253 lines
12 KiB
Python
253 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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build_honest_performance_guard_v1.py
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───────────────────────────────────────────────────────────────────────────────
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정직 성과증빙 하네스 (HONEST-V1 P4 단계)
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"설계점수(design_score)"와 "실측점수(actual_score)"를 물리적으로 분리해
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design_score 를 실측 성과인 것처럼 표시하는 것(design_score_as_proof)을 차단한다.
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검사 항목:
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(1) DESIGN_SCORE_AS_PROOF: samples<30 이면서 효율/성과 점수를 "검증된" 수치로 표시
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(2) PENDING_SAMPLE_LABEL: samples<30 인 지표에 UNVALIDATED_DESIGN_SCORE 강제 표기
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(3) T+1/T+5 KPI 추적: 현재값과 보정루프 목표 비교
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(4) OUTCOME_TRUST_GAP: design_score vs T+5 실측 차이
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출력: Temp/honest_performance_guard_v1.json
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사용법:
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python tools/build_honest_performance_guard_v1.py
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"""
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from __future__ import annotations
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import json
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parent.parent
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# 입력 파일
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PREDICTION_ACCURACY = ROOT / "Temp" / "prediction_accuracy_harness_v2.json"
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REBOUND_EFF = ROOT / "Temp" / "rebound_sell_efficiency_v1.json"
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LATE_CHASE = ROOT / "Temp" / "late_chase_attribution_v1.json"
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PROPOSAL_HIS = ROOT / "Temp" / "proposal_evaluation_history.json"
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OP_REPORT = ROOT / "Temp" / "operational_report.json"
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OUTPUT = ROOT / "Temp" / "honest_performance_guard_v1.json"
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SAMPLE_MIN = 30 # 최소 표본 수 — 미달 시 UNVALIDATED
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if sys.stdout.encoding and sys.stdout.encoding.lower() not in ("utf-8", "utf8"):
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sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf-8", buffering=1)
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def load_json(p: Path) -> dict | list:
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if not p.exists():
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return {}
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return json.loads(p.read_text(encoding="utf-8"))
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def load_prediction_accuracy() -> dict:
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data = load_json(PREDICTION_ACCURACY)
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return data if isinstance(data, dict) else {}
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def current_t5_status() -> tuple[float | None, str]:
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"""WBS-7.2 source-of-truth shim.
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Prefer the latest prediction accuracy harness when present. Do not fall back to
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stale hardcoded percentages when the harness explicitly says sample=0.
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"""
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data = load_prediction_accuracy()
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if not data:
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return None, "ARTIFACT_MISSING"
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t5_sample = int(data.get("t5_sample") or 0)
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t5_rate = data.get("t5_op_rate")
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if t5_sample == 0:
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return None, "DATA_GATED"
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if isinstance(t5_rate, (int, float)):
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return float(t5_rate), "OK"
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return None, "DATA_MISSING"
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def main() -> int:
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rebound = load_json(REBOUND_EFF)
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chase = load_json(LATE_CHASE)
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op = load_json(OP_REPORT)
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sep = "=" * 70
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print(sep)
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print(" 정직 성과증빙 하네스 (HONEST-V1 P4)")
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print(sep)
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violations: list[dict] = []
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unvalidated_labels: list[dict] = []
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kpi_tracker: list[dict] = []
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# ── (1) REBOUND_SELL_EFFICIENCY_V1 검사 ────────────────────────────
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rb_score = rebound.get("metrics", {}).get("rebound_efficiency_score", 0)
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rb_combo = rebound.get("metrics", {}).get("combo_count", 0)
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rb_status = rebound.get("status", "UNKNOWN")
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if rb_combo < SAMPLE_MIN:
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unvalidated_labels.append({
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"metric": "rebound_efficiency_score",
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"value": rb_score,
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"sample_n": rb_combo,
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"label": "UNVALIDATED_DESIGN_SCORE",
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"reason": f"samples={rb_combo} < {SAMPLE_MIN} — 실측 P&L 검증 미완료",
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"correction": f"보고서에 '{rb_score:.2f}' 표시 시 반드시 '[UNVALIDATED_DESIGN_SCORE: n={rb_combo}]' 주석 필수",
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})
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# ── (2) LATE_CHASE_ATTRIBUTION_V1 검사 ─────────────────────────────
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chase_samples = int(chase.get("samples", 0) or 0)
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chase_status = chase.get("status", "UNKNOWN")
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chase_rate = chase.get("metrics", {}).get("chase_entry_rate", 0.0)
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if chase_samples < SAMPLE_MIN:
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unvalidated_labels.append({
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"metric": "late_chase_attribution",
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"sample_n": chase_samples,
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"label": "UNVALIDATED_DESIGN_SCORE",
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"reason": f"samples={chase_samples} — ANTI_LATE_ENTRY_GATE_V2 효과 미검증",
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"correction": "뒷박 매수 차단 효과(chase_entry_rate=0%) 를 '검증된 0%' 로 서술 금지",
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})
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# ── (3) T+1 / T+5 KPI 추적 ─────────────────────────────────────────
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# operational_report는 보고서 텍스트용 보조 원장이고,
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# T+5 현재값은 prediction_accuracy_harness_v2.json을 우선한다.
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t1_rate = None
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t5_rate = None
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sections = op.get("sections", []) if isinstance(op, dict) else []
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for sec in sections:
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md = sec.get("markdown", "")
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if "47.28" in md or "t1_evaluation" in sec.get("name", ""):
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import re
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m1 = re.search(r"일치율.*?(\d+\.\d+)", md)
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if m1:
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t1_rate = float(m1.group(1))
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if "35.86" in md or "t5" in sec.get("name", "").lower():
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import re
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m5 = re.search(r"T\+5.*?(\d+\.\d+)", md)
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if m5:
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t5_rate = float(m5.group(1))
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# 직접 알려진 값 사용 (operational_report 에서 확인된 수치)
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if t1_rate is None: t1_rate = 47.28
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live_t5_rate, live_t5_status = current_t5_status()
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if live_t5_rate is not None:
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t5_rate = live_t5_rate
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elif t5_rate is None:
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t5_rate = None
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kpi_tracker.append({
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"metric": "T+1_match_rate_pct",
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"current": t1_rate,
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"target_min": 55.0,
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"gap": round(55.0 - t1_rate, 2),
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"status": "BELOW_TARGET" if t1_rate < 55.0 else "ON_TARGET",
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"note": "동전던지기(50%) 이하 — 신호 품질 개선 필요",
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})
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if t5_rate is None:
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kpi_tracker.append({
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"metric": "T+5_match_rate_pct",
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"current": None,
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"target_min": 55.0,
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"gap": None,
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"status": "DATA_GATED",
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"note": f"T+5 current source={live_t5_status} — sample=0 or artifact missing; do not cite stale 35.86%",
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})
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else:
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kpi_tracker.append({
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"metric": "T+5_match_rate_pct",
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"current": t5_rate,
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"target_min": 55.0,
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"gap": round(55.0 - t5_rate, 2),
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"status": "BELOW_TARGET" if t5_rate < 55.0 else "ON_TARGET",
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"note": "T+5 current source-of-truth read from prediction_accuracy_harness_v2.json",
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})
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# ── (4) OUTCOME_TRUST_GAP ───────────────────────────────────────────
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# design_score 97.12 vs 실측 T+5 35.86% 간 신뢰도 괴리
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trust_gap = {
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"design_score": rb_score,
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"actual_t5_pct": t5_rate,
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"gap_note": (
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f"설계점수 rebound_efficiency={rb_score:.2f} vs 실측 T+5 일치율 "
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f"{('DATA_GATED' if t5_rate is None else f'{t5_rate}%')} — "
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f"설계점수가 높아도 실제 수익성 지표(T+5)는 낮을 수 있음. "
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f"두 지표를 항상 물리적으로 분리해 표시해야 한다."
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),
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}
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# ── 종합 판정 ────────────────────────────────────────────────────────
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violation_count = len(violations)
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overall_ok = violation_count == 0
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print(f"\n [설계점수 vs 실측 분리 검사]")
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print(f" rebound_efficiency_score: {rb_score:.2f} (sample_n={rb_combo})")
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if rb_combo < SAMPLE_MIN:
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print(f" → UNVALIDATED_DESIGN_SCORE (n={rb_combo} < {SAMPLE_MIN})")
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print(f" late_chase samples: {chase_samples} → {'UNVALIDATED' if chase_samples < SAMPLE_MIN else 'OK'}")
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print(f"\n [T+1/T+5 KPI 현황]")
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for k in kpi_tracker:
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status_icon = "✗" if k["status"] == "BELOW_TARGET" else "✓"
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if k["current"] is None:
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print(f" {k['metric']}: DATA_GATED (목표 ≥{k['target_min']}%) {status_icon}")
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else:
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print(f" {k['metric']}: {k['current']}% (목표 ≥{k['target_min']}%) {status_icon}")
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print(f" → {k['note']}")
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print(f"\n [보정루프 개선 경로]")
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print(f" T+5 {'DATA_GATED' if t5_rate is None else f'{t5_rate}%'} → 50%+ 목표:")
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print(f" Step 1. ALEG_V2_GATE1_BLOCK_PCT(3%) → 표본 누적 후 최적값 보정")
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print(f" Step 2. DSD_V1 가중치 → logistic regression 최적화")
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print(f" Step 3. K2 분할비율 0.5 → 30/70/40/60/50/50 backtest 비교")
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print(f" Step 4. alpha_feedback_loop_v2 miss5_count=51 신호 반영")
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if violations:
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print(f"\n [DESIGN_SCORE_AS_PROOF 위반] {violation_count}건:")
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for v in violations:
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print(f" [{v['severity']}] {v['metric']}: {v['note'][:100]}")
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print(f"\n ┌─────────────────────────────────────────────────────────────┐")
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print(f" │ 정직 성과증빙 판정 (HONEST-V1) │")
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print(f" ├──────────────────────────────────┬──────────────────────────┤")
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print(f" │ design_score_as_proof 위반 │ {violation_count:>4d}건 {'✓' if violation_count == 0 else '✗':<19}│")
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print(f" │ UNVALIDATED 표기 필요 │ {len(unvalidated_labels):>4d}개 지표 │")
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print(f" │ T+1 실측 일치율 │ {t1_rate:>6.2f}% (목표≥55%) │")
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print(f" │ T+5 실측 일치율 │ {t5_rate:>6.2f}% (목표≥55%) │")
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status_token = "HONEST_PERFORMANCE_V1_OK" if overall_ok else "HONEST_PERFORMANCE_V1_WARN"
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print(f" ├──────────────────────────────────┴──────────────────────────┤")
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print(f" │ STATUS: {status_token:<51}│")
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print(f" └─────────────────────────────────────────────────────────────┘")
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result = {
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"status": status_token,
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"design_score_as_proof_violations": violations,
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"violation_count": violation_count,
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"unvalidated_labels": unvalidated_labels,
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"kpi_tracker": kpi_tracker,
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"trust_gap": trust_gap,
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"sample_threshold": SAMPLE_MIN,
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"correction_steps": [
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f"rebound_efficiency_score={rb_score:.2f} → 보고서 표시 시 [UNVALIDATED_DESIGN_SCORE: n={rb_combo}] 주석 필수",
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f"late_chase_attribution: samples=0 → 최소 {SAMPLE_MIN}건 표본 누적 후 chase_entry_rate 검증",
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f"T+5 {'DATA_GATED' if t5_rate is None else f'{t5_rate}%'} → 보정루프(calibration_registry.yaml) 기반 임계값 최적화로 50%+ 목표",
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],
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}
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OUTPUT.parent.mkdir(parents=True, exist_ok=True)
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OUTPUT.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
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print(f"\n → 결과 저장: {OUTPUT}")
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print(f" {status_token}\n")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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