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SKLL 3.0.0
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@desilinguist desilinguist released this 21 Dec 20:12
· 197 commits to main since this release
v3.0
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This is a major new release with with dependency updates and bugfixes!

⚡️ SKLL 3.0 is backwards incompatible with previous versions of SKLL and might yield different results compared to previous versions even with the same data and same settings. ⚡️

💥 Breaking Changes 💥

  • Python 3.7 is no longer officially supported while official support for Python 3.10 has been added (Issue #701, PR #711).

  • scikit-learn has been updated to v1.0.1 (Issue #699, PR #702).

  • The configuration field pos_label_str from the “Tuning" section has been renamed to pos_label. Older configuration files with pos_label_str will now raise an exception (Issue #569, PR #706).

  • The configuration field log from the “Output” section that was renamed to logs in SKLL v2.5 has now been completely deprecated. Older configuration files with log will now raise an exception (Issue #671, PR #705).

💡 New features 💡

  • SKLL now supports specifying custom seed values for cross-validation tasks. This option may be useful for running the same cross-validation experiment multiple times (with the same number of differently constituted folds) to get a sense of the variance across replicates (Issue #593, PR #707).

🛠 Bugfixes & Improvements 🛠

  • Using the --drop-blanks option with filter_features now raises a more useful error for the case when every single row in a tabular feature file has a blank column (Issue #693, PR #703).

  • SKLL conda packages are again generic Python packages instead of platform-specific ones (Issue #710, PR #711).

📖 Documentation Updates 📖

  • Add a new section to the hands-on tutorial explaining how to first install SKLL in a virtual environment (Issue #689, PR #709).

  • Add missing link to SKLL repository in the tutorial data section (Issue #688, PR #691).

  • Update CONTRIBUTING.md to include more detailed instructions for pushing to the SKLL repository (Issue #680, PR #704).

  • Link to the RSMTool implementation of quadratic_weighted_kappa which supports continuous values and can be used as a custom metric in SKLL for both hyper-parameter tuning as well as validation. See the quadratic_weighted_kappa bullet under the objectives section (Issue #512, PR #704).

  • Continued readability improvements to function and method docstrings.

✔️ Tests ✔️

  • All tests now specify local=True when making run_configuration() calls. This ensures that tests always run in local mode and prevent an unnecessary check for the gridmap library. (Issue #616, PR #708).

👩‍🔬 Contributors 👨‍🔬

(Note: This list is sorted alphabetically by last name and not by the quality/quantity of contributions to this release.)

Binod Gyawali (@bndgyawali), Robbie Imbrie (@RobertImbrie), Sanjna Kashyap (@Frost45), Sözen Ozkan Grigoras (@sozkangrigoras), Nitin Madnani (@desilinguist), Matt Mulholland (@mulhod), and Damien Xie (@damien2012eng),

Contributors

desilinguist, mulhod, and 4 other contributors
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