Code for the concordant partial AUC and the partial C statistic
by André Carrington and Yusuf Sheikh

Citation: André M Carrington, Paul W Fieguth, Hammad Qazi, Andreas Holzinger, Helen H Chen, Franz Mayr, and Douglas G Manuel. A new concordant partial AUC and partial C statistic for imbalanced data in the evaluation of machine learning algorithms. BMC Medical Informatics and Decision Making, 20(1):1–12, 2020.

Ensure you have a Python 3.7 interpreter.
Copy files in Python3.7 folder to a local folder.
Create a subfolder called output.
Edit the variables (explained below) at the top of the file test_pAUCc.py as input settings.

python test_pAUCc.py

The program will block (wait) for you to close each plot generated and each plot is saved automatically along with the log file to the output folder using the test number from the test vector or the input file.

useFile = False
useSingleTestVector = True
useAllTestVectors = False

fileName = 'input-matlab/result581.mat' # a matlab file (or future: csv file) for input
singleTestVectorNum = 1 # which of 11 test vectors in the function get_ROC_test_scores_labels_ranges below

rangeAxis = 'FPR' # examine ranges (next) by FPR or TPR
filePAUCranges = [[0, 0.33], [0.33, 0.67], [0.67, 1.0]] # ranges, as few or many as you like
useCloseRangePoint = True # automatically alter the ranges to match with the closest points in data
costs = dict(cFP=1, cFN=1, cTP=0, cTN=0) # specify relative costs explicitly (default shown)
rates = False # treat costs as rates, e.g. cFPR (default False)

sanityCheckWholeAUC = True
showPlot = True
showData = False
showError = True

Created and tested in PyCharm with a Python 3.7 interpreter.