inFairness.utils.datautils module#

inFairness.utils.datautils.convert_tensor_to_numpy(tensor)[source]#

Converts a PyTorch tensor to numpy array

If the provided tensor is not a PyTorch tensor, it returns the same object back with no modifications

Parameters:

tensor (torch.Tensor) – Tensor to be converted to numpy array

Returns:

array_np – Numpy array of the provided tensor

Return type:

numpy.ndarray

inFairness.utils.datautils.generate_data_pairs(n_pairs, datasamples_1, datasamples_2=None, comparator=None)[source]#

Utility function to generate (in)comparable data pairs given data samples. Use case includes creating a dataset of comparable and incomparable data for the EXPLORE distance metric which learns from such data samples.

Parameters:

  • n_pairs (int) – Number of pairs to construct

  • datasamples_1 (numpy.ndarray) – Array of data samples of shape (N_1, *)

  • datasamples_2 (numpy.ndarray) – (Optional) array of data samples of shape (N_2, *). If datasamples_2 is provided, then data pairs are constructed between datasamples_1 and datasamples_2. If datasamples_2 is not provided, then data pairs are constructed within datasamples_1

  • comparator (function) – A lambda function that given two samples returns True if they should be paired, and False if not. If comparator is not defined, then random data samples are paired together. Example: comparator = lambda x, y: (x == y)

Returns:

idxs – A (n_pairs, 2) shaped array with indices of data sample pairs

Return type:

numpy.ndarray

inFairness.utils.datautils.get_device(obj)[source]#

Returns a device (cpu/cuda) based on the type of the reference object

Parameters:

obj (torch.Tensor) –

inFairness.utils.datautils.include_exclude_terms(data_terms: Iterable[str], include: Iterable[str] = (), exclude: Iterable[str] = ())[source]#

given a set of data terms, return a resulting set depending on specified included and excluded terms.

Parameters:

  • data_terms (string iterable) – set of terms to be filtered

  • include (string iterable) – set of terms to be included, if not specified all data_terms are included

  • exclude (string iterable) – set of terms to be excluded from data_terms

Returns:

terms – resulting terms in alphabetical order.

Return type:

list of strings