CondVAEModel.forward_evaluation
- CondVAEModel.forward_evaluation(data: ndarray | torch.Tensor, format_out: str = 'df', input_transformed: bool = False, return_untransformed: bool = False) DataFrame | ndarray | List[List] | Dict | List[Dict] | torch.Tensor [source]
Wrapper function of CondAEModel.predict_y() to evaluate the model on the provided data, and return the output in the desired format.
- Parameters:
data (Union[pd.DataFrame, np.ndarray, List[List], Dict, List[Dict], torch.Tensor]) – Input data to evaluate in the surrogate model
format_out (str, optional, default=”df”) – The format for the returned output. The possible formats are [“dict”, “dict_list”, “df_per_obj”, “df”, “array”, “torch”, “list”], and default is “df”.
input_transformed (bool, optional, default=False) – If True, the input data is assumed to be already transformed, and no transformation is applied before evaluating the model
return_untransformed (bool, optional, default=False) – If True, the output is returned in the original space, by applying the inverse transformation.
- Returns:
Union[pd.DataFrame, np.ndarray, List[List], Dict, List[Dict], torch.Tensor] – The predictions in the desired format, based on the format_out argument, transformed or in original space based on the return_untransformed argument.