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))
#Remove training experts from the output lists
trainOutputLists = []
testOutputLists = []
for outputList in outputLists:
newTrainOutputList = []
newTestOutputList = []
for item in outputList:
if item not in testExpertMatchesInds:
newTrainOutputList.append(item)
if item not in trainExpertMatchesInds:
newTestOutputList.append(item)
