Ex-Fuzzy Documentation#

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Ex-Fuzzy is a powerful Python library for explainable fuzzy logic inference and approximate reasoning. It provides comprehensive tools for building, training, and analyzing fuzzy rule-based classifiers with a special focus on interpretability and explainability.

🚀 Quick Start

Get up and running with Ex-Fuzzy in minutes. Learn the basics of fuzzy classification and see practical examples.

Getting Started
📖 User Guide

Comprehensive tutorials and examples for building fuzzy classifiers, analyzing patterns, and visualizing results.

User Guide
🔍 API Reference

Complete reference for all classes, functions, and modules with detailed descriptions and examples.

API Reference
🧪 Examples

Real-world examples and case studies demonstrating Ex-Fuzzy’s capabilities across different domains.

Examples

Key Features#

🧠 Explainable AI

Generate interpretable fuzzy rules that provide transparent decision-making processes for your machine learning models.

⚡ High Performance

Optimized implementations with support for both Type-1 and Type-2 fuzzy systems, multiprocessing, and evolutionary algorithms.

📊 Rich Visualizations

Built-in plotting capabilities for fuzzy sets, rules, pattern stability analysis, and model performance metrics.

🔧 Flexible Architecture

Modular design allows easy customization of fuzzy sets, membership functions, and inference mechanisms.

📈 Pattern Analysis

Advanced tools for analyzing pattern stability, variable importance, and rule discovery consistency across multiple runs.

🔬 Research Ready

Designed for academic research with comprehensive statistical testing, bootstrapping, and experimental validation tools.

Quick Example#

Here’s a simple example to get you started:

import ex_fuzzy.evolutionary_fit as evf
import ex_fuzzy.eval_tools as eval_tools
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pandas as pd

# Load and prepare data
iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = iris.target

# Split the data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=0
)

# Create and train fuzzy classifier
classifier = evf.BaseFuzzyRulesClassifier(nRules=10, nAnts=4)
classifier.fit(X_train, y_train, n_gen=50, pop_size=30)

# Evaluate and visualize
evaluator = eval_tools.FuzzyEvaluator(classifier)
evaluator.eval_fuzzy_model(
    X_train, y_train, X_test, y_test,
    plot_rules=True, print_rules=True, plot_partitions=True
)

Installation#

Install Ex-Fuzzy using pip:

pip install ex-fuzzy

Or install from source:

git clone https://github.com/fuminides/ex-fuzzy.git
cd ex-fuzzy
pip install -e .

What’s New#

Version 1.0.0

  • 🎉 New comprehensive test suite with >90% code coverage

  • 📚 Modernized documentation with interactive examples

  • 🔧 Improved API consistency across all modules

  • ⚡ Performance optimizations for large datasets

  • 🐛 Bug fixes and stability improvements

Documentation Contents#

Getting Started

User Guide

Examples

API Reference

Development

Legacy Documentation

Community and Support#

💬 Discussion

Join our community discussions, ask questions, and share your projects.

https://github.com/fuminides/ex-fuzzy/discussions
🐛 Report Issues

Found a bug or have a feature request? Let us know on GitHub.

https://github.com/fuminides/ex-fuzzy/issues
📖 Contributing

Help improve Ex-Fuzzy by contributing code, documentation, or examples.

Contributing
📧 Contact

Get in touch with the development team for collaboration or support.

mailto:your-email@example.com

Citation#

If you use Ex-Fuzzy in your research, please cite:

@article{ex_fuzzy_2023,
    title={Ex-Fuzzy: A Python Library for Explainable Fuzzy Logic Inference},
    author={Fumanal Idocin, Javier},
    journal={Software Impacts},
    year={2023},
    publisher={Elsevier}
}

Indices and Tables#