Runs hyperparameter optimization using Optuna and
returns the best hyperparameters found merged with the user defined parameters
"""
- identifier = self.freqai_info.get("identifier")
+ identifier = self.freqai_info.get("identifier", "no_id_provided")
if self.rl_config_optuna.get("per_pair", False):
study_name = f"{identifier}-{dk.pair}"
storage = self.get_storage(dk.pair)
y_test,
test_weights,
) -> tuple[dict, float] | tuple[None, None]:
- identifier = self.freqai_info.get("identifier")
+ identifier = self.freqai_info.get("identifier", "no_id_provided")
study_namespace = "hp"
study_name = f"{identifier}-{study_namespace}-{pair}"
storage = self.optuna_storage(pair)
test_weights,
model_training_parameters,
) -> tuple[dict, float] | tuple[None, None]:
- identifier = self.freqai_info.get("identifier")
+ identifier = self.freqai_info.get("identifier", "no_id_provided")
study_namespace = "period"
study_name = f"{identifier}-{study_namespace}-{pair}"
storage = self.optuna_storage(pair)
y_test,
test_weights,
) -> tuple[dict, float] | tuple[None, None]:
- identifier = self.freqai_info.get("identifier")
+ identifier = self.freqai_info.get("identifier", "no_id_provided")
study_namespace = "hp"
study_name = f"{identifier}-{study_namespace}-{pair}"
storage = self.optuna_storage(pair)
test_weights,
model_training_parameters,
) -> tuple[dict, float] | tuple[None, None]:
- identifier = self.freqai_info.get("identifier")
+ identifier = self.freqai_info.get("identifier", "no_id_provided")
study_namespace = "period"
study_name = f"{identifier}-{study_namespace}-{pair}"
storage = self.optuna_storage(pair)