Updated black

extra_model/_adjectives.py

@@ -212,27 +212,29 @@ def adjective_info(dataframe_topics, dataframe_aspects, vectorizer):
     )
 
     dataframe_aspects["sentiment_compound"] = dataframe_aspects["descriptor"].apply(
-        lambda descriptor: sentiment_dict[descriptor][0]
-        if descriptor in sentiment_dict
-        else 0
+        lambda descriptor: (
+            sentiment_dict[descriptor][0] if descriptor in sentiment_dict else 0
+        )
     )
     dataframe_aspects["sentiment_binary"] = dataframe_aspects["descriptor"].apply(
-        lambda descriptor: sentiment_dict[descriptor][1]
-        if descriptor in sentiment_dict
-        else 0
+        lambda descriptor: (
+            sentiment_dict[descriptor][1] if descriptor in sentiment_dict else 0
+        )
     )
 
     # flip sentiment if adjective is negated
     dataframe_aspects["sentiment_compound"] = dataframe_aspects.apply(
-        lambda x: x["sentiment_compound"] * (-1)
-        if x["is_negated"]
-        else x["sentiment_compound"],
+        lambda x: (
+            x["sentiment_compound"] * (-1)
+            if x["is_negated"]
+            else x["sentiment_compound"]
+        ),
         axis=1,
     )
     dataframe_aspects["sentiment_binary"] = dataframe_aspects.apply(
-        lambda x: x["sentiment_binary"] * (-1)
-        if x["is_negated"]
-        else x["sentiment_binary"],
+        lambda x: (
+            x["sentiment_binary"] * (-1) if x["is_negated"] else x["sentiment_binary"]
+        ),
         axis=1,
     )
     return dataframe_topics, dataframe_aspects

extra_model/_disambiguate.py

@@ -1,4 +1,5 @@
 """Functions to do word-sense disambiguation using artifical contexts."""
+
 import logging
 import math
 

extra_model/_summarize.py

@@ -1,4 +1,5 @@
 """Fill out and link the dataframes for topics, aspects and texts. Provide some summary ouput if debug-level is set to info or lower."""
+
 import logging
 
 logger = logging.getLogger(__name__)

extra_model/_topics.py

@@ -1,4 +1,5 @@
 """Aggregate aspects into semantic clusters ('topics')."""
+
 import logging
 from collections import Counter
 
@@ -419,9 +420,14 @@ def get_topics(dataframe_aspects, vectors):
     aspects, synsets_match = match(aspect_counts, vectors)
     disambiguation_dict = dict(zip(aspects, synsets_match))
     dataframe_aspects.loc[:, "wordnet_node"] = dataframe_aspects["aspect"].apply(
-        lambda aspect: disambiguation_dict[aspect].name()
-        if (aspect in disambiguation_dict and disambiguation_dict[aspect] is not None)
-        else None
+        lambda aspect: (
+            disambiguation_dict[aspect].name()
+            if (
+                aspect in disambiguation_dict
+                and disambiguation_dict[aspect] is not None
+            )
+            else None
+        )
     )
 
     # build the hypernym graph

tests/test_init.py

@@ -1,4 +1,5 @@
 """Test package init."""
+
 import extra_model