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[entelecheia]

_{Mention [stepsize] in a comment if you'd like to report some technical debt. See examples here.}

[pull-request-quantifier-deprecated]

This PR has 92 quantified lines of changes. In general, a change size of upto 200 lines is ideal for the best PR experience!

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Quantification details

Label      : Small
Size       : +79 -13
Percentile : 36.8%

Total files changed: 5

Change summary by file extension:
.toml : +2 -0
.py : +77 -13

Change counts above are quantified counts, based on the PullRequestQuantifier customizations.

Why proper sizing of changes matters

Optimal pull request sizes drive a better predictable PR flow as they strike a
balance between between PR complexity and PR review overhead. PRs within the
optimal size (typical small, or medium sized PRs) mean:

• Fast and predictable releases to production:
  • Optimal size changes are more likely to be reviewed faster with fewer
    iterations.
  • Similarity in low PR complexity drives similar review times.
• Review quality is likely higher as complexity is lower:
  • Bugs are more likely to be detected.
  • Code inconsistencies are more likely to be detected.
• Knowledge sharing is improved within the participants:
  • Small portions can be assimilated better.
• Better engineering practices are exercised:
  • Solving big problems by dividing them in well contained, smaller problems.
  • Exercising separation of concerns within the code changes.

What can I do to optimize my changes

• Use the PullRequestQuantifier to quantify your PR accurately
  • Create a context profile for your repo using the context generator
  • Exclude files that are not necessary to be reviewed or do not increase the review complexity. Example: Autogenerated code, docs, project IDE setting files, binaries, etc. Check out the Excluded section from your prquantifier.yaml context profile.
  • Understand your typical change complexity, drive towards the desired complexity by adjusting the label mapping in your prquantifier.yaml context profile.
  • Only use the labels that matter to you, see context specification to customize your prquantifier.yaml context profile.
• Change your engineering behaviors
  • For PRs that fall outside of the desired spectrum, review the details and check if:
    • Your PR could be split in smaller, self-contained PRs instead
    • Your PR only solves one particular issue. (For example, don't refactor and code new features in the same PR).

How to interpret the change counts in git diff output

• One line was added: +1 -0
• One line was deleted: +0 -1
• One line was modified: +1 -1 (git diff doesn't know about modified, it will
  interpret that line like one addition plus one deletion)
• Change percentiles: Change characteristics (addition, deletion, modification)
  of this PR in relation to all other PRs within the repository.

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[sourcery-ai]

Hey @entelecheia - I've reviewed your changes and they look great!

General suggestions:

• Ensure that the expanded environment variables for paths are validated to prevent potential runtime errors.
• Review the newly added docstrings for accuracy and completeness, especially in utility classes where the functionality is complex.
• Consider the impact of removing parameters from method calls, such as the removal of the 'project' parameter in 'HyFI.run_task', to ensure it aligns with the intended design and usage patterns.

Here's what I looked at during the review

• 🟡 General issues: 2 issues found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟢 Complexity: all looks good
• 🟢 Docstrings: all looks good

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[sourcery-ai]

suggestion (code_refinement): Consider validating expanded environment variables for project_root and global_hyfi_root.

Expanding environment variables is a great feature, but it's important to ensure the expanded paths are valid and accessible to prevent runtime errors.

[sourcery-ai]

suggestion (code_clarification): Ensure the new docstring for DATASETs class aligns with the actual functionality and usage.

While the addition of a docstring is commendable, it's crucial to ensure it accurately reflects the class's purpose, especially since the class methods and attributes are not detailed within.