threshold = max(stats.values()) / 10 ^^^^^^^^^^^^^^^^^^^ ValueError: max() arg is an empty sequence
时间: 2024-11-08 14:13:13 浏览: 14
这个错误`ValueError: max() arg is an empty sequence`通常发生在尝试对一个空序列(即长度为零的列表或元组)调用`max()`函数时。在这个例子中,`stats.values()`返回的是一个值的集合,如果这个集合为空,意味着`stats`字典中没有可迭代的值。
`threshold`变量的计算依赖于`stats.values()`的结果除以10得到最大值的十分之一。当你试图获取空序列的最大值时,Python抛出了这个错误。
要修复这个问题,你需要确保`stats.values()`至少包含一个元素。这可以通过检查`stats.values()`是否为空先进行判断:
```python
if stats.values():
threshold = max(stats.values()) / 10
else:
# 如果 stats 是空的,你可以选择一个默认值、None 或者抛出异常
print("stats values are empty, cannot calculate threshold")
```
如果你希望即使在`stats`为空的情况下也能设置一个默认阈值,可以提供一个备选值:
```python
default_threshold = 0.0 if not stats.values() else max(stats.values()) / 10
threshold = default_threshold
```
相关问题
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
好的,我了解了你的要求。首先,需要安装pytorch和pandas库。
以下是代码实现:
```python
import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from scipy.stats import uniform
from sko.PSO import PSO
# 定义LSTMAttention模型
class LSTMAttention(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers, dropout):
super(LSTMAttention, self).__init__()
self.hidden_size = hidden_size
self.num_layers = num_layers
self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
self.dropout = nn.Dropout(dropout)
self.fc1 = nn.Linear(hidden_size * 2, output_size)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
h0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
c0 = torch.zeros(self.num_layers * 2, x.size(0), self.hidden_size).to(device)
out, _ = self.lstm(x, (h0, c0))
out = self.dropout(out)
out = self.fc1(out[:, -1, :])
out = self.softmax(out)
return out
# 加载数据
X_train = pd.read_csv('X_train.csv')
y_train = pd.read_csv('y_train.csv')
X_test = pd.read_csv('X_test.csv')
y_test = pd.read_csv('y_test.csv')
# 转换数据格式
X_train = torch.from_numpy(X_train.values).float()
y_train = torch.from_numpy(y_train.values).long().squeeze()
X_test = torch.from_numpy(X_test.values).float()
y_test = torch.from_numpy(y_test.values).long().squeeze()
# 定义超参数空间
dim = 4
lb = [16, 0.1, 64, 0.0001]
ub = [256, 0.5, 256, 0.1]
pso_bound = np.array([lb, ub])
# 定义FA_CPSO优化算法
class FA_CPSO(PSO):
def __init__(self, func, lb, ub, dimension, size_pop=50, max_iter=300, w=0.8, c1=2, c2=2, c3=2, p=0.5):
super().__init__(func, lb, ub, dimension, size_pop, max_iter, w, c1, c2, p)
self.c3 = c3 # FA_CPSO新增参数
self.S = np.zeros((self.size_pop, self.dimension)) # 储存每个个体的历代最优位置
self.F = np.zeros(self.size_pop) # 储存每个个体的当前适应度值
self.Fbest = np.zeros(self.max_iter + 1) # 储存每次迭代的最优适应度值
self.Fbest[0] = self.gbest_y
self.S = self.X.copy()
def evolve(self):
self.F = self.cal_fitness(self.X)
self.Fbest[self.gbest_iter] = self.gbest_y
for i in range(self.size_pop):
if uniform.rvs() < self.p:
# 个体位置更新
self.X[i] = self.S[i] + self.c3 * (self.gbest - self.X[i]) + self.c1 * \
(self.pbest[i] - self.X[i]) + self.c2 * (self.pbest[np.random.choice(self.neighbor[i])] - self.X[i])
else:
# 个体位置更新
self.X[i] = self.S[i] + self.c1 * (self.pbest[i] - self.X[i]) + self.c2 * (self.pbest[np.random.choice(self.neighbor[i])] - self.X[i])
# 边界处理
self.X[i] = np.clip(self.X[i], self.lb, self.ub)
# 适应度值更新
self.F[i] = self.func(self.X[i])
# 个体历代最优位置更新
if self.F[i] < self.func(self.S[i]):
self.S[i] = self.X[i]
# 全局最优位置更新
self.gbest = self.S[self.F.argmin()]
self.gbest_y = self.F.min()
# 定义优化目标函数
def objective_function(para):
hidden_size, dropout, batch_size, learning_rate = para
model = LSTMAttention(10, hidden_size, 2, 2, dropout).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
train_dataset = torch.utils.data.TensorDataset(X_train, y_train)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
for epoch in range(100):
for i, (inputs, labels) in enumerate(train_loader):
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
test_dataset = torch.utils.data.TensorDataset(X_test, y_test)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=len(test_dataset))
for inputs, labels in test_loader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
pred = torch.argmax(outputs, dim=1)
test_loss = criterion(outputs, labels)
rmse = torch.sqrt(torch.mean((pred - labels) ** 2))
return test_loss.item() + rmse.item()
# 运行FA_CPSO算法进行超参数优化
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
fa_cpso = FA_CPSO(objective_function, lb, ub, dim, size_pop=50, max_iter=100)
fa_cpso.run()
# 输出最优超参数
best_hidden_size, best_dropout, best_batch_size, best_learning_rate = fa_cpso.gbest
# 使用最优超参数训练模型
model = LSTMAttention(10, best_hidden_size, 2, 2, best_dropout).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=best_learning_rate)
train_dataset = torch.utils.data.TensorDataset(X_train, y_train)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=best_batch_size, shuffle=True)
for epoch in range(100):
for i, (inputs, labels) in enumerate(train_loader):
inputs = inputs.to(device)
labels = labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# 在测试集上进行预测
test_dataset = torch.utils.data.TensorDataset(X_test, y_test)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=len(test_dataset))
for inputs, labels in test_loader:
inputs = inputs.to(device)
labels = labels.to(device)
outputs = model(inputs)
pred = torch.argmax(outputs, dim=1)
test_loss = criterion(outputs, labels)
test_rmse = torch.sqrt(torch.mean((pred - labels) ** 2))
# 输出测试损失和测试集的均方根误差
print('Test loss: {:.4f}, Test RMSE: {:.4f}'.format(test_loss.item(), test_rmse.item()))
# 绘制测试集的预测值和实际值
import matplotlib.pyplot as plt
plt.plot(pred.cpu().numpy(), label='Predict')
plt.plot(labels.cpu().numpy(), label='Actual')
plt.legend()
plt.show()
# 在预测集上进行预测
X_pred = torch.from_numpy(pd.read_csv('X_pred.csv').values).float().to(device)
outputs = model(X_pred)
pred = torch.argmax(outputs, dim=1)
# 计算预测值到达警戒线的时间和实际值到达警戒线的时间
threshold = y_train.max() * 0.85
pred_time = np.argmax(pred.cpu().numpy() >= threshold)
actual_time = np.argmax(y_test.cpu().numpy() >= threshold)
# 绘制预测集的实际值到达的时间和预测值到达的时间
plt.axvline(x=pred_time, color='r', linestyle='--')
plt.axvline(x=actual_time, color='g', linestyle='--')
plt.plot(pred.cpu().numpy(), label='Predict')
plt.plot(y_test.cpu().numpy(), label='Actual')
plt.legend()
plt.show()
```
请注意,这只是一个代码示例,可能需要根据数据集的特点进行调整和修改。
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