Metadata-Version: 2.1
Name: scikit-physlearn
Version: 0.1.4
Summary: A Python package for single-target and multi-target regression tasks.
Home-page: https://github.com/a-wozniakowski/scikit-physlearn
Maintainer: Alex Wozniakowski
Maintainer-email: wozn0001@e.ntu.edu.sg
License: MIT
Download-URL: https://github.com/a-wozniakowski/scikit-physlearn
Project-URL: Paper, https://arxiv.org/abs/2005.06194
Description: .. -*- mode: rst -*-
        
        |SOTA|_ |DOCS|_ |PyPI|_
        
        .. |SOTA| image:: https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/boosting-on-the-shoulders-of-giants-in/multi-target-regression-on-google-5-qubit
        .. _SOTA: https://paperswithcode.com/sota/multi-target-regression-on-google-5-qubit?p=boosting-on-the-shoulders-of-giants-in
        
        .. |DOCS| image:: https://readthedocs.org/projects/scikit-physlearn/badge/?version=latest
        .. _DOCS: https://scikit-physlearn.readthedocs.io/en/latest/?badge=latest
        
        .. |PyPI| image:: https://badge.fury.io/py/scikit-physlearn.svg
        .. _PyPI: https://badge.fury.io/py/scikit-physlearn
        
        ################
        Scikit-physlearn
        ################
        
        **Scikit-physlearn** is a machine learning library designed to amalgamate 
        `Scikit-learn <https://scikit-learn.org/>`_,
        `LightGBM <https://lightgbm.readthedocs.io/en/latest/index.html>`_,
        `XGBoost <https://xgboost.readthedocs.io/en/latest/>`_,
        `CatBoost <https://catboost.ai/>`_,
        and `Mlxtend <http://rasbt.github.io/mlxtend/>`_ 
        regressors into a flexible framework that:
        
        - Follows the Scikit-learn API.
        - Processes pandas data representations.
        - Solves single-target and multi-target regression tasks.
        - Interprets regressors with `SHAP <https://shap.readthedocs.io/en/latest/>`_.
        
        Additionally, the library contains the official implementation of
        `base boosting <https://arxiv.org/abs/2005.06194>`_, which is an algorithmic
        paradigm for building additive expansions based upon the output of any
        base-level regressor. The implementation:
        
        - Supplants the statistical initialization in gradient boosting
          with the output of any base-level regressor.
        - Boosts arbitrary basis functions, i.e., it is not limited to boosting
          decision trees.
        - Efficiently learns in the low data regime.
        
        The `library <https://github.com/a-wozniakowski/scikit-physlearn>`_ was
        started by Alex Wozniakowski during his graduate studies at Nanyang Technological
        University.
        
        ************
        Installation
        ************
        
        Scikit-physlearn can be installed from `PyPI <https://pypi.org/project/scikit-physlearn/>`__::
        
            pip install scikit-physlearn
        
        To build from source, see the `installation guide <https://scikit-physlearn.readthedocs.io/en/latest/install.html>`_.
        
        ********
        Citation
        ********
        
        If you use this library, please consider adding the corresponding citation:
        
        .. code-block:: latex
        
            @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 C.},
              journal={arXiv preprint arXiv:2005.06194},
              year={2020}
            }
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/x-rst
Provides-Extra: docs
Provides-Extra: tests
