拿神经网络写一下泰坦尼克

1. 导入数据，用dataset&dataloader

import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd

class MyDataset(Dataset):
    def __init__(self, filepath):
        # 从中提取5个特征
        features = ["Pclass", "Sex", "SibSp", "Parch", "Fare"]
        data = pd.read_csv(filepath)
        self.len = data.shape[0]
        #将dataframe转为张量并分为x和y
        #get_dummies会自动帮你处理好特征的转换，好东西！
        self.x_data = torch.from_numpy(np.array(pd.get_dummies(data[features])))
        self.y_data = torch.from_numpy(np.array(data["Survived"]))
        
    
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]
        
    def __len__(self):
        return self.len
    
dataset = MyDataset(r'.\titanic\train.csv')
train_loader = DataLoader(dataset = dataset
                         ,batch_size = 1
                         ,shuffle = True
                         ,num_workers = 0
                         )

注意，之前的各种数据预处理在这里直接调用pd.get_dummies就可以完成了，挺方便的

2. 定义模型

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(6, 3)
        self.linear2 = torch.nn.Linear(3, 1)
        self.sigmoid = torch.nn.Sigmoid()

    def forward(self, x):
        x = self.sigmoid(self.linear1(x))
        x = self.sigmoid(self.linear2(x))
        return x
    
    # 定义一个预测函数
    def predict(self, x):
        with torch.no_grad():
            x = self.sigmoid(self.linear1(x))
            x = self.sigmoid(self.linear2(x))
            y = []
            for i in x:
                if i>0.5:
                    y.append(1)
                else :
                    y.append(0)
            return y

model = Model()

3. 构造损失函数和优化器

criterion = torch.nn.BCELoss(reduction="mean")
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

4. 进行训练

if __name__ == '__main__':
    loss_list = []
    for epoch in range(200):
        for i, data in enumerate(train_loader, 0):
            inputs, labels = data
            #进行类型转换不然报错
            inputs = inputs.float()
            labels = labels.float()
            print(inputs, labels)
        
            y_pred = model(inputs)
            #压缩成1维
            y_pred = y_pred.squeeze(-1)
            loss = criterion(y_pred, labels)
            print(epoch, i, loss.item())
        
            optimizer.zero_grad()
            loss.backward()
            
            optimizer.step()
        loss_list.append(loss.item())
    
    # 画图
    plt.plot(np.arange(epoch), loss_list)
    plt.xlabel('epoch')
    plt.ylabel('loss')
    plt.show()

跑代码的时候没加画图的部分，而且用Jupyter跑的话很慢。。。还是用pycharm跑吧以后

5. 进行预测

test_data = pd.read_csv("./titanic/test.csv")
features = ["Pclass", "Sex", "SibSp", "Parch", "Fare"]
test = torch.from_numpy(np.array(pd.get_dummies(test_data[features])))

#预测
y = model.predict(test.float())
y

#输出预测结果
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': y})
output.to_csv('my_predict.csv', index=False)

输出全是0，结果上传之后有0.6+的分数。。。我不太明白

还要继续优化的话就是多加一个隐藏层来提升性能，但这也意味着要更多的时间