0.4.0

New algorithms, a datasets module, large speed-ups and benchmarks against fcapy and concepts. Documentation: https://conceptflow.readthedocs.io

New

• Boolean matrix factorization: GreConD and ASSO as scikit-learn transformers (like NMF).
• Ord2Factor: large ordinal two-factorizations of any context (Dürrschnabel & Stumme 2023), with an exact SAT method and greedy/annealing heuristics.
• conceptflow.datasets: the 20 contexts of the FCA repository bundled, fetch_fca_repository, the UCI Mushroom data and random contexts.
• ConceptualScaler(handle_unknown="ignore") for values unseen during fit, e.g. in cross-validation.
• Benchmarks against fcapy and concepts, an API stability and deprecation policy, CITATION.cff, CONTRIBUTING.md and continuous integration.

Faster (same results)

• Canonical basis with LinCbO: UCI Mushroom 134 s → 17 s; 40–85× faster with many attributes.
• Concept enumeration with FCbO pruning: Mushroom 26 s → 14 s.
• Exact stability, now per concept: all of Mushroom in about 40 s; also works on iceberg lattices.
• Conceptual scaling: Mushroom 49 s → 0.1 s.

Changed

• Concept ids and feature names are derived from the intent: stable across reordered or resampled data, but different from earlier versions.
• NominalScale(0) means the first column (as in ColumnTransformer).
• inf in DataFrames is rejected.
• Python 3.14 is supported.

Full list in the CHANGELOG.

pip install --upgrade conceptflow