Add unit tests and CLAUDE.md #236
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- Add tests for previously uncovered modules: - store.py (data storage classes) - latent_class_mnl.py (latent class model) - assortment_optimizer.py (optimizer factory) - Create CLAUDE.md with build/test commands and code style guidelines - Focus on convergence testing for model classes - Improve code coverage 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]>
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Hello @chicham, 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!
Summary of Changes
This pull request focuses on enhancing the testing and documentation of the choice-learn library. It introduces unit tests for previously uncovered modules, specifically store.py, latent_class_mnl.py, and assortment_optimizer.py, to improve test coverage and ensure the reliability of these components. Additionally, a CLAUDE.md file is created to provide essential information for coding agents, including build/test commands and code style guidelines. The pull request also includes updates to GitHub Actions workflows to use newer versions of actions.
Highlights
- Unit Tests: Added comprehensive unit tests for
store.py,latent_class_mnl.py, andassortment_optimizer.pyto increase code coverage and ensure proper functionality. The tests forlatent_class_mnl.pyinclude convergence tests to verify model training stability. - Documentation: Created
CLAUDE.mdto provide coding standards, build, test, and linting commands, and other essential information for developers contributing to the project. - GitHub Actions Updates: Updated GitHub Actions workflows (
publish.yaml,ci.yaml,deploy_docs.yaml) to use the latest versions ofactions/checkout(v4) andactions/setup-python(v5).
Changelog
Click here to see the changelog
- .github/actions/publish/action.yaml
- Updated
actions/checkoutto v4. - Updated
actions/setup-pythonto v5.
- Updated
- .github/workflows/ci.yaml
- Updated
actions/checkoutto v4. - Updated
actions/setup-pythonto v5.
- Updated
- .github/workflows/deploy_docs.yaml
- Updated
actions/checkoutto v4. - Updated
actions/setup-pythonto v5.
- Updated
- CLAUDE.md
- Created
CLAUDE.mdwith coding standards and commands for choice-learn. - Added build, test, and lint commands.
- Included code style guidelines such as import structure, formatting rules, and naming conventions.
- Created
- tests/unit_tests/data/test_store.py
- Created
test_store.pyto test data storage classes. - Implemented tests for
Store,FeaturesStore, andOneHotStoreclasses, covering initialization, data handling, and property access.
- Created
- tests/unit_tests/models/test_latent_class_mnl.py
- Created
test_latent_class_mnl.pyto test theLatentClassSimpleMNLmodel. - Added tests for model instantiation, dataset integration, convergence during training, and prediction functionality.
- Created
- tests/unit_tests/toolbox/test_assortment_optimizer.py
- Created
test_assortment_optimizer.pyto test assortment optimizer factory functions. - Implemented tests for
MNLAssortmentOptimizer,LatentClassAssortmentOptimizer, andLatentClassPricingOptimizerclasses, covering solver selection and instantiation.
- Created
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Code Review
This pull request introduces unit tests for previously uncovered modules and creates a CLAUDE.md file with build/test commands and code style guidelines. The addition of unit tests significantly improves the robustness and reliability of the codebase. The CLAUDE.md file provides valuable guidance for developers, ensuring consistency and maintainability. Overall, this is a well-structured and valuable contribution.
Merge Readiness
The pull request is well-structured and addresses the need for increased test coverage and coding guidelines. The addition of unit tests and the CLAUDE.md file are valuable contributions to the project. I am unable to approve the pull request, and recommend that others review and approve this code before merging. There are no critical or high severity issues, and the changes can be merged after addressing the medium and low severity issues.
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| def test_latent_class_mnl_convergence(test_dataset): | ||
| """Test that the LatentClassSimpleMNL model converges during training.""" | ||
| tf.config.run_functions_eagerly(True) |
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It's generally good practice to disable eager execution only when necessary, as it can impact performance. Consider if it's possible to run these tests without disabling eager execution.
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| def test_latent_class_mnl_prediction(test_dataset): | ||
| """Test that the LatentClassSimpleMNL model can make predictions.""" | ||
| tf.config.run_functions_eagerly(True) |
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It's generally good practice to disable eager execution only when necessary, as it can impact performance. Consider if it's possible to run these tests without disabling eager execution.
Coverage Report for Python 3.11
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Coverage Report for Python 3.10
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Coverage Report for Python 3.12
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Summary
Details
test_store.pyfor testing data storage classes (100% coverage)test_latent_class_mnl.pyfor testing latent class models with convergence teststest_assortment_optimizer.pyfor optimizer factory functionsTest plan
pytest --cov🤖 Generated with Claude Code