Fix cv accuracy

tests/test_cv_accuracy.py

@@ -18,42 +18,42 @@ if not base_path.endswith("/"):
 
 FILENAME="sa_data.tsv"
 
-sa_val = file(FILENAME)
 
-scores=[]
-texts=[]
-lines=sa_val.readlines()
-
-eset=essay_set.EssaySet(type="train")
-
-for i in xrange(1,len(lines)):
-    score,text=lines[i].split("\t\"")
-    scores.append(int(score))
-    texts.append(text)
-    eset.add_essay(text,int(score))
-    #if int(score)==0:
-    #    eset.generate_additional_essays(text,int(score))
-
-extractor=feature_extractor.FeatureExtractor()
-extractor.initialize_dictionaries(eset)
-train_feats=extractor.gen_feats(eset)
-
-clf=GradientBoostingClassifier(n_estimators=100, learn_rate=.05,
-    max_depth=4, random_state=1,
-    min_samples_leaf=3)
-
-cv_preds=util_functions.gen_cv_preds(clf,train_feats,scores)
-
-err=numpy.mean(numpy.abs(cv_preds-scores))
-print err
-
-kappa=util_functions.quadratic_weighted_kappa(list(cv_preds),scores)
-print kappa
-
-outfile=open("full_cvout.tsv",'w+')
-outfile.write("cv_pred" + "\t" + "actual")
-for i in xrange(0,len(cv_preds)):
-    outfile.write("{0}\t{1}".format(cv_preds[i],scores[i]))
+all_err=[]
+all_kappa=[]
+
+for t_len in [0,50,100,200,300]:
+    sa_val = file(FILENAME)
+    scores=[]
+    texts=[]
+    lines=sa_val.readlines()
+    eset=essay_set.EssaySet(type="train")
+    for i in xrange(1,len(lines)):
+        score,text=lines[i].split("\t\"")
+        if len(text)>t_len:
+            scores.append(int(score))
+            texts.append(text)
+            eset.add_essay(text,int(score))
+            #if int(score)==0:
+            #    eset.generate_additional_essays(text,int(score))
+    extractor=feature_extractor.FeatureExtractor()
+    extractor.initialize_dictionaries(eset)
+    train_feats=extractor.gen_feats(eset)
+    clf=GradientBoostingClassifier(n_estimators=100, learn_rate=.05,max_depth=4, random_state=1,min_samples_leaf=3)
+    cv_preds=util_functions.gen_cv_preds(clf,train_feats,scores)
+    err=numpy.mean(numpy.abs(cv_preds-scores))
+    print err
+    kappa=util_functions.quadratic_weighted_kappa(list(cv_preds),scores)
+    print kappa
+    all_err.append(err)
+    all_kappa.append(kappa)
+
+    """
+    outfile=open("full_cvout.tsv",'w+')
+    outfile.write("cv_pred" + "\t" + "actual")
+    for i in xrange(0,len(cv_preds)):
+        outfile.write("{0}\t{1}".format(cv_preds[i],scores[i]))
+    """