使用pytorch预测房价预测比赛，顺便给自己写一个标准的pytorch模板。
1. 数据预处理
引入必要的包，tqdm监控训练进程，colorama彩色打印
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from tqdm import tqdm
from datetime import datetime
from colorama import Fore, Back
import warnings
warnings.filterwarnings('ignore')
这是一个简单的回归问题，这里只做简单的预处理，对于数值型数据转化为正态分布然后缺失填充0，标称型数据做onehot编码
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('test.csv')
# 第一列为id，最后一列为标签，把这两列取出后合并训练集与测试集
all_features = pd.concat((train_df.iloc[:, 1:-1], test_df.iloc[:, 1:]))
# 数值型数据减均值除方差标准化
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()))
# 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
all_features[numeric_features] = all_features[numeric_features].fillna(0)
# 标称数据直接onehot
all_features = pd.get_dummies(all_features, dummy_na=True)
print(f'预处理之后数据形状: {all_features.shape}')
[out]: 预处理之后数据形状: (2919, 331)
这里是回归问题，就简单的2，8分割训练集与验证集。一般来说训练集还会被分割为训练集和验证集，这两个部分都是有类标签的。测试集是最终预测提交的数据，因此没有类标签。
train_num = train_df.shape[0] # 训练集总样本
train_data = all_features.iloc[:train_num, :] # 训练集样本
test_data = all_features.iloc[train_num:, :] # 测试集样本
# 2, 8 分训练集，验证集
train_features, val_features, train_labels, val_labels = train_test_split(
train_data, train_df.iloc[:,-1], test_size=0.2, random_state=42)
# 转化为tensor
# 训练集
train_features = torch.tensor(train_features.values, dtype=torch.float)
# 验证集
val_features = torch.tensor(val_features.values, dtype=torch.float)
# 测试集
test_features = torch.tensor(test_data.values, dtype=torch.float)
# 类标签
# 类标签需要加一维, 为了作为损失函数的输入
# eg:[100,]--->[100, 1]
train_labels = torch.tensor(train_labels.values, dtype=torch.float)
train_labels = train_labels.unsqueeze(1)
val_labels = torch.tensor(val_labels.values, dtype=torch.float)
val_labels = val_labels.unsqueeze(1)
print(f'训练集数据: {train_features.shape}')
print(f'验证集数据: {val_features.shape}')
print(f'测试集数据: {test_features.shape}')
[out]:
训练集数据: torch.Size([1168, 331])
验证集数据: torch.Size([292, 331])
测试集数据: torch.Size([1459, 331])
2. 编写网络
首先，处理输入数据集，pytorch标准写法，编写Dataset类，然后将其装入一个迭代器
# 生成数据集类
class myDataset:
def __init__(self, data, label):
self.data = data
self.label = label
def __len__(self):
return len(self.label)
def __getitem__(self, idx):
return self.data[idx, :], self.label[idx]
train_dataset = myDataset(train_features, train_labels)
val_dataset = myDataset(val_features, val_labels)
# 变为迭代器
train_iter = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True, num_workers=4)
val_iter = DataLoader(dataset=val_dataset, batch_size=64, shuffle=False, num_workers=4)
然后，定义网络初始化参数，定义损失函数，优化器，自定义得分函数
# 初始化权重
def _weight_init(m):
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# 网络
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(331, 512)
self.fc2 = nn.Linear(512, 256)
self.fc3 = nn.Linear(256, 1)
self.apply(_weight_init) # 初始化参数
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# 使用rmse作为自定义得分函数，这也是比赛的判定标准
def custom_score(y_true, y_pred):
rmse = mean_squared_error(np.log1p(y_true), np.log1p(y_pred), squared=False)
return rmse
net = Net()
criterion = torch.nn.MSELoss() # 损失函数为MSE
net = net.to(device) # 将网络和损失函数转化为GPU或CPU
criterion = criterion.to(device)
optimizer = torch.optim.Adam(params=net.parameters(), lr=0.005, weight_decay=0)
在后， 定义训练函数
# 这是训练函数，分为train和val
# train时前向传播后向更新参数
# val时只计算损失函数
def train(net, data_iter, phase, criterion, optimizer=None):
y_true = []
y_pred = []
mean_loss = []
is_grad = True if phase == 'train' else False
with torch.set_grad_enabled(is_grad):
net.train()
for step, (X, y) in enumerate(data_iter):
X = X.to(device)
y = y.to(device)
out = net(X)
loss = criterion(out, y) # 计算损失
mean_loss.append(loss.item())
if phase == 'train':
optimizer.zero_grad() # optimizer 0
loss.backward() # back propragation
optimizer.step() # update the paramters
# 将每一个step的结果加入列表，最后统一生产这一个epoch的指标
# 添加预测值和真实类标签
y_pred.extend(out.detach().cpu().squeeze().numpy().tolist())
y_true.extend(y.detach().cpu().squeeze().numpy().tolist())
# 全量样本的rmse和平均loss
rmse = custom_score(y_true, y_pred)
mean_loss = np.mean(mean_loss)
# 保留4位小数
rmse = np.round(rmse, 4)
mean_loss = np.round(mean_loss, 4)
return mean_loss, rmse
最后，训练函数提交结果
epochs = 100
print(f'{datetime.now()} 开始训练结束...')
for epoch in tqdm(range(epochs)):
train_mean_loss, train_score = train(net=net,
data_iter=train_iter,
phase='train',
criterion=criterion,
optimizer=optimizer)
val_mean_loss, val_score = train(net=net,
data_iter=train_iter,
phase='val',
criterion=criterion,
optimizer=None)
if epoch%10 == 0:
print(Fore.CYAN + Back.BLACK, end='')
tqdm.write(f'Epoch: {epoch} Train loss: {train_mean_loss} Val loss: {val_mean_loss}', end=' ')
tqdm.write(f'Train score: {train_score} Val score: {val_score}')
print(f'{datetime.now()} 训练结束...')
训练结果
# 提交结果
submission = pd.read_csv('sample_submission.csv')
predict = net(test_features)
predict = predict.detach().squeeze().numpy()
submission['SalePrice'] = predict
submission.to_csv('torch_submission.csv', index=False)
这里可以看出验证集的最终结果应该是0.11，我们将数据提交到kaggle得到最终分数
