Merge pull request #4 from MITx/vik/deployment_work

Minor feedback updates (only display spelling/grammar for low scoring re...

feature_extractor.py

@@ -224,7 +224,9 @@ class FeatureExtractor(object):
                                  'topicality' : "Topicality: Ok.", 'markup_text' : "",
                                  'prompt_overlap' : "Prompt Overlap: Ok.",
                                  'grammar_per_char' : set_grammar_per_character[m],
-                                 'spelling_per_char' : set_spell_errors_per_character[m]}
+                                 'spelling_per_char' : set_spell_errors_per_character[m],
+                                 'too_similar_to_prompt' : False,
+                                 }
             markup_tokens=e_set._markup_text[m].split(" ")
 
             #This loop ensures that sequences of bad grammar get put together into one sequence instead of staying
@@ -256,6 +258,7 @@ class FeatureExtractor(object):
 
                 if(features[m,9]>.6):
                     individual_feedback['prompt_overlap']="Prompt Overlap: Too much overlap with prompt."
+                    individual_feedback['too_similar_to_prompt']=True
                     log.debug(features[m,9])
 
             #Create string representation of markup text

grade.py

@@ -55,6 +55,7 @@ def grade(grader_data,grader_config,submission):
     #Try to determine confidence level
     try:
         min_score=min(numpy.asarray(grader_data['score']))
+        max_score=max(numpy.asarray(grader_data['score']))
         raw_confidence=grader_data['model'].predict_proba(grader_feats)[0,(results['score']-min_score)]
         #TODO: Normalize confidence somehow here
         results['confidence']=raw_confidence
@@ -64,7 +65,7 @@ def grade(grader_data,grader_config,submission):
 
     if not has_error:
 
-        if(len(feedback['prompt_overlap'])>20):
+        if(feedback['too_similar_to_prompt']):
             results['score']=0
             results['correct']=False
 
@@ -80,10 +81,14 @@ def grade(grader_data,grader_config,submission):
         results['feedback']={
             'topicality' : feedback['topicality'],
             'prompt-overlap' : feedback['prompt_overlap'],
-            'spelling' : feedback['spelling'],
+        }
+
+        if results['score']/float(max_score)<.5:
+            results['feedback'].update(
+                {'spelling' : feedback['spelling'],
             'grammar' : feedback['grammar'],
             'markup-text' : feedback['markup_text'],
-        }
+            })
 
     else:
         #If error, success is False.