This project uses unit and integration tests with Python files and notebooks.
- In the unit tests we just make sure the notebook runs.
- In the integration tests we use a bigger dataset for more epochs and we test that the metrics are what we expect.
For more information, see a quick introduction to unit and integration tests. To manually execute the unit tests in the different environments, first make sure you are in the correct environment as described in the README.md
Tests are automatically run as part of a DevOps pipeline. The pipelines are defined in the .yml files in tests/ci with filenames that align with pipeline names.
Click on the following menus to see more details on how to execute the unit and integration tests:
Unit tests (click to expand)
Unit tests ensure that each class or function behaves as it should. Every time a developer makes a pull request to main branch, a battery of unit tests is executed.
Note that the next instructions execute the tests from the root folder.
Integration tests (click to expand)
Integration tests make sure that the program results are acceptable
Note that the next instructions execute the tests from the root folder.
In the notebooks of these repo we use Papermill in unit and integration tests.
In the unit tests we just make sure the notebook runs. In the integration tests, we use a bigger dataset for more epochs and we test that the metrics are what we expect.
For a deep overview on how to integrate papermill on unit and integration test, please refer to this guide from Recommenders repo.
More details on how to integrate Papermill with notebooks can be found in their repo.