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) maxLocalAuc.validationSize = 5 maxLocalAuc.validationUsers = 0 t0s = 2.0**-numpy.arange(-2, 8, 1) #maxLocalAuc.maxIterations = 1 #maxLocalAuc.numProcesses = 1 meanObjsList = [] stdObjsList = [] initialAlgs = ["rand", "svd"] rates = ["optimal", "constant"] if saveResults:
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)
#maxLocalAuc.learningRateSelect(trainX)
U, V, trainMeasures, testMeasures, iterations, time = maxLocalAuc.learnModel(trainX, U=U, V=V, verbose=True)
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
if saveResults:
X = DatasetUtils.getDataset(dataset, nnz=100000)
print(X.shape, X.nnz)
print(maxLocalAuc)
maxLocalAuc.lmbdaU = 0.25
maxLocalAuc.lmbdaV = 0.25
meanObjs1, paramDict = maxLocalAuc.learningRateSelect(X)
maxLocalAuc.lmbdaU = 0.03125
maxLocalAuc.lmbdaV = 0.25
meanObjs2, paramDict = maxLocalAuc.learningRateSelect(X)
