def testDerivativeUiApprox(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 derivative
"""
m = 20
n = 30
k = 3
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, csarray=True)
w = 0.1
learner = MaxAUCSquare(k, w)
learner.normalise = False
learner.lmbdaU = 0
learner.lmbdaV = 0
learner.rho = 1.0
learner.numAucSamples = 10
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
gp = numpy.random.rand(n)
gp /= gp.sum()
gq = numpy.random.rand(n)
gq /= gq.sum()
numRuns = 200
numTests = 5
indPtr, colInds = SparseUtils.getOmegaListPtr(X)
permutedRowInds = numpy.arange(m, dtype=numpy.uint32)
permutedColInds = numpy.arange(n, dtype=numpy.uint32)
#Test with small number of AUC samples, but normalise
learner.numAucSamples = 50
numRuns = 200
for i in numpy.random.permutation(m)[0:numTests]:
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
du1 = numpy.zeros(k)
for j in range(numRuns):
VDot, VDotDot, WDot, WDotDot = learner.computeMeansVW(indPtr, colInds, U, V, permutedRowInds, permutedColInds, gp, gq)
du1 += learner.derivativeUiApprox(U, V, VDot, VDotDot, WDot, WDotDot, i)
du1 /= numRuns
du2 = learner.derivativeUi(indPtr, colInds, U, V, gp, gq, i)
#print(du1, du2)
print(du1/numpy.linalg.norm(du1), du2/numpy.linalg.norm(du2))
#print(numpy.linalg.norm(du1 - du2)/numpy.linalg.norm(du1))
nptst.assert_array_almost_equal(du1, du2, 2)
#Let's compare against using the exact derivative
for i in numpy.random.permutation(m)[0:numTests]:
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
du1 = numpy.zeros(k)
for j in range(numRuns):
VDot, VDotDot, WDot, WDotDot = learner.computeMeansVW(indPtr, colInds, U, V, permutedRowInds, permutedColInds, gp, gq)
du1 += learner.derivativeUiApprox(U, V, VDot, VDotDot, WDot, WDotDot, i)
du1 /= numRuns
du2 = learner.derivativeUi(indPtr, colInds, U, V, gp, gq, i)
print(du1/numpy.linalg.norm(du1), du2/numpy.linalg.norm(du2))
nptst.assert_array_almost_equal(du1, du2, 2)
learner.lmbdaV = 0.5
for i in numpy.random.permutation(m)[0:numTests]:
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
du1 = numpy.zeros(k)
for j in range(numRuns):
VDot, VDotDot, WDot, WDotDot = learner.computeMeansVW(indPtr, colInds, U, V, permutedRowInds, permutedColInds, gp, gq)
du1 += learner.derivativeUiApprox(U, V, VDot, VDotDot, WDot, WDotDot, i)
du1 /= numRuns
du2 = learner.derivativeUi(indPtr, colInds, U, V, gp, gq, i)
nptst.assert_array_almost_equal(du1, du2, 2)
print(du1/numpy.linalg.norm(du1), du2/numpy.linalg.norm(du2))
def testDerivativeUiApprox(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 derivative
"""
m = 20
n = 30
k = 3
X = SparseUtils.generateSparseBinaryMatrix((m, n), k, csarray=True)
w = 0.1
learner = MaxAUCSquare(k, w)
learner.normalise = False
learner.lmbdaU = 0
learner.lmbdaV = 0
learner.rho = 1.0
learner.numAucSamples = 10
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
gp = numpy.random.rand(n)
gp /= gp.sum()
gq = numpy.random.rand(n)
gq /= gq.sum()
numRuns = 200
numTests = 5
indPtr, colInds = SparseUtils.getOmegaListPtr(X)
permutedRowInds = numpy.arange(m, dtype=numpy.uint32)
permutedColInds = numpy.arange(n, dtype=numpy.uint32)
#Test with small number of AUC samples, but normalise
learner.numAucSamples = 50
numRuns = 200
for i in numpy.random.permutation(m)[0:numTests]:
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
du1 = numpy.zeros(k)
for j in range(numRuns):
VDot, VDotDot, WDot, WDotDot = learner.computeMeansVW(
indPtr, colInds, U, V, permutedRowInds, permutedColInds,
gp, gq)
du1 += learner.derivativeUiApprox(U, V, VDot, VDotDot, WDot,
WDotDot, i)
du1 /= numRuns
du2 = learner.derivativeUi(indPtr, colInds, U, V, gp, gq, i)
#print(du1, du2)
print(du1 / numpy.linalg.norm(du1), du2 / numpy.linalg.norm(du2))
#print(numpy.linalg.norm(du1 - du2)/numpy.linalg.norm(du1))
nptst.assert_array_almost_equal(du1, du2, 2)
#Let's compare against using the exact derivative
for i in numpy.random.permutation(m)[0:numTests]:
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
du1 = numpy.zeros(k)
for j in range(numRuns):
VDot, VDotDot, WDot, WDotDot = learner.computeMeansVW(
indPtr, colInds, U, V, permutedRowInds, permutedColInds,
gp, gq)
du1 += learner.derivativeUiApprox(U, V, VDot, VDotDot, WDot,
WDotDot, i)
du1 /= numRuns
du2 = learner.derivativeUi(indPtr, colInds, U, V, gp, gq, i)
print(du1 / numpy.linalg.norm(du1), du2 / numpy.linalg.norm(du2))
nptst.assert_array_almost_equal(du1, du2, 2)
learner.lmbdaV = 0.5
for i in numpy.random.permutation(m)[0:numTests]:
U = numpy.random.rand(X.shape[0], k)
V = numpy.random.rand(X.shape[1], k)
du1 = numpy.zeros(k)
for j in range(numRuns):
VDot, VDotDot, WDot, WDotDot = learner.computeMeansVW(
indPtr, colInds, U, V, permutedRowInds, permutedColInds,
gp, gq)
du1 += learner.derivativeUiApprox(U, V, VDot, VDotDot, WDot,
WDotDot, i)
du1 /= numRuns
du2 = learner.derivativeUi(indPtr, colInds, U, V, gp, gq, i)
nptst.assert_array_almost_equal(du1, du2, 2)
print(du1 / numpy.linalg.norm(du1), du2 / numpy.linalg.norm(du2))
