Pytorch房价预测
数值稳定性和激活函数总结
- relu容易导致梯度爆炸、sigmoid容易导致梯度消失
- xavier模型初始化方法
- Adam适应学习的范围更大一点
房价预测demo
下载数据
import hashlib
import os
import tarfile
import zipfile
import requests
DATA_HUB = dict()
DATA_URL = 'http://d2l-data.s3-accelerate.amazonaws.com/'
- 断言 assert 等价于
if not expression:
raise AssertionError
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
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)
import numpy as np
import pandas as pd
import torch
from torch import nn
from d2l import torch as d2l
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)
(1460, 81)
(1459, 80)
# 前四个和最后两个特征，以及相应标签
print(train_data.iloc[0:4,[0,1,2,3,-3,-2,-1]])
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
特征工程
- 需要注意，这里用的是所有数据集的均值和方差处理数据，实际中不一定能够拿到测试集
# 在每个样本中，第一个特征是ID，我们将其从数据集中删除，同时删除训练集中的标签
all_features = pd.concat((train_data.iloc[:,1:-1],test_data.iloc[:,1:]))
all_features.head()
| MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | ... | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 60 | RL | 65.0 | 8450 | Pave | NaN | Reg | Lvl | AllPub | Inside | ... | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2008 | WD | Normal |
| 1 | 20 | RL | 80.0 | 9600 | Pave | NaN | Reg | Lvl | AllPub | FR2 | ... | 0 | 0 | NaN | NaN | NaN | 0 | 5 | 2007 | WD | Normal |
| 2 | 60 | RL | 68.0 | 11250 | Pave | NaN | IR1 | Lvl | AllPub | Inside | ... | 0 | 0 | NaN | NaN | NaN | 0 | 9 | 2008 | WD | Normal |
| 3 | 70 | RL | 60.0 | 9550 | Pave | NaN | IR1 | Lvl | AllPub | Corner | ... | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2006 | WD | Abnorml |
| 4 | 60 | RL | 84.0 | 14260 | Pave | NaN | IR1 | Lvl | AllPub | FR2 | ... | 0 | 0 | NaN | NaN | NaN | 0 | 12 | 2008 | WD | Normal |
5 rows × 79 columns
all_features.info()
Int64Index: 2919 entries, 0 to 1458
Data columns (total 79 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 MSSubClass 2919 non-null int64
1 MSZoning 2915 non-null object
2 LotFrontage 2433 non-null float64
3 LotArea 2919 non-null int64
4 Street 2919 non-null object
5 Alley 198 non-null object
6 LotShape 2919 non-null object
7 LandContour 2919 non-null object
8 Utilities 2917 non-null object
9 LotConfig 2919 non-null object
10 LandSlope 2919 non-null object
11 Neighborhood 2919 non-null object
12 Condition1 2919 non-null object
13 Condition2 2919 non-null object
14 BldgType 2919 non-null object
15 HouseStyle 2919 non-null object
16 OverallQual 2919 non-null int64
17 OverallCond 2919 non-null int64
18 YearBuilt 2919 non-null int64
19 YearRemodAdd 2919 non-null int64
20 RoofStyle 2919 non-null object
21 RoofMatl 2919 non-null object
22 Exterior1st 2918 non-null object
23 Exterior2nd 2918 non-null object
24 MasVnrType 2895 non-null object
25 MasVnrArea 2896 non-null float64
26 ExterQual 2919 non-null object
27 ExterCond 2919 non-null object
28 Foundation 2919 non-null object
29 BsmtQual 2838 non-null object
30 BsmtCond 2837 non-null object
31 BsmtExposure 2837 non-null object
32 BsmtFinType1 2840 non-null object
33 BsmtFinSF1 2918 non-null float64
34 BsmtFinType2 2839 non-null object
35 BsmtFinSF2 2918 non-null float64
36 BsmtUnfSF 2918 non-null float64
37 TotalBsmtSF 2918 non-null float64
38 Heating 2919 non-null object
39 HeatingQC 2919 non-null object
40 CentralAir 2919 non-null object
41 Electrical 2918 non-null object
42 1stFlrSF 2919 non-null int64
43 2ndFlrSF 2919 non-null int64
44 LowQualFinSF 2919 non-null int64
45 GrLivArea 2919 non-null int64
46 BsmtFullBath 2917 non-null float64
47 BsmtHalfBath 2917 non-null float64
48 FullBath 2919 non-null int64
49 HalfBath 2919 non-null int64
50 BedroomAbvGr 2919 non-null int64
51 KitchenAbvGr 2919 non-null int64
52 KitchenQual 2918 non-null object
53 TotRmsAbvGrd 2919 non-null int64
54 Functional 2917 non-null object
55 Fireplaces 2919 non-null int64
56 FireplaceQu 1499 non-null object
57 GarageType 2762 non-null object
58 GarageYrBlt 2760 non-null float64
59 GarageFinish 2760 non-null object
60 GarageCars 2918 non-null float64
61 GarageArea 2918 non-null float64
62 GarageQual 2760 non-null object
63 GarageCond 2760 non-null object
64 PavedDrive 2919 non-null object
65 WoodDeckSF 2919 non-null int64
66 OpenPorchSF 2919 non-null int64
67 EnclosedPorch 2919 non-null int64
68 3SsnPorch 2919 non-null int64
69 ScreenPorch 2919 non-null int64
70 PoolArea 2919 non-null int64
71 PoolQC 10 non-null object
72 Fence 571 non-null object
73 MiscFeature 105 non-null object
74 MiscVal 2919 non-null int64
75 MoSold 2919 non-null int64
76 YrSold 2919 non-null int64
77 SaleType 2918 non-null object
78 SaleCondition 2919 non-null object
dtypes: float64(11), int64(25), object(43)
memory usage: 1.8+ MB
# 存在缺失值的列的数目
all_features.isnull().any(axis=0).sum()
34
# 存在缺失值的行的数目
all_features.isnull().any(axis=1).sum()
2919
# 将所有缺失的值替换为相应特征的平均值。 通过将特征重新缩放到零均值和单位方差来标准化数据
numeric_features = all_features.dtypes[all_features.dtypes != "object"].index # 在pandas中object就是字符串类型
all_features[numeric_features] = all_features[numeric_features].apply(\
lambda x: (x- x.mean() / x.std())) # 对每一列进行操作
all_features[numeric_features] = all_features[numeric_features].fillna(0)
# 再看一下存在缺失值的列的数目
all_features.isnull().any(axis=0).sum()
23
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-RvstCcp5-1668172706049)(attachment:faedb1f0-d795-4196-861c-164478db64e4.png)]
# 处理字符串，one-hot编码
all_features = pd.get_dummies(all_features, dummy_na=True)
all_features.shape
(2919, 331)
# 再看一下存在缺失值的列的数目
all_features.isnull().any(axis=0).sum()
0
转为张量
# 从pandas格式中提取Numpy格式，并将其转为张量
# 切记将其转换为float32，因为tensor常用的是float32
n_train = train_data.shape[0] # 行数
train_features = torch.tensor(all_features[:n_train].values, dtype=torch.float32)
train_features.shape
torch.Size([1460, 331])
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)
# 不将训练标签转换成矩阵的话训练过程中会有警告
模型及训练
模型
loss = nn.MSELoss()
in_features = train_features.shape[1]
def get_net(): # 简单的线性回归
net = nn.Sequential(nn.Linear(in_features=in_features,out_features=1))
return net
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-aslA2oL1-1668172706050)(attachment:9ebb6e60-ef9c-433d-bc4e-0ae619824975.png)]
def log_rmse(net, features, labels):
clipped_preds = torch.clamp(net(features), 1, float('inf'))
rmse = torch.sqrt(loss(torch.log(clipped_preds), torch.log(labels)))
return rmse.item()
训练函数
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)
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
k折交叉验证
注意：我们这里的验证集是从训练集中分出来的
slice(1,4) # 切片函数 跟python序列数据类型的切片一毛一样
slice(1, 4, None)
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) # 行拼接
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'fold {i + 1}, train log rmse {float(train_ls[-1]):f}, '
f'valid log rmse {float(valid_ls[-1]):f}')
return train_l_sum / k, valid_l_sum / k
模型选择
调整超参数和模型架构
k, num_epochs, lr, weight_decay, batch_size = 5, 100, 0.03, 0.01, 64
# lr这么大的原因是选择了Adam优化器，他能接受的学习率范围更大
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}')
fold 1, train log rmse 0.258938, valid log rmse 0.234451
fold 2, train log rmse 0.250196, valid log rmse 0.281742
fold 3, train log rmse 0.255146, valid log rmse 0.254656
fold 4, train log rmse 0.255734, valid log rmse 0.259894
fold 5, train log rmse 0.252717, valid log rmse 0.259565
5-折验证: 平均训练log rmse: 0.254546, 平均验证log rmse: 0.258062
提交kaggle预测num_epochs
def train_and_prde(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'train log rmse {float(train_ls[-1]):f}') # 保留六位小数
preds = net(test_features).detach().numpy()
test_data['SalePrice'] = pd.Series(preds.reshape(1,-1)[0])
# print(test_data['SalePrice'])
submission = pd.concat([test_data['Id'], test_data['SalePrice']],axis=1)
submission.to_csv('submission.csv', index=False)
train_and_prde(train_features, test_features, train_labels, test_data,
num_epochs, lr,weight_decay, batch_size)
train log rmse 0.245931
