Pytorch每日一练——预测泰坦尼克号船上的生存乘客

训练数据：
Survived是输出标签，其他年龄、性别、名字等等都当做输入。当然会有数据缺失的情况，需要提前进行清洗。
测试的目的就是输入样本特征，输出是否能生存下来(0或1)

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

class DiabetesDataset(Dataset):
    def __init__(self, filepath):
        xy = pd.read_csv(filepath)
        self.len = xy.shape[0]
        features = ["Pclass", "Sex", "SibSp", "Parch", "Fare"]
        self.x_data = torch.from_numpy(np.array(pd.get_dummies(xy[features])))
        self.y_data = torch.from_numpy(np.array(xy['Survived']))
        
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]
    
    def __len__(self):
        return self.len
    
dataset = DiabetesDataset('Dataset\\titanic\\train.csv')
train_loader = DataLoader(dataset = dataset,
                          batch_size = 32,
                          shuffle = True,
                         num_workers = 0)
batch_size = 32
batch = np.round(dataset.__len__() / batch_size)

class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear1 = torch.nn.Linear(6, 4)
        self.linear2 = torch.nn.Linear(4, 2)
        self.linear3 = torch.nn.Linear(2, 1)
        self.relu = torch.nn.ReLU()
        self.sigmoid = torch.nn.Sigmoid()
        
    def forward(self, x):
        x = self.relu(self.linear1(x))
        x = self.relu(self.linear2(x))
        x = self.sigmoid(self.linear3(x))#注意最后一步不能使用relu，避免无法计算梯度
        return x

mymodel = Model() 

criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.SGD(mymodel.parameters(), lr = 0.01)

epoch_list = []
loss_list = []
sum_loss = 0
if __name__ == '__main__':
    for epoch in range(500):
        for index, data in enumerate(train_loader, 0):  #train_loader存的是分割组合后的小批量训练样本和对应的标签
            inputs, labels = data #inputs labels都是张量
            inputs = inputs.float()
            labels = labels.float()
            y_pred = mymodel(inputs)
            y_pred = y_pred.squeeze(-1)
            loss = criterion(y_pred, labels)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            sum_loss += loss.item()

            print('epoch = ', epoch + 1,'index = ', index+1, 'loss = ', loss.item())
        epoch_list.append(epoch)
        loss_list.append(sum_loss/batch)
        print(sum_loss/batch)
        sum_loss = 0

test_x = pd.read_csv('Dataset\\titanic\\test.csv')
features = ["Pclass", "Sex", "SibSp", "Parch", "Fare"]
test_x_data = torch.from_numpy(np.array(pd.get_dummies(test_x[features])))
test_x_data = test_x_data.float()
y_test_pred = mymodel(test_x_data)
len_y = y_test_pred.shape[0]
y = []
for i in range(len_y):
    if(y_test_pred[i].item()<0.5):
        y.append(0)
    else:
        y.append(1)
for i in range(len(y)):
    print(y[i])

最后把输出的y保存到gender_submission.csv中，提交kaggle即可。

刚开始练习基础，后面再慢慢改进…