xanfis

X-ANFIS: Modular ANFIS Library with Classic, Gradient, and Bio-inspired Training Strategies

X-ANFIS is a Python library offering a powerful and extensible implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) using PyTorch and Scikit-Learn. The library is written with object-oriented principles and modular architecture, enabling easy customization, integration, and experimentation.It supports:

• 🔧 Classic ANFIS (Hybrid: Gradient Descent + Least Squares)
• 🌊 Gradient-based ANFIS (Fully differentiable training)
• 🧠 Bio-inspired ANFIS (Metaheuristics + Least Squares)

✨ Key Features

• Modular, object-oriented design for ease of extension.
• Scikit-Learn API: .fit(), .predict(), .score() and compatible with GridSearchCV, Pipeline, etc.
• Wide range of Gradient-based training optimizers: SGD, Adam, RMSprop, Adagrad, AdamW, ...
• Wide range of Bio-inspired optimizers: GA, PSO, ACO, WOA, BA, FFA, CSA, GWO, ABC, DE, ...
• Rich membership functions: Triangular, Gaussian, Bell, ...
• Over 67 built-in metrics and losses.

🧠 Model Zoo

Model Class	Training Method	Type
AnfisRegressor, AnfisClassifier	Classic Hybrid	Regression / Classification
GdAnfisRegressor, GdAnfisClassifier	Gradient-based	Regression / Classification
BioAnfisRegressor, BioAnfisClassifier	Metaheuristic-based	Regression / Classification

📌 Citation

Please include these citations if you plan to use this library:

@software{thieu20250414,
  author  = {Nguyen Van Thieu},
  title   = {X-ANFIS: An Extensible and Cross-Learning ANFIS Framework for Machine Learning Tasks},
  month   = June,
  year    = 2025,
  doi     = {10.6084/m9.figshare.28802531},
  url     = {https://github.com/thieu1995/X-ANFIS}
}

@article{van2023mealpy,
  title   = {MEALPY: An open-source library for latest meta-heuristic algorithms in Python},
  author  = {Van Thieu, Nguyen and Mirjalili, Seyedali},
  journal = {Journal of Systems Architecture},
  year    = {2023},
  publisher = {Elsevier},
  doi     = {10.1016/j.sysarc.2023.102871}
}

⚙️ General tutorial

Below tutorial is how you can install and use this library. For more complex examples and documentation please check the examples folder and documentation website.

📦 Installation

Install the latest version using pip:

pip install xanfis

After that, check the version to ensure successful installation:

$ python
>>> import xanfis
>>> xanfis.__version__

🧪 Quick Example

Let's say I want to use Adam optimization-based ANFIS for Iris classification dataset. Here how to do it.

from xanfis import Data, GdAnfisClassifier
from sklearn.datasets import load_iris

## Load data object
X, y = load_iris(return_X_y=True)
data = Data(X, y)

## Split train and test
data.split_train_test(test_size=0.2, random_state=2, inplace=True, shuffle=True)
print(data.X_train.shape, data.X_test.shape)

## Scaling dataset
data.X_train, scaler_X = data.scale(data.X_train, scaling_methods=("standard", "minmax"))
data.X_test = scaler_X.transform(data.X_test)

data.y_train, scaler_y = data.encode_label(data.y_train)
data.y_test = scaler_y.transform(data.y_test)

print(type(data.X_train), type(data.y_train))

## Create model
model = GdAnfisClassifier(num_rules=20, mf_class="Gaussian",
                          act_output=None, vanishing_strategy="blend", reg_lambda=None,
                          epochs=50, batch_size=16, optim="Adam", optim_params={"lr": 0.01},
                          early_stopping=True, n_patience=10, epsilon=0.001, valid_rate=0.1,
                          seed=42, verbose=True)
## Train the model
model.fit(X=data.X_train, y=data.y_train)

## Test the model
y_pred = model.predict(data.X_test)
print(y_pred)
print(model.predict_proba(data.X_test))

## Calculate some metrics
print(model.evaluate(y_true=data.y_test, y_pred=y_pred, list_metrics=["F2S", "CKS", "FBS", "PS", "RS", "NPV", "F1S"]))

💬 Contributing

We welcome contributions to X-ANFIS! If you have suggestions, improvements, or bug fixes, feel free to fork the repository, create a pull request, or open an issue.

X-ANFIS allows for customization at various levels, including:

• Membership Functions: You can define custom membership functions to fit your problem requirements.

• Learning Strategies: Easily switch between gradient-based or bio-inspired algorithms for training.

• Model Components: Customize the architecture of the ANFIS model, including the number of input and output nodes, output activation function, number of rules, and rule strengths, L2 regularization, training methods.

📞 Community & Support

• 📖 Official Source Code
• 📖 Official Releases
• 📖 Official Docs
• 💬 Telegram Chat
• 🐛 Report Issues
• 🔄 Changelog

🧩 Related Projects

Explore other projects by the author:

• 🔧 MEALPY
• 🔍 Metaheuristics
• 🧪 Permetrics
• 📦 Opfunu
• 🔬 PFEvaluator
• 🧠 IntelELM
• 🔥 Reflame
• 🧭 MetaCluster
• 🧠 Enoppy
• 🤖 AIIR Team

Developed by: Thieu @ 2025