hookeai.utilities.fit_data_scalers.min_max_batch_fit

min_max_batch_fit(dataset, features_type, n_features, is_verbose=False)

Perform batch fitting of min-max data scaler.

Parameters:

• dataset (torch.utils.data.Dataset) – Time series data set. Each sample is stored as a dictionary where each feature (key, str) data is a torch.Tensor(2d) of shape (sequence_length, n_features).

• features_type (str) – Features for which data scaler is fitted (e.g., ‘features_in’, ‘features_out’). Must be directly available from data set samples.

• n_features (int) – Number of features (dimensionality).

• is_verbose (bool, default=False) – If True, enable verbose output.

Returns:

• minimum (torch.Tensor) – Features normalization minimum tensor stored as a torch.Tensor with shape (n_features,).

• maximum (torch.Tensor) – Features normalization maximum tensor stored as a torch.Tensor with shape (n_features,).