feat: Add CLI Commands for Browsing and Searching OpenML Studies
Nadie ha tomado este issue todavía.
Evaluación
- Dificultad
- 3/5
- Tiempo estimado
- 1-2 días
- Aptitud para principiantes
- 52/100
Línea de trabajo
Start in openml/cli.py with the existing resource CLI patterns and review tests/test_openml/test_cli.py, including its mocked API calls. Verify the studies list, info, and search commands and their documented filtering, pagination, format, and type options. Done means the command behavior is covered by the test suite and the listed pre-commit checks pass.
Escrito por el modelo de indexación a partir del texto del issue.
Descripción
Metadata
New Tests Added: Yes
Documentation Updated: No (CLI help text serves as documentation)
Change Log Entry: "Add CLI commands for browsing and searching OpenML studies: openml studies list, openml studies info, and openml studies search"
Details
What does this PR implement/fix?
This PR adds three new CLI subcommands under openml studies to improve the user experience of the study/benchmark suite catalogue:
openml studies list - List studies with optional filtering (status, uploader, pagination, output format)
openml studies info <study_id> - Display detailed information about a specific study including tasks, datasets, and metadata
openml studies search - Search studies by name/alias with case-insensitive matching
Why is this change necessary? What is the problem it solves?
Currently, users must write Python code to browse or search OpenML studies and benchmark suites, even for simple tasks like listing available studies or finding a specific benchmark. This creates a barrier to entry and makes the study catalogue less accessible. Adding CLI commands allows users to interact with the study catalogue directly from the command line without writing code.
This directly addresses the ESoC 2025 goal of "Improving user experience of the study catalogue in AIoD and OpenML".
How can I reproduce the issue this PR is solving and its solution?
Before (requires Python code):
import openml
studies = openml.study.list_studies(size=10)
for sid in studies:
study = openml.study.get_study(sid)
print(f"{sid}: {study.name}")
After (CLI commands):
# List first 10 studies
openml studies list --size 10
# Search for benchmark suites by name
openml studies search "OpenML-CC18"
# Get detailed info about a study
openml studies info 14
# List studies by a specific uploader
openml studies list --uploader "openml-bot" --format table
# List benchmark suites (studies with main_entity_type='task')
openml studies list --type suite
Implementation Details:
Added three new functions in openml/cli.py: studies_list(), studies_info(), and studies_search()
Integrated into main CLI parser with proper argument handling
Added comprehensive test suite in tests/test_openml/test_cli.py
Uses existing openml.study.list_studies() and openml.study.get_study() functions - no changes to core API
Follows existing CLI patterns (similar to other resource CLI commands)
All tests use mocked API calls to avoid requiring server connections
Any other comments?
All pre-commit hooks pass (ruff, mypy, formatting)
No breaking changes
Follows project code style and patterns
Ready for review
- Lenguaje dominante
- Python
- Estrellas
- 361
- Forks
- 296
- Métricas de merge de PR
- Sin PR fusionados en 30 d
Guía de contribución
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
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