利用pytorch两层线性网络对titanic数据集进行分类（kaggle)
import torch
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
import numpy as np
from torchvision import datasets
from torchvision import transforms
import pandas as pd
class titanicDataset(Dataset):
def __init__(self,filepath):
xy=np.loadtxt(filepath,delimiter=',',skiprows=1,usecols=[1,2,7,8],dtype=np.float32)
self.len=xy.shape[0]
# print(self.len)
self.y_data=torch.from_numpy(xy[:,[0]])
self.x_data=torch.from_numpy(xy[:,1:])
def __getitem__(self,index):#获取索引元素
return self.x_data[index],self.y_data[index]
def __len__(self):
return self.len
dataset=titanicDataset('./pytorch/dataset/titanic/train.csv')
train_loader=DataLoader(dataset=dataset,batch_size=32,shuffle=True,num_workers=0)
# print(dataset.x_data,dataset.y_data)
test_loader=DataLoader(dataset=np.loadtxt('./pytorch/dataset/titanic/test.csv',delimiter=',',skiprows=1,usecols=[1,6,7],dtype=np.float32),batch_size=32,shuffle=False,num_workers=0)
print(next(iter(test_loader)))
class Model(torch.nn.Module):
def __init__(self):
super(Model,self).__init__()
# self.linear1=torch.nn.Linear(4,3)
self.linear2=torch.nn.Linear(3,2)
self.linear3=torch.nn.Linear(2,1)
self.sigmoid=torch.nn.Sigmoid()
def forward(self,x):
# x=self.sigmoid(self.linear1(x))
x=self.sigmoid(self.linear2(x))
x=self.sigmoid(self.linear3(x))
return x
model=Model()
criterion=torch.nn.BCELoss(size_average=True)
optimizer=torch.optim.SGD(model.parameters(),lr=0.1,momentum=0.9)
for epoch in range(10000):
acc_num=0
for i,data in enumerate(train_loader,0):
#1.Prepare data
inputs,labels=data
# print(inputs.shape[0])
#2.Forward
y_pred=model(inputs)
loss=criterion(y_pred,labels)
# print(epoch,i,loss.item())
#3.Backward
optimizer.zero_grad()
loss.backward()
#4.Update
optimizer.step()
y_pred_label=torch.where(y_pred>0.5,torch.tensor([1.0]),torch.tensor([0.0]))
acc_num+=torch.eq(y_pred_label,labels).sum().item()
# print(acc_num,len(dataset),len(train_loader.dataset))
acc=acc_num/len(dataset)
print(acc)
# print(test_loader)
# print(test_loader.dataset.shape)
out = model(torch.tensor(test_loader.dataset))
y_pred = torch.where(out>0.5,torch.tensor([1.0]),torch.tensor([0.0]))[:,0]
print(y_pred)
print(pd.Series(y_pred))
id=pd.read_csv('./pytorch/dataset/titanic/test.csv',usecols=['PassengerId']).iloc[:,0]
# print(type(id))
pd.DataFrame({'PassengerId':id,'Survived':pd.Series(y_pred,dtype=int)}).to_csv('pred.csv',index=None)
a=pd.DataFrame([id,pd.Series(y_pred)])
print(a)
# print(y_pred[-10:])
# for x in test_loader:
# print(x.shape)
# out = model(x)
# y_pred = torch.where(out>0.5,torch.tensor([1.0]),torch.tensor([0.0]))
# print(y_pred)