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# cython: cdivision=True
# cython: boundscheck=False
# cython: wraparound=False
# cython: nonecheck=False
# cython: language_level=3
# Author: Nicolas Hug
cimport cython
import numpy as np
cimport numpy as np
from numpy.math cimport INFINITY
from cython.parallel import prange
from libc.math cimport isnan
from .common cimport X_DTYPE_C, X_BINNED_DTYPE_C
np.import_array()
def _map_to_bins(const X_DTYPE_C [:, :] data,
list binning_thresholds,
const unsigned char missing_values_bin_idx,
X_BINNED_DTYPE_C [::1, :] binned):
"""Bin numerical values to discrete integer-coded levels.
Parameters
----------
data : ndarray, shape (n_samples, n_features)
The numerical data to bin.
binning_thresholds : list of arrays
For each feature, stores the increasing numeric values that are
used to separate the bins.
binned : ndarray, shape (n_samples, n_features)
Output array, must be fortran aligned.
"""
cdef:
int feature_idx
for feature_idx in range(data.shape[1]):
_map_num_col_to_bins(data[:, feature_idx],
binning_thresholds[feature_idx],
missing_values_bin_idx,
binned[:, feature_idx])
cdef void _map_num_col_to_bins(const X_DTYPE_C [:] data,
const X_DTYPE_C [:] binning_thresholds,
const unsigned char missing_values_bin_idx,
X_BINNED_DTYPE_C [:] binned):
"""Binary search to find the bin index for each value in the data."""
cdef:
int i
int left
int right
int middle
for i in prange(data.shape[0], schedule='static', nogil=True):
if isnan(data[i]):
binned[i] = missing_values_bin_idx
else:
# for known values, use binary search
left, right = 0, binning_thresholds.shape[0]
while left < right:
middle = (right + left - 1) // 2
if data[i] <= binning_thresholds[middle]:
right = middle
else:
left = middle + 1
binned[i] = left
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