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MAINT Improve the _middle_term_sparse_sparse_{32, 64} routines #25449

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Merged
merged 4 commits into from
Feb 6, 2023
Merged

MAINT Improve the _middle_term_sparse_sparse_{32, 64} routines #25449

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d29fe05
remove TODOs and simplify variable names
Vincent-Maladiere Jan 21, 2023
a8ad977
Merge branch 'main' into optimize_csr_csr_routine
Vincent-Maladiere Jan 24, 2023
777e012
Update sklearn/metrics/_pairwise_distances_reduction/_middle_term_com…
Vincent-Maladiere Feb 1, 2023
109c248
Merge branch 'main' into optimize_csr_csr_routine
Vincent-Maladiere Feb 1, 2023
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22 changes: 7 additions & 15 deletions sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx.tp
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Original file line number Diff line number Diff line change
Expand Up @@ -38,14 +38,6 @@ import numpy as np
from scipy.sparse import issparse, csr_matrix
from ...utils._typedefs import DTYPE, SPARSE_INDEX_TYPE

# TODO: If possible optimize this routine to efficiently treat cases where
# `n_samples_X << n_samples_Y` met in practise when X_test consists of a
# few samples, and thus when there's a single chunk of X whose number of
# samples is less than the default chunk size.

# TODO: compare this routine with the similar ones in SciPy, especially
# `csr_matmat` which might implement a better algorithm.
# See: https://github.com/scipy/scipy/blob/e58292e066ba2cb2f3d1e0563ca9314ff1f4f311/scipy/sparse/sparsetools/csr.h#L603-L669 # noqa
cdef void _middle_term_sparse_sparse_64(
const DTYPE_t[:] X_data,
const SPARSE_INDEX_TYPE_t[:] X_indices,
Expand All @@ -66,17 +58,17 @@ cdef void _middle_term_sparse_sparse_64(
ITYPE_t i, j, k
ITYPE_t n_X = X_end - X_start
ITYPE_t n_Y = Y_end - Y_start
ITYPE_t X_i_col_idx, X_i_ptr, Y_j_col_idx, Y_j_ptr
ITYPE_t x_col, x_ptr, y_col, y_ptr

for i in range(n_X):
for X_i_ptr in range(X_indptr[X_start+i], X_indptr[X_start+i+1]):
X_i_col_idx = X_indices[X_i_ptr]
for x_ptr in range(X_indptr[X_start+i], X_indptr[X_start+i+1]):
x_col = X_indices[x_ptr]
for j in range(n_Y):
k = i * n_Y + j
for Y_j_ptr in range(Y_indptr[Y_start+j], Y_indptr[Y_start+j+1]):
Y_j_col_idx = Y_indices[Y_j_ptr]
if X_i_col_idx == Y_j_col_idx:
D[k] += -2 * X_data[X_i_ptr] * Y_data[Y_j_ptr]
for y_ptr in range(Y_indptr[Y_start+j], Y_indptr[Y_start+j+1]):
y_col = Y_indices[y_ptr]
if x_col == y_col:
D[k] += -2 * X_data[x_ptr] * Y_data[y_ptr]


{{for name_suffix, upcast_to_float64, INPUT_DTYPE_t, INPUT_DTYPE in implementation_specific_values}}
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