asp个人网站模板下载/关键词优化策略

DAY 36 复习日

对应5.25作业

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。

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
import matplotlib.pyplot as plt
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")
# 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。
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
# 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)
# 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)
# 原始模型
class MLP(nn.Module):def __init__(self):super(MLP, self).__init__()self.fc1 = nn.Linear(31, 20)  # 输入层到隐藏层self.relu = nn.ReLU()self.fc2 = nn.Linear(20, 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
# 实例化原始模型并移至GPU
original_model = MLP().to(device)
# 分类问题使用交叉熵损失函数
original_criterion = nn.CrossEntropyLoss()
# 使用随机梯度下降优化器
original_optimizer = optim.SGD(original_model.parameters(), lr=0.01)
# 训练原始模型
num_epochs = 20000
original_losses = []
from tqdm import tqdm
with tqdm(total=num_epochs, desc="原始模型训练进度", unit="epoch") as pbar:for epoch in range(num_epochs):# 前向传播outputs = original_model(X_train)  # 隐式调用forward函数loss = original_criterion(outputs, y_train)# 反向传播和优化original_optimizer.zero_grad()loss.backward()original_optimizer.step()# 记录损失值并更新进度条if (epoch + 1) % 200 == 0:original_losses.append(loss.item())# 更新进度条的描述信息pbar.set_postfix({'Loss': f'{loss.item():.4f}'})# 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)  # 计算剩余的进度并更新
# 评估原始模型
original_model.eval()
with torch.no_grad():outputs = original_model(X_test)_, predicted = torch.max(outputs, 1)correct = (predicted == y_test).sum().item()original_accuracy = correct / y_test.size(0)print(f'原始模型测试集准确率: {original_accuracy * 100:.2f}%')

使用设备: cuda:0

原始模型训练进度: 100%|██████████| 20000/20000 [00:10<00:00, 1844.71epoch/s, Loss=0.4630]

原始模型测试集准确率: 77.07%