Bugfix for similarity return types (#10051)

spacy/lexeme.pyx

@@ -130,8 +130,10 @@ cdef class Lexeme:
             return 0.0
         vector = self.vector
         xp = get_array_module(vector)
-        return (xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm))
-
+        result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
+        # ensure we get a scalar back (numpy does this automatically but cupy doesn't)
+        return result.item()
+    
     @property
     def has_vector(self):
         """RETURNS (bool): Whether a word vector is associated with the object.

spacy/tests/vocab_vectors/test_similarity.py

@@ -35,6 +35,7 @@ def test_vectors_similarity_LL(vocab, vectors):
     assert lex1.vector_norm != 0
     assert lex2.vector_norm != 0
     assert lex1.vector[0] != lex2.vector[0] and lex1.vector[1] != lex2.vector[1]
+    assert isinstance(lex1.similarity(lex2), float)
     assert numpy.isclose(lex1.similarity(lex2), get_cosine(vec1, vec2))
     assert numpy.isclose(lex2.similarity(lex2), lex1.similarity(lex1))
 
@@ -47,25 +48,46 @@ def test_vectors_similarity_TT(vocab, vectors):
     assert doc[0].vector_norm != 0
     assert doc[1].vector_norm != 0
     assert doc[0].vector[0] != doc[1].vector[0] and doc[0].vector[1] != doc[1].vector[1]
+    assert isinstance(doc[0].similarity(doc[1]), float)
     assert numpy.isclose(doc[0].similarity(doc[1]), get_cosine(vec1, vec2))
     assert numpy.isclose(doc[1].similarity(doc[0]), doc[0].similarity(doc[1]))
 
 
+def test_vectors_similarity_SS(vocab, vectors):
+    [(word1, vec1), (word2, vec2)] = vectors
+    doc = Doc(vocab, words=[word1, word2])
+    assert isinstance(doc[0:1].similarity(doc[0:2]), float)
+    assert doc[0:1].similarity(doc[0:2]) == doc[0:2].similarity(doc[0:1])
+
+
+def test_vectors_similarity_DD(vocab, vectors):
+    [(word1, vec1), (word2, vec2)] = vectors
+    doc1 = Doc(vocab, words=[word1, word2])
+    doc2 = Doc(vocab, words=[word2, word1])
+    assert isinstance(doc1.similarity(doc2), float)
+    assert doc1.similarity(doc2) == doc2.similarity(doc1)
+
+
 def test_vectors_similarity_TD(vocab, vectors):
     [(word1, vec1), (word2, vec2)] = vectors
     doc = Doc(vocab, words=[word1, word2])
     with pytest.warns(UserWarning):
+        assert isinstance(doc.similarity(doc[0]), float)
+        assert isinstance(doc[0].similarity(doc), float)
         assert doc.similarity(doc[0]) == doc[0].similarity(doc)
 
 
-def test_vectors_similarity_DS(vocab, vectors):
-    [(word1, vec1), (word2, vec2)] = vectors
-    doc = Doc(vocab, words=[word1, word2])
-    assert doc.similarity(doc[:2]) == doc[:2].similarity(doc)
-
-
 def test_vectors_similarity_TS(vocab, vectors):
     [(word1, vec1), (word2, vec2)] = vectors
     doc = Doc(vocab, words=[word1, word2])
     with pytest.warns(UserWarning):
+        assert isinstance(doc[:2].similarity(doc[0]), float)
+        assert isinstance(doc[0].similarity(doc[-2]), float)
         assert doc[:2].similarity(doc[0]) == doc[0].similarity(doc[:2])
+
+
+def test_vectors_similarity_DS(vocab, vectors):
+    [(word1, vec1), (word2, vec2)] = vectors
+    doc = Doc(vocab, words=[word1, word2])
+    assert isinstance(doc.similarity(doc[:2]), float)
+    assert doc.similarity(doc[:2]) == doc[:2].similarity(doc)

spacy/tokens/span.pyx

@@ -364,8 +364,10 @@ cdef class Span:
             return 0.0
         vector = self.vector
         xp = get_array_module(vector)
-        return xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
-
+        result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
+        # ensure we get a scalar back (numpy does this automatically but cupy doesn't)
+        return result.item()
+    
     cpdef np.ndarray to_array(self, object py_attr_ids):
         """Given a list of M attribute IDs, export the tokens to a numpy
         `ndarray` of shape `(N, M)`, where `N` is the length of the document.

spacy/tokens/token.pyx

@@ -209,8 +209,10 @@ cdef class Token:
             return 0.0
         vector = self.vector
         xp = get_array_module(vector)
-        return (xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm))
-
+        result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
+        # ensure we get a scalar back (numpy does this automatically but cupy doesn't)
+        return result.item()
+    
     def has_morph(self):
         """Check whether the token has annotated morph information.
         Return False when the morph annotation is unset/missing.