DAY 36 复习日-优快云博客

本文链接：https://blog.youkuaiyun.com/awsndy/article/details/148833056

简单神经网络训练信贷预测分类模型

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split  # 划分数据集
from sklearn.preprocessing import MinMaxScaler
import time
from tqdm import tqdm


import torch  # PyTorch的主库，提供了张量操作、自动微分、GPU加速等核心功能
import torch.nn as nn  # 神经网络模块，包含了各种层（如线性层、卷积层）、激活函数、损失函数等构建神经网络的基础组件
import torch.optim as optim  # 优化器模块，提供了各种优化算法（如SGD、Adam等）用于更新神经网络参数


import matplotlib.pyplot as plt    #用于绘制各种类型的图表
import warnings
warnings.filterwarnings("ignore")

 # 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

# 设置GPU设备
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"使用设备: {device}")

data = pd.read_csv('data.csv')    #读取数据


# 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

data.drop(columns=['Id'], inplace=True) # 删除 Loan ID 列
# data.info()


# 划分数据集
# 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
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


# 归一化数据
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

# 将数据转换为PyTorch张量并移至GPU
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)

class MLP(nn.Module):
  def __init__(self):
    super(MLP, self).__init__()
    self.fc1 = nn.Linear(30, 50)  # 从输入层到隐藏层
    self.relu = nn.ReLU()
    self.fc2 = nn.Linear(50, 2)  # 隐藏层到输出层

  def forward(self, x):
    out = self.fc1(x)
    out = self.relu(out)
    out = self.fc2(out)
    return out

# 实例化模型并移至GPU
model = MLP().to(device)

# 分类问题使用交叉熵损失函数
criterion = nn.CrossEntropyLoss()

# 使用随机梯度下降优化器
optimizer = optim.SGD(model.parameters(), lr=0.01)

# 训练模型
num_epochs = 20000  # 训练的轮数

# 存储每100个epoch的损失值和对应的epoch数
losses = []
epochs = []

start_time = time.time()

with tqdm(total=num_epochs, desc="训练进度", unit="epoch") as pbar:
  for epoch in range(num_epochs):
    # 前向传播
    outputs = model(X_train)  # 隐式调用forward函数
    loss = criterion(outputs, y_train)

    # 反向传播和优化
    optimizer.zero_grad( )  # 梯度清零，因为PyTorch会累积梯度，所以每次迭代需要梯度清零
    loss.backward()  # 反向传播计算梯度
    optimizer.step()  # 更新参数

    # 记录损失值
    if (epoch + 1) % 200 ==0:
      losses.append(loss.item())  # item()方法返回一个Python数值，loss是一个标量张量
      epochs.append(epoch + 1)
      # 更新进度条描述信息
      pbar.set_postfix({'Loss': f'{loss.item():.3f}' })

    # 每1000个epoch更新一次进度条
    if (epoch + 1) % 1000 == 0:
      pbar.update(1000)  # 更新进度条

# 确保进度条达到100%
if pbar.n < num_epochs:
  pbar.update(num_epochs - pbar.n)

end_time = time.time()
print(f"Training time: {end_time - start_time:.2f} seconds")

# 可视化损失曲线
plt.plot(range(len(losses)), losses)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title("Training Loss over Epochs")
plt.show()

输出：

使用设备: cuda:0
训练进度: 100%|██████████| 20000/20000 [00:17<00:00, 1119.23epoch/s, Loss=0.463]
Training time: 17.88 seconds

模型结构可视化

from torchinfo import summary
summary(model, input_size=(30,))

输出：

==========================================================================================
Layer (type:depth-idx)                   Output Shape              Param #
==========================================================================================
MLP                                      [2]                       --
├─Linear: 1-1                            [50]                      1,550
├─ReLU: 1-2                              [50]                      --
├─Linear: 1-3                            [2]                       102
==========================================================================================
Total params: 1,652
Trainable params: 1,652
Non-trainable params: 0
Total mult-adds (M): 0.08
==========================================================================================
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.01
Estimated Total Size (MB): 0.01
==========================================================================================

@浙大疏锦行