import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import hashlib
import tarfile
import zipfile
import requests
import numpy as np
import pandas as pd
import torch
from torch import nn
from d2l import torch as d2l
DATA_HUB = dict()
DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'
def download(name, cache_dir=os.path.join('..', 'data')):
"""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):
"""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():
"""下载DATA_HUB中的所有文件"""
for name in DATA_HUB:
download(name)
DATA_HUB['kaggle_house_train'] = (
DATA_URL + 'kaggle_house_pred_train.csv',
'585e9cc93e70b39160e7921475f9bcd7d31219ce')
DATA_HUB['kaggle_house_test'] = (
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'))
print(train_data.shape)
print(test_data.shape)
# 先看一下数据
print(train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]])
# 把id列去掉在把数据合并
all_features = pd.concat((train_data.iloc[:,1:-1],test_data.iloc[:,1:]))
# 如果无法获得测试数据，则可根据训练数据计算均值和标准差
# 找到所有数值类型的标签
numeric_features = all_features.dtypes[all_features.dtypes!='object'].index
# 将列的数值类型 进行标准化操作，均值为0 方差为1
all_features[numeric_features] = all_features[numeric_features].apply(
lambda x:((x-x.mean())/(x.std()))
)
# 在标准化数据之后，所有均值消失，因此可以将缺失值设置位0
all_features[numeric_features] = all_features[numeric_features].fillna(0)
# dummy_na=True将na视为有效值的特征值，并为其创建指示符特征，将离散值用独热编码替换
all_features = pd.get_dummies(all_features,dummy_na=True)
shape = all_features.shape
print(shape)
# 获得train数据有多少条
n_train = train_data.shape[0]
print('n_train:',n_train)
# 将train和test分开
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)
# 损失
loss = nn.MSELoss()
# 特征选取331列
in_features = train_features.shape[1]
def get_net():
# 单层线性回归
net = nn.Sequential(nn.Linear(in_features,1))
return net
# 计算相对误差
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'))
rmse = torch.sqrt(loss(torch.log(clipped_preds),torch.log(labels)))
return rmse.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)
print('train_iter:',train_iter)
#使用Adam优化算法，对学习率不那么敏感
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:
# print('x是：',x)
# print('y是：',y)
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))
print('train_ls:',train_ls)
print('test_ls:',test_ls)
return train_ls,test_ls
# k折交叉验证，返回对应的测试集和验证集
def get_k_fold_data(k,i,x,y):
assert k>1
# //表示整除
fold_size = x.shape[0] // k
x_train,y_train = None,None
for j in range(k):
# 设定切片区间
idx = slice(j *fold_size,(j+1)*fold_size)
print('idx:',idx)
x_part,y_part = x[idx,:],y[idx]
print('x_part:',x_part)
print('y_part:',y_part)
# i表示当前第几折，做成验证集
if j==i:
x_valid,y_valid = x_part,y_part
# 将其余的折合并起来
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)
print('x_train:',x_train)
print('y_train:',y_train)
print('x_valid:',x_valid)
print('y_valid:',y_valid)
# 返回训练集和验证集
return x_train,y_train,x_valid,y_valid
# 做k折交叉验证
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):
# 每次拿第i折，包含了训练集和验证集
data = get_k_fold_data(k,i,x_train,y_train)
# 初始化net
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}')
# 训练损失求和做平均，验证损失求和做平均
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}')
# 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)
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)