实现kaggle房价预测

导入所需模块：

%matplotlib inlineimport torchimport torch.nn as nnimport numpy as npimport pandas as pdprint(torch.__version__)torch.set_default_tensor_type(torch.FloatTensor)

读取数据集：

（具体以自己存放的数据集的位置为准）

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

数据集的基本情况：

train_data.shape # 输出 (1460, 81)，训练数据集包括1460个样本、80个特征和1个标签。test_data.shape # 输出 (1459, 80)，测试数据集包括1459个样本、80个特征#查看前5个样本的前4个特征、后2个特征和标签（SalePrice）：train_data.iloc[0:5, [0, 1, 2, 3, -3, -2, -1]]

第一个特征是Id，对于机器学习来说，id不能带来有效的特征信息，所以我们不使用这个属性作为特征。

# 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:]))

对数据进行预处理：

numeric_features = all_features.dtypes[all_features.dtypes != 'object'].indexall_features[numeric_features] = all_features[numeric_features].apply(lambda x: (x - x.mean()) / (x.std())) #标准化处理，对所有的特征减去均值，再除以标准差# 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)# 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这一步操作相当于进行one_hot编码all_features = pd.get_dummies(all_features, dummy_na=True)all_features.shape # (2919, 331)，这一步转换将特征数从79增加到了331#将numpy数据转化为tensor，便于后面的训练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)

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

使用one_hot编码将离散的特征分解成多个特征，分解之后的特征可以用0/1来表示，这样，这个转换将特征数从79增加到了331

定义模型所需的函数：

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#对数均方差损失的实现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()

给定预测值y^1,…,y^n\hat y_1, \ldots, \hat y_ny^​1​,…,y^​n​和对应的真实标签y1,…,yny_1,\ldots, y_ny1​,…,yn​，对数均方根误差的定义为

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}.n1​i=1∑n​(log(yi​)−log(y^​i​))2​.

实现K折交叉验证：

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_validdef 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

。

。

训练模型：

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_lsk, num_epochs, lr, weight_decay, batch_size = 5, 100, 5, 0, 64train_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))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)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('./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，该文件符合kaggle的提交格式，可以直接将结果再kaggle比赛链接进行提交。