Refactor temporalscope #17
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…n and core TimeFrame will utilize Universal Time Series model assumptions - assuming end-user will pre-filter or transform data without grouping from TemporalScope Other time series ML/AI time series packages do not explicitly state modelling assumptions and nomenclature - API design allows for flexibility but enforces best-practice standards
…istent dataframe handlin
…nting-errors-from-ruff,-including-docstring-format-and-magic-values---Resolved-issues-with-pre-commit-hooks-for-linting,-security,-and-style-checks---Ensured-codespell,-bandit,-mypy,-and-other-pre-commit-checks-pass-cleanly---Updated-and-cleaned-up-CI/CD-pipeline-to-ensure-smooth-code-integration): fix pre-commit linting errors and update ci/cd pipeline
…ocking-ci/cd-due-to-low-coverage.-set-project-threshold-to-50%-and-patch-threshold-to-10%,-making-it-highly-lenient-during-beta.): lower codecov thresholds for beta phase
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Pull Request
PR Type
Description
Refactored the SlidingWindowPartitioner class and the core utility files for consistent DataFrame handling across supported backends (Pandas, Modin, Polars). Also updated and renamed tutorial notebooks to reflect changes.
Related Issues
Testing
Added/Updated Tests
How Has This Been Tested?
The refactored code has been tested with updated unit tests that reflect changes to the backend handling of DataFrames. Existing tests were run successfully.
Test Configuration
Checklist
Additional Notes
Additional documentation will be added in future updates. For now, this refactor focuses on ensuring consistent DataFrame handling.