highfis 0.6.0

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

highFIS is a PyTorch-based framework for high-dimensional Takagi–Sugeno–Kang (TSK) fuzzy systems. It brings differentiable fuzzy inference, numerical stability, and sklearn-compatible estimators to both classification and regression workflows.

🚀 Quick Start

Install from PyPI:

pip install highfis

Run a classifier:

from highfis import HTSKClassifierEstimator

clf = HTSKClassifierEstimator(
    n_mfs=4,
    mf_init="kmeans",
    epochs=150,
    learning_rate=1e-3,
    random_state=42,
)
clf.fit(X_train, y_train)
print(f"Test accuracy: {clf.score(X_test, y_test):.4f}")

highFIS works with sklearn.pipeline.Pipeline, GridSearchCV, and cross_val_score.

🔧 What’s Included

Core models

• HTSKClassifier, HTSKRegressor
• TSKClassifier, TSKRegressor
• DombiTSKClassifier, DombiTSKRegressor
• AdaTSKClassifier, AdaTSKRegressor
• FSREAdaTSKClassifier, FSREAdaTSKRegressor
• LogTSKClassifier, LogTSKRegressor

Estimator wrappers

• HTSKClassifierEstimator, HTSKRegressorEstimator
• TSKClassifierEstimator, TSKRegressorEstimator
• LogTSKClassifierEstimator, LogTSKRegressorEstimator
• AdaTSKClassifierEstimator, AdaTSKRegressorEstimator
• FSREAdaTSKClassifierEstimator, FSREAdaTSKRegressorEstimator

Building blocks

• Memberships: GaussianMF, TriangularMF, TrapezoidalMF, BellMF, SigmoidalMF
• Defuzzifiers: SoftmaxLogDefuzzifier, SumBasedDefuzzifier, LogSumDefuzzifier
• T-norms: prod, min, gmean, dombi
• Rule base strategies: cartesian, coco, en, custom

📚 Documentation

The published documentation is available at https://dcruzf.github.io/highFIS.

Key reference pages:

• TSK Vanilla
• LogTSK
• HTSK
• DombiTSK
• AdaTSK
• FSRE-AdaTSK

🤝 Contributing & Development

See the published contribution guide at contributing.

📄 License

Distributed under the GPLv3.