编写pytorch代码,定义LSTMAttention模型,定义一个FA_CPSO优化算法,用FA_CPSO算法有优化模型中神经元个数、dropout比率、batch_size、学习率等超参数,将优化好的超参数传递给模型,在特征训练集X_train.csv和标签训练集y_train.csv上训练模型,将最优的参数设置给模型,在特征测试集X_test.csv和标签测试集y_test.csv上测试模型,进一步优化模型,将优化后的参数设置给模型,并输出测试损失,绘制测试集的预测值和实际值,计算测试集的均方根误差,在预测集上进行预测,设定标签数据最大值的85%为警戒线,绘制预测集的实际值到达的时间和预测值到达的时间
时间: 2024-03-22 17:41:52 浏览: 32
好的,这是一个比较复杂的任务,需要比较多的代码实现。以下是一个参考实现,您可以根据自己的数据集和需求进行修改和调整。
首先是模型的定义,这里定义了一个带有注意力机制的LSTM模型。
``` python
import torch
import torch.nn as nn
import torch.nn.functional as F
class LSTMAttention(nn.Module):
def __init__(self, input_size, hidden_size, output_size, dropout):
super(LSTMAttention, self).__init__()
self.hidden_size = hidden_size
self.dropout = dropout
self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True, bidirectional=True)
self.dropout = nn.Dropout(dropout)
self.fc = nn.Linear(hidden_size*2, output_size) # bidirectional LSTM,所以hidden_size乘2
self.attn = nn.Linear(hidden_size*2, hidden_size*2)
def forward(self, input):
lstm_out, (h_n, c_n) = self.lstm(input)
lstm_out = self.dropout(lstm_out)
attn_weights = F.softmax(self.attn(lstm_out), dim=1)
attn_applied = torch.bmm(attn_weights.transpose(1,2), lstm_out)
output = self.fc(attn_applied)
return output
```
接下来是FA_CPSO算法的定义,这里使用了PySwarm库实现。
``` python
from pyswarm import pso
class FA_CPSO():
def __init__(self, model, X_train, y_train, X_test, y_test):
self.model = model
self.X_train = X_train
self.y_train = y_train
self.X_test = X_test
self.y_test = y_test
def optimize(self):
def objective_function(params):
hidden_size, dropout, batch_size, learning_rate = params
self.model.hidden_size = int(hidden_size)
self.model.dropout = dropout
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(self.model.parameters(), lr=learning_rate)
train_dataset = torch.utils.data.TensorDataset(torch.from_numpy(self.X_train), torch.from_numpy(self.y_train))
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=int(batch_size), shuffle=True)
self.model.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = self.model(data.float())
loss = criterion(output, target.float())
loss.backward()
optimizer.step()
self.model.eval()
test_dataset = torch.utils.data.TensorDataset(torch.from_numpy(self.X_test), torch.from_numpy(self.y_test))
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=int(batch_size), shuffle=True)
test_loss = 0
with torch.no_grad():
for data, target in test_loader:
output = self.model(data.float())
test_loss += criterion(output, target.float()).item()
return test_loss
lb = [16, 0.1, 32, 1e-6]
ub = [512, 0.9, 128, 0.1]
xopt, fopt = pso(objective_function, lb, ub, swarmsize=10, maxiter=100)
return xopt
```
最后是整个流程的实现,包括数据的读取、模型的初始化、FA_CPSO算法优化、模型的训练和测试、预测结果的输出等。
``` python
import pandas as pd
import numpy as np
# 读取数据集
X_train = pd.read_csv('X_train.csv').values
y_train = pd.read_csv('y_train.csv').values
X_test = pd.read_csv('X_test.csv').values
y_test = pd.read_csv('y_test.csv').values
# 模型初始化
input_size = X_train.shape[1]
output_size = y_train.shape[1]
model = LSTMAttention(input_size, 16, output_size, 0.1)
# FA_CPSO算法优化
fcpso = FA_CPSO(model, X_train, y_train, X_test, y_test)
xopt = fcpso.optimize()
hidden_size, dropout, batch_size, learning_rate = xopt
# 模型训练和测试
model.hidden_size = int(hidden_size)
model.dropout = dropout
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
train_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train))
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=int(batch_size), shuffle=True)
for epoch in range(100):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = model(data.float())
loss = criterion(output, target.float())
loss.backward()
optimizer.step()
model.eval()
test_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_test), torch.from_numpy(y_test))
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=int(batch_size), shuffle=True)
test_loss = 0
with torch.no_grad():
for data, target in test_loader:
output = model(data.float())
test_loss += criterion(output, target.float()).item()
print('Epoch: {}, Test Loss: {}'.format(epoch+1, test_loss))
# 预测结果输出
model.eval()
pred_dataset = torch.utils.data.TensorDataset(torch.from_numpy(X_test))
pred_loader = torch.utils.data.DataLoader(pred_dataset, batch_size=1, shuffle=False)
pred = []
with torch.no_grad():
for data in pred_loader:
output = model(data[0].float())
pred.append(output.numpy())
pred = np.concatenate(pred, axis=0)
rmse = np.sqrt(np.mean((y_test - pred)**2))
print('RMSE: {}'.format(rmse))
# 绘制预测值和实际值
import matplotlib.pyplot as plt
plt.plot(y_test, label='Actual')
plt.plot(pred, label='Predicted')
plt.legend()
plt.show()
# 计算警戒线
threshold = np.max(y_test) * 0.85
# 绘制预测值和实际值到达的时间
actual_time = np.argmax(y_test > threshold)
pred_time = np.argmax(pred > threshold)
plt.axvline(x=actual_time, color='r', linestyle='--')
plt.axvline(x=pred_time, color='g', linestyle='--')
plt.plot(y_test, label='Actual')
plt.plot(pred, label='Predicted')
plt.legend()
plt.show()
```
请注意,这只是一个参考实现,对于您的数据集和问题,可能需要进行适当的修改和调整。另外,如果您的数据集较大,可能需要使用GPU进行加速。
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