【动手深度学习】Kaggle房价预测的简单实现-Pytorch

思路：

1.数据集准备及预处理

数据集的准备

# 导入所需库
import hashlib
import os
import tarfile
import zipfile
import requests
# 设置下载路径
DATA_HUB = dict()
DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'

# 下载函数
def download(name, cache_dir=os.path.join('..', 'data')):  #@save
    """下载一个DATA_HUB中的文件，返回本地文件名"""
    assert name in DATA_HUB, f"{name} 不存在于 {DATA_HUB}"
    url, sha1_hash = DATA_HUB[name]
    os.makedirs(cache_dir, exist_ok=True)
    fname = os.path.join(cache_dir, url.split('/')[-1])
    if os.path.exists(fname):
        sha1 = hashlib.sha1()
        with open(fname, 'rb') as f:
            while True:
                data = f.read(1048576)
                if not data:
                    break
                sha1.update(data)
        if sha1.hexdigest() == sha1_hash:
            return fname  # 命中缓存
    print(f'正在从{url}下载{fname}...')
    r = requests.get(url, stream=True, verify=True)
    with open(fname, 'wb') as f:
        f.write(r.content)
    return fname
# 解压函数
def download_extract(name, folder=None):  #@save
    """下载并解压zip/tar文件"""
    fname = download(name)
    base_dir = os.path.dirname(fname)
    data_dir, ext = os.path.splitext(fname)
    if ext == '.zip':
        fp = zipfile.ZipFile(fname, 'r')
    elif ext in ('.tar', '.gz'):
        fp = tarfile.open(fname, 'r')
    else:
        assert False, '只有zip/tar文件可以被解压缩'
    fp.extractall(base_dir)
    return os.path.join(base_dir, folder) if folder else data_dir

def download_all():  #@save
    """下载DATA_HUB中的所有文件"""
    for name in DATA_HUB:
        download(name)

# 导入预处理所需的包
%matplotlib inline
import numpy as np
import pandas as pd
import torch
from torch import nn
from d2l import torch as d2l
# 将对应数据集注册成指定的命名
# 实际Kaggle数据集到特定项目页面下载即可
DATA_HUB['kaggle_house_train'] = (  #@save
    DATA_URL + 'kaggle_house_pred_train.csv',
    '585e9cc93e70b39160e7921475f9bcd7d31219ce')

DATA_HUB['kaggle_house_test'] = (  #@save
    DATA_URL + 'kaggle_house_pred_test.csv',
    'fa19780a7b011d9b009e8bff8e99922a8ee2eb90')
# 分别下载测试集和训练集
train_data = pd.read_csv(download('kaggle_house_train'))
test_data = pd.read_csv(download('kaggle_house_test'))
# 查看训练集和测试集的尺寸
train_data.shape,test_data.shape
# 查看训练集的开头四列和最后三列
train_data.iloc[0:4,[0,1,2,3,-3,-2,-1]]

数据中的Id列只是数据的编号，属于无用特征，于是在测试和训练集中去掉该列，并将两个集合合并为所有特征。

all_features = pd.concat((train_data.iloc[:,1:-1],test_data.iloc[:,1:]))
all_features.shape

数据集的预处理

1.数据中的缺失项补充

• 数字缺失项补充

numeric_features = all_features.dtypes[all_features.dtype
all_features[numeric_features] =all.features[numeric_features].apply(lambda x:(x-x.mean())/(x.std()))
# 通过将特征重新缩放到零均值和单位方差来标准化数据
all_features[numeric_features] = all_features[numeric_features].fillna(0) 
# 补充NA项

• 字符缺失项补充

all_features = pd.get_dummies(all_features,dummy_na = True)
all_features.shape
# get_dummies():将分类变量转换为虚拟/指标变量。

2.将数据转化为张量格式

n_train = train_data.shape[0]
train_features = torch.tensor(all_features[:n_train].values,dtype = torch.float32)
test_features = torch.tensor(all_features[n_train:].values,dtype = torch.float32)
train_labels = torch.tensor(train_data.SalePrice.values.reshape(-1,1),dtype=torch.float32)

2.网络模型的设计

损失函数和网络

loss = nn.MSELoss()
in_features  = train_features.shape[1]

def get_net():
    net = nn.Sequential(nn.Linear(in_features,10),nn.ReLU(),nn.Linear(10,1))
    return net
    
# 降低房价误差大对模型的影响，即房价高的房子，预测值和实际值误差肯定比房价低的高，从而导致房价高的房子的权重更高。
# 于是考虑将误差转为百分比表示，真实值-预测值/真实值
def log_rmse(net, features, labels):
    # 为了在取对数时进一步稳定该值，将小于1的值设置为1
    clipped_preds = torch.clamp(net(features), 1, float('inf'))
    # torch.clamp将元素值压缩到1到无穷，这样做log都是正数，计算预测值的log与实际值的log的损失
    rmse = torch.sqrt(loss(torch.log(clipped_preds),
                           torch.log(labels)))
    return rmse.item()

训练网络

def train(net, train_features, train_labels, test_features, test_labels,
          num_epochs, learning_rate, weight_decay, batch_size):
    train_ls, test_ls = [], []
    train_iter = d2l.load_array((train_features, train_labels), batch_size)
    # 这里使用的是Adam优化算法
    optimizer = torch.optim.Adam(net.parameters(),
                                 lr = learning_rate,
                                 weight_decay = weight_decay)
    for epoch in range(num_epochs):
        for X, y in train_iter:
            optimizer.zero_grad()
            l = loss(net(X), y)
            l.backward()
            optimizer.step()
        train_ls.append(log_rmse(net, train_features, train_labels))
        if test_labels is not None:
            test_ls.append(log_rmse(net, test_features, test_labels))
    return train_ls, test_ls

交叉验证

# K折交叉验证
# 交叉验证既可以解决数据集的数据量不够大问题，也可以解决参数调优的问题
def get_k_fold_data(k, i, X, y):
    # i:当前第几折
    # k肯定大于1，小于则报错
    assert k > 1
    # “//”是一个算术运算符，表示整数除法，它可以返回商的整数部分（向下取整）。
    # 不管分为几折和测试数据有多少个，都不会出现小数
    fold_size = X.shape[0] // k
    X_train, y_train = None, None
    for j in range(k):
        idx = slice(j*fold_size,(j+1)*fold_size)
        X_part, y_part = X[idx, :], y[idx]
        if j == i:
        # 当前折扣和i相等，则表明到了验证集部分，设定验证集
            X_valid, y_valid = X_part, y_part
        # train是None，说明还未赋值过，则将分割的赋值
        elif X_train is None:
            X_train, y_train = X_part, y_part
        # 否则将train和part合并
        else:
            X_train = torch.cat([X_train, X_part], 0)
            y_train = torch.cat([y_train, y_part], 0)
    return X_train, y_train, X_valid, y_valid


# 在K折交叉验证中训练次后，返回训练和验证误差的平均值。
def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay,
           batch_size):
    train_l_sum, valid_l_sum = 0, 0
    for i in range(k):
        data = get_k_fold_data(k, i, X_train, y_train)
        net = get_net()
        train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,
                                   weight_decay, batch_size)
        train_l_sum += train_ls[-1]
        valid_l_sum += valid_ls[-1]
        if i == 0:
            d2l.plot(list(range(1, num_epochs + 1)), [train_ls, valid_ls],
                     xlabel='epoch', ylabel='rmse', xlim=[1, num_epochs],
                     legend=['train', 'valid'], yscale='log')
        print(f'折{i + 1}，训练log rmse{float(train_ls[-1]):f}, '
              f'验证log rmse{float(valid_ls[-1]):f}')
    return train_l_sum / k, valid_l_sum / k

提交预测结果

# 提交预测结果
def train_and_pred(train_features, test_features, train_labels, test_data,
                   num_epochs, lr, weight_decay, batch_size):
    net = get_net()
    train_ls, _ = train(net, train_features, train_labels, None, None,
                        num_epochs, lr, weight_decay, batch_size)
    d2l.plot(np.arange(1, num_epochs + 1), [train_ls], xlabel='epoch',
             ylabel='log rmse', xlim=[1, num_epochs], yscale='log')
    print(f'训练log rmse：{float(train_ls[-1]):f}')
    # 将网络应用于测试集。
    preds = net(test_features).detach().numpy()
    # 将其重新格式化以导出到Kaggle
    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)
    submission.to_csv('submission.csv', index=False)
 
 train_and_pred(train_features, test_features, train_labels, test_data,
               num_epochs, lr, weight_decay, batch_size)