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# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Joel Nothman <joel.nothman@gmail.com>
# Arnaud Joly <arnaud.v.joly@gmail.com>
# Jacob Schreiber <jmschreiber91@gmail.com>
#
# License: BSD 3 clause
# See _splitter.pyx for details.
cimport numpy as cnp
from ._criterion cimport Criterion
from ..utils._typedefs cimport float32_t, float64_t, intp_t, int32_t, uint32_t
cdef struct SplitRecord:
# Data to track sample split
intp_t feature # Which feature to split on.
intp_t pos # Split samples array at the given position,
# # i.e. count of samples below threshold for feature.
# # pos is >= end if the node is a leaf.
float64_t threshold # Threshold to split at.
float64_t improvement # Impurity improvement given parent node.
float64_t impurity_left # Impurity of the left split.
float64_t impurity_right # Impurity of the right split.
float64_t lower_bound # Lower bound on value of both children for monotonicity
float64_t upper_bound # Upper bound on value of both children for monotonicity
unsigned char missing_go_to_left # Controls if missing values go to the left node.
intp_t n_missing # Number of missing values for the feature being split on
cdef class Splitter:
# The splitter searches in the input space for a feature and a threshold
# to split the samples samples[start:end].
#
# The impurity computations are delegated to a criterion object.
# Internal structures
cdef public Criterion criterion # Impurity criterion
cdef public intp_t max_features # Number of features to test
cdef public intp_t min_samples_leaf # Min samples in a leaf
cdef public float64_t min_weight_leaf # Minimum weight in a leaf
cdef object random_state # Random state
cdef uint32_t rand_r_state # sklearn_rand_r random number state
cdef intp_t[::1] samples # Sample indices in X, y
cdef intp_t n_samples # X.shape[0]
cdef float64_t weighted_n_samples # Weighted number of samples
cdef intp_t[::1] features # Feature indices in X
cdef intp_t[::1] constant_features # Constant features indices
cdef intp_t n_features # X.shape[1]
cdef float32_t[::1] feature_values # temp. array holding feature values
cdef intp_t start # Start position for the current node
cdef intp_t end # End position for the current node
cdef const float64_t[:, ::1] y
# Monotonicity constraints for each feature.
# The encoding is as follows:
# -1: monotonic decrease
# 0: no constraint
# +1: monotonic increase
cdef const cnp.int8_t[:] monotonic_cst
cdef bint with_monotonic_cst
cdef const float64_t[:] sample_weight
# The samples vector `samples` is maintained by the Splitter object such
# that the samples contained in a node are contiguous. With this setting,
# `node_split` reorganizes the node samples `samples[start:end]` in two
# subsets `samples[start:pos]` and `samples[pos:end]`.
# The 1-d `features` array of size n_features contains the features
# indices and allows fast sampling without replacement of features.
# The 1-d `constant_features` array of size n_features holds in
# `constant_features[:n_constant_features]` the feature ids with
# constant values for all the samples that reached a specific node.
# The value `n_constant_features` is given by the parent node to its
# child nodes. The content of the range `[n_constant_features:]` is left
# undefined, but preallocated for performance reasons
# This allows optimization with depth-based tree building.
# Methods
cdef int init(
self,
object X,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight,
const unsigned char[::1] missing_values_in_feature_mask,
) except -1
cdef int node_reset(
self,
intp_t start,
intp_t end,
float64_t* weighted_n_node_samples
) except -1 nogil
cdef int node_split(
self,
float64_t impurity, # Impurity of the node
SplitRecord* split,
intp_t* n_constant_features,
float64_t lower_bound,
float64_t upper_bound,
) except -1 nogil
cdef void node_value(self, float64_t* dest) noexcept nogil
cdef void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil
cdef float64_t node_impurity(self) noexcept nogil
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