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| """ | |
| Unit tests for model functionality | |
| """ | |
| import unittest | |
| import torch | |
| import numpy as np | |
| import sys | |
| import os | |
| # Add src to path | |
| sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'src')) | |
| from src.model import LoanPredictionDeepANN | |
| class TestLoanPredictionModel(unittest.TestCase): | |
| def setUp(self): | |
| """Set up test fixtures before each test method.""" | |
| self.model = LoanPredictionDeepANN(input_size=9) | |
| self.sample_input = torch.randn(10, 9) # Batch of 10 samples | |
| def test_model_initialization(self): | |
| """Test model initialization""" | |
| self.assertIsInstance(self.model, LoanPredictionDeepANN) | |
| self.assertEqual(self.model.fc1.in_features, 9) | |
| self.assertEqual(self.model.fc5.out_features, 1) | |
| def test_forward_pass(self): | |
| """Test forward pass""" | |
| output = self.model(self.sample_input) | |
| # Check output shape | |
| self.assertEqual(output.shape, (10, 1)) | |
| # Check output range (should be between 0 and 1 due to sigmoid) | |
| self.assertTrue(torch.all(output >= 0)) | |
| self.assertTrue(torch.all(output <= 1)) | |
| def test_model_parameters(self): | |
| """Test model has parameters""" | |
| params = list(self.model.parameters()) | |
| self.assertTrue(len(params) > 0) | |
| # Check parameter shapes | |
| self.assertEqual(params[0].shape, (128, 9)) # First layer weights | |
| self.assertEqual(params[1].shape, (128,)) # First layer bias | |
| def test_training_mode(self): | |
| """Test training and eval modes""" | |
| self.model.train() | |
| self.assertTrue(self.model.training) | |
| self.model.eval() | |
| self.assertFalse(self.model.training) | |
| if __name__ == '__main__': | |
| unittest.main() | |