MAINT Clean-up comments and rename variables in _middle_term_sparse_sparse_{32, 64} (scikit-learn#25449)

Vincent-Maladiere · jjerphan · web-flow · commit 4acd91d88198 · 2023-02-06T16:12:11.000+01:00
Co-authored-by: Julien Jerphanion &lt;git@jjerphan.xyz&gt;
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_middle_term_computer.pyx.tp
@@ -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,
@@ -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}}
