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chengsoonong / mclass-sky

Multiclass methods for astronomical data
BSD 3-Clause "New" or "Revised" License
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mclass-sky

Multiclass Active Learning Algorithms with Application in Astronomy.

:Contributors: Alasdair Tran <http://alasdairtran.com>, Cheng Soon Ong <http://www.ong-home.my>, Jakub Nabaglo <https://github.com/nbgl>, David Wu <https://github.com/davidjwu>, Wei Yen Lee <https://weiyen.net> :License: This package is distributed under a a 3-clause ("Simplified" or "New") BSD license. :Source: <https://github.com/chengsoonong/mclass-sky> :Doc: <https://mclearn.readthedocs.io/en/latest/> :Publications: Combining Active Learning Suggestions <projects/peerjcs16/paper> by Alasdair Tran, Cheng Soon Ong, and Christian Wolf

           `Active Learning with Gaussian Processes <projects/jakub/thesis/nabaglo17photometric-redshift.pdf>`_ by Jakub Nabaglo

           `Photometric Classification with Thompson Sampling <projects/alasdair/thesis/tran15honours-thesis.pdf>`__ by Alasdair Tran

           `Cutting-Plane Methods with Active Learning <projects/david/report/dwu_asc_report_16s2.pdf>`_ by David Wu

.. image:: https://travis-ci.org/chengsoonong/mclass-sky.svg :target: https://travis-ci.org/chengsoonong/mclass-sky

.. image:: https://coveralls.io/repos/chengsoonong/mclass-sky/badge.svg?branch=master&service=github :target: https://coveralls.io/github/chengsoonong/mclass-sky?branch=master

.. image:: https://zenodo.org/badge/doi/10.5281/zenodo.58500.svg :target: https://doi.org/10.5281/zenodo.58500

Introduction

This repository contains a collection of projects related to active learning methods with application in astronomy. Click on one of the links below to go to the directory of a particular project.

  1. Combining Active Learning Suggestions <projects/peerjcs16>_ by Alasdair Tran, Cheng Soon Ong, and Christian Wolf

  2. Active Learning with Gaussian Processes for Photometric Redshift Prediction <projects/jakub>_

  3. Cutting-plane Methods with Applications in Convex Optimization and Active Learning <projects/david>_

  4. Photometric Classification with Thompson Sampling <projects/alasdair>__

mclearn

mclearn is a Python package that implement selected multiclass active learning algorithms, with a focus in astronomical data.

The dependencies are Python 3.4, numpy, pandas, matplotlib, seaborn, ephem, scipy, ipython, and scikit-learn. It's best to first install the Anaconda distribution for Python 3, then install mclearn using pip::

pip install mclearn