4.10.1 下载和缓存数据集

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:breaksha1.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_dirdef download_all():  #@save"""下载DATA_HUB中的所有文件"""for name in DATA_HUB:download(name)

4.10.2 Kaggle

好久没用的老帐号给我删了？

4.10.3 访问和读取数据集

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

# 使用前面定义的脚本下载并缓存数据DATA_HUB['kaggle_house_train'] = (  #@saveDATA_URL + 'kaggle_house_pred_train.csv','585e9cc93e70b39160e7921475f9bcd7d31219ce')DATA_HUB['kaggle_house_test'] = (  #@saveDATA_URL + 'kaggle_house_pred_test.csv','fa19780a7b011d9b009e8bff8e99922a8ee2eb90')

# 使用pandas分别加载数据train_data = pd.read_csv(download('kaggle_house_train'))
test_data = pd.read_csv(download('kaggle_house_test'))

print(train_data.shape)
print(test_data.shape)
print(train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]])  # 查看前四个和后两个

(1460, 81)
(1459, 80)Id  MSSubClass MSZoning  LotFrontage SaleType SaleCondition  SalePrice
0   1          60       RL         65.0       WD        Normal     208500
1   2          20       RL         80.0       WD        Normal     181500
2   3          60       RL         68.0       WD        Normal     223500
3   4          70       RL         60.0       WD       Abnorml     140000

all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))  # 删除不带预测信息的Id

4.10.4 数据预处理

numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index  # 定位数值列
all_features[numeric_features] = all_features[numeric_features].apply(lambda x: (x - x.mean()) / (x.std()))  # 标准化数据
all_features[numeric_features] = all_features[numeric_features].fillna(0)  # 将缺失值设为0

# 处理离散值 “Dummy_na=True”将“na”（缺失值）视为有效的特征值，并为其创建指示符特征all_features = pd.get_dummies(all_features, dummy_na=True)
all_features.shape

(2919, 331)

n_train = train_data.shape[0]  # 获取样本数
# 从pandas格式中提取NumPy格式，并将其转换为张量表示用于训练
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)

4.10.5 训练

# 整一个带有损失平方的线性模型作为基线模型loss = nn.MSELoss()
in_features = train_features.shape[1]def get_net():# net = nn.Sequential(nn.Linear(in_features, 1))net = nn.Sequential(nn.Linear(in_features, 256),nn.ReLU(),nn.Linear(256, 64),nn.ReLU(),nn.Linear(64, 1))return net

# 由于房价预测更在意相对误差，故进行取对数处理def log_rmse(net, features, labels):clipped_preds = torch.clamp(net(features), 1, float('inf'))  # 将房价范围限制在1到无穷大，进一步稳定其值rmse = torch.sqrt(loss(torch.log(clipped_preds),torch.log(labels)))  # 取对数再算均方根误差return rmse.item()

# 使用对学习率不敏感的Adam优化器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)  # 加载训练集数据optimizer = torch.optim.Adam(net.parameters(),lr = learning_rate,weight_decay = weight_decay)  # 使用Adam优化算法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

4.10.6 K折交叉验证

def get_k_fold_data(k, i, X, y):assert k > 1fold_size = X.shape[0] // k  # 计算子集数据量X_train, y_train = None, Nonefor j in range(k):idx = slice(j * fold_size, (j + 1) * fold_size)X_part, y_part = X[idx, :], y[idx]  # 截取当前子集数据if j == i:X_valid, y_valid = X_part, y_partelif X_train is None:X_train, y_train = X_part, y_partelse: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

# 完成训练后需要求误差的平均值def k_fold(k, X_train, y_train, num_epochs, learning_rate, weight_decay,batch_size):train_l_sum, valid_l_sum = 0, 0for 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

4.10.7 模型选择

k, num_epochs, lr, weight_decay, batch_size = 10, 100, 0.03, 0.05, 256
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 rmse0.099098, 验证log rmse0.162470
折2，训练log rmse0.091712, 验证log rmse0.114310
折3，训练log rmse0.107151, 验证log rmse0.151471
折4，训练log rmse0.103659, 验证log rmse0.167303
折5，训练log rmse0.102100, 验证log rmse0.165151
折6，训练log rmse0.110199, 验证log rmse0.131012
折7，训练log rmse0.105075, 验证log rmse0.146769
折8，训练log rmse0.109164, 验证log rmse0.123824
折9，训练log rmse0.096305, 验证log rmse0.174747
折10，训练log rmse0.096146, 验证log rmse0.136332
10-折验证: 平均训练log rmse: 0.102061, 平均验证log rmse: 0.147339

4.10.8 提交 Kaggle 预测

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Kaggletest_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：0.091832