Correct the ROC AUC computations and add relevant tests.

[potocpav]

The old ROC AUC computations were wrong (at least) in cases of duplicate y_hat values. I added a test demonstrating said issue and fixed the computations.

The table below summarizes the resulting AUCs for each test. Tests 0,1 and tests 2,3 differ only in data-point order, so they should obviously return the same AUCs. I checked the correctness of the new values on paper.

EDIT: also checked using Python's sklearn.metrics.roc_auc_score

test	old AUC	new AUC
0	Ok(1)	Ok(0.75)
1	Ok(0.25)	Ok(0.75)
2	Ok(0.625)	Ok(0.875)
3	Ok(1)	Ok(0.875)
4	Ok(0.16666666)	Ok(0.5)
5	Ok(NaN)	Ok(0.25)

[maciejkula]

Thanks for the fix!

[potocpav]

I saw your different coding style and tried at first to just correct your code, but then I got lazy and copied-and-pasted the code from my own project... So yeah, is this better?

[maciejkula]

Great, thanks a lot!