feat(graph-mixer): implement L0 sparsity with Hard-Concrete gate for channel selection

layers/SeasonPatch.py

@@ -27,7 +27,15 @@ class SeasonPatch(nn.Module):
                  d_state: int = 64,
                  d_conv: int = 4,
                  expand: int = 2,
-                 headdim: int = 64):
+                 headdim: int = 64,
+                 # Mixergraph 可选超参数
+                 thr_graph: float = 0.5,
+                 symmetric_graph: bool = True,
+                 degree_rescale: str = "count-sqrt",     # "none" | "count" | "count-sqrt" | "sum"
+                 gate_temperature: float = 2./3.,
+                 tau_attn: float = 1.0,
+                 l0_lambda: float = 1e-4):
+
         super().__init__()
         
         # Store patch parameters
@@ -46,7 +54,17 @@ class SeasonPatch(nn.Module):
                 c_in=c_in, patch_num=patch_num, patch_len=patch_len,
                 d_model=d_model, n_layers=n_layers, n_heads=n_heads
             )
-            self.mixer = HierarchicalGraphMixer(c_in, dim=d_model, k=k_graph)
+            # 集成新 HierarchicalGraphMixer（非归一化）
+            self.mixer = HierarchicalGraphMixer(
+                n_channel=c_in,
+                dim=d_model,
+                max_degree=k_graph,
+                thr=thr_graph,
+                temperature=gate_temperature,
+                tau_attn=tau_attn,
+                symmetric=symmetric_graph,
+                degree_rescale=degree_rescale
+            )
             # Prediction head（Transformer 路径用到，输入维度为 patch_num * d_model）
             self.head = nn.Sequential(
                 nn.Linear(patch_num * d_model, patch_num * d_model),
@@ -97,3 +115,11 @@ class SeasonPatch(nn.Module):
             y_pred = self.head(z_last)      # y_pred: [B, C, pred_len]
 
         return y_pred  # [B, C, pred_len]
+
+    def reg_loss(self):
+        """
+        可选：把 L0 正则暴露出去，训练时加到总loss。
+        """
+        if self.encoder_type == "Transformer" and hasattr(self, "mixer"):
+            return self.mixer.l0_loss(self.l0_lambda)
+        return torch.tensor(0.0, device=self.head[0].weight.device)