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feat: Add CLI Commands for Browsing and Searching OpenML Runs

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Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
35/100
Tipo de issue
Nueva funcionalidad
Claridad
Bien especificado
Estado de actividad
Estancado
Stack tecnológico
python
Área
cli, machine-learning

Línea de trabajo

Start with the runs_list(), runs_info(), and runs_download() entry points in openml/cli.py, then review the existing CLI patterns for configure, models, datasets, and tasks. Run the CLI tests in tests/test_openml/test_cli.py; done means the three commands support the documented filters, details, downloads, formatting, and mocked API behavior.

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 runs: openml runs list, openml runs info, and openml runs download"

Details
What does this PR implement/fix?
This PR adds three new CLI subcommands under openml runs to improve the user experience of the run catalogue:

openml runs list - List runs with optional filtering (task_id, flow_id, uploader, tag, pagination, output format)
openml runs info <run_id> - Display detailed information about a specific run including task, flow, evaluations, and parameter settings
openml runs download <run_id> - Download a run and save predictions to local cache
Why is this change necessary? What is the problem it solves?
Currently, users must write Python code to browse or search OpenML runs, even for simple tasks like listing runs for a specific task or downloading run results. This creates a barrier to entry and makes the run catalogue less accessible. Adding CLI commands allows users to interact with the run catalogue directly from the command line without writing code.

This directly addresses the ESoC 2025 goal of "Improving user experience of the run catalogue in AIoD and OpenML".

How can I reproduce the issue this PR is solving and its solution?
Before (requires Python code):

import openml
runs = openml.runs.list_runs(task=[1], size=10)
for rid, run_dict in runs.items():
    print(f"{rid}: Task {run_dict['task_id']}")

After (CLI commands):

# List first 10 runs for a specific task
openml runs list --task 1 --size 10
# List runs by a specific uploader
openml runs list --uploader "John Doe"
# Get detailed info about a run
openml runs info 12345
# Download a run and cache predictions
openml runs download 12345
# List runs for a specific flow, formatted as table
openml runs list --flow 42 --format table --verbose
# Filter by both task and flow
openml runs list --task 1 --flow 42

Implementation Details:
Added three new functions in openml/cli.py: runs_list(), runs_info(), and runs_download()
Integrated into main CLI parser with proper argument handling
Added comprehensive test suite in tests/test_openml/test_cli.py
Uses existing openml.runs.list_runs() and openml.runs.get_run() functions - no changes to core API
Follows existing CLI patterns (similar to configure, models, datasets, and tasks 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
362
Forks
304
Merge medio
2 d 20 h
PR fusionados (30 d)
2

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