INNER CODE UNIT · Python
tune_pars_TPE_algorithm_optuna
fabsig/GPBoost · python-package/gpboost/engine.py:1249
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
Parameters
----------
search_space : dict
The range for every parameter over which a search is done.
The format for every entry of the dict must be
'parameter_name': [lower, upper].
See https://github.com/fabsig/GPBoost/blob/master/docs/Main_parameters.rst#tuning-parameters--hyperparameters-for-the-tree-boosting-part
n_trials: int
The number of trials for the TPESampler.