1.8.Kaggle房价预测

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筋斗云
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House Prices - Advanced Regression Techniques | Kaggle

在这里下载数据,然后使用pandas读。

课本:4.10. 实战Kaggle比赛:预测房价 — 动手学深度学习 2.0.0 documentation (d2l.ai)

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一层线性层

def get_net():     net = nn.Sequential(nn.Linear(in_features, 1))  # 输出房价     return net   k, num_epochs, lr, weight_decay, batch_size = 5, 100, 5, 0, 64   

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MLP

net = nn.Sequential(nn.Flatten(), nn.Linear(in_features, 128), nn.ReLU(), nn.Linear(128, 1)) k, num_epochs, lr, weight_decay, batch_size = 5, 300, 5, 6, 64 

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Xarvier初始化,MLP

def get_net():     #net = nn.Sequential(nn.Linear(in_features, 1))  # 输出房价     net = nn.Sequential(nn.Flatten(), nn.Linear(in_features, 128), nn.ReLU(), nn.Linear(128, 1))     return net  def init_weights(m):     if type(m) == nn.Linear:         nn.init.xavier_normal_(m.weight)         if m.bias is not None:             nn.init.zeros_(m.bias)              k, num_epochs, lr, weight_decay, batch_size = 5, 100, 0.1, 0.2, 128 

完整代码

import numpy as np import pandas as pd import torch from torch import nn from d2l import torch as d2l   train_data = pd.read_csv('D:/a-learn/summer_AI/kaggle/HousePrices/train.csv') test_data = pd.read_csv('D:/a-learn/summer_AI/kaggle/HousePrices/test.csv')  print(train_data.shape) print(test_data.shape)  print(train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]]) # 可以看到第一列特征是ID,对预测没有帮助,直接去掉 # train里面的最后一列是需要预测的值,这样train和test都是80行了 all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))  '''数据预处理     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     下面先处理值为数字的特征,在处理值离散的特征 ''' numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index  # 如果dtype不是object,就是数值特征 all_features[numeric_features] = all_features[numeric_features].apply(     lambda x: (x - x.mean()) / (x.std())  # 归一化 )  # 将方差变为1 all_features[numeric_features] = all_features[numeric_features].fillna(0)  # 归一化后再将NaN填为0 # 处理离散值  # dummy_na意为值为NaN意为没有特征,pandas会帮我们处理NaN的值,注意get_dummies自动赋的是布尔值,需要自己使用dtype来调整 all_features = pd.get_dummies(all_features, dummy_na=True, dtype=int)  # 至此已经全部处理好了,最后通过values属性,可以从pandas格式中提取NumPy格式,并将其转换为张量表示用于训练 n_train = train_data.shape[0]  # 训练集数据的个数 # 将数据转换成为张量 train_features = torch.tensor(all_features[:n_train].values, dtype=torch.float32) test_features = torch.tensor(all_features[n_train:].values, dtype=torch.float32) # reshape(-1,1)将Numpy数组形状转换为一个二维数组,确保每个样本都有一个输出,即从形状(n,)转换为(n,1),n为样本数量 train_labels = torch.tensor(train_data.SalePrice.values.reshape(-1, 1), dtype=torch.float32)  '''训练'''  loss = nn.MSELoss() in_features = train_features.shape[1]  # 输入的特征数   def get_net():     #net = nn.Sequential(nn.Linear(in_features, 1))  # 输出房价     net = nn.Sequential(nn.Flatten(), nn.Linear(in_features, 128), nn.ReLU(), nn.Linear(128, 1))     return net  def init_weights(m):     if type(m) == nn.Linear:         nn.init.xavier_normal_(m.weight)         if m.bias is not None:             nn.init.zeros_(m.bias)  # 对于房价,我们更关心相对误差(y-y')/y.可以使用对数来衡量差异 '''对数均方根误差'''   def log_rmse(net, features, labels):     clipped_preds = torch.clamp(net(features), 1, float('inf'))  # 在取对数时,确保所有预测值至少为 1,以避免对数计算时出现负无穷或未定义的情况     rmse = torch.sqrt(loss(torch.log(clipped_preds), torch.log(labels)))     return rmse.item()  # 将张量转换为Python标量值   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)     # 这里使用的是Adam优化算法,对初始学习率不是很敏感     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()  # 梯度清0             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 # 得到第i折的数据 def get_k_fold_data(k, i, X, y):  # 分别是划分数,选取第几部分为验证集,输入,输出     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:  # 验证集             X_valid, y_valid = X_part, y_part         elif X_train is None:             X_train, y_train = X_part, y_part  # 训练集为空则赋值         else:             X_train = torch.cat([X_train, X_part], 0)  # 不为空则连接,直接接在后面就行,dim=0             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()         net.apply(init_weights)         # *data是对数据解码(取括号),得到get_k_fold_data返回的4个数据列表,依次传入train函数中         train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,                                    weight_decay, batch_size)         train_l_sum += train_ls[-1]  # 注意最后一列是对数均方根误差,没问题的         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'折{i + 1},训练log rmse{float(train_ls[-1]):f}, '               f'验证log rmse{float(valid_ls[-1]):f}')     return train_l_sum / k, valid_l_sum / k   # k, num_epochs, lr, weight_decay, batch_size = 5, 100, 0.1, 0.2, 128 # 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}') # d2l.plt.show()  #调好参数后,使用所有的数据作为训练,然后预测 def train_and_pred(train_features, test_features, train_labels, test_data,                    num_epochs, lr, weight_decay, batch_size):     net = get_net()     net.apply(init_weights)     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'训练log rmse:{float(train_ls[-1]):f}')     d2l.plt.show()     # 将网络应用于测试集。     preds = net(test_features).detach().numpy()     # 将其重新格式化以导出到Kaggle     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,                    100, 0.1, 0.2, 128) 

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