169 lines
7.4 KiB
Python
169 lines
7.4 KiB
Python
"""赛道映射（硬门槛 C 的实现载体，设计 §3.2）。
|
||
|
||
config/frontier_tracks.yml 是唯一事实源，三条映射路径并行：
|
||
kg_themes 主题名 → industry_pools（弱锚 fallback，source_rule=pool_theme）
|
||
kg_chains 链名 → 图谱 IN_SEGMENT 边的产业链修饰符（强锚，graph_chain）
|
||
kg_segments 环节名 → 图谱环节投影（强锚，graph_segment）
|
||
后两条读基座第五插槽视图 v_factor_segment_members（akg.project_segments
|
||
每晨 06:10 刷新）。同一只股票多路命中时**图谱路径优先记账**——这就是设计
|
||
文档"两级 source_rule：graph 强 / fallback 弱"的落地。成员表快照落
|
||
data/track_members_<日期>.csv，每行带 source_rule，可审计到入选依据。
|
||
"""
|
||
import datetime as dt
|
||
import json
|
||
import os
|
||
|
||
import pandas as pd
|
||
|
||
import config
|
||
import db
|
||
|
||
try:
|
||
import yaml
|
||
except ImportError: # 镜像未装 PyYAML 时给出可执行的修复指令，而不是裸崩
|
||
yaml = None
|
||
|
||
|
||
def _need_yaml():
|
||
if yaml is None:
|
||
raise SystemExit(
|
||
"缺 PyYAML：requirements.txt 已加，请在桥机重建镜像——\n"
|
||
" docker compose build akg-factor-bridge && "
|
||
"docker compose up -d akg-factor-bridge")
|
||
|
||
|
||
def load_yml(path: str | None = None) -> dict:
|
||
_need_yaml()
|
||
with open(path or config.TRACKS_YML, "r", encoding="utf-8") as f:
|
||
return yaml.safe_load(f)
|
||
|
||
|
||
def _pool_theme_members() -> dict:
|
||
"""industry_pools 当前态：主题名 → 成员列表（dict，含 ts_code/name）。"""
|
||
pools = db.read_pg("SELECT theme, members FROM industry_pools")
|
||
by_theme = {}
|
||
for _, r in pools.iterrows():
|
||
ms = r["members"]
|
||
if isinstance(ms, str):
|
||
ms = json.loads(ms)
|
||
by_theme[str(r["theme"]).strip()] = ms or []
|
||
return by_theme
|
||
|
||
|
||
def _graph_members():
|
||
"""环节投影（第五插槽）→ 两个索引：环节名→成员、链名→成员（仅上市成员）。
|
||
|
||
视图不可用（投影表未建/基座不可达）时返回空索引并提示——图谱路径跳过，
|
||
主题路径照常工作，不挡任何调用方。空串链名（抽取未写修饰的边）在此忽略。
|
||
"""
|
||
try:
|
||
df = db.read_pg(
|
||
"SELECT segment_name, ts_code, member_name, chain "
|
||
"FROM v_factor_segment_members WHERE ts_code IS NOT NULL")
|
||
except Exception as e: # noqa: BLE001
|
||
print(f" (环节投影视图不可用，图谱映射路径跳过: {e!r})")
|
||
return {}, {}
|
||
by_seg, by_chain = {}, {}
|
||
for r in df.itertuples():
|
||
m = (str(r.ts_code), None if pd.isna(r.member_name) else str(r.member_name))
|
||
by_seg.setdefault(str(r.segment_name).strip(), []).append(m)
|
||
if r.chain is not None and not pd.isna(r.chain) and str(r.chain).strip():
|
||
by_chain.setdefault(str(r.chain).strip(), []).append(m)
|
||
return by_seg, by_chain
|
||
|
||
|
||
def resolve_members(only_confirmed: bool = True, path: str | None = None,
|
||
dedup: bool = True):
|
||
"""yml + 三路数据源 → (成员表, 未命中键清单)。
|
||
|
||
成员表列：ts_code, name, track, status, theme, source_rule——theme 列放
|
||
命中它的那个键（主题名/链名/环节名）。图谱路径的行排在主题路径之前，
|
||
去重时同股同赛道保留最先出现的行 ⇒ 图谱锚优先记账。
|
||
未命中 = yml 里写了、数据源里查无此键，元素为 (赛道, 键类型, 键名)。
|
||
"""
|
||
d = load_yml(path)
|
||
by_theme = _pool_theme_members()
|
||
by_seg, by_chain = _graph_members()
|
||
excl = {str(x).strip() for x in (d.get("exclude_themes") or [])}
|
||
rows, missing = [], []
|
||
for tr in d.get("tracks") or []:
|
||
if only_confirmed and tr.get("status") != "confirmed":
|
||
continue
|
||
tname, status = tr["name"], tr.get("status")
|
||
for seg in tr.get("kg_segments") or []: # 图谱路径（强锚）在前
|
||
s = str(seg).strip()
|
||
if s not in by_seg:
|
||
missing.append((tname, "环节", s))
|
||
continue
|
||
for ts, nm in by_seg[s]:
|
||
rows.append((ts, nm, tname, status, s, "graph_segment"))
|
||
for ch in tr.get("kg_chains") or []:
|
||
c = str(ch).strip()
|
||
if c not in by_chain:
|
||
missing.append((tname, "链", c))
|
||
continue
|
||
for ts, nm in by_chain[c]:
|
||
rows.append((ts, nm, tname, status, c, "graph_chain"))
|
||
for theme in tr.get("kg_themes") or []: # 主题路径（fallback）在后
|
||
t = str(theme).strip()
|
||
if t in excl:
|
||
continue
|
||
if t not in by_theme:
|
||
missing.append((tname, "主题", t))
|
||
continue
|
||
for m in by_theme[t]:
|
||
ts = (m or {}).get("ts_code")
|
||
if ts:
|
||
rows.append((ts, m.get("name"), tname, status, t, "pool_theme"))
|
||
df = pd.DataFrame(rows, columns=["ts_code", "name", "track", "status",
|
||
"theme", "source_rule"])
|
||
if dedup:
|
||
df = df.drop_duplicates(["ts_code", "track"])
|
||
return df, missing
|
||
|
||
|
||
def snapshot(only_confirmed: bool = True, path: str | None = None):
|
||
"""成员表落 data/ 版本化快照（每行带 source_rule 与 updated_at，审计列）。"""
|
||
df, missing = resolve_members(only_confirmed, path)
|
||
os.makedirs("data", exist_ok=True)
|
||
out = f"data/track_members_{dt.date.today().isoformat()}.csv"
|
||
(df.assign(layer="",
|
||
updated_at=dt.datetime.now().isoformat(timespec="seconds"))
|
||
.to_csv(out, index=False))
|
||
return out, df, missing
|
||
|
||
|
||
def coverage_report(path: str | None = None) -> pd.DataFrame:
|
||
"""赛道覆盖体检：逐赛道给出成员数（含图谱锚数）、各路径命中率，含 candidate。"""
|
||
# dedup=False：命中率要在去重前数——成员完全被同赛道更早键覆盖的键，
|
||
# 去重后一行不剩，会被误计为"没命中"（07-30 实测教训）。
|
||
df, missing = resolve_members(only_confirmed=False, path=path, dedup=False)
|
||
d = load_yml(path)
|
||
print("赛道覆盖体检（主题=industry_pools 当前态；图谱=环节投影；"
|
||
"成员数已去重，命中率按去重前算）：")
|
||
for tr in d.get("tracks") or []:
|
||
sub = df[df["track"] == tr["name"]]
|
||
graph_sub = sub[sub["source_rule"] != "pool_theme"]
|
||
n_all = sub["ts_code"].nunique()
|
||
n_graph = graph_sub["ts_code"].nunique()
|
||
n_tkeys = len(tr.get("kg_themes") or [])
|
||
hit_t = sub[sub["source_rule"] == "pool_theme"]["theme"].nunique()
|
||
n_gkeys = (len(tr.get("kg_segments") or [])
|
||
+ len(tr.get("kg_chains") or []))
|
||
hit_g = graph_sub["theme"].nunique()
|
||
miss = [f"{kind}:{key}" for name, kind, key in missing
|
||
if name == tr["name"]]
|
||
tag = "" if tr.get("status") == "confirmed" else "（candidate）"
|
||
line = (f" {tr['name']}{tag}: 成员 {n_all} 只（图谱锚 {n_graph}）"
|
||
f" | 主题命中 {hit_t}/{n_tkeys}")
|
||
if n_gkeys:
|
||
line += f" | 图谱键命中 {hit_g}/{n_gkeys}"
|
||
if miss:
|
||
line += f" | 未命中: {'、'.join(miss)}"
|
||
print(line)
|
||
conf = df[df["status"] == "confirmed"]
|
||
n_conf = conf["ts_code"].nunique()
|
||
n_conf_graph = conf[conf["source_rule"] != "pool_theme"]["ts_code"].nunique()
|
||
print(f" —— confirmed 合计（去重）: {n_conf} 只，其中带图谱锚 {n_conf_graph} 只")
|
||
return df
|