old_contents: import os
  import sys
  base_path = os.path.dirname(__file__)
  sys.path.append(base_path)
  
  one_up_path=os.path.abspath(os.path.join(os.path.dirname(__file__),'..'))
  sys.path.append(one_up_path)
  
  import util_functions
  import essay_set
  import feature_extractor
  
  from sklearn.ensemble import GradientBoostingClassifier
  
  if not base_path.endswith("/"):
      base_path=base_path+"/"
  
  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 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)
  
  kappa=util_functions.quadratic_weighted_kappa(cv_preds,scores)
  
  outfile=open("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]))
  
  
  
  
new_contents: import os
  import sys
  base_path = os.path.dirname(__file__)
  sys.path.append(base_path)
  
  one_up_path=os.path.abspath(os.path.join(os.path.dirname(__file__),'..'))
  sys.path.append(one_up_path)
  
  import util_functions
  import essay_set
  import feature_extractor
  import numpy
  
  from sklearn.ensemble import GradientBoostingClassifier
  
  if not base_path.endswith("/"):
      base_path=base_path+"/"
  
  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]))
  
  
  
  
subject: Test cv script