semvec/pypi-packages huggingface dataset:
qc-utils 20.9.1
Tool for representing ENCODE QC metrics
License: MIT
Requires Python: >=3.5

==============================================
``qc-utils``: Utility for ENCODE pipeline QC
==============================================

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/ambv/black
    :alt: Code Style: Black

.. image:: https://img.shields.io/badge/License-MIT-blue.svg
   :target: https://lbesson.mit-license.org/
   :alt: License: MIT

.. image:: https://circleci.com/gh/ENCODE-DCC/qc-utils.svg?style=svg
    :target: https://circleci.com/gh/ENCODE-DCC/qc-utils
    :alt: CircleCI status

.. short-intro-begin

``qc-utils`` is a python package that provides ``QCMetric`` and ``QCMetricRecord`` classes that can be used for representing ENCODE pipeline quality control metrics.

.. short-intro-end

Installation
==============

.. code-block:: console

    $ pip install qc-utils


Project Information
====================

``qc-utils`` is released under the `MIT <https://choosealicense.com/licenses/mit/>`_ license, documentation lives in `readthedocs <https://qc-utils.readthedocs.io/en/latest/>`_, code is hosted on `github <https://github.com/ENCODE-DCC/qc-utils>`_ and the releases on `PyPI <https://pypi.org/project/qc-utils/>`_.
mulan-2020-hd-full-online-free 0.1
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License: MIT
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mulan-2020-full-online-free 0.1
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License: MIT
Keywords: Full Movie,Watch online,Putlocker

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corankcolight 1.1.3
Kemeny-Young method for rank aggregation of incomplete rankings with ties
License: MIT

corankcolight
===============

This package implements methods for rank aggregation of incomplete rankings with ties 

Installation
------------

Install from PyPI:

``pip3 install --user corankcolight``


Example usage
-------------

>>> from corankcolight.dataset import Dataset
>>> from corankcolight.scoringscheme import ScoringScheme
>>> from corankcolight.kemrankagg import KemRankAgg
>>> from corankcolight.algorithms.enumeration import Algorithm
>>>
>>> d = Dataset([[[1], [2]], [[1, 3]]])
>>> print(d.description())
Dataset description:
	elements:3
	rankings:2
	complete:False
	with ties: True
	rankings:
		r1 = [[1], [2]]
		r2 = [[1, 3]]
>>>
>>> sc = ScoringScheme([[0., 1., 1., 0., 0., 0.], [1., 1., 0., 0., 0., 0.]])
>>>
>>> consensus = KemRankAgg.compute_with_heuristic(dataset=d, scoring_scheme=sc, algorithm=Algorithm.AllTied)
>>>
>>> print(consensus.description())
Consensus description:
	necessarily optimal:False
	kemeny score:-1.0
	consensus:
		c1 = [[1, 2, 3]]