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[0.4.4] - 2026-06-15

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@jiro-iwanaga jiro-iwanaga released this 15 Jun 12:51
· 23 commits to main since this release
f332e88

Added

  • save(path=None) / load(path): persist a fitted model to a pickle file and restore it
    without retraining. path=None saves rfscorer.pkl to the current directory; a directory
    path saves rfscorer.pkl inside it; a file path saves directly. On major or minor version
    mismatch, load() emits a UserWarning and continues loading.
  • save_zip(path=None) / load_zip(path): save/restore the model as a zip archive bundling
    rfscorer.pkl, metadata.json (version, parameters, fit statistics), probability-table CSVs,
    and plot PNGs for all computed model kinds. path=None saves scorer.zip to the current
    directory. Intended for research sharing and artifact management.
  • Tutorial notebooks (tutorial_beginner_en.ipynb / tutorial_beginner_ja.ipynb): added
    Section 10 covering save() / load() usage with a Google Colab persistence guide.
    Also added a commented # !pip install rfscorer line to the import cell.

Changed

  • Terminology unification: renamed all eval-prefixed names to gt (ground truth)
    to align with the unified terminology in docs/glossary.md (正解データ / ground truth data).
    Breaking changes:
    • fit(df_obs, df_eval, ...)fit(df_obs, df_gt, ...)
    • evaluate(df_rec, df_eval, ...)evaluate(df_rec, df_gt, ...)
    • split_by_date(..., evaluation_days=7, ...)split_by_date(..., gt_days=7, ...);
      return value documented as (df_obs, df_gt) instead of (df_obs, df_eval).
    • Attribute record_num_evalrecord_num_gt
    • Attribute evaluation_start_gt_start_
    • Attribute evaluation_end_gt_end_
    • show() output label evaluation:ground_truth:
    • Error message "No events observed in evaluation period" → "No events observed in ground truth period"
  • fit(): the datetime column in df_gt is now optional. Only user and item columns
    are required for fitting. The gt_start_ and gt_end_ attributes (which depended on
    df_gt's datetime column) have been removed.