UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation
This provides a tool for researchers and practitioners needing efficient uncertainty estimation, but it is incremental as it builds on existing methods without new algorithmic breakthroughs.
The paper introduces UncertaintyPlayground, a Python library for fast uncertainty estimation in supervised learning using Gaussian processes and mixed density networks, offering GPU/CPU training, visualization, and comprehensive testing and documentation.
This paper introduces UncertaintyPlayground, a Python library built on PyTorch and GPyTorch for uncertainty estimation in supervised learning tasks. The library offers fast training for Gaussian and multi-modal outcome distributions through Sparse and Variational Gaussian Process Regressions (SVGPRs) for normally distributed outcomes and Mixed Density Networks (MDN) for mixed distributions. In addition to model training with various hyperparameters, UncertaintyPlayground can visualize the prediction intervals of one or more instances. Due to using tensor operations, the library can be trained both on CPU and GPU and offers various PyTorch-specific techniques for speed optimization. The library contains unit tests for each module and ensures multi-platform continuous integration with GitHub Workflows (online integration) and Tox (local integration). Finally, the code is documented with Google-style docstrings and offers a documentation website created with MkDocs and MkDocStrings.