Implement "Prejudice Remover Regularized" Estimator

[cosmicBboy]

PRR as an optimization technique that extends the standard L1/L2-norm regularization method by adding a prejudice index term to the objective function. This term is equivalent to normalized mutual information, which measures the degree to which predictions ŷ and s are dependent on each other.

With values ranging from 0 to 1, 0 means that ŷ and s are independent and a value of 1 means that they are dependent. The goal of the objective function is to find model parameters that minimize the difference between ŷ and y in addition to the degree to which ŷ depends on s. See reference below for exact implementation details.

Reference:
Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012). Fairness-aware classifier with prejudice remover regularizer. Machine Learning and Knowledge Discovery in Databases, 35-50.
http://www.kamishima.net/archive/2011-ws-icdm_padm.pdf