scikit-physlearn 0.1.2

A Python package for single-target and multi-target regression tasks.

Scikit-physlearn

Scikit-physlearn is a Python package for single-target and multi-target regression. It is designed to amalgamate Scikit-learn, LightGBM, XGBoost, CatBoost, and Mlxtend regressors into a unified Regressor, which:

• Follows the Scikit-learn API.
• Represents data in pandas.
• Supports base boosting.

The repository was started by Alex Wozniakowski during his graduate studies at Nanyang Technological University.

Install

Scikit-physlearn can be installed from PyPi:

pip install scikit-physlearn

Quick Start

See below for a quick tour of the Scikit-physlearn package.

• Follow the introduction module to get started with single-target regression.
• Check out the multi-target module to get started with multi-target regression.
• Explore the model search module to learn about (hyper)parameter optimization.

Base boosting

Inspired by the process of human research, base boosting is a modification of the standard version of gradient boosting, which is designed to emulate the paradigm of "standing on the shoulders of giants." To evaluate the efficacy of this approach in a quantum device calibration application with a limited supply of experimental data:

• Start with the learning curve module, and use it to generate an augmented learning curve.
• Next, run the benchmark module, and use it to obtain the base regressor's test error.
• Then, run the main body module, and compare the test error of base boosting with the benchmark error.
• Lastly, explore the difficulty in learning without the base regressor's inductive bias in the supplementary module.

Citation

If you use this package, please consider adding the corresponding citation:

@article{wozniakowski_2020_boosting,
  title={Boosting on the shoulders of giants in quantum device calibration},
  author={Wozniakowski, Alex and Thompson, Jayne and Gu, Mile and Binder, Felix},
  journal={arXiv preprint arXiv:2005.06194},
  year={2020}
}