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.t0s = 2.0**-numpy.arange(7, 12, 1) maxLocalAuc.validationSize = 3 maxLocalAuc.validationUsers = 0 newM = X.shape[0]/4
