Multilabel support in ROC

[SkafteNicki]

Before submitting

• Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
• Did you read the contributor guideline, Pull Request section?
• Did you make sure to update the docs?
• Did you write any new necessary tests?

What does this PR do?

Fixes #103
Adds multilabel support to ROC metric

PR review

Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.

Did you have fun?

Make sure you had fun coding 🙃

[tchaton]

LGTM ! Nice addition !

[Borda]

@SkafteNicki mind resolve conflict, then ready to land :]

[codecov]

Codecov Report

Merging #114 (c95f4e1) into master (264cc6f) will decrease coverage by 17.36%.
The diff coverage is 86.64%.

@@             Coverage Diff             @@
##           master     #114       +/-   ##
===========================================
- Coverage   97.24%   79.88%   -17.37%     
===========================================
  Files         122       70       -52     
  Lines        3990     2177     -1813     
===========================================
- Hits         3880     1739     -2141     
- Misses        110      438      +328     

Flag	Coverage Δ
Linux	79.88% <86.64%> (+1.08%)	⬆️
Windows	79.88% <86.64%> (+1.08%)	⬆️
cpu	79.88% <86.64%> (-17.37%)	⬇️
gpu	?
macOS	79.88% <86.64%> (-17.37%)	⬇️
pytest	79.88% <86.64%> (-17.37%)	⬇️
python3.6	?
python3.8	?
python3.9	?
torch1.3.1	?
torch1.4.0	?
torch1.8.0	?

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
torchmetrics/metric.py	55.25% <25.00%> (-38.98%)	⬇️
torchmetrics/functional/classification/auroc.py	46.15% <38.46%> (-39.10%)	⬇️
torchmetrics/functional/classification/accuracy.py	68.96% <50.00%> (-31.04%)	⬇️
torchmetrics/functional/regression/psnr.py	62.50% <57.14%> (-34.28%)	⬇️
...chmetrics/functional/classification/cohen_kappa.py	74.07% <66.66%> (-25.93%)	⬇️
torchmetrics/functional/regression/r2score.py	70.00% <66.66%> (-24.88%)	⬇️
torchmetrics/classification/auroc.py	83.78% <71.42%> (-7.89%)	⬇️
...rics/functional/classification/confusion_matrix.py	66.66% <75.00%> (-33.34%)	⬇️
...hmetrics/functional/retrieval/average_precision.py	80.00% <80.00%> (ø)
torchmetrics/regression/r2score.py	92.85% <80.00%> (+0.26%)	⬆️
... and 151 more

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update 264cc6f...c95f4e1. Read the comment docs.

[Borda]

@SkafteNicki seems that the tests are not correct

[SkafteNicki]

@Borda thanks, had not run all test locally but they should be good now.