from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_splitX = df.iloc[:,:-1]
y = df.iloc[:,-1]# 將每一列特征標準化為標準正太分布，注意，標準化是針對每一列而言的
sc = StandardScaler()
X  = sc.fit_transform(X)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.shapefrom 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)

class model_rnn(nn.Module):def __init__(self):super(model_rnn, self).__init__()self.rnn0 = nn.RNN(input_size=32, 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 out   model = model_rnn().to(device)
model

# 訓練循環
def train(dataloader, model, loss_fn, optimizer):size = len(dataloader.dataset) # 訓練集的大小num_batches = len(dataloader) # 批次數目, (size/batch_size，向上取整)train_loss, train_acc = 0, 0 # 初始化訓練損失和正確率for X, y in dataloader: # 獲取圖片及其標簽X, y = X.to(device), y.to(device)# 計算預測誤差pred = model(X) # 網絡輸出loss = loss_fn(pred, y) # 計算網絡輸出和真實值之間的差距，targets為真實值，計算二者差值即為損失# 反向傳播optimizer.zero_grad() # grad屬性歸零loss.backward() # 反向傳播optimizer.step() # 每一步自動更新# 記錄acc與losstrain_acc += (pred.argmax(1) == y).type(torch.float).sum().item()train_loss += loss.item()train_acc /= sizetrain_loss /= num_batchesreturn train_acc, train_lossdef test (dataloader, model, loss_fn):size = len(dataloader.dataset) # 測試集的大小num_batches = len(dataloader) # 批次數目, (size/batch_size，向上取整)test_loss, test_acc = 0, 0# 當不進行訓練時，停止梯度更新，節省計算內存消耗with torch.no_grad():for imgs, target in dataloader:imgs, target = imgs.to(device), target.to(device)# 計算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_lossloss_fn = nn.CrossEntropyLoss() # 創建損失函數
learn_rate = 1e-4 # 學習率
opt = torch.optim.Adam(model.parameters(),lr=learn_rate)
epochs = 30train_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)# 獲取當前的學習率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
#隱藏警告
import warnings
warnings.filterwarnings("ignore") #忽略警告信息
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用來正常顯示中文標簽
plt.rcParams['axes.unicode_minus'] = False # 用來正常顯示負號
plt.rcParams['figure.dpi'] = 100 #分辨率from datetime import datetime
current_time = datetime.now() # 獲取當前時間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) # 打卡請帶上時間戳，否則代碼截圖無效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()import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay# 計算混淆矩陣
cm = confusion_matrix(y_test, pred)plt.figure(figsize=(6,5))
plt.suptitle('')
sns.heatmap(cm, annot=True, fmt="d", cmap="Blues")# 修改字體大小
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)# 顯示圖
plt.tight_layout()  # 調整布局防止重疊
plt.show()

test_X = X_test[0].reshape(1, -1) # X_test[0]即我們的輸入數據pred = model(test_X.to(device)).argmax(1).item()
print("模型預測結果為：",pred)
print("=="*20)
print("0：未患病")
print("1：已患病")