feat(datasets): use scikit-learn datasets downloader

[homksei]

Description

This PR updates the dataset downloading mechanism to ensure data integrity by implementing SHA256 checksum verification. It replaces the custom retrieve function with sklearn.datasets._base.fetch_file.

Changes:

• sklbench/datasets/downloaders.py:

  • Modified download_and_read_csv to accept a tuple containing (filename, url, sha256) instead of a raw URL.
  • Replaced the local retrieve function with sklearn.datasets._base.fetch_file to handle downloads and hash validation.
  • Added logging for download operations.

• sklbench/datasets/loaders.py:

  • Updated all dataset loading functions (e.g., load_airline_depdelay, load_hepmass, load_higgs, load_sift, etc.) to provide the specific filename, base URL, and corresponding SHA256 hash.
  • Refactored load_ann_dataset_template to support the new metadata structure.

Motivation:
To prevent the usage of corrupted or tampered data files and to standardize the downloading logic using scikit-learn's internal utilities.

---

Checklist:

Completeness and readability

• I have commented my code, particularly in hard-to-understand areas.
• I have updated the documentation to reflect the changes or created a separate PR with updates and provided its number in the description, if necessary.
• Git commit message contains an appropriate signed-off-by string (see CONTRIBUTING.md for details).
• I have resolved any merge conflicts that might occur with the base branch.

Testing

• I have run it locally and tested the changes extensively.
• All CI jobs are green or I have provided justification why they aren't.
• I have extended testing suite if new functionality was introduced in this PR.

[david-cortes-intel]

/intelci: run

[david-cortes-intel]

/intelci: run ml-benchmarks