【MXNet】（十六）：Kaggle房价预测

用到的数据集可以到kaggle的官网上下载，也可以在我的网盘下载：kaggle house数据集，提取码：t5hn

这里要用到pandas，请提前安装好。

首先导入包，

%matplotlib inline
from mxnet import autograd, gluon, init, nd
from mxnet.gluon import data as gdata, loss as gloss, nn
import numpy as np
import pandas as pd

读取数据，

train_data = pd.read_csv('data/kaggle_house_pred_train.csv')
test_data = pd.read_csv('data/kaggle_house_pred_test.csv')

训练数据集包括1460个样本、80个特征和1个标签。测试数据集包括1459个样本和80个特征。

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，所以不使用它来训练。下面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:]))

已经读取的数据还需要做一些预处理。

先对对连续数值的特征做标准化。

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()))
all_features = all_features.fillna(all_features.mean())

接下来将离散数值转成指示特征。

all_features = pd.get_dummies(all_features, dummy_na=True)

然后将数据转化成NDArray。

n_train = train_data.shape[0]
train_features = nd.array(all_features[:n_train].values)
test_features = nd.array(all_features[n_train:].values)
train_labels = nd.array(train_data.SalePrice.values).reshape((-1, 1))

下面定义模型和损失函数，

loss = gloss.L2Loss()

def get_net():
    net = nn.Sequential()
    net.add(nn.Dense(10, activation='relu'),nn.Dense(1))
    net.initialize(init.Normal(sigma=0.01))
    return net

评价模型使用对数均方根误差，

def log_rmse(net, features, labels):
    clipped_preds = nd.clip(net(features), 1, float('inf'))
    rmse = nd.sqrt(2 * loss(clipped_preds.log(), labels.log()).mean())
    return rmse.asscalar()

定义训练函数，这里的优化函数使用adam。

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 = gdata.DataLoader(gdata.ArrayDataset(train_features, train_labels), batch_size, shuffle=True)
    trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': learning_rate, 'wd': weight_decay})
    for epoch in range(num_epochs):
        for X, y in train_iter:
            with autograd.record():
                l = loss(net(X), y)
            l.backward()
            trainer.step(batch_size)
        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

因为样本比较少，这里使用k折交叉验证的方式。

先定义一个获取k折交叉验证时第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):
    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 = nd.concat(X_train, X_part, dim=0)
            y_train = nd.concat(y_train, y_part, dim=0)
    return X_train, y_train, X_valid, y_valid

在K折交叉验证中训练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()
        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

其中semilogy是一个作图函数，

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)

set_figsize()函数的定义请参考链接：【MXNet】（九）：NDArray实现一个简单的线性回归模型。

下面设置超参数进行模型调优。

k, num_epochs, lr, weight_decay, batch_size = 5, 50, 0.3, 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))

 

定义一个预测函数，

def train_and_pred(train_features, test_feature, 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.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse')
    print('train rmse %f' % train_ls[-1])
    preds = net(test_feature).asnumpy()
    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)
    submission.to_csv('result.csv', index=False)

使用调好的参数来训练和预测，

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

打开result.csv，