from __future__ import annotations
|
|
import hashlib
|
import json
|
from datetime import datetime
|
from pathlib import Path
|
|
import pandas as pd
|
|
|
RUN_ID = "RUN-ANA-WUJI-BASELINE-PILOT-20260607-001"
|
ROOT = Path(__file__).resolve().parents[1]
|
REQUIRED_EVIDENCE_COLUMNS = [
|
"amount",
|
"prev60_high_ref_date",
|
"prev60_high_ref_policy",
|
]
|
|
|
def sha256_file(path: Path) -> str:
|
h = hashlib.sha256()
|
with path.open("rb") as f:
|
for chunk in iter(lambda: f.read(1024 * 1024), b""):
|
h.update(chunk)
|
return h.hexdigest()
|
|
|
def select_candidates(case_index: pd.DataFrame, candidate_ledger: pd.DataFrame) -> pd.DataFrame:
|
selected_rows = []
|
for _, case in case_index.iterrows():
|
rows = candidate_ledger[candidate_ledger["entry_trade_date"] == case["entry_trade_date"]].copy()
|
if rows.empty:
|
continue
|
if case["selection_bucket"] == "PREV_HIGH_REVIEW_RISK":
|
review = rows[rows["candidate_status"] != "PASS"].sort_values("candidate_rank").head(2)
|
strict = rows[rows["candidate_status"] == "PASS"].sort_values("candidate_rank").head(3)
|
chosen = pd.concat([strict, review], ignore_index=True).sort_values("candidate_rank").head(5)
|
else:
|
strict = rows[rows["candidate_status"] == "PASS"].sort_values("candidate_rank").head(5)
|
chosen = strict if len(strict) >= 5 else rows.sort_values("candidate_rank").head(5)
|
chosen = chosen.copy()
|
chosen["case_id"] = case["case_id"]
|
chosen["case_status"] = case["case_status"]
|
chosen["selection_bucket"] = case["selection_bucket"]
|
selected_rows.append(chosen)
|
return pd.concat(selected_rows, ignore_index=True) if selected_rows else pd.DataFrame()
|
|
|
def update_case_manifest(case_dir: Path) -> None:
|
case_files = [
|
"candidate_ledger.csv",
|
"image_manifest.csv",
|
"case_image_board.md",
|
"case_story_board.md",
|
]
|
existing_manifest = {}
|
manifest_path = case_dir / "manifest.json"
|
if manifest_path.exists():
|
existing_manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
|
manifest = {
|
"case_id": case_dir.name,
|
"run_id": RUN_ID,
|
"stage": existing_manifest.get("stage", "CANDIDATE_DAILY_IMAGE_PACKAGE_READY"),
|
"files": [
|
{
|
"path": name,
|
"size": (case_dir / name).stat().st_size,
|
"sha256": sha256_file(case_dir / name),
|
}
|
for name in case_files
|
if (case_dir / name).exists()
|
],
|
"image_count": existing_manifest.get("image_count", 0),
|
}
|
manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
|
|
|
def update_candidate_image_summary(selected_path: Path, image_manifest_path: Path, root_board_path: Path) -> None:
|
summary_path = ROOT / "candidate_image_generation_summary.json"
|
if not summary_path.exists():
|
return
|
summary = json.loads(summary_path.read_text(encoding="utf-8"))
|
summary["generated_at"] = datetime.now().astimezone().isoformat(timespec="seconds")
|
summary["repair_note"] = (
|
"Candidate evidence columns amount / prev60_high_ref_date / prev60_high_ref_policy "
|
"were propagated without regenerating images."
|
)
|
summary.setdefault("artifacts", {})
|
summary["artifacts"]["selected_candidate_ledger.csv"] = {
|
"size": selected_path.stat().st_size,
|
"sha256": sha256_file(selected_path),
|
}
|
if image_manifest_path.exists():
|
summary["artifacts"]["image_manifest.csv"] = {
|
"size": image_manifest_path.stat().st_size,
|
"sha256": sha256_file(image_manifest_path),
|
}
|
if root_board_path.exists():
|
summary["artifacts"]["case_image_board.md"] = {
|
"size": root_board_path.stat().st_size,
|
"sha256": sha256_file(root_board_path),
|
}
|
summary_path.write_text(json.dumps(summary, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
|
|
|
def main() -> None:
|
candidate_ledger = pd.read_csv(ROOT / "candidate_ledger.csv", encoding="utf-8-sig")
|
case_index = pd.read_csv(ROOT / "case_index.csv", encoding="utf-8-sig")
|
missing = [col for col in REQUIRED_EVIDENCE_COLUMNS if col not in candidate_ledger.columns]
|
if missing:
|
raise RuntimeError(f"candidate_ledger.csv missing evidence columns: {missing}")
|
|
old_selected_path = ROOT / "selected_candidate_ledger.csv"
|
old_selected_ids: list[str] = []
|
if old_selected_path.exists():
|
old_selected = pd.read_csv(old_selected_path, encoding="utf-8-sig")
|
old_selected_ids = old_selected["candidate_id"].astype(str).tolist()
|
|
selected = select_candidates(case_index, candidate_ledger)
|
if selected.empty:
|
raise RuntimeError("No selected candidates after evidence repair.")
|
new_selected_ids = selected["candidate_id"].astype(str).tolist()
|
selected_id_changed = bool(old_selected_ids and old_selected_ids != new_selected_ids)
|
if selected_id_changed:
|
raise RuntimeError("Selected candidate IDs changed during evidence-only repair.")
|
|
selected.to_csv(old_selected_path, index=False, encoding="utf-8-sig")
|
case_rows = []
|
for _, case in case_index.iterrows():
|
case_id = case["case_id"]
|
case_dir = ROOT / "cases" / case_id
|
case_dir.mkdir(parents=True, exist_ok=True)
|
case_candidates = selected[selected["case_id"] == case_id].copy()
|
case_candidates.to_csv(case_dir / "candidate_ledger.csv", index=False, encoding="utf-8-sig")
|
update_case_manifest(case_dir)
|
case_rows.append(
|
{
|
"case_id": case_id,
|
"candidate_rows": int(len(case_candidates)),
|
"candidate_ledger_size": (case_dir / "candidate_ledger.csv").stat().st_size,
|
"candidate_ledger_sha256": sha256_file(case_dir / "candidate_ledger.csv"),
|
}
|
)
|
|
update_candidate_image_summary(
|
old_selected_path,
|
ROOT / "image_manifest.csv",
|
ROOT / "case_image_board.md",
|
)
|
|
summary = {
|
"schema_version": "1.0",
|
"run_id": RUN_ID,
|
"generated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
|
"stage": "CANDIDATE_POOL_EVIDENCE_REPAIR_DONE",
|
"repair_scope": "evidence_only_no_candidate_selection_change_no_image_regeneration",
|
"required_evidence_columns": REQUIRED_EVIDENCE_COLUMNS,
|
"candidate_rows": int(len(candidate_ledger)),
|
"selected_candidate_rows": int(len(selected)),
|
"selected_candidate_ids_changed": selected_id_changed,
|
"case_count": int(case_index["case_id"].nunique()),
|
"case_candidate_ledgers": case_rows,
|
"artifacts": {
|
"candidate_ledger.csv": {
|
"size": (ROOT / "candidate_ledger.csv").stat().st_size,
|
"sha256": sha256_file(ROOT / "candidate_ledger.csv"),
|
},
|
"selected_candidate_ledger.csv": {
|
"size": old_selected_path.stat().st_size,
|
"sha256": sha256_file(old_selected_path),
|
},
|
"candidate_generation_summary.json": {
|
"size": (ROOT / "candidate_generation_summary.json").stat().st_size,
|
"sha256": sha256_file(ROOT / "candidate_generation_summary.json"),
|
},
|
},
|
"boundary": (
|
"This repair only exposes amount sorting evidence and previous-high reference volume policy. "
|
"It does not change candidate IDs, selected pilot cases, buy/sell decisions, return stats, or images."
|
),
|
}
|
(ROOT / "candidate_pool_evidence_repair_summary.json").write_text(
|
json.dumps(summary, ensure_ascii=False, indent=2) + "\n",
|
encoding="utf-8",
|
)
|
(ROOT / "candidate_pool_evidence_repair_summary.md").write_text(
|
"\n".join(
|
[
|
"# candidate_pool_evidence_repair_summary",
|
"",
|
f"run_id：`{RUN_ID}`",
|
"阶段：`CANDIDATE_POOL_EVIDENCE_REPAIR_DONE`",
|
"",
|
"## 修复范围",
|
"",
|
"- `candidate_ledger.csv` 保留 `amount` 作为候选排名兜底排序证据。",
|
"- `candidate_ledger.csv` 保留 `prev60_high_ref_date` 和 `prev60_high_ref_policy`。",
|
"- 前高参考成交量口径为 `FIRST_PREVIOUS_HIGH_IN_60D_WINDOW`。",
|
"- 同步 `selected_candidate_ledger.csv` 和各 case 的 `candidate_ledger.csv`。",
|
"- 不重新生成图片，不修改买卖裁决，不修改收益准备包。",
|
"",
|
"## 校验",
|
"",
|
f"- 候选行数：{summary['candidate_rows']}",
|
f"- selected candidate 行数：{summary['selected_candidate_rows']}",
|
f"- selected candidate IDs 是否变化：{summary['selected_candidate_ids_changed']}",
|
"",
|
]
|
),
|
encoding="utf-8",
|
)
|
|
|
if __name__ == "__main__":
|
main()
|
