Refactor(#83): 정상 SMS 데이터 정비 및 실제 데이터 기반 holdout 재구성 - #86
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📝 WalkthroughWalkthroughThe SMS pipeline now supports label-aware taxonomy annotation, real-message holdout construction, type-aware grouped splitting, and v3 training and evaluation reporting. ChangesSMS dataset v3 pipeline
Estimated code review effort: 5 (Critical) | ~120 minutes Merge Risk: 🟡 Moderate · up to This PR changes dataset auditing, holdout evaluation, and split generation. At the current head, normal-data reports can be mislabeled, default evaluation can mix primary and stress metrics, and disabling type weighting can still alter split selection; these may produce misleading results or inconsistent datasets, so the issues should be fixed or explicitly accepted before merge. Sequence Diagram(s)sequenceDiagram
participant DatasetBuilder
participant Splitter
participant Trainer
participant Evaluator
DatasetBuilder->>DatasetBuilder: build and tag real holdout
DatasetBuilder->>Splitter: provide grouped dataset
Splitter->>Splitter: score class and type distributions
Splitter->>Trainer: provide v3 split manifest
Trainer->>Evaluator: evaluate trained stacking model
🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
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Actionable comments posted: 6
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Inline comments:
In `@data_science/SMSModel/analyze_sms_taxonomy.py`:
- Around line 424-433: Update build_audit_report and its CLI output to use
target-neutral audit fields: include target_label, current_types,
raw_target_count, and unique_target_count instead of phishing-specific keys.
Bump the audit schema version and update associated tests and report artifacts
to match the new schema.
In `@data_science/SMSModel/dataset_splitting/splitter.py`:
- Around line 152-153: Update the type-candidate generation guard in the
splitter logic to return no candidates when config.type_weight is zero, treating
it the same as config.type_column being unset or unavailable. Preserve existing
candidate generation for positive weights.
In `@data_science/SMSModel/reports/stacking_v3/test_evaluation.md`:
- Line 1: Update the report heading from “Stacking v2 Test Evaluation” to
“Stacking v3 Test Evaluation” so it matches the artifact version.
In `@data_science/SMSModel/run_stacking_training.py`:
- Line 38: Update the v2 labels in the docstring around the stacking training
flow and the completion output near the artifact-generation path to derive their
displayed version from STACKING_ARTIFACT_VERSION, ensuring both consistently
report v3 without hardcoded version text.
In `@data_science/SMSModel/train_sms.py`:
- Around line 56-61: Update the default evaluation flow around
evaluate_new_holdout so the combined HOLDOUT_SOURCES data is split into
REAL_HOLDOUT_SOURCES and SYNTHETIC_STRESS_SOURCES subsets, with each subset
evaluated and reported separately instead of emitting only one combined report.
In `@docs/DATASET_V3_REVISION.md`:
- Line 7: Fix the Markdown lint violations in the report: update the references
to `#78` near the affected headings so they are formatted as inline code or
preceded by text, and add an appropriate language identifier such as text to the
fenced code blocks near the other reported locations. Preserve the document’s
content and structure.
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Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
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ℹ️ Review info
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Run ID: c7140000-2a67-4b52-8d76-e1e9cb685ee8
⛔ Files ignored due to path filters (4)
data_science/Data/SMSData/normal_type_annotations_v3.csvis excluded by!**/*.csvdata_science/Data/SMSData/phishing_total_dataset_reclassified.csvis excluded by!**/*.csvdata_science/Data/SMSData/sms_normal_additions_v3.csvis excluded by!**/*.csvdata_science/SMSModel/splits/sms_split_v3.csvis excluded by!**/*.csv
📒 Files selected for processing (25)
data_science/SMSModel/analyze_sms_taxonomy.pydata_science/SMSModel/apply_sms_type_annotations.pydata_science/SMSModel/artifacts/stacking/v3/metadata.jsondata_science/SMSModel/artifacts/stacking/v3/model.joblibdata_science/SMSModel/build_real_holdout.pydata_science/SMSModel/dataset_splitting/config.pydata_science/SMSModel/dataset_splitting/splitter.pydata_science/SMSModel/merge_sms_diversity.pydata_science/SMSModel/reports/dataset_composition.jsondata_science/SMSModel/reports/dataset_split_summary.jsondata_science/SMSModel/reports/dataset_split_summary.mddata_science/SMSModel/reports/normal_additions_merge_v3.jsondata_science/SMSModel/reports/normal_taxonomy_audit_v3.jsondata_science/SMSModel/reports/normal_type_change_report_v3.jsondata_science/SMSModel/reports/real_holdout_build.jsondata_science/SMSModel/reports/stacking_v3/test_evaluation.jsondata_science/SMSModel/reports/stacking_v3/test_evaluation.mddata_science/SMSModel/run_dataset_composition_report.pydata_science/SMSModel/run_stacking_holdout_comparison.pydata_science/SMSModel/run_stacking_training.pydata_science/SMSModel/train_sms.pydocs/DATASET_V3_REVISION.mdtests/data_science/SMSModel/test_dataset_split_type_stratification.pytests/data_science/SMSModel/test_normal_type_annotations.pytests/data_science/SMSModel/test_stacking_holdout_comparison.py
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📝 개요
#78에서 Stacking v2가 정상 메시지 60건 중 59건을 피싱으로 분류한(오탐률 98.33%) 원인을 데이터 관점에서 규명하고 데이터셋을 정비했습니다.
원인은 두 가지였습니다.
첫째, 학습 pool의 정상 415건 중 392건(94%)이 기타정상·일상대화 두 덩어리였고 피싱과 짝이 되는 정상 알림은 통틀어 23건뿐이었습니다(금융기관사칭 91건 ↔ 정상금융알림 1건).
둘째, holdout 210건이 전부 합성 데이터라 실제 성능을 판정할 수 없었습니다.
정비 후 동일 파이프라인으로 학습한 결과, 실제 문자 평가셋에서 ROC-AUC 0.9357이 측정됐습니다.
정비 전 v2 artifact를 실제 split에 적용했을 때는 0.72~0.83이었습니다.
모델 채택 판정은 이 PR의 범위가 아닙니다.
artifacts/stacking/v3는 파이프라인 동작 확인용 스모크 테스트 결과이며, v2 artifact와 #78 리포트는 그대로 보존했습니다.
🔗 관련 이슈
🎯 주요 변경 사항
데이터셋 정비
실제 데이터 기반 holdout — build_real_holdout.py 신규
-기존 합성 셋 123건은 보조 FP 스트레스 셋으로 강등, select_real_holdout() / select_synthetic_stress()로 구분
Split 유형 커버리지 — dataset_splitting/
도구 파라미터화
파이프라인 가드
📸 사진
✅ PR 체크리스트
uvicorn구동 또는 테스트 코드)를 통과했습니다.Summary by CodeRabbit
New Features
Tests
Documentation