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"
