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)