aif360.algorithms.preprocessing.LFR

class aif360.algorithms.preprocessing.LFR(unprivileged_groups, privileged_groups, k=5, Ax=0.01, Ay=1.0, Az=50.0, print_interval=250, verbose=0, seed=None)

Learning fair representations is a pre-processing technique that finds a latent representation which encodes the data well but obfuscates information about protected attributes [2]. .. rubric:: References

[2]

R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning Fair Representations.” International Conference on Machine Learning, 2013.

Based on code from https://github.com/zjelveh/learning-fair-representations

Parameters:

• unprivileged_groups (tuple) – Representation for unprivileged group.

• privileged_groups (tuple) – Representation for privileged group.

• k (int, optional) – Number of prototypes.

• Ax (float, optional) – Input recontruction quality term weight.

• Az (float, optional) – Fairness constraint term weight.

• Ay (float, optional) – Output prediction error.

• print_interval (int, optional) – Print optimization objective value every print_interval iterations.

• verbose (int, optional) – If zero, then no output.

• seed (int, optional) – Seed to make predict repeatable.

Methods

fit	Compute the transformation parameters that leads to fair representations.
fit_predict	Train a model on the input and predict the labels.
fit_transform	Fit and transform methods sequentially.
predict	Return a new dataset with labels predicted by running this Transformer on the input.
transform	Transform the dataset using learned model parameters.

__init__(unprivileged_groups, privileged_groups, k=5, Ax=0.01, Ay=1.0, Az=50.0, print_interval=250, verbose=0, seed=None)

Parameters:

• unprivileged_groups (tuple) – Representation for unprivileged group.

• privileged_groups (tuple) – Representation for privileged group.

• k (int, optional) – Number of prototypes.

• Ax (float, optional) – Input recontruction quality term weight.

• Az (float, optional) – Fairness constraint term weight.

• Ay (float, optional) – Output prediction error.

• print_interval (int, optional) – Print optimization objective value every print_interval iterations.

• verbose (int, optional) – If zero, then no output.

• seed (int, optional) – Seed to make predict repeatable.

fit(dataset, maxiter=5000, maxfun=5000)

Compute the transformation parameters that leads to fair representations. :param dataset: Dataset containing true labels. :type dataset: BinaryLabelDataset :param maxiter: Maximum number of iterations. :type maxiter: int :param maxfun: Maxinum number of function evaluations. :type maxfun: int

Returns:

LFR – Returns self.

fit_transform(dataset, maxiter=5000, maxfun=5000, threshold=0.5)

Fit and transform methods sequentially.

Parameters:

• dataset (BinaryLabelDataset) – Dataset containing labels that needs to be transformed.

• maxiter (int) – Maximum number of iterations.

• maxfun (int) – Maxinum number of function evaluations.

• threshold (float, optional) – threshold parameter used for binary label prediction.

Returns:

dataset (BinaryLabelDataset) – Transformed Dataset.

transform(dataset, threshold=0.5)

Transform the dataset using learned model parameters. :param dataset: Dataset containing labels that needs to be transformed. :type dataset: BinaryLabelDataset :param threshold: threshold parameter used for binary label prediction. :type threshold: float, optional

Returns:

dataset (BinaryLabelDataset) – Transformed Dataset.