Satisfy pep8

src/word2vec_worker.py

@@ -93,8 +93,8 @@ class W2vWorker(lex_ws.LexWorker):
     def _evaluate_operation(self, operation_type, input_to_evaluate):
         """Evaluates operation passed by task.
 
-        :param operation_type: task which is supposed to be done. Can be one of either:
-            vector, similarity, kbest.
+        :param operation_type: task which is supposed to be done.
+            Can be one of either: vector, similarity, kbest.
         :type operation_type: str
         :raise Exception: If provided operation is not supported.
         :param input_to_evaluate: Data to be evaluated by the worker.
@@ -106,8 +106,10 @@ class W2vWorker(lex_ws.LexWorker):
         if operation_type == "notmatch":
             return model.most_not_match(input_to_evaluate["options"]["words"])
         if operation_type == "kbestposneg":
-            return model.most_similar_full(input_to_evaluate["options"]["positive"],
-                                           input_to_evaluate["options"]["negative"])
+            return model.most_similar_full(
+                input_to_evaluate["options"]["positive"],
+                input_to_evaluate["options"]["negative"]
+            )
         if operation_type == "kbest":
             return model.most_similar(input_to_evaluate["lexeme"])
         elif operation_type == "similarity":
@@ -121,12 +123,18 @@ class W2vWorker(lex_ws.LexWorker):
                 input_to_evaluate["lexeme2"]
             )
         elif operation_type == "doc2vec":
-            return model.doc2vec(input_to_evaluate["lexeme1"], input_to_evaluate["counts1"])
+            return model.doc2vec(
+                input_to_evaluate["lexeme1"],
+                input_to_evaluate["counts1"]
+            )
         elif operation_type == "vector":
             return model.vector_representations(input_to_evaluate["lexeme"])
         elif operation_type == "all" or not operation_type:
-            return {"vector": model.vector_representations(input_to_evaluate["lexeme"]),
-                    "kbest": model.most_similar(input_to_evaluate["lexeme"])}
+            return {
+                "vector": model.vector_representations(
+                    input_to_evaluate["lexeme"]),
+                "kbest": model.most_similar(input_to_evaluate["lexeme"])
+            }
 
         else:
             raise Exception(