def train_gbtree(X_train, y_train):
# Training
print('Training model...')
# shuffle X and y
X_train, y_train = shuffle(X_train, y_train, random_state=0)
if args.gb_tool == 'xgboost':
model = XGBClassifier(
objective='binary:logistic',
booster='gbtree',
learning_rate=0.05,
n_estimators=200,
max_depth=3,
min_child_weight=6,
verbosity=1,
)
model.fit(X_train, y_train)
params = model.get_params()
else:
model = CatBoostClassifier(
verbose=0,
cat_features=cat_features,
random_state=args.rs_model,
# scale_pos_weight=(1 - pos_rate) / pos_rate
)
model.fit(X_train, y_train)
params = model.get_all_params()
print('Parameters:', params)
print('Done.')
return model
def train_gbtree(X_train, y_train, pos_rate, args):
# Training
print('Training model...')
if args.gb_tool == 'xgboost':
model = XGBClassifier(objective='binary:logistic',
booster='gbtree',
learning_rate=0.05,
n_estimators=200,
max_depth=3,
min_child_weight=6,
verbosity=1
)
else:
model = CatBoostClassifier(verbose=0,
# scale_pos_weight=(1 - pos_rate) / pos_rate,
learning_rate=args.lr,
depth=args.depth,
l2_leaf_reg=args.l2
)
model.fit(X_train, y_train)
params = model.get_params() if args.gb_tool == 'xgboost' else model.get_all_params()
print('Parameters:', params)
print('Done.')
return model