def profileLearnModel(self): #Profile full gradient descent X, U, V = DatasetUtils.syntheticDataset1(u=0.01, m=1000, n=2000) #X, U, V = DatasetUtils.syntheticDataset1() #X, U, V = DatasetUtils.syntheticDataset1(u=0.2, sd=0.2) #X = DatasetUtils.flixster() u = 0.2 w = 1 - u eps = 10**-6 alpha = 0.5 maxLocalAuc = MaxLocalAUC(self.k, w, alpha=alpha, eps=eps, stochastic=True) maxLocalAuc.maxNormU = 10 maxLocalAuc.maxNormV = 10 maxLocalAuc.maxIterations = 100 maxLocalAuc.initialAlg = "rand" maxLocalAuc.rate = "constant" maxLocalAuc.parallelSGD = True maxLocalAuc.numProcesses = 8 maxLocalAuc.numAucSamples = 10 maxLocalAuc.numRowSamples = 30 maxLocalAuc.scaleAlpha = False maxLocalAuc.loss = "hinge" maxLocalAuc.validationUsers = 0.0 print(maxLocalAuc) ProfileUtils.profile('maxLocalAuc.learnModel(X)', globals(), locals())
def testParallelLearnModel(self): numpy.random.seed(21) m = 500 n = 200 k = 5 X = SparseUtils.generateSparseBinaryMatrix((m, n), k, csarray=True) from wallhack.rankingexp.DatasetUtils import DatasetUtils X, U, V = DatasetUtils.syntheticDataset1() u = 0.1 w = 1-u eps = 0.05 maxLocalAuc = MaxLocalAUC(k, w, alpha=1.0, eps=eps, stochastic=True) maxLocalAuc.maxIterations = 3 maxLocalAuc.recordStep = 1 maxLocalAuc.rate = "optimal" maxLocalAuc.t0 = 2.0 maxLocalAuc.validationUsers = 0.0 maxLocalAuc.numProcesses = 4 os.system('taskset -p 0xffffffff %d' % os.getpid()) print(X.nnz/maxLocalAuc.numAucSamples) U, V = maxLocalAuc.parallelLearnModel(X)
def __init__(self): numpy.random.seed(21) #Create a low rank matrix m = 1000 n = 500 self.k = 8 #self.X = SparseUtils.generateSparseBinaryMatrix((m, n), self.k, csarray=True) self.X, U, V = DatasetUtils.syntheticDataset1(u=0.2, sd=0.2)
def computeRProfile(self): X, U, V = DatasetUtils.syntheticDataset1(m=1000, n=20000) w = 0.9 indsPerRow = 50 numRuns = 1000 def run(): for i in range(numRuns): SparseUtilsCython.computeR(U, V, w, indsPerRow) ProfileUtils.profile('run()', globals(), locals())
def profileRestrictOmega(self): X, U, V = DatasetUtils.syntheticDataset1(u=0.01, m=1000, n=2000) m, n = X.shape indPtr, colInds = SparseUtils.getOmegaListPtr(X) colIndsSubset = numpy.random.choice(n, 500, replace=False) def run(): for i in range(100): newIndPtr, newColInds = restrictOmega(indPtr, colInds, colIndsSubset) ProfileUtils.profile('run()', globals(), locals())
def profileLearnModel2(self): #Profile stochastic case #X = DatasetUtils.flixster() #X = Sampling.sampleUsers(X, 1000) X, U, V = DatasetUtils.syntheticDataset1(u=0.001, m=10000, n=1000) rho = 0.00 u = 0.2 w = 1 - u eps = 10**-6 alpha = 0.5 k = self.k maxLocalAuc = MaxLocalAUC(k, w, alpha=alpha, eps=eps, stochastic=True) maxLocalAuc.numRowSamples = 2 maxLocalAuc.numAucSamples = 10 maxLocalAuc.maxIterations = 1 maxLocalAuc.numRecordAucSamples = 100 maxLocalAuc.recordStep = 10 maxLocalAuc.initialAlg = "rand" maxLocalAuc.rate = "optimal" #maxLocalAuc.parallelSGD = True trainTestX = Sampling.shuffleSplitRows(X, maxLocalAuc.folds, 5) trainX, testX = trainTestX[0] def run(): U, V, trainMeasures, testMeasures, iterations, time = maxLocalAuc.learnModel( trainX, True) #logging.debug("Train Precision@5=" + str(MCEvaluator.precisionAtK(trainX, U, V, 5))) #logging.debug("Train Precision@10=" + str(MCEvaluator.precisionAtK(trainX, U, V, 10))) #logging.debug("Train Precision@20=" + str(MCEvaluator.precisionAtK(trainX, U, V, 20))) #logging.debug("Train Precision@50=" + str(MCEvaluator.precisionAtK(trainX, U, V, 50))) #logging.debug("Test Precision@5=" + str(MCEvaluator.precisionAtK(testX, U, V, 5))) #logging.debug("Test Precision@10=" + str(MCEvaluator.precisionAtK(testX, U, V, 10))) #logging.debug("Test Precision@20=" + str(MCEvaluator.precisionAtK(testX, U, V, 20))) #logging.debug("Test Precision@50=" + str(MCEvaluator.precisionAtK(testX, U, V, 50))) ProfileUtils.profile('run()', globals(), locals())
def testParallelLearnModel(self): numpy.random.seed(21) m = 500 n = 200 k = 5 X = SparseUtils.generateSparseBinaryMatrix((m, n), k, csarray=True) from wallhack.rankingexp.DatasetUtils import DatasetUtils X, U, V = DatasetUtils.syntheticDataset1() u = 0.1 w = 1 - u eps = 0.05 maxLocalAuc = MaxLocalAUC(k, w, alpha=1.0, eps=eps, stochastic=True) maxLocalAuc.maxIterations = 3 maxLocalAuc.recordStep = 1 maxLocalAuc.rate = "optimal" maxLocalAuc.t0 = 2.0 maxLocalAuc.validationUsers = 0.0 maxLocalAuc.numProcesses = 4 os.system('taskset -p 0xffffffff %d' % os.getpid()) print(X.nnz / maxLocalAuc.numAucSamples) U, V = maxLocalAuc.parallelLearnModel(X)
import numpy import logging import sys import argparse from wallhack.rankingexp.RankingExpHelper import RankingExpHelper from wallhack.rankingexp.DatasetUtils import DatasetUtils from sandbox.util.Util import Util Util.setupScript() #Create a low rank matrix X, U, V = DatasetUtils.syntheticDataset1(u=0.2, sd=0.2) m, n = X.shape u = 0.1 w = 1-u # Arguments related to the dataset dataArgs = argparse.Namespace() # Arguments related to the algorithm defaultAlgoArgs = argparse.Namespace() defaultAlgoArgs.u = 5/float(n) #defaultAlgoArgs.validationUsers = 0.0 defaultAlgoArgs.ks = numpy.array([8]) # data args parser # dataParser = argparse.ArgumentParser(description="", add_help=False) dataParser.add_argument("-h", "--help", action="store_true", help="show this help message and exit") devNull, remainingArgs = dataParser.parse_known_args(namespace=dataArgs) if dataArgs.help: helpParser = argparse.ArgumentParser(description="", add_help=False, parents=[dataParser, RankingExpHelper.newAlgoParser(defaultAlgoArgs)])