abroca

abroca

"abroca" is a python package to provide basic functionality for computing and visualizing the Absolute Between-ROC Area (ABROCA).

Install

The source code is currently hosted on github and Python package at PyPi

# PyPI
pip install abroca

Example

You can find the .ipynb file under example folder. It is a basic example which demonstrates the use of the abroca package to compute the ABROCA for a simple logistic regression classifier.

#Compute Abroca
slice = compute_abroca(df_test, pred_col = 'pred_proba' , label_col = 'returned',         
                       protected_attr_col = 'Gender', compare_type = 'binary',
                       n_grid = 10000, plot_slices = True)

The plot is automatically saved to a file and is displayed on-screen. The link to download the data is given in the comments in the example file. Parameters are self explainatory through the example file. Parameter details below.

• df - dataframe containing colnames matching pred_col, label_col and protected_attr_col
• pred_col - name of column containing predicted probabilities (string)
• label_col - name of column containing true labels (should be 0,1 only) (string)
• protected_attr_col - name of column containing protected attribute (string)
• compare_type - comparison group being 'overall', 'multiple' (compare one majority with all classes) or 'binary'
• majority_protected_attr_val(optional) - name of 'majority' group with respect to protected attribute (string)
• n_grid (optional) - number of grid points to use in approximation (numeric) (default of 10000 is more than adequate for most cases)
• plot_slices (optional) - if TRUE, ROC slice plots are generated and saved to file_name (boolean)
• lb (optional) - Lower limit of integration (use -numpy.inf for -infinity) Default is 0
• ub (optional) - Upper limit of integration (use -numpy.inf for -infinity) Default is 1
• limit (optional) - An upper bound on the number of subintervals used in the adaptive algorithm.Default is 1000

Reference Paper: Josh Gardner, Christopher Brooks, and Ryan Baker (2019). Evaluating the Fairness of Predictive Student Models Through Slicing Analysis. Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK19); March 4-8, 2019; Tempe, AZ, USA. https://doi.org/10.1145/3303772.3303791

Getting Help

If you encounter a clear bug, please file a minimal reproducible example on github, or contact the package maintainers directly (see the package documentation).