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Calculates pairwise semantic similarity scores in a list of annotation sets

        def GO_Similarity(G, allGO, metric="GS2", **kargs):
    """
    Calculates pairwise semantic similarity scores in a list of annotation sets
    """
    
    if len(allGO)<2:
        sim=1.0
        l=[1.0]
    else:
        if metric=="GS2":
            sim,l=G.GS2( [G.GOtoInt(GO)  for GO in allGO ])
        elif metric=="CzekanowskiDice":
            allGO=[G.GOtoInt(GO)  for GO in allGO ]

            allD=dict()
            l=list()
            for i,GO1 in enumerate(allGO):
                foo=[allD.setdefault(sort([i,j])[0],dict()).setdefault(sort([i,j])[1], G.CzekanowskiDice(GO1, GO2)) for j,GO2 in enumerate(allGO) if not j==i]
                l.append(mean(foo))

            sim=mean(l)

        elif metric=="Resnik":
            allGO=[G.GOtoInt(GO)  for GO in allGO ]

            allD=dict()
            l=list()
            for i,GO1 in enumerate(allGO):
                foo=[allD.setdefault(sort([i,j])[0],dict()).setdefault(sort([i,j])[1], G.Resnik(GO1, GO2, kargs.get('IC', dict()))) for j,GO2 in enumerate(allGO) if not j==i]
                l.append(mean(foo))

            sim=mean(l)
            
        else:
            logger.handleWarning ("Sorry, unknown semnatic similarity %s " % metric)
            sim,l=None,None
    
    return sim,l
        


src/a/i/AIGO-0.1.0/AIGO/Analyse.py   AIGO(Download)
from AIGO import logger, logFun
 
from AIGO.Similarity import GOSet_Similarity, GO_Similarity
 
class AnalyseFA(dict):
            allCompactness=dict()
            for a in FA.G.aspect:
                sim, l = GO_Similarity(FA.G, FA.GPtoGO[a].values())
                allCompactness[a]=l