<s> "<STR_LIT>" <EOL> "<STR_LIT>" <EOL> import pandas as pd <EOL> from sklearn . cross_validation import train_test_split <EOL> from sklearn . pipeline import Pipeline <EOL> from sklearn . preprocessing import Normalizer , PolynomialFeatures <EOL> from sklearn . preprocessing import MaxAbsScaler , MinMaxScaler , StandardScaler , RobustScaler <EOL> from sklearn . linear_model import LogisticRegression as LR <EOL> from sklearn . metrics import roc_auc_score as AUC , accuracy_score as accuracy , log_loss <EOL> def train_and_evaluate ( y_train , x_train , y_val , x_val ) : <EOL> lr = LR ( ) <EOL> lr . fit ( x_train , y_train ) <EOL> p = lr . predict_proba ( x_val ) <EOL> auc = AUC ( y_val , p [ : , <NUM_LIT:1> ] ) <EOL> ll = log_loss ( y_val , p [ : , <NUM_LIT:1> ] ) <EOL> return ( auc , ll ) <EOL> def transform_train_and_evaluate ( transformer ) : <EOL> global x_train , x_val , y_train <EOL> x_train_new = transformer . fit_transform ( x_train ) <EOL> x_val_new = transformer . transform ( x_val ) <EOL> return train_and_evaluate ( y_train , x_train_new , y_val , x_val_new ) <EOL> input_file = '<STR_LIT>' <EOL> d = pd . read_csv ( input_file ) <EOL> train , val = train_test_split ( d , test_size = <NUM_LIT> ) <EOL> y_train = train . target . values <EOL> y_val = val . target . values <EOL> x_train = train . drop ( '<STR_LIT:target>' , axis = <NUM_LIT:1> ) <EOL> x_val = val . drop ( '<STR_LIT:target>' , axis = <NUM_LIT:1> ) <EOL> auc , ll = train_and_evaluate ( y_train , x_train , y_val , x_val ) <EOL> print "<STR_LIT>" <EOL> print "<STR_LIT>" . format ( auc , ll ) <EOL> transformers = [ MaxAbsScaler ( ) , MinMaxScaler ( ) , RobustScaler ( ) , StandardScaler ( ) , <EOL> Normalizer ( norm = '<STR_LIT>' ) , Normalizer ( norm = '<STR_LIT>' ) , Normalizer ( norm = '<STR_LIT>' ) ] <EOL> for transformer in transformers : <EOL> print transformer <EOL> auc , ll = transform_train_and_evaluate ( transformer ) <EOL> print "<STR_LIT>" . format ( auc , ll ) <EOL> """<STR_LIT>""" </s>
