仔细回顾一下神经网络到目前的内容，没跟上进度的同学补一下进度。
- 作业：对之前的信贷项目，利用神经网络训练下，尝试用到目前的知识点让代码更加规范和美观。
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import pandas as pd #用于数据处理和分析，可处理表格数据。 import numpy as np #用于数值计算，提供了高效的数组操作。 import matplotlib.pyplot as plt #用于绘制各种类型的图表 import seaborn as sns #基于matplotlib的高级绘图库，能绘制更美观的统计图形。 import warnings warnings.filterwarnings("ignore") import torch import torch.nn as nn import torch.optim as optim from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler import time from tqdm import tqdm # 导入tqdm库用于进度条显示 # 设置中文字体（解决中文显示问题） plt.rcParams['font.sans-serif'] = ['SimHei'] # Windows系统常用黑体字体 plt.rcParams['axes.unicode_minus'] = False # 正常显示负号 data = pd.read_csv('data.csv') #读取数据 # 设置GPU设备 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(f"使用设备: {device}") # 先筛选字符串变量 discrete_features = data.select_dtypes(include=['object']).columns.tolist() # Home Ownership 标签编码 home_ownership_mapping = { 'Own Home': 1, 'Rent': 2, 'Have Mortgage': 3, 'Home Mortgage': 4 } data['Home Ownership'] = data['Home Ownership'].map(home_ownership_mapping) # Years in current job 标签编码 years_in_job_mapping = { '< 1 year': 1, '1 year': 2, '2 years': 3, '3 years': 4, '4 years': 5, '5 years': 6, '6 years': 7, '7 years': 8, '8 years': 9, '9 years': 10, '10+ years': 11 } data['Years in current job'] = data['Years in current job'].map(years_in_job_mapping) # Purpose 独热编码，记得需要将bool类型转换为数值 data = pd.get_dummies(data, columns=['Purpose']) data2 = pd.read_csv("data.csv") # 重新读取数据，用来做列名对比 list_final = [] # 新建一个空列表，用于存放独热编码后新增的特征名 for i in data.columns: if i not in data2.columns: list_final.append(i) # 这里打印出来的就是独热编码后的特征名 for i in list_final: data[i] = data[i].astype(int) # 这里的i就是独热编码后的特征名 # Term 0 - 1 映射 term_mapping = { 'Short Term': 0, 'Long Term': 1 } data['Term'] = data['Term'].map(term_mapping) data.rename(columns={'Term': 'Long Term'}, inplace=True) # 重命名列 continuous_features = data.select_dtypes(include=['int64', 'float64']).columns.tolist() #把筛选出来的列名转换成列表 # 连续特征用中位数补全 for feature in continuous_features: mode_value = data[feature].mode()[0] #获取该列的众数。 data[feature].fillna(mode_value, inplace=True) #用众数填充该列的缺失值，inplace=True表示直接在原数据上修改。 # 最开始也说了 很多调参函数自带交叉验证，甚至是必选的参数，你如果想要不交叉反而实现起来会麻烦很多 # 所以这里我们还是只划分一次数据集 from sklearn.model_selection import train_test_split X = data.drop(['Credit Default'], axis=1) # 特征，axis=1表示按列删除 y = data['Credit Default'] # 标签 # 按照8:2划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 80%训练集，20%测试集 # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 归一化数据 scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) X_train = torch.FloatTensor(X_train).to(device) y_train = torch.LongTensor(y_train.values).to(device) X_test = torch.FloatTensor(X_test).to(device) y_test = torch.LongTensor(y_test.values).to(device) batch_size = 64 train_dataset = torch.utils.data.TensorDataset(X_train, y_train) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) input_size=X_train.shape[1] class MLP(nn.Module): def __init__(self, input_size): # 添加input_size参数 super(MLP, self).__init__() self.fc1 = nn.Linear(input_size, 64) # 输入维度为实际特征数 self.relu = nn.ReLU() self.dropout = nn.Dropout(0.2) # 新增dropout层防止过拟合 self.fc2 = nn.Linear(64, 2) # 输出改为2个神经元（二分类问题） def forward(self, x): out = self.fc1(x) out = self.relu(out) out = self.dropout(out) out = self.fc2(out) return out # 实例化模型 model = MLP(input_size=X_train.shape[1]).to(device) # 分类问题使用交叉熵损失函数 criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # 改为Adam优化器 criterion = nn.CrossEntropyLoss() num_epochs = 20000 best_loss = float('inf') patience = 5 min_delta = 0.001 counter = 0 # 添加记录列表 loss_history = [] epoch_list = [] start_time = time.time() with tqdm(total=num_epochs, desc="训练进度", unit="epoch") as pbar: for epoch in range(num_epochs): model.train() epoch_loss = 0.0 # 训练步骤 for inputs, labels in train_loader: optimizer.zero_grad() outputs = model(inputs) loss = criterion(outputs, labels) loss.backward() optimizer.step() epoch_loss += loss.item() * inputs.size(0) # 计算平均epoch损失 avg_loss = epoch_loss / len(train_loader.dataset) loss_history.append(avg_loss) epoch_list.append(epoch+1) # 更新进度条 pbar.set_postfix({'Train Loss': f'{avg_loss:.4f}'}) pbar.update(1) # 早停逻辑（基于训练损失） if avg_loss < best_loss - min_delta: best_loss = avg_loss counter = 0 best_weights = model.state_dict().copy() # 保存最佳权重 else: counter += 1 if counter >= patience: print(f"\n早停触发！第 {epoch+1} 个epoch后停止") break # 加载最佳模型权重 model.load_state_dict(best_weights) time_all = time.time() - start_time print(f'训练时间: {time_all:.2f}秒') # 可视化损失曲线 plt.figure(figsize=(10, 6)) plt.plot(epoch_list, loss_history) plt.xlabel('Epoch') plt.ylabel('Loss') plt.title('Training Loss over Epochs') plt.grid(True) plt.show() # 评估模型 model.eval() # 设置模型为评估模式 with torch.no_grad(): # torch.no_grad()的作用是禁用梯度计算，可以提高模型推理速度 outputs = model(X_test) # 对测试数据进行前向传播，获得预测结果 _, predicted = torch.max(outputs, 1) # torch.max(outputs, 1)返回每行的最大值和对应的索引 correct = (predicted == y_test).sum().item() # 计算预测正确的样本数 accuracy = correct / y_test.size(0) print(f'测试集准确率: {accuracy * 100:.2f}%')
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