Aif360

=====================

Fixes
-----
* Fixed `fit_predict` arguments in `RejectOptionClassification` (111)
* Removed Orange3 from requirements (113)

Fixes
* Removed Gender Classification tutorial (see 101 for details and discussion)
* Bug fix in Optimized Preprocessing to check for optimality correctly

Backwards-Incompatible Changes

* Deprecated support for Python 2.7

Fixes

* See issues 80, 83
* Also PRs 86, 90

Highlights
New Algorithm:
* Meta-Algorithm for Fair Classification ([Celis et al.. 2018](https://arxiv.org/abs/1806.06055))

New Features/Improvements
* Added download script for MEPS data
* Added ability to choose protected attribute for `DisparateImpactRemover`
* Updated `OptimPreproc` to use the latest version of `cvxpy`
* Added a threshold value to update `labels` from predicted `scores` in `CalibratedEqOddsPostprocessing`
* New `scores_names` arg in `StructuredDataset` allows for easier importing of predictions run elsewhere
* `tutorial_gender_classification` notebook now uses `skimage` instead of `cv2`
* `aif360.__version__` now returns the correct version string

Fixes
* Changed Credit Scoring Tutorial to use `Reweighing`; added new demo using `AdversarialDebiasing` on Adult Dataset
* Removed dependency on `subprocess.run` in `PrejudiceRemover` for Python 2.7 compatibility
* Fixed bug where `categorical_features` would not take into account `features_to_drop` in `StandardDataset`

New Contributors
ckadner, cclauss, vijaykeswani, ffosilva, kant, adrinjalali, mariaborbones

This update contains no feature changes.

Fixes
* changes to description for PyPI

The AI Fairness 360 toolkit is an open-source library to help detect and remove bias in machine learning models. The AI Fairness 360 Python package includes a comprehensive set of metrics for datasets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

Highlights
A brief list of features provided in this release include:
* Algorithms:
* Optimized Preprocessing ([Calmon et al., 2017](http://papers.nips.cc/paper/6988-optimized-pre-processing-for-discrimination-prevention))
* Disparate Impact Remover ([Feldman et al., 2015](https://doi.org/10.1145/2783258.2783311))
* Equalized Odds Postprocessing ([Hardt et al., 2016](https://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning))
* Reweighing ([Kamiran and Calders, 2012](http://doi.org/10.1007/s10115-011-0463-8))
* Reject Option Classification ([Kamiran et al., 2012](https://doi.org/10.1109/ICDM.2012.45))
* Prejudice Remover Regularizer ([Kamishima et al., 2012](https://rd.springer.com/chapter/10.1007/978-3-642-33486-3_3))
* Calibrated Equalized Odds Postprocessing ([Pleiss et al., 2017](https://papers.nips.cc/paper/7151-on-fairness-and-calibration))
* Learning Fair Representations ([Zemel et al., 2013](http://proceedings.mlr.press/v28/zemel13.html))
* Adversarial Debiasing ([Zhang et al., 2018](http://www.aies-conference.com/wp-content/papers/main/AIES_2018_paper_162.pdf))
* Datasets Interface (raw data not included)
* UCI ML Repository: Adult, German Credit, Bank Marketing
* ProPublica Recidivism
* Medical Expenditure Panel Survey
* Metrics
* Comprehensive set of group fairness metrics derived from selection rates and error rates
* Comprehensive set of sample distortion metrics
* Generalized Entropy Index ([Speicher et al., 2018](https://doi.org/10.1145/3219819.3220046))
* Metric Explanations
* Text and JSON output formats supported