computeInfluence = True
graphRanker = GraphRanker(k=100, numRuns=100, computeInfluence=computeInfluence, p=0.05, inputRanking=[1, 2])
methodNames = graphRanker.getNames()
methodNames.append("MC2")
numMethods = len(methodNames)
averageTrainPrecisions = numpy.zeros((len(dataset.fields), len(ns), numMethods))
averageTestPrecisions = numpy.zeros((len(dataset.fields), len(ns), numMethods))
coverages = numpy.load(dataset.coverageFilename)
print("==== Coverages ====")
print(coverages)
for s, field in enumerate(dataset.fields):
if ranLSI:
outputFilename = dataset.getOutputFieldDir(field) + "outputListsLSI.npz"
documentFilename = dataset.getOutputFieldDir(field) + "relevantDocsLSI.npy"
else:
outputFilename = dataset.getOutputFieldDir(field) + "outputListsLDA.npz"
documentFilename = dataset.getOutputFieldDir(field) + "relevantDocsLDA.npy"
try:
print(field)
print("-----------")
outputLists, trainExpertMatchesInds, testExpertMatchesInds = Util.loadPickle(outputFilename)
graph, authorIndexer = Util.loadPickle(dataset.getCoauthorsFilename(field))
trainPrecisions = numpy.zeros((len(ns), numMethods))
testPrecisions = numpy.zeros((len(ns), numMethods))
fich.write(str(ListeAuthorsFinale)+"\n")
#Save relevant authors
#numpy.save(dataset.dataDir + "relevantAuthorsReputation" + field + ".txt", outputLists)
fich.write("-----------------------------------------------")
#for line in outputLists:
#fich.write(line[i])
#Ajout du score de l'expertise
#outputLists.append(expertAuthorsInds)
itemList = RankAggregator.generateItemList(outputLists)
methodNames = graphRanker.getNames()
if runLSI:
outputFilename = dataset.getOutputFieldDir(field) + "outputListsLSI.npz"
else:
outputFilename = dataset.getOutputFieldDir(field) + "outputListsLDA.npz"
Util.savePickle([outputLists, trainExpertMatchesInds, testExpertMatchesInds], outputFilename, debug=True)
numMethods = len(outputLists)
precisions = numpy.zeros((len(ns), numMethods))
averagePrecisions = numpy.zeros(numMethods)
for i, n in enumerate(ns):
for j in range(len(outputLists)):
precisions[i, j] = Evaluator.precisionFromIndLists(testExpertMatchesInds, outputLists[j][0:n])
for j in range(len(outputLists)):
averagePrecisions[j] = Evaluator.averagePrecisionFromLists(testExpertMatchesInds, outputLists[j][0:averagePrecisionN], averagePrecisionN)