R7周：糖尿病预测模型优化探索

import torch.nn.functional as F
import torch.nn as nn
import torch, torchvision

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

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
# 支持中文
plt.rcParams['savefig.dpi'] = 500
plt.rcParams['figure.dpi'] = 500
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('F:/jupyter lab/DL-100-days/datasets/diabetes_pre/dia.xls')
DataFrame.head()

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()

import plotly
import plotly.express as px

#删除列'卡号'
DataFrame.drop(columns=['卡号'], inplace=True)
# 计算各列之间的相关系数
df_corr = DataFrame.corr()

#相关矩阵生成函数
def corr_generate(df):
    fig = px.imshow(df,text_auto=True,aspect="auto",color_continuous_scale='RdBu_r')
    fig.show()
    
#生成相关矩阵
corr_generate(df_corr)

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_transform(X)

X = 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, DataLoader

train_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)

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=1
        if 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 out

model = model_lstm().to(device)
print(model)

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 3D
        if X.dim() == 2:
            X = X.unsqueeze(1)  # [batch_size, 1, input_size]，即假设 seq_len=1
        
        X, 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loss
        train_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_loss

def 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loss
            target_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 /= size
    test_loss /= num_batches
    return 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 = 30
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()