贝叶斯优化神经网络模型：提升HIV预测精度

导入必要的库

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd

# 安装贝叶斯优化库
!pip install bayesian-optimization
from bayes_opt import BayesianOptimization

读取数据并进行预处理

# 读取Excel表格
data = pd.read_excel('C:\Users\lenovo\Desktop\HIV\GSE6740GSE50011基因降低\output_data.xlsx')

# 数据标准化
data.iloc[:, 1:] = (data.iloc[:, 1:] - data.iloc[:, 1:].mean()) / data.iloc[:, 1:].std()

# 划分数据集
x_train = torch.tensor(data.iloc[:, 1:].values, dtype=torch.float32)
y_train = torch.tensor(data.iloc[:, 0].values, dtype=torch.long)
num_features = x_train.shape[1]

定义神经网络模型

# 定义第一个模型
class Model1(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(Model1, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, output_dim)
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, x):
        out = nn.functional.relu(self.fc1(x))
        out = self.dropout(out)
        out = nn.functional.relu(self.fc2(out))
        out = self.dropout(out)
        out = self.fc3(out)
        return out


# 定义第二个模型
class Model2(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(Model2, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, output_dim)
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, x):
        out = nn.functional.relu(self.fc1(x))
        out = self.dropout(out)
        out = self.fc2(out)
        return out

定义贝叶斯优化函数

def optimize_model(hidden_dim1, hidden_dim2, dropout1, dropout2, lr1, lr2):
    # 初始化第一个模型
    input_dim = num_features
    output_dim = 4
    model1 = Model1(input_dim, int(hidden_dim1), int(hidden_dim2))
    model1.dropout.p = dropout1
    
    # 初始化第二个模型
    input_dim = output_dim
    output_dim = 1
    model2 = Model2(input_dim, int(hidden_dim2), output_dim)
    model2.dropout.p = dropout2
    
    # 定义损失函数和优化器
    criterion1 = nn.CrossEntropyLoss()
    criterion2 = nn.BCEWithLogitsLoss()
    optimizer1 = optim.Adam(model1.parameters(), lr=lr1)
    optimizer2 = optim.Adam(model2.parameters(), lr=lr2)

    # 训练第一个模型
    num_epochs1 = 1000
    for epoch in range(num_epochs1):
        model1.train()
        optimizer1.zero_grad()
        outputs = model1(x_train)
        loss = criterion1(outputs, y_train)
        loss.backward()
        optimizer1.step()

    # 训练第二个模型
    num_epochs2 = 500
    for epoch in range(num_epochs2):
        model2.train()
        optimizer2.zero_grad()
        inputs = model1(x_train)
        outputs = model2(inputs)
        loss = criterion2(outputs, y_train.float().unsqueeze(1))
        loss.backward()
        optimizer2.step()

    # 测试模型
    model1.eval()
    model2.eval()
    inputs = model1(x_train)
    outputs = model2(inputs)
    predicted = outputs.ge(0.5).view(-1).long()
    accuracy = (predicted == y_train).sum().item() / y_train.size(0)

    return accuracy

运行贝叶斯优化

# 设置搜索空间
pbounds = {'hidden_dim1': (64, 256), 'hidden_dim2': (32, 128), 'dropout1': (0.1, 0.5), 'dropout2': (0.1, 0.5),
           'lr1': (0.0001, 0.01), 'lr2': (0.0001, 0.01)}

# 初始化贝叶斯优化器
optimizer = BayesianOptimization(f=optimize_model, pbounds=pbounds, random_state=1)

# 运行贝叶斯优化
optimizer.maximize(init_points=5, n_iter=25)

# 输出最优参数和准确率
print(optimizer.max)

总结

通过贝叶斯优化，我们可以自动搜索最优的神经网络模型参数，从而提高模型的预测精度。此方法可以有效地帮助我们找到更适合数据的模型结构和超参数，提高模型的泛化能力，最终获得更好的预测结果。