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第R3周：RNN-心脏病预测

news 来源：原创 2025/4/27 18:39:35

文章目录

一、前期准备工作
- 1. 设置硬件设备
- 2. 导入数据
二、构建数据集
- 1. 标准化
- 2. 划分数据集
- 3. 构建数据加载器
三、模型训练
- 1. 构建模型
- 2. 定义训练函数
- 3.定义测试函数
- 4. 正式训练模型
四、模型评估
- 1. Loss与Accuracy图
- 2. 混淆矩阵
- 3. 调用模型进行预测
五、总结

🍨 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

一、前期准备工作

1. 设置硬件设备

import numpy as np
import pandas as pd
import torch
from torch import nn
import torch.nn.functional as F
import seaborn as snsdevice = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device

device(type=‘cpu’)

2. 导入数据

df = pd.read_csv("heart.csv")df

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0

303 rows × 14 columns

二、构建数据集

1. 标准化

from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_splitX = df.iloc[:,: -1]
y = df.iloc[:, -1]# Standardize the data
sc = StandardScaler()
X = sc.fit_transform(X)

2. 划分数据集

X = torch.tensor(np.array(X), dtype=torch.float32)
y = torch.tensor(np.array(y), dtype=torch.int64)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1,random_state=1)X_train.shape, y_train.shape

(torch.Size([272, 13]), torch.Size([272]))

3. 构建数据加载器

from torch.utils.data import TensorDataset, DataLoadertrain_dl = DataLoader(TensorDataset(X_train, y_train),batch_size = 64, shuffle = False)test_dl = DataLoader(TensorDataset(X_test, y_test),batch_size = 64,shuffle = False)

三、模型训练

1. 构建模型

class model_rnn(nn.Module):def __init__(self):super(model_rnn, self).__init__()self.rnn0 = nn.RNN(input_size=13, hidden_size=200,num_layers=1, batch_first=True)self.fc0 = nn.Linear(200, 50)self.fc1 = nn.Linear(50, 2)def forward(self, x):out, hidden1 = self.rnn0(x)out = self.fc0(out)out = self.fc1(out)return outmodel = model_rnn().to(device)
model

model_rnn(
(rnn0): RNN(13, 200, batch_first=True)
(fc0): Linear(in_features=200, out_features=50, bias=True)
(fc1): Linear(in_features=50, out_features=2, bias=True)
)

model(torch.rand(30,13).to(device)).shape

torch.Size([30, 2])

2. 定义训练函数

# 训练循环
def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset)num_batches = len(dataloader)train_loss, train_acc = 0, 0for X, y in dataloader:X, y = X.to(device), y.to(device)pred = model(X)loss = loss_fn(pred, y)optimizer.zero_grad()loss.backward()optimizer.step()train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= sizetrain_loss /= num_batchesreturn train_acc, train_loss

3.定义测试函数

def test (dataloader, model, loss_fn):size = len(dataloader.dataset)num_batches = len(dataloader)test_loss, test_acc = 0, 0with torch.no_grad():for imgs, target in dataloader:imgs, target = imgs.to(device), target.to(device)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 /= sizetest_loss /= num_batchesreturn test_acc, test_loss

4. 正式训练模型

loss_fn = nn.CrossEntropyLoss()
learn_rate = 1e-4
opt = torch.optim.Adam(model.parameters(), lr=learn_rate)
epochs = 50train_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)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)

Epoch: 1, Train_acc:41.9%, Train_loss:0.699, Test_acc:61.3%, Test_loss:0.679, Lr:1.00E-04
==================== Done ====================
Epoch: 2, Train_acc:48.9%, Train_loss:0.685, Test_acc:67.7%, Test_loss:0.664, Lr:1.00E-04
==================== Done ====================
Epoch: 3, Train_acc:59.6%, Train_loss:0.673, Test_acc:67.7%, Test_loss:0.649, Lr:1.00E-04
==================== Done ====================
Epoch: 4, Train_acc:69.5%, Train_loss:0.661, Test_acc:83.9%, Test_loss:0.635, Lr:1.00E-04
==================== Done ====================
Epoch: 5, Train_acc:74.6%, Train_loss:0.649, Test_acc:83.9%, Test_loss:0.622, Lr:1.00E-04
==================== Done ====================
Epoch: 6, Train_acc:77.6%, Train_loss:0.637, Test_acc:87.1%, Test_loss:0.608, Lr:1.00E-04
==================== Done ====================
Epoch: 7, Train_acc:80.1%, Train_loss:0.625, Test_acc:87.1%, Test_loss:0.595, Lr:1.00E-04
==================== Done ====================
Epoch: 8, Train_acc:81.6%, Train_loss:0.613, Test_acc:83.9%, Test_loss:0.581, Lr:1.00E-04
==================== Done ====================
Epoch: 9, Train_acc:81.6%, Train_loss:0.600, Test_acc:83.9%, Test_loss:0.568, Lr:1.00E-04
==================== Done ====================
Epoch:10, Train_acc:81.6%, Train_loss:0.588, Test_acc:83.9%, Test_loss:0.555, Lr:1.00E-04
==================== Done ====================
Epoch:11, Train_acc:82.4%, Train_loss:0.575, Test_acc:83.9%, Test_loss:0.542, Lr:1.00E-04
==================== Done ====================
Epoch:12, Train_acc:83.1%, Train_loss:0.561, Test_acc:83.9%, Test_loss:0.529, Lr:1.00E-04
==================== Done ====================
Epoch:13, Train_acc:83.5%, Train_loss:0.548, Test_acc:83.9%, Test_loss:0.517, Lr:1.00E-04
==================== Done ====================
Epoch:14, Train_acc:83.8%, Train_loss:0.534, Test_acc:83.9%, Test_loss:0.506, Lr:1.00E-04
==================== Done ====================
Epoch:15, Train_acc:83.5%, Train_loss:0.520, Test_acc:83.9%, Test_loss:0.495, Lr:1.00E-04
==================== Done ====================
Epoch:16, Train_acc:83.5%, Train_loss:0.505, Test_acc:83.9%, Test_loss:0.486, Lr:1.00E-04
==================== Done ====================
Epoch:17, Train_acc:83.5%, Train_loss:0.491, Test_acc:83.9%, Test_loss:0.478, Lr:1.00E-04
==================== Done ====================
Epoch:18, Train_acc:83.8%, Train_loss:0.477, Test_acc:83.9%, Test_loss:0.471, Lr:1.00E-04
==================== Done ====================
Epoch:19, Train_acc:83.5%, Train_loss:0.463, Test_acc:83.9%, Test_loss:0.465, Lr:1.00E-04
==================== Done ====================
Epoch:20, Train_acc:83.1%, Train_loss:0.450, Test_acc:80.6%, Test_loss:0.459, Lr:1.00E-04
==================== Done ====================
Epoch:21, Train_acc:83.5%, Train_loss:0.437, Test_acc:80.6%, Test_loss:0.454, Lr:1.00E-04
==================== Done ====================
Epoch:22, Train_acc:83.8%, Train_loss:0.424, Test_acc:80.6%, Test_loss:0.449, Lr:1.00E-04
==================== Done ====================
Epoch:23, Train_acc:84.2%, Train_loss:0.412, Test_acc:80.6%, Test_loss:0.442, Lr:1.00E-04
==================== Done ====================
Epoch:24, Train_acc:84.2%, Train_loss:0.399, Test_acc:83.9%, Test_loss:0.434, Lr:1.00E-04
==================== Done ====================
Epoch:25, Train_acc:84.2%, Train_loss:0.387, Test_acc:87.1%, Test_loss:0.426, Lr:1.00E-04
==================== Done ====================
Epoch:26, Train_acc:84.6%, Train_loss:0.376, Test_acc:87.1%, Test_loss:0.419, Lr:1.00E-04
==================== Done ====================
Epoch:27, Train_acc:85.3%, Train_loss:0.365, Test_acc:87.1%, Test_loss:0.412, Lr:1.00E-04
==================== Done ====================
Epoch:28, Train_acc:86.0%, Train_loss:0.354, Test_acc:87.1%, Test_loss:0.406, Lr:1.00E-04
==================== Done ====================
Epoch:29, Train_acc:85.3%, Train_loss:0.343, Test_acc:87.1%, Test_loss:0.401, Lr:1.00E-04
==================== Done ====================
Epoch:30, Train_acc:87.1%, Train_loss:0.333, Test_acc:87.1%, Test_loss:0.397, Lr:1.00E-04
==================== Done ====================
Epoch:31, Train_acc:87.5%, Train_loss:0.324, Test_acc:87.1%, Test_loss:0.394, Lr:1.00E-04
==================== Done ====================
Epoch:32, Train_acc:88.2%, Train_loss:0.314, Test_acc:87.1%, Test_loss:0.390, Lr:1.00E-04
==================== Done ====================
Epoch:33, Train_acc:88.2%, Train_loss:0.306, Test_acc:87.1%, Test_loss:0.388, Lr:1.00E-04
==================== Done ====================
Epoch:34, Train_acc:88.6%, Train_loss:0.297, Test_acc:87.1%, Test_loss:0.386, Lr:1.00E-04
==================== Done ====================
Epoch:35, Train_acc:89.0%, Train_loss:0.289, Test_acc:87.1%, Test_loss:0.384, Lr:1.00E-04
==================== Done ====================
Epoch:36, Train_acc:88.6%, Train_loss:0.282, Test_acc:87.1%, Test_loss:0.384, Lr:1.00E-04
==================== Done ====================
Epoch:37, Train_acc:89.0%, Train_loss:0.274, Test_acc:83.9%, Test_loss:0.384, Lr:1.00E-04
==================== Done ====================
Epoch:38, Train_acc:89.3%, Train_loss:0.267, Test_acc:80.6%, Test_loss:0.385, Lr:1.00E-04
==================== Done ====================
Epoch:39, Train_acc:89.3%, Train_loss:0.261, Test_acc:80.6%, Test_loss:0.386, Lr:1.00E-04
==================== Done ====================
Epoch:40, Train_acc:90.1%, Train_loss:0.254, Test_acc:80.6%, Test_loss:0.389, Lr:1.00E-04
==================== Done ====================
Epoch:41, Train_acc:90.1%, Train_loss:0.247, Test_acc:80.6%, Test_loss:0.392, Lr:1.00E-04
==================== Done ====================
Epoch:42, Train_acc:90.1%, Train_loss:0.240, Test_acc:80.6%, Test_loss:0.396, Lr:1.00E-04
==================== Done ====================
Epoch:43, Train_acc:90.1%, Train_loss:0.234, Test_acc:77.4%, Test_loss:0.400, Lr:1.00E-04
==================== Done ====================
Epoch:44, Train_acc:90.4%, Train_loss:0.227, Test_acc:77.4%, Test_loss:0.404, Lr:1.00E-04
==================== Done ====================
Epoch:45, Train_acc:91.9%, Train_loss:0.221, Test_acc:77.4%, Test_loss:0.409, Lr:1.00E-04
==================== Done ====================
Epoch:46, Train_acc:93.4%, Train_loss:0.214, Test_acc:77.4%, Test_loss:0.414, Lr:1.00E-04
==================== Done ====================
Epoch:47, Train_acc:93.4%, Train_loss:0.208, Test_acc:77.4%, Test_loss:0.418, Lr:1.00E-04
==================== Done ====================
Epoch:48, Train_acc:93.8%, Train_loss:0.202, Test_acc:77.4%, Test_loss:0.422, Lr:1.00E-04
==================== Done ====================
Epoch:49, Train_acc:93.8%, Train_loss:0.197, Test_acc:77.4%, Test_loss:0.426, Lr:1.00E-04
==================== Done ====================
Epoch:50, Train_acc:94.5%, Train_loss:0.191, Test_acc:77.4%, Test_loss:0.429, Lr:1.00E-04
==================== Done ====================

四、模型评估

1. Loss与Accuracy图

import matplotlib.pyplot as plt
from datetime import datetime
#隐藏警告
import warnings
warnings.filterwarnings("ignore")        #忽略警告信息current_time = datetime.now() # 获取当前时间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']         = 200        #分辨率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) # 打卡请带上时间戳，否则代码截图无效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()

在这里插入图片描述

2. 混淆矩阵

print("==============输入数据Shape为==============")
print("X_test.shape：",X_test.shape)
print("y_test.shape：",y_test.shape)pred = model(X_test.to(device)).argmax(1).cpu().numpy()print("\n==============输出数据Shape为==============")
print("pred.shape：",pred.shape)

输入数据Shape为
X_test.shape： torch.Size([31, 13])
y_test.shape： torch.Size([31])

输出数据Shape为
pred.shape： (31,)

import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay# 计算混淆矩阵
cm = confusion_matrix(y_test, pred)plt.figure(figsize=(6,5))
plt.suptitle('')
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")# 修改字体大小
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.title("Confusion Matrix", fontsize=12)
plt.xlabel("Predicted Label", fontsize=10)
plt.ylabel("True Label", fontsize=10)# 显示图
plt.tight_layout()  # 调整布局防止重叠
plt.show()

在这里插入图片描述

3. 调用模型进行预测

test_X = X_test[0].reshape(1, -1) # X_test[0]即我们的输入数据pred = model(test_X.to(device)).argmax(1).item()
print("模型预测结果为：",pred)
print("=="*20)
print("0：不会患心脏病")
print("1：可能患心脏病")

模型预测结果为： 0
========================================
0：不会患心脏病
1：可能患心脏病

五、总结

本周主要学习了LSTM和RNN，通过实践项目更加深入地了解了RNN模型的结构。

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	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0

	age	sex	cp	trestbps	chol	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	63	1	3	145	233	1	0	150	0	2.3	0	0	1	1
1	37	1	2	130	250	0	1	187	0	3.5	0	0	2	1
2	41	0	1	130	204	0	0	172	0	1.4	2	0	2	1
3	56	1	1	120	236	0	1	178	0	0.8	2	0	2	1
4	57	0	0	120	354	0	1	163	1	0.6	2	0	2	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
298	57	0	0	140	241	0	1	123	1	0.2	1	0	3	0
299	45	1	3	110	264	0	1	132	0	1.2	1	0	3	0
300	68	1	0	144	193	1	1	141	0	3.4	1	2	3	0
301	57	1	0	130	131	0	1	115	1	1.2	1	1	3	0
302	57	0	1	130	236	0	0	174	0	0.0	1	1	2	0