kaggle房价预测

比赛地址：https://www.kaggle.com/c/house-prices-advanced-regression-techniques

读取数据

% matplotlib inline
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import d2lzh_pytorch as d2l  # 或者直接安装d2l包：pip install d2l==0.17.6  然后导入 import d2l

print(torch.__version__)
torch.set_default_tensor_type(torch.FloatTensor)  # 设置默认的tensor类型,

1.12.0

train_data = pd.read_csv('../data/kaggle_house/train.csv')
test_data = pd.read_csv('../data/kaggle_house/test.csv')

print(train_data.shape, test_data.shape)

(1460, 81) (1459, 80)

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

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），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  # 包含所有数值特征的列名
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)

(2919, 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

loss = torch.nn.MSELoss()


def get_net(feature_num):
    net = nn.Linear(feature_num, 1)
    for param in net.parameters():  # 初始化模型参数
        nn.init.normal_(param, mean=0, std=0.01)
    return net

下面定义比赛用来评价模型的对数均方根误差。其定义为 1 n ∑ i = 1 n ( log ⁡ ( y i ) − log ⁡ ( y ^ i ) ) 2 \sqrt{\frac{1}{n}\sum_{i=1}^n(\log(y_i) - \log(\hat{y}_i))^2} n1i=1∑n(log(yi)−log(y^i))2

对数均方根误差的实现如下。

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()

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。

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 > 1
    fold_size = X.shape[0] // k  # 计算每一折的数据量
    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), dim=0)
            y_train = torch.cat((y_train, y_part), dim=0)
    return X_train, y_train, X_valid, y_valid

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。

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(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:
            d2l.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

我们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))

fold 0, train rmse 0.169539, valid rmse 0.156727
fold 1, train rmse 0.162395, valid rmse 0.190494
fold 2, train rmse 0.163862, valid rmse 0.168476
fold 3, train rmse 0.167720, valid rmse 0.154207
fold 4, train rmse 0.162936, valid rmse 0.182907
5-fold validation: avg train rmse 0.165290, avg valid rmse 0.170562

预测并在Kaggle提交结果

下面定义预测函数。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。

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,
                        num_epochs, lr, weight_decay, batch_size)
    d2l.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('../data/kaggle_house/submission_1.csv', index=False)

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。如果我们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。

train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size)

train rmse 0.162583

上述模型无调参的情况下在Kaggle上的得分是0.16719。

参考李沐《动手学深度学习》