n_estimators=100,
objective='binary:logistic',
base_score=0.5,
colsample_bylevel=1,
gamma=0,
colsample_bytree=1,
max_delta_step=0,
min_child_weight=1,
missing=None,
reg_alpha=0,
reg_lambda=1,
scale_pos_weight=1,
seed=0,
subsample=1)
model.feature_names = feature_names
print(model)
model.fit(X_train, np.ravel(Y_train))
#save model
model_save_name = save_directory + '/antgc_' + signal + '_bdt'
model._Booster.dump_model(model_save_name + '.xgb')
model._Booster.save_model(model_save_name + '_bin.xgb')
pk.dump(model, open(model_save_name + '.pickle', 'wb'))
print 'Saved model ' + model_save_name + '(*.xgb, *.pickle)'
# save test and test sets
train_save_file = save_directory + '/train_set' + signal + '.txt'
test_save_file = save_directory + '/test_set' + signal + '.txt'
train_save = np.append(X_train, Y_train, axis=1)
