CondAEModel.forward_evaluation

CondAEModel.forward_evaluation(data: DataFrame | ndarray | List[List] | Dict | List[Dict] | torch.Tensor, format_out: str = 'df', input_normalized: bool = False, output_normalized: bool = False, transform_output: bool = False) → DataFrame | ndarray | List[List] | Dict | List[Dict] | torch.Tensor[source]

Receives some values of inputML, and returns the corresponding outputML, as predicted by the model. The data can be provided in any format, and returned also in any format. It just relies on the encode method

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_normalized (bool, optional, default=False) – To indicate if the input data is already normalized

  • output_normalized (bool, optional, default=False) – To indicate if the output data should be left normalized when returned

  • transform_output (bool, optional, default=False) – If False, the data is returned in the original format of performance attributes, otherwise it is returned in the format of the outputML

Returns:

Union[pd.DataFrame, np.ndarray, List[List], Dict, List[Dict], torch.Tensor] – The outputML in the indicated format