aif360.datasets.BinaryLabelDataset

class aif360.datasets.BinaryLabelDataset(favorable_label=1.0, unfavorable_label=0.0, **kwargs)[source]

Base class for all structured datasets with binary labels.

Parameters:
  • favorable_label (float) – Label value which is considered favorable (i.e. “positive”).
  • unfavorable_label (float) – Label value which is considered unfavorable (i.e. “negative”).
  • **kwargs – StructuredDataset arguments.

Methods

align_datasets Align the other dataset features, labels and protected_attributes to this dataset.
convert_to_dataframe Convert the StructuredDataset to a pandas.DataFrame.
copy Convenience method to return a copy of this dataset.
export_dataset Export the dataset and supporting attributes TODO: The preferred file format is HDF
import_dataset Import the dataset and supporting attributes TODO: The preferred file format is HDF
split Split this dataset into multiple partitions.
subset Subset of dataset based on position :param indexes: iterable which contains row indexes
temporarily_ignore Temporarily add the fields provided to ignore_fields.
validate_dataset Error checking and type validation.
__init__(favorable_label=1.0, unfavorable_label=0.0, **kwargs)[source]
Parameters:
  • favorable_label (float) – Label value which is considered favorable (i.e. “positive”).
  • unfavorable_label (float) – Label value which is considered unfavorable (i.e. “negative”).
  • **kwargs – StructuredDataset arguments.
validate_dataset()[source]

Error checking and type validation.

Raises:
  • ValueError – labels must be shape [n, 1].
  • ValueError – favorable_label and unfavorable_label must be the only values present in labels.