gpclarity 0.1.2

Interpretability and Diagnostics Tools for Gaussian Processes

GPClarity: Gaussian Process Interpretability Toolkit

GPClarity is a production-ready library that transforms black-box Gaussian Process models into interpretable, debuggable, and trustworthy tools. Built on GPy and emukit, it provides human-readable insights into kernel behavior, uncertainty patterns, and model complexity.

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🎯 Features

• 🔍 Kernel Interpretation: Translate raw kernel math into human meaning
• 📊 Uncertainty Profiling: Visualize and diagnose uncertainty behavior
• 📈 Hyperparameter Tracking: Monitor optimization dynamics in real-time
• 🧮 Complexity Quantification: Measure and prevent overfitting
• 🎯 Data Influence Analysis: Identify impactful training points
• 🔗 Emukit Integration: Seamless Bayesian optimization support

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🚀 Quick Start

import gpclarity
import GPy
import numpy as np

# Train a Gaussian Process
X = np.linspace(0, 10, 50).reshape(-1, 1)
y = np.sin(X).flatten() + 0.1 * np.random.randn(50)

kernel = GPy.kern.RBF(1) + GPy.kern.White(1)
model = GPy.models.GPRegression(X, y[:, None], kernel)
model.optimize()

summary = gpclarity.summarize_kernel(model)

profiler = gpclarity.UncertaintyProfiler(model)
X_test = np.linspace(-2, 12, 200).reshape(-1, 1)
profiler.plot(X_test, X_train=X, y_train=y)

tracker = gpclarity.HyperparameterTracker(model)
history = tracker.wrapped_optimize(max_iters=50)
tracker.plot_evolution()

complexity = gpclarity.compute_complexity_score(model, X)
print(f"Complexity: {complexity['score']:.2f} - {complexity['interpretation']}")

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📦 Installation

Stable Release

pip install gpclarity

Development Version

git clone https://github.com/AngadKumar16/gpclarity.git
cd gpclarity
pip install -e ".[dev]"

Conda (coming soon)

conda install -c conda-forge gpclarity

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🏗️ Architecture

gpclarity/
├── kernel_summary
├── uncertainty_analysis
├── hyperparam_tracker
├── model_complexity
├── data_influence
└── utils

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🔬 Advanced Usage

Emukit Integration

from gpclarity import ClarityBayesianOptimizationLoop

loop = ClarityBayesianOptimizationLoop(model, space)
loop.run_loop(user_function, stopping_condition)
loop.plot_diagnostics()

Batch Processing

models = [model1, model2, model3]
reports = [gpclarity.summarize_kernel(m, verbose=False) for m in models]

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📊 Example Outputs

Kernel Summary

🔍 KERNEL SUMMARY
Structure: ['RBF', 'White']
Components: 2

📦 RBF (lengthscale)
  └─ lengthscale: 1.23
  💡 Moderate flexibility

📦 White (variance)
  └─ variance: 0.01
  💡 Low observation noise

Complexity Report

{
  "score": 2.34,
  "interpretation": "Moderate complexity (well-balanced)",
  "components": {
    "n_kernel_parts": 2,
    "roughness_score": 0.81,
    "noise_ratio": 4.5
  }
}

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🎓 Citation

@software{gpclarity2026,
  title={gpclarity: Gaussian Process Interpretability Toolkit},
  author={Angad Kumar},
  year={2026},
  url={https://github.com/AngadKumar16/gpclarity},
  version={0.1.0}
}

📝 License

GPClarity is licensed under the MIT License. See LICENSE for details.

🤝 Contributing

Contributions are welcome!

• Report bugs or request features via GitHub Issues
• Submit pull requests for fixes or enhancements
• Make sure to follow the code style and write tests for new features

Author: Angad Kumar (GitHub, Email)

🛣️ Roadmap

• Conda package support
• More visualization tools for kernel decomposition
• Automated tutorials / example notebooks
• More features overall