Datasets API

class compresso_recsys.datasets.SplitBundle(train, val, test)[source]
Parameters:

  • train (DataFrame)

  • val (DataFrame)

  • test (DataFrame)

class compresso_recsys.datasets.RecSysDataset(data_dir='data')[source]

Thin base class for interaction datasets used in example pipelines.

Canonical interactions schema: - user_id: str - item_id: str - value: float - timestamp: int | float | None

Parameters:

data_dir (str | Path)

static preprocess_interactions_for_recsys(df, *, min_value_to_keep=4.0, user_min_support=5, item_min_support=1, set_all_values_to=1.0, max_steps=0)[source]

Paper-style preprocessing: threshold, binarize, iterative pruning, categorical cleanup.

Return type:

DataFrame

Parameters:

  • df (DataFrame)

  • min_value_to_keep (float | None)

  • user_min_support (int)

  • item_min_support (int)

  • set_all_values_to (float | None)

  • max_steps (int)

to_hf_dataset(df=None)[source]

Convert interactions to HuggingFace Dataset.

Import is optional so core library does not hard-depend on datasets.

Parameters:

df (DataFrame | None)

static to_sparse_matrix(df)[source]

Return (X, user_ids, item_ids) where X is user x item CSR.

Parameters:

df (DataFrame)

class compresso_recsys.datasets.MovieLens1M(data_dir='data', *, metadata_text_fields=None, min_entity_text_words=0)[source]
Parameters:

  • data_dir (str)

  • metadata_text_fields (Iterable[str] | None)

  • min_entity_text_words (int)

class compresso_recsys.datasets.MovieLens20M(data_dir='data', *, metadata_text_fields=None, min_entity_text_words=0)[source]
Parameters:

  • data_dir (str)

  • metadata_text_fields (Iterable[str] | None)

  • min_entity_text_words (int)

class compresso_recsys.datasets.Goodbooks(data_dir='data', *, metadata_text_fields=None, min_entity_text_words=0)[source]
Parameters:

  • data_dir (str)

  • metadata_text_fields (Iterable[str] | None)

  • min_entity_text_words (int)

class compresso_recsys.datasets.AmazonReviews2023(data_dir='data', *, category='Toys_and_Games', metadata_text_fields=('title', 'features', 'description', 'categories'), min_entity_text_words=0, include_image_urls=False, show_progress=True)[source]

Amazon Reviews 2023 category dataset loaded from McAuley’s files.

The recommender pipeline only needs compact rating-only interactions plus item metadata. Reviews are intentionally not downloaded.

Parameters:

  • data_dir (str)

  • category (str)

  • metadata_text_fields (Iterable[str])

  • min_entity_text_words (int)

  • include_image_urls (bool)

  • show_progress (bool)

load_source_dataframe(config, *, split='full')

Load a McAuley Amazon 2023 config into a DataFrame.

Kept under its historical name for compatibility with tests and subclasses, but this no longer uses Hugging Face datasets. Recent datasets releases reject repositories that still expose loading scripts, so we read direct Hugging Face/McAuley data files instead.

Return type:

DataFrame

Parameters:

  • config (str)

  • split (str)

load_timestamp_splits_with_history()[source]

Load McAuley’s timestamp split with per-row history fields.

Return type:

dict[str, DataFrame]