Unified XAI

Production-Ready Explainable AI Library for Deep Learning

Documentation | Tutorials | API Reference

🎯 Overview

Unified XAI is a comprehensive, production-ready library for explaining deep learning models across multiple frameworks and modalities. It provides a unified API for various explainability methods, making it easy to understand, debug, and improve your AI models.

✨ Key Features

• 🔄 Framework Agnostic: Seamless support for PyTorch, TensorFlow, Keras, and ONNX
• 📊 Multiple Modalities: Image, text, tabular, time-series, and multimodal data
• 🎨 Rich Visualizations: Interactive plots, heatmaps, and dashboards
• 📈 Comprehensive Metrics: Faithfulness, stability, complexity evaluations
• ⚡ High Performance: Optimized implementations with caching and parallelization
• 🔧 Production Ready: Type hints, extensive testing, and robust error handling
• 🚀 Easy to Use: Simple API with sensible defaults
• 📦 Extensible: Plugin architecture for custom methods

🚀 Quick Start

Installation

# Basic installation
pip install unified-xai

# With specific framework support
pip install unified-xai[torch]  # PyTorch support
pip install unified-xai[tf]     # TensorFlow support
pip install unified-xai[all]    # All frameworks

# Development installation
pip install unified-xai[dev]

# With dashboard support
pip install unified-xai[dashboard]

Basic Usage

import torch
from unified_xai import XAIAnalyzer, XAIConfig
from unified_xai.config import Framework, Modality

# Load your model
model = torch.load('your_model.pth')

# Configure XAI
config = XAIConfig(
    framework=Framework.PYTORCH,
    modality=Modality.IMAGE
)

# Initialize analyzer
analyzer = XAIAnalyzer(model, config)

# Generate explanation
explanation = analyzer.explain(
    input_data, 
    method='integrated_gradients',
    target=class_idx
)

# Visualize
fig = analyzer.visualize(explanation, original_input=input_data)

📚 Supported Methods

Gradient-Based Methods

• ✅ Vanilla Gradient
• ✅ Integrated Gradients
• ✅ SmoothGrad
• ✅ Grad-CAM / Grad-CAM++
• ✅ Guided Backpropagation
• ✅ DeepLIFT

Perturbation-Based Methods

• ✅ LIME (Local Interpretable Model-agnostic Explanations)
• ✅ SHAP (SHapley Additive exPlanations)
• ✅ Occlusion Sensitivity
• ✅ Meaningful Perturbations

Attention-Based Methods

• ✅ Attention Rollout
• ✅ Attention Flow
• ✅ LRP (Layer-wise Relevance Propagation)

Example-Based Methods

• ✅ Influence Functions
• ✅ Prototype Selection
• ✅ Counterfactual Explanations

🎯 Use Cases

# Explain image classification
explanation = analyzer.explain(image, method='gradcam')

# Compare multiple methods
comparison = analyzer.compare_methods(
    image,
    methods=['gradcam', 'integrated_gradients', 'lime'],
    metrics=['faithfulness', 'complexity']
)

# Explain text classification
config = XAIConfig(framework=Framework.PYTORCH, modality=Modality.TEXT)
analyzer = XAIAnalyzer(bert_model, config)

explanation = analyzer.explain(
    text_tokens, 
    method='integrated_gradients'
)

# Explain tabular predictions
config = XAIConfig(modality=Modality.TABULAR)
analyzer = XAIAnalyzer(model, config)

explanation = analyzer.explain(
    tabular_data,
    method='shap',
    background_data=train_data
)

📊 Evaluation Metrics

Unified XAI provides comprehensive metrics to evaluate explanation quality:

# Evaluate explanation
metrics = analyzer.evaluator.evaluate(
    explanation,
    input_data,
    metrics=['faithfulness', 'stability', 'complexity', 'sensitivity']
)

# Compare methods quantitatively
rankings = analyzer.compare_methods(
    input_data,
    methods=['gradcam', 'lime', 'shap'],
    metrics=['faithfulness', 'stability']
)

🎨 Visualization Dashboard

Launch interactive dashboard for exploration:

# Command line
unified-xai dashboard --model path/to/model --port 8080

# Or in Python
from unified_xai.dashboard import launch_dashboard
launch_dashboard(model, port=8080)

🏗️ Architecture

unified-xai/
├── core/              # Core abstractions and base classes
├── methods/           # Explanation method implementations
│   ├── gradient/      # Gradient-based methods
│   ├── perturbation/  # Perturbation-based methods
│   ├── attention/     # Attention-based methods
│   └── example/       # Example-based methods
├── frameworks/        # Framework-specific adapters
├── visualization/     # Visualization utilities
├── metrics/          # Evaluation metrics
├── utils/            # Helper utilities
└── dashboard/        # Web dashboard

🔧 Configuration

Unified XAI supports various configuration options:

# From file
config = XAIConfig.from_file('config.yaml')

# Programmatic
config = XAIConfig(
    framework=Framework.PYTORCH,
    modality=Modality.IMAGE,
    gradient_config={
        'normalize': True,
        'smooth_samples': 50
    },
    visualization_config={
        'cmap': 'RdBu_r',
        'overlay': True
    }
)

Example config.yaml:

framework: pytorch
modality: image
batch_size: 32
device: cuda

gradient_config:
  normalize: true
  smooth_samples: 50
  
lime_config:
  num_samples: 1000
  num_features: 10
  
visualization_config:
  cmap: RdBu_r
  alpha: 0.7

🧪 Testing

# Run all tests
pytest

# Run with coverage
pytest --cov=unified_xai

# Run specific test module
pytest tests/test_methods.py

# Run benchmarks
pytest tests/benchmarks/ --benchmark-only

📖 Documentation

Full documentation is available at https://unified-xai.readthedocs.io

• Getting Started Guide
• API Reference
• Tutorials
• Method Descriptions
• Best Practices

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

# Setup development environment
git clone https://github.com/yourusername/unified-xai.git
cd unified-xai
pip install -e ".[dev]"
pre-commit install

# Run checks before committing
make lint
make test
make docs

📊 Benchmarks

Performance comparisons across different methods and frameworks:

🎓 Citation

If you use Unified XAI in your research, please cite:

@software{unified_xai,
  title = {Unified XAI: A Production-Ready Explainable AI Library},
  author = {Satyam Singh},
  year = {2025},
  url = {https://github.com/SatyamSingh8306/unified-xai}
}

📝 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• Thanks to all contributors and the open-source community
• Inspired by Captum, SHAP, LIME, and other XAI libraries
• Supported by [Your Organization]

📬 Contact

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: satyamsingh7734@gmail.com
• Twitter: @unified_xai

🗺️ Roadmap

• Support for Vision Transformers
• Additional evaluation metrics
• Model-specific explanations
• Distributed computing support
• AutoML integration
• Mobile deployment