testSize = 5 folds = 2 trainTestXs = Sampling.shuffleSplitRows(X, folds, testSize) u = 0.1 w2 = 1-u k = 64 eps = 10**-8 maxLocalAuc = MaxLocalAUC(k, w2, eps=eps, stochastic=True) maxLocalAuc.maxIterations = 50 maxLocalAuc.numRowSamples = 30 maxLocalAuc.numAucSamples = 10 maxLocalAuc.initialAlg = "rand" maxLocalAuc.recordStep = 10 maxLocalAuc.rate = "optimal" maxLocalAuc.alpha = 1.0 maxLocalAuc.t0 = 0.1 maxLocalAuc.lmbdaU = 0.0 maxLocalAuc.lmbdaV = 1.0 maxLocalAuc.rho = 0.5 maxItems = 10 chunkSize = 1 startAverages = numpy.array([2, 5, 10, 20, 30, 40]) learningRateParams = [(4.0, 1.0), (4.0, 0.5), (4.0, 0.1), (1.0, 1.0), (1.0, 0.5), (1.0, 0.1), (0.25, 1.0), (0.25, 0.5), (0.25, 0.1)] print(startAverages) def computeTestObj(args): trainX, maxLocalAuc = args numpy.random.seed(21)
u = 0.1 w = 1-u testSize = 5 folds = 5 trainTestXs = Sampling.shuffleSplitRows(X, folds, testSize) numRecordAucSamples = 200 k2 = 8 u2 = 0.5 w2 = 1-u2 eps = 10**-4 lmbda = 0.0 maxLocalAuc = MaxLocalAUC(k2, w2, eps=eps, lmbdaU=lmbda, lmbdaV=lmbda, stochastic=True) maxLocalAuc.alpha = 0.05 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.lmbdas = numpy.linspace(0.5, 2.0, 7) maxLocalAuc.maxIterations = 500 maxLocalAuc.metric = "f1" maxLocalAuc.normalise = True maxLocalAuc.numAucSamples = 10 maxLocalAuc.numProcesses = 1 maxLocalAuc.numRecordAucSamples = 100 maxLocalAuc.numRowSamples = 30 maxLocalAuc.rate = "constant"
trainOmegaList = SparseUtils.getOmegaList(trainX) trainOmegaPtr = SparseUtils.getOmegaListPtr(trainX) testOmegaList = SparseUtils.getOmegaList(testX) testOmegaPtr = SparseUtils.getOmegaListPtr(testX) allOmegaPtr = SparseUtils.getOmegaListPtr(X) numRecordAucSamples = 200 logging.debug("Number of non-zero elements: " + str((trainX.nnz, testX.nnz))) 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 = 32 maxLocalAuc.alphas = 2.0**-numpy.arange(0, 5, 1) maxLocalAuc.beta = 2 maxLocalAuc.bound = False 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 = numpy.linspace(0.5, 2.0, 7) maxLocalAuc.loss = "hinge" maxLocalAuc.maxIterations = 100 maxLocalAuc.maxNorm = 100 maxLocalAuc.metric = "f1"