Metadata-Version: 1.2
Name: corankco
Version: 1.0.0
Summary: Kemeny-Young method for rank aggregation of incomplete rankings with ties
Home-page: https://github.com/pierreandrieu/corankco
Author: Pierre Andrieu
Author-email: pierre.andrieu@lilo.org
License: MIT
Description: corankco
        ===============
        
        This package implements methods for rank aggregation of incomplete rankings with ties 
        
        Installation
        ------------
        
        Install from PyPI:
        
        ``pip3 install --user corankco``
        
        
        Example usage
        -------------
        
        >>> from corankco.dataset import Dataset
        >>> from corankco.scoringscheme import ScoringScheme
        >>> from corankco.kemrankagg import KemRankAgg
        >>> from corankco.algorithms.enumeration import Algorithm
        >>>
        >>> d = Dataset([
        ...               [[1], [2, 3]],
        ...               [[3, 1], [4]],
        ...               [[1], [5], [3, 2]]
        ...              ])
        >>> print(d.description())
        Dataset description:
        	elements:5
        	rankings:3
        	complete:False
        	with ties: True
        	rankings:
        		r1 = [[1], [2, 3]]
        		r2 = [[3, 1], [4]]
        		r3 = [[1], [5], [3, 2]]
        
        >>> # Generates default scoring scheme
        >>> sc = ScoringScheme()
        
        >>> # Consensus computation with an exact algorithm
        >>> consensus = KemRankAgg.compute_consensus(d, sc, Algorithm.Exact)
        
        
        >>> print(consensus.description())
        Consensus description:
        	computed by:Exact algorithm ILP Cplex
        	necessarily optimal:True
        	kemeny score:6.0
        	consensus:
        		c1 = [[1], [2, 3], [4], [5]]
        		c2 = [[1], [2, 3], [5], [4]]
        
        >>> # Consensus computation with an heuristic
        consensus = KemRankAgg.compute_consensus(d, sc, Algorithm.ParCons)
        
        
        >>> print(consensus.description())
        Consensus description:
        	necessarily optimal:True
        	computed by:ParCons, uses  "BioConsert with input rankings as starters" on groups of size >  80
        	weak partitioning (at least one optimal solution)[{1}, {2, 3}, {5}, {4}]
        	kemeny score:8.0
        	consensus:
        		c1 = [[1], [2, 3], [5], [4]]
        
        >>> # example of computing score ('distance') between two ranking
        >>> r1 = [[1], [2], [3, 4]]
        >>> r2 = [[3], [2]]
        >>> print(KemenyScoreFactory.score_between_rankings(r1, r2, sc))
        5.
        
        
Platform: UNKNOWN
Requires-Python: >=3
