<s> from pynet . datasets . i2r import * <EOL> from sklearn . ensemble import GradientBoostingClassifier <EOL> from sklearn . metrics import * <EOL> clf = GradientBoostingClassifier ( n_estimators = <NUM_LIT:100> , learning_rate = <NUM_LIT> , max_depth = <NUM_LIT:6> ) <EOL> data = I2R_Posterior_ClnDNN_NoisyFeat ( <NUM_LIT> , <NUM_LIT> , one_hot = False ) <EOL> data . __iter__ ( ) <EOL> data = next ( data ) <EOL> X = data . get_train ( ) . X <EOL> y = data . get_train ( ) . y <EOL> clf . fit ( X , y ) <EOL> test_X = data . get_test ( ) . X <EOL> test_y = data . get_test ( ) . y <EOL> test_X = test_X <EOL> test_y = test_y <EOL> pred_y = clf . predict ( test_X ) <EOL> print '<STR_LIT>' , accuracy_score ( test_y , pred_y ) <EOL> print '<STR_LIT>' , accuracy_score ( test_X . argmax ( axis = <NUM_LIT:1> ) , test_y ) </s>
