Make WmdSimilarity and SoftCosineSimilarity handle empty queries

gensim/similarities/docsim.py

@@ -639,7 +639,7 @@ def get_similarities(self, query):
             # Convert document indexes to actual documents.
             query = [self.corpus[i] for i in query]
 
-        if not isinstance(query[0], list):
+        if not query or not isinstance(query[0], list):
             query = [query]
 
         n_queries = len(query)
@@ -724,7 +724,7 @@ def get_similarities(self, query):
             # Convert document indexes to actual documents.
             query = [self.corpus[i] for i in query]
 
-        if not isinstance(query[0], list):
+        if not query or not isinstance(query[0], list):
             query = [query]
 
         n_queries = len(query)

gensim/test/test_similarities.py

@@ -40,6 +40,9 @@ class _TestSimilarityABC(object):
     Base class for SparseMatrixSimilarity and MatrixSimilarity unit tests.
     """
 
+    def factoryMethod(self):
+        return self.cls(corpus, num_features=len(dictionary))
+
     def testFull(self, num_best=None, shardsize=100):
         if self.cls == similarities.Similarity:
             index = self.cls(None, corpus, num_features=len(dictionary), shardsize=shardsize)
@@ -104,6 +107,15 @@ def test_scipy2scipy_clipped(self):
         self.assertTrue(scipy.sparse.issparse(matrix_scipy_clipped))
         self.assertTrue([matutils.scipy2sparse(x) for x in matrix_scipy_clipped], [expected] * 3)
 
+    def testEmptyQuery(self):
+        index = self.factoryMethod()
+        query = []
+        try:
+            sims = index[query]
+            self.assertTrue(sims is not None)
+        except IndexError:
+            self.assertTrue(False)
+
     def testChunking(self):
         if self.cls == similarities.Similarity:
             index = self.cls(None, corpus, num_features=len(dictionary), shardsize=5)
@@ -156,14 +168,7 @@ def testPersistency(self):
             return
 
         fname = get_tmpfile('gensim_similarities.tst.pkl')
-        if self.cls == similarities.Similarity:
-            index = self.cls(None, corpus, num_features=len(dictionary), shardsize=5)
-        elif self.cls == similarities.WmdSimilarity:
-            index = self.cls(texts, self.w2v_model)
-        elif self.cls == similarities.SoftCosineSimilarity:
-            index = self.cls(self.corpus, self.similarity_matrix)
-        else:
-            index = self.cls(corpus, num_features=len(dictionary))
+        index = self.factoryMethod()
         index.save(fname)
         index2 = self.cls.load(fname)
         if self.cls == similarities.Similarity:
@@ -183,14 +188,7 @@ def testPersistencyCompressed(self):
             return
 
         fname = get_tmpfile('gensim_similarities.tst.pkl.gz')
-        if self.cls == similarities.Similarity:
-            index = self.cls(None, corpus, num_features=len(dictionary), shardsize=5)
-        elif self.cls == similarities.WmdSimilarity:
-            index = self.cls(texts, self.w2v_model)
-        elif self.cls == similarities.SoftCosineSimilarity:
-            index = self.cls(self.corpus, self.similarity_matrix)
-        else:
-            index = self.cls(corpus, num_features=len(dictionary))
+        index = self.factoryMethod()
         index.save(fname)
         index2 = self.cls.load(fname)
         if self.cls == similarities.Similarity:
@@ -210,14 +208,7 @@ def testLarge(self):
             return
 
         fname = get_tmpfile('gensim_similarities.tst.pkl')
-        if self.cls == similarities.Similarity:
-            index = self.cls(None, corpus, num_features=len(dictionary), shardsize=5)
-        elif self.cls == similarities.WmdSimilarity:
-            index = self.cls(texts, self.w2v_model)
-        elif self.cls == similarities.SoftCosineSimilarity:
-            index = self.cls(self.corpus, self.similarity_matrix)
-        else:
-            index = self.cls(corpus, num_features=len(dictionary))
+        index = self.factoryMethod()
         # store all arrays separately
         index.save(fname, sep_limit=0)
 
@@ -239,14 +230,7 @@ def testLargeCompressed(self):
             return
 
         fname = get_tmpfile('gensim_similarities.tst.pkl.gz')
-        if self.cls == similarities.Similarity:
-            index = self.cls(None, corpus, num_features=len(dictionary), shardsize=5)
-        elif self.cls == similarities.WmdSimilarity:
-            index = self.cls(texts, self.w2v_model)
-        elif self.cls == similarities.SoftCosineSimilarity:
-            index = self.cls(self.corpus, self.similarity_matrix)
-        else:
-            index = self.cls(corpus, num_features=len(dictionary))
+        index = self.factoryMethod()
         # store all arrays separately
         index.save(fname, sep_limit=0)
 
@@ -268,14 +252,7 @@ def testMmap(self):
             return
 
         fname = get_tmpfile('gensim_similarities.tst.pkl')
-        if self.cls == similarities.Similarity:
-            index = self.cls(None, corpus, num_features=len(dictionary), shardsize=5)
-        elif self.cls == similarities.WmdSimilarity:
-            index = self.cls(texts, self.w2v_model)
-        elif self.cls == similarities.SoftCosineSimilarity:
-            index = self.cls(self.corpus, self.similarity_matrix)
-        else:
-            index = self.cls(corpus, num_features=len(dictionary))
+        index = self.factoryMethod()
         # store all arrays separately
         index.save(fname, sep_limit=0)
 
@@ -298,14 +275,7 @@ def testMmapCompressed(self):
             return
 
         fname = get_tmpfile('gensim_similarities.tst.pkl.gz')
-        if self.cls == similarities.Similarity:
-            index = self.cls(None, corpus, num_features=len(dictionary), shardsize=5)
-        elif self.cls == similarities.WmdSimilarity:
-            index = self.cls(texts, self.w2v_model)
-        elif self.cls == similarities.SoftCosineSimilarity:
-            index = self.cls(self.corpus, self.similarity_matrix)
-        else:
-            index = self.cls(corpus, num_features=len(dictionary))
+        index = self.factoryMethod()
         # store all arrays separately
         index.save(fname, sep_limit=0)
 
@@ -323,6 +293,9 @@ def setUp(self):
         self.cls = similarities.WmdSimilarity
         self.w2v_model = Word2Vec(texts, min_count=1)
 
+    def factoryMethod(self):
+        return self.cls(texts, self.w2v_model)
+
     def testFull(self, num_best=None):
         # Override testFull.
 
@@ -405,6 +378,9 @@ def setUp(self):
         similarity_matrix[dictionary.token2id["human"], dictionary.token2id["user"]] = 0.5
         self.similarity_matrix = similarity_matrix.tocsc()
 
+    def factoryMethod(self):
+        return self.cls(self.corpus, self.similarity_matrix)
+
     def testFull(self, num_best=None):
         # Override testFull.
 
@@ -504,6 +480,9 @@ class TestSimilarity(unittest.TestCase, _TestSimilarityABC):
     def setUp(self):
         self.cls = similarities.Similarity
 
+    def factoryMethod(self):
+        return self.cls(None, corpus, num_features=len(dictionary), shardsize=5)
+
     def testSharding(self):
         for num_best in [None, 0, 1, 9, 1000]:
             for shardsize in [1, 2, 9, 1000]: