maxLocalAuc = MaxLocalAUC(k2, w2, eps=eps, lmbdaU=0.1, lmbdaV=0.1, stochastic=True) maxLocalAuc.alpha = 0.5 maxLocalAuc.alphas = 2.0**-numpy.arange(2, 9, 2) maxLocalAuc.beta = 2 maxLocalAuc.bound = False maxLocalAuc.delta = 0.1 maxLocalAuc.eta = 20 maxLocalAuc.folds = 2 maxLocalAuc.initialAlg = "svd" 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
lmbda = 0.0 maxLocalAuc = MaxLocalAUC(k2, w2, eps=eps, lmbdaU=lmbda, lmbdaV=lmbda, stochastic=True) maxLocalAuc.alpha = 0.1 maxLocalAuc.alphas = 2.0**-numpy.arange(0, 5, 1) maxLocalAuc.bound = True maxLocalAuc.delta = 0.1 maxLocalAuc.eta = 0 maxLocalAuc.folds = 2 maxLocalAuc.initialAlg = "rand" 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, 10) maxLocalAuc.loss = "hinge" maxLocalAuc.maxIterations = 100 maxLocalAuc.maxNorm = 1/numpy.sqrt(2) maxLocalAuc.metric = "f1" maxLocalAuc.normalise = True maxLocalAuc.numAucSamples = 10 maxLocalAuc.numProcesses = 1 maxLocalAuc.numRecordAucSamples = 200 maxLocalAuc.numRowSamples = 30 maxLocalAuc.rate = "constant" maxLocalAuc.recordStep = 10 maxLocalAuc.reg = False maxLocalAuc.rho = 1.0 maxLocalAuc.t0 = 1.0 maxLocalAuc.t0s = 2.0**-numpy.arange(7, 12, 1) maxLocalAuc.validationSize = 5 maxLocalAuc.validationUsers = 1.0