Added algorithms and test file for Celis et al. paper.

[vijaykeswani]

As per discussion with Sameep Mehta, I have added support for the Fair Classification meta-algorithm that we developed. The implementation is based on the following work:
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees - https://arxiv.org/abs/1806.06055.

The algorithm we suggest in the paper takes the fairness metric as input and corresponding to the metric specified, the optimization problem is different. In this implementation, we provide code for optimizing two fairness metrics: Statistical Rate and False Discovery Rate.

The main implementation file is meta_fair_classifier.py. The algorithm is implemented in the folder celisMeta. I have added a Jupyter test notebook for this algorithm as well - demo_meta_classifier.ipynb.

I have tried to loosely follow the coding style used for other implemented algorithms. Any comments on the code/algorithm will be very helpful and please let me know any changes I should make before this can be merged.

[hoffmansc]

Would you mind adding the MetaFairClassifier to inprocessing/__init__.py similarly to the other examples there? This allows it to be imported like from aif360.algorithms.inprocessing import MetaFairClassifier consistent with the other classes.

Additionally, I see you have a demo notebook, which is great! Could you add a couple assert statements testing that the relevant metrics are debiased? You can see an example here: https://github.com/IBM/AIF360/blob/master/examples/demo_optim_data_preproc.ipynb (the cells marked ### Testing). Then we can add this to the unit test framework with a corresponding file like https://github.com/IBM/AIF360/blob/master/tests/test_demo_optim_data_preproc.py (all you have to do is change the name and path).

Thanks for your contribution!

[hoffmansc]

I think this should be ... y == [dataset.favorable_label] ... since we assume the general case where the favorable label may not be 1

[hoffmansc]

do you want to make this a parameter in __init__ to handle the StatisticalRate case as well?

[hoffmansc]

If these helper functions aren't strictly necessary maybe we can move the code inside fit or predict? Or rename them to something more descriptive? I don't want users to accidentally do _runTrain when they mean to do fit etc.

[hoffmansc]

It looks like the demo notebook is taking a too long to run. We could up the time limit but I don't think we should have such a lengthy unit test anyway. I propose we do a few things:

• keep the notebook but remove the test_meta_classifier.py file
• add a short unit test similar to test_disparate_impact_remover.py which tests basic usage and corner cases and such (should only run fit and predict one or two times since these are expensive)

Also, a few other things:

• there's a typo -- MetaFairClassifer should be MetaFairClassifier
• if you could add a line describing the new arg type in __init__ i.e. state the valid values ('fdr', 'sr')
• it seems that there are some unused variables in meta_fair_classifier.py like class_attr, y_test, and x_control_test and some variables like sensitive_attr are assigned default values after they've been used
• the relative imports for files in the celisMeta/ folder need to look like from .General import * and from . import utils as ut to work with Python 3
• you can add a link to your paper in the main README under "Supported bias mitigation algorithms"

Thanks for staying patient

[vijaykeswani]

Thanks a lot for the review. I made the suggested changes and created a new test file instead of the earlier one based on the notebook. Its passed the tests now.

Please let me know if there's any other change required before it can be merged.