saveResults = False prefix = "LearningRate2" outputFile = PathDefaults.getOutputDir() + "ranking/" + prefix + dataset.title() + "Results.npz" X = DatasetUtils.getDataset(dataset) m, n = X.shape k2 = 64 u2 = 5/float(n) w2 = 1-u2 eps = 10**-8 lmbda = 0.01 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()
prefix = "Rademacher"
outputFile = PathDefaults.getOutputDir() + "ranking/" + prefix + dataset.title() + "Results.npz"
X = DatasetUtils.getDataset(dataset, nnz=20000)
m, n = X.shape
k2 = 16
u2 = 5/float(n)
w2 = 1-u2
eps = 10**-8
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
