INNER CODE UNIT · Python
best_score
fabsig/GPBoost · python-package/gpboost/engine.py:1239
if best_num_boost_round < 0 or best_score == (1e99 if not higher_better else -1e99):
raise ValueError("Did not find any valid parameter combination. " \
"Check the 'metric' (is it supported?), search space, and the data provided ")
if return_all_combinations:
return {'best_params': best_params, 'best_iter': best_num_boost_round, 'best_score': best_score,
'all_combinations': all_combinations}
else:
return {'best_params': best_params, 'best_iter': best_num_boost_round, 'best_score': best_score}
def tune_pars_TPE_algorithm_optuna(search_space, n_trials, X, y, gp_model = None,
max_num_boost_round=1000, early_stopping_rounds=None,
metric=None, folds=None, nfold=5,
cv_seed=0, tpe_seed=0,
params=None, verbose_train=0, verbose_eval=1,
use_gp_model_for_validation=True, train_gp_model_cov_pars=True, feval=None,
categorical_feature='auto'):
"""Function for choosing tuning parameters using the TPE (Tree-structured Parzen Estimator) algorithm implemented in optuna