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)
maxLocalAuc.validationSize = 3
maxLocalAuc.validationUsers = 0
os.system('taskset -p 0xffffffff %d' % os.getpid())
logging.debug("Starting training")
losses = [("tanh", 0.25), ("tanh", 0.5), ("tanh", 1.0), ("tanh", 2.0), ("hinge", 1), ("square", 1), ("logistic", 0.5), ("logistic", 1.0), ("logistic", 2.0), ("sigmoid", 0.5), ("sigmoid", 1.0), ("sigmoid", 2.0)]
def computeTestAuc(args):
trainX, testX, maxLocalAuc, U, V = args
numpy.random.seed(21)
logging.debug(maxLocalAuc)
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, V, trainMeasures, testMeasures, iterations, totalTime = maxLocalAuc.learnModel(trainX, verbose=True)
return U, V, trainMeasures[-1, 0], testMeasures[-1, 0]
if saveResults:
