Merge pull request #2083 from recommenders-team/miguel/scipy_sar_issue

SAR sparse multiplcation modification due to a breaking change in scipy

recommenders/utils/python_utils.py

@@ -62,7 +62,7 @@ def jaccard(cooccurrence):
     with np.errstate(invalid="ignore", divide="ignore"):
         result = cooccurrence / (diag_rows + diag_cols - cooccurrence)
 
-    return np.array(result)
+    return np.array(result) if isinstance(result, np.ndarray) else result.toarray()
 
 
 def lift(cooccurrence):
@@ -85,7 +85,7 @@ def lift(cooccurrence):
     with np.errstate(invalid="ignore", divide="ignore"):
         result = cooccurrence / (diag_rows * diag_cols)
 
-    return np.array(result)
+    return np.array(result) if isinstance(result, np.ndarray) else result.toarray()
 
 
 def mutual_information(cooccurrence):
@@ -106,7 +106,7 @@ def mutual_information(cooccurrence):
     with np.errstate(invalid="ignore", divide="ignore"):
         result = np.log2(cooccurrence.shape[0] * lift(cooccurrence))
 
-    return np.array(result)
+    return np.array(result) if isinstance(result, np.ndarray) else result.toarray()
 
 
 def lexicographers_mutual_information(cooccurrence):
@@ -128,7 +128,7 @@ def lexicographers_mutual_information(cooccurrence):
     with np.errstate(invalid="ignore", divide="ignore"):
         result = cooccurrence * mutual_information(cooccurrence)
 
-    return np.array(result)
+    return np.array(result) if isinstance(result, np.ndarray) else result.toarray()
 
 
 def cosine_similarity(cooccurrence):
@@ -151,7 +151,7 @@ def cosine_similarity(cooccurrence):
     with np.errstate(invalid="ignore", divide="ignore"):
         result = cooccurrence / np.sqrt(diag_rows * diag_cols)
 
-    return np.array(result)
+    return np.array(result) if isinstance(result, np.ndarray) else result.toarray()
 
 
 def inclusion_index(cooccurrence):
@@ -173,7 +173,7 @@ def inclusion_index(cooccurrence):
     with np.errstate(invalid="ignore", divide="ignore"):
         result = cooccurrence / np.minimum(diag_rows, diag_cols)
 
-    return np.array(result)
+    return np.array(result) if isinstance(result, np.ndarray) else result.toarray()
 
 
 def get_top_k_scored_items(scores, top_k, sort_top_k=False):

setup.py

@@ -43,7 +43,7 @@
     "retrying>=1.3.4,<2",
     "scikit-learn>=1.2.0,<2",  # requires scipy, and introduce breaking change affects feature_extraction.text.TfidfVectorizer.min_df
     "scikit-surprise>=1.1.3",
-    "scipy>=1.10.1,<1.11.0",  # FIXME: We limit <1.11.0 until #1954 is fixed
+    "scipy>=1.10.1",
     "seaborn>=0.13.0,<1",  # requires matplotlib, packaging
     "transformers>=4.27.0,<5",  # requires packaging, pyyaml, requests, tqdm
 ]