python informer

Python Informer是一个Python库，用于实现信息的自动化收集、处理和转发。它可以帮助用户快速获取和整理各种信息，并提供灵活的方式将信息传递给用户。Python Informer提供了丰富的功能，包括网页数据提取、API调用、文件处理等，以满足用户对不同信息源的需求。

使用Python Informer，用户可以通过指定的规则从网页中提取所需的数据，如文字、链接、图片、表格等。这个功能特别适用于需要从网页中提取大量信息的场景，比如爬虫任务、新闻资讯等。Python Informer还可以进行API调用，用户可以通过简单的配置信息和参数，快速实现数据的采集和整合。这对于需要获取实时数据或者从不同的API中汇总数据的用户非常有用。

另外，Python Informer还提供了多种文件处理功能，包括文件的读取、写入、格式转换等。用户可以使用Python Informer来处理各种不同类型的文件，如文本文件、CSV文件、Excel文件等。同时，Python Informer还支持文件的解析和提取，用户可以根据需要从文件中获取所需的信息。

总的来说，Python Informer是一个功能强大的工具，可以帮助用户实现各种信息的自动化收集和处理。它的灵活性和易用性使得用户能够快速应对不同的信息需求，并将信息按照自己的需要传递给其他系统或用户。无论是对于开发者还是普通用户，Python Informer都是一个非常有价值的工具。

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informer代码python

### Informer Python Code Example

Informer是一种用于处理长时间序列数据的深度学习模型，特别适用于具有长依赖关系的时间序列预测任务。下面是一个基于PyTorch框架实现Informer模型的基础代码示例：

import torch
from torch import nn


class AttentionLayer(nn.Module):
    """Attention机制层"""
    
    def __init__(self, attention_hidden_dim=64):
        super().__init__()
        self.query_linear = nn.Linear(attention_hidden_dim, attention_hidden_dim)
        self.key_linear = nn.Linear(attention_hidden_dim, attention_hidden_dim)
        self.value_linear = nn.Linear(attention_hidden_dim, attention_hidden_dim)

    def forward(self, queries, keys, values):
        Q = self.query_linear(queries)
        K = self.key_linear(keys)
        V = self.value_linear(values)
        
        scores = torch.matmul(Q, K.transpose(-2, -1)) / (K.size(-1) ** 0.5)
        attn_weights = torch.softmax(scores, dim=-1)
        context_vector = torch.matmul(attn_weights, V)
        return context_vector


class EncoderLayer(nn.Module):
    """编码器单层结构"""

    def __init__(self, d_model=512, n_heads=8, d_ff=2048, dropout=0.1):
        super().__init__()
        self.self_attn = AttentionLayer(d_model)
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(d_ff, d_model)
        )
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src):
        src2 = self.self_attn(src, src, src)
        src = src + self.dropout(src2)
        src = self.norm1(src)
        src2 = self.feed_forward(src)
        src = src + self.dropout(src2)
        src = self.norm2(src)
        return src


class DecoderLayer(nn.Module):
    """解码器单层结构"""

    def __init__(self, d_model=512, n_heads=8, d_ff=2048, dropout=0.1):
        super().__init__()
        self.self_attn = AttentionLayer(d_model)
        self.multihead_attn = AttentionLayer(d_model)
        self.feed_forward = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.ReLU(),
            nn.Dropout(dropout),
            nn.Linear(d_ff, d_model)
        )
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, tgt, memory):
        tgt2 = self.self_attn(tgt, tgt, tgt)
        tgt = tgt + self.dropout(tgt2)
        tgt = self.norm1(tgt)
        tgt2 = self.multihead_attn(tgt, memory, memory)
        tgt = tgt + self.dropout(tgt2)
        tgt = self.norm2(tgt)
        tgt2 = self.feed_forward(tgt)
        tgt = tgt + self.dropout(tgt2)
        tgt = self.norm3(tgt)
        return tgt


class Informer(nn.Module):
    """完整的Informer网络架构定义"""

    def __init__(self,
                 enc_in=7, dec_in=7, c_out=7,
                 seq_len=96, label_len=48, out_len=24,
                 factor=5, d_model=512, n_heads=8, e_layers=3, d_layers=2,
                 d_ff=2048, dropout=0.0, activation='gelu'):
        super(Informer, self).__init__()

        # 编码部分
        self.enc_embedding = DataEmbedding(enc_in, d_model, dropout)
        self.encoder = Encoder([
            EncoderLayer(
                AttentionLayer(True, factor, attention_dropout=dropout), 
                d_model, d_ff, dropout=dropout
            ) for _ in range(e_layers)])

        # 解码部分
        self.dec_embedding = DataEmbedding(dec_in, d_model, dropout)
        self.decoder = Decoder([
            DecoderLayer(
                AttentionLayer(False, factor, attention_dropout=dropout),
                AttentionLayer(False, factor, attention_dropout=dropout),
                d_model, d_ff, dropout=dropout
            ) for _ in range(d_layers)])
        
        self.projection = nn.Linear(d_model, c_out, bias=True)


def main():
    model = Informer()
    print(model)


if __name__ == '__main__':
    main()

此段代码展示了如何构建一个简单的Informer模型[^4]。

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)