Catching `roc_auc_score` error 🚫

[KristinaUlicna]

PR contribution summary

Why is this PR useful / good for? Please describe the problem(s) you're trying to address.

• With small random graphs / poorly performing models, it may happen that there is only one class in y_true.
• This PR implements a safe version of the roc_auc_score function from sklearn which catches this exception.
• This is especially important for tests (i.e. test_run.py ) to pass.

List of proposed changes / linked issues & discussions

• [x] 🛠️ Fixes #315

What should a reviewer concentrate their feedback on?

• 💻 Code quality
• 📝 Everything looks OK?

What type of PR is this? (check all applicable)

• 🐛 Bug fix
• 🧑‍💻 Code refactor / style
• 🔥 Performance Improvements
• ✅ Test

Added tests?

• 🙅 no, but caught an edge case

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PR review summary

Describe what this PR does & how you reviewed the individual items, where needed:

Some helper checks to tick off:

• Focus on image annotation
• Focus on model training
• Could any optimization be applied?
• Is there any redundant code?
• Are there any spelling errors?

In conclusion, after my review, I'd like to:

• 🙋 ask some clarifying questions
• 🙅 suggest some specific changes

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[KristinaUlicna]

Closing this one & rebasing with edits only relevant to this PR.