fix: 改用类别加权 FocalLoss 替代 WeightedSampler，更快更稳定

src/models/train.py

@@ -14,7 +14,7 @@ import torch.nn.functional as F
 from sklearn.metrics import accuracy_score, f1_score
 from torch.optim import AdamW
 from torch.optim.lr_scheduler import ReduceLROnPlateau
-from torch.utils.data import DataLoader, TensorDataset, WeightedRandomSampler
+from torch.utils.data import DataLoader, TensorDataset
 
 from src.models.lstm_attention import HeatRiskPredictor
 from src.utils.config import (
@@ -29,15 +29,18 @@ from src.utils.config import (
 
 
 class FocalLoss(nn.Module):
-    """Focal Loss — 聚焦困难样本，缓解类别不平衡"""
+    """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 = 0.5, gamma: float = 2.0,
+                 class_weight: torch.Tensor | None = None):
         super().__init__()
         self.alpha = alpha
         self.gamma = gamma
+        self.class_weight = class_weight
 
     def forward(self, logits: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
-        ce = F.cross_entropy(logits, targets, reduction="none")
+        ce = F.cross_entropy(logits, targets, reduction="none",
+                            weight=self.class_weight)
         pt = torch.exp(-ce)
         focal = self.alpha * (1 - pt) ** self.gamma * ce
         return focal.mean()
@@ -147,21 +150,11 @@ def train() -> HeatRiskPredictor:
     X_test_t = torch.tensor(X_test_np, dtype=torch.float32)
     y_test_t = torch.tensor(y_test_np, dtype=torch.long)
 
-    # -------------------- DataLoader (加权采样) --------------------
-    # 基于 y_short 的类别权重，解决极度不平衡问题
-    y_short_labels = y_train_np[:, 0]
-    class_counts = np.bincount(y_short_labels)
-    class_weights = 1.0 / class_counts
-    sample_weights = class_weights[y_short_labels]
-    sampler = WeightedRandomSampler(
-        weights=torch.from_numpy(sample_weights).float(),
-        num_samples=len(y_short_labels),
-        replacement=True,
-    )
+    # -------------------- DataLoader --------------------
     train_loader = DataLoader(
         TensorDataset(X_train_t, y_train_t),
         batch_size=BATCH_SIZE,
-        sampler=sampler,
+        shuffle=True,
     )
     val_loader = DataLoader(
         TensorDataset(X_val_t, y_val_t),
@@ -175,7 +168,13 @@ 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 --------------------
-    focal_loss = FocalLoss()
+    # 基于训练集类别分布计算权重
+    class_counts = np.bincount(y_train_np[:, 0])
+    class_weights_tensor = torch.tensor(
+        1.0 / (class_counts + 1), 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)