Refactored the feature_extractor file to include just what we want it to, in a logical manner.

ease/essay_set.py

@@ -52,7 +52,7 @@ class EssaySet(object):
         self._cleaned_spelled_essays = []
         self._tokens = []
         self._pos_tags = []
-        self._cleaned_stem_essays = []
+        self._cleaned_stemmed_essays = []
         self._generated = []
         self._prompt = ""
         self._spelling_errors = []
@@ -147,7 +147,7 @@ class EssaySet(object):
         # from words in English.
         porter = nltk.PorterStemmer()
         porter_tokens = " ".join([porter.stem(token) for token in tokens])
-        self._cleaned_stem_essays.append(porter_tokens)
+        self._cleaned_stemmed_essays.append(porter_tokens)
 
         return "Essay Added. Text: " + cleaned_essay + " Score: " + str(essay_score)
 

ease/grade.py

@@ -66,36 +66,15 @@ def grade(grader_data, submission):
     grader_features = None
     try:
         grader_features = extractor.generate_features(grader_set)
-        feedback = extractor.generate_feedback(grader_set, grader_features)[0]
         results['score'] = int(model.predict(grader_features)[0])
     except:
         error_message = "Could not extract features and score essay."
         log.exception(error_message)
         results['errors'].append(error_message)
 
-    # If we have errors above, we do not continue here, but return the dictionary of failure
-    if len(results['errors']) < 0:
-
     # We have gotten through without an error, so we have been successful
+    if len(results['errors']) < 0:
         results['success'] = True
-
-        # If the essay is just a copy of the prompt (or too similar), return a 0 as the score
-        if 'too_similar_to_prompt' in feedback and feedback['too_similar_to_prompt']:
-            results['score'] = 0
-
-        # Generate feedback, identifying a number of explicable problem areas
-        results['feedback'] = {
-            'spelling': feedback['spelling'],
-            'grammar': feedback['grammar'],
-            'markup-text': feedback['markup_text'],
-        }
-
-        if 'topicality' in feedback and 'prompt_overlap' in feedback:
-            results['feedback'].update({
-                'topicality': feedback['topicality'],
-                'prompt-overlap': feedback['prompt_overlap'],
-            })
-
     # If we get here, that means there was 1+ error above. Set success to false and return
     else:
         results['success'] = False