Lethe: Comprehensive Machine Unlearning Library

Named after the Greek river of forgetfulness, Lethe provides state-of-the-art machine unlearning algorithms with comprehensive evaluation and verification capabilities.

Overview

Lethe is a comprehensive Python library for machine unlearning - the process of selectively removing the influence of specific training data from machine learning models. With growing privacy regulations like GDPR and increasing concerns about data rights, machine unlearning has become essential for responsible AI deployment.

Key Features

• Multiple Unlearning Algorithms: Naive retraining, gradient ascent, SISA, influence functions, and more
• Comprehensive Evaluation: Performance metrics, privacy verification, and utility assessment
• Privacy Testing: Membership inference attacks and privacy loss estimation
• Production Ready: Industry-standard APIs with proper error handling and logging
• Extensive Documentation: Complete examples, tutorials, and API reference
• Framework Agnostic: Works with scikit-learn, PyTorch, TensorFlow models
• Benchmarking Suite: Compare different unlearning methods systematically

Quick Start

Installation

pip install lethe-ml

Or with uv:

uv add lethe-ml

Basic Usage

import lethe
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

# Create synthetic dataset
X, y = make_classification(n_samples=1000, n_features=10, n_classes=3, random_state=42)

# Train model
model = RandomForestClassifier(n_estimators=50, random_state=42)
model.fit(X, y)

# Create data splits for unlearning
loader = lethe.DatasetLoader()
dataset = loader.load_from_arrays(X, y)
splitter = lethe.UnlearningDataSplitter()
data_split = splitter.create_unlearning_split(dataset, forget_ratio=0.1)

# Perform unlearning
result = lethe.unlearn(
    model=model,
    method='gradient_ascent',
    forget_data=data_split.forget,
    retain_data=data_split.retain
)

print(f"Unlearning completed in {result.execution_time:.4f}s")
print(f"Metrics: {result.metrics}")

Comprehensive Evaluation

# Evaluate unlearning quality
evaluator = lethe.UnlearningEvaluator(task_type="classification")
eval_result = evaluator.evaluate_unlearning(
    original_model=model,
    unlearned_model=result.unlearned_model,
    data_split=data_split
)

# Verify privacy and security
verifier = lethe.UnlearningVerifier()
verify_result = verifier.verify_unlearning(
    original_model=model,
    unlearned_model=result.unlearned_model,
    data_split=data_split
)

print(f"Unlearning Quality: {eval_result.unlearning_quality:.4f}")
print(f"Privacy Score: {verify_result.overall_score:.4f}")

Supported Algorithms

Advanced Usage

Custom Unlearning Pipeline

from lethe import UnlearningAlgorithmFactory, ExperimentConfig

# Configure experiment
config = ExperimentConfig(
    experiment_name="privacy_evaluation",
    forget_ratio=0.15,
    unlearning_method="gradient_ascent",
    save_results=True
)

# Create custom algorithm
algorithm = UnlearningAlgorithmFactory.create_algorithm(
    "gradient_ascent",
    learning_rate=0.01,
    n_epochs=20
)

# Run unlearning
result = algorithm.unlearn(model, data_split.forget, data_split.retain)

Batch Processing

# Test multiple methods
methods = ['naive_retraining', 'gradient_ascent', 'sisa']
results = {}

for method in methods:
    result = lethe.unlearn(model, method, data_split.forget, data_split.retain)
    results[method] = result
    print(f"{method}: {result.execution_time:.4f}s")

Command Line Interface

# Run basic demo
python -m lethe

# Run advanced benchmarks
python -m lethe --benchmark --results-dir ./experiments

# Run specific demo
python -m lethe --demo advanced --log-level DEBUG

Documentation

• Quick Start Guide: Get up and running in minutes
• API Reference: Complete function and class documentation
• Algorithm Guide: Detailed explanation of unlearning methods
• Examples: Jupyter notebooks and scripts
• Contributing: How to contribute to the project

Requirements

• Python 3.8+
• NumPy >= 1.21.0
• scikit-learn >= 1.0.0
• pandas >= 1.3.0
• pydantic >= 2.0.0

Optional dependencies:

• matplotlib >= 3.3.0 (for visualization)
• seaborn >= 0.11.0 (for plotting)
• jupyter (for examples)

Installation from Source

git clone https://github.com/yourusername/lethe.git
cd lethe
pip install -e .

With uv:

git clone https://github.com/yourusername/lethe.git
cd lethe
uv pip install -e .

Examples

Real-world Dataset

# Load real dataset
from sklearn.datasets import load_breast_cancer
data = load_breast_cancer()

# Create Lethe dataset
dataset = lethe.Dataset(
    X=data.data, 
    y=data.target,
    feature_names=data.feature_names.tolist(),
    target_names=data.target_names.tolist()
)

# Perform privacy-preserving unlearning
result = lethe.unlearn(
    model=LogisticRegression(),
    method='influence_function',
    forget_data=sensitive_data,
    retain_data=public_data
)

Model Comparison

from lethe.evaluation import EvaluationReport

# Compare multiple unlearning methods
comparison = lethe.compare_methods(
    model=model,
    methods=['naive_retraining', 'gradient_ascent', 'sisa'],
    data_split=data_split
)

# Generate comprehensive report
report = EvaluationReport.generate_text_report(comparison)
print(report)

Benchmarks

Performance on standard datasets:

Contributing

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

Development Setup

git clone https://github.com/yourusername/lethe.git
cd lethe
uv sync --dev
uv run pytest

Running Tests

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=lethe --cov-report=html

# Run specific test
uv run pytest tests/test_algorithms.py -v

License

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