highfis 0.1.0a3

highFIS is a comprehensive Python package for training and evaluating high-dimensional TSK fuzzy systems, built on PyTorch and compatible with the scikit-learn API.

highFIS

Python library for high-dimensional Takagi–Sugeno–Kang (TSK) fuzzy inference systems, built on PyTorch with a scikit-learn compatible API.

📦 Installation

Install from PyPI:

pip install highfis

🧠 Quick Start

from highfis import HTSKClassifierEstimator

clf = HTSKClassifierEstimator(
    n_mfs=3,
    rule_base="en",
    epochs=200,
    learning_rate=1e-3,
    ur_weight=0.01,
    random_state=42,
)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))

Works with sklearn.pipeline.Pipeline, GridSearchCV, and cross_val_score.

🧩 Key Components

Class	Module	Description
GaussianMF	highfis.memberships	Differentiable Gaussian membership function.
MembershipLayer	highfis.layers	Evaluates all membership functions.
RuleLayer	highfis.layers	Computes firing strengths with configurable t-norm and rule base.
NormalizationLayer	highfis.layers	Normalizes firing strengths.
ClassificationConsequentLayer	highfis.layers	Linear TSK consequent aggregation.
HTSKClassifier	highfis.models	Full TSK pipeline as nn.Module.
HTSKClassifierEstimator	highfis.estimators	sklearn-compatible estimator.
InputConfig	highfis.estimators	Per-feature membership function configuration.

🧪 Testing & Quality

Running tests

Run the full test suite with coverage:

hatch test -c -a

This project is tested on Python 3.11 | 3.12 | 3.13 | 3.14 across Linux, Windows and macOS.

Linting & Formatting

hatch fmt

Typing

hatch run typing

Security

hatch run security

📚 Documentation

Comprehensive guides, API reference, and examples: dcruzf.github.io/highFIS.

🤝 Contributing

Issues and pull requests are welcome! Please open a discussion if you'd like to propose larger changes.

📄 License

Distributed under the GPLv3.