample[X_indices[k]] = X_data[k]

                # While node not a leaf
                while node.left_child != _TREE_LEAF:
                    # ... and node.right_child != _TREE_LEAF:

                    indices_ptr[indptr_ptr[i + 1]] = <SIZE_t>(node - self.nodes)
                    indptr_ptr[i + 1] += 1

                    if feature_to_sample[node.feature] == i:
                        feature_value = X_sample[node.feature]

                    else:
                        feature_value = 0.

                    if feature_value <= node.threshold:
                        node = &self.nodes[node.left_child]
                    else:
                        node = &self.nodes[node.right_child]

                # Add the leave node
                indices_ptr[indptr_ptr[i + 1]] = <SIZE_t>(node - self.nodes)
                indptr_ptr[i + 1] += 1

            # Free auxiliary arrays
            free(X_sample)
            free(feature_to_sample)

        indices = indices[:indptr[n_samples]]
        cdef np.ndarray[SIZE_t] data = np.ones(shape=len(indices),
                                               dtype=np.intp)
        out = csr_matrix((data, indices, indptr),
                         shape=(n_samples, self.node_count))

        return out

    cdef np.ndarray _get_value_ndarray(self):
        """Wraps value as a 3-d NumPy array.

        The array keeps a reference to this Tree, which manages the underlying
        memory.
        """
        cdef np.npy_intp shape[3]
        shape[0] = <np.npy_intp> self.node_count
        shape[1] = <np.npy_intp> self.n_outputs
        shape[2] = <np.npy_intp> self.max_n_classes
        cdef np.ndarray arr
        arr = np.PyArray_SimpleNewFromData(3, shape, np.NPY_DOUBLE, self.value)
        Py_INCREF(self)
        arr.base = <PyObject*> self
        return arr

    cdef np.ndarray _get_node_ndarray(self):
        """Wraps nodes as a NumPy struct array.

        The array keeps a reference to this Tree, which manages the underlying
        memory. Individual fields are publicly accessible as properties of the
        Tree.
        """
        cdef np.npy_intp shape[1]
        shape[0] = <np.npy_intp> self.node_count
        cdef np.npy_intp strides[1]
        strides[0] = sizeof(Node)
        cdef np.ndarray arr
        Py_INCREF(NODE_DTYPE)
        arr = PyArray_NewFromDescr(np.ndarray, <np.dtype> NODE_DTYPE, 1, shape,
                                   strides, <void*> self.nodes,
                                   np.NPY_DEFAULT, None)
        Py_INCREF(self)
        arr.base = <PyObject*> self
        return arr
