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Replaced unsigned char with uint8_t in Cython Codebase (9 files) #29597

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Merged
merged 5 commits into from
Aug 1, 2024
Merged

Replaced unsigned char with uint8_t in Cython Codebase (9 files) #29597

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ae55467
Replace unsigned char with uint8_t in _radius_neighbors_classmode.pyx.tp
norgera Jul 31, 2024
0f8d7e2
Replace unsigned char with uint8_t in 8 other files
norgera Jul 31, 2024
e819e47
fix linting
norgera Jul 31, 2024
294ad5d
linting again
norgera Jul 31, 2024
b6b4809
linting again lol
norgera Jul 31, 2024
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11 changes: 6 additions & 5 deletions sklearn/ensemble/_hist_gradient_boosting/_binning.pyx
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Expand Up @@ -4,12 +4,13 @@ from cython.parallel import prange
from libc.math cimport isnan

from .common cimport X_DTYPE_C, X_BINNED_DTYPE_C
from ...utils._typedefs cimport uint8_t


def _map_to_bins(const X_DTYPE_C [:, :] data,
list binning_thresholds,
const unsigned char[::1] is_categorical,
const unsigned char missing_values_bin_idx,
const uint8_t[::1] is_categorical,
const uint8_t missing_values_bin_idx,
int n_threads,
X_BINNED_DTYPE_C [::1, :] binned):
"""Bin continuous and categorical values to discrete integer-coded levels.
Expand All @@ -24,7 +25,7 @@ def _map_to_bins(const X_DTYPE_C [:, :] data,
binning_thresholds : list of arrays
For each feature, stores the increasing numeric values that are
used to separate the bins.
is_categorical : ndarray of unsigned char of shape (n_features,)
is_categorical : ndarray of uint8_t of shape (n_features,)
Indicates categorical features.
n_threads : int
Number of OpenMP threads to use.
Expand All @@ -48,8 +49,8 @@ def _map_to_bins(const X_DTYPE_C [:, :] data,
cdef void _map_col_to_bins(
const X_DTYPE_C [:] data,
const X_DTYPE_C [:] binning_thresholds,
const unsigned char is_categorical,
const unsigned char missing_values_bin_idx,
const uint8_t is_categorical,
const uint8_t missing_values_bin_idx,
int n_threads,
X_BINNED_DTYPE_C [:] binned
):
Expand Down
12 changes: 7 additions & 5 deletions sklearn/ensemble/_hist_gradient_boosting/_bitset.pxd
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Expand Up @@ -2,17 +2,19 @@ from .common cimport X_BINNED_DTYPE_C
from .common cimport BITSET_DTYPE_C
from .common cimport BITSET_INNER_DTYPE_C
from .common cimport X_DTYPE_C
from ...utils._typedefs cimport uint8_t


cdef void init_bitset(BITSET_DTYPE_C bitset) noexcept nogil

cdef void set_bitset(BITSET_DTYPE_C bitset, X_BINNED_DTYPE_C val) noexcept nogil

cdef unsigned char in_bitset(BITSET_DTYPE_C bitset, X_BINNED_DTYPE_C val) noexcept nogil
cdef uint8_t in_bitset(BITSET_DTYPE_C bitset, X_BINNED_DTYPE_C val) noexcept nogil

cpdef unsigned char in_bitset_memoryview(const BITSET_INNER_DTYPE_C[:] bitset,
X_BINNED_DTYPE_C val) noexcept nogil
cpdef uint8_t in_bitset_memoryview(const BITSET_INNER_DTYPE_C[:] bitset,
X_BINNED_DTYPE_C val) noexcept nogil

cdef unsigned char in_bitset_2d_memoryview(
const BITSET_INNER_DTYPE_C [:, :] bitset,
cdef uint8_t in_bitset_2d_memoryview(
const BITSET_INNER_DTYPE_C[:, :] bitset,
X_BINNED_DTYPE_C val,
unsigned int row) noexcept nogil
16 changes: 8 additions & 8 deletions sklearn/ensemble/_hist_gradient_boosting/_bitset.pyx
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Expand Up @@ -2,6 +2,7 @@ from .common cimport BITSET_INNER_DTYPE_C
from .common cimport BITSET_DTYPE_C
from .common cimport X_DTYPE_C
from .common cimport X_BINNED_DTYPE_C
from ...utils._typedefs cimport uint8_t


# A bitset is a data structure used to represent sets of integers in [0, n]. We
Expand All @@ -25,20 +26,19 @@ cdef inline void set_bitset(BITSET_DTYPE_C bitset, # OUT
bitset[val // 32] |= (1 << (val % 32))


cdef inline unsigned char in_bitset(BITSET_DTYPE_C bitset,
X_BINNED_DTYPE_C val) noexcept nogil:

cdef inline uint8_t in_bitset(BITSET_DTYPE_C bitset,
X_BINNED_DTYPE_C val) noexcept nogil:
return (bitset[val // 32] >> (val % 32)) & 1


cpdef inline unsigned char in_bitset_memoryview(const BITSET_INNER_DTYPE_C[:] bitset,
X_BINNED_DTYPE_C val) noexcept nogil:
cpdef inline uint8_t in_bitset_memoryview(const BITSET_INNER_DTYPE_C[:] bitset,
X_BINNED_DTYPE_C val) noexcept nogil:
return (bitset[val // 32] >> (val % 32)) & 1

cdef inline unsigned char in_bitset_2d_memoryview(const BITSET_INNER_DTYPE_C [:, :] bitset,
X_BINNED_DTYPE_C val,
unsigned int row) noexcept nogil:

cdef inline uint8_t in_bitset_2d_memoryview(const BITSET_INNER_DTYPE_C[:, :] bitset,
X_BINNED_DTYPE_C val,
unsigned int row) noexcept nogil:
# Same as above but works on 2d memory views to avoid the creation of 1d
# memory views. See https://github.com/scikit-learn/scikit-learn/issues/17299
return (bitset[row, val // 32] >> (val % 32)) & 1
Expand Down
6 changes: 3 additions & 3 deletions sklearn/ensemble/_hist_gradient_boosting/_predictor.pyx
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Expand Up @@ -4,7 +4,7 @@ from cython.parallel import prange
from libc.math cimport isnan
import numpy as np

from ...utils._typedefs cimport intp_t
from ...utils._typedefs cimport intp_t, uint8_t
from .common cimport X_DTYPE_C
from .common cimport Y_DTYPE_C
from .common import Y_DTYPE
Expand Down Expand Up @@ -89,7 +89,7 @@ def _predict_from_binned_data(
node_struct [:] nodes,
const X_BINNED_DTYPE_C [:, :] binned_data,
BITSET_INNER_DTYPE_C [:, :] binned_left_cat_bitsets,
const unsigned char missing_values_bin_idx,
const uint8_t missing_values_bin_idx,
int n_threads,
Y_DTYPE_C [:] out):

Expand All @@ -109,7 +109,7 @@ cdef inline Y_DTYPE_C _predict_one_from_binned_data(
const X_BINNED_DTYPE_C [:, :] binned_data,
const BITSET_INNER_DTYPE_C [:, :] binned_left_cat_bitsets,
const int row,
const unsigned char missing_values_bin_idx) noexcept nogil:
const uint8_t missing_values_bin_idx) noexcept nogil:
# Need to pass the whole array and the row index, else prange won't work.
# See issue Cython #2798

Expand Down
6 changes: 3 additions & 3 deletions sklearn/ensemble/_hist_gradient_boosting/common.pxd
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Expand Up @@ -24,14 +24,14 @@ cdef packed struct node_struct:
unsigned int count
intp_t feature_idx
X_DTYPE_C num_threshold
unsigned char missing_go_to_left
uint8_t missing_go_to_left
unsigned int left
unsigned int right
Y_DTYPE_C gain
unsigned int depth
unsigned char is_leaf
uint8_t is_leaf
X_BINNED_DTYPE_C bin_threshold
unsigned char is_categorical
uint8_t is_categorical
# The index of the corresponding bitsets in the Predictor's bitset arrays.
# Only used if is_categorical is True
unsigned int bitset_idx
Expand Down
9 changes: 5 additions & 4 deletions sklearn/ensemble/_hist_gradient_boosting/histogram.pyx
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Expand Up @@ -12,6 +12,7 @@ from .common import HISTOGRAM_DTYPE
from .common cimport hist_struct
from .common cimport X_BINNED_DTYPE_C
from .common cimport G_H_DTYPE_C
from ...utils._typedefs cimport uint8_t


# Notes:
Expand Down Expand Up @@ -79,13 +80,13 @@ cdef class HistogramBuilder:
G_H_DTYPE_C [::1] hessians
G_H_DTYPE_C [::1] ordered_gradients
G_H_DTYPE_C [::1] ordered_hessians
unsigned char hessians_are_constant
uint8_t hessians_are_constant
int n_threads

def __init__(self, const X_BINNED_DTYPE_C [::1, :] X_binned,
unsigned int n_bins, G_H_DTYPE_C [::1] gradients,
G_H_DTYPE_C [::1] hessians,
unsigned char hessians_are_constant,
uint8_t hessians_are_constant,
int n_threads):

self.X_binned = X_binned
Expand Down Expand Up @@ -130,7 +131,7 @@ cdef class HistogramBuilder:
int f_idx
int i
# need local views to avoid python interactions
unsigned char hessians_are_constant = self.hessians_are_constant
uint8_t hessians_are_constant = self.hessians_are_constant
int n_allowed_features = self.n_features
G_H_DTYPE_C [::1] ordered_gradients = self.ordered_gradients
G_H_DTYPE_C [::1] gradients = self.gradients
Expand Down Expand Up @@ -195,7 +196,7 @@ cdef class HistogramBuilder:
self.ordered_gradients[:n_samples]
G_H_DTYPE_C [::1] ordered_hessians = \
self.ordered_hessians[:n_samples]
unsigned char hessians_are_constant = \
uint8_t hessians_are_constant = \
self.hessians_are_constant

# Set histograms to zero.
Expand Down
50 changes: 25 additions & 25 deletions sklearn/ensemble/_hist_gradient_boosting/splitting.pyx
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Expand Up @@ -32,7 +32,7 @@ cdef struct split_info_struct:
Y_DTYPE_C gain
int feature_idx
unsigned int bin_idx
unsigned char missing_go_to_left
uint8_t missing_go_to_left
Y_DTYPE_C sum_gradient_left
Y_DTYPE_C sum_gradient_right
Y_DTYPE_C sum_hessian_left
Expand All @@ -41,7 +41,7 @@ cdef struct split_info_struct:
unsigned int n_samples_right
Y_DTYPE_C value_left
Y_DTYPE_C value_right
unsigned char is_categorical
uint8_t is_categorical
BITSET_DTYPE_C left_cat_bitset


Expand Down Expand Up @@ -168,11 +168,11 @@ cdef class Splitter:
const X_BINNED_DTYPE_C [::1, :] X_binned
unsigned int n_features
const unsigned int [::1] n_bins_non_missing
unsigned char missing_values_bin_idx
const unsigned char [::1] has_missing_values
const unsigned char [::1] is_categorical
uint8_t missing_values_bin_idx
const uint8_t [::1] has_missing_values
const uint8_t [::1] is_categorical
const signed char [::1] monotonic_cst
unsigned char hessians_are_constant
uint8_t hessians_are_constant
Y_DTYPE_C l2_regularization
Y_DTYPE_C min_hessian_to_split
unsigned int min_samples_leaf
Expand All @@ -188,15 +188,15 @@ cdef class Splitter:
def __init__(self,
const X_BINNED_DTYPE_C [::1, :] X_binned,
const unsigned int [::1] n_bins_non_missing,
const unsigned char missing_values_bin_idx,
const unsigned char [::1] has_missing_values,
const unsigned char [::1] is_categorical,
const uint8_t missing_values_bin_idx,
const uint8_t [::1] has_missing_values,
const uint8_t [::1] is_categorical,
const signed char [::1] monotonic_cst,
Y_DTYPE_C l2_regularization,
Y_DTYPE_C min_hessian_to_split=1e-3,
unsigned int min_samples_leaf=20,
Y_DTYPE_C min_gain_to_split=0.,
unsigned char hessians_are_constant=False,
uint8_t hessians_are_constant=False,
Y_DTYPE_C feature_fraction_per_split=1.0,
rng=np.random.RandomState(),
unsigned int n_threads=1):
Expand Down Expand Up @@ -307,14 +307,14 @@ cdef class Splitter:
cdef:
int n_samples = sample_indices.shape[0]
X_BINNED_DTYPE_C bin_idx = split_info.bin_idx
unsigned char missing_go_to_left = split_info.missing_go_to_left
unsigned char missing_values_bin_idx = self.missing_values_bin_idx
uint8_t missing_go_to_left = split_info.missing_go_to_left
uint8_t missing_values_bin_idx = self.missing_values_bin_idx
int feature_idx = split_info.feature_idx
const X_BINNED_DTYPE_C [::1] X_binned = \
self.X_binned[:, feature_idx]
unsigned int [::1] left_indices_buffer = self.left_indices_buffer
unsigned int [::1] right_indices_buffer = self.right_indices_buffer
unsigned char is_categorical = split_info.is_categorical
uint8_t is_categorical = split_info.is_categorical
# Cython is unhappy if we set left_cat_bitset to
# split_info.left_cat_bitset directly, so we need a tmp var
BITSET_INNER_DTYPE_C [:] cat_bitset_tmp = split_info.left_cat_bitset
Expand All @@ -334,7 +334,7 @@ cdef class Splitter:
int thread_idx
int sample_idx
int right_child_position
unsigned char turn_left
uint8_t turn_left
int [:] left_offset = np.zeros(n_threads, dtype=np.int32)
int [:] right_offset = np.zeros(n_threads, dtype=np.int32)

Expand Down Expand Up @@ -482,8 +482,8 @@ cdef class Splitter:
int n_allowed_features
split_info_struct split_info
split_info_struct * split_infos
const unsigned char [::1] has_missing_values = self.has_missing_values
const unsigned char [::1] is_categorical = self.is_categorical
const uint8_t [::1] has_missing_values = self.has_missing_values
const uint8_t [::1] is_categorical = self.is_categorical
const signed char [::1] monotonic_cst = self.monotonic_cst
int n_threads = self.n_threads
bint has_interaction_cst = False
Expand Down Expand Up @@ -622,7 +622,7 @@ cdef class Splitter:
cdef void _find_best_bin_to_split_left_to_right(
Splitter self,
unsigned int feature_idx,
unsigned char has_missing_values,
uint8_t has_missing_values,
const hist_struct [:, ::1] histograms, # IN
unsigned int n_samples,
Y_DTYPE_C sum_gradients,
Expand Down Expand Up @@ -658,7 +658,7 @@ cdef class Splitter:
Y_DTYPE_C sum_gradient_right
Y_DTYPE_C loss_current_node
Y_DTYPE_C gain
unsigned char found_better_split = False
uint8_t found_better_split = False

Y_DTYPE_C best_sum_hessian_left
Y_DTYPE_C best_sum_gradient_left
Expand Down Expand Up @@ -771,7 +771,7 @@ cdef class Splitter:
Y_DTYPE_C loss_current_node
Y_DTYPE_C gain
unsigned int start = self.n_bins_non_missing[feature_idx] - 2
unsigned char found_better_split = False
uint8_t found_better_split = False

Y_DTYPE_C best_sum_hessian_left
Y_DTYPE_C best_sum_gradient_left
Expand Down Expand Up @@ -851,7 +851,7 @@ cdef class Splitter:
cdef void _find_best_bin_to_split_category(
self,
unsigned int feature_idx,
unsigned char has_missing_values,
uint8_t has_missing_values,
const hist_struct [:, ::1] histograms, # IN
unsigned int n_samples,
Y_DTYPE_C sum_gradients,
Expand Down Expand Up @@ -890,7 +890,7 @@ cdef class Splitter:
unsigned int n_samples_left, n_samples_right
Y_DTYPE_C gain
Y_DTYPE_C best_gain = -1.0
unsigned char found_better_split = False
uint8_t found_better_split = False
Y_DTYPE_C best_sum_hessian_left
Y_DTYPE_C best_sum_gradient_left
unsigned int best_n_samples_left
Expand Down Expand Up @@ -1139,12 +1139,12 @@ cdef inline Y_DTYPE_C _loss_from_value(
"""
return sum_gradient * value

cdef inline unsigned char sample_goes_left(
unsigned char missing_go_to_left,
unsigned char missing_values_bin_idx,
cdef inline uint8_t sample_goes_left(
uint8_t missing_go_to_left,
uint8_t missing_values_bin_idx,
X_BINNED_DTYPE_C split_bin_idx,
X_BINNED_DTYPE_C bin_value,
unsigned char is_categorical,
uint8_t is_categorical,
BITSET_DTYPE_C left_cat_bitset) noexcept nogil:
"""Helper to decide whether sample should go to left or right child."""

Expand Down
6 changes: 3 additions & 3 deletions sklearn/linear_model/_sgd_fast.pyx.tp
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Expand Up @@ -33,7 +33,7 @@ from cython cimport floating
from libc.math cimport exp, fabs, isfinite, log, pow, INFINITY

from .._loss._loss cimport CyLossFunction
from ..utils._typedefs cimport uint32_t
from ..utils._typedefs cimport uint32_t, uint8_t
from ..utils._weight_vector cimport WeightVector32, WeightVector64
from ..utils._seq_dataset cimport SequentialDataset32, SequentialDataset64

Expand Down Expand Up @@ -287,7 +287,7 @@ def _plain_sgd{{name_suffix}}(
double C,
double l1_ratio,
SequentialDataset{{name_suffix}} dataset,
const unsigned char[::1] validation_mask,
const uint8_t[::1] validation_mask,
bint early_stopping,
validation_score_cb,
int n_iter_no_change,
Expand Down Expand Up @@ -333,7 +333,7 @@ def _plain_sgd{{name_suffix}}(
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
dataset : SequentialDataset
A concrete ``SequentialDataset`` object.
validation_mask : ndarray[unsigned char, ndim=1]
validation_mask : ndarray[uint8_t, ndim=1]
Equal to True on the validation set.
early_stopping : boolean
Whether to use a stopping criterion based on the validation set.
Expand Down
4 changes: 2 additions & 2 deletions sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp
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Expand Up @@ -4,7 +4,7 @@ from cython cimport floating, final, integral
from cython.operator cimport dereference as deref
from cython.parallel cimport parallel, prange
from ._classmode cimport WeightingStrategy
from ...utils._typedefs cimport intp_t, float64_t
from ...utils._typedefs cimport intp_t, float64_t, uint8_t

import numpy as np
from scipy.sparse import issparse
Expand All @@ -25,7 +25,7 @@ cdef class RadiusNeighborsClassMode{{name_suffix}}(RadiusNeighbors{{name_suffix}
intp_t outlier_label_index
bint outlier_label_exists
bint outliers_exist
unsigned char[::1] outliers
uint8_t[::1] outliers
object outlier_label
float64_t[:, ::1] class_scores
WeightingStrategy weight_type
Expand Down