Sparse dot fix (#22)

docs/releases.md

@@ -1,3 +1,6 @@
+v0.3.1  
+- Fix exploding memory usage when using `top_n`   
+
 v0.3.0  
 - Use `top_n` in `polyfuzz.models.TFIDF` and `polyfuzz.models.Embeddings`   
 
@@ -16,8 +19,7 @@ v0.1.0
 - More thorough documentation  
 - Prepare for public release  
 
-v0.0.1
-
+v0.0.1  
 - First release of `PolyFuzz`
 - Matching through:
     - Edit Distance

polyfuzz/__init__.py

@@ -1,2 +1,2 @@
 from .polyfuzz import PolyFuzz
-__version__ = "0.3.0"
+__version__ = "0.3.1"

polyfuzz/models/_utils.py

@@ -85,9 +85,9 @@ def cosine_similarity(from_vector: np.ndarray,
             similarity_matrix.setdiag(0.)
             similarity_matrix = similarity_matrix.tocsr()
 
-        indices = np.flip(np.argsort(similarity_matrix.toarray(), axis=-1), axis=1)[:, :top_n]
-        similarities = np.flip(np.sort(similarity_matrix.toarray(), axis=-1), axis=1)[:, :top_n]
-        similarities = [np.round(similarities[:, i], 3) for i in range(similarities.shape[1])]
+        indices = _top_n_idx_sparse(similarity_matrix, top_n)
+        similarities = _top_n_similarities_sparse(similarity_matrix, top_n, indices)
+        indices = np.array(np.nan_to_num(np.array(indices, dtype=np.float), nan=0), dtype=np.int)
 
     # Faster than knn and slower than sparse but uses more memory
     else:
@@ -101,9 +101,9 @@ def cosine_similarity(from_vector: np.ndarray,
         similarities = [np.round(similarities[:, i], 3) for i in range(similarities.shape[1])]
 
     # Convert results to df
-    columns =  (["From"] +
-                ["To" if i == 0 else f"To_{i+1}" for i in range(top_n)] +
-                ["Similarity" if i ==0 else f"Similarity_{i+1}" for i in range(top_n)]); columns
+    columns = (["From"] +
+               ["To" if i == 0 else f"To_{i+1}" for i in range(top_n)] +
+               ["Similarity" if i ==0 else f"Similarity_{i+1}" for i in range(top_n)])
     matches = [[to_list[idx] for idx in indices[:, i]] for i in range(indices.shape[1])]
     matches = pd.DataFrame(np.vstack(([from_list], matches, similarities)).T, columns = columns)
 
@@ -119,3 +119,24 @@ def cosine_similarity(from_vector: np.ndarray,
             matches.loc[matches[column] < 0.001, column.replace("Similarity", "To")] = None
 
     return matches
+
+
+def _top_n_idx_sparse(matrix, n):
+    """ Return index of top n values in each row of a sparse matrix """
+    top_n_idx = []
+    for le, ri in zip(matrix.indptr[:-1], matrix.indptr[1:]):
+        n_row_pick = min(n, ri - le)
+        values = list(matrix.indices[le + np.argpartition(matrix.data[le:ri], -n_row_pick)[-n_row_pick:]])[::-1]
+        values = [values[index] if len(values) >= index + 1 else None for index in range(n)]
+        top_n_idx.append(values)
+    return np.array(top_n_idx)
+
+
+def _top_n_similarities_sparse(matrix, n, indices):
+    """ Return similarity scores of top n values in each row of a sparse matrix """
+    similarity_scores = []
+    for row, values in enumerate(indices):
+        scores = [round(matrix[row, value], n) if value is not None else 0 for value in values]
+        similarity_scores.append(scores)
+    similarity_scores = np.array(similarity_scores).T
+    return similarity_scores

setup.py

@@ -37,7 +37,7 @@
 setup(
     name="polyfuzz",
     packages=find_packages(exclude=["notebooks", "docs"]),
-    version="0.3.0",
+    version="0.3.1",
     author="Maarten Grootendorst",
     author_email="maartengrootendorst@gmail.com",
     description="PolyFuzz performs fuzzy string matching, grouping, and evaluation.",