Remove prompt overlap and topicality, fix cv error

feature_extractor.py

@@ -220,9 +220,9 @@ class FeatureExtractor(object):
         all_feedback=[]
         for m in xrange(0,len(e_set._text)):
             #Be very careful about changing these messages!
-            individual_feedback={'grammar' : "Grammar: Ok.", 'spelling' : "Spelling: Ok.",
-                                 'topicality' : "Topicality: Ok.", 'markup_text' : "",
-                                 'prompt_overlap' : "Prompt Overlap: Ok.",
+            individual_feedback={'grammar' : "Grammar: Ok.",
+                                 'spelling' : "Spelling: Ok.",
+                                 'markup_text' : "",
                                  'grammar_per_char' : set_grammar_per_character[m],
                                  'spelling_per_char' : set_spell_errors_per_character[m],
                                  'too_similar_to_prompt' : False,

grade.py

@@ -81,10 +81,12 @@ def grade(grader_data,grader_config,submission):
                 problem_areas+=len(feedback[tag])>5
 
         #Add feedback to results
-        results['feedback']={
-            'topicality' : feedback['topicality'],
-            'prompt-overlap' : feedback['prompt_overlap'],
-        }
+        results['feedback'] = {}
+        if 'topicality' in feedback and 'prompt_overlap' in feedback:
+            results['feedback'].update({
+                'topicality' : feedback['topicality'],
+                'prompt-overlap' : feedback['prompt_overlap'],
+            })
 
         if results['score']/float(max_score)<.33:
             results['feedback'].update(

tests/test_cv_full.py

@@ -36,8 +36,7 @@ def run_single_worker(args):
     texts=[]
     lines=sa_val.readlines()
     eset=essay_set.EssaySet(type="train")
-    #len(lines)
-    for i in xrange(1,10):
+    for i in xrange(1,len(lines)):
         id_val,essay_set_num,score1,score2,text=lines[i].split("\t")
         score1s.append(int(score1))
         score2s.append(int(score2))
@@ -57,7 +56,7 @@ def run_single_worker(args):
         clf=GradientBoostingRegressor(n_estimators=100, learn_rate=.05, max_depth=4, random_state=1, min_samples_leaf=3)
 
     try:
-        cv_preds=util_functions.gen_cv_preds(clf,train_feats,score1s, num_chunks = 3) # int(math.floor(len(texts)/2)
+        cv_preds=util_functions.gen_cv_preds(clf,train_feats,score1s, num_chunks = 10) # int(math.floor(len(texts)/2)
     except:
         cv_preds = score1s
 
@@ -79,7 +78,7 @@ def run_single_worker(args):
     return err, kappa,percent_error,human_err,human_kappa,human_percent_error
 
 length = len(filenames)
-np=12
+np=8
 p = Pool(processes=np)
 errs, kappas,percent_errors,human_errs,human_kappas,human_percent_errors = zip(*p.map(run_single_worker,[(filenames[i],data_path) for i in xrange(0,length)]))