3_16_.py

import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import d2lzh_pytorch as d2l

#readdata
train_data =pd.read_csv('E:/house-prices-advanced-regression-techniques/train.csv ')
test_data =pd.read_csv('E:/house-prices-advanced-regression-techniques/test.csv ')

print(train_data.shape)
print(test_data.shape)
print(train_data.iloc[0:4, [0, 1, 2, 3, -3, -2, -1]])

all_features = pd.concat((train_data.iloc[:, 1:-1], test_data.iloc[:, 1:]))

#对连续数值的特征做标准化（standardization）：设该特征在整个数据集上的均值为μ，标准差为σ。
# 那么，我们可以将该特征的每个值先减去μ再除以σ得到标准化后的每个特征值。对于缺失的特征值，我们将其替换成该特征的均值。

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)#将拥有不同值的变量转换为0/1数值
print(all_features.shape)

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)

#train
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,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):
    # 返回第i折交叉验证时所需要的训练和验证数据
    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:
            d2l.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, num_epochs, lr, weight_decay, batch_size = 5, 100, 5, 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))

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)
    d2l.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)