Condition sparse-dot-topn version on Python version

polyfuzz/models/_utils.py

@@ -1,16 +1,12 @@
-import importlib.util
-
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
 from typing import List
 from sklearn.neighbors import NearestNeighbors
 from sklearn.metrics.pairwise import cosine_similarity as scikit_cosine_similarity
 
-_HAVE_SPARSE_DOT = importlib.util.find_spec("sparse_dot_topn") is not None
-if _HAVE_SPARSE_DOT:
-    from sparse_dot_topn import sp_matmul_topn
 
+from polyfuzz.models._utils_sdtn import _HAVE_SPARSE_DOT, sp_matmul_topn
 
 def cosine_similarity(from_vector: np.ndarray,
                       to_vector: np.ndarray,

polyfuzz/models/_utils_sdtn.py

@@ -0,0 +1,33 @@
+import sys
+import importlib.util
+from scipy.sparse import csr_matrix
+
+from typing import Optional
+
+_HAVE_SPARSE_DOT = importlib.util.find_spec("sparse_dot_topn") is not None
+if _HAVE_SPARSE_DOT:
+    if sys.version_info >= (3, 8):
+        from sparse_dot_topn import sp_matmul_topn
+    else:
+        from sparse_dot_topn import awesome_cossim_topn
+
+        def sp_matmul_topn(
+            A: csr_matrix,
+            B: csr_matrix,
+            top_n: int,
+            threshold: float,
+            sort: bool = True,
+            n_threads: Optional[int] = None,
+        ):
+            n_threads = n_threads or 1
+            use_threads = n_threads > 1
+            return awesome_cossim_topn(
+                A,
+                B.T,
+                ntop=max(top_n, 2),
+                lower_bound=threshold,
+                use_threads=use_threads,
+                n_jobs=n_threads,
+            )
+
+    __all__ = ["sp_matmul_topn"]

setup.py

@@ -32,7 +32,10 @@
     "sentence-transformers>=0.4.1"
 ]
 
-fast_cosine = ["sparse_dot_topn>=1.1.1"]
+fast_cosine = [
+    "sparse_dot_topn<1.0; python_version < '3.8'",
+    "sparse_dot_topn>=1.1.1; python_version >= '3.8'",
+]
 
 embeddings_packages = [
     "torch>=1.4.0",