Spaces:
Sleeping
Sleeping
File size: 1,797 Bytes
7eccd3a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
"""
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()
|