Document feedback function

feature_extractor.py

@@ -201,14 +201,23 @@ class FeatureExtractor(object):
         in order to get off topic feedback.
         Returns a list of lists (one list per essay in e_set)
         """
+
+        #Set ratio to modify thresholds for grammar/spelling errors
         modifier_ratio=1.05
+
+        #Calc number of grammar and spelling errors per character
         set_grammar,bad_pos_positions=self._get_grammar_errors(e_set._pos,e_set._text,e_set._tokens)
         set_grammar_per_character=[set_grammar[m]/float(len(e_set._text[m])) for m in xrange(0,len(e_set._text))]
         set_spell_errors_per_character=[e_set._spelling_errors[m]/float(len(e_set._text[m])) for m in xrange(0,len(e_set._text))]
+
+        #Iterate through essays and create a feedback dict for each
         all_feedback=[]
         for m in xrange(0,len(e_set._text)):
             individual_feedback={'grammar' : "Ok.", 'spelling' : "Ok.", 'topicality' : "Ok.", 'markup_text' : ""}
             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
+            #disjointed
             bad_pos_starts=[z[0] for z in bad_pos_positions[m]]
             bad_pos_ends=[z[1]-1 for z in bad_pos_positions[m]]
             for z in xrange(0,len(markup_tokens)):
@@ -220,15 +229,21 @@ class FeatureExtractor(object):
                 if max(bad_pos_ends)>(len(markup_tokens)-1) and max(bad_pos_starts)<(len(markup_tokens)-1):
                     markup_tokens[len(markup_tokens)-1]+="</bg>"
 
+            #Display messages if grammar/spelling errors greater than average in training set
             if set_grammar_per_character[m]>(self._grammar_errors_per_character*modifier_ratio):
                 individual_feedback['grammar']="Potential grammar errors."
             if set_spell_errors_per_character[m]>(self._spell_errors_per_character*modifier_ratio):
                 individual_feedback['spelling']="Potential spelling errors."
+
+            #Test topicality by calculating # of on topic words per character and comparing to the training set
+            #mean.  Requires features to be passed in
             if features is not None:
                 f_row_sum=numpy.sum(features[m,12:])
                 f_row_prop=f_row_sum/len(e_set._text[m])
                 if f_row_prop<(self._mean_f_prop/1.5) or len(e_set._text[m])<20:
                     individual_feedback['topicality']="Essay may be off topic."
+
+            #Create string representation of markup text
             markup_string=" ".join(markup_tokens)
             individual_feedback['markup_text']=markup_string
             all_feedback.append(individual_feedback)