34 lines
847 B
Python
34 lines
847 B
Python
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import torch
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import os
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import pandas as pd
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import numpy as np
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from NeuralNetwork import NeuralNetwork
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# Load model if it exists
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if os.path.exists('./models/model.pth'):
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# Create model
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model = torch.load('./models/model.pth')
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# Load test data
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test = pd.read_csv('./datasets/test.csv')
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# Split data
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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_test = torch.FloatTensor(X_test)
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y_test = torch.FloatTensor(y_test).view(-1, 1)
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# Predict
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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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# Save predictions to CSV
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pd.DataFrame(y_pred, columns=['Prediction']).to_csv('predictions.csv', index=False)
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else:
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raise FileNotFoundError('Model not found')
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