relevantAuthors = set(relAuthorsDocSimilarity).union(set(relAuthorsDocCitations))
logging.debug("Total number of relevant authors : " + str(len(relevantAuthors)))
graph, authorIndexer = dataset.coauthorsGraph(field, relevantAuthors)
#f = open(dataset.getCoauthorsFilename(field),'rb') #Save the graph of co-authors
#storedlist=pickle.load(f)
#for i in storedlist:
#fgraph.write(str(i))
#fgraph.write("\n"+ "Author vertices")
#fgraph.write(str(authorIndexer.getIdDict()))
#fgraph.write(str(numpy.ones(graph.ecount())))
#print(i)
#fgraph.close()
graph1, authorIndexer1 = dataset.GetIndexer(field, expertAuthors) #Get Index for the expert list BM25
trainExpertMatches = dataset.matchExperts(relevantAuthors, dataset.trainExpertDict[field])
testExpertMatches = dataset.matchExperts(relevantAuthors, dataset.testExpertDict[field])
trainExpertMatchesInds = authorIndexer.translate(trainExpertMatches)
testExpertMatchesInds = authorIndexer.translate(testExpertMatches)
relevantAuthorInds1 = authorIndexer.translate(relAuthorsDocSimilarity)
relevantAuthorInds2 = authorIndexer.translate(relAuthorsDocCitations)
relevantAuthorsInds = authorIndexer.translate(relevantAuthors)
expertAuthorsInds = authorIndexer1.translate(expertAuthors)#Get Ids our BM25 List
assert (numpy.array(relevantAuthorInds1) < len(relevantAuthorsInds)).all()
assert (numpy.array(relevantAuthorInds2) < len(relevantAuthorsInds)).all()
if len(testExpertMatches) != 0:
fich.write(field)
fich.write("\n")
