🧪 Add unit tests for calculate_qwf_enhanced_qag_model_loss#13
🧪 Add unit tests for calculate_qwf_enhanced_qag_model_loss#13Sir-Ripley wants to merge 1 commit intomainfrom
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Co-authored-by: Sir-Ripley <31619989+Sir-Ripley@users.noreply.github.com>
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Reviewer's guide (collapsed on small PRs)Reviewer's GuideAdds focused unit tests around calculate_qwf_enhanced_qag_model_loss in the QuantumAffinityGravity notebook, covering normal behavior, integration failures, and default-parameter edge cases to increase safety for future refactors. File-Level Changes
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Summary of ChangesHello, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request introduces comprehensive unit tests for the Highlights
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Code Review
This pull request adds a valuable set of unit tests for the calculate_qwf_enhanced_qag_model_loss function, covering the happy path, error conditions, and default parameter usage. The tests increase confidence in the model's loss calculation. I've provided a couple of suggestions to improve the maintainability and conciseness of the test code by removing a magic number and simplifying the mocking approach. Overall, this is a great addition to improve the robustness of the codebase.
| " loss = calculate_qwf_enhanced_qag_model_loss(self.params, self.target_data)\n", | ||
| " self.assertIsInstance(loss, float)\n", | ||
| " self.assertGreaterEqual(loss, 0.0)\n", | ||
| " self.assertLess(loss, 1e10) # Assuming the normal loss is smaller than the large penalty\n", |
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The value 1e10 is a magic number representing LARGE_PENALTY from the function under test. It's also used in test_loss_integration_failure (line 592). To improve maintainability and avoid hardcoding this value in multiple places, consider defining it as a class constant, for example:
class TestCalculateQWFEnhancedQAGModelLoss(unittest.TestCase):
LARGE_PENALTY = 1e10
# ... testsThen you can use self.LARGE_PENALTY in your assertions, making the tests cleaner and easier to update if the penalty value changes.
| " class MockSol:\n", | ||
| " status = -1\n", | ||
| " y = np.array([[-1.0]])\n", | ||
| " mock_solve_ivp.return_value = MockSol()\n", |
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Instead of defining a nested class MockSol to create a mock object, you can directly configure the return_value of the mock_solve_ivp object. The @patch decorator provides a MagicMock object by default, which allows you to set attributes on its return_value directly. This makes the test more concise and idiomatic.
mock_solve_ivp.return_value.status = -1
mock_solve_ivp.return_value.y = np.array([[-1.0]])
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🎯 What: The testing gap for the
calculate_qwf_enhanced_qag_model_lossfunction in¡QuantumAffinityGravity!.ipynbhas been addressed.📊 Coverage: The new tests cover:
unittest.mock.patchto simulate an ODE solver integration failure and verify that theLARGE_PENALTYis appropriately applied.QAG_DEFINITIONS.✨ Result: Increased test coverage and confidence in the reliability of the QWF-enhanced loss calculations, enabling safer future refactoring.
PR created automatically by Jules for task 16899225476645283569 started by @Sir-Ripley
Summary by Sourcery
Tests: