Performance optimization

[mzueva]

No description provided.

[gemini-code-assist]

Summary of Changes

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This pull request introduces significant performance optimizations by refactoring the liability calculation and risk classification logic to leverage Polars' vectorized operations. By replacing iterative, row-wise Python processing with highly optimized Polars expressions and pre-compiling regular expressions, the script's execution speed for large datasets is substantially improved. This change enhances efficiency without altering the core functionality or output of the liability analysis.

Highlights

• Vectorized Polars Operations: Replaced inefficient row-wise Python functions with native Polars vectorized expressions for liability identification, risk classification, and summary generation, significantly boosting processing speed.
• Regex Pre-compilation: Implemented pre-compilation of regular expressions to optimize pattern matching operations, reducing overhead during repeated use, especially within the annotation loop.
• Code Simplification: Removed several debug print statements and deprecated row-wise processing functions, leading to cleaner and more maintainable code.

Changelog

• .changeset/upset-sites-eat.md
  • Added a new changeset file to document the performance optimization patch.
• liabilities-calc-script/src/main.py
  • Introduced global variables POLARS_W_CDR3_PATTERN and COMPILED_LIABILITY_REGEX to support vectorized regex operations and address Polars Rust regex limitations.
  • Modified get_active_liability_definitions to populate COMPILED_LIABILITY_REGEX with pre-compiled patterns.
  • Removed the identify_liabilities, classify_risk, overall_risk_func, and _create_sequence_liabilities_summary_str functions.
  • Added new vectorized Polars expression builder functions: _build_vectorized_liability_expr, _build_vectorized_risk_expr, _build_vectorized_overall_risk_expr, and _build_vectorized_summary_expr.
  • Updated the main function to utilize the new vectorized Polars expressions for liability calculation, risk classification, and summary generation, replacing map_elements calls.
  • Modified the annotation loop in main to use pre-compiled regex patterns for improved performance.
  • Removed several debug print statements from the main function.

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[gemini-code-assist]

Code Review

This pull request introduces a significant performance optimization by refactoring the liability and risk calculation logic to use vectorized Polars expressions instead of row-wise Python functions with map_elements. This is an excellent improvement that leverages the power of Polars for better performance. The new expression-building functions are well-structured. I've added a few suggestions to make the expression-building code even more concise and idiomatic by using polars.any_horizontal. The removal of leftover debug print statements is also a good cleanup.

[gemini-code-assist]

The manual reduction of boolean expressions using loops can be simplified. Using polars.any_horizontal is more concise and idiomatic for this purpose, and it directly operates on a list of expressions.

            any_in_bounds = pl.any_horizontal(any_in_bounds_parts)
            any_has_c = pl.any_horizontal(any_has_c_parts)

[gemini-code-assist]

This manual reduction of a list of boolean conditions can be simplified by using polars.any_horizontal(conditions). This is more idiomatic and improves readability.

        result = pl.any_horizontal(conditions)

[gemini-code-assist]

This loop can be refactored to be more declarative and potentially more performant by using polars.any_horizontal for each risk level check. This avoids iterating in Python and builds the expression tree more directly.

    has_high = pl.any_horizontal((pl.col(c).cast(pl.Utf8) == "High") for c in risk_col_names)
    has_medium = pl.any_horizontal((pl.col(c).cast(pl.Utf8) == "Medium") for c in risk_col_names)
    has_low = pl.any_horizontal((pl.col(c).cast(pl.Utf8) == "Low") for c in risk_col_names)