Release v3.2.0

[rossarmstrong]

Enhancements

• Refactored metrics computation architecture to eliminate np.vectorize() overhead by replacing it with a C-level batch processing loop (_metrics_batch()). This provides cleaner code structure and establishes a foundation for future performance optimizations without introducing any performance regression.

• Fixed double computation bug where error_handler() was calling metrics() for validation, then wrapper functions were computing metrics again. The error_handler() function is now validation-only, and all metric calculations happen through a single unified metrics() entry point, improving efficiency and code maintainability.

• Standardized internal metrics return format to row-based (n, 9) array structure instead of columnar format. This simplifies DataFrame construction in summary() and summaryp() functions by eliminating complex transpose operations and reducing code complexity.

• Improved code organization with unified metrics() router function that dispatches to either single-pair calculations() or batch _metrics_batch() processing, providing a cleaner and more maintainable architecture for metric computation.

• Updated Pylint configuration to suppress import errors for Cython modules during static analysis and exclude benchmark/development directories from linting. This resolves CI/CD build failures while maintaining code quality standards for the core package.

• Optimized Levenshtein distance algorithm in calculations() function with C-level performance improvements: replaced np.zeros() with np.empty() to eliminate redundant initialization, moved boundary condition initialization outside the main DP loop to remove conditional branches from the hot path, and replaced Python's min() function with manual C-level sequential comparisons.

• Implemented dual-path architecture with fast path optimization for functions that don't require word tracking. Added three new functions (calculations_fast(), _metrics_batch_fast(), metrics_fast()) that skip word list construction and return float64 arrays instead of object arrays. Updated wer(), wers(), werp(), and werps() functions to use the fast path, achieving performance improvement on synthetic benchmarks. Functions requiring word tracking (summary() and summaryp()) continue using the full path.

Bug Fixes

• Expanded try/except scope in all wrapper functions (wer.py, wers.py, werp.py, werps.py, summary.py, summaryp.py) to properly catch exceptions from both validation (error_handler()) and computation (metrics()/metrics_fast()). This fixes 6 pre-existing test failures where invalid input types (e.g., lists of integers) would crash instead of returning None with an error message.

• Added division-by-zero guards in calculations_fast() function (wer = (<double>ld) / m if m > 0 else 0.0) and corpus-level wrapper functions (wer.py, werp.py) to prevent crashes on empty input. Also added per-row masked division in werps.py to handle cases where individual samples have zero reference length.