aif360.datasets.BinaryLabelDataset

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

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)

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()

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.