def testOverfit(self):
"""
See if we can get a zero objective on the hinge loss
"""
m = 10
n = 20
k = 5
u = 0.5
w = 1 - u
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w, csarray=True)
eps = 0.001
k = 10
maxLocalAuc = MaxLocalAUC(k, u, eps=eps, stochastic=True)
maxLocalAuc.rate = "constant"
maxLocalAuc.maxIterations = 500
maxLocalAuc.numProcesses = 1
maxLocalAuc.loss = "hinge"
maxLocalAuc.validationUsers = 0
maxLocalAuc.lmbda = 0
print("Overfit example")
U, V, trainMeasures, testMeasures, iterations, time = maxLocalAuc.learnModel(
X, verbose=True)
self.assertAlmostEquals(trainMeasures[-1, 0], 0, 3)
def testOverfit(self):
"""
See if we can get a zero objective on the hinge loss
"""
m = 10
n = 20
k = 5
u = 0.5
w = 1-u
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, w, csarray=True)
eps = 0.001
k = 10
maxLocalAuc = MaxLocalAUC(k, u, eps=eps, stochastic=True)
maxLocalAuc.rate = "constant"
maxLocalAuc.maxIterations = 500
maxLocalAuc.numProcesses = 1
maxLocalAuc.loss = "hinge"
maxLocalAuc.validationUsers = 0
maxLocalAuc.lmbda = 0
print("Overfit example")
U, V, trainMeasures, testMeasures, iterations, time = maxLocalAuc.learnModel(X, verbose=True)
self.assertAlmostEquals(trainMeasures[-1, 0], 0, 3)
def testLearnModel(self):
m = 50
n = 20
k = 5
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, csarray=True)
u = 0.1
w = 1 - u
eps = 0.05
maxLocalAuc = MaxLocalAUC(k, w, alpha=5.0, eps=eps, stochastic=False)
U, V = maxLocalAuc.learnModel(X)
maxLocalAuc.stochastic = True
U, V = maxLocalAuc.learnModel(X)
#Test case where we do not have validation set
maxLocalAuc.validationUsers = 0.0
U, V = maxLocalAuc.learnModel(X)
def testLearnModel(self):
m = 50
n = 20
k = 5
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, csarray=True)
u = 0.1
w = 1-u
eps = 0.05
maxLocalAuc = MaxLocalAUC(k, w, alpha=5.0, eps=eps, stochastic=False)
U, V = maxLocalAuc.learnModel(X)
maxLocalAuc.stochastic = True
U, V = maxLocalAuc.learnModel(X)
#Test case where we do not have validation set
maxLocalAuc.validationUsers = 0.0
U, V = maxLocalAuc.learnModel(X)
maxLocalAuc.t0 = 1.0
maxLocalAuc.t0s = 2.0**-numpy.arange(7, 12, 1)
maxLocalAuc.validationSize = 5
maxLocalAuc.validationUsers = 1.0
os.system('taskset -p 0xffffffff %d' % os.getpid())
logging.debug("Starting training")
logging.debug(maxLocalAuc)
#modelSelectX = trainX[0:100, :]
#maxLocalAuc.learningRateSelect(trainX)
#maxLocalAuc.modelSelect(trainX)
#ProfileUtils.profile('U, V, trainObjs, trainAucs, testObjs, testAucs, iterations, time = maxLocalAuc.learnModel(trainX, testX=testX, verbose=True)', globals(), locals())
U, V, trainMeasures, testMeasures, iterations, time = maxLocalAuc.learnModel(trainX, verbose=True)
p = 10
trainOrderedItems = MCEvaluator.recommendAtk(U, V, p)
testOrderedItems = MCEvaluatorCython.recommendAtk(U, V, p, trainX)
r = SparseUtilsCython.computeR(U, V, maxLocalAuc.w, maxLocalAuc.numRecordAucSamples)
trainObjVec = maxLocalAuc.objectiveApprox(trainOmegaPtr, U, V, r, maxLocalAuc.gi, maxLocalAuc.gp, maxLocalAuc.gq, full=True)
testObjVec = maxLocalAuc.objectiveApprox(testOmegaPtr, U, V, r, maxLocalAuc.gi, maxLocalAuc.gp, maxLocalAuc.gq, allArray=allOmegaPtr, full=True)
itemCounts = numpy.array(X.sum(0)+1, numpy.int32)
beta = 0.5
for p in [1, 3, 5, 10]:
trainPrecision = MCEvaluator.precisionAtK(trainOmegaPtr, trainOrderedItems, p)
