fix: 手动温和权重 [1,3,5,8] + gamma 3.0

src/models/train.py

@@ -31,7 +31,7 @@ from src.utils.config import (
 class FocalLoss(nn.Module):
     """Focal Loss with class weights — 解决极度不平衡"""
 
-    def __init__(self, alpha: float = 0.5, gamma: float = 2.0,
+    def __init__(self, alpha: float = 0.75, gamma: float = 3.0,
                  class_weight: torch.Tensor | None = None):
         super().__init__()
         self.alpha = alpha
@@ -168,12 +168,10 @@ def train() -> HeatRiskPredictor:
     print(f"模型参数量: {sum(p.numel() for p in model.parameters()):,}")
 
     # -------------------- 损失、优化器、调度器 --------------------
-    # 基于训练集类别分布计算权重 (sqrt inverse freq, 温和加权)
-    class_counts = np.bincount(y_train_np[:, 0])
+    # 手动温和类别权重: [低, 中, 高, 严重]
     class_weights_tensor = torch.tensor(
-        1.0 / np.sqrt(class_counts + 1), dtype=torch.float32
+        [1.0, 3.0, 5.0, 8.0], dtype=torch.float32
     ).to(device)
-    class_weights_tensor = class_weights_tensor / class_weights_tensor.sum() * 4
     focal_loss = FocalLoss(class_weight=class_weights_tensor)
     optimizer = AdamW(model.parameters(), lr=LEARNING_RATE, weight_decay=1e-4)
     scheduler = ReduceLROnPlateau(optimizer, factor=0.5, patience=5)