Remove skartifacts

[4Freye]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @4Freye, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request modernizes the panelsplit library by updating its core Python and scikit-learn dependencies, streamlining the internal metrics module for better organization and expanded functionality, and enhancing the SequentialCVPipeline's operational logic. These changes aim to improve the library's compatibility with recent ecosystem advancements, clarify its API through updated documentation, and bolster its reliability with new, thorough test cases, particularly around how pipelines manage data indices and final estimator outputs.

Highlights

• Dependency Updates: The required Python version has been updated from 3.10 to 3.11, and the scikit-learn dependency has been upgraded to version 1.8.0.
• Metrics Module Enhancements: The panelsplit.metrics module has undergone significant refactoring, including consolidated scikit-learn imports, the addition of new classification metrics (e.g., f1, precision, recall, jaccard, d2 scores), and improved docstrings for key functions like make_SequentialCV_scorer and get_scorer.
• Pipeline Logic Refinement: The SequentialCVPipeline now features refined internal logic for handling data indices and distinguishing between intermediate and final steps, leading to more robust prediction and transformation behavior. The classes_ property has also been improved with better error handling and a note on class aggregation.
• Documentation and Test Coverage: Documentation for GridSearch and RandomizedSearch has been updated with direct links to scikit-learn resources. New comprehensive tests have been added for SequentialCVPipeline to cover various cross-validation and index handling scenarios, ensuring stability and correctness.

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[gemini-code-assist]

Code Review

This pull request is a significant and valuable refactoring effort to remove dependencies on scikit-learn's internal artifacts and align the codebase with modern versions of the library. The changes, including cleaning up imports, improving docstrings, and removing deprecated code paths, greatly enhance maintainability. The addition of comprehensive tests, especially for the SequentialCVPipeline indexing logic, is also a fantastic improvement.

I've found two issues:

1. A high-severity bug in the classes_ property of SequentialCVPipeline that causes it to fail when the pipeline is fitted with methods like fit_transform.
2. A medium-severity issue in the docstring of GridSearch where the example output for cv_results_ is misleading.

Details and suggestions are in the review comments. Overall, this is a great PR that moves the project forward.