tsicl.utils.data_adapter.validate_target_inputs

tsicl.utils.data_adapter.validate_target_inputs(inputs: list | torch.Tensor | numpy.ndarray | pandas.DataFrame, batch_size: int) → Tuple[int, bool, int, list | torch.Tensor]

Check dims and standardize all target tensor shapes.

Ignore covariates.

Returns:

• num_batches (int) – The total number of mini batches to process.

• is_tensor (bool) – Whether the returned inputs are torch Tensor.

• num_var (int) – Number of target variables now hidden in batch dimension.

• out (torch.Tensor or list) – Preprocessed inputs as Tensors of shape (batch, seq_len, 1). If multiple variables to predict, all are stacked in the batch dimension.