利用随机数据生成完整的基于python的Informer时序预测模型代码

在Python中，我们可以使用`PyTorch`库结合`TIMM`（Transformers in PyTorch）来构建基于注意力机制的时间序列预测模型如Informer。请注意，以下代码示例将假设你已经安装了必要的库，如果没有，你可以通过`pip install torch timm`进行安装。

import torch
from torch import nn
import torch.nn.functional as F
from timm.models.layers import DropPath
from einops import rearrange

class MHA(nn.Module):
    def __init__(self, d_model, n_heads, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.fc_q = nn.Linear(d_model, d_model)
        self.fc_k = nn.Linear(d_model, d_model)
        self.fc_v = nn.Linear(d_model, d_model)
        self.attn_dropout = nn.Dropout(dropout)
        self.proj = nn.Linear(d_model, d_model)

    def forward(self, x):
        b, seq_len, _ = x.shape
        q = self.fc_q(x).view(b, seq_len, self.n_heads, self.head_dim)
        k = self.fc_k(x).view(b, seq_len, self.n_heads, self.head_dim)
        v = self.fc_v(x).view(b, seq_len, self.n_heads, self.head_dim)

        q = rearrange(q, 'b t (h d) -> b h t d', h=self.n_heads)
        k = rearrange(k, 'b t (h d) -> b h t d', h=self.n_heads)
        v = rearrange(v, 'b t (h d) -> b h t d', h=self.n_heads)

        attn_weights = torch.matmul(q, k.transpose(-1, -2)) / (self.head_dim ** 0.5)
        attn_weights = F.softmax(attn_weights, dim=-1)
        attn_weights = self.attn_dropout(attn_weights)

        out = torch.matmul(attn_weights, v)
        out = rearrange(out, 'b h t d -> b t (h d)')
        out = self.proj(out)
        return out

class InformerBlock(nn.Module):
    def __init__(self, d_model, n_heads, dropout=0.1):
        super().__init__()
        self.mha = MHA(d_model, n_heads, dropout)
        self.ln_1 = nn.LayerNorm(d_model)
        self.ffn = nn.Sequential(
            nn.Linear(d_model, d_model * 4),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(d_model * 4, d_model),
        )
        self.ln_2 = nn.LayerNorm(d_model)

    def forward(self, x):
        residual = x
        x = self.ln_1(x)
        x = x + self.mha(x)
        x = self.ln_2(x)
        x = x + self.ffn(residual)
        return x

class Informer(nn.Module):
    def __init__(self, input_size, output_size, num_layers, d_model, n_heads, dropout=0.1):
        super().__init__()
        self.encoder = nn.ModuleList([
            InformerBlock(d_model, n_heads, dropout)
            for _ in range(num_layers)
        ])
        self.decoder = nn.Linear(d_model, output_size)
        self.dropout = nn.Dropout(dropout)
        self.linear_transpose = nn.Linear(output_size, input_size)

    def forward(self, x, past_seq=None):
        if past_seq is not None:
            # For autoregressive prediction with a given context
            x, past_seq = x[:, :-1], past_seq
        for block in self.encoder:
            x = block(x)
        encoded_x = self.dropout(x)
        decoded_x = self.decoder(encoded_x.mean(dim=1))  # Assuming average pooling over sequence
        predicted_seq = self.linear_transpose(decoded_x)
        return predicted_seq, encoded_x

# 使用示例
input_size = ...  # 输入时间步数特征维度
output_size = ...  # 预测时间步数特征维度
num_layers = ...
d_model = ...
n_heads = ...

model = Informer(input_size, output_size, num_layers, d_model, n_heads)
past_seq = None  # 如果你想进行自回归预测，可以提供一个过去序列
x = torch.randn(1, input_size, requires_grad=True)
predicted_seq, encoded_x = model(x, past_seq)