feat: implement dynamic threshold scheduling for GraphMixer

layers/GraphMixer.py

@@ -53,6 +53,10 @@ class HierarchicalGraphMixer(nn.Module):
         dim: int,
         max_degree: int = None,    # 可选：限制每行最多边数
         thr: float = 0.5,          # 保留边阈值，例如 0.5/0.7
+        thr_min: float = None,            # 动态阈值起点，不传则用 thr
+        thr_max: float = None,            # 动态阈值终点，不传则用 thr
+        thr_steps: int = 0,               # 从 thr_min -> thr_max 的步数，>0 时启用动态调度
+        thr_schedule: str = "linear",     # "linear" | "cosine" | "exp"
         temperature: float = 2./3.,
         tau_attn: float = 1.0,     # Patch attention 温度（可选）
         symmetric: bool = True,    # 是否对称化通道图
@@ -67,6 +71,13 @@ class HierarchicalGraphMixer(nn.Module):
         self.tau_attn = tau_attn
         self.symmetric = symmetric
         self.degree_rescale = degree_rescale
+        self.thr_min = thr if (thr_min is None) else float(thr_min)
+        self.thr_max = thr if (thr_max is None) else float(thr_max)
+        self.thr_steps = int(thr_steps) if thr_steps is not None else 0
+        self.thr_schedule = thr_schedule
+        self._use_dynamic_thr = (self.thr_steps > 0) and (abs(self.thr_max - self.thr_min) > 1e-12)
+        # 用 buffer 记录已步进次数（不保存到权重里）
+        self.register_buffer("_thr_step", torch.zeros((), dtype=torch.long), persistent=False)
 
         # Level 1: 非归一化门控
         self.gate = HardConcreteGate(
@@ -88,6 +99,30 @@ class HierarchicalGraphMixer(nn.Module):
         self.out_proj = nn.Linear(dim, dim)
         self.norm = nn.LayerNorm(dim)
 
+    def _compute_thr_by_progress(self, progress: float) -> float:
+        # progress in [0,1]
+        progress = max(0.0, min(1.0, float(progress)))
+        if self.thr_schedule == "linear":
+            g = progress
+        elif self.thr_schedule == "cosine":
+            # 慢起步，后期加速
+            import math
+            g = 0.5 - 0.5 * math.cos(math.pi * progress)
+        elif self.thr_schedule == "exp":
+            # 更快从 thr_min 过渡到 thr_max（指数式）
+            import math
+            k = 5.0
+            g = (math.exp(k * progress) - 1.0) / (math.exp(k) - 1.0)
+        else:
+            g = progress
+        return self.thr_min + (self.thr_max - self.thr_min) * g
+    def _maybe_update_thr(self):
+        if self.training and self._use_dynamic_thr:
+            step = int(self._thr_step.item())
+            progress = step / float(self.thr_steps)
+            self.thr = float(self._compute_thr_by_progress(progress))
+            self._thr_step += 1
+
     def _build_sparse_neighbors(self, z_gate):
         """
         基于 z_gate 构造每行的邻接列表（按阈值与可选top-k）。
@@ -151,6 +186,7 @@ class HierarchicalGraphMixer(nn.Module):
         return lam * self.gate.expected_l0().sum()
 
     def forward(self, z):
+        self._maybe_update_thr()
         # z: [B, C, N, D]
         B, C, N, D = z.shape
         assert C == self.C and D == self.dim

layers/SeasonPatch.py

@@ -30,6 +30,10 @@ class SeasonPatch(nn.Module):
                  headdim: int = 64,
                  # Mixergraph 可选超参数
                  thr_graph: float = 0.5,
+                 thr_graph_min: float = None,
+                 thr_graph_max: float = None,
+                 thr_graph_steps: int = 0,
+                 thr_graph_schedule: str = "linear",
                  symmetric_graph: bool = True,
                  degree_rescale: str = "count-sqrt",     # "none" | "count" | "count-sqrt" | "sum"
                  gate_temperature: float = 2./3.,
@@ -38,6 +42,9 @@ class SeasonPatch(nn.Module):
 
         super().__init__()
         
+        # ===== 新增：保存 l0_lambda，防止 reg_loss 访问报错 =====
+        self.l0_lambda = l0_lambda
+
         # Store patch parameters
         self.patch_len = patch_len  # patch 长度
         self.stride = stride        # patch 步幅
@@ -60,6 +67,10 @@ class SeasonPatch(nn.Module):
                 dim=d_model,
                 max_degree=k_graph,
                 thr=thr_graph,
+                thr_min=thr_graph_min,
+                thr_max=thr_graph_max,
+                thr_steps=thr_graph_steps,
+                thr_schedule=thr_graph_schedule,
                 temperature=gate_temperature,
                 tau_attn=tau_attn,
                 symmetric=symmetric_graph,

models/xPatch_SparseChannel.py

@@ -54,6 +54,10 @@ class Model(nn.Module):
             # GraphMixer相关（非归一化）
             k_graph=getattr(configs, 'k_graph', 8),                     # -> max_degree
             thr_graph=getattr(configs, 'thr_graph', 0.5),
+            thr_graph_min=getattr(configs, 'thr_graph_min', None),
+            thr_graph_max=getattr(configs, 'thr_graph_max', None),
+            thr_graph_steps=getattr(configs, 'thr_graph_steps', 0),
+            thr_graph_schedule=getattr(configs, 'thr_graph_schedule', 'linear'),
             symmetric_graph=getattr(configs, 'symmetric_graph', True),
             degree_rescale=getattr(configs, 'degree_rescale', 'count-sqrt'),  # 'none' | 'count' | 'count-sqrt' | 'sum'
             gate_temperature=getattr(configs, 'gate_temperature', 2.0/3.0),

scripts/long_term_forecast/xPatch_SparseChannel_all-Copy1.sh

@@ -2,45 +2,6 @@
 
 model_name=xPatch_SparseChannel
 
-# ETTm1 dataset
-for pred_len in 96 192 336 720
-do
-python -u run.py \
-  --task_name long_term_forecast \
-  --is_training 1 \
-  --root_path ./dataset/ETT-small/ \
-  --data_path ETTm1.csv \
-  --model_id ETTm1_$pred_len'_'$pred_len \
-  --model $model_name \
-  --data ETTm1 \
-  --features M \
-  --seq_len 96 \
-  --label_len 48 \
-  --pred_len $pred_len \
-  --e_layers 2 \
-  --d_layers 1 \
-  --enc_in 7 \
-  --c_out 7 \
-  --d_model 128 \
-  --lradj 'sigmoid' \
-  --d_ff 256 \
-  --n_heads 16 \
-  --patch_len 16 \
-  --stride 8 \
-  --k_graph 5 \
-  --dropout 0.1 \
-  --revin 1 \
-  --des 'Exp' \
-  --itr 1 \
-  --season_encoder 'Transformer' \
-  --thr_graph 0.6 \
-  --symmetric_graph 1 \
-  --degree_rescale 'none' \
-  --gate_temperature 0.6667 \
-  --tau_attn 1.0 \
-  --season_l0_lambda 0.0000
-done
-
 # Weather dataset
 for pred_len in 96 192 336 720
 do
@@ -78,7 +39,11 @@ python -u run.py \
   --degree_rescale 'none' \
   --gate_temperature 0.6667 \
   --tau_attn 1.0 \
-  --season_l0_lambda 0.0000
+  --season_l0_lambda 0.0000 \
+  --thr_graph_min 0.1 \
+  --thr_graph_max 0.6 \
+  --thr_graph_steps 1000 \
+  --thr_graph_schedule 'cosine'
 done
 
 # Exchange dataset
@@ -117,9 +82,57 @@ python -u run.py \
   --degree_rescale 'none' \
   --gate_temperature 0.6667 \
   --tau_attn 1.0 \
-  --season_l0_lambda 0.0000
+  --season_l0_lambda 0.0000 \
+  --thr_graph_min 0.1 \
+  --thr_graph_max 0.6 \
+  --thr_graph_steps 1000 \
+  --thr_graph_schedule 'cosine'
 done
 
+# ETTm1 dataset
+for pred_len in 96 192 336 720
+do
+python -u run.py \
+  --task_name long_term_forecast \
+  --is_training 1 \
+  --root_path ./dataset/ETT-small/ \
+  --data_path ETTm1.csv \
+  --model_id ETTm1_$pred_len'_'$pred_len \
+  --model $model_name \
+  --data ETTm1 \
+  --features M \
+  --seq_len 96 \
+  --label_len 48 \
+  --pred_len $pred_len \
+  --e_layers 2 \
+  --d_layers 1 \
+  --enc_in 7 \
+  --c_out 7 \
+  --d_model 128 \
+  --lradj 'sigmoid' \
+  --d_ff 256 \
+  --n_heads 16 \
+  --patch_len 16 \
+  --stride 8 \
+  --k_graph 5 \
+  --dropout 0.1 \
+  --revin 1 \
+  --des 'Exp' \
+  --itr 1 \
+  --season_encoder 'Transformer' \
+  --thr_graph 0.6 \
+  --symmetric_graph 1 \
+  --degree_rescale 'none' \
+  --gate_temperature 0.6667 \
+  --tau_attn 1.0 \
+  --season_l0_lambda 0.0000 \
+  --thr_graph_min 0.1 \
+  --thr_graph_max 0.6 \
+  --thr_graph_steps 1000 \
+  --thr_graph_schedule 'cosine'
+done
+
+
 
 # ETTm2 dataset
 for pred_len in 96 192 336 720