Update core/external_scoring.py

core/external_scoring.py

@@ -2,7 +2,6 @@
 from __future__ import annotations
 from typing import Dict, Any, List, Tuple
 import pandas as pd
-import math
 
 __all__ = [
     "get_external_template_df",
@@ -71,7 +70,7 @@ def fill_missing_with_external(df: pd.DataFrame, suggestions: Dict[str, Any] | N
             df2.at[idx, "値"] = suggestions[key]
     return df2
 
-# ===== スコア計算（定量的・ルールベース） =====
+# ===== スコア計算（定量・ばらつき強化） =====
 _WEIGHTS = {
     ("経営者能力", "経営姿勢"): 8,
     ("経営者能力", "事業経験"): 5,
@@ -81,7 +80,7 @@ _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,  # 市場成長率を反映
+    ("成長率", "市場成長調整"): 6,
 
     ("安定性", "自己資本"): 8,
     ("安定性", "決済振り"): 10,
@@ -95,34 +94,21 @@ _WEIGHTS = {
 _WEIGHT_NORM = 100.0 / float(sum(_WEIGHTS.values()))
 
 def _clamp(v, a, b): return max(a, min(b, v))
-
-def _add(items, cat, name, raw, weight, reason):
-    raw_s = None if raw is None else round(raw, 2)
-    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
-    })
-
 def _to_float(x):
     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
     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
     return None
-
 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
@@ -135,8 +121,27 @@ def _ramp(x, good, bad, lo=0.0, hi=10.0, neutral=None):
         if x <= good: return hi
         return lo + (hi-lo) * (x-good)/(bad-good)
 
+def _stretch_0_10(x: float, k: float = 1.2) -> float:
+    """
+    0-10 を保ちつつ中央付近(4-6)の密集をほぐす単調変換。
+    k>1で両端を強調。k≈1.2~1.4 推奨。
+    """
+    if x is None: return None
+    t = (x/10.0)
+    t = t**(1.0/k) if t >= 0.5 else (t**k)
+    return _clamp(t*10.0, 0.0, 10.0)
+
+def _add(items, cat, name, raw, weight, reason):
+    raw2 = _stretch_0_10(raw, k=1.25) if raw is not None else None
+    w = round(weight * _WEIGHT_NORM, 2)
+    sc = 0.0 if raw2 is None else round((raw2 / 10.0) * w, 2)
+    items.append({
+        "category": cat, "name": name, "raw": None if raw is None else round(raw,2),
+        "raw_stretched": None if raw2 is None else round(raw2,2),
+        "weight": w, "score": sc, "reason": reason
+    })
+
 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
@@ -225,7 +230,7 @@ def score_external_from_df(df: pd.DataFrame) -> Dict[str, Any]:
          _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
+    # 商品
     if prod_total is None or prod_total <= 0:
         pr_sc = 5.0; rs = "不明→中立"
     else:
@@ -273,7 +278,7 @@ def score_external_from_df(df: pd.DataFrame) -> Dict[str, Any]:
     _add(items,"安定性","業歴", _ramp(years,20,1),
          _WEIGHTS[("安定性","業歴")], f"{years or '—'}年")
 
-    # 公平性（←ここを二行に分割して修正）
+    # 公平性
     sc_dis = 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
@@ -290,7 +295,6 @@ def score_external_from_df(df: pd.DataFrame) -> Dict[str, Any]:
 
     total = round(sum(x["score"] for x in items),1)
 
-    # 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:
@@ -303,5 +307,5 @@ def score_external_from_df(df: pd.DataFrame) -> Dict[str, Any]:
         "external_total": total,
         "items": items,
         "category_scores": cat_scores,
-        "notes": "欠損は中立。市場成長率を成長評価に加味（過熱/低迷の補正）。",
+        "notes": "欠損は中立＋市場成長補正。ストレッチでばらつきを拡大。",
     }