import numpy as np import pandas as pd import torch import torch.nn as nn import torch.optim as optim import os from sklearn.metrics import classification_report from NeuralNetwork import NeuralNetwork # Seed for reproducibility torch.manual_seed(1234) # Load data train = pd.read_csv('./datasets/train.csv') test = pd.read_csv('./datasets/test.csv') # Split data X_train = train.drop(columns=['id', 'diagnosis']).values y_train = train['diagnosis'].values X_test = test.drop(columns=['id', 'diagnosis']).values y_test = test['diagnosis'].values # Convert data to PyTorch tensors X_train = torch.FloatTensor(X_train) y_train = torch.FloatTensor(y_train).view(-1, 1) X_test = torch.FloatTensor(X_test) y_test = torch.FloatTensor(y_test).view(-1, 1) # Parameters input_size = X_train.shape[1] hidden_size = 128 learning_rate = 0.001 weight_decay = 0.001 num_epochs = 1000 # Model initialization model = NeuralNetwork(input_size, hidden_size) # Loss function and optimizer criterion = nn.BCELoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay) # Training loop model.train() for epoch in range(num_epochs): # Zero the gradients optimizer.zero_grad() # Forward pass outputs = model(X_train) # Compute loss loss = criterion(outputs, y_train) # Backward pass loss.backward() # Update weights optimizer.step() # Print loss if (epoch + 1) % 100 == 0: print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item()}') # Test the model model.eval() with torch.no_grad(): y_pred = model(X_test) y_pred = np.where(y_pred > 0.5, 1, 0) print(classification_report(y_test, y_pred, target_names=['B', 'M'])) # If directory models does not exist, create it if not os.path.exists('./models'): os.makedirs('./models') # Save the model torch.save(model, './models/model.pth')