365打卡第R6周: LSTM实现糖尿病探索与预测

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊

🏡 我的环境：

语言环境：Python3.10
编译器：Jupyter Lab
深度学习环境：torch==2.5.1    torchvision==0.20.1
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#设置GPU 
import torch.nn as nn 
import torch.nn.functional as F 
import torchvision,torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu")print(device) 

(数据规模很小，在普通笔记本电脑就可以流畅跑完)

#导入数据
import numpy   as np
import pandas  as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
plt.rcParams['savefig.dpi'] = 500 #图片像素
plt.rcParams['figure.dpi'] = 500 #分辨率plt.rcParams['font.sans-serif'] = ['SimHei'] #用来正常显示中文标签import warnings
warnings.filterwarnings('ignore')DataFrame = pd.read_excel('./dia.xls')
print(DataFrame.head())print(DataFrame.shape)

# 查看是否有缺失值
print("数据缺失值------------------")
print(DataFrame.isnull().sum())

# 查看数据是否有重复值
print("数据重复值------------------")
print('数据的重复值为：'f'{DataFrame.duplicated().sum()}')

feature_map = {'年龄': '年龄','高密度脂蛋白胆固醇': '高密度脂蛋白胆固醇','低密度脂蛋白胆固醇': '低密度脂蛋白胆固醇','极低密度脂蛋白胆固醇': '极低密度脂蛋白胆固醇','甘油三酯': '甘油三酯','总胆固醇': '总胆固醇','脉搏': '脉搏','舒张压': '舒张压','高血压史': '高血压史','尿素氮': '尿素氮','尿酸': '尿酸','肌酐': '肌酐','体重检查结果': '体重检查结果'
}plt.figure(figsize=(15, 10))
for i, (col, col_name) in enumerate(feature_map.items(), 1):plt.subplot(3, 5, i)sns.boxplot(x=DataFrame['是否糖尿病'], y=DataFrame[col])plt.title(f'{col_name}的箱线图', fontsize=14)plt.ylabel('数值', fontsize=12)plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

# 构建数据集
from sklearn.preprocessing import StandardScaler# '高密度脂蛋白胆固醇'字段与糖尿病负相关，故在X 中去掉该字段
X = DataFrame.drop(['卡号', '是否糖尿病', '高密度脂蛋白胆固醇'], axis=1)
y = DataFrame['是否糖尿病']# sc_X = StandardScaler()
# X = sc_X.fit_transformX = torch.tensor(np.array(X), dtype=torch.float32)
y = torch.tensor(np.array(y), dtype=torch.int64)train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=1)train_X.shape, train_y.shape

from torch.utils.data import TensorDataset, DataLoadertrain_dl = DataLoader(TensorDataset(train_X, train_y), batch_size=64, shuffle=False)
test_dl = DataLoader(TensorDataset(test_X, test_y), batch_size=64, shuffle=False)# 定义模型
class model_lstm(nn.Module):def __init__(self):super(model_lstm, self).__init__()self.lstm0 = nn.LSTM(input_size=13, hidden_size=200,num_layers=1, batch_first=True)self.lstm1 = nn.LSTM(input_size=200, hidden_size=200,num_layers=1, batch_first=True)self.fc0 = nn.Linear(200, 2)  # 输出 2 类def forward(self, x):# 如果 x 是 2D 的，转换为 3D 张量，假设 seq_len=1if x.dim() == 2:x = x.unsqueeze(1)  # [batch_size, 1, input_size]# LSTM 处理数据out, (h_n, c_n) = self.lstm0(x)  # 第一层 LSTM# 使用第二个 LSTM，并传递隐藏状态out, (h_n, c_n) = self.lstm1(out, (h_n, c_n))  # 第二层 LSTM# 获取最后一个时间步的输出out = out[:, -1, :]  # 选择序列的最后一个时间步的输出out = self.fc0(out)  # [batch_size, 2]return outmodel = model_lstm().to(device)
print(model)

# 训练模型def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)  # 训练集的大小num_batches = len(dataloader)  # 批次数目train_loss, train_acc = 0, 0  # 初始化训练损失和正确率model.train()  # 设置模型为训练模式for X, y in dataloader:  # 获取数据和标签# 如果 X 是 2D 的，调整为 3Dif X.dim() == 2:X = X.unsqueeze(1)  # [batch_size, 1, input_size]，即假设 seq_len=1X, y = X.to(device), y.to(device)  # 将数据移动到设备# 计算预测误差pred = model(X)  # 网络输出loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距# 反向传播optimizer.zero_grad()  # 清除上一步的梯度loss.backward()  # 反向传播optimizer.step()  # 更新权重# 记录acc与losstrain_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= size  # 平均准确率train_loss /= num_batches  # 平均损失return train_acc, train_lossdef test(dataloader, model, loss_fn):size = len(dataloader.dataset)  # 测试集的大小num_batches = len(dataloader)  # 批次数目, (size/batch_size，向上取test_loss, test_acc = 0, 0# 当不进行训练时，停止梯度更新，节省计算内存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target = imgs.to(device), target.to(device)# 计算losstarget_pred = model(imgs)loss = loss_fn(target_pred, target)test_loss += loss.item()test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()test_acc /= sizetest_loss /= num_batchesreturn test_acc, test_loss

loss_fn = nn.CrossEntropyLoss()  # 创建损失函数
learn_rate = 1e-4  # 学习率
opt = torch.optim.Adam(model.parameters(), lr=learn_rate)
epochs = 50
train_loss = []
train_acc = []
test_loss = []
test_acc = []
for epoch in range(epochs):model.train()epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt)model.eval()epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)train_acc.append(epoch_train_acc)train_loss.append(epoch_train_loss)test_acc.append(epoch_test_acc)test_loss.append(epoch_test_loss)# 获取当前的学习率lr = opt.state_dict()['param_groups'][0]['lr']template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f},Lr:{:.2E}')print(template.format(epoch + 1, epoch_train_acc * 100, epoch_train_loss, epoch_test_acc * 100, epoch_test_loss, lr))print("=" * 20, 'Done', "=" * 20)

import matplotlib.pyplot as plt
#隐藏警告
import warnings
warnings.filterwarnings("ignore")               #忽略警告信息
plt.rcParams['font.sans-serif']    = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号
plt.rcParams['figure.dpi']         = 100        #分辨率from datetime import datetime
current_time = datetime.now()epochs_range = range(epochs)plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.xlabel(current_time)plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

--------------小结和改进思路（顺便把R7周的任务一起完成）---------------------

针对前述代码，主要在下面几个方面进行改进：

• 性能提升​​：通过双向LSTM和Dropout增强特征提取能力，标准化和类别权重缓解数据问题。
• ​​防止过拟合​​：在全连接层前添加BatchNorm提高泛化性。
• ​​训练稳定性​​：学习率调度器和训练集启用shuffle随机打乱数据使训练更稳定。

通过上述优化，模型在保持LSTM核心结构的同时，能够更有效地处理表格数据并提升预测性能，最终训练集和测试集分别提高了5个百分点。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import TensorDataset, DataLoader
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import f1_score, confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns# 环境设置
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
torch.manual_seed(42) # 打印随机seed使随机分配可重复

# 数据加载与预处理
def load_data():df = pd.read_excel('./dia.xls')# 数据清洗print("缺失值:", df.isnull().sum().sum())print("重复值:", df.duplicated().sum())df = df.drop_duplicates().reset_index(drop=True)# 特征工程X = df.drop(['卡号', '是否糖尿病', '高密度脂蛋白胆固醇'], axis=1)y = df['是否糖尿病']# 处理类别不平衡class_counts = y.value_counts().valuesclass_weights = torch.tensor([1/count for count in class_counts], dtype=torch.float32).to(device)# 数据标准化（先分割后标准化）X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)scaler = StandardScaler()X_train = scaler.fit_transform(X_train)X_test = scaler.transform(X_test)# 转换为TensorX_train = torch.tensor(X_train, dtype=torch.float32).to(device)X_test = torch.tensor(X_test, dtype=torch.float32).to(device)y_train = torch.tensor(y_train.values, dtype=torch.long).to(device)y_test = torch.tensor(y_test.values, dtype=torch.long).to(device)return X_train, X_test, y_train, y_test, class_weights

# 双向LSTM模型
class BiLSTM(nn.Module):def __init__(self, input_size=12, hidden_size=100, num_layers=2):super().__init__()self.lstm = nn.LSTM(input_size=input_size,hidden_size=hidden_size,num_layers=num_layers,bidirectional=True,batch_first=True,dropout=0.3 if num_layers>1 else 0)self.bn = nn.BatchNorm1d(hidden_size*2)self.dropout = nn.Dropout(0.5)self.fc = nn.Linear(hidden_size*2, 2)def forward(self, x):if x.dim() == 2:x = x.unsqueeze(1)out, _ = self.lstm(x)out = out[:, -1, :]  # 取最后时间步out = self.bn(out)out = self.dropout(out)return self.fc(out)

# 训练与验证函数
def train_epoch(model, loader, criterion, optimizer):model.train()total_loss, correct = 0, 0for X, y in loader:X = X.unsqueeze(1) if X.dim()==2 else Xoptimizer.zero_grad()outputs = model(X)loss = criterion(outputs, y)loss.backward()optimizer.step()total_loss += loss.item()correct += (outputs.argmax(1) == y).sum().item()return correct/len(loader.dataset), total_loss/len(loader)def evaluate(model, loader, criterion):model.eval()total_loss, correct = 0, 0all_preds, all_labels = [], []with torch.no_grad():for X, y in loader:X = X.unsqueeze(1) if X.dim()==2 else Xoutputs = model(X)loss = criterion(outputs, y)total_loss += loss.item()correct += (outputs.argmax(1) == y).sum().item()all_preds.extend(outputs.argmax(1).cpu().numpy())all_labels.extend(y.cpu().numpy())f1 = f1_score(all_labels, all_preds)cm = confusion_matrix(all_labels, all_preds)return correct/len(loader.dataset), total_loss/len(loader), f1, cmprint(model)

# 数据准备X_train, X_test, y_train, y_test, class_weights = load_data()train_dataset = TensorDataset(X_train, y_train)test_dataset = TensorDataset(X_test, y_test)train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)test_loader = DataLoader(test_dataset, batch_size=64)# 模型初始化model = BiLSTM(input_size=13).to(device)criterion = nn.CrossEntropyLoss(weight=class_weights)optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=3)# 训练循环best_f1 = 0train_losses, test_losses = [], []train_accs, test_accs = [], []for epoch in range(50):train_acc, train_loss = train_epoch(model, train_loader, criterion, optimizer)test_acc, test_loss, f1, cm = evaluate(model, test_loader, criterion)# 学习率调整scheduler.step(test_loss)# 记录指标train_losses.append(train_loss)test_losses.append(test_loss)train_accs.append(train_acc)test_accs.append(test_acc)# 打印信息print(f"Epoch {epoch+1:02d} | "f"Train Acc: {train_acc:.2%} | Test Acc: {test_acc:.2%} | "f"F1: {f1:.4f} | LR: {optimizer.param_groups[0]['lr']:.2e}")# 可视化plt.figure(figsize=(12,5))plt.subplot(1,2,1)plt.plot(train_losses, label='Train Loss')plt.plot(test_losses, label='Test Loss')plt.legend()plt.title("Loss Curve")plt.subplot(1,2,2)plt.plot(train_accs, label='Train Acc')plt.plot(test_accs, label='Test Acc')plt.legend()plt.title("Accuracy Curve")plt.show()