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Pruthviraj Gojare — R23-T045: How CI tests machine_learning/random_forest_classifier.py #46
Description
Student: Pruthviraj Gojare
Task: R23-T045 · 5 of 10
Repository: https://github.com/S-AI-Projects/TheAlgorithms-Python
Assigned working branch: student/e26b07f0834
Goal
Explore how the repository's CI collects, runs and reports on this file. This file has no doctests and only a main() using scikit-learn. What does CI actually test here? Make it testable: a function returning accuracy with a fixed random_state, plus a doctest.
Steps
- Read .github/workflows/build.yml (the pytest command, the --ignore list, coverage) and pyproject.toml [tool.pytest].
- Run the CI-equivalent command for just this file with
-vand coverage:uv run pytest machine_learning/random_forest_classifier.py --cov=machine_learning --cov-report=term-missing -v. - Find this file in DIRECTORY.md and explain how scripts/build_directory_md.py and scripts/validate_filenames.py treat it.
- Write work/R23-T045/CI_NOTES.md answering the question in the goal with evidence.
- Make the code change in the repository: machine_learning/random_forest_classifier.py: a function returning accuracy with a fixed random_state, type hints and a doctest, keeping main() as a demo. Re-run the automated check and the repo's own tests afterwards.
- Prepare the PR: on branch R23-T045, commit the code change plus your work/R23-T045/ evidence, put your findings in the PR description and open the PR against the org fork. For a later upstream PR, create a second branch containing only the code change (no work/ folder).
Done when
☐ CI_NOTES.md explains that pytest currently collects nothing testable from this file (0 doctests, only main()).
☐ machine_learning/random_forest_classifier.py gains a typed function returning accuracy with random_state fixed, plus a doctest asserting accuracy above a stated threshold; main() still runs.
☐ uv run pytest machine_learning/random_forest_classifier.py -v now shows the new doctest passing.
Additional task guidance
Additional curated task guidance
Item:
machine_learning/random_forest_classifier.py (43 lines; 0 doctests)
Task type:
CI & repo tooling
Code change (goes into the PR):
machine_learning/random_forest_classifier.py: a function returning accuracy with a fixed random_state, type hints and a doctest, keeping main() as a demo.
Automated check:
uv run pytest machine_learning/random_forest_classifier.py --cov=machine_learning --cov-report=term-missing -v
Prerequisites:
Linux/WSL2/Codespaces with uv (https://docs.astral.sh/uv/getting-started/installation/); run uv sync --group=test once – it installs the Python version CI uses (.python-version = 3.15t) and the test dependencies, about 1–2 GB
Upstream PR rules:
Follow CONTRIBUTING.md: type hints on every parameter/return, doctests for valid and erroneous input, snake_case file names, no duplicate of an existing implementation, no hand edits to DIRECTORY.md. CI (pytest with doctests, ruff) must pass – bots close PRs without tests or type hints. Keep work/ (benchmarks, notes, property tests) out of the upstream branch; put results in the PR description.
Source (pinned):
https://github.com/TheAlgorithms/Python/blob/574d100b7181956e9c8384552fd078165e4cc326/machine_learning/random_forest_classifier.py
Change:
No change recorded
PR destination:
Preserved pilot guidance
Submission checklist
- Complete this task and its checks.
- Add the required evidence and link your pull request here.
- Select this S-AI-Projects fork as the PR base repository.
- Request instructor review.
Keep this PR focused on this task. Do not submit assignment PRs to the upstream repository.
Activity