Bugfixes

ease/feature_extractor.py

@@ -128,7 +128,10 @@ class FeatureExtractor(object):
                 divisor=len(pos_ngrams)/len(pos_seq)
             else:
                 divisor=1
-            good_pos_tags.append((len(pos_ngrams)-len(overlap_ngrams))/divisor)
+            if divisor == 0:
+                divisor=1
+            good_grammar_ratio = (len(pos_ngrams)-len(overlap_ngrams))/divisor
+            good_pos_tags.append(good_grammar_ratio)
         return good_pos_tags,bad_pos_positions
 
     def gen_length_feats(self, e_set):

ease/grade.py

@@ -161,14 +161,16 @@ def get_confidence_value(algorithm,model,grader_feats,score, scores):
     """
     min_score=min(numpy.asarray(scores))
     max_score=max(numpy.asarray(scores))
-    if algorithm == util_functions.AlgorithmTypes.classification:
+    if algorithm == util_functions.AlgorithmTypes.classification and hasattr(model, "predict_proba"):
         #If classification, predict with probability, which gives you a matrix of confidences per score point
         raw_confidence=model.predict_proba(grader_feats)[0,(float(score)-float(min_score))]
         #TODO: Normalize confidence somehow here
         confidence=raw_confidence
-    else:
+    elif hasattr(model, "predict"):
         raw_confidence = model.predict(grader_feats)[0]
         confidence = max(float(raw_confidence) - math.floor(float(raw_confidence)), math.ceil(float(raw_confidence)) - float(raw_confidence))
+    else:
+        confidence = 0
 
     return confidence