fklearn: Functional Machine Learning

fklearn uses functional programming principles to make it easier to solve real problems with Machine Learning.

The name is a reference to the widely known scikit-learn library.

fklearn Principles

1. Validation should reflect real-life situations.
2. Production models should match validated models.
3. Models should be production-ready with few extra steps.
4. Reproducibility and in-depth analysis of model results should be easy to achieve.

Documentation | Getting Started | API Docs | Contributing |

Installation

To install via pip:

pip install fklearn

To install with optional dependencies:

pip install fklearn[lgbm]       # LightGBM support
pip install fklearn[xgboost]    # XGBoost support
pip install fklearn[catboost]   # CatBoost support
pip install fklearn[all_models] # All model backends
pip install fklearn[all]        # All models + tools

Development with UV

fklearn uses uv for dependency management. uv sync creates a virtual environment, installs all locked dependencies, and installs fklearn itself in editable mode (the default for uv projects) so changes under src/ are picked up without reinstalling.

Setup

uv sync                 # core deps + dev group
uv sync --all-extras    # also installs lgbm / xgboost / catboost / tools / demos / docs

The dev dependency group (pytest, ruff, mypy, hypothesis) is included by default via tool.uv.default-groups, so uv sync alone is enough for most development workflows.

Running Tests

uv run pytest --cov=src/

Linting

uv run ruff check src/ tests/
uv run ruff format src/ tests/

Adding Dependencies

uv add <package-name>          # runtime dependency
uv add --dev <package-name>    # dev dependency

Note for Nubank contributors

Regenerate the lockfile with --default-index https://pypi.org/simple/:

uv lock --default-index https://pypi.org/simple/

License

Apache License 2.0