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