Python MaxLocalAUC.maxNorm Examples

Example #1

File: LearningRateExp2.py Project: charanpald/wallhack

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

Example #2

File: RademacherExp.py Project: charanpald/wallhack

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