Fix transpose_matvec for categorical matrix with read-only indices (replaces #489)

CHANGELOG.rst

@@ -7,6 +7,15 @@
 Changelog
 =========
 
+
+4.1.4 - 2025-10-27
+------------------
+
+**Bug fix:**
+
+- Fixed :meth:`CategoricalMatrix.transpose_matvec` to operate on read-only buffers as well.
+
+
 4.1.3 - 2025-10-14
 ------------------
 

src/tabmat/ext/cat_split_helpers-tmpl.cpp

@@ -5,7 +5,7 @@
 template <typename Int, typename F>
 void _transpose_matvec_${type}(
     Int n_rows,
-    Int* indices,
+    const Int* indices,
     F* other,
     F* res,
     Int res_size

src/tabmat/ext/categorical.pyx

@@ -16,12 +16,12 @@ ctypedef fused win_numeric:
     long long
 
 cdef extern from "cat_split_helpers.cpp":
-    void _transpose_matvec_all_rows_fast[Int, F](Int, Int*, F*, F*, Int)
-    void _transpose_matvec_all_rows_complex[Int, F](Int, Int*, F*, F*, Int, bool)
+    void _transpose_matvec_all_rows_fast[Int, F](Int, const int*, F*, F*, Int)
+    void _transpose_matvec_all_rows_complex[Int, F](Int, const int*, F*, F*, Int, bool)
 
 
 def transpose_matvec_fast(
-    int[:] indices,
+    const int[:] indices,
     floating[:] other,
     int n_cols,
     dtype,
@@ -68,7 +68,7 @@ def transpose_matvec_fast(
 
 
 def transpose_matvec_complex(
-    int[:] indices,
+    const int[:] indices,
     floating[:] other,
     int n_cols,
     dtype,

tests/test_transpose_matvec.py

@@ -0,0 +1,24 @@
+import narwhals as nw
+import numpy as np
+import pandas as pd
+
+import tabmat as tm
+
+
+def test_transpose_matvec_does_not_crash():
+    # With high n_categories CategoricalMatrix.indices is read-only.
+    # As a result, transpose_matvec method crashed with "buffer
+    # source array is read-only".
+
+    n = 797_586
+    n_categories = 58_059
+    categories = [f"cat[{i}]" for i in range(n_categories)]
+    indices = np.linspace(0, n_categories - 1, n).round().astype(int)
+    cat_vec = pd.Series(pd.Categorical.from_codes(indices, categories=categories))
+    cat_vec_nw = nw.from_native(cat_vec, allow_series=True)
+    weights = np.ones(n)
+    cat_matrix_tm = tm.CategoricalMatrix(cat_vec_nw)
+
+    result = cat_matrix_tm.transpose_matvec(weights)
+
+    assert result is not None