31 lines
1019 B
Python
31 lines
1019 B
Python
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import torch
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from model_train import MyNeuralNetwork, load_data
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from torch.utils.data import DataLoader
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import csv
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def main() -> None:
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model: MyNeuralNetwork = MyNeuralNetwork()
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model.load_state_dict(torch.load('model.pt'))
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model.eval()
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test_dataset = load_data("home_loan_test.csv")
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batch_size: int = 32
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test_dataloader = DataLoader(test_dataset, batch_size=batch_size)
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predictions = []
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labels = []
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with torch.no_grad():
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for batch_data, batch_labels in test_dataloader:
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batch_predictions = model(batch_data)
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predictions.extend(batch_predictions)
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labels.extend(batch_labels)
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filename = "results.csv"
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column_name = "predict"
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with open(filename, 'w', newline='') as file:
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writer = csv.writer(file)
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writer.writerow([column_name])
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for result in predictions:
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loan_decision = 1 if result.item() > 0.5 else 0
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writer.writerow([loan_decision])
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if __name__ == "__main__":
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main()
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