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Unit Tests

Updated May 19, 2023 ·

Testing in Machine Learning

Before deploying a model, testing ensures it works as expected and handles real-world scenarios correctly.

  • Testing

    • Verifies that the model runs without errors
    • Ensures predictions are accurate and fast
  • Unit Tests

    • Detect errors before deployment
    • Check specific parts of the model’s behavior
    • Run automatically during development

Writing Unit Tests

Python's unittest library helps define and run tests for models.

To create a test case, we can first subclass unittest.TestCase, then add test methods that start with test_. Use assertions to check expected behavior.

import unittest

class TestModelInference(unittest.TestCase):

def setUp(self):
self.model = fitted_model
self.X_test = X_test

def test_prediction_output_shape(self):
y_pred = self.model.predict(self.X_testt)
self.assertEqual(y_pred.shape[0], self.X_test.shape[0])

if __name__ == '__main__'
unittest.main()

We can also use other built-in methods like assertIn() to verify that input values fall within a valid range, ensuring data integrity.

def test_input_values(self):
print("Running test_input_values test case")
for row in X_test:
for value in row:
self.assertIn(value, range(0, 501))

Key Considerations

Key Considerations

Writing unit tests helps catch errors early and ensures models work as expected.

  • Running Unit Tests

    • Run tests after every change
    • Ensure new functionality is covered
  • Avoiding Redundant Tests

    • Don’t test well-established library functions
    • Focus on critical features
  • Benefits of Testing

    • Prevents errors in production
    • Increases confidence in model updates

Test-Driven Development

Test-Driven Development (TDD) ensures code reliability by writing tests before implementation.

  • Write Tests First

    • Define expected behavior before coding
    • Guides development and prevents unexpected issues
  • Iterate and Improve

    • Refactor code while keeping tests intact
    • Ensures long-term stability and maintainability