diff --git a/pinnacle_experiments.py b/pinnacle_experiments.py
index db9d7e1..f497683 100644
--- a/pinnacle_experiments.py
+++ b/pinnacle_experiments.py
@@ -5,11 +5,13 @@ from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import log_loss
 
 
-def compute_metrics(df: pd.DataFrame,
-                    n_bins: int = 10,
-                    bin_strategy: str = 'uniform',  # 'uniform' or 'quantile'
-                    include_draws: bool = True,
-                    eps: float = 1e-6) -> dict:
+def compute_metrics(
+    df: pd.DataFrame,
+    n_bins: int = 10,
+    bin_strategy: str = "uniform",  # 'uniform' or 'quantile'
+    include_draws: bool = True,
+    eps: float = 1e-6,
+) -> dict:
     """
     计算预测评估指标并拟合校准关系。
 
@@ -25,14 +27,14 @@ def compute_metrics(df: pd.DataFrame,
     """
     # 处理 refunded
     if include_draws:
-        mask = df['res'].isin(['won', 'refunded', 'lost'])
+        mask = df["res"].isin(["won", "refunded", "lost"])
     else:
-        mask = df['res'].isin(['won', 'lost'])
+        mask = df["res"].isin(["won", "lost"])
     df = df[mask].copy()
 
     # 标签: won=1, others=0 (包括 refunded)
-    y = df['res'].map({'won': 1, 'refunded': 0, 'lost': 0}).astype(int).values
-    p = df['win_prob'].astype(float).values
+    y = df["res"].map({"won": 1, "refunded": 0, "lost": 0}).astype(int).values
+    p = df["win_prob"].astype(float).values
 
     # 裁剪概率以保证数值稳定
     p_clip = np.clip(p, eps, 1 - eps)
@@ -48,7 +50,7 @@ def compute_metrics(df: pd.DataFrame,
     brier = float(np.mean((p_clip - y) ** 2))
 
     # ECE 计算（支持 uniform 或 quantile）
-    if bin_strategy == 'quantile':
+    if bin_strategy == "quantile":
         # quantile bin edges
         try:
             edges = np.unique(np.percentile(p_clip, np.linspace(0, 100, n_bins + 1)))
@@ -57,7 +59,9 @@ def compute_metrics(df: pd.DataFrame,
                 bin_idxs = np.minimum((p_clip * n_bins).astype(int), n_bins - 1)
             else:
                 # searchsorted to assign bins
-                bin_idxs = np.clip(np.searchsorted(edges, p_clip, side='right') - 1, 0, len(edges) - 2)
+                bin_idxs = np.clip(
+                    np.searchsorted(edges, p_clip, side="right") - 1, 0, len(edges) - 2
+                )
         except Exception:
             bin_idxs = np.minimum((p_clip * n_bins).astype(int), n_bins - 1)
     else:
@@ -70,39 +74,40 @@ def compute_metrics(df: pd.DataFrame,
         idx = bin_idxs == b
         count = int(idx.sum())
         if count == 0:
-            bin_stats.append({'count': 0, 'mean_pred': float('nan'), 'emp_freq': float('nan')})
+            bin_stats.append(
+                {"count": 0, "mean_pred": float("nan"), "emp_freq": float("nan")}
+            )
             continue
         mean_pred = float(p_clip[idx].mean())
         emp_freq = float(y[idx].mean())
         ece += abs(mean_pred - emp_freq) * count
-        bin_stats.append({'count': count, 'mean_pred': mean_pred, 'emp_freq': emp_freq})
-    ece = float(ece / total) if total > 0 else float('nan')
+        bin_stats.append({"count": count, "mean_pred": mean_pred, "emp_freq": emp_freq})
+    ece = float(ece / total) if total > 0 else float("nan")
 
     # accuracy
     acc = float(np.mean((p_clip >= 0.5) == (y == 1)))
 
     # 校准拟合: 使用 LogisticRegression 拟合 logit(E[y]) = alpha + beta * logit(p)
     X = sp_logit(p_clip).reshape(-1, 1)
-    clf = LogisticRegression(C=1e6, solver='lbfgs', max_iter=200)
+    clf = LogisticRegression(C=1e6, solver="lbfgs", max_iter=200)
     clf.fit(X, y)
     alpha = float(clf.intercept_[0])
     beta = float(clf.coef_[0][0])
 
     return {
-        'logloss': logloss,
-        'brier': brier,
-        'ece': ece,
-        'accuracy': acc,
-        'reg_alpha': alpha,
-        'reg_beta': beta,
+        "logloss": logloss,
+        "brier": brier,
+        "ece": ece,
+        "accuracy": acc,
+        "reg_alpha": alpha,
+        "reg_beta": beta,
         # 'ece_bins': bin_stats,
-        'n_samples': int(total)
+        "n_samples": int(total),
     }
 
 
-
-if __name__ == '__main__':
+if __name__ == "__main__":
     df = pd.read_feather("data/p_res.feather")
-    df['win_prob'] = df['power_p']
+    df["win_prob"] = df["power_p"]
     res = compute_metrics(df)
     print(res)