logging.debug("Number of non-zero elements: " + str((trainX.nnz, testX.nnz)))
u = 0.1
w = 1-u
k2 = 64
eps = 10**-6
maxLocalAuc = MaxLocalAUC(k2, w, eps=eps, stochastic=True)
maxLocalAuc.alpha = 0.1
maxLocalAuc.alphas = 2.0**-numpy.arange(0, 5, 1)
maxLocalAuc.folds = 1
maxLocalAuc.initialAlg = "rand"
maxLocalAuc.itemExpP = 0.0
maxLocalAuc.itemExpQ = 0.0
maxLocalAuc.ks = numpy.array([k2])
maxLocalAuc.lmbdaU = 0.0
maxLocalAuc.lmbdaV = 0.0
maxLocalAuc.lmbdas = 2.0**-numpy.arange(0, 8)
maxLocalAuc.loss = "hinge"
maxLocalAuc.maxIterations = 500
maxLocalAuc.maxNorms = 2.0**numpy.arange(-2, 5, 0.5)
maxLocalAuc.metric = "f1"
maxLocalAuc.normalise = True
maxLocalAuc.numAucSamples = 10
maxLocalAuc.numProcesses = multiprocessing.cpu_count()
maxLocalAuc.numRecordAucSamples = 100
maxLocalAuc.numRowSamples = 30
maxLocalAuc.rate = "constant"
maxLocalAuc.recordStep = 10
maxLocalAuc.rho = 1.0
maxLocalAuc.t0 = 1.0
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)
maxLocalAuc.lmbdaU = 0.03125
maxLocalAuc.lmbdaV = 0.25
meanObjs2, paramDict = maxLocalAuc.learningRateSelect(X)
maxLocalAuc.lmbdaU = 0.25
maxLocalAuc.lmbdaV = 0.03125
meanObjs3, paramDict = maxLocalAuc.learningRateSelect(X)
maxLocalAuc.lmbdaU = 0.03125
maxLocalAuc.lmbdaV = 0.03125
meanObjs4, paramDict = maxLocalAuc.learningRateSelect(X)
