sentence sorting by classification confidence (ml options)

src/Backend/classify.py

@@ -1,5 +1,6 @@
 import aspect_searcher
 import object_comparer
+from pandas import DataFrame
 
 import os, sys
 sys.path.append(os.path.join(os.path.dirname(__file__), "cam_pretrained"))
@@ -11,7 +12,9 @@ def classify_sentences(sentences, model):
         model = load_model('data/model.pkl', glove_path='data/glove.840B.300d.txt', infersent_path='data/infersent.allnli.pickle')
     else:
         model = load_model('data/bow_model.pkl', glove_path=None, infersent_path=None)
-    return model.predict(sentences)
+    df = DataFrame(model.predict_proba(sentences), columns=model.classes_)
+    df['max'] = df.idxmax(axis=1)
+    return df
 
 
 def evaluate(sentences, prepared_sentences, classification_results, obj_a, obj_b, aspects):
@@ -20,24 +23,34 @@ def evaluate(sentences, prepared_sentences, classification_results, obj_a, obj_b
         max_sentscore = max(sentences.values())
 
     for index, row in prepared_sentences.iterrows():
-        label = classification_results[index]
+        label = classification_results['max'][index]
         if label == 'NONE':
             continue
 
+        classification_confidence = classification_results[label][index]
         sentence = row['sentence']
         contained_aspects = aspect_searcher.find_aspects(sentence, aspects)
         if (label == 'BETTER' and row['object_a'] == obj_a.name) or (label == 'WORSE' and row['object_b'] == obj_a.name):
-            add_points(contained_aspects, obj_a, sentences[sentence], sentence, max_sentscore)
+            add_points(contained_aspects, obj_a, sentences[sentence], sentence, max_sentscore, classification_confidence)
         else:
-            add_points(contained_aspects, obj_b, sentences[sentence], sentence, max_sentscore)
+            add_points(contained_aspects, obj_b, sentences[sentence], sentence, max_sentscore, classification_confidence)
+
+    obj_a.sentence_with_aspect = prepare_sentence_list(obj_a.sentence_with_aspect)
+    obj_b.sentence_with_aspect = prepare_sentence_list(obj_b.sentence_with_aspect)
+    obj_a.sentences = prepare_sentence_list(obj_a.sentences)
+    obj_b.sentences = prepare_sentence_list(obj_b.sentences)
+
     return object_comparer.build_final_dict(obj_a, obj_b)
 
+def prepare_sentence_list(sentences_with_confidence):
+    sentences_with_confidence.sort()
+    return list(DataFrame(sentences_with_confidence, columns=['confidence', 'sentence'])['sentence'])
 
-def add_points(contained_aspects, winner, sentence_score, sentence, max_sentscore):
+def add_points(contained_aspects, winner, sentence_score, sentence, max_sentscore, classification_confidence):
     if contained_aspects:
         for aspect in contained_aspects:
             winner.add_points((sentence_score / max_sentscore) * aspect.weight * max_sentscore)
-        winner.add_sentence_with_aspect(sentence)
+        winner.add_sentence_with_aspect([classification_confidence, sentence])
     else:
-        winner.add_sentence(sentence)
         winner.add_points(sentence_score / max_sentscore)
+        winner.add_sentence([classification_confidence, sentence])