15Kaggle房价预测+课程竞赛.zip

import pandas as pd import numpy as np import matplotlib.pyplot as plt import torch import torch.nn as nn from torch import tensor from torch.utils.data import DataLoader from torch.utils.data import Dataset ##第一步：读取数据 path_train = './house-prices-advanced-regression-techniques/train.csv' #训练数据集地址 path_test = './house-prices-advanced-regression-techniques/test.csv' #测试数据集地址 train_data = pd.read_csv(path_train) #训练数据集 print(train_data) test_data = pd.read_csv(path_test) #测试数据集 print(test_data) ##第二步：预处理数据集 all_features = pd.concat([train_data.iloc[:,1:-1],test_data.iloc[:,1:]],axis=0) print(all_features) # print(all_features) index_Missing_data = all_features.isnull()##全部确实值的索引 numeric_featrues = all_features.dtypes[all_features.dtypes != 'object'].index ##返回所有标签不是object的索引 # print(all_features[numeric_featrues])##输出所有列索引不为object的数据，表示这些列的数据可以用数值表示，即：可以观察到 all_features[numeric_featrues] = all_features[numeric_featrues].apply(lambda x: (x-x.mean())/(x.std())) # 在标准化数据之后，所有均值消失，因此我们可以将缺失值设置为0 all_features[numeric_featrues] = all_features[numeric_featrues].fillna(0) # “Dummy_na=True”将“na”（缺失值）视为有效的特征值，并为其创建指示符特征 all_features = pd.get_dummies(all_features, dummy_na=True) #从Dataframe数据库中读取数据并将其转化为tensor形式 n_train = len(train_data) train_features = torch.tensor(all_features[:n_train].values,dtype = torch.float32) test_features = torch.tensor(all_features[n_train:].values,dtype = torch.float32) train_labels = torch.tensor(train_data.SalePrice.values.reshape(-1, 1), dtype=torch.float32) ''' ##代码测试：检测构成的小批量数据 from torch.utils.data import DataLoader, TensorDataset train_iter = DataLoader(TensorDataset(train_features, train_labels), batch_size=64,shuffle=True) for X,Y in train_iter: print(X) print(Y) #画一下原始的房子销售价格，并分析一下数据，选择使用的预测方法（本节理解即可） a = train_labels.numpy().reshape(-1) b = len(a) plt.plot(np.arange(100), a[0:100]) plt.show() ''' ###第三部：开始模型的训练 #定义平方损失函数 loss = nn.MSELoss() #定义一个简单的线性回归模型 in_features = train_features.shape[1] def get_net(): net = nn.Sequential(nn.Linear(in_features,1)) return net #定义评级模型的指标：对数均方根误差 def log_RMSE(net, features, labels): # 为了在取对数时进一步稳定该值，将小于1的值设置为1 clipped_preds = torch.clamp(net(features), 1, float('inf')) RMSE = torch.sqrt(loss(torch.log(clipped_preds), torch.log(labels))) return RMSE.item() #定义训练模型的过程 def train(net,train_features,train_labels,test_features,test_labels,num_epochs,learning_rate,weight_decay,batch_size): ''' net:定义的网络模型 train_features :训练的特征值 train_labels :训练的标签 test_features :测试的特征值 test_labels :测试的标签 num_epochs :训练迭代的次数 learning_rate :学习率 weight_decay :权重值 batch_size :小批量大小 return :train_ls表示训练数据集的损失, test_ls表示测试数据集的损失 ''' from torch.utils.data import DataLoader, TensorDataset ##使用Dataloader进行小批量读取数据 train_iter = DataLoader(TensorDataset(train_features, train_labels), batch_size=batch_size,shuffle=True) train_ls = [] # 用来保存训练集的对数均方根误差 test_ls = [] # 用来保存测试集的对数均方跟误差 optimizer = torch.optim.Adam(net.parameters(), lr = learning_rate , weight_decay = weight_decay)#优化函数 for epoch in range(num_epochs): for X,Y in train_iter: optimizer.zero_grad() 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 # 第三部分：K折交叉验证 def get_k_fold_data(k, i, X, y): ''' 函数功能：将训练集数据进行 k 折 切 分 k:表示将数据进行 k 次切分 i: X:表示训练集的数据输入 y:表示训练集的数据输出 return:函数返回切分好的数据集 ''' assert k > 1 #只有当k大于1时，后面的代码才会开始运行 fold_size = X.shape[0] // k # 读取训练数据的第一维的数据（行），并将测试的数据集进行相对的切分 X_train, y_train = None, None for j in range(k): idx = slice(j * fold_size, (j + 1) * fold_size) #对数据开始切片，并返回切片位置的索引 X_part = X[idx, :] # 进行训练集的输入数据切分 y_part = y[idx] # 进行训练集的输出数据切分 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], 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): ''' k: 进行 k 折交叉检验 X_train: 训练数据集的输入数据 y_train: 训练数据集的输出的数据 num_epochs:迭代的次数 learning_rate: 学习率 weight_decay: 权重 batch_size: 小批量大小 return: 返回函数的 ''' train_l_sum = 0 #用来累计训练数据集的损失 valid_l_sum = 0 #用来累计测试数据集的损失 for i in range(k): ''' 依次从K个交叉验证集中选取第i个数据集 ''' data = get_k_fold_data(k, i, X_train, y_train) net = get_net() train_ls, valid_ls = train(net, *data, num_epochs, learning_rate,weight_decay, batch_size) train_l_sum += train_ls[-1] #累加返回训练的损失 valid_l_sum += valid_ls[-1] #累加返回测试的损失 '输出第一次的损失函数的图像' if i : plt.plot(list(range(1, num_epochs + 1)), train_ls) plt.plot(list(range(1, num_epochs + 1)), valid_ls) plt.xlim([1, num_epochs]) plt.xlabel('epoch') plt.ylabel('RMSE') plt.legend(['train', 'valid']) plt.yscale('log') plt.show() print(f'k折交叉检验，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 = 5 num_epochs = 100 lr = 5 weight_decay = 0 batch_size =64 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}') ##第四部分： 开始进行测试集的预测，并提交预测结果 def train_and_pred(train_features, test_features, train_labels, test_data,num_epochs, lr, weight_decay, batch_size): ''' :param train_features: 训练集的特征 :param test_features: 测试集的特征 :param train_labels: 训练集的标签 :param test_data: 测试集的标签 :param num_epochs: 迭代次数 :param lr: 学习率 :param weight_decay: 权重 :param batch_size: 小批量大小 :return: ''' net = get_net() train_ls, _ = train(net, train_features, train_labels, None, None,num_epochs, lr, weight_decay, batch_size) plt.plot(np.arange(1, num_epochs + 1), train_ls) plt.xlabel('epoch') plt.y