房价预测

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import utils

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

numeric_features = all_features.dtypes[all_features.dtypes != 'object'].index
# all_features.dtypes 返回 列名:dtype，类型为Series
# all_features.dtypes != 'object' 返回 列名:True/False，类型为Series
# all_features.dtypes[all_features.dtypes != 'object'] 选择不是object（为数值）的列的子集，列名:dtype，类型为Series
# Series.index 得到列名

all_features[numeric_features] = all_features[numeric_features].apply(
    lambda x: (x - x.mean()) / (x.std()))
# all_features[numeric_features] 取出数值列
# .apply(...) 默认按列应用括号中的函数（axis=0）
# 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
# lambda为匿名函数，格式为 lambda 参数: 表达式

all_features[numeric_features] = all_features[numeric_features].fillna(0)
# 用均值填充na，而标准化后均值为0，故 .fillna(0)

n_train = train_data.shape[0]
# n_train表示 train data的个数（行数）

train_features = torch.tensor(all_features[:n_train].to_numpy(dtype='float32'), dtype=torch.float)
test_features = torch.tensor(all_features[n_train:].to_numpy(dtype='float32'), dtype=torch.float)
# 从合并的all_features中分离出train_features和test_features
# 由于all_features中仍有多种类型（bool和float），而to_numpy()会返回object数组导致报错，因此先转为float的numpy，再转为float的tensor

train_labels = torch.tensor(train_data.SalePrice.to_numpy(), dtype=torch.float).view(-1, 1)
# train_data.SalePrice 从train_data（类型未DataFrame）中取出SalePrice列
# 将SalePrice列作为labels，转化为tensor

loss = torch.nn.MSELoss()
# 定义train时的loss为均方误差，即各个参数作差平方的平均的开根号

def get_net(feature_num):
    # 定义网络模型

    net = nn.Linear(feature_num, 1)
    # 定义线性模型，行数为输入features数，列数为输出类型数

    for param in net.parameters():
        nn.init.normal_(param, mean=0, std=0.01)
    # net.parameters() 遍历网络里所有可学习参数，包括权重和偏移
    # nn.init.normal_ 用正态分布N(0,0.01)给每个参数初始化

    return net

def log_rmse(net, features, labels):
    # 定义test时用对数均方根误差

    with torch.no_grad():
        clipped_preds = torch.max(net(features), torch.tensor(1.0))
        # net(features) 得到net预测的结果
        # torch.max() 将预测结果中小于1的数置为1，取log时更稳定

        rmse = torch.sqrt(loss(clipped_preds.log(), labels.log()))
        # 定义对数均方根误差，即用对数做均方误差的计算

    return rmse.item()

def train(net, train_features, train_labels, test_features, test_labels,
          num_epochs, learning_rate, weight_decay, batch_size):
    # 训练模型，返回train、test的log‑RMSE列表

    train_ls, test_ls = [], []
    # 用列表记录每个epoch的训练、测试的log‑RMSE

    dataset = torch.utils.data.TensorDataset(train_features, train_labels)
    # 把特征和标签打包成数据集对象

    train_iter = torch.utils.data.DataLoader(dataset, batch_size, shuffle=True)
    # 创建小批量迭代器，并打乱数据

    optimizer = torch.optim.Adam(params=net.parameters(), lr=learning_rate, weight_decay=weight_decay)
    # 定义优化器（Adam），并设置lr和weight_decay

    net = net.float()
    # 确保模型参数是float类型

    for epoch in range(num_epochs):
        # 遍历num_epochs个阶段

        for X, y in train_iter:
            # 从迭代器中取出batch

            l = loss(net(X.float()), y.float())
            optimizer.zero_grad()
            l.backward()
            optimizer.step()

        train_ls.append(log_rmse(net, train_features, train_labels))
        # 每个epoch结束后，计算并记录训练集的log‑RMSE

        if test_labels is not None:
            test_ls.append(log_rmse(net, test_features, test_labels))
        # 如果有测试集标签，就计算验证集的log‑RMSE

    return train_ls, test_ls

def get_k_fold_data(k, i, X, y):
    # 返回第i个fold交叉验证时所需要的训练、验证数据

    assert k > 1
    # 检查k>1是否成立但不改变k的值，成立则程序继续，不成立则抛出AssertionError

    fold_size = X.shape[0] // k
    # 每个fold的大小为总数除以fold数

    X_train, y_train = None, None

    for j in range(k):
        # 遍历每个fold

        idx = slice(j * fold_size, (j + 1) * fold_size)
        # 创建第j个fold的索引切片

        X_part, y_part = X[idx, :], y[idx]
        # 选取第j个fold的数据X_part和标签y_part
        # X_part和y_part都按idx选择行，其中X_part选择所有列

        if j == i:
            X_valid, y_valid = X_part, y_part
            # X_valid, y_valid 为返回的验证集数据

        elif X_train is None:
            X_train, y_train = X_part, y_part
            # 如果这是第一个fold，用于将训练集数据初始化
        else:
            X_train = torch.cat((X_train, X_part), dim=0)
            y_train = torch.cat((y_train, y_part), dim=0)
            # 如果训练集数据已完成初始化，竖直拼接

    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):
    # k折交叉验证，返回train、test中的平均误差

    train_l_sum, valid_l_sum = 0, 0

    for i in range(k):
        # 遍历每个fold

        data = get_k_fold_data(k, i, X_train, y_train)
        # 返回第k个训练数据、验证数据

        net = get_net(X_train.shape[1])
        # 定义网络

        train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,
                                   weight_decay, batch_size)
        # 得到每个epoch中train、test记录的log‑RMSE

        train_l_sum += train_ls[-1]
        valid_l_sum += valid_ls[-1]
        # 每次叠加这一fold训练完后的误差，后续除以fold数得到平均误差

        if i == 0:
            utils.semilogy(range(1, num_epochs + 1), train_ls, 'epochs', 'rmse',
                           range(1, num_epochs + 1), valid_ls,
                           ['train', 'valid'])
        # 在第一次画训练/验证的误差曲线

        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

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)
    utils.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)

# import torch
# import torch.nn as nn
# import numpy as np
# import pandas as pd
# import utils
#
# train_data = pd.read_csv(r'/kaggle/house_price/HousePrices\train.csv')
# test_data = pd.read_csv(r'/kaggle/house_price/HousePrices\test.csv')
#
# all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))
#
# 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[numeric_features] = all_features[numeric_features].fillna(0)
#
# all_features = pd.get_dummies(all_features, dummy_na=True)
#
# n_train = train_data.shape[0]
# train_features = torch.tensor(all_features[:n_train].to_numpy(dtype='float32'), dtype=torch.float)
# test_features = torch.tensor(all_features[n_train:].to_numpy(dtype='float32'), dtype=torch.float)
# train_labels = torch.tensor(train_data.SalePrice.to_numpy(), dtype=torch.float).view(-1, 1)
#
# 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
#
#
# def log_rmse(net, features, labels):
#     with torch.no_grad():
#         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)
#     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
#
#
# 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 = 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
#
#
# 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:
#             utils.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
#
#
# k = 5
# num_epochs = 100
# learning_rate = 5
# weight_decay = 0.1
# batch_size = 64
#
# train_l, valid_l = k_fold(k, train_features, train_labels,
#                           num_epochs, learning_rate, weight_decay, batch_size)
# print('k-fold avg train rmse: %f, avg valid rmse: %f' % (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)
#     utils.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, learning_rate, weight_decay,
#                batch_size)