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import unittest
import pandas as pd
import numpy as np
import os
import sys
# Add the src directory to the path
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from src.models.loan_model import LoanApprovalModel
class TestLoanModel(unittest.TestCase):
def setUp(self):
"""Set up test fixtures"""
self.model = LoanApprovalModel()
# Create sample data
self.sample_data = pd.DataFrame({
"income": [20000, 50000, 80000, 30000],
"credit_score": [580, 720, 800, 650],
"debt_to_income": [40, 25, 15, 30],
"loan_amount": [150000, 200000, 300000, 180000],
"loan_term": [30, 30, 15, 30],
"employment_length": [2, 5, 10, 3],
"home_ownership": ["RENT", "OWN", "OWN", "RENT"],
"loan_purpose": ["HOME", "HOME", "HOME", "HOME"]
})
def test_predict(self):
"""Test prediction functionality"""
predictions = self.model.predict(self.sample_data)
# Check that predictions are binary (0 or 1)
self.assertTrue(all(pred in [0, 1] for pred in predictions))
# Check that higher income and credit score leads to approval
self.assertEqual(predictions[2], 1, "High income and credit score should lead to approval")
def test_predict_proba(self):
"""Test probability prediction functionality"""
probabilities = self.model.predict_proba(self.sample_data)
# Check that probabilities are between 0 and 1
self.assertTrue(all(0 <= prob[1] <= 1 for prob in probabilities))
# Check that probabilities are higher for better applications
self.assertGreater(probabilities[1][1], probabilities[0][1],
"Better application should have higher approval probability")
def test_feature_importance(self):
"""Test feature importance functionality"""
importance = self.model.feature_importance()
# Check that feature importance is returned for all features
self.assertEqual(len(importance), len(self.sample_data.columns))
# Check that importance values sum to approximately 1
self.assertAlmostEqual(sum(importance.values()), 1.0, places=5)
if __name__ == "__main__":
unittest.main() |