def testModelSelect(self): m = 50 n = 50 k = 5 u = 0.5 w = 1 - u X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w) os.system('taskset -p 0xffffffff %d' % os.getpid()) u = 0.2 lmbda = 0.1 gamma = 0.01 learner = BprRecommender(k, lmbda, gamma) learner.maxIterations = 2 learner.ks = 2**numpy.arange(3, 5) learner.lmbdaUsers = 2.0**-numpy.arange(1, 3) learner.lmbdaPoses = 2.0**-numpy.arange(1, 3) learner.lmbdaNegs = 2.0**-numpy.arange(1, 3) learner.gammas = 2.0**-numpy.arange(1, 3) learner.folds = 2 learner.numProcesses = 1 colProbs = numpy.array(X.sum(1)).ravel() colProbs /= colProbs.sum() print(colProbs, colProbs.shape) learner.modelSelect(X, colProbs=colProbs)
def testModelSelect(self): m = 50 n = 50 k = 5 u = 0.5 w = 1-u X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w) os.system('taskset -p 0xffffffff %d' % os.getpid()) u = 0.2 lmbda = 0.1 gamma = 0.01 learner = BprRecommender(k, lmbda, gamma) learner.maxIterations = 2 learner.ks = 2**numpy.arange(3, 5) learner.lmbdaUsers = 2.0**-numpy.arange(1, 3) learner.lmbdaPoses = 2.0**-numpy.arange(1, 3) learner.lmbdaNegs = 2.0**-numpy.arange(1, 3) learner.gammas = 2.0**-numpy.arange(1, 3) learner.folds = 2 learner.numProcesses = 1 colProbs = numpy.array(X.sum(1)).ravel() colProbs /= colProbs.sum() print(colProbs, colProbs.shape) learner.modelSelect(X, colProbs=colProbs)