How to store and retrieve model test cases in version control

Storing and retrieving model test cases in version control involves systematically managing test case files alongside your code, ensuring they are linked to the model’s lifecycle. Here’s how you can do this effectively:

1. Organize Test Cases in a Directory Structure

• Create a dedicated folder for test cases within your project, such as /tests or /model_tests.

• Subdivide by model versions or components: If you have multiple models or versions, you could organize test cases in subfolders like /tests/v1, /tests/v2, etc.

Example:

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/project
├── /models
├── /tests
│   ├── /v1
│   │   ├── test_model.py
│   │   ├── test_data_preprocessing.py
│   ├── /v2
│   │   ├── test_model.py
│   │   ├── test_feature_engineering.py
├── /scripts
├── /data
└── /README.md

2. Define Test Cases Clearly

• Name the test cases logically: Each test case should be descriptive, specifying what feature or part of the model it is testing. Use names like test_model_accuracy.py, test_data_integrity.py, or test_training_stability.py.

• Versioning in filenames: To track test case versions, use version tags in the filenames or include them in the comments. For example, test_model_v1.py, test_model_v2.py.

3. Integrate with Version Control (Git)

• Commit Test Cases: Treat test cases like any other code. Whenever a model is updated or changed, commit the corresponding test cases.

• Use branches: For major updates to models, create branches to handle test cases that are linked to the specific model version. After finalizing the changes, merge the test cases back to the main branch.

Example Git commands:

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git add tests/v1/test_model.py
git commit -m "Add test case for model v1"
git push origin main

4. Use Descriptive Test Case Content

• Include metadata in the test case file itself (e.g., version number, dependencies, description). This can be done in docstrings at the start of each test case.

Example for a Python test file:

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"""
Test case: test_model_accuracy.py
Model version: v1.0
Description: Tests the accuracy of the model on the validation set
Dependencies: Model should be trained and stored at 'model/v1'
"""
import unittest

class TestModelAccuracy(unittest.TestCase):
    def test_accuracy(self):
        # Load model, dataset, etc.
        self.assertGreater(model_accuracy, 0.9)

5. Version Control for Test Dependencies

• Track dependencies: If your tests depend on specific libraries or datasets, store the versions of those libraries in a requirements.txt file or equivalent. This ensures that the environment is replicable across versions.

• Lock dependencies: Tools like pipenv or conda can be used to lock dependencies and ensure consistency between environments.

6. Link Test Cases to the Model Development Process

• Reference test cases in documentation: Each model version should link to the relevant test cases, either in the model’s README or as part of the release notes.

• Automate test execution: Integrate test execution into CI/CD pipelines (e.g., using GitHub Actions, Jenkins, or GitLab CI) so that test cases are run automatically whenever the model or code changes.

7. Retrieving Test Cases

• To retrieve a test case from version control, simply use Git’s checkout or log to fetch a specific version or commit containing the test case.

Example:

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git checkout v1 -- tests/v1/test_model.py

You can also view the test history with:

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git log -- tests/v1/test_model.py

8. Example Workflow for Updating and Testing

• Update model: After updating the model (e.g., improving accuracy or adding features), modify or add test cases.

• Run tests: Use the test suite to verify the new version.

• Commit changes: Commit both the model and test cases together, ensuring consistency.

• Deploy: Push the changes and deploy the updated model with its corresponding tests.

This version control setup will allow you to keep your test cases organized, versioned alongside your model, and ensure that every change is well-tested and reproducible.