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" maxLocalAuc.recordStep = 10 maxLocalAuc.rho = 1.0 maxLocalAuc.t0 = 1.0 maxLocalAuc.t0s = 2.0**-numpy.arange(7, 12, 1)
trainTestXs = Sampling.shuffleSplitRows(X, 1, testSize)
trainX, testX = trainTestXs[0]
logging.debug("Number of non-zero elements: " + str((trainX.nnz, testX.nnz)))
k2 = 32
u2 = 0.1
w2 = 1-u2
eps = 10**-8
lmbda = 1.0
maxLocalAuc = MaxLocalAUC(k2, w2, eps=eps, lmbdaU=0.0, lmbdaV=lmbda, stochastic=True)
maxLocalAuc.alpha = 1.0
maxLocalAuc.alphas = 2.0**-numpy.arange(-5, 5, 1)
maxLocalAuc.folds = 5
maxLocalAuc.initialAlg = "rand"
maxLocalAuc.itemExpP = 1.0
maxLocalAuc.itemExpQ = 1.0
maxLocalAuc.lmbdas = numpy.linspace(0.5, 2.0, 7)
maxLocalAuc.maxIterations = 100
maxLocalAuc.metric = "f1"
maxLocalAuc.normalise = True
maxLocalAuc.numAucSamples = 10
#maxLocalAuc.numProcesses = 1
maxLocalAuc.numRecordAucSamples = 100
maxLocalAuc.numRowSamples = 30
maxLocalAuc.rate = "optimal"
maxLocalAuc.recommendSize = 5
maxLocalAuc.recordStep = 1
maxLocalAuc.rho = 1.0
maxLocalAuc.t0 = 1.0
maxLocalAuc.t0s = 2.0**-numpy.arange(-1, 6, 1)
