1021 字
5 分钟
pytorch学习20：处理结构数据实例
2021-10-09
学习记录
PyTorch
/
Python
/
深度学习
/
人工智能

简介#

在对训练数据进行预处理时，我们有时会遇到结构化数据。结构化数据，是高度组织和整齐格式化的数据，是可以放入表格和电子表格中的数据类型，可以理解为一张表格。

本次使用adult.csv数据作为样例进行模型训练，数据连接如下：

https://github.com/zergtant/pytorch-handbook/blob/master/chapter5/data/adult.csv

数据结构如下图：

请添加图片描述

数据预处理#

在处理数据之前，首先要读取数据。使用pandas对数据进行读取：

#读入文件
df = pd.read_csv('./data/adult.csv')

由于模型只能处理数值型数据，因此要先将结构化数据中的不同类型数据进行划分，如下：

#训练结果
result_var = 'salary'
#分类型数据
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race','sex','native-country']
#数值型数据
cont_names = ['age', 'fnlwgt', 'education-num','capital-gain','capital-loss','hours-per-week']

然后需要将分类型数据转化为数值型数据，并进行缺失数据填充：

for col in df.columns:
    if col in cat_names:
        df[col].fillna('---')
        df[col] = LabelEncoder().fit_transform(df[col].astype(str))
    if col in cont_names:
        df[col]=df[col].fillna(0)

数据预处理完后就可以划分出训练数据和结果了

#分割下训练数据和标签
Y = df['salary']
Y_label = LabelEncoder()
Y=Y_label.fit_transform(Y)
X=df.drop(columns=result_var)

定义数据集#

当数据预处理完成后，就可以准备DataLoader了。

首先自定义一个Dataset类：

class tabularDataset(Dataset):
    def __init__(self, X, Y):
        self.x = X.values
        self.y = Y


    def __len__(self):
        return len(self.y)


    def __getitem__(self, idx):
        return (self.x[idx], self.y[idx])


train_ds = tabularDataset(X, Y)

然后创建DataLoader对象：

#DataLoader加载数据
train_dl = DataLoader(train_ds, batch_size=batch_size,shuffle=True)

训练模型和测试结果#

完整代码#

在定义好数据集后，就可以进行模型训练了，训练过程与之前一样。完整代码如下：

import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from sklearn.preprocessing import LabelEncoder
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torch.utils.tensorboard import SummaryWriter


# 定义数据集
class TabularDataset(Dataset):
    def __init__(self, X, Y):
        self.x = X.values
        self.y = Y


    def __len__(self):
        return len(self.y)


    def __getitem__(self, idx):
        return (self.x[idx], self.y[idx])


# 定义模型
class TabularModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(14, 500)
        self.fc2 = nn.Linear(500, 100)
        self.fc3 = nn.Linear(100, 2)
        self.bn_in = nn.BatchNorm1d(14)
        self.bn1 = nn.BatchNorm1d(500)
        self.bn2 = nn.BatchNorm1d(100)


    def forward(self, x):
        x = self.bn_in(x)
        x = F.relu(self.fc1(x))
        x = self.bn1(x)


        x = F.relu(self.fc2(x))
        x = self.bn2(x)


        x = self.fc3(x)
        x = torch.sigmoid(x)


        return x


def getData(batch_size):
    '''
    获得数据集
    :param batch_size: 批大小
    :return: 获得相应的DataLoader
    '''


    df = pd.read_csv("./data/adult.csv")
    # 训练结果
    result_var = "salary"
    # 分类型数据
    cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
                 'native-country']
    # 数值型数据
    cont_names = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']


    # 将分类型数据转化为数字数据，并进行缺失数据填充
    for col in df.columns:
        if col in cat_names:
            df[col].fillna("---")
            df[col] = LabelEncoder().fit_transform(df[col].astype(str))
        if col in cont_names:
            df[col] = df[col].fillna(0)


    # 分割训练数据和标签
    Y = df["salary"]
    Y_label = LabelEncoder()
    Y = Y_label.fit_transform(Y)
    X = df.drop(columns=result_var)
    train_ds = TabularDataset(X, Y)
    # 使用DataLoader加载数据
    train_dl = DataLoader(train_ds, batch_size=batch_size, shuffle=True)
    return train_dl


def train(model, train_dl, DEVICE, epochs=20, learning_rate=0.01):
    '''
    训练


    :param model:
    :param train_dl:
    :param DEVICE:
    :param epochs:
    :param learning_rate:
    :return:
    '''


    model.train()
    writer = SummaryWriter(log_dir="runs/train_data")


    # 损失函数
    criterion = nn.CrossEntropyLoss()
    # 优化器
    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)


    # 记录损失
    losses = []
    for epoch in range(epochs):
        for i, (x, y) in enumerate(train_dl):
            x = x.float().to(DEVICE)
            y = y.long().to(DEVICE)
            # 清除梯度累积
            optimizer.zero_grad()
            # 前向计算
            outputs = model(x)
            # 计算损失
            loss = criterion(outputs, y)
            # 反向传播
            loss.backward()
            optimizer.step()


            losses.append(loss.cpu().data.item())


        writer.add_scalar("loss", loss.cpu().data.item(), global_step=epoch)
        test(model, train_dl, DEVICE, global_step=epoch)
        if (epoch + 1) % 10 == 0:
            print('Epoch : %d/%d,   Loss: %.4f' % (epoch + 1, epochs, np.mean(losses)))


    writer.close()


def test(model, train_dl, DEVICE, global_step=0):
    '''
    测试


    :param model:
    :param train_dl:
    :param DEVICE:
    :param global_step:
    :return:
    '''


    writer = SummaryWriter("runs/test_data")


    # 测试模型
    model.eval()
    correct = 0
    total = 0
    for i, (x, y) in enumerate(train_dl):
        x = x.float().to(DEVICE)
        y = y.long().to(DEVICE)
        outputs = model(x).cpu()
        pred = torch.argmax(outputs, dim=1)
        total += y.size(0)
        correct += (pred == y).sum()
    print('准确率: %.4f %%' % (100 * correct / total))
    writer.add_scalar("accurary", (100 * correct / total), global_step=global_step)
    writer.close()


if __name__ == "__main__":
    # 设置训练趟数
    epochs = 20
    learning_rate = 0.01


    # 准备设备
    DEVICE = torch.device("cpu")
    if torch.cuda.is_available():
        DEVICE = torch.device("cuda")
    print(DEVICE)
    # 获得数据集
    train_dl = getData(batch_size=1024)
    # 实例化模型
    model = TabularModel().to(DEVICE)


    writer = SummaryWriter("runs/model")
    writer.add_graph(model, torch.randn(1, 14))
    writer.close()


    train(model, train_dl, DEVICE, epochs=epochs, learning_rate=learning_rate)

测试结果#

模型结构图#

请添加图片描述

正确率和损失的变化图#

请添加图片描述

注意#

里面的模型结构和变化图都是用tensorboard完成的。关于tensorboard相关应用，可以参考pytorch学习19：pytorch下tensorboard的使用

原文链接：CSDN

pytorch学习20：处理结构数据实例
https://blog.homura.work/posts/csdn/120680192/
作者
HMT
发布于
2021-10-09
许可协议
CC BY-NC-SA 4.0
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