实战Kaggle比赛：预测房价

下载数据集

import hashlib
import os
import tarfile
import zipfile
import requests

#@save
DATA_HUB = dict()
DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'


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


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)

%matplotlib inline
import numpy as np
import pandas as pd
import torch
from torch import nn
from d2l import torch as d2l

pd.set_option('display.width', 10)  # 设置字符显示宽度
pd.set_option('display.max_rows', None)  # 设置显示最大

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')
train_data = pd.read_csv(download('kaggle_house_train'))
test_data = pd.read_csv(download('kaggle_house_test'))

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

((1460, 81), (1459, 80))

预处理

train_data.head()

# 数据据预处理
train_data.drop(columns=list(columns_null_rate[columns_null_rate["缺失率"] > 0.25].index), inplace=True)
for name in list(columns_null_rate[columns_null_rate["缺失率"] <= 0.25].index):
    if all_features[name].dtype == object:
        all_features[name].fillna(all_features[name].mode()[0] # 众数
                                  , inplace=True)
    else:
        all_features[name].fillna(all_features[name].median() # 中位数
                                  , inplace=True)

temp = all_features.dtypes
columns_not_object_name = list(temp[temp != object].index)

# 标准化
all_features[columns_not_object_name] = all_features[columns_not_object_name].apply(
    lambda x: (x - x.mean()) / x.std()
)
# 硬编码效果经过测试表现不好
# columns_object_name = list(temp[temp == object].index)
# for i in columns_object_name:
#     all_features[i] = pd.factorize(all_features[i])[0]
all_features = pd.get_dummies(all_features, dummy_na=True)

训练

# 生成训练测试集
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)

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


def get_net():
    net = nn.Sequential(nn.Linear(in_features, 1))
    return net


def log_rmse(net, features, y):  # 题目要求的损失函数
    # 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'))
    return torch.sqrt(loss(torch.log(clipped_preds), torch.log(y))).item()


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)
    optimzer = torch.optim.Adam(
        net.parameters(),
        lr=learning_rate,
        weight_decay=weight_decay
    )
    for epoch in range(num_epochs):
        for X, y in train_iter:
            optimzer.zero_grad()
            l = loss(net(X), y)
            l.backward()
            optimzer.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 Cross-Validation

# K折交叉验证
def get_k_fold_data(k, i, X, y):
    assert k > 1
    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:
            X_valid, y_valid = X_part, y_part
        elif X_train is None:
            X_train, y_train = X_part, y_part
        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


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

k, num_epochs, lr, weight_decay, batch_size = 5, 100, 5, 0, 64
train_l, valid_l = k_fold(k, train_features, train_labels, num_epochs, lr,
                          weight_decay, batch_size)
print(f'{k}-折验证: 平均训练log rmse: {float(train_l):f}, '
      f'平均验证log rmse: {float(valid_l):f}')

折1，训练log rmse0.166534, 验证log rmse0.153954
折2，训练log rmse0.160931, 验证log rmse0.181701
折3，训练log rmse0.160867, 验证log rmse0.165777
折4，训练log rmse0.165114, 验证log rmse0.154417
折5，训练log rmse0.159945, 验证log rmse0.181938
5-折验证: 平均训练log rmse: 0.162678, 平均验证log rmse: 0.167557

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)

训练log rmse：0.162785