import numpy as np
from aif360.datasets import StructuredDataset
[docs]class BinaryLabelDataset(StructuredDataset):
"""Base class for all structured datasets with binary labels."""
def __init__(self, favorable_label=1., unfavorable_label=0., **kwargs):
"""
Args:
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.
"""
self.favorable_label = float(favorable_label)
self.unfavorable_label = float(unfavorable_label)
super(BinaryLabelDataset, self).__init__(**kwargs)
[docs] def validate_dataset(self):
"""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`.
"""
# fix scores before validating
if np.all(self.scores == self.labels):
self.scores = np.float64(self.scores == self.favorable_label)
super(BinaryLabelDataset, self).validate_dataset()
# =========================== SHAPE CHECKING ===========================
# Verify if the labels are only 1 column
if self.labels.shape[1] != 1:
raise ValueError("BinaryLabelDataset only supports single-column "
"labels:\n\tlabels.shape = {}".format(self.labels.shape))
# =========================== VALUE CHECKING ===========================
# Check if the favorable and unfavorable labels match those in the dataset
if (not set(self.labels.ravel()) <=
set([self.favorable_label, self.unfavorable_label])):
raise ValueError("The favorable and unfavorable labels provided do "
"not match the labels in the dataset.")