fuzzycocopython 0.1.2

Python API for FuzzyCoco using C++ bindings

FuzzyCocoPython

Documentation

Python bindings and scikit-learn style estimators for fuzzycoco, an evolutionary fuzzy rule learning engine written in C++. This package wraps the C++ core as a Python module and exposes FuzzyCocoClassifier and FuzzyCocoRegressor with a familiar fit/predict API.

Features

• Train fuzzy rule-based classifiers and regressors using fit, predict, and score
• Inspect learned linguistic variables, rules, and activation statistics from Python
• Persist trained estimators with save/load helpers based on joblib

Installation

Install from PyPI:

pip install fuzzycocopython

Or install from source. Make sure the following prerequisites are available:

• A C++17 compiler toolchain (GCC/Clang on Linux & macOS, MSVC on Windows)
• CMake ≥ 3.21 and Ninja on your PATH
• uv is recommended for dependency management, but pip works just as well

Clone the repository, initialise the fuzzycoco submodule, then build and install in editable mode with uv:

git clone https://github.com/CI4CB-lab/fuzzycocopython.git
cd fuzzycocopython
git submodule update --init --recursive

uv venv
source .venv/bin/activate
uv pip install -e .

If you prefer the standard tooling, create a virtual environment manually and install with pip:

python -m venv .venv
source .venv/bin/activate

pip install -e .

When you only need the published bindings and not the repository, install straight from GitHub:

pip install git+https://github.com/CI4CB-lab/fuzzycocopython.git

For development tasks (tests, linting, docs) install the optional toolchain:

uv pip install -e '.[dev]'

The build compiles the bundled C++ bindings. Refer to the fuzzycoco project for background on the engine itself.

Quick start

import pandas as pd
from sklearn.datasets import load_iris
from fuzzycocopython import FuzzyCocoClassifier

data = load_iris(as_frame=True)
clf = FuzzyCocoClassifier(random_state=0)
clf.fit(data.data, data.target)

preds = clf.predict(data.data)
score = clf.score(data.data, data.target)

print(f"Accuracy: {score:.3f}")
print(clf.rules_df_.head())

The estimators now expose scikit-learn style hyper-parameters for direct tuning:

from fuzzycocopython import FuzzyCocoClassifier

model = FuzzyCocoClassifier(nb_rules=10, nb_sets_in=3, random_state=42)
model.fit(X, y)
preds = model.predict(X)

Bit widths for variable and set indices are automatically derived from the training data. Override them with nb_bits_vars_in, nb_bits_sets_in, nb_bits_vars_out, or nb_bits_sets_out when you need full manual control.

For a guided tour, open demo.ipynb. Additional usage examples live in tests/test_fuzzycocopython.py.

Documentation

Full API documentation is available at CI4CB-lab.github.io/fuzzycocopython. To build the docs locally run:

uv pip install -e '.[docs]'
uv run sphinx-build -W -b html docs docs/_build/html

Pre-commit hooks

Install and activate the provided hooks (Ruff lint/format, mypy, general hygiene) once per clone:

uv run pre-commit install

Run them against the full tree when needed:

uv run pre-commit run --all-files

License

This fuzzycoco software is licensed under the GNU Affero General Public License v3.0 (AGPL-3.0).