def profileLearnModel(self):
#Profile full gradient descent
u = 0.2
w = 1-u
eps = 10**-6
alpha = 0.5
learner = BprRecommender(self.k)
learner.maxIterations = 10
learner.recordStep = 10
learner.numAucSamples = 5
print(learner)
print(self.X.nnz)
ProfileUtils.profile('learner.learnModel(self.X)', globals(), locals())
def testLearnModel(self):
m = 50
n = 20
k = 5
u = 0.1
w = 1-u
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w, csarray=True)
lmbda = 0.1
gamma = 0.01
learner = BprRecommender(k, lmbda, gamma)
learner.max_iters = 50
learner.learnModel(X)
Z = learner.predict(n)
def testLearnModel(self):
m = 50
n = 20
k = 5
u = 0.1
w = 1 - u
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w, csarray=True)
lmbda = 0.1
gamma = 0.01
learner = BprRecommender(k, lmbda, gamma)
learner.max_iters = 50
learner.learnModel(X)
Z = learner.predict(n)
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)
