citylearn.end_use_load_profiles.lstm_model.preprocessing module

citylearn.end_use_load_profiles.lstm_model.preprocessing.dataset_dataloader(x: ndarray, y: ndarray, batch_size: int, shuffle: bool = None, drop_last: bool = None) → Tuple[TensorDataset, DataLoader]

citylearn.end_use_load_profiles.lstm_model.preprocessing.preprocess_df(config: Mapping[str, Any], df: DataFrame, train_references: List[int] = None, validation_references: List[int] = None, test_references: List[int] = None) → Mapping[str, Any]

citylearn.end_use_load_profiles.lstm_model.preprocessing.sliding_windows(data: ndarray, seq_length: int, output_len: int)

Check that the variable to be predicted is the last column of the dataframe :param data: dataframe :param seq_length: lookback :param output_len: how many timetep ahead will be predicted :return: x = matrix [number of timestep - lookback, lookback, number of input variables];

y = matrix [number of timestep - lookback, number of output variables]