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.
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Testing
- Verifies that the model runs without errors
- Ensures predictions are accurate and fast
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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
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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.
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Write Tests First
- Define expected behavior before coding
- Guides development and prevents unexpected issues
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Iterate and Improve
- Refactor code while keeping tests intact
- Ensures long-term stability and maintainability