fuzzytool 0.5.0

A clean, extensible fuzzy-logic toolkit in pure Python + NumPy

fuzzytool

A clean, extensible fuzzy-logic toolkit in pure Python + NumPy. Its design priorities are a composable API, algorithm comparison, visualization and code clarity — a modern alternative to the verbose control API of scikit-fuzzy.

import fuzzytool as fz

# Credit-risk premium: a lender turns a credit score + debt-to-income ratio
# into the risk points it adds on top of its base interest rate.
score   = fz.Variable("score", (300, 850), terms=["poor", "fair", "good", "excellent"])
dti     = fz.Variable("dti", (0, 50), terms=["low", "moderate", "high"])
premium = fz.Variable("premium", (0, 12), terms=["low", "medium", "high"])

sys = fz.Mamdani(defuzz="centroid")
sys.rule(score["poor"] | dti["high"], premium["high"])        # |=OR  &=AND  ~=NOT
sys.rule(score["fair"] & dti["moderate"], premium["medium"])
sys.rule(score["good"] | score["excellent"], premium["low"])

print(sys(score=800, dti=10))    # the system is just callable -> a low premium

The design idea (extensibility)

The inference loop knows nothing about any concrete variant. Everything that changes lives behind small Python Protocols:

• MembershipFunction (fuzzytool/membership.py) — a callable x -> degree. A new shape = a new callable.
• Norm (fuzzytool/norms.py) — t-norms (AND) and s-norms (OR), resolved by name. A new connective = one registered function.
• defuzzifiers (fuzzytool/defuzz.py) — centroid, bisector, MOM/SOM/LOM, resolved by name.

Rules read like logic thanks to operator overloading: & is the t-norm, | the s-norm, ~ the complement.

What it includes / roadmap

Phase	Content	Status
1	Core: membership functions, t-/s-norms, Variable, operator rules, Mamdani + defuzzification, fraud-alert example, tests	✅
2	Takagi-Sugeno (TSK) inference + viz (membership plots, control surface)	✅ (TSK + viz)
3	Type-2 / interval type-2 sets (footprint of uncertainty) + Karnik-Mendel type reduction	✅
4	Fuzzy clustering: fuzzy c-means, Gustafson-Kessel, possibilistic	✅
5	ANFIS (trainable TSK) + F-transform (direct/inverse)	✅
6	Notebooks, JOSS paper.md, Zenodo DOI, PyPI release	✅
7	Ecosystem integrations: pandas, scikit-learn, PyTorch, SciPy, turboswarm, Optuna, Joblib/Dask, LLM agents	✅

v0.5.0

• General type-2 (GT2) fuzzy sets via the zSlices / alpha-plane representation: GeneralType2MF, the gt2_* constructors, GeneralType2Mamdani inference and centroid_gt2 type reduction — reusing the existing interval type-2 / Karnik-Mendel machinery.

v0.4.0

• turboswarm integration: gradient-free, global tuning of a fuzzy system's membership functions with Particle Swarm Optimization (integrations.turboswarm.tune) — the metaheuristic sibling of the SciPy tuner.

v0.3.0

• Integrations (fuzzytool.integrations.*, each behind its own extra): pandas (DataFrame I/O), scikit-learn (Fuzzifier, regressors), PyTorch (differentiable FuzzyLayer), SciPy (MF tuning), Optuna (hyperparameter search), Joblib/Dask (parallel inference) and LLM agents (explainable inference_tool).
• Tutorials and an Integrations guide page, with rendered plots and computed outputs throughout the docs.

v0.2.0

• Fuzzy numbers & MCDM: triangular/trapezoidal fuzzy-number arithmetic, Fuzzy TOPSIS and Fuzzy AHP.
• Rule learning: Wang-Mendel rule-base generation from data; Tsukamoto inference (monotonic consequents).
• Engineering: vectorized batch inference (predict), JSON save/load, and a scikit-learn estimator interface for ANFIS.

See ROADMAP.md.

Install

pip install fuzzytool            # core (NumPy only)
pip install fuzzytool[viz]       # + matplotlib visualization

From source, for development:

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev,viz,docs]"
pytest -q
python examples/fraud_alert.py

Documentation

A documentation portal (narrative guide + API reference from docstrings) is built with MkDocs Material and published to GitHub Pages: https://fuzzytool.github.io/.

pip install -e ".[docs]"
mkdocs serve        # live portal at http://127.0.0.1:8000

License

MIT. See LICENSE.