def testObjectiveApprox(self):
"""
We'll test the case in which we apprormate using a large number of samples
for the AUC and see if we get close to the exact objective
"""
m = 20
n = 30
k = 3
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, csarray=True)
learner = MaxAUCSquare(k)
learner.normalise = False
learner.lmbdaU = 0
learner.lmbdaV = 0
learner.rho = 1.0
learner.numAucSamples = n
indPtr, colInds = SparseUtils.getOmegaListPtr(X)
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
numRuns = 100
numTests = 5
gi = numpy.random.rand(m)
gi /= gi.sum()
gp = numpy.random.rand(n)
gp /= gp.sum()
gq = numpy.random.rand(n)
gq /= gq.sum()
#gi = numpy.ones(m)
#gp = numpy.ones(n)
#gq = numpy.ones(n)
#Let's compare against using the exact derivative
for i in range(numTests):
obj = 0
for j in range(numRuns):
obj += learner.objectiveApprox(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
obj /= numRuns
obj2 = learner.objective(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
self.assertAlmostEquals(obj, obj2, 2)
learner.rho = 0.2
for i in range(numTests):
obj = 0
for j in range(numRuns):
obj += learner.objectiveApprox(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
obj /= numRuns
obj2 = learner.objective(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
self.assertAlmostEquals(obj, obj2, 2)
learner.lmbdaV = 0.2
for i in range(numTests):
obj = 0
for j in range(numRuns):
obj += learner.objectiveApprox(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
obj /= numRuns
obj2 = learner.objective(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
self.assertAlmostEquals(obj, obj2, 2)
#Check full and summary versions are the same
obj = learner.objective(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
obj2 = learner.objective(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
self.assertAlmostEquals(obj, obj2, 2)
def testObjectiveApprox(self):
"""
We'll test the case in which we apprormate using a large number of samples
for the AUC and see if we get close to the exact objective
"""
m = 20
n = 30
k = 3
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, csarray=True)
learner = MaxAUCSquare(k)
learner.normalise = False
learner.lmbdaU = 0
learner.lmbdaV = 0
learner.rho = 1.0
learner.numAucSamples = n
indPtr, colInds = SparseUtils.getOmegaListPtr(X)
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
numRuns = 100
numTests = 5
gi = numpy.random.rand(m)
gi /= gi.sum()
gp = numpy.random.rand(n)
gp /= gp.sum()
gq = numpy.random.rand(n)
gq /= gq.sum()
#gi = numpy.ones(m)
#gp = numpy.ones(n)
#gq = numpy.ones(n)
#Let's compare against using the exact derivative
for i in range(numTests):
obj = 0
for j in range(numRuns):
obj += learner.objectiveApprox(indPtr, colInds, indPtr,
colInds, U, V, gp, gq)
obj /= numRuns
obj2 = learner.objective(indPtr, colInds, indPtr, colInds, U, V,
gp, gq)
self.assertAlmostEquals(obj, obj2, 2)
learner.rho = 0.2
for i in range(numTests):
obj = 0
for j in range(numRuns):
obj += learner.objectiveApprox(indPtr, colInds, indPtr,
colInds, U, V, gp, gq)
obj /= numRuns
obj2 = learner.objective(indPtr, colInds, indPtr, colInds, U, V,
gp, gq)
self.assertAlmostEquals(obj, obj2, 2)
learner.lmbdaV = 0.2
for i in range(numTests):
obj = 0
for j in range(numRuns):
obj += learner.objectiveApprox(indPtr, colInds, indPtr,
colInds, U, V, gp, gq)
obj /= numRuns
obj2 = learner.objective(indPtr, colInds, indPtr, colInds, U, V,
gp, gq)
self.assertAlmostEquals(obj, obj2, 2)
#Check full and summary versions are the same
obj = learner.objective(indPtr, colInds, indPtr, colInds, U, V, gp, gq)
obj2 = learner.objective(indPtr, colInds, indPtr, colInds, U, V, gp,
gq)
self.assertAlmostEquals(obj, obj2, 2)
