def testModelSelectLmbda(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.rate = "constant"
maxLocalAuc.ks = numpy.array([4, 8])
maxLocalAuc.modelSelectLmbda(X)
def testModelSelectLmbda(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.rate = "constant"
maxLocalAuc.ks = numpy.array([4, 8])
maxLocalAuc.modelSelectLmbda(X)
softImpute.rho = 0.1
softImpute.eps = 10**-4
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:
numRecordAucSamples = 200 k2 = 8 u2 = 0.5 w2 = 1-u2 eps = 10**-4 lmbda = 0.0 maxLocalAuc = MaxLocalAUC(k2, w2, eps=eps, lmbdaU=lmbda, lmbdaV=lmbda, stochastic=True) maxLocalAuc.alpha = 0.05 maxLocalAuc.alphas = 2.0**-numpy.arange(0, 5, 1) maxLocalAuc.folds = 1 maxLocalAuc.initialAlg = "rand" 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
u2 = 5/float(n) w2 = 1-u2 eps = 10**-8 lmbda = 0.01 maxLocalAuc = MaxLocalAUC(k2, w2, eps=eps, lmbdaU=0.1, lmbdaV=0.1, stochastic=True) maxLocalAuc.alpha = 0.5 maxLocalAuc.alphas = 2.0**-numpy.arange(2, 9, 2) maxLocalAuc.beta = 2 maxLocalAuc.bound = False maxLocalAuc.delta = 0.1 maxLocalAuc.eta = 20 maxLocalAuc.folds = 2 maxLocalAuc.initialAlg = "svd" maxLocalAuc.itemExpP = 0.0 maxLocalAuc.itemExpQ = 0.0 maxLocalAuc.ks = numpy.array([4, 8, 16, 32, 64, 128]) maxLocalAuc.lmbdas = 2.0**-numpy.arange(1, 5) 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
