maxLocalAuc.itemExpP = 0.0
maxLocalAuc.itemExpQ = 0.0
maxLocalAuc.ks = numpy.array([4, 8, 16, 32, 64, 128])
maxLocalAuc.lmbdas = 2.0**-numpy.arange(1, 5)
maxLocalAuc.loss = "hinge"
maxLocalAuc.maxIterations = 500
maxLocalAuc.maxNorm = 100
maxLocalAuc.metric = "f1"
maxLocalAuc.normalise = False
maxLocalAuc.numAucSamples = 10
maxLocalAuc.numProcesses = multiprocessing.cpu_count()
maxLocalAuc.numRecordAucSamples = 200
maxLocalAuc.numRowSamples = 15
maxLocalAuc.rate = "optimal"
maxLocalAuc.recordStep = 10
maxLocalAuc.reg = False
maxLocalAuc.rho = 1.0
maxLocalAuc.startAverage = 100
maxLocalAuc.t0 = 1.0
maxLocalAuc.t0s = 2.0**-numpy.arange(1, 12, 2)
maxLocalAuc.validationSize = 5
maxLocalAuc.validationUsers = 0.0
if saveResults:
X = DatasetUtils.getDataset(dataset, nnz=100000)
print(X.shape, X.nnz)
print(maxLocalAuc)
maxLocalAuc.lmbdaU = 0.25
maxLocalAuc.lmbdaV = 0.25
meanObjs1, paramDict = maxLocalAuc.learningRateSelect(X)
