218 lines
6.7 KiB
Python
Executable File
218 lines
6.7 KiB
Python
Executable File
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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"""
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在原 EXP 模型基础上，添加 Haar 小波变换实现的一层小波去噪，增强时序特征。
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"""
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class WaveletDenoise(nn.Module):
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"""
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单层 Haar 小波去噪：
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- 使用低通滤波器提取近似系数
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- 通过转置卷积重构时序信号
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"""
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def __init__(self):
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super().__init__()
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# Haar 低通滤波器 [1/√2, 1/√2]
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lp = torch.tensor([1.0, 1.0]) / (2**0.5)
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self.register_buffer("lp_filter", lp.view(1, 1, 2))
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# 转置卷积同滤波器
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self.register_buffer("lp_rec", lp.view(1, 1, 2))
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def forward(self, x):
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"""
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x: (B, T, N)
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返回去噪后的 (B, T, N)
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"""
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B, T, N = x.shape
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# reshape for conv1d: (B*N, 1, T)
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x_flat = x.permute(0, 2, 1).contiguous().view(-1, 1, T)
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# 分解
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cA = F.conv1d(x_flat, self.lp_filter, stride=2, padding=0)
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# 重构
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# 反卷积: stride=2, output_padding=T%2
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out = F.conv_transpose1d(cA, self.lp_rec, stride=2, output_padding=(T % 2))
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# 裁剪至原始长度
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out = out[:, :, :T]
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# reshape back
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x_dn = out.view(B, N, T).permute(0, 2, 1)
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return x_dn
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class DynamicGraphConstructor(nn.Module):
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def __init__(self, node_num, embed_dim):
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super().__init__()
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self.nodevec1 = nn.Parameter(
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torch.randn(node_num, embed_dim), requires_grad=True
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)
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self.nodevec2 = nn.Parameter(
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torch.randn(node_num, embed_dim), requires_grad=True
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)
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def forward(self):
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adj = torch.matmul(self.nodevec1, self.nodevec2.T)
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adj = F.relu(adj)
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adj = F.softmax(adj, dim=-1)
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return adj
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class GraphConvBlock(nn.Module):
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def __init__(self, input_dim, output_dim):
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super().__init__()
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self.theta = nn.Linear(input_dim, output_dim)
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self.residual = input_dim == output_dim
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if not self.residual:
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self.res_proj = nn.Linear(input_dim, output_dim)
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def forward(self, x, adj):
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res = x
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x = torch.matmul(adj, x)
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x = self.theta(x)
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x = x + (res if self.residual else self.res_proj(res))
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return F.relu(x)
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class MANBA_Block(nn.Module):
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def __init__(self, input_dim, hidden_dim):
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super().__init__()
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self.attn = nn.MultiheadAttention(
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embed_dim=input_dim, num_heads=4, batch_first=True
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)
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self.ffn = nn.Sequential(
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nn.Linear(input_dim, hidden_dim),
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nn.ReLU(),
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nn.Linear(hidden_dim, input_dim),
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)
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self.norm1 = nn.LayerNorm(input_dim)
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self.norm2 = nn.LayerNorm(input_dim)
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def forward(self, x):
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res = x
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x_attn, _ = self.attn(x, x, x)
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x = self.norm1(res + x_attn)
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res2 = x
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x_ffn = self.ffn(x)
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x = self.norm2(res2 + x_ffn)
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return x
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class SandwichBlock(nn.Module):
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def __init__(self, num_nodes, embed_dim, hidden_dim):
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super().__init__()
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self.manba1 = MANBA_Block(hidden_dim, hidden_dim * 2)
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self.graph_constructor = DynamicGraphConstructor(num_nodes, embed_dim)
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self.gc = GraphConvBlock(hidden_dim, hidden_dim)
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self.manba2 = MANBA_Block(hidden_dim, hidden_dim * 2)
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def forward(self, h):
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h1 = self.manba1(h)
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adj = self.graph_constructor()
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h2 = self.gc(h1, adj)
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h3 = self.manba2(h2)
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return h3
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class MLP(nn.Module):
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def __init__(self, in_dim, hidden_dims, out_dim, activation=nn.ReLU):
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super().__init__()
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dims = [in_dim] + hidden_dims + [out_dim]
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layers = []
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for i in range(len(dims) - 2):
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layers += [nn.Linear(dims[i], dims[i + 1]), activation()]
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layers += [nn.Linear(dims[-2], dims[-1])]
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self.net = nn.Sequential(*layers)
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def forward(self, x):
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return self.net(x)
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class EXP(nn.Module):
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def __init__(self, args):
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super().__init__()
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self.horizon = args["horizon"]
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self.output_dim = args["output_dim"]
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self.seq_len = args.get("in_len", 12)
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self.hidden_dim = args.get("hidden_dim", 64)
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self.num_nodes = args["num_nodes"]
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self.embed_dim = args.get("embed_dim", 16)
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# ==== NEW: discrete time embeddings ====
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self.time_slots = args.get("time_slots", 24 * 60 // args.get("time_slot", 5))
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self.time_embedding = nn.Embedding(self.time_slots, self.hidden_dim)
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self.day_embedding = nn.Embedding(7, self.hidden_dim)
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# ==== NEW: 小波去噪层 ====
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self.wavelet = WaveletDenoise()
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# input projection now only takes the denoised flow history
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self.input_proj = MLP(
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in_dim=self.seq_len, hidden_dims=[self.hidden_dim], out_dim=self.hidden_dim
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)
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# two Sandwich blocks remain unchanged
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self.sandwich1 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)
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self.sandwich2 = SandwichBlock(self.num_nodes, self.embed_dim, self.hidden_dim)
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# output projection unchanged
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self.out_proj = MLP(
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in_dim=self.hidden_dim,
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hidden_dims=[2 * self.hidden_dim],
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out_dim=self.horizon * self.output_dim,
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)
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def forward(self, x):
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"""
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x: (B, T, N, D_total)
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D_total >= 3 where
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x[...,0] = flow,
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x[...,1] = time_in_day (0 … 1),
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x[...,2] = day_in_week (0–6)
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"""
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x_flow = x[..., 0] # (B, T, N)
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x_time = x[..., 1] # (B, T, N)
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x_day = x[..., 2] # (B, T, N)
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B, T, N = x_flow.shape
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assert T == self.seq_len
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# 1) 小波去噪
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x_dn = self.wavelet(x_flow) # (B, T, N)
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# 2) project the denoised flow history
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x_flat = x_dn.permute(0, 2, 1).reshape(B * N, T)
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h0 = self.input_proj(x_flat).view(B, N, self.hidden_dim)
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# 3) lookup discrete time indexes at the last time step
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t_idx = (
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x_time[
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:,
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-1,
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:,
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]
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* (self.time_slots - 1)
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).long()
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d_idx = x_day[
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:,
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-1,
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:,
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].long()
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time_emb = self.time_embedding(t_idx) # (B, N, hidden_dim)
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day_emb = self.day_embedding(d_idx) # (B, N, hidden_dim)
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# 4) inject them into the initial hidden state
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h0 = h0 + time_emb + day_emb
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# 5) the usual Sandwich + residuals
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h1 = self.sandwich1(h0)
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h1 = h1 + h0
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h2 = self.sandwich2(h1)
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# 6) output projection
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out = self.out_proj(h2) # (B, N, horizon*output_dim)
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out = out.view(B, N, self.horizon, self.output_dim)
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out = out.permute(0, 2, 1, 3) # (B, horizon, N, output_dim)
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return out
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