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3RNN/Lib/site-packages/sklearn/tree/_criterion.pyx

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2024-05-26 19:49:15 +02:00
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Lars Buitinck
# Arnaud Joly <arnaud.v.joly@gmail.com>
# Joel Nothman <joel.nothman@gmail.com>
# Fares Hedayati <fares.hedayati@gmail.com>
# Jacob Schreiber <jmschreiber91@gmail.com>
# Nelson Liu <nelson@nelsonliu.me>
#
# License: BSD 3 clause
from libc.string cimport memcpy
from libc.string cimport memset
from libc.math cimport fabs, INFINITY
import numpy as np
cimport numpy as cnp
cnp.import_array()
from scipy.special.cython_special cimport xlogy
from ._utils cimport log
from ._utils cimport WeightedMedianCalculator
# EPSILON is used in the Poisson criterion
cdef float64_t EPSILON = 10 * np.finfo('double').eps
cdef class Criterion:
"""Interface for impurity criteria.
This object stores methods on how to calculate how good a split is using
different metrics.
"""
def __getstate__(self):
return {}
def __setstate__(self, d):
pass
cdef int init(
self,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight,
float64_t weighted_n_samples,
const intp_t[:] sample_indices,
intp_t start,
intp_t end,
) except -1 nogil:
"""Placeholder for a method which will initialize the criterion.
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
Parameters
----------
y : ndarray, dtype=float64_t
y is a buffer that can store values for n_outputs target variables
stored as a Cython memoryview.
sample_weight : ndarray, dtype=float64_t
The weight of each sample stored as a Cython memoryview.
weighted_n_samples : float64_t
The total weight of the samples being considered
sample_indices : ndarray, dtype=intp_t
A mask on the samples. Indices of the samples in X and y we want to use,
where sample_indices[start:end] correspond to the samples in this node.
start : intp_t
The first sample to be used on this node
end : intp_t
The last sample used on this node
"""
pass
cdef void init_missing(self, intp_t n_missing) noexcept nogil:
"""Initialize sum_missing if there are missing values.
This method assumes that caller placed the missing samples in
self.sample_indices[-n_missing:]
Parameters
----------
n_missing: intp_t
Number of missing values for specific feature.
"""
pass
cdef int reset(self) except -1 nogil:
"""Reset the criterion at pos=start.
This method must be implemented by the subclass.
"""
pass
cdef int reverse_reset(self) except -1 nogil:
"""Reset the criterion at pos=end.
This method must be implemented by the subclass.
"""
pass
cdef int update(self, intp_t new_pos) except -1 nogil:
"""Updated statistics by moving sample_indices[pos:new_pos] to the left child.
This updates the collected statistics by moving sample_indices[pos:new_pos]
from the right child to the left child. It must be implemented by
the subclass.
Parameters
----------
new_pos : intp_t
New starting index position of the sample_indices in the right child
"""
pass
cdef float64_t node_impurity(self) noexcept nogil:
"""Placeholder for calculating the impurity of the node.
Placeholder for a method which will evaluate the impurity of
the current node, i.e. the impurity of sample_indices[start:end]. This is the
primary function of the criterion class. The smaller the impurity the
better.
"""
pass
cdef void children_impurity(self, float64_t* impurity_left,
float64_t* impurity_right) noexcept nogil:
"""Placeholder for calculating the impurity of children.
Placeholder for a method which evaluates the impurity in
children nodes, i.e. the impurity of sample_indices[start:pos] + the impurity
of sample_indices[pos:end].
Parameters
----------
impurity_left : float64_t pointer
The memory address where the impurity of the left child should be
stored.
impurity_right : float64_t pointer
The memory address where the impurity of the right child should be
stored
"""
pass
cdef void node_value(self, float64_t* dest) noexcept nogil:
"""Placeholder for storing the node value.
Placeholder for a method which will compute the node value
of sample_indices[start:end] and save the value into dest.
Parameters
----------
dest : float64_t pointer
The memory address where the node value should be stored.
"""
pass
cdef void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
pass
cdef float64_t middle_value(self) noexcept nogil:
"""Compute the middle value of a split for monotonicity constraints
This method is implemented in ClassificationCriterion and RegressionCriterion.
"""
pass
cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
"""Compute a proxy of the impurity reduction.
This method is used to speed up the search for the best split.
It is a proxy quantity such that the split that maximizes this value
also maximizes the impurity improvement. It neglects all constant terms
of the impurity decrease for a given split.
The absolute impurity improvement is only computed by the
impurity_improvement method once the best split has been found.
"""
cdef float64_t impurity_left
cdef float64_t impurity_right
self.children_impurity(&impurity_left, &impurity_right)
return (- self.weighted_n_right * impurity_right
- self.weighted_n_left * impurity_left)
cdef float64_t impurity_improvement(self, float64_t impurity_parent,
float64_t impurity_left,
float64_t impurity_right) noexcept nogil:
"""Compute the improvement in impurity.
This method computes the improvement in impurity when a split occurs.
The weighted impurity improvement equation is the following:
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where N is the total number of samples, N_t is the number of samples
at the current node, N_t_L is the number of samples in the left child,
and N_t_R is the number of samples in the right child,
Parameters
----------
impurity_parent : float64_t
The initial impurity of the parent node before the split
impurity_left : float64_t
The impurity of the left child
impurity_right : float64_t
The impurity of the right child
Return
------
float64_t : improvement in impurity after the split occurs
"""
return ((self.weighted_n_node_samples / self.weighted_n_samples) *
(impurity_parent - (self.weighted_n_right /
self.weighted_n_node_samples * impurity_right)
- (self.weighted_n_left /
self.weighted_n_node_samples * impurity_left)))
cdef bint check_monotonicity(
self,
cnp.int8_t monotonic_cst,
float64_t lower_bound,
float64_t upper_bound,
) noexcept nogil:
pass
cdef inline bint _check_monotonicity(
self,
cnp.int8_t monotonic_cst,
float64_t lower_bound,
float64_t upper_bound,
float64_t value_left,
float64_t value_right,
) noexcept nogil:
cdef:
bint check_lower_bound = (
(value_left >= lower_bound) &
(value_right >= lower_bound)
)
bint check_upper_bound = (
(value_left <= upper_bound) &
(value_right <= upper_bound)
)
bint check_monotonic_cst = (
(value_left - value_right) * monotonic_cst <= 0
)
return check_lower_bound & check_upper_bound & check_monotonic_cst
cdef void init_sum_missing(self):
"""Init sum_missing to hold sums for missing values."""
cdef inline void _move_sums_classification(
ClassificationCriterion criterion,
float64_t[:, ::1] sum_1,
float64_t[:, ::1] sum_2,
float64_t* weighted_n_1,
float64_t* weighted_n_2,
bint put_missing_in_1,
) noexcept nogil:
"""Distribute sum_total and sum_missing into sum_1 and sum_2.
If there are missing values and:
- put_missing_in_1 is True, then missing values to go sum_1. Specifically:
sum_1 = sum_missing
sum_2 = sum_total - sum_missing
- put_missing_in_1 is False, then missing values go to sum_2. Specifically:
sum_1 = 0
sum_2 = sum_total
"""
cdef intp_t k, c, n_bytes
if criterion.n_missing != 0 and put_missing_in_1:
for k in range(criterion.n_outputs):
n_bytes = criterion.n_classes[k] * sizeof(float64_t)
memcpy(&sum_1[k, 0], &criterion.sum_missing[k, 0], n_bytes)
for k in range(criterion.n_outputs):
for c in range(criterion.n_classes[k]):
sum_2[k, c] = criterion.sum_total[k, c] - criterion.sum_missing[k, c]
weighted_n_1[0] = criterion.weighted_n_missing
weighted_n_2[0] = criterion.weighted_n_node_samples - criterion.weighted_n_missing
else:
# Assigning sum_2 = sum_total for all outputs.
for k in range(criterion.n_outputs):
n_bytes = criterion.n_classes[k] * sizeof(float64_t)
memset(&sum_1[k, 0], 0, n_bytes)
memcpy(&sum_2[k, 0], &criterion.sum_total[k, 0], n_bytes)
weighted_n_1[0] = 0.0
weighted_n_2[0] = criterion.weighted_n_node_samples
cdef class ClassificationCriterion(Criterion):
"""Abstract criterion for classification."""
def __cinit__(self, intp_t n_outputs,
cnp.ndarray[intp_t, ndim=1] n_classes):
"""Initialize attributes for this criterion.
Parameters
----------
n_outputs : intp_t
The number of targets, the dimensionality of the prediction
n_classes : numpy.ndarray, dtype=intp_t
The number of unique classes in each target
"""
self.start = 0
self.pos = 0
self.end = 0
self.missing_go_to_left = 0
self.n_outputs = n_outputs
self.n_samples = 0
self.n_node_samples = 0
self.weighted_n_node_samples = 0.0
self.weighted_n_left = 0.0
self.weighted_n_right = 0.0
self.weighted_n_missing = 0.0
self.n_classes = np.empty(n_outputs, dtype=np.intp)
cdef intp_t k = 0
cdef intp_t max_n_classes = 0
# For each target, set the number of unique classes in that target,
# and also compute the maximal stride of all targets
for k in range(n_outputs):
self.n_classes[k] = n_classes[k]
if n_classes[k] > max_n_classes:
max_n_classes = n_classes[k]
self.max_n_classes = max_n_classes
# Count labels for each output
self.sum_total = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
self.sum_left = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
self.sum_right = np.zeros((n_outputs, max_n_classes), dtype=np.float64)
def __reduce__(self):
return (type(self),
(self.n_outputs, np.asarray(self.n_classes)), self.__getstate__())
cdef int init(
self,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight,
float64_t weighted_n_samples,
const intp_t[:] sample_indices,
intp_t start,
intp_t end
) except -1 nogil:
"""Initialize the criterion.
This initializes the criterion at node sample_indices[start:end] and children
sample_indices[start:start] and sample_indices[start:end].
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
Parameters
----------
y : ndarray, dtype=float64_t
The target stored as a buffer for memory efficiency.
sample_weight : ndarray, dtype=float64_t
The weight of each sample stored as a Cython memoryview.
weighted_n_samples : float64_t
The total weight of all samples
sample_indices : ndarray, dtype=intp_t
A mask on the samples. Indices of the samples in X and y we want to use,
where sample_indices[start:end] correspond to the samples in this node.
start : intp_t
The first sample to use in the mask
end : intp_t
The last sample to use in the mask
"""
self.y = y
self.sample_weight = sample_weight
self.sample_indices = sample_indices
self.start = start
self.end = end
self.n_node_samples = end - start
self.weighted_n_samples = weighted_n_samples
self.weighted_n_node_samples = 0.0
cdef intp_t i
cdef intp_t p
cdef intp_t k
cdef intp_t c
cdef float64_t w = 1.0
for k in range(self.n_outputs):
memset(&self.sum_total[k, 0], 0, self.n_classes[k] * sizeof(float64_t))
for p in range(start, end):
i = sample_indices[p]
# w is originally set to be 1.0, meaning that if no sample weights
# are given, the default weight of each sample is 1.0.
if sample_weight is not None:
w = sample_weight[i]
# Count weighted class frequency for each target
for k in range(self.n_outputs):
c = <intp_t> self.y[i, k]
self.sum_total[k, c] += w
self.weighted_n_node_samples += w
# Reset to pos=start
self.reset()
return 0
cdef void init_sum_missing(self):
"""Init sum_missing to hold sums for missing values."""
self.sum_missing = np.zeros((self.n_outputs, self.max_n_classes), dtype=np.float64)
cdef void init_missing(self, intp_t n_missing) noexcept nogil:
"""Initialize sum_missing if there are missing values.
This method assumes that caller placed the missing samples in
self.sample_indices[-n_missing:]
"""
cdef intp_t i, p, k, c
cdef float64_t w = 1.0
self.n_missing = n_missing
if n_missing == 0:
return
memset(&self.sum_missing[0, 0], 0, self.max_n_classes * self.n_outputs * sizeof(float64_t))
self.weighted_n_missing = 0.0
# The missing samples are assumed to be in self.sample_indices[-n_missing:]
for p in range(self.end - n_missing, self.end):
i = self.sample_indices[p]
if self.sample_weight is not None:
w = self.sample_weight[i]
for k in range(self.n_outputs):
c = <intp_t> self.y[i, k]
self.sum_missing[k, c] += w
self.weighted_n_missing += w
cdef int reset(self) except -1 nogil:
"""Reset the criterion at pos=start.
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
"""
self.pos = self.start
_move_sums_classification(
self,
self.sum_left,
self.sum_right,
&self.weighted_n_left,
&self.weighted_n_right,
self.missing_go_to_left,
)
return 0
cdef int reverse_reset(self) except -1 nogil:
"""Reset the criterion at pos=end.
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
"""
self.pos = self.end
_move_sums_classification(
self,
self.sum_right,
self.sum_left,
&self.weighted_n_right,
&self.weighted_n_left,
not self.missing_go_to_left
)
return 0
cdef int update(self, intp_t new_pos) except -1 nogil:
"""Updated statistics by moving sample_indices[pos:new_pos] to the left child.
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
Parameters
----------
new_pos : intp_t
The new ending position for which to move sample_indices from the right
child to the left child.
"""
cdef intp_t pos = self.pos
# The missing samples are assumed to be in
# self.sample_indices[-self.n_missing:] that is
# self.sample_indices[end_non_missing:self.end].
cdef intp_t end_non_missing = self.end - self.n_missing
cdef const intp_t[:] sample_indices = self.sample_indices
cdef const float64_t[:] sample_weight = self.sample_weight
cdef intp_t i
cdef intp_t p
cdef intp_t k
cdef intp_t c
cdef float64_t w = 1.0
# Update statistics up to new_pos
#
# Given that
# sum_left[x] + sum_right[x] = sum_total[x]
# and that sum_total is known, we are going to update
# sum_left from the direction that require the least amount
# of computations, i.e. from pos to new_pos or from end to new_po.
if (new_pos - pos) <= (end_non_missing - new_pos):
for p in range(pos, new_pos):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
self.sum_left[k, <intp_t> self.y[i, k]] += w
self.weighted_n_left += w
else:
self.reverse_reset()
for p in range(end_non_missing - 1, new_pos - 1, -1):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
self.sum_left[k, <intp_t> self.y[i, k]] -= w
self.weighted_n_left -= w
# Update right part statistics
self.weighted_n_right = self.weighted_n_node_samples - self.weighted_n_left
for k in range(self.n_outputs):
for c in range(self.n_classes[k]):
self.sum_right[k, c] = self.sum_total[k, c] - self.sum_left[k, c]
self.pos = new_pos
return 0
cdef float64_t node_impurity(self) noexcept nogil:
pass
cdef void children_impurity(self, float64_t* impurity_left,
float64_t* impurity_right) noexcept nogil:
pass
cdef void node_value(self, float64_t* dest) noexcept nogil:
"""Compute the node value of sample_indices[start:end] and save it into dest.
Parameters
----------
dest : float64_t pointer
The memory address which we will save the node value into.
"""
cdef intp_t k, c
for k in range(self.n_outputs):
for c in range(self.n_classes[k]):
dest[c] = self.sum_total[k, c] / self.weighted_n_node_samples
dest += self.max_n_classes
cdef inline void clip_node_value(
self, float64_t * dest, float64_t lower_bound, float64_t upper_bound
) noexcept nogil:
"""Clip the values in dest such that predicted probabilities stay between
`lower_bound` and `upper_bound` when monotonic constraints are enforced.
Note that monotonicity constraints are only supported for:
- single-output trees and
- binary classifications.
"""
if dest[0] < lower_bound:
dest[0] = lower_bound
elif dest[0] > upper_bound:
dest[0] = upper_bound
# Values for binary classification must sum to 1.
dest[1] = 1 - dest[0]
cdef inline float64_t middle_value(self) noexcept nogil:
"""Compute the middle value of a split for monotonicity constraints as the simple average
of the left and right children values.
Note that monotonicity constraints are only supported for:
- single-output trees and
- binary classifications.
"""
return (
(self.sum_left[0, 0] / (2 * self.weighted_n_left)) +
(self.sum_right[0, 0] / (2 * self.weighted_n_right))
)
cdef inline bint check_monotonicity(
self,
cnp.int8_t monotonic_cst,
float64_t lower_bound,
float64_t upper_bound,
) noexcept nogil:
"""Check monotonicity constraint is satisfied at the current classification split"""
cdef:
float64_t value_left = self.sum_left[0][0] / self.weighted_n_left
float64_t value_right = self.sum_right[0][0] / self.weighted_n_right
return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
cdef class Entropy(ClassificationCriterion):
r"""Cross Entropy impurity criterion.
This handles cases where the target is a classification taking values
0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
then let
count_k = 1 / Nm \sum_{x_i in Rm} I(yi = k)
be the proportion of class k observations in node m.
The cross-entropy is then defined as
cross-entropy = -\sum_{k=0}^{K-1} count_k log(count_k)
"""
cdef float64_t node_impurity(self) noexcept nogil:
"""Evaluate the impurity of the current node.
Evaluate the cross-entropy criterion as impurity of the current node,
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
better.
"""
cdef float64_t entropy = 0.0
cdef float64_t count_k
cdef intp_t k
cdef intp_t c
for k in range(self.n_outputs):
for c in range(self.n_classes[k]):
count_k = self.sum_total[k, c]
if count_k > 0.0:
count_k /= self.weighted_n_node_samples
entropy -= count_k * log(count_k)
return entropy / self.n_outputs
cdef void children_impurity(self, float64_t* impurity_left,
float64_t* impurity_right) noexcept nogil:
"""Evaluate the impurity in children nodes.
i.e. the impurity of the left child (sample_indices[start:pos]) and the
impurity the right child (sample_indices[pos:end]).
Parameters
----------
impurity_left : float64_t pointer
The memory address to save the impurity of the left node
impurity_right : float64_t pointer
The memory address to save the impurity of the right node
"""
cdef float64_t entropy_left = 0.0
cdef float64_t entropy_right = 0.0
cdef float64_t count_k
cdef intp_t k
cdef intp_t c
for k in range(self.n_outputs):
for c in range(self.n_classes[k]):
count_k = self.sum_left[k, c]
if count_k > 0.0:
count_k /= self.weighted_n_left
entropy_left -= count_k * log(count_k)
count_k = self.sum_right[k, c]
if count_k > 0.0:
count_k /= self.weighted_n_right
entropy_right -= count_k * log(count_k)
impurity_left[0] = entropy_left / self.n_outputs
impurity_right[0] = entropy_right / self.n_outputs
cdef class Gini(ClassificationCriterion):
r"""Gini Index impurity criterion.
This handles cases where the target is a classification taking values
0, 1, ... K-2, K-1. If node m represents a region Rm with Nm observations,
then let
count_k = 1/ Nm \sum_{x_i in Rm} I(yi = k)
be the proportion of class k observations in node m.
The Gini Index is then defined as:
index = \sum_{k=0}^{K-1} count_k (1 - count_k)
= 1 - \sum_{k=0}^{K-1} count_k ** 2
"""
cdef float64_t node_impurity(self) noexcept nogil:
"""Evaluate the impurity of the current node.
Evaluate the Gini criterion as impurity of the current node,
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
better.
"""
cdef float64_t gini = 0.0
cdef float64_t sq_count
cdef float64_t count_k
cdef intp_t k
cdef intp_t c
for k in range(self.n_outputs):
sq_count = 0.0
for c in range(self.n_classes[k]):
count_k = self.sum_total[k, c]
sq_count += count_k * count_k
gini += 1.0 - sq_count / (self.weighted_n_node_samples *
self.weighted_n_node_samples)
return gini / self.n_outputs
cdef void children_impurity(self, float64_t* impurity_left,
float64_t* impurity_right) noexcept nogil:
"""Evaluate the impurity in children nodes.
i.e. the impurity of the left child (sample_indices[start:pos]) and the
impurity the right child (sample_indices[pos:end]) using the Gini index.
Parameters
----------
impurity_left : float64_t pointer
The memory address to save the impurity of the left node to
impurity_right : float64_t pointer
The memory address to save the impurity of the right node to
"""
cdef float64_t gini_left = 0.0
cdef float64_t gini_right = 0.0
cdef float64_t sq_count_left
cdef float64_t sq_count_right
cdef float64_t count_k
cdef intp_t k
cdef intp_t c
for k in range(self.n_outputs):
sq_count_left = 0.0
sq_count_right = 0.0
for c in range(self.n_classes[k]):
count_k = self.sum_left[k, c]
sq_count_left += count_k * count_k
count_k = self.sum_right[k, c]
sq_count_right += count_k * count_k
gini_left += 1.0 - sq_count_left / (self.weighted_n_left *
self.weighted_n_left)
gini_right += 1.0 - sq_count_right / (self.weighted_n_right *
self.weighted_n_right)
impurity_left[0] = gini_left / self.n_outputs
impurity_right[0] = gini_right / self.n_outputs
cdef inline void _move_sums_regression(
RegressionCriterion criterion,
float64_t[::1] sum_1,
float64_t[::1] sum_2,
float64_t* weighted_n_1,
float64_t* weighted_n_2,
bint put_missing_in_1,
) noexcept nogil:
"""Distribute sum_total and sum_missing into sum_1 and sum_2.
If there are missing values and:
- put_missing_in_1 is True, then missing values to go sum_1. Specifically:
sum_1 = sum_missing
sum_2 = sum_total - sum_missing
- put_missing_in_1 is False, then missing values go to sum_2. Specifically:
sum_1 = 0
sum_2 = sum_total
"""
cdef:
intp_t i
intp_t n_bytes = criterion.n_outputs * sizeof(float64_t)
bint has_missing = criterion.n_missing != 0
if has_missing and put_missing_in_1:
memcpy(&sum_1[0], &criterion.sum_missing[0], n_bytes)
for i in range(criterion.n_outputs):
sum_2[i] = criterion.sum_total[i] - criterion.sum_missing[i]
weighted_n_1[0] = criterion.weighted_n_missing
weighted_n_2[0] = criterion.weighted_n_node_samples - criterion.weighted_n_missing
else:
memset(&sum_1[0], 0, n_bytes)
# Assigning sum_2 = sum_total for all outputs.
memcpy(&sum_2[0], &criterion.sum_total[0], n_bytes)
weighted_n_1[0] = 0.0
weighted_n_2[0] = criterion.weighted_n_node_samples
cdef class RegressionCriterion(Criterion):
r"""Abstract regression criterion.
This handles cases where the target is a continuous value, and is
evaluated by computing the variance of the target values left and right
of the split point. The computation takes linear time with `n_samples`
by using ::
var = \sum_i^n (y_i - y_bar) ** 2
= (\sum_i^n y_i ** 2) - n_samples * y_bar ** 2
"""
def __cinit__(self, intp_t n_outputs, intp_t n_samples):
"""Initialize parameters for this criterion.
Parameters
----------
n_outputs : intp_t
The number of targets to be predicted
n_samples : intp_t
The total number of samples to fit on
"""
# Default values
self.start = 0
self.pos = 0
self.end = 0
self.n_outputs = n_outputs
self.n_samples = n_samples
self.n_node_samples = 0
self.weighted_n_node_samples = 0.0
self.weighted_n_left = 0.0
self.weighted_n_right = 0.0
self.weighted_n_missing = 0.0
self.sq_sum_total = 0.0
self.sum_total = np.zeros(n_outputs, dtype=np.float64)
self.sum_left = np.zeros(n_outputs, dtype=np.float64)
self.sum_right = np.zeros(n_outputs, dtype=np.float64)
def __reduce__(self):
return (type(self), (self.n_outputs, self.n_samples), self.__getstate__())
cdef int init(
self,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight,
float64_t weighted_n_samples,
const intp_t[:] sample_indices,
intp_t start,
intp_t end,
) except -1 nogil:
"""Initialize the criterion.
This initializes the criterion at node sample_indices[start:end] and children
sample_indices[start:start] and sample_indices[start:end].
"""
# Initialize fields
self.y = y
self.sample_weight = sample_weight
self.sample_indices = sample_indices
self.start = start
self.end = end
self.n_node_samples = end - start
self.weighted_n_samples = weighted_n_samples
self.weighted_n_node_samples = 0.
cdef intp_t i
cdef intp_t p
cdef intp_t k
cdef float64_t y_ik
cdef float64_t w_y_ik
cdef float64_t w = 1.0
self.sq_sum_total = 0.0
memset(&self.sum_total[0], 0, self.n_outputs * sizeof(float64_t))
for p in range(start, end):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
y_ik = self.y[i, k]
w_y_ik = w * y_ik
self.sum_total[k] += w_y_ik
self.sq_sum_total += w_y_ik * y_ik
self.weighted_n_node_samples += w
# Reset to pos=start
self.reset()
return 0
cdef void init_sum_missing(self):
"""Init sum_missing to hold sums for missing values."""
self.sum_missing = np.zeros(self.n_outputs, dtype=np.float64)
cdef void init_missing(self, intp_t n_missing) noexcept nogil:
"""Initialize sum_missing if there are missing values.
This method assumes that caller placed the missing samples in
self.sample_indices[-n_missing:]
"""
cdef intp_t i, p, k
cdef float64_t y_ik
cdef float64_t w_y_ik
cdef float64_t w = 1.0
self.n_missing = n_missing
if n_missing == 0:
return
memset(&self.sum_missing[0], 0, self.n_outputs * sizeof(float64_t))
self.weighted_n_missing = 0.0
# The missing samples are assumed to be in self.sample_indices[-n_missing:]
for p in range(self.end - n_missing, self.end):
i = self.sample_indices[p]
if self.sample_weight is not None:
w = self.sample_weight[i]
for k in range(self.n_outputs):
y_ik = self.y[i, k]
w_y_ik = w * y_ik
self.sum_missing[k] += w_y_ik
self.weighted_n_missing += w
cdef int reset(self) except -1 nogil:
"""Reset the criterion at pos=start."""
self.pos = self.start
_move_sums_regression(
self,
self.sum_left,
self.sum_right,
&self.weighted_n_left,
&self.weighted_n_right,
self.missing_go_to_left
)
return 0
cdef int reverse_reset(self) except -1 nogil:
"""Reset the criterion at pos=end."""
self.pos = self.end
_move_sums_regression(
self,
self.sum_right,
self.sum_left,
&self.weighted_n_right,
&self.weighted_n_left,
not self.missing_go_to_left
)
return 0
cdef int update(self, intp_t new_pos) except -1 nogil:
"""Updated statistics by moving sample_indices[pos:new_pos] to the left."""
cdef const float64_t[:] sample_weight = self.sample_weight
cdef const intp_t[:] sample_indices = self.sample_indices
cdef intp_t pos = self.pos
# The missing samples are assumed to be in
# self.sample_indices[-self.n_missing:] that is
# self.sample_indices[end_non_missing:self.end].
cdef intp_t end_non_missing = self.end - self.n_missing
cdef intp_t i
cdef intp_t p
cdef intp_t k
cdef float64_t w = 1.0
# Update statistics up to new_pos
#
# Given that
# sum_left[x] + sum_right[x] = sum_total[x]
# and that sum_total is known, we are going to update
# sum_left from the direction that require the least amount
# of computations, i.e. from pos to new_pos or from end to new_pos.
if (new_pos - pos) <= (end_non_missing - new_pos):
for p in range(pos, new_pos):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
self.sum_left[k] += w * self.y[i, k]
self.weighted_n_left += w
else:
self.reverse_reset()
for p in range(end_non_missing - 1, new_pos - 1, -1):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
self.sum_left[k] -= w * self.y[i, k]
self.weighted_n_left -= w
self.weighted_n_right = (self.weighted_n_node_samples -
self.weighted_n_left)
for k in range(self.n_outputs):
self.sum_right[k] = self.sum_total[k] - self.sum_left[k]
self.pos = new_pos
return 0
cdef float64_t node_impurity(self) noexcept nogil:
pass
cdef void children_impurity(self, float64_t* impurity_left,
float64_t* impurity_right) noexcept nogil:
pass
cdef void node_value(self, float64_t* dest) noexcept nogil:
"""Compute the node value of sample_indices[start:end] into dest."""
cdef intp_t k
for k in range(self.n_outputs):
dest[k] = self.sum_total[k] / self.weighted_n_node_samples
cdef inline void clip_node_value(self, float64_t* dest, float64_t lower_bound, float64_t upper_bound) noexcept nogil:
"""Clip the value in dest between lower_bound and upper_bound for monotonic constraints."""
if dest[0] < lower_bound:
dest[0] = lower_bound
elif dest[0] > upper_bound:
dest[0] = upper_bound
cdef float64_t middle_value(self) noexcept nogil:
"""Compute the middle value of a split for monotonicity constraints as the simple average
of the left and right children values.
Monotonicity constraints are only supported for single-output trees we can safely assume
n_outputs == 1.
"""
return (
(self.sum_left[0] / (2 * self.weighted_n_left)) +
(self.sum_right[0] / (2 * self.weighted_n_right))
)
cdef bint check_monotonicity(
self,
cnp.int8_t monotonic_cst,
float64_t lower_bound,
float64_t upper_bound,
) noexcept nogil:
"""Check monotonicity constraint is satisfied at the current regression split"""
cdef:
float64_t value_left = self.sum_left[0] / self.weighted_n_left
float64_t value_right = self.sum_right[0] / self.weighted_n_right
return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
cdef class MSE(RegressionCriterion):
"""Mean squared error impurity criterion.
MSE = var_left + var_right
"""
cdef float64_t node_impurity(self) noexcept nogil:
"""Evaluate the impurity of the current node.
Evaluate the MSE criterion as impurity of the current node,
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
better.
"""
cdef float64_t impurity
cdef intp_t k
impurity = self.sq_sum_total / self.weighted_n_node_samples
for k in range(self.n_outputs):
impurity -= (self.sum_total[k] / self.weighted_n_node_samples)**2.0
return impurity / self.n_outputs
cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
"""Compute a proxy of the impurity reduction.
This method is used to speed up the search for the best split.
It is a proxy quantity such that the split that maximizes this value
also maximizes the impurity improvement. It neglects all constant terms
of the impurity decrease for a given split.
The absolute impurity improvement is only computed by the
impurity_improvement method once the best split has been found.
The MSE proxy is derived from
sum_{i left}(y_i - y_pred_L)^2 + sum_{i right}(y_i - y_pred_R)^2
= sum(y_i^2) - n_L * mean_{i left}(y_i)^2 - n_R * mean_{i right}(y_i)^2
Neglecting constant terms, this gives:
- 1/n_L * sum_{i left}(y_i)^2 - 1/n_R * sum_{i right}(y_i)^2
"""
cdef intp_t k
cdef float64_t proxy_impurity_left = 0.0
cdef float64_t proxy_impurity_right = 0.0
for k in range(self.n_outputs):
proxy_impurity_left += self.sum_left[k] * self.sum_left[k]
proxy_impurity_right += self.sum_right[k] * self.sum_right[k]
return (proxy_impurity_left / self.weighted_n_left +
proxy_impurity_right / self.weighted_n_right)
cdef void children_impurity(self, float64_t* impurity_left,
float64_t* impurity_right) noexcept nogil:
"""Evaluate the impurity in children nodes.
i.e. the impurity of the left child (sample_indices[start:pos]) and the
impurity the right child (sample_indices[pos:end]).
"""
cdef const float64_t[:] sample_weight = self.sample_weight
cdef const intp_t[:] sample_indices = self.sample_indices
cdef intp_t pos = self.pos
cdef intp_t start = self.start
cdef float64_t y_ik
cdef float64_t sq_sum_left = 0.0
cdef float64_t sq_sum_right
cdef intp_t i
cdef intp_t p
cdef intp_t k
cdef float64_t w = 1.0
cdef intp_t end_non_missing
for p in range(start, pos):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
y_ik = self.y[i, k]
sq_sum_left += w * y_ik * y_ik
if self.missing_go_to_left:
# add up the impact of these missing values on the left child
# statistics.
# Note: this only impacts the square sum as the sum
# is modified elsewhere.
end_non_missing = self.end - self.n_missing
for p in range(end_non_missing, self.end):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
y_ik = self.y[i, k]
sq_sum_left += w * y_ik * y_ik
sq_sum_right = self.sq_sum_total - sq_sum_left
impurity_left[0] = sq_sum_left / self.weighted_n_left
impurity_right[0] = sq_sum_right / self.weighted_n_right
for k in range(self.n_outputs):
impurity_left[0] -= (self.sum_left[k] / self.weighted_n_left) ** 2.0
impurity_right[0] -= (self.sum_right[k] / self.weighted_n_right) ** 2.0
impurity_left[0] /= self.n_outputs
impurity_right[0] /= self.n_outputs
cdef class MAE(RegressionCriterion):
r"""Mean absolute error impurity criterion.
MAE = (1 / n)*(\sum_i |y_i - f_i|), where y_i is the true
value and f_i is the predicted value."""
cdef cnp.ndarray left_child
cdef cnp.ndarray right_child
cdef void** left_child_ptr
cdef void** right_child_ptr
cdef float64_t[::1] node_medians
def __cinit__(self, intp_t n_outputs, intp_t n_samples):
"""Initialize parameters for this criterion.
Parameters
----------
n_outputs : intp_t
The number of targets to be predicted
n_samples : intp_t
The total number of samples to fit on
"""
# Default values
self.start = 0
self.pos = 0
self.end = 0
self.n_outputs = n_outputs
self.n_samples = n_samples
self.n_node_samples = 0
self.weighted_n_node_samples = 0.0
self.weighted_n_left = 0.0
self.weighted_n_right = 0.0
self.node_medians = np.zeros(n_outputs, dtype=np.float64)
self.left_child = np.empty(n_outputs, dtype='object')
self.right_child = np.empty(n_outputs, dtype='object')
# initialize WeightedMedianCalculators
for k in range(n_outputs):
self.left_child[k] = WeightedMedianCalculator(n_samples)
self.right_child[k] = WeightedMedianCalculator(n_samples)
self.left_child_ptr = <void**> cnp.PyArray_DATA(self.left_child)
self.right_child_ptr = <void**> cnp.PyArray_DATA(self.right_child)
cdef int init(
self,
const float64_t[:, ::1] y,
const float64_t[:] sample_weight,
float64_t weighted_n_samples,
const intp_t[:] sample_indices,
intp_t start,
intp_t end,
) except -1 nogil:
"""Initialize the criterion.
This initializes the criterion at node sample_indices[start:end] and children
sample_indices[start:start] and sample_indices[start:end].
"""
cdef intp_t i, p, k
cdef float64_t w = 1.0
# Initialize fields
self.y = y
self.sample_weight = sample_weight
self.sample_indices = sample_indices
self.start = start
self.end = end
self.n_node_samples = end - start
self.weighted_n_samples = weighted_n_samples
self.weighted_n_node_samples = 0.
cdef void** left_child = self.left_child_ptr
cdef void** right_child = self.right_child_ptr
for k in range(self.n_outputs):
(<WeightedMedianCalculator> left_child[k]).reset()
(<WeightedMedianCalculator> right_child[k]).reset()
for p in range(start, end):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
# push method ends up calling safe_realloc, hence `except -1`
# push all values to the right side,
# since pos = start initially anyway
(<WeightedMedianCalculator> right_child[k]).push(self.y[i, k], w)
self.weighted_n_node_samples += w
# calculate the node medians
for k in range(self.n_outputs):
self.node_medians[k] = (<WeightedMedianCalculator> right_child[k]).get_median()
# Reset to pos=start
self.reset()
return 0
cdef void init_missing(self, intp_t n_missing) noexcept nogil:
"""Raise error if n_missing != 0."""
if n_missing == 0:
return
with gil:
raise ValueError("missing values is not supported for MAE.")
cdef int reset(self) except -1 nogil:
"""Reset the criterion at pos=start.
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
"""
cdef intp_t i, k
cdef float64_t value
cdef float64_t weight
cdef void** left_child = self.left_child_ptr
cdef void** right_child = self.right_child_ptr
self.weighted_n_left = 0.0
self.weighted_n_right = self.weighted_n_node_samples
self.pos = self.start
# reset the WeightedMedianCalculators, left should have no
# elements and right should have all elements.
for k in range(self.n_outputs):
# if left has no elements, it's already reset
for i in range((<WeightedMedianCalculator> left_child[k]).size()):
# remove everything from left and put it into right
(<WeightedMedianCalculator> left_child[k]).pop(&value,
&weight)
# push method ends up calling safe_realloc, hence `except -1`
(<WeightedMedianCalculator> right_child[k]).push(value,
weight)
return 0
cdef int reverse_reset(self) except -1 nogil:
"""Reset the criterion at pos=end.
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
"""
self.weighted_n_right = 0.0
self.weighted_n_left = self.weighted_n_node_samples
self.pos = self.end
cdef float64_t value
cdef float64_t weight
cdef void** left_child = self.left_child_ptr
cdef void** right_child = self.right_child_ptr
# reverse reset the WeightedMedianCalculators, right should have no
# elements and left should have all elements.
for k in range(self.n_outputs):
# if right has no elements, it's already reset
for i in range((<WeightedMedianCalculator> right_child[k]).size()):
# remove everything from right and put it into left
(<WeightedMedianCalculator> right_child[k]).pop(&value,
&weight)
# push method ends up calling safe_realloc, hence `except -1`
(<WeightedMedianCalculator> left_child[k]).push(value,
weight)
return 0
cdef int update(self, intp_t new_pos) except -1 nogil:
"""Updated statistics by moving sample_indices[pos:new_pos] to the left.
Returns -1 in case of failure to allocate memory (and raise MemoryError)
or 0 otherwise.
"""
cdef const float64_t[:] sample_weight = self.sample_weight
cdef const intp_t[:] sample_indices = self.sample_indices
cdef void** left_child = self.left_child_ptr
cdef void** right_child = self.right_child_ptr
cdef intp_t pos = self.pos
cdef intp_t end = self.end
cdef intp_t i, p, k
cdef float64_t w = 1.0
# Update statistics up to new_pos
#
# We are going to update right_child and left_child
# from the direction that require the least amount of
# computations, i.e. from pos to new_pos or from end to new_pos.
if (new_pos - pos) <= (end - new_pos):
for p in range(pos, new_pos):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
# remove y_ik and its weight w from right and add to left
(<WeightedMedianCalculator> right_child[k]).remove(self.y[i, k], w)
# push method ends up calling safe_realloc, hence except -1
(<WeightedMedianCalculator> left_child[k]).push(self.y[i, k], w)
self.weighted_n_left += w
else:
self.reverse_reset()
for p in range(end - 1, new_pos - 1, -1):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
for k in range(self.n_outputs):
# remove y_ik and its weight w from left and add to right
(<WeightedMedianCalculator> left_child[k]).remove(self.y[i, k], w)
(<WeightedMedianCalculator> right_child[k]).push(self.y[i, k], w)
self.weighted_n_left -= w
self.weighted_n_right = (self.weighted_n_node_samples -
self.weighted_n_left)
self.pos = new_pos
return 0
cdef void node_value(self, float64_t* dest) noexcept nogil:
"""Computes the node value of sample_indices[start:end] into dest."""
cdef intp_t k
for k in range(self.n_outputs):
dest[k] = <float64_t> self.node_medians[k]
cdef inline float64_t middle_value(self) noexcept nogil:
"""Compute the middle value of a split for monotonicity constraints as the simple average
of the left and right children values.
Monotonicity constraints are only supported for single-output trees we can safely assume
n_outputs == 1.
"""
return (
(<WeightedMedianCalculator> self.left_child_ptr[0]).get_median() +
(<WeightedMedianCalculator> self.right_child_ptr[0]).get_median()
) / 2
cdef inline bint check_monotonicity(
self,
cnp.int8_t monotonic_cst,
float64_t lower_bound,
float64_t upper_bound,
) noexcept nogil:
"""Check monotonicity constraint is satisfied at the current regression split"""
cdef:
float64_t value_left = (<WeightedMedianCalculator> self.left_child_ptr[0]).get_median()
float64_t value_right = (<WeightedMedianCalculator> self.right_child_ptr[0]).get_median()
return self._check_monotonicity(monotonic_cst, lower_bound, upper_bound, value_left, value_right)
cdef float64_t node_impurity(self) noexcept nogil:
"""Evaluate the impurity of the current node.
Evaluate the MAE criterion as impurity of the current node,
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
better.
"""
cdef const float64_t[:] sample_weight = self.sample_weight
cdef const intp_t[:] sample_indices = self.sample_indices
cdef intp_t i, p, k
cdef float64_t w = 1.0
cdef float64_t impurity = 0.0
for k in range(self.n_outputs):
for p in range(self.start, self.end):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
impurity += fabs(self.y[i, k] - self.node_medians[k]) * w
return impurity / (self.weighted_n_node_samples * self.n_outputs)
cdef void children_impurity(self, float64_t* p_impurity_left,
float64_t* p_impurity_right) noexcept nogil:
"""Evaluate the impurity in children nodes.
i.e. the impurity of the left child (sample_indices[start:pos]) and the
impurity the right child (sample_indices[pos:end]).
"""
cdef const float64_t[:] sample_weight = self.sample_weight
cdef const intp_t[:] sample_indices = self.sample_indices
cdef intp_t start = self.start
cdef intp_t pos = self.pos
cdef intp_t end = self.end
cdef intp_t i, p, k
cdef float64_t median
cdef float64_t w = 1.0
cdef float64_t impurity_left = 0.0
cdef float64_t impurity_right = 0.0
cdef void** left_child = self.left_child_ptr
cdef void** right_child = self.right_child_ptr
for k in range(self.n_outputs):
median = (<WeightedMedianCalculator> left_child[k]).get_median()
for p in range(start, pos):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
impurity_left += fabs(self.y[i, k] - median) * w
p_impurity_left[0] = impurity_left / (self.weighted_n_left *
self.n_outputs)
for k in range(self.n_outputs):
median = (<WeightedMedianCalculator> right_child[k]).get_median()
for p in range(pos, end):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
impurity_right += fabs(self.y[i, k] - median) * w
p_impurity_right[0] = impurity_right / (self.weighted_n_right *
self.n_outputs)
cdef class FriedmanMSE(MSE):
"""Mean squared error impurity criterion with improvement score by Friedman.
Uses the formula (35) in Friedman's original Gradient Boosting paper:
diff = mean_left - mean_right
improvement = n_left * n_right * diff^2 / (n_left + n_right)
"""
cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
"""Compute a proxy of the impurity reduction.
This method is used to speed up the search for the best split.
It is a proxy quantity such that the split that maximizes this value
also maximizes the impurity improvement. It neglects all constant terms
of the impurity decrease for a given split.
The absolute impurity improvement is only computed by the
impurity_improvement method once the best split has been found.
"""
cdef float64_t total_sum_left = 0.0
cdef float64_t total_sum_right = 0.0
cdef intp_t k
cdef float64_t diff = 0.0
for k in range(self.n_outputs):
total_sum_left += self.sum_left[k]
total_sum_right += self.sum_right[k]
diff = (self.weighted_n_right * total_sum_left -
self.weighted_n_left * total_sum_right)
return diff * diff / (self.weighted_n_left * self.weighted_n_right)
cdef float64_t impurity_improvement(self, float64_t impurity_parent, float64_t
impurity_left, float64_t impurity_right) noexcept nogil:
# Note: none of the arguments are used here
cdef float64_t total_sum_left = 0.0
cdef float64_t total_sum_right = 0.0
cdef intp_t k
cdef float64_t diff = 0.0
for k in range(self.n_outputs):
total_sum_left += self.sum_left[k]
total_sum_right += self.sum_right[k]
diff = (self.weighted_n_right * total_sum_left -
self.weighted_n_left * total_sum_right) / self.n_outputs
return (diff * diff / (self.weighted_n_left * self.weighted_n_right *
self.weighted_n_node_samples))
cdef class Poisson(RegressionCriterion):
"""Half Poisson deviance as impurity criterion.
Poisson deviance = 2/n * sum(y_true * log(y_true/y_pred) + y_pred - y_true)
Note that the deviance is >= 0, and since we have `y_pred = mean(y_true)`
at the leaves, one always has `sum(y_pred - y_true) = 0`. It remains the
implemented impurity (factor 2 is skipped):
1/n * sum(y_true * log(y_true/y_pred)
"""
# FIXME in 1.0:
# min_impurity_split with default = 0 forces us to use a non-negative
# impurity like the Poisson deviance. Without this restriction, one could
# throw away the 'constant' term sum(y_true * log(y_true)) and just use
# Poisson loss = - 1/n * sum(y_true * log(y_pred))
# = - 1/n * sum(y_true * log(mean(y_true))
# = - mean(y_true) * log(mean(y_true))
# With this trick (used in proxy_impurity_improvement()), as for MSE,
# children_impurity would only need to go over left xor right split, not
# both. This could be faster.
cdef float64_t node_impurity(self) noexcept nogil:
"""Evaluate the impurity of the current node.
Evaluate the Poisson criterion as impurity of the current node,
i.e. the impurity of sample_indices[start:end]. The smaller the impurity the
better.
"""
return self.poisson_loss(self.start, self.end, self.sum_total,
self.weighted_n_node_samples)
cdef float64_t proxy_impurity_improvement(self) noexcept nogil:
"""Compute a proxy of the impurity reduction.
This method is used to speed up the search for the best split.
It is a proxy quantity such that the split that maximizes this value
also maximizes the impurity improvement. It neglects all constant terms
of the impurity decrease for a given split.
The absolute impurity improvement is only computed by the
impurity_improvement method once the best split has been found.
The Poisson proxy is derived from:
sum_{i left }(y_i * log(y_i / y_pred_L))
+ sum_{i right}(y_i * log(y_i / y_pred_R))
= sum(y_i * log(y_i) - n_L * mean_{i left}(y_i) * log(mean_{i left}(y_i))
- n_R * mean_{i right}(y_i) * log(mean_{i right}(y_i))
Neglecting constant terms, this gives
- sum{i left }(y_i) * log(mean{i left}(y_i))
- sum{i right}(y_i) * log(mean{i right}(y_i))
"""
cdef intp_t k
cdef float64_t proxy_impurity_left = 0.0
cdef float64_t proxy_impurity_right = 0.0
cdef float64_t y_mean_left = 0.
cdef float64_t y_mean_right = 0.
for k in range(self.n_outputs):
if (self.sum_left[k] <= EPSILON) or (self.sum_right[k] <= EPSILON):
# Poisson loss does not allow non-positive predictions. We
# therefore forbid splits that have child nodes with
# sum(y_i) <= 0.
# Since sum_right = sum_total - sum_left, it can lead to
# floating point rounding error and will not give zero. Thus,
# we relax the above comparison to sum(y_i) <= EPSILON.
return -INFINITY
else:
y_mean_left = self.sum_left[k] / self.weighted_n_left
y_mean_right = self.sum_right[k] / self.weighted_n_right
proxy_impurity_left -= self.sum_left[k] * log(y_mean_left)
proxy_impurity_right -= self.sum_right[k] * log(y_mean_right)
return - proxy_impurity_left - proxy_impurity_right
cdef void children_impurity(self, float64_t* impurity_left,
float64_t* impurity_right) noexcept nogil:
"""Evaluate the impurity in children nodes.
i.e. the impurity of the left child (sample_indices[start:pos]) and the
impurity of the right child (sample_indices[pos:end]) for Poisson.
"""
cdef intp_t start = self.start
cdef intp_t pos = self.pos
cdef intp_t end = self.end
impurity_left[0] = self.poisson_loss(start, pos, self.sum_left,
self.weighted_n_left)
impurity_right[0] = self.poisson_loss(pos, end, self.sum_right,
self.weighted_n_right)
cdef inline float64_t poisson_loss(
self,
intp_t start,
intp_t end,
const float64_t[::1] y_sum,
float64_t weight_sum
) noexcept nogil:
"""Helper function to compute Poisson loss (~deviance) of a given node.
"""
cdef const float64_t[:, ::1] y = self.y
cdef const float64_t[:] sample_weight = self.sample_weight
cdef const intp_t[:] sample_indices = self.sample_indices
cdef float64_t y_mean = 0.
cdef float64_t poisson_loss = 0.
cdef float64_t w = 1.0
cdef intp_t i, k, p
cdef intp_t n_outputs = self.n_outputs
for k in range(n_outputs):
if y_sum[k] <= EPSILON:
# y_sum could be computed from the subtraction
# sum_right = sum_total - sum_left leading to a potential
# floating point rounding error.
# Thus, we relax the comparison y_sum <= 0 to
# y_sum <= EPSILON.
return INFINITY
y_mean = y_sum[k] / weight_sum
for p in range(start, end):
i = sample_indices[p]
if sample_weight is not None:
w = sample_weight[i]
poisson_loss += w * xlogy(y[i, k], y[i, k] / y_mean)
return poisson_loss / (weight_sum * n_outputs)
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