DataModule

class aixd.mlmodel.data.data_loader.DataModule(*args: Any, **kwargs: Any)[source]

Bases: LightningDataModule

Data module for the ML model. It takes care of splitting the data into train, val and test sets, normalizing the data.

Parameters:

x (Union[np.ndarray, torch.Tensor]) – The input fata matrix.
y (Union[np.ndarray, torch.Tensor]) – The output data matrix.
input_ml_dblock (InputML) – Input data block, defining normalizations, the type of the input data, and the heads for the ML model.
output_ml_dblock (OutputML) – Output data block, defining normalizations, the type of the output data, and the heads for the ML model.
batch_size (int, optional) – Batch size. The default is 512.
split_ratios (List[Union[int, float]], optional) – List of ratios for splitting the data into train, val and test sets. The default is [0.8, 0.1, 0.1].
random_seed (int, optional) – Random seed for splitting the data. The default is RANDOM_SEED_SPLIT.
predict (bool, optional) – Whether to create a predict set. The default is False.

Methods

`from_dataset`	Creates a data module from a dataset.
`from_parameters`	Creates a data module from parameters returned by the get_parameters(...) method.
`get_checksum`	Computes a checksum for the training/validation/test data.
`get_parameters`	Get parameters defining the data module.
`normalize`	Normalize the input and output data.
`normalize_x`	Normalize only the input data.
`normalize_y`	Normalize only the output data.
`predict_dataloader`
`print_ml_configuration`
`summary_input_output_dimensions`	Calculates the dimensions of the input and output of the ML model.
`test_dataloader`
`train_dataloader`
`unnorm_inv_transf_x`	Unnormalize and do the inverse transformation of the data.
`unnorm_inv_transf_y`	Unnormalize and do the inverse transformation of the data.
`unnormalize`	Unnormalize the input and output data.
`unnormalize_x`	Unnormalize only the input data.
`unnormalize_y`	Unnormalize only the output data.
`val_dataloader`