⚡ Bolt: Optimize _factor_fit with vectorized aggregations - #179
⚡ Bolt: Optimize _factor_fit with vectorized aggregations#179seonghobae wants to merge 1 commit into
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Pull request overview
OpenCode cannot approve yet because required coverage evidence did not pass.
Review outcome
1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
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Problem: The required coverage-evidence job result was
failure, so OpenCode cannot establish approval sufficiency for this head. -
Root cause: Automated approval is only valid when the same-head coverage-evidence job proves supported repository test suites passed and configured docstring gates passed or were advisory, or reports not applicable because no supported source files or package manifests exist. Missing, failed, skipped, unavailable, or unsupported-tooling test evidence is a blocker.
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Fix: Install or configure the repository test/docstring evidence tooling when source files or package manifests exist, rerun the current-head coverage-evidence job, and approve only after it reports
successwith required evidence or explicit no-source not-applicable evidence. -
Regression test: Keep the approval branch checking
needs.coverage-evidence.result == successbefore posting APPROVE, and publish REQUEST_CHANGES when coverage-evidence blocker states such as cancelled, skipped, failed, unsupported-tooling, or below-100 evidence are present. -
Result: REQUEST_CHANGES
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Reason: coverage-evidence result was
failure, so required test/docstring evidence was not proven for current head7d4ca15245d53137981ac500a40f98b1bd76631c. -
Head SHA:
7d4ca15245d53137981ac500a40f98b1bd76631c -
Workflow run: 29617236539
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Workflow attempt: 1
Coverage evidence
Coverage Decision
- Result: FAIL
- Test evidence: not proven passing
- Docstring evidence: not proven passing when configured
- Failure count: 1
Changed-File Evidence Map
flowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file: diagnostics.py"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file: diagnostics.py"]
R1 --> V1["required checks"]
Evidence --> S2["Test: test_objective.py"]
S2 --> I2["regression suite"]
I2 --> R2["Review risk: Test: test_objective.py"]
R2 --> V2["targeted test run"]
OpenCode Review Overview
Pull request overviewOpenCode cannot approve yet because required coverage evidence did not pass. Review outcome1. HIGH .github/workflows/opencode-review.yml:1 - Coverage evidence did not prove required test/docstring evidence
Coverage evidenceCoverage Decision
Changed-File Evidence Mapflowchart LR
PR["PR changed files"] --> Evidence["OpenCode bounded evidence"]
Evidence --> S1["Changed file: diagnostics.py"]
S1 --> I1["repository behavior"]
I1 --> R1["Review risk: Changed file: diagnostics.py"]
R1 --> V1["required checks"]
Evidence --> S2["Test: test_objective.py"]
S2 --> I2["regression suite"]
I2 --> R2["Review risk: Test: test_objective.py"]
R2 --> V2["targeted test run"]
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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 a subset of #169 and is now obsolete, and stopping work on this task. |
1 participant
What
fast_mlsirm/diagnostics.py의_factor_fit함수에서 Pythonfor루프를 제거하고,axis=0기준 병합 및 boolean masking 행렬 곱셈(@)을 통한 완전 벡터화(vectorized) 구현으로 변경하였습니다. 또한 최적화에 대한 설명 주석을 추가했습니다.Why
NumPy 환경에서 Python 레벨의 명시적
for루프를 반복하며 범주형 데이터(factor) 단위로 집계 연산을 수행하면 인터프리터 오버헤드와 C 레벨 루프 최적화 부재로 인해 병목이 발생합니다..sum(axis=0)을 통한 선제적 아이템 집계 및 매핑 마스크(boolean mapping mask)에 대한 행렬 곱(@)을 활용하면 C/BLAS 수준의 최적화를 이끌어낼 수 있습니다. 이로 인해 대형 데이터셋에서 메모리 추가 할당(allocation)과 실행 시간을 극적으로 절감할 수 있습니다. (참고:.jules/bolt.md)Impact
배열을 3차원으로 브로드캐스팅하거나 Python 스칼라 호출을 하는 대신 마스킹과 행렬 곱으로 일괄 치환함으로써, 요소와 팩터의 수가 많은 환경에서 실행 시간을 약 1/14로 획기적으로 줄였습니다.
benchmark_factor_fit.py벤치마크 결과 (N=5000, J=500):Measurement
Python pytest(
python -m pytest tests) 및 Rust 단위 테스트(cargo test --workspace)가 정상적으로 통과되며, 원래 출력 형태와 완전히 동일한 값을 반환하는 것을 확인하였습니다.PR created automatically by Jules for task 10043797980699195769 started by @seonghobae