Machine_Learning/Logistic_regretion.py at main · glitcher007/Machine_Learning · GitHub

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import torch
import torch.nn as nn
import numpy as np
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

#0) prepare data
bc=datasets.load_breast_cancer()
X,y=bc.data,bc.target


n_samples,n_features=X.shape

# print(n_samples,n_features)

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=1234)

sc=StandardScaler()
X_train=sc.fit_transform(X_train)
X_test=sc.transform(X_test)

X_train=torch.from_numpy(X_train.astype(np.float32))
X_test=torch.from_numpy(X_test.astype(np.float32))
y_train=torch.from_numpy(y_train.astype(np.float32))
y_test=torch.from_numpy(y_test.astype(np.float32))

y_train=y_train.view(y_train.shape[0],1)
y_test=y_test.view(y_test.shape[0],1)


#1) prepare model


class LogsisticRegression(nn.Module):
    def __init__(self,n_input_features):
        super(LogsisticRegression,self).__init__()
        self.linear=nn.Linear(n_input_features,1)


    def forward(self,x):
        y_prdicted=torch.sigmoid(self.linear(x))

        return y_prdicted


model=LogsisticRegression(n_features)


#2)Loss and optimiser

learning_rate=0.01
criterion=nn.BCELoss()

optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)


num_epochs=1000
for epoch in range(num_epochs):
    #forward pass
    y_predicted=model(X_train)
    loss=criterion(y_predicted,y_train)


    #loss backward pas
    loss.backward()


    optimizer.step()


    optimizer.zero_grad()

    if((epoch+1)%50==0):
      print(f'epoch:{epoch+1},loss=={loss.item():.4f}')


with torch.no_grad():
    y_predicted=model(X_test)

    y_predicted_cls=y_predicted.round()


    acc=y_predicted_cls.eq(y_test).sum()/float(y_test.shape[0])
    print(f'accuracy={acc:.4f}')