(self, trans_params: numpy.ndarray)
return params
Untransform a parameter configuration from continuous space to actual values.

        Args:
            trans_params:
                A 1-dimensional ``numpy.ndarray`` in the transformed space corresponding to a
                parameter configuration.

        Returns:
            A dictionary of an untransformed parameter configuration. Keys are parameter names.
            Values are untransformed parameter values.
Untransform a parameter configuration from continuous space to actual values.
Untransform a parameter configuration from continuous space to actual values .
def untransform(self, trans_params: numpy.ndarray) -> Dict[str, Any]:
        """Untransform a parameter configuration from continuous space to actual values.

        Args:
            trans_params:
                A 1-dimensional ``numpy.ndarray`` in the transformed space corresponding to a
                parameter configuration.

        Returns:
            A dictionary of an untransformed parameter configuration. Keys are parameter names.
            Values are untransformed parameter values.

        """
        assert trans_params.shape == (self._bounds.shape[0],)

        params = {}

        for (name, distribution), encoded_columns in zip(
            self._search_space.items(), self.column_to_encoded_columns
        ):
            trans_param = trans_params[encoded_columns]

            if isinstance(distribution, CategoricalDistribution):
                # Select the highest rated one-hot encoding.
                param = distribution.to_external_repr(trans_param.argmax())
            else:
                param = _untransform_numerical_param(
                    trans_param.item(), distribution, self._transform_log
                )

            params[name] = param

        return params
def untransform ( self , trans_params : numpy . ndarray ) -> Dict [ str , Any ] : assert trans_params . shape == ( self . _bounds . shape [ 0 ] , ) params = { } for ( name , distribution ) , encoded_columns in zip ( self . _search_space . items ( ) , self . column_to_encoded_columns ) : trans_param = trans_params [ encoded_columns ] if isinstance ( distribution , CategoricalDistribution ) : # Select the highest rated one-hot encoding. param = distribution . to_external_repr ( trans_param . argmax ( ) ) else : param = _untransform_numerical_param ( trans_param . item ( ) , distribution , self . _transform_log ) params [ name ] = param return params