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. The library also includes DGTSK dynamic-gating models for feature and rule selection in high-dimensional fuzzy systems.

🚀 Overview

• Differentiable TSK fuzzy systems built for high-dimensional data.
• Supports both concrete PyTorch model classes and sklearn-compatible estimators.
• Includes adaptive and gated inference variants for feature selection and sparse rule extraction.
• Designed for numerical stability with log-space and inverse-log defuzzifiers.

📦 Installation

Install from PyPI:

pip install highfis

🧠 Quick Start

from highfis import HTSKClassifier

clf = HTSKClassifier(
    n_mfs=5,
    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 integrates with sklearn.pipeline.Pipeline, GridSearchCV, and cross_val_score.

🧩 Model families

highFIS provides a full family of TSK models, each tuned for a specific high-dimensional inference strategy.

• TSK — vanilla TSK with product antecedent aggregation and sum-based normalization.
• HTSK — high-dimensional TSK with geometric mean aggregation and log-space normalization.
• LogTSK — log-domain inverse-log normalization for stable aggregation.
• MHTSK — multihead sparse TSK with feature-subset heads and sparse consequents.
• DombiTSK — Dombi T-norm aggregation with a learnable shape parameter.
• ADMTSK — adaptive Dombi TSK with composite Gaussian Pi membership functions.
• AYATSK — Yager T-norm aggregation for flexible antecedent behavior.
• ADATSK — adaptive softmin-style inference with dynamic rule weighting.
• ADPTSK — adaptive double-parameter softmin inference with stable normalized rule weights.
• FSRE-ADATSK — adaptive model with gated feature selection and rule extraction.
• DGTSK — double-gated training for feature selection and rule extraction.
• DGALETSK — adaptive Ln-Exp softmin with embedded feature and rule gates.
• HDFIS — high-dimensional inference with product T-norm (HDFISProd) and minimum T-norm (HDFISMin) variants.

Each family exposes classifier and regressor variants.

🔧 Core components

highFIS exposes a compact, model-family-driven API with both concrete model classes and sklearn-compatible estimator wrappers.

• Model families: TSK, HTSK, LogTSK, MHTSK, DombiTSK, ADMTSK, AYATSK, ADATSK, ADPTSK, FSRE-ADATSK, DGTSK, DGALETSK, HDFIS
• Estimators: *Classifier and *Regressor variants for each model family, accessible directly from import highfis
• Building blocks: membership functions (highfis.memberships), defuzzifiers (highfis.defuzzifiers), T-norms (highfis.t_norms), and PyTorch model classes (highfis.models)

For the full class list and API reference, see the documentation:

• Models
• Estimators

🛠️ Training options

highFIS uses gradient-based optimization and supports:

• adaptive optimizers like Adam/W and standard SGD
• early stopping with validation
• uniform rule regularization for balanced rule activation
• custom T-norms, custom rule bases, and custom defuzzifiers

📚 Documentation

The published documentation is available at:

https://dcruzf.github.io/highFIS

Key reference pages:

• TSK Vanilla
• LogTSK
• AYATSK
• HTSK
• DombiTSK
• ADPTSK
• ADMTSK
• ADATSK
• DGTSK
• DG-ALETSK
• FSRE-ADATSK
• MHTSK
• HDFIS

🧪 Testing & quality

Format, lint, and type check:

hatch check --fix

Run the test suite with coverage:

hatch test -c -a

Run security scan:

hatch run security

🤝 Contributing

Contributions are welcome! Please open issues or pull requests, and refer to our development guide in the documentation: contributing.

📄 License

Distributed under the GPLv3.