Update core/external_scoring.py

core/external_scoring.py

@@ -61,9 +61,7 @@ def get_external_template_df() -> pd.DataFrame:
                         columns=["カテゴリー", "入力項目", "値"])
 
 def fill_missing_with_external(df: pd.DataFrame, suggestions: Dict[str, Any] | None = None) -> pd.DataFrame:
-    """
-    LLM等からの suggestions を {入力項目: 値} で受け取り、空欄のみ埋める
-    """
+    """LLM等からの suggestions を {入力項目: 値} で受け取り、空欄のみ埋める"""
     if not suggestions:
         return df.copy()
     df2 = df.copy()
@@ -83,7 +81,7 @@ _WEIGHTS = {
     ("成長率", "売上高伸長性"): 10,
     ("成長率", "利益伸長性"): 10,
     ("成長率", "商品"): 6,
-    ("成長率", "市場成長調整"): 6,  # ★ 追加：市場成長率を反映
+    ("成長率", "市場成長調整"): 6,  # 市場成長率を反映
 
     ("安定性", "自己資本"): 8,
     ("安定性", "決済振り"): 10,
@@ -103,59 +101,47 @@ def _add(items, cat, name, raw, weight, reason):
     w = round(weight * _WEIGHT_NORM, 2)
     sc = 0.0 if raw is None else round((raw / 10.0) * w, 2)
     items.append({
-        "category": cat,
-        "name": name,
-        "raw": raw_s,
-        "weight": w,
-        "score": sc,
-        "reason": reason
+        "category": cat, "name": name, "raw": raw_s,
+        "weight": w, "score": sc, "reason": reason
     })
 
 def _to_float(x):
-    if x is None:
-        return None
+    if x is None: return None
     try:
         return float(str(x).replace(",", "").replace("▲", "-").replace("△", "-"))
     except Exception:
         return None
 
 def _to_bool(x):
-    if x is None:
-        return None
+    if x is None: return None
     s = str(x).strip().lower()
-    if s in ("true", "t", "1", "yes", "y", "有", "あり"):
-        return True
-    if s in ("false", "f", "0", "no", "n", "無", "なし"):
-        return False
+    if s in ("true","t","1","yes","y","有","あり"): return True
+    if s in ("false","f","0","no","n","無","なし"): return False
     return None
 
-def _ratio(a, b):
-    if a is None or b is None or b == 0:
-        return None
-    return a / b
+def _ratio(a,b):
+    if a is None or b is None or b == 0: return None
+    return a/b
 
 def _ramp(x, good, bad, lo=0.0, hi=10.0, neutral=None):
     if x is None:
-        return neutral if neutral is not None else (lo + hi) / 2.0
+        return neutral if neutral is not None else (lo+hi)/2.0
     if good > bad:
         if x <= bad: return lo
         if x >= good: return hi
-        return lo + (hi - lo) * (x - bad) / (good - bad)
+        return lo + (hi-lo) * (x-bad)/(good-bad)
     else:
         if x >= bad: return lo
         if x <= good: return hi
-        return lo + (hi - lo) * (x - good) / (bad - good)
+        return lo + (hi-lo) * (x-good)/(bad-good)
 
 def score_external_from_df(df: pd.DataFrame) -> Dict[str, Any]:
-    """
-    入力DF（カテゴリー/入力項目/値）を定量スコア化。
-    欠損は中立。市場の年成長率(%) を「市場成長調整」に反映し、成長率評価の過剰/過小を補正。
-    """
+    """入力DF（カテゴリー/入力項目/値）を定量スコア化。欠損は中立。市場成長率も反映。"""
     def ref(label: str):
         m = df["入力項目"].eq(label)
         return df.loc[m, "値"].values[0] if m.any() else None
 
-    items = []
+    items: List[Dict[str, Any]] = []
 
     yoy3 = _to_float(ref("予実達成率_3年平均(%)"))
     audit_bad = _to_float(ref("監査・内部統制の重大な不備 件数(過去3年)"))
@@ -199,12 +185,9 @@ def score_external_from_df(df: pd.DataFrame) -> Dict[str, Any]:
     coll_to_ms = _ratio(collat, sales_m2)
 
     def cagr(v1, v3):
-        if v1 is None or v3 is None or v1 <= 0:
-            return None
-        try:
-            return (v3 / v1) ** (1 / 2) - 1.0
-        except Exception:
-            return None
+        if v1 is None or v3 is None or v1 <= 0: return None
+        try: return (v3/v1)**(1/2) - 1.0
+        except Exception: return None
 
     s_cagr = cagr(s1, s3)
     p_cagr = cagr(p1, p3)
@@ -214,100 +197,100 @@ def score_external_from_df(df: pd.DataFrame) -> Dict[str, Any]:
               _ramp(0 if not audit_bad else -audit_bad, 0, -3) +
               _ramp(0 if not comp_bad else -comp_bad, 0, -2) +
               _ramp(indep, 33, 0)) / 4
-    _add(items, "経営者能力", "経営姿勢", mg_att, _WEIGHTS[("経営者能力", "経営姿勢")],
+    _add(items, "経営者能力", "経営姿勢", mg_att, _WEIGHTS[("経営者能力","経営姿勢")],
          f"予実{yoy3 or '—'}%/監査{audit_bad or 0}/違反{comp_bad or 0}/社外{indep or '—'}%")
 
     mg_exp = _ramp(exp_years if exp_years is not None else 5.0, 15, 0)
-    _add(items, "経営者能力", "事業経験", mg_exp, _WEIGHTS[("経営者能力", "事業経験")],
+    _add(items, "経営者能力", "事業経験", mg_exp, _WEIGHTS[("経営者能力","事業経験")],
          f"経験{exp_years if exp_years is not None else '不明→中立'}年")
 
     mg_asset = _ramp(cash_to_ms, 1.5, 0.2)
-    _add(items, "経営者能力", "資産担保力", mg_asset, _WEIGHTS[("経営者能力", "資産担保力")],
-         f"現預金/月商≈{round(cash_to_ms, 2) if cash_to_ms else '—'}")
+    _add(items, "経営者能力", "資産担保力", mg_asset, _WEIGHTS[("経営者能力","資産担保力")],
+         f"現預金/月商≈{round(cash_to_ms,2) if cash_to_ms else '—'}")
 
-    if incidents and incidents > 0:
-        pen = 0.0; rs = f"重大事故{int(incidents)}件→大幅減点"
+    if incidents and incidents>0:
+        pen=0.0; rs=f"重大事故{int(incidents)}件→大幅減点"
     elif has_bk:
-        pen = 6.0 if (bk_years and bk_years >= 10) else 3.0; rs = f"倒産歴あり（{bk_years or '不明'}年）"
+        pen=6.0 if (bk_years and bk_years>=10) else 3.0; rs=f"倒産歴あり（{bk_years or '不明'}年）"
     else:
-        pen = 10.0; rs = "事故/倒産なし"
-    _add(items, "経営者能力", "減点事項", pen, _WEIGHTS[("経営者能力", "減点事項")], rs)
+        pen=10.0; rs="事故/倒産なし"
+    _add(items,"経営者能力","減点事項",pen,_WEIGHTS[("経営者能力","減点事項")],rs)
 
-    # 成長率（市場成長で調整）
-    _add(items, "成長率", "売上高伸長性", _ramp(s_cagr, 0.08, -0.05),
-         _WEIGHTS[("成長率", "売上高伸長性")],
+    # 成長率
+    _add(items,"成長率","売上高伸長性", _ramp(s_cagr,0.08,-0.05),
+         _WEIGHTS[("成長率","売上高伸長性")],
          f"CAGR売上{round((s_cagr or 0)*100,1) if s_cagr is not None else '—'}%")
 
-    _add(items, "成長率", "利益伸長性", _ramp(p_cagr, 0.08, -0.05),
-         _WEIGHTS[("成長率", "利益伸長性")],
+    _add(items,"成長率","利益伸長性", _ramp(p_cagr,0.08,-0.05),
+         _WEIGHTS[("成長率","利益伸長性")],
          f"CAGR営業{round((p_cagr or 0)*100,1) if p_cagr is not None else '—'}%")
 
-    # 商品スコア：総数と成長中の比率で
+    # 商品スコア
     if prod_total is None or prod_total <= 0:
         pr_sc = 5.0; rs = "不明→中立"
     else:
         ratio = _ratio(prod_growing, prod_total) or 0.0
         pr_sc = ( _ramp(prod_total, 3, 0) + _ramp(ratio, 0.7, 0.1) ) / 2
         rs = f"主力{int(prod_total)}/成長中比{round(ratio*100,1)}%"
-    _add(items, "成長率", "商品", pr_sc, _WEIGHTS[("成長率", "商品")], rs)
+    _add(items,"成長率","商品", pr_sc, _WEIGHTS[("成長率","商品")], rs)
 
-    # 市場成長調整：市場 >10% なら高評価、マイナス成長は減点
-    _add(items, "成長率", "市場成長調整",
-         _ramp(market_growth, 15, -5),
-         _WEIGHTS[("成長率", "市場成長調整")],
+    # 市場成長調整
+    _add(items,"成長率","市場成長調整",
+         _ramp(market_growth,15,-5),
+         _WEIGHTS[("成長率","市場成長調整")],
          f"市場年成長{market_growth or '—'}%")
 
     # 安定性
-    _add(items, "安定性", "自己資本", _ramp(equity, 40, 5),
-         _WEIGHTS[("安定性", "自己資本")], f"自己資本比率{equity or '—'}%")
+    _add(items,"安定性","自己資本", _ramp(equity,40,5),
+         _WEIGHTS[("安定性","自己資本")], f"自己資本比率{equity or '—'}%")
 
     if (delay_cnt is not None) or (boun_cnt is not None) or (delay_days is not None):
-        sc = ( _ramp(-(delay_cnt or 0), 0, -6) +
-               _ramp(-(boun_cnt or 0), 0, -1) +
-               _ramp(-(delay_days or 0), 0, -30) ) / 3
-        rs = f"遅延{int(delay_cnt or 0)}/不渡{int(boun_cnt or 0)}/平均{int(delay_days or 0)}日"
+        sc=( _ramp(-(delay_cnt or 0),0,-6) +
+             _ramp(-(boun_cnt or 0),0,-1) +
+             _ramp(-(delay_days or 0),0,-30) )/3
+        rs=f"遅延{int(delay_cnt or 0)}/不渡{int(boun_cnt or 0)}/平均{int(delay_days or 0)}日"
     else:
-        sc = _ramp(cash_to_ms, 1.0, 0.2)
-        rs = f"代理：現預金/月商≈{round(cash_to_ms,2) if cash_to_ms else '—'}"
-    _add(items, "安定性", "決済振り", sc, _WEIGHTS[("安定性", "決済振り")], rs)
+        sc=_ramp(cash_to_ms,1.0,0.2); rs=f"代理：現預金/月商≈{round(cash_to_ms,2) if cash_to_ms else '—'}"
+    _add(items,"安定性","決済振り", sc, _WEIGHTS[("安定性","決済振り")], rs)
 
     sc_mb = 5.0
     sc_mb += 2.0 if mainbank else (-0.5 if mainbank is False else 0)
     sc_mb += 1.0 if has_line else 0
-    sc_mb = _clamp(sc_mb, 0, 10)
-    _add(items, "安定性", "金融取引", sc_mb, _WEIGHTS[("安定性", "金融取引")],
+    sc_mb = _clamp(sc_mb,0,10)
+    _add(items,"安定性","金融取引", sc_mb, _WEIGHTS[("安定性","金融取引")],
          f"メイン{'有' if mainbank else '無' if mainbank is False else '—'}/与信枠{'有' if has_line else '無' if has_line is False else '—'}")
 
-    _add(items, "安定性", "資産担保余力", _ramp(coll_to_ms, 4.0, 0.0),
-         _WEIGHTS[("安定性", "資産担保余力")], f"担保/月商≈{round(coll_to_ms,2) if coll_to_ms else '—'}")
+    _add(items,"安定性","資産担保余力", _ramp(coll_to_ms,4.0,0.0),
+         _WEIGHTS[("安定性","資産担保余力")], f"担保/月商≈{round(coll_to_ms,2) if coll_to_ms else '—'}")
 
-    _add(items, "安定性", "取引先",
-         ( _ramp(-(top1 or 50), 0, -80) +
-           _ramp(cust_score, 80, 50) +
-           _ramp(-(npl_cnt or 1), 0, -3) ) / 3,
-         _WEIGHTS[("安定性", "取引先")],
+    _add(items,"安定性","取引先",
+         ( _ramp(-(top1 or 50),0,-80) +
+           _ramp(cust_score,80,50) +
+           _ramp(-(npl_cnt or 1),0,-3) )/3,
+         _WEIGHTS[("安定性","取引先")],
          f"上位1社{top1 or '—'}%/信用{cust_score or '—'}/不良{int(npl_cnt or 0)}")
 
-    _add(items, "安定性", "業歴", _ramp(years, 20, 1),
-         _WEIGHTS[("安定性", "業歴")], f"{years or '—'}年")
+    _add(items,"安定性","業歴", _ramp(years,20,1),
+         _WEIGHTS[("安定性","業歴")], f"{years or '—'}年")
 
-    # 公平性
+    # 公平性（←ここを二行に分割して修正）
     sc_dis = 0.0
-    sc_dis += 10.0 if has_sec := _to_bool(ref("有価証券報告書提出企業か(TRUE/FALSE)")) else 0.0
+    has_sec = _to_bool(ref("有価証券報告書提出企業か(TRUE/FALSE)"))
+    sc_dis += 10.0 if has_sec else 0.0
     if sc_dis == 0.0:
         pub_off = _to_bool(ref("決算公告や官報での公開あり(TRUE/FALSE)"))
         pub_web = _to_bool(ref("HP/IRサイトで財務資料公開あり(TRUE/FALSE)"))
         sc_dis += 7.0 if (pub_off or pub_web) else 4.0
     upd_on = _to_bool(ref("直近更新が定め通りか(TRUE/FALSE)"))
     if upd_on: sc_dis += 1.0
-    sc_dis = _clamp(sc_dis, 0, 10)
-    _add(items, "公平性・総合世評", "ディスクロージャー", sc_dis,
-         _WEIGHTS[("公平性・総合世評", "ディスクロージャー")],
+    sc_dis = _clamp(sc_dis,0,10)
+    _add(items,"公平性・総合世評","ディスクロージャー", sc_dis,
+         _WEIGHTS[("公平性・総合世評","ディスクロージャー")],
          f"{'有報' if has_sec else '公開あり' if sc_dis>=7.0 else '公開乏しい'} / 更新{'◯' if upd_on else '—'}")
 
-    total = round(sum(x["score"] for x in items), 1)
+    total = round(sum(x["score"] for x in items),1)
 
-    # レーダ用にカテゴリ集計（重み付き平均→0-100）
+    # レーダ用カテゴリスコア（重み付き）
     from collections import defaultdict
     cat_sum, cat_w = defaultdict(float), defaultdict(float)
     for it in items: