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:

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.
x (Union[np.ndarray, torch.Tensor]) – The input fata matrix.
y (Union[np.ndarray, torch.Tensor]) – The output data matrix.
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

`convert_to_dataloader`	Converts input and output data to a dataloader.
`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.
`inverse_transform`	Unnormalize the input and output data.
`inverse_transform_x`	Unnormalize only the input data.
`inverse_transform_y`	Un-transform only the output data.
`predict_dataloader`
`summary_input_output_dimensions`	Calculates the dimensions of the input and output of the ML model.
`test_dataloader`
`train_dataloader`
`transform`	Transform the input and output data.
`transform_x`	Transform only the input data.
`transform_y`	Transform only the output data.
`val_dataloader`