(self)
Perform the hyperparameter-tuning with given parameters.
Perform the hyperparameter-tuning with given parameters.
Perform the hyperparameter - tuning with given parameters .
def run(self) -> None:
        """Perform the hyperparameter-tuning with given parameters."""
        verbosity = self.auto_options["verbosity"]
        if verbosity is not None:
            if verbosity > 1:
                optuna.logging.set_verbosity(optuna.logging.DEBUG)
            elif verbosity == 1:
                optuna.logging.set_verbosity(optuna.logging.INFO)
            elif verbosity == 0:
                optuna.logging.set_verbosity(optuna.logging.WARNING)
            else:
                optuna.logging.set_verbosity(optuna.logging.CRITICAL)

        # Handling aliases.
        _handling_alias_parameters(self.lgbm_params)

        # Sampling.
        self.sample_train_set()

        self.tune_feature_fraction()
        self.tune_num_leaves()
        self.tune_bagging()
        self.tune_feature_fraction_stage2()
        self.tune_regularization_factors()
        self.tune_min_data_in_leaf()
def run ( self ) -> None : verbosity = self . auto_options [ "verbosity" ] if verbosity is not None : if verbosity > 1 : optuna . logging . set_verbosity ( optuna . logging . DEBUG ) elif verbosity == 1 : optuna . logging . set_verbosity ( optuna . logging . INFO ) elif verbosity == 0 : optuna . logging . set_verbosity ( optuna . logging . WARNING ) else : optuna . logging . set_verbosity ( optuna . logging . CRITICAL ) # Handling aliases. _handling_alias_parameters ( self . lgbm_params ) # Sampling. self . sample_train_set ( ) self . tune_feature_fraction ( ) self . tune_num_leaves ( ) self . tune_bagging ( ) self . tune_feature_fraction_stage2 ( ) self . tune_regularization_factors ( ) self . tune_min_data_in_leaf ( )