<s> import os <EOL> import pandas as pd <EOL> import numpy as np <EOL> from sklearn . cross_validation import train_test_split <EOL> from sklearn . feature_extraction . text import TfidfVectorizer <EOL> from sklearn . linear_model import LogisticRegression as LR <EOL> from sklearn . metrics import roc_auc_score as AUC <EOL> from KaggleWord2VecUtility import KaggleWord2VecUtility <EOL> data_file = '<STR_LIT>' <EOL> data = pd . read_csv ( data_file , header = <NUM_LIT:0> , delimiter = "<STR_LIT:\t>" , quoting = <NUM_LIT:3> ) <EOL> train_i , test_i = train_test_split ( np . arange ( len ( data ) ) , train_size = <NUM_LIT> , random_state = <NUM_LIT> ) <EOL> train = data . ix [ train_i ] <EOL> test = data . ix [ test_i ] <EOL> print "<STR_LIT>" <EOL> clean_train_reviews = [ ] <EOL> for review in train [ '<STR_LIT>' ] : <EOL> clean_train_reviews . append ( "<STR_LIT:U+0020>" . join ( KaggleWord2VecUtility . review_to_wordlist ( review ) ) ) <EOL> print "<STR_LIT>" <EOL> clean_test_reviews = [ ] <EOL> for review in test [ '<STR_LIT>' ] : <EOL> clean_test_reviews . append ( "<STR_LIT:U+0020>" . join ( KaggleWord2VecUtility . review_to_wordlist ( review ) ) ) <EOL> print "<STR_LIT>" <EOL> vectorizer = TfidfVectorizer ( max_features = <NUM_LIT> , ngram_range = ( <NUM_LIT:1> , <NUM_LIT:3> ) , <EOL> sublinear_tf = True ) <EOL> train_data_features = vectorizer . fit_transform ( clean_train_reviews ) <EOL> test_data_features = vectorizer . transform ( clean_test_reviews ) <EOL> def train_and_eval_auc ( model , train_x , train_y , test_x , test_y ) : <EOL> model . fit ( train_x , train_y ) <EOL> p = model . predict_proba ( test_x ) <EOL> auc = AUC ( test_y , p [ : , <NUM_LIT:1> ] ) <EOL> return auc <EOL> lr = LR ( ) <EOL> auc = train_and_eval_auc ( lr , train_data_features , train [ "<STR_LIT>" ] , test_data_features , test [ "<STR_LIT>" ] . values ) <EOL> print "<STR_LIT>" , auc </s>
