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
