FDFI Documentation

FDFI (Flow-Disentangled Feature Importance) is a Python library for computing feature importance using disentangled methods, inspired by SHAP. This package implements both OT-based DFI and flow-based FDFI methods.

Key Features

🎯 Multiple Explainer Types: Tree, Linear, Kernel, and Optimal Transport explainers
🧭 OT-Based DFI: Gaussian OT (OTExplainer) and Entropic OT (EOTExplainer)
🔍 Shared Diagnostics: Latent independence and fidelity checks for OT/EOT/Flow
📊 Statistical Inference: Confidence intervals and hypothesis testing
🔧 Easy to Use: Simple API similar to SHAP
🚀 Extensible: Built with modularity for future enhancements

Quick Example

import numpy as np
from fdfi.explainers import OTExplainer

# Define your model
def model(X):
    return X.sum(axis=1)

# Create background data
X_background = np.random.randn(100, 10)

# Create an explainer
explainer = OTExplainer(model, data=X_background, nsamples=50)

# Explain test instances
X_test = np.random.randn(10, 10)
results = explainer(X_test)

# Get confidence intervals
ci = explainer.conf_int(alpha=0.05, target="X", alternative="two-sided")

Installation

git clone https://github.com/jaydu1/FDFI.git
cd FDFI
pip install -e .

# With optional dependencies
pip install -e ".[plots]"   # For visualization
pip install -e ".[flow]"    # For flow matching models
pip install -e ".[dev]"     # For development

User Guide

Tutorials

Tutorials

API Reference

API Reference

Development

Changelog

Indices and tables