def testModelSelectMaxNorm(self): m = 10 n = 20 k = 5 u = 0.5 w = 1 - u X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w, csarray=True) os.system('taskset -p 0xffffffff %d' % os.getpid()) eps = 0.001 k = 5 maxLocalAuc = MaxLocalAUC(k, w, eps=eps, stochastic=True) maxLocalAuc.maxIterations = 5 maxLocalAuc.recordStep = 1 maxLocalAuc.validationSize = 3 maxLocalAuc.metric = "f1" maxLocalAuc.modelSelectNorm(X)
def testModelSelectMaxNorm(self): m = 10 n = 20 k = 5 u = 0.5 w = 1-u X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w, csarray=True) os.system('taskset -p 0xffffffff %d' % os.getpid()) eps = 0.001 k = 5 maxLocalAuc = MaxLocalAUC(k, w, eps=eps, stochastic=True) maxLocalAuc.maxIterations = 5 maxLocalAuc.recordStep = 1 maxLocalAuc.validationSize = 3 maxLocalAuc.metric = "f1" maxLocalAuc.modelSelectNorm(X)
softImpute.numProcesses = args.processes wrmf = WeightedMf(k=k, maxIterations=maxIterations, alpha=1.0) wrmf.ks = ks wrmf.folds = folds wrmf.lmbdas = 2.0**-numpy.arange(-1, 12, 2) wrmf.metric = "f1" wrmf.numProcesses = args.processes maxLocalAuc = MaxLocalAUC(k=k, w=0.9, maxIterations=50, lmbdaU=0.1, lmbdaV=0.1, stochastic=True) maxLocalAuc.numRowSamples = 10 maxLocalAuc.parallelSGD = True maxLocalAuc.initialAlg = "rand" maxLocalAuc.ks = ks maxLocalAuc.folds = folds maxLocalAuc.metric = "f1" maxLocalAuc.numProcesses = args.processes kNeighbours = 25 knn = CosineKNNRecommender(kNeighbours) numFeatures = 200 slim = SLIM(num_selected_features=numFeatures) learners = [("SoftImpute", softImpute), ("WRMF", wrmf), ("KNN", knn), ("MLAUC", maxLocalAuc), ("SLIM", slim)] #Figure out the correct learner for tempLearnerName, tempLearner in learners: if args.alg == tempLearnerName: learnerName = tempLearnerName learner = tempLearner