from skopt.space import Real, Integer
xgboost_params_space = [Real(1e-7, 1, prior='log-uniform', name='learning_rate'),
Real(0.5, 1.0, name='subsample'),
Integer(2, 10, name='max_depth'),
Real(1e-16, 1e5, prior='log-uniform', name='min_child_weight'),
Real(0.5, 1.0, name='colsample_bylevel'),
Real(0.5, 1.0, name='colsample_bytree'),
Real(0.5, 1.0, name='colsample_bynode'),
Real(1.0, 16.0, name='scale_pos_weight'),
Real(0.0, 100, name='alpha'),
Real(0.0, 100, name='lambda'),
Real(0.0, 100, name='gamma')]from skopt.space import Real, Integer
lgbm_params_space = [Real(1e-7, 1, prior='log-uniform', name='learning_rate'),
Real(1, 1e7, prior='log-uniform', name='num_leaves'),
Real(0.5, 1.0, name='feature_fraction'),
Real(0.5, 1.0, name='bagging_fraction'),
Real(1e-16, 1e5, prior='log-uniform', name='min_child_weight'),
Real(1e-16, 1e2, prior='log-uniform', name='min_child_samples'),
Integer(2, 10, name='max_depth'),
Real(0.5, 1.0, name='subsample'),
Real(0.5, 1.0, name='colsample_bylevel'),
Real(0.5, 1.0, name='colsample_bytree'),
Real(0.5, 1.0, name='colsample_bynode'),
Real(1.0, 16.0, name='scale_pos_weight'),
Real(0.0, 100, name='lambda_l1'),
Real(0.0, 100, name='lambda_l2')]from skopt.space import Real, Integer
catboost_params_space = [Real(1e-7, 1, prior='log-uniform', name='learning_rate'),
Integer(2, 10, name='max_depth'),
Real(0.5, 1.0, name='subsample'),
Real(0.5, 1.0, name='colsample_bylevel'),
Integer(1, 10, name='gradient_iterations'),
Real(1.0, 16.0, name='scale_pos_weight'),
Real(0.0, 1.0, name='bagging_temperature'),
Integer(1, 20, name='random_strength'),
Integer(2, 25, name='one_hot_max_size'),
Real(1.0, 100, name='reg_lambda')]