⚡ Bolt: _factor_fit 함수의 Python 루프를 행렬 곱셈 기반 완전 벡터화 연산으로 최적화 - #205
⚡ Bolt: _factor_fit 함수의 Python 루프를 행렬 곱셈 기반 완전 벡터화 연산으로 최적화#205seonghobae wants to merge 1 commit into
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Replaced the explicit python `for` loop in `_factor_fit` with a fast vectorized boolean masking approach using NumPy matrix multiplication (`@`). This significantly reduces intermediate array memory allocation and iteration overhead when calculating aggregations over categorical factor mappings.
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Pull request overview
This PR refactors the Python diagnostics factor-level fit aggregation to use NumPy linear-algebra style vectorization instead of a per-factor Python loop, aiming to reduce loop overhead and intermediate slice allocations for large response matrices. It also updates the Rust binding crate’s lockfile to reflect a dependency version change.
Changes:
- Refactored
python/fast_mlsirm/diagnostics.py::_factor_fitfrom a Pythonforloop over factors to a vectorized aggregation approach. - Updated
crates/fast-mlsirm-py/Cargo.lockto bumppollsterto1.0.1.
Reviewed changes
Copilot reviewed 1 out of 2 changed files in this pull request and generated 2 comments.
| File | Description |
|---|---|
| python/fast_mlsirm/diagnostics.py | Reworks factor-level fit aggregation to a vectorized approach (removing Python loop). |
| crates/fast-mlsirm-py/Cargo.lock | Updates the resolved Rust dependency version for pollster. |
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중복 정리: 배경: 2026-07-14 이후 조직 coverage-evidence 인프라 문제로 모든 PR이 REQUEST_CHANGES 상태였습니다(인프라 수정: ContextualWisdomLab/.github#611). 필요 시 재오픈 가능합니다. Generated by Claude Code |
Understood. Acknowledging that this work is already covered by PR #169 and is now obsolete. I will stop work on this task. |
💡 What (무엇을 변경했나요?)
python/fast_mlsirm/diagnostics.py파일 내의_factor_fit함수에서 요인(factor)별로 데이터를 집계하는 파이썬for루프를 제거하고,NumPy의 행렬 곱셈(@)을 활용한 완전한 벡터화 연산으로 리팩터링했습니다.🎯 Why (왜 이 변경이 필요한가요?)
요인별로 부울 인덱싱(
cols = factors == factor)을 사용하여 배열 슬라이스 복사본을 생성하고 루프를 순회하는 기존 방식은 데이터 크기와 범주 수가 늘어남에 따라 심각한 병목 현상을 유발합니다. 파이썬 수준의 루프 오버헤드와 잦은 중간 메모리 할당을 C/BLAS 최적화된 행렬 연산으로 대체하여 실행 속도를 획기적으로 개선하기 위함입니다. (.jules/bolt.md의 학습 내용 적용)📊 Impact (어떤 성능 향상이 있나요?)
🔬 Measurement (어떻게 검증할 수 있나요?)
uv run pytest tests를 실행하여 Python 전체 테스트 스위트가 통과하는지 확인합니다.cargo test --workspace를 실행하여 Rust 코어 파리티 테스트가 문제 없는지 교차 검증합니다.np.allclose로 동등함).PR created automatically by Jules for task 17298375324016726764 started by @seonghobae