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口碑好的东莞网站建设,百度指数指的是什么,百度app打开,wordpress怎样将小工具放到左侧实战Kaggle比赛：房价预测让我们动手实战一个Kaggle比赛：房价预测House Prices - Advanced Regression Techniques | Kaggle。本文将提供未经调优的数据的预处理、模型的设计和超参数的选择。通过动手操作、仔细观察实验现象、认真分析实验结果并不断调…

实战Kaggle比赛：房价预测

让我们动手实战一个Kaggle比赛：房价预测House Prices - Advanced Regression Techniques | Kaggle。本文将提供未经调优的数据的预处理、模型的设计和超参数的选择。通过动手操作、仔细观察实验现象、认真分析实验结果并不断调整方法，得到满意的结果。

获取和读取数据集

比赛数据分为训练数据集和测试数据集。两个数据集都包括每栋房子的特征，如街道类型、建造年份、房顶类型、地下室状况等特征值。这些特征值有连续的数字、离散的标签甚至是缺失值“na”。只有训练数据集包括了每栋房子的价格，也就是标签。可以访问比赛网页中的“Data”标签，并下载这些数据集。【You have accepted the rules for this competition. Good luck!】
将通过pandas库读入并处理数据。

%matplotlib inline
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
print(torch.__version__)
torch.set_default_tensor_type(torch.FloatTensor)
train_data = pd.read_csv('./house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('./house-prices-advanced-regression-techniques/test.csv')
print(train_data,test_data)

1.13.1Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \
0        1          60       RL         65.0     8450   Pave   NaN      Reg   
1        2          20       RL         80.0     9600   Pave   NaN      Reg   
2        3          60       RL         68.0    11250   Pave   NaN      IR1   
3        4          70       RL         60.0     9550   Pave   NaN      IR1   
4        5          60       RL         84.0    14260   Pave   NaN      IR1   
...    ...         ...      ...          ...      ...    ...   ...      ...   
1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   
1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   
1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   
1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   
1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \
0            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   
1            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   
2            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   
3            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   
4            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   
...          ...       ...  ...      ...    ...    ...         ...     ...   
1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   
1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   
1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   
1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   
1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   MoSold YrSold  SaleType  SaleCondition  SalePrice  
0         2   2008        WD         Normal     208500  
1         5   2007        WD         Normal     181500  
2         9   2008        WD         Normal     223500  
3         2   2006        WD        Abnorml     140000  
4        12   2008        WD         Normal     250000  
...     ...    ...       ...            ...        ...  
1455      8   2007        WD         Normal     175000  
1456      2   2010        WD         Normal     210000  
1457      5   2010        WD         Normal     266500  
1458      4   2010        WD         Normal     142125  
1459      6   2008        WD         Normal     147500  [1460 rows x 81 columns]         Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \
0     1461          20       RH         80.0    11622   Pave   NaN      Reg   
1     1462          20       RL         81.0    14267   Pave   NaN      IR1   
2     1463          60       RL         74.0    13830   Pave   NaN      IR1   
3     1464          60       RL         78.0     9978   Pave   NaN      IR1   
4     1465         120       RL         43.0     5005   Pave   NaN      IR1   
...    ...         ...      ...          ...      ...    ...   ...      ...   
1454  2915         160       RM         21.0     1936   Pave   NaN      Reg   
1455  2916         160       RM         21.0     1894   Pave   NaN      Reg   
1456  2917          20       RL        160.0    20000   Pave   NaN      Reg   
1457  2918          85       RL         62.0    10441   Pave   NaN      Reg   
1458  2919          60       RL         74.0     9627   Pave   NaN      Reg   LandContour Utilities  ... ScreenPorch PoolArea PoolQC  Fence  \
0            Lvl    AllPub  ...         120        0    NaN  MnPrv   
1            Lvl    AllPub  ...           0        0    NaN    NaN   
2            Lvl    AllPub  ...           0        0    NaN  MnPrv   
3            Lvl    AllPub  ...           0        0    NaN    NaN   
4            HLS    AllPub  ...         144        0    NaN    NaN   
...          ...       ...  ...         ...      ...    ...    ...   
1454         Lvl    AllPub  ...           0        0    NaN    NaN   
1455         Lvl    AllPub  ...           0        0    NaN    NaN   
1456         Lvl    AllPub  ...           0        0    NaN    NaN   
1457         Lvl    AllPub  ...           0        0    NaN  MnPrv   
1458         Lvl    AllPub  ...           0        0    NaN    NaN   MiscFeature MiscVal MoSold  YrSold  SaleType  SaleCondition  
0            NaN       0      6    2010        WD         Normal  
1           Gar2   12500      6    2010        WD         Normal  
2            NaN       0      3    2010        WD         Normal  
3            NaN       0      6    2010        WD         Normal  
4            NaN       0      1    2010        WD         Normal  
...          ...     ...    ...     ...       ...            ...  
1454         NaN       0      6    2006        WD         Normal  
1455         NaN       0      4    2006        WD        Abnorml  
1456         NaN       0      9    2006        WD        Abnorml  
1457        Shed     700      7    2006        WD         Normal  
1458         NaN       0     11    2006        WD         Normal  [1459 rows x 80 columns]

查看前4个样本的前4个特征、后2个特征和标签（SalePrice）：
可以看到第一个特征是Id，它能帮助模型记住每个训练样本，但难以推广到测试样本，所以我们不使用它来训练。我们将所有的训练数据和测试数据的79个特征按样本连结。

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

预处理数据

对连续数值的特征做标准化（standardization）：设该特征在整个数据集上的均值为 $μ\mu$ ，标准差为 $σ\sigma$ 。那么，我们可以将该特征的每个值先减去 $μ\mu$ 再除以 $σ\sigma$ 得到标准化后的每个特征值。对于缺失的特征值，将其替换成该特征的均值。

numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index
all_features[numeric_features] = all_features[numeric_features].apply(lambda x: (x - x.mean()) / (x.std()))
# 标准化后，每个数值特征的均值变为0，所以可以直接用0来替换缺失值
all_features[numeric_features] = all_features[numeric_features].fillna(0)

接下来将离散数值转成指示特征。举个例子，假设特征MSZoning里面有两个不同的离散值RL和RM，那么这一步转换将去掉MSZoning特征，并新加两个特征MSZoning_RL和MSZoning_RM，其值为0或1。如果一个样本原来在MSZoning里的值为RL，那么有MSZoning_RL=1且MSZoning_RM=0。

# dummy_na=True将缺失值也当作合法的特征值并为其创建指示特征
all_features = pd.get_dummies(all_features, dummy_na=True)
all_features.shape # (2919, 331)

可以看到这一步转换将特征数从79增加到了331。最后，通过values属性得到NumPy格式的数据，并转成Tensor方便后面的训练。

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

训练模型

使用一个基本的线性回归模型和平方损失函数来训练模型。下面定义比赛用来评价模型的对数均方根误差。给定预测值 $y^1,…,y^n\hat y_1, \ldots, \hat y_n$ 和对应的真实标签 $y1,…,yny_1,\ldots, y_n$ ，它的定义为 $1n∑i=1n(log⁡(yi)−log⁡(y^i))2\sqrt{\frac{1}{n}\sum_{i=1}^n\left(\log(y_i)-\log(\hat y_i)\right)^2}$ .对数均方根误差的实现如下。

loss = torch.nn.MSELoss()
def get_net(feature_num):net = nn.Linear(feature_num, 1)for param in net.parameters():nn.init.normal_(param, mean=0, std=0.01)return net
def log_rmse(net, features, labels):with torch.no_grad():# 将小于1的值设成1，使得取对数时数值更稳定clipped_preds = torch.max(net(features), torch.tensor(1.0))rmse = torch.sqrt(loss(clipped_preds.log(), labels.log()))return rmse.item()

下面的训练函数跟本章中前几节的不同在于使用了Adam优化算法。相对之前使用的小批量随机梯度下降，它对学习率相对不那么敏感。

def train(net, train_features, train_labels, test_features, test_labels,num_epochs, learning_rate, weight_decay, batch_size):train_ls, test_ls = [], []dataset = torch.utils.data.TensorDataset(train_features, train_labels)train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True)# 这里使用了Adam优化算法optimizer = torch.optim.Adam(params=net.parameters(), lr=learning_rate, weight_decay=weight_decay) net = net.float()for epoch in range(num_epochs):for X, y in train_iter:l = loss(net(X.float()), y.float())optimizer.zero_grad()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

它将被用来选择模型设计并调节超参数。下面实现了一个函数，它返回第i折交叉验证时所需要的训练和验证数据。在K折交叉验证中我们训练K次并返回训练和验证的平均误差。

from IPython import display
from matplotlib import pyplot as plt
def use_svg_display():# 用矢量图显示display.display_svg()
def set_figsize(figsize=(3.5, 2.5)):use_svg_display()# 设置图的尺寸plt.rcParams['figure.figsize'] = figsize
def semilogy(x_vals, y_vals, x_label, y_label, x2_vals=None, y2_vals=None,legend=None, figsize=(3.5, 2.5)):set_figsize(figsize)plt.xlabel(x_label)plt.ylabel(y_label)plt.semilogy(x_vals, y_vals)if x2_vals and y2_vals:plt.semilogy(x2_vals, y2_vals, linestyle=':')plt.legend(legend)
def get_k_fold_data(k, i, X, y):# 返回第i折交叉验证时所需要的训练和验证数据assert k > 1fold_size = X.shape[0] // kX_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]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), dim=0)y_train = torch.cat((y_train, y_part), dim=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, 0for i in range(k):data = get_k_fold_data(k, i, X_train, y_train)net = get_net(X_train.shape[1])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:semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse', range(1, num_epochs + 1), valid_ls, ['train', 'valid'])print('fold %d, train rmse %f, valid rmse %f' % (i, train_ls[-1], valid_ls[-1]))return train_l_sum / k, valid_l_sum / k

模型选择

使用一组未经调优的超参数并计算交叉验证误差。可以改动这些超参数来尽可能减小平均测试误差。

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('%d-fold validation: avg train rmse %f, avg valid rmse %f' % (k, train_l, valid_l))

有时候你会发现一组参数的训练误差可以达到很低，但是在K折交叉验证上的误差可能反而较高。这种现象很可能是由过拟合造成的。因此，当训练误差降低时，要观察K折交叉验证上的误差是否也相应降低。

预测并在Kaggle提交结果

定义预测函数。在预测之前，我们会使用完整的训练数据集来重新训练模型，并将预测结果存成提交所需要的格式。设计好模型并调好超参数之后，下一步就是对测试数据集上的房屋样本做价格预测。如果得到与交叉验证时差不多的训练误差，那么这个结果很可能是理想的，可以在Kaggle上提交结果。

def train_and_pred(train_features, test_features, train_labels, test_data,num_epochs, lr, weight_decay, batch_size):net = get_net(train_features.shape[1])train_ls, _ = train(net, train_features, train_labels, None, None,ag-0-1gqj3d1amag-1-1gqj3d1am num_epochs, lr, weight_decay, batch_size)semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse')print('train rmse %f' % train_ls[-1])preds = net(test_features).detach().numpy()test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)submission.to_csv('./house-prices-advanced-regression-techniques/submission.csv', index=False)
train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size)

上述代码执行完之后会生成一个submission.csv文件。这个文件是符合Kaggle比赛要求的提交格式的。这时，我们可以在Kaggle上提交我们预测得出的结果，并且查看与测试数据集上真实房价（标签）的误差。
具体来说有以下几个步骤：登录Kaggle网站，访问房价预测比赛网页，并点击右侧“Submit Predictions”或“Late Submission”按钮；然后，点击页面下方“Upload Submission File”图标所在的虚线框选择需要提交的预测结果文件；最后，点击页面最下方的“Make Submission”按钮就可以查看结果了.
通常需要对真实数据做预处理。
可以使用K折交叉验证来选择模型并调节超参数。