1630 lines
59 KiB
Cython
1630 lines
59 KiB
Cython
# 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>
|
|
#
|
|
# License: BSD 3 clause
|
|
|
|
cimport numpy as cnp
|
|
|
|
from ._criterion cimport Criterion
|
|
|
|
from libc.stdlib cimport qsort
|
|
from libc.string cimport memcpy
|
|
from libc.math cimport isnan
|
|
from cython cimport final
|
|
|
|
import numpy as np
|
|
|
|
from scipy.sparse import issparse
|
|
|
|
from ._utils cimport log
|
|
from ._utils cimport rand_int
|
|
from ._utils cimport rand_uniform
|
|
from ._utils cimport RAND_R_MAX
|
|
|
|
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 SparsePartitioner
|
|
cdef DTYPE_t EXTRACT_NNZ_SWITCH = 0.1
|
|
|
|
cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) noexcept nogil:
|
|
self.impurity_left = INFINITY
|
|
self.impurity_right = INFINITY
|
|
self.pos = start_pos
|
|
self.feature = 0
|
|
self.threshold = 0.
|
|
self.improvement = -INFINITY
|
|
self.missing_go_to_left = False
|
|
self.n_missing = 0
|
|
|
|
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,
|
|
const cnp.int8_t[:] monotonic_cst,
|
|
):
|
|
"""
|
|
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
|
|
|
|
monotonic_cst : const cnp.int8_t[:]
|
|
Monotonicity constraints
|
|
|
|
"""
|
|
|
|
self.criterion = criterion
|
|
|
|
self.n_samples = 0
|
|
self.n_features = 0
|
|
|
|
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.monotonic_cst = monotonic_cst
|
|
self.with_monotonic_cst = monotonic_cst is not None
|
|
|
|
def __getstate__(self):
|
|
return {}
|
|
|
|
def __setstate__(self, d):
|
|
pass
|
|
|
|
def __reduce__(self):
|
|
return (type(self), (self.criterion,
|
|
self.max_features,
|
|
self.min_samples_leaf,
|
|
self.min_weight_leaf,
|
|
self.random_state,
|
|
self.monotonic_cst), self.__getstate__())
|
|
|
|
cdef int init(
|
|
self,
|
|
object X,
|
|
const DOUBLE_t[:, ::1] y,
|
|
const DOUBLE_t[:] sample_weight,
|
|
const unsigned char[::1] missing_values_in_feature_mask,
|
|
) except -1:
|
|
"""Initialize the splitter.
|
|
|
|
Take in the input data X, the target Y, and optional sample weights.
|
|
|
|
Returns -1 in case of failure to allocate memory (and raise MemoryError)
|
|
or 0 otherwise.
|
|
|
|
Parameters
|
|
----------
|
|
X : object
|
|
This contains the inputs. Usually it is a 2d numpy array.
|
|
|
|
y : ndarray, dtype=DOUBLE_t
|
|
This is the vector of targets, or true labels, for the samples represented
|
|
as a Cython memoryview.
|
|
|
|
sample_weight : ndarray, dtype=DOUBLE_t
|
|
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. This is represented
|
|
as a Cython memoryview.
|
|
|
|
has_missing : bool
|
|
At least one missing values is in X.
|
|
"""
|
|
|
|
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
|
|
self.samples = np.empty(n_samples, dtype=np.intp)
|
|
cdef SIZE_t[::1] samples = self.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 is None or sample_weight[i] != 0.0:
|
|
samples[j] = i
|
|
j += 1
|
|
|
|
if sample_weight is not None:
|
|
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]
|
|
self.features = np.arange(n_features, dtype=np.intp)
|
|
self.n_features = n_features
|
|
|
|
self.feature_values = np.empty(n_samples, dtype=np.float32)
|
|
self.constant_features = np.empty(n_features, dtype=np.intp)
|
|
|
|
self.y = y
|
|
|
|
self.sample_weight = sample_weight
|
|
if missing_values_in_feature_mask is not None:
|
|
self.criterion.init_sum_missing()
|
|
return 0
|
|
|
|
cdef int node_reset(self, SIZE_t start, SIZE_t end,
|
|
double* weighted_n_node_samples) except -1 nogil:
|
|
"""Reset splitter on node samples[start:end].
|
|
|
|
Returns -1 in case of failure to allocate memory (and raise MemoryError)
|
|
or 0 otherwise.
|
|
|
|
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 : ndarray, dtype=double pointer
|
|
The total weight of those samples
|
|
"""
|
|
|
|
self.start = start
|
|
self.end = end
|
|
|
|
self.criterion.init(
|
|
self.y,
|
|
self.sample_weight,
|
|
self.weighted_n_samples,
|
|
self.samples,
|
|
start,
|
|
end
|
|
)
|
|
|
|
weighted_n_node_samples[0] = self.criterion.weighted_n_node_samples
|
|
return 0
|
|
|
|
cdef int node_split(
|
|
self,
|
|
double impurity,
|
|
SplitRecord* split,
|
|
SIZE_t* n_constant_features,
|
|
double lower_bound,
|
|
double upper_bound,
|
|
) except -1 nogil:
|
|
|
|
"""Find the best split on node samples[start:end].
|
|
|
|
This is a placeholder method. The majority of computation will be done
|
|
here.
|
|
|
|
It should return -1 upon errors.
|
|
"""
|
|
|
|
pass
|
|
|
|
cdef void node_value(self, double* dest) noexcept nogil:
|
|
"""Copy the value of node samples[start:end] into dest."""
|
|
|
|
self.criterion.node_value(dest)
|
|
|
|
cdef inline void clip_node_value(self, double* dest, double lower_bound, double upper_bound) noexcept nogil:
|
|
"""Clip the value in dest between lower_bound and upper_bound for monotonic constraints."""
|
|
|
|
self.criterion.clip_node_value(dest, lower_bound, upper_bound)
|
|
|
|
cdef double node_impurity(self) noexcept nogil:
|
|
"""Return the impurity of the current node."""
|
|
|
|
return self.criterion.node_impurity()
|
|
|
|
cdef inline void shift_missing_values_to_left_if_required(
|
|
SplitRecord* best,
|
|
SIZE_t[::1] samples,
|
|
SIZE_t end,
|
|
) nogil:
|
|
cdef SIZE_t i, p, current_end
|
|
# The partitioner partitions the data such that the missing values are in
|
|
# samples[-n_missing:] for the criterion to consume. If the missing values
|
|
# are going to the right node, then the missing values are already in the
|
|
# correct position. If the missing values go left, then we move the missing
|
|
# values to samples[best.pos:best.pos+n_missing] and update `best.pos`.
|
|
if best.n_missing > 0 and best.missing_go_to_left:
|
|
for p in range(best.n_missing):
|
|
i = best.pos + p
|
|
current_end = end - 1 - p
|
|
samples[i], samples[current_end] = samples[current_end], samples[i]
|
|
best.pos += best.n_missing
|
|
|
|
# Introduce a fused-class to make it possible to share the split implementation
|
|
# between the dense and sparse cases in the node_split_best and node_split_random
|
|
# functions. The alternative would have been to use inheritance-based polymorphism
|
|
# but it would have resulted in a ~10% overall tree fitting performance
|
|
# degradation caused by the overhead frequent virtual method lookups.
|
|
ctypedef fused Partitioner:
|
|
DensePartitioner
|
|
SparsePartitioner
|
|
|
|
cdef inline int node_split_best(
|
|
Splitter splitter,
|
|
Partitioner partitioner,
|
|
Criterion criterion,
|
|
double impurity,
|
|
SplitRecord* split,
|
|
SIZE_t* n_constant_features,
|
|
bint with_monotonic_cst,
|
|
const cnp.int8_t[:] monotonic_cst,
|
|
double lower_bound,
|
|
double upper_bound,
|
|
) except -1 nogil:
|
|
"""Find the best split on node samples[start:end]
|
|
|
|
Returns -1 in case of failure to allocate memory (and raise MemoryError)
|
|
or 0 otherwise.
|
|
"""
|
|
# Find the best split
|
|
cdef SIZE_t start = splitter.start
|
|
cdef SIZE_t end = splitter.end
|
|
cdef SIZE_t end_non_missing
|
|
cdef SIZE_t n_missing = 0
|
|
cdef bint has_missing = 0
|
|
cdef SIZE_t n_searches
|
|
cdef SIZE_t n_left, n_right
|
|
cdef bint missing_go_to_left
|
|
|
|
cdef SIZE_t[::1] samples = splitter.samples
|
|
cdef SIZE_t[::1] features = splitter.features
|
|
cdef SIZE_t[::1] constant_features = splitter.constant_features
|
|
cdef SIZE_t n_features = splitter.n_features
|
|
|
|
cdef DTYPE_t[::1] feature_values = splitter.feature_values
|
|
cdef SIZE_t max_features = splitter.max_features
|
|
cdef SIZE_t min_samples_leaf = splitter.min_samples_leaf
|
|
cdef double min_weight_leaf = splitter.min_weight_leaf
|
|
cdef UINT32_t* random_state = &splitter.rand_r_state
|
|
|
|
cdef SplitRecord best_split, current_split
|
|
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 p_prev
|
|
|
|
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
|
|
|
|
_init_split(&best_split, end)
|
|
|
|
partitioner.init_node_split(start, 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[
|
|
features[n_drawn_constants], features[f_j] = features[f_j], features[n_drawn_constants]
|
|
|
|
n_drawn_constants += 1
|
|
continue
|
|
|
|
# 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_split.feature = features[f_j]
|
|
partitioner.sort_samples_and_feature_values(current_split.feature)
|
|
n_missing = partitioner.n_missing
|
|
end_non_missing = end - n_missing
|
|
|
|
if (
|
|
# All values for this feature are missing, or
|
|
end_non_missing == start or
|
|
# This feature is considered constant (max - min <= FEATURE_THRESHOLD)
|
|
feature_values[end_non_missing - 1] <= feature_values[start] + FEATURE_THRESHOLD
|
|
):
|
|
# We consider this feature constant in this case.
|
|
# Since finding a split among constant feature is not valuable,
|
|
# we do not consider this feature for splitting.
|
|
features[f_j], features[n_total_constants] = features[n_total_constants], features[f_j]
|
|
|
|
n_found_constants += 1
|
|
n_total_constants += 1
|
|
continue
|
|
|
|
f_i -= 1
|
|
features[f_i], features[f_j] = features[f_j], features[f_i]
|
|
has_missing = n_missing != 0
|
|
if has_missing:
|
|
criterion.init_missing(n_missing)
|
|
# Evaluate all splits
|
|
|
|
# If there are missing values, then we search twice for the most optimal split.
|
|
# The first search will have all the missing values going to the right node.
|
|
# The second search will have all the missing values going to the left node.
|
|
# If there are no missing values, then we search only once for the most
|
|
# optimal split.
|
|
n_searches = 2 if has_missing else 1
|
|
|
|
for i in range(n_searches):
|
|
missing_go_to_left = i == 1
|
|
criterion.missing_go_to_left = missing_go_to_left
|
|
criterion.reset()
|
|
|
|
p = start
|
|
|
|
while p < end_non_missing:
|
|
partitioner.next_p(&p_prev, &p)
|
|
|
|
if p >= end_non_missing:
|
|
continue
|
|
|
|
if missing_go_to_left:
|
|
n_left = p - start + n_missing
|
|
n_right = end_non_missing - p
|
|
else:
|
|
n_left = p - start
|
|
n_right = end_non_missing - p + n_missing
|
|
|
|
# Reject if min_samples_leaf is not guaranteed
|
|
if n_left < min_samples_leaf or n_right < min_samples_leaf:
|
|
continue
|
|
|
|
current_split.pos = p
|
|
criterion.update(current_split.pos)
|
|
|
|
# Reject if monotonicity constraints are not satisfied
|
|
if (
|
|
with_monotonic_cst and
|
|
monotonic_cst[current_split.feature] != 0 and
|
|
not criterion.check_monotonicity(
|
|
monotonic_cst[current_split.feature],
|
|
lower_bound,
|
|
upper_bound,
|
|
)
|
|
):
|
|
continue
|
|
|
|
# Reject if min_weight_leaf is not satisfied
|
|
if ((criterion.weighted_n_left < min_weight_leaf) or
|
|
(criterion.weighted_n_right < min_weight_leaf)):
|
|
continue
|
|
|
|
current_proxy_improvement = criterion.proxy_impurity_improvement()
|
|
|
|
if current_proxy_improvement > best_proxy_improvement:
|
|
best_proxy_improvement = current_proxy_improvement
|
|
# sum of halves is used to avoid infinite value
|
|
current_split.threshold = (
|
|
feature_values[p_prev] / 2.0 + feature_values[p] / 2.0
|
|
)
|
|
|
|
if (
|
|
current_split.threshold == feature_values[p] or
|
|
current_split.threshold == INFINITY or
|
|
current_split.threshold == -INFINITY
|
|
):
|
|
current_split.threshold = feature_values[p_prev]
|
|
|
|
current_split.n_missing = n_missing
|
|
if n_missing == 0:
|
|
current_split.missing_go_to_left = n_left > n_right
|
|
else:
|
|
current_split.missing_go_to_left = missing_go_to_left
|
|
|
|
best_split = current_split # copy
|
|
|
|
# Evaluate when there are missing values and all missing values goes
|
|
# to the right node and non-missing values goes to the left node.
|
|
if has_missing:
|
|
n_left, n_right = end - start - n_missing, n_missing
|
|
p = end - n_missing
|
|
missing_go_to_left = 0
|
|
|
|
if not (n_left < min_samples_leaf or n_right < min_samples_leaf):
|
|
criterion.missing_go_to_left = missing_go_to_left
|
|
criterion.update(p)
|
|
|
|
if not ((criterion.weighted_n_left < min_weight_leaf) or
|
|
(criterion.weighted_n_right < min_weight_leaf)):
|
|
current_proxy_improvement = criterion.proxy_impurity_improvement()
|
|
|
|
if current_proxy_improvement > best_proxy_improvement:
|
|
best_proxy_improvement = current_proxy_improvement
|
|
current_split.threshold = INFINITY
|
|
current_split.missing_go_to_left = missing_go_to_left
|
|
current_split.n_missing = n_missing
|
|
current_split.pos = p
|
|
best_split = current_split
|
|
|
|
# Reorganize into samples[start:best_split.pos] + samples[best_split.pos:end]
|
|
if best_split.pos < end:
|
|
partitioner.partition_samples_final(
|
|
best_split.pos,
|
|
best_split.threshold,
|
|
best_split.feature,
|
|
best_split.n_missing
|
|
)
|
|
if best_split.n_missing != 0:
|
|
criterion.init_missing(best_split.n_missing)
|
|
criterion.missing_go_to_left = best_split.missing_go_to_left
|
|
|
|
criterion.reset()
|
|
criterion.update(best_split.pos)
|
|
criterion.children_impurity(
|
|
&best_split.impurity_left, &best_split.impurity_right
|
|
)
|
|
best_split.improvement = criterion.impurity_improvement(
|
|
impurity,
|
|
best_split.impurity_left,
|
|
best_split.impurity_right
|
|
)
|
|
|
|
shift_missing_values_to_left_if_required(&best_split, samples, end)
|
|
|
|
# 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[0], &constant_features[0], 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_split
|
|
n_constant_features[0] = n_total_constants
|
|
return 0
|
|
|
|
|
|
# Sort n-element arrays pointed to by feature_values and samples, simultaneously,
|
|
# by the values in feature_values. Algorithm: Introsort (Musser, SP&E, 1997).
|
|
cdef inline void sort(DTYPE_t* feature_values, SIZE_t* samples, SIZE_t n) noexcept nogil:
|
|
if n == 0:
|
|
return
|
|
cdef int maxd = 2 * <int>log(n)
|
|
introsort(feature_values, samples, n, maxd)
|
|
|
|
|
|
cdef inline void swap(DTYPE_t* feature_values, SIZE_t* samples,
|
|
SIZE_t i, SIZE_t j) noexcept nogil:
|
|
# Helper for sort
|
|
feature_values[i], feature_values[j] = feature_values[j], feature_values[i]
|
|
samples[i], samples[j] = samples[j], samples[i]
|
|
|
|
|
|
cdef inline DTYPE_t median3(DTYPE_t* feature_values, SIZE_t n) noexcept 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 = feature_values[0], b = feature_values[n / 2], c = feature_values[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* feature_values, SIZE_t *samples,
|
|
SIZE_t n, int maxd) noexcept nogil:
|
|
cdef DTYPE_t pivot
|
|
cdef SIZE_t i, l, r
|
|
|
|
while n > 1:
|
|
if maxd <= 0: # max depth limit exceeded ("gone quadratic")
|
|
heapsort(feature_values, samples, n)
|
|
return
|
|
maxd -= 1
|
|
|
|
pivot = median3(feature_values, n)
|
|
|
|
# Three-way partition.
|
|
i = l = 0
|
|
r = n
|
|
while i < r:
|
|
if feature_values[i] < pivot:
|
|
swap(feature_values, samples, i, l)
|
|
i += 1
|
|
l += 1
|
|
elif feature_values[i] > pivot:
|
|
r -= 1
|
|
swap(feature_values, samples, i, r)
|
|
else:
|
|
i += 1
|
|
|
|
introsort(feature_values, samples, l, maxd)
|
|
feature_values += r
|
|
samples += r
|
|
n -= r
|
|
|
|
|
|
cdef inline void sift_down(DTYPE_t* feature_values, SIZE_t* samples,
|
|
SIZE_t start, SIZE_t end) noexcept nogil:
|
|
# Restore heap order in feature_values[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 feature_values[maxind] < feature_values[child]:
|
|
maxind = child
|
|
if child + 1 < end and feature_values[maxind] < feature_values[child + 1]:
|
|
maxind = child + 1
|
|
|
|
if maxind == root:
|
|
break
|
|
else:
|
|
swap(feature_values, samples, root, maxind)
|
|
root = maxind
|
|
|
|
|
|
cdef void heapsort(DTYPE_t* feature_values, SIZE_t* samples, SIZE_t n) noexcept nogil:
|
|
cdef SIZE_t start, end
|
|
|
|
# heapify
|
|
start = (n - 2) / 2
|
|
end = n
|
|
while True:
|
|
sift_down(feature_values, 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(feature_values, samples, 0, end)
|
|
sift_down(feature_values, samples, 0, end)
|
|
end = end - 1
|
|
|
|
cdef inline int node_split_random(
|
|
Splitter splitter,
|
|
Partitioner partitioner,
|
|
Criterion criterion,
|
|
double impurity,
|
|
SplitRecord* split,
|
|
SIZE_t* n_constant_features,
|
|
bint with_monotonic_cst,
|
|
const cnp.int8_t[:] monotonic_cst,
|
|
double lower_bound,
|
|
double upper_bound,
|
|
) except -1 nogil:
|
|
"""Find the best random split on node samples[start:end]
|
|
|
|
Returns -1 in case of failure to allocate memory (and raise MemoryError)
|
|
or 0 otherwise.
|
|
"""
|
|
# Draw random splits and pick the best
|
|
cdef SIZE_t start = splitter.start
|
|
cdef SIZE_t end = splitter.end
|
|
|
|
cdef SIZE_t[::1] features = splitter.features
|
|
cdef SIZE_t[::1] constant_features = splitter.constant_features
|
|
cdef SIZE_t n_features = splitter.n_features
|
|
|
|
cdef SIZE_t max_features = splitter.max_features
|
|
cdef SIZE_t min_samples_leaf = splitter.min_samples_leaf
|
|
cdef double min_weight_leaf = splitter.min_weight_leaf
|
|
cdef UINT32_t* random_state = &splitter.rand_r_state
|
|
|
|
cdef SplitRecord best_split, current_split
|
|
cdef double current_proxy_improvement = - INFINITY
|
|
cdef double best_proxy_improvement = - INFINITY
|
|
|
|
cdef SIZE_t f_i = n_features
|
|
cdef SIZE_t f_j
|
|
# 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
|
|
|
|
_init_split(&best_split, end)
|
|
|
|
partitioner.init_node_split(start, 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[
|
|
features[n_drawn_constants], features[f_j] = features[f_j], features[n_drawn_constants]
|
|
n_drawn_constants += 1
|
|
continue
|
|
|
|
# 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_split.feature = features[f_j]
|
|
|
|
# Find min, max
|
|
partitioner.find_min_max(
|
|
current_split.feature, &min_feature_value, &max_feature_value
|
|
)
|
|
|
|
if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
|
|
features[f_j], features[n_total_constants] = features[n_total_constants], current_split.feature
|
|
|
|
n_found_constants += 1
|
|
n_total_constants += 1
|
|
continue
|
|
|
|
f_i -= 1
|
|
features[f_i], features[f_j] = features[f_j], features[f_i]
|
|
|
|
# Draw a random threshold
|
|
current_split.threshold = rand_uniform(
|
|
min_feature_value,
|
|
max_feature_value,
|
|
random_state,
|
|
)
|
|
|
|
if current_split.threshold == max_feature_value:
|
|
current_split.threshold = min_feature_value
|
|
|
|
# Partition
|
|
current_split.pos = partitioner.partition_samples(current_split.threshold)
|
|
|
|
# Reject if min_samples_leaf is not guaranteed
|
|
if (((current_split.pos - start) < min_samples_leaf) or
|
|
((end - current_split.pos) < min_samples_leaf)):
|
|
continue
|
|
|
|
# Evaluate split
|
|
# At this point, the criterion has a view into the samples that was partitioned
|
|
# by the partitioner. The criterion will use the partition to evaluating the split.
|
|
criterion.reset()
|
|
criterion.update(current_split.pos)
|
|
|
|
# Reject if min_weight_leaf is not satisfied
|
|
if ((criterion.weighted_n_left < min_weight_leaf) or
|
|
(criterion.weighted_n_right < min_weight_leaf)):
|
|
continue
|
|
|
|
# Reject if monotonicity constraints are not satisfied
|
|
if (
|
|
with_monotonic_cst and
|
|
monotonic_cst[current_split.feature] != 0 and
|
|
not criterion.check_monotonicity(
|
|
monotonic_cst[current_split.feature],
|
|
lower_bound,
|
|
upper_bound,
|
|
)
|
|
):
|
|
continue
|
|
|
|
current_proxy_improvement = criterion.proxy_impurity_improvement()
|
|
|
|
if current_proxy_improvement > best_proxy_improvement:
|
|
best_proxy_improvement = current_proxy_improvement
|
|
best_split = current_split # copy
|
|
|
|
# Reorganize into samples[start:best.pos] + samples[best.pos:end]
|
|
if best_split.pos < end:
|
|
if current_split.feature != best_split.feature:
|
|
# TODO: Pass in best.n_missing when random splitter supports missing values.
|
|
partitioner.partition_samples_final(
|
|
best_split.pos, best_split.threshold, best_split.feature, 0
|
|
)
|
|
|
|
criterion.reset()
|
|
criterion.update(best_split.pos)
|
|
criterion.children_impurity(
|
|
&best_split.impurity_left, &best_split.impurity_right
|
|
)
|
|
best_split.improvement = criterion.impurity_improvement(
|
|
impurity, best_split.impurity_left, best_split.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[0], &constant_features[0], 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_split
|
|
n_constant_features[0] = n_total_constants
|
|
return 0
|
|
|
|
|
|
@final
|
|
cdef class DensePartitioner:
|
|
"""Partitioner specialized for dense data.
|
|
|
|
Note that this partitioner is agnostic to the splitting strategy (best vs. random).
|
|
"""
|
|
cdef:
|
|
const DTYPE_t[:, :] X
|
|
cdef SIZE_t[::1] samples
|
|
cdef DTYPE_t[::1] feature_values
|
|
cdef SIZE_t start
|
|
cdef SIZE_t end
|
|
cdef SIZE_t n_missing
|
|
cdef const unsigned char[::1] missing_values_in_feature_mask
|
|
|
|
def __init__(
|
|
self,
|
|
const DTYPE_t[:, :] X,
|
|
SIZE_t[::1] samples,
|
|
DTYPE_t[::1] feature_values,
|
|
const unsigned char[::1] missing_values_in_feature_mask,
|
|
):
|
|
self.X = X
|
|
self.samples = samples
|
|
self.feature_values = feature_values
|
|
self.missing_values_in_feature_mask = missing_values_in_feature_mask
|
|
|
|
cdef inline void init_node_split(self, SIZE_t start, SIZE_t end) noexcept nogil:
|
|
"""Initialize splitter at the beginning of node_split."""
|
|
self.start = start
|
|
self.end = end
|
|
self.n_missing = 0
|
|
|
|
cdef inline void sort_samples_and_feature_values(
|
|
self, SIZE_t current_feature
|
|
) noexcept nogil:
|
|
"""Simultaneously sort based on the feature_values.
|
|
|
|
Missing values are stored at the end of feature_values.
|
|
The number of missing values observed in feature_values is stored
|
|
in self.n_missing.
|
|
"""
|
|
cdef:
|
|
SIZE_t i, current_end
|
|
DTYPE_t[::1] feature_values = self.feature_values
|
|
const DTYPE_t[:, :] X = self.X
|
|
SIZE_t[::1] samples = self.samples
|
|
SIZE_t n_missing = 0
|
|
const unsigned char[::1] missing_values_in_feature_mask = self.missing_values_in_feature_mask
|
|
|
|
# Sort samples along that feature; by
|
|
# copying the values into an array and
|
|
# sorting the array in a manner which utilizes the cache more
|
|
# effectively.
|
|
if missing_values_in_feature_mask is not None and missing_values_in_feature_mask[current_feature]:
|
|
i, current_end = self.start, self.end - 1
|
|
# Missing values are placed at the end and do not participate in the sorting.
|
|
while i <= current_end:
|
|
# Finds the right-most value that is not missing so that
|
|
# it can be swapped with missing values at its left.
|
|
if isnan(X[samples[current_end], current_feature]):
|
|
n_missing += 1
|
|
current_end -= 1
|
|
continue
|
|
|
|
# X[samples[current_end], current_feature] is a non-missing value
|
|
if isnan(X[samples[i], current_feature]):
|
|
samples[i], samples[current_end] = samples[current_end], samples[i]
|
|
n_missing += 1
|
|
current_end -= 1
|
|
|
|
feature_values[i] = X[samples[i], current_feature]
|
|
i += 1
|
|
else:
|
|
# When there are no missing values, we only need to copy the data into
|
|
# feature_values
|
|
for i in range(self.start, self.end):
|
|
feature_values[i] = X[samples[i], current_feature]
|
|
|
|
sort(&feature_values[self.start], &samples[self.start], self.end - self.start - n_missing)
|
|
self.n_missing = n_missing
|
|
|
|
cdef inline void find_min_max(
|
|
self,
|
|
SIZE_t current_feature,
|
|
DTYPE_t* min_feature_value_out,
|
|
DTYPE_t* max_feature_value_out,
|
|
) noexcept nogil:
|
|
"""Find the minimum and maximum value for current_feature."""
|
|
cdef:
|
|
SIZE_t p
|
|
DTYPE_t current_feature_value
|
|
const DTYPE_t[:, :] X = self.X
|
|
SIZE_t[::1] samples = self.samples
|
|
DTYPE_t min_feature_value = X[samples[self.start], current_feature]
|
|
DTYPE_t max_feature_value = min_feature_value
|
|
DTYPE_t[::1] feature_values = self.feature_values
|
|
|
|
feature_values[self.start] = min_feature_value
|
|
|
|
for p in range(self.start + 1, self.end):
|
|
current_feature_value = X[samples[p], current_feature]
|
|
feature_values[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
|
|
|
|
min_feature_value_out[0] = min_feature_value
|
|
max_feature_value_out[0] = max_feature_value
|
|
|
|
cdef inline void next_p(self, SIZE_t* p_prev, SIZE_t* p) noexcept nogil:
|
|
"""Compute the next p_prev and p for iteratiing over feature values.
|
|
|
|
The missing values are not included when iterating through the feature values.
|
|
"""
|
|
cdef:
|
|
DTYPE_t[::1] feature_values = self.feature_values
|
|
SIZE_t end_non_missing = self.end - self.n_missing
|
|
|
|
while (
|
|
p[0] + 1 < end_non_missing and
|
|
feature_values[p[0] + 1] <= feature_values[p[0]] + FEATURE_THRESHOLD
|
|
):
|
|
p[0] += 1
|
|
|
|
p_prev[0] = p[0]
|
|
|
|
# By adding 1, we have
|
|
# (feature_values[p] >= end) or (feature_values[p] > feature_values[p - 1])
|
|
p[0] += 1
|
|
|
|
cdef inline SIZE_t partition_samples(self, double current_threshold) noexcept nogil:
|
|
"""Partition samples for feature_values at the current_threshold."""
|
|
cdef:
|
|
SIZE_t p = self.start
|
|
SIZE_t partition_end = self.end
|
|
SIZE_t[::1] samples = self.samples
|
|
DTYPE_t[::1] feature_values = self.feature_values
|
|
|
|
while p < partition_end:
|
|
if feature_values[p] <= current_threshold:
|
|
p += 1
|
|
else:
|
|
partition_end -= 1
|
|
|
|
feature_values[p], feature_values[partition_end] = (
|
|
feature_values[partition_end], feature_values[p]
|
|
)
|
|
samples[p], samples[partition_end] = samples[partition_end], samples[p]
|
|
|
|
return partition_end
|
|
|
|
cdef inline void partition_samples_final(
|
|
self,
|
|
SIZE_t best_pos,
|
|
double best_threshold,
|
|
SIZE_t best_feature,
|
|
SIZE_t best_n_missing,
|
|
) noexcept nogil:
|
|
"""Partition samples for X at the best_threshold and best_feature.
|
|
|
|
If missing values are present, this method partitions `samples`
|
|
so that the `best_n_missing` missing values' indices are in the
|
|
right-most end of `samples`, that is `samples[end_non_missing:end]`.
|
|
"""
|
|
cdef:
|
|
# Local invariance: start <= p <= partition_end <= end
|
|
SIZE_t start = self.start
|
|
SIZE_t p = start
|
|
SIZE_t end = self.end - 1
|
|
SIZE_t partition_end = end - best_n_missing
|
|
SIZE_t[::1] samples = self.samples
|
|
const DTYPE_t[:, :] X = self.X
|
|
DTYPE_t current_value
|
|
|
|
if best_n_missing != 0:
|
|
# Move samples with missing values to the end while partitioning the
|
|
# non-missing samples
|
|
while p < partition_end:
|
|
# Keep samples with missing values at the end
|
|
if isnan(X[samples[end], best_feature]):
|
|
end -= 1
|
|
continue
|
|
|
|
# Swap sample with missing values with the sample at the end
|
|
current_value = X[samples[p], best_feature]
|
|
if isnan(current_value):
|
|
samples[p], samples[end] = samples[end], samples[p]
|
|
end -= 1
|
|
|
|
# The swapped sample at the end is always a non-missing value, so
|
|
# we can continue the algorithm without checking for missingness.
|
|
current_value = X[samples[p], best_feature]
|
|
|
|
# Partition the non-missing samples
|
|
if current_value <= best_threshold:
|
|
p += 1
|
|
else:
|
|
samples[p], samples[partition_end] = samples[partition_end], samples[p]
|
|
partition_end -= 1
|
|
else:
|
|
# Partitioning routine when there are no missing values
|
|
while p < partition_end:
|
|
if X[samples[p], best_feature] <= best_threshold:
|
|
p += 1
|
|
else:
|
|
samples[p], samples[partition_end] = samples[partition_end], samples[p]
|
|
partition_end -= 1
|
|
|
|
|
|
@final
|
|
cdef class SparsePartitioner:
|
|
"""Partitioner specialized for sparse CSC data.
|
|
|
|
Note that this partitioner is agnostic to the splitting strategy (best vs. random).
|
|
"""
|
|
cdef SIZE_t[::1] samples
|
|
cdef DTYPE_t[::1] feature_values
|
|
cdef SIZE_t start
|
|
cdef SIZE_t end
|
|
cdef SIZE_t n_missing
|
|
cdef const unsigned char[::1] missing_values_in_feature_mask
|
|
|
|
cdef const DTYPE_t[::1] X_data
|
|
cdef const INT32_t[::1] X_indices
|
|
cdef const INT32_t[::1] X_indptr
|
|
|
|
cdef SIZE_t n_total_samples
|
|
|
|
cdef SIZE_t[::1] index_to_samples
|
|
cdef SIZE_t[::1] sorted_samples
|
|
|
|
cdef SIZE_t start_positive
|
|
cdef SIZE_t end_negative
|
|
cdef bint is_samples_sorted
|
|
|
|
def __init__(
|
|
self,
|
|
object X,
|
|
SIZE_t[::1] samples,
|
|
SIZE_t n_samples,
|
|
DTYPE_t[::1] feature_values,
|
|
const unsigned char[::1] missing_values_in_feature_mask,
|
|
):
|
|
if not (issparse(X) and X.format == "csc"):
|
|
raise ValueError("X should be in csc format")
|
|
|
|
self.samples = samples
|
|
self.feature_values = feature_values
|
|
|
|
# Initialize X
|
|
cdef SIZE_t n_total_samples = X.shape[0]
|
|
|
|
self.X_data = X.data
|
|
self.X_indices = X.indices
|
|
self.X_indptr = X.indptr
|
|
self.n_total_samples = n_total_samples
|
|
|
|
# Initialize auxiliary array used to perform split
|
|
self.index_to_samples = np.full(n_total_samples, fill_value=-1, dtype=np.intp)
|
|
self.sorted_samples = np.empty(n_samples, dtype=np.intp)
|
|
|
|
cdef SIZE_t p
|
|
for p in range(n_samples):
|
|
self.index_to_samples[samples[p]] = p
|
|
|
|
self.missing_values_in_feature_mask = missing_values_in_feature_mask
|
|
|
|
cdef inline void init_node_split(self, SIZE_t start, SIZE_t end) noexcept nogil:
|
|
"""Initialize splitter at the beginning of node_split."""
|
|
self.start = start
|
|
self.end = end
|
|
self.is_samples_sorted = 0
|
|
self.n_missing = 0
|
|
|
|
cdef inline void sort_samples_and_feature_values(
|
|
self, SIZE_t current_feature
|
|
) noexcept nogil:
|
|
"""Simultaneously sort based on the feature_values."""
|
|
cdef:
|
|
DTYPE_t[::1] feature_values = self.feature_values
|
|
SIZE_t[::1] index_to_samples = self.index_to_samples
|
|
SIZE_t[::1] samples = self.samples
|
|
|
|
self.extract_nnz(current_feature)
|
|
# Sort the positive and negative parts of `feature_values`
|
|
sort(&feature_values[self.start], &samples[self.start], self.end_negative - self.start)
|
|
if self.start_positive < self.end:
|
|
sort(
|
|
&feature_values[self.start_positive],
|
|
&samples[self.start_positive],
|
|
self.end - self.start_positive
|
|
)
|
|
|
|
# Update index_to_samples to take into account the sort
|
|
for p in range(self.start, self.end_negative):
|
|
index_to_samples[samples[p]] = p
|
|
for p in range(self.start_positive, self.end):
|
|
index_to_samples[samples[p]] = p
|
|
|
|
# Add one or two zeros in feature_values, if there is any
|
|
if self.end_negative < self.start_positive:
|
|
self.start_positive -= 1
|
|
feature_values[self.start_positive] = 0.
|
|
|
|
if self.end_negative != self.start_positive:
|
|
feature_values[self.end_negative] = 0.
|
|
self.end_negative += 1
|
|
|
|
# XXX: When sparse supports missing values, this should be set to the
|
|
# number of missing values for current_feature
|
|
self.n_missing = 0
|
|
|
|
cdef inline void find_min_max(
|
|
self,
|
|
SIZE_t current_feature,
|
|
DTYPE_t* min_feature_value_out,
|
|
DTYPE_t* max_feature_value_out,
|
|
) noexcept nogil:
|
|
"""Find the minimum and maximum value for current_feature."""
|
|
cdef:
|
|
SIZE_t p
|
|
DTYPE_t current_feature_value, min_feature_value, max_feature_value
|
|
DTYPE_t[::1] feature_values = self.feature_values
|
|
|
|
self.extract_nnz(current_feature)
|
|
|
|
if self.end_negative != self.start_positive:
|
|
# There is a zero
|
|
min_feature_value = 0
|
|
max_feature_value = 0
|
|
else:
|
|
min_feature_value = feature_values[self.start]
|
|
max_feature_value = min_feature_value
|
|
|
|
# Find min, max in feature_values[start:end_negative]
|
|
for p in range(self.start, self.end_negative):
|
|
current_feature_value = feature_values[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 feature_values[start_positive:end]
|
|
for p in range(self.start_positive, self.end):
|
|
current_feature_value = feature_values[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
|
|
|
|
min_feature_value_out[0] = min_feature_value
|
|
max_feature_value_out[0] = max_feature_value
|
|
|
|
cdef inline void next_p(self, SIZE_t* p_prev, SIZE_t* p) noexcept nogil:
|
|
"""Compute the next p_prev and p for iteratiing over feature values."""
|
|
cdef:
|
|
SIZE_t p_next
|
|
DTYPE_t[::1] feature_values = self.feature_values
|
|
|
|
if p[0] + 1 != self.end_negative:
|
|
p_next = p[0] + 1
|
|
else:
|
|
p_next = self.start_positive
|
|
|
|
while (p_next < self.end and
|
|
feature_values[p_next] <= feature_values[p[0]] + FEATURE_THRESHOLD):
|
|
p[0] = p_next
|
|
if p[0] + 1 != self.end_negative:
|
|
p_next = p[0] + 1
|
|
else:
|
|
p_next = self.start_positive
|
|
|
|
p_prev[0] = p[0]
|
|
p[0] = p_next
|
|
|
|
cdef inline SIZE_t partition_samples(self, double current_threshold) noexcept nogil:
|
|
"""Partition samples for feature_values at the current_threshold."""
|
|
return self._partition(current_threshold, self.start_positive)
|
|
|
|
cdef inline void partition_samples_final(
|
|
self,
|
|
SIZE_t best_pos,
|
|
double best_threshold,
|
|
SIZE_t best_feature,
|
|
SIZE_t n_missing,
|
|
) noexcept nogil:
|
|
"""Partition samples for X at the best_threshold and best_feature."""
|
|
self.extract_nnz(best_feature)
|
|
self._partition(best_threshold, best_pos)
|
|
|
|
cdef inline SIZE_t _partition(self, double threshold, SIZE_t zero_pos) noexcept nogil:
|
|
"""Partition samples[start:end] based on threshold."""
|
|
cdef:
|
|
SIZE_t p, partition_end
|
|
SIZE_t[::1] index_to_samples = self.index_to_samples
|
|
DTYPE_t[::1] feature_values = self.feature_values
|
|
SIZE_t[::1] samples = self.samples
|
|
|
|
if threshold < 0.:
|
|
p = self.start
|
|
partition_end = self.end_negative
|
|
elif threshold > 0.:
|
|
p = self.start_positive
|
|
partition_end = self.end
|
|
else:
|
|
# Data are already split
|
|
return zero_pos
|
|
|
|
while p < partition_end:
|
|
if feature_values[p] <= threshold:
|
|
p += 1
|
|
|
|
else:
|
|
partition_end -= 1
|
|
|
|
feature_values[p], feature_values[partition_end] = (
|
|
feature_values[partition_end], feature_values[p]
|
|
)
|
|
sparse_swap(index_to_samples, samples, p, partition_end)
|
|
|
|
return partition_end
|
|
|
|
cdef inline void extract_nnz(self, SIZE_t feature) noexcept nogil:
|
|
"""Extract and partition values for a given feature.
|
|
|
|
The extracted values are partitioned between negative values
|
|
feature_values[start:end_negative[0]] and positive values
|
|
feature_values[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.
|
|
"""
|
|
cdef SIZE_t[::1] samples = self.samples
|
|
cdef DTYPE_t[::1] feature_values = self.feature_values
|
|
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
|
|
cdef SIZE_t[::1] index_to_samples = self.index_to_samples
|
|
cdef SIZE_t[::1] sorted_samples = self.sorted_samples
|
|
cdef const INT32_t[::1] X_indices = self.X_indices
|
|
cdef const DTYPE_t[::1] X_data = self.X_data
|
|
|
|
# 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 - self.is_samples_sorted) * n_samples * log(n_samples) +
|
|
n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices):
|
|
extract_nnz_binary_search(X_indices, X_data,
|
|
indptr_start, indptr_end,
|
|
samples, self.start, self.end,
|
|
index_to_samples,
|
|
feature_values,
|
|
&self.end_negative, &self.start_positive,
|
|
sorted_samples, &self.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(X_indices, X_data,
|
|
indptr_start, indptr_end,
|
|
samples, self.start, self.end,
|
|
index_to_samples,
|
|
feature_values,
|
|
&self.end_negative, &self.start_positive)
|
|
|
|
|
|
cdef int compare_SIZE_t(const void* a, const void* b) noexcept nogil:
|
|
"""Comparison function for sort."""
|
|
return <int>((<SIZE_t*>a)[0] - (<SIZE_t*>b)[0])
|
|
|
|
|
|
cdef inline void binary_search(const INT32_t[::1] sorted_array,
|
|
INT32_t start, INT32_t end,
|
|
SIZE_t value, SIZE_t* index,
|
|
INT32_t* new_start) noexcept 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(const INT32_t[::1] X_indices,
|
|
const DTYPE_t[::1] X_data,
|
|
INT32_t indptr_start,
|
|
INT32_t indptr_end,
|
|
SIZE_t[::1] samples,
|
|
SIZE_t start,
|
|
SIZE_t end,
|
|
SIZE_t[::1] index_to_samples,
|
|
DTYPE_t[::1] feature_values,
|
|
SIZE_t* end_negative,
|
|
SIZE_t* start_positive) noexcept 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
|
|
feature_values[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:
|
|
feature_values[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(const INT32_t[::1] X_indices,
|
|
const DTYPE_t[::1] X_data,
|
|
INT32_t indptr_start,
|
|
INT32_t indptr_end,
|
|
SIZE_t[::1] samples,
|
|
SIZE_t start,
|
|
SIZE_t end,
|
|
SIZE_t[::1] index_to_samples,
|
|
DTYPE_t[::1] feature_values,
|
|
SIZE_t* end_negative,
|
|
SIZE_t* start_positive,
|
|
SIZE_t[::1] sorted_samples,
|
|
bint* is_samples_sorted) noexcept 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
|
|
feature_values[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:
|
|
feature_values[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[::1] index_to_samples, SIZE_t[::1] samples,
|
|
SIZE_t pos_1, SIZE_t pos_2) noexcept 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 BestSplitter(Splitter):
|
|
"""Splitter for finding the best split on dense data."""
|
|
cdef DensePartitioner partitioner
|
|
cdef int init(
|
|
self,
|
|
object X,
|
|
const DOUBLE_t[:, ::1] y,
|
|
const DOUBLE_t[:] sample_weight,
|
|
const unsigned char[::1] missing_values_in_feature_mask,
|
|
) except -1:
|
|
Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask)
|
|
self.partitioner = DensePartitioner(
|
|
X, self.samples, self.feature_values, missing_values_in_feature_mask
|
|
)
|
|
|
|
cdef int node_split(
|
|
self,
|
|
double impurity,
|
|
SplitRecord* split,
|
|
SIZE_t* n_constant_features,
|
|
double lower_bound,
|
|
double upper_bound
|
|
) except -1 nogil:
|
|
return node_split_best(
|
|
self,
|
|
self.partitioner,
|
|
self.criterion,
|
|
impurity,
|
|
split,
|
|
n_constant_features,
|
|
self.with_monotonic_cst,
|
|
self.monotonic_cst,
|
|
lower_bound,
|
|
upper_bound
|
|
)
|
|
|
|
cdef class BestSparseSplitter(Splitter):
|
|
"""Splitter for finding the best split, using the sparse data."""
|
|
cdef SparsePartitioner partitioner
|
|
cdef int init(
|
|
self,
|
|
object X,
|
|
const DOUBLE_t[:, ::1] y,
|
|
const DOUBLE_t[:] sample_weight,
|
|
const unsigned char[::1] missing_values_in_feature_mask,
|
|
) except -1:
|
|
Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask)
|
|
self.partitioner = SparsePartitioner(
|
|
X, self.samples, self.n_samples, self.feature_values, missing_values_in_feature_mask
|
|
)
|
|
|
|
cdef int node_split(
|
|
self,
|
|
double impurity,
|
|
SplitRecord* split,
|
|
SIZE_t* n_constant_features,
|
|
double lower_bound,
|
|
double upper_bound
|
|
) except -1 nogil:
|
|
return node_split_best(
|
|
self,
|
|
self.partitioner,
|
|
self.criterion,
|
|
impurity,
|
|
split,
|
|
n_constant_features,
|
|
self.with_monotonic_cst,
|
|
self.monotonic_cst,
|
|
lower_bound,
|
|
upper_bound
|
|
)
|
|
|
|
cdef class RandomSplitter(Splitter):
|
|
"""Splitter for finding the best random split on dense data."""
|
|
cdef DensePartitioner partitioner
|
|
cdef int init(
|
|
self,
|
|
object X,
|
|
const DOUBLE_t[:, ::1] y,
|
|
const DOUBLE_t[:] sample_weight,
|
|
const unsigned char[::1] missing_values_in_feature_mask,
|
|
) except -1:
|
|
Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask)
|
|
self.partitioner = DensePartitioner(
|
|
X, self.samples, self.feature_values, missing_values_in_feature_mask
|
|
)
|
|
|
|
cdef int node_split(
|
|
self,
|
|
double impurity,
|
|
SplitRecord* split,
|
|
SIZE_t* n_constant_features,
|
|
double lower_bound,
|
|
double upper_bound
|
|
) except -1 nogil:
|
|
return node_split_random(
|
|
self,
|
|
self.partitioner,
|
|
self.criterion,
|
|
impurity,
|
|
split,
|
|
n_constant_features,
|
|
self.with_monotonic_cst,
|
|
self.monotonic_cst,
|
|
lower_bound,
|
|
upper_bound
|
|
)
|
|
|
|
cdef class RandomSparseSplitter(Splitter):
|
|
"""Splitter for finding the best random split, using the sparse data."""
|
|
cdef SparsePartitioner partitioner
|
|
cdef int init(
|
|
self,
|
|
object X,
|
|
const DOUBLE_t[:, ::1] y,
|
|
const DOUBLE_t[:] sample_weight,
|
|
const unsigned char[::1] missing_values_in_feature_mask,
|
|
) except -1:
|
|
Splitter.init(self, X, y, sample_weight, missing_values_in_feature_mask)
|
|
self.partitioner = SparsePartitioner(
|
|
X, self.samples, self.n_samples, self.feature_values, missing_values_in_feature_mask
|
|
)
|
|
cdef int node_split(
|
|
self,
|
|
double impurity,
|
|
SplitRecord* split,
|
|
SIZE_t* n_constant_features,
|
|
double lower_bound,
|
|
double upper_bound
|
|
) except -1 nogil:
|
|
return node_split_random(
|
|
self,
|
|
self.partitioner,
|
|
self.criterion,
|
|
impurity,
|
|
split,
|
|
n_constant_features,
|
|
self.with_monotonic_cst,
|
|
self.monotonic_cst,
|
|
lower_bound,
|
|
upper_bound
|
|
)
|