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)

一层线性层

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

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

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 netdef 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 d2ltrain_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 netdef 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0l = 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 > 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:  # 验证集X_valid, y_valid = X_part, y_partelif 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=0y_train = torch.cat([y_train, y_part], 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()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Kaggletest_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)