feat(fast_seqfunc)✨: Add support for custom alphabets and integer sequences #5
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ericmjl
commented
Mar 26, 2025
ericmjl
commented
- Introduced the Alphabet class for handling custom sequence alphabets.
- Enhanced OneHotEmbedder to support custom alphabets.
- Added synthetic data generation for integer sequences.
- Implemented tests for new features and functionalities.
…uences - Introduced the Alphabet class for handling custom sequence alphabets. - Enhanced OneHotEmbedder to support custom alphabets. - Added synthetic data generation for integer sequences. - Implemented tests for new features and functionalities.
…eling - Introduced a new example script demonstrating sequence-function modeling with mixed amino acids. - The script includes data generation, model training, and prediction functionalities. - Provides visualization and evaluation of model performance.
…dling and compatibility - Introduced getter and setter for the alphabet property to enhance encapsulation. - Updated tests to align with the new alphabet handling logic. - Refactored test cases to dynamically calculate expected dimensions.
…lass - Updated tests to dynamically verify alphabet size and token mappings. - Improved parameterized tests for encoding and decoding sequences. - Added checks for sequence padding and truncation scenarios.
- Adjusted expected embedding shapes in tests to account for the updated token type count. - Modified assertions to validate the new token type structure, including gap values and characters. - Ensured all test cases align with the revised alphabet size and token handling.
- Updated the GitHub Actions workflow to include a test matrix for fast and slow tests. - Introduced a new pytest marker for slow tests in the configuration. - Added slow test markers to relevant test cases in the test suite.
…els directly - Updated the prediction function to directly use scikit-learn models if available. - Simplified the test cases to focus on embedding and serialization without PyCaret dependencies.
…ction logic for improved maintainability - Unified test execution logic in CI workflows to reduce redundancy. - Simplified prediction logic in core.py by consolidating conditional branches.
…flow - Removed manual test output capturing and exit code handling. - Utilized pytest's cache for counting test results. - Streamlined the test summary creation process.
- Updated the test result parsing logic to extract counts from pytest output. - Replaced direct cache file parsing with output analysis for better reliability.
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