def testPredict(self):
#Create a set of indices
lmbda = 0.0
iterativeSoftImpute = IterativeSoftImpute(lmbda, k=10)
matrixIterator = iter(self.matrixList)
ZList = iterativeSoftImpute.learnModel(matrixIterator)
XhatList = iterativeSoftImpute.predict(ZList, self.indsList)
#Check we get the exact matrices returned
for i, Xhat in enumerate(XhatList):
nptst.assert_array_almost_equal(numpy.array(Xhat.todense()), self.matrixList[i].todense())
self.assertEquals(Xhat.nnz, self.indsList[i].shape[0])
self.assertAlmostEquals(MCEvaluator.meanSqError(Xhat, self.matrixList[i]), 0)
self.assertAlmostEquals(MCEvaluator.rootMeanSqError(Xhat, self.matrixList[i]), 0)
#Try moderate lambda
lmbda = 0.1
iterativeSoftImpute = IterativeSoftImpute(lmbda, k=10)
matrixIterator = iter(self.matrixList)
ZList = list(iterativeSoftImpute.learnModel(matrixIterator))
XhatList = iterativeSoftImpute.predict(iter(ZList), self.indsList)
for i, Xhat in enumerate(XhatList):
for ind in self.indsList[i]:
U, s, V = ZList[i]
Z = (U*s).dot(V.T)
self.assertEquals(Xhat[numpy.unravel_index(ind, Xhat.shape)], Z[numpy.unravel_index(ind, Xhat.shape)])
self.assertEquals(Xhat.nnz, self.indsList[i].shape[0])
def testModelSelect2(self):
rho = 0.1
shape = (20, 20)
r = 20
numInds = 100
noise = 0.2
X = ExpSU.SparseUtils.generateSparseLowRank(shape, r, numInds, noise)
X = X.tocsc()
U, s, V = numpy.linalg.svd(X.todense())
k = 15
iterativeSoftImpute = IterativeSoftImpute(rho, k=None, svdAlg="propack", updateAlg="initial")
rhos = numpy.linspace(0.5, 0.001, 5)
ks = numpy.array([5, 10, 15], numpy.int)
folds = 3
cvInds = []
for i in range(folds):
cvInds.append((numpy.arange(X.nnz), numpy.arange(X.nnz)))
meanTestErrors, stdTestErrors = iterativeSoftImpute.modelSelect(X, rhos, ks, cvInds)
self.assertAlmostEquals(numpy.linalg.norm(stdTestErrors), 0, 3)
meanTestErrors2 = numpy.zeros((rhos.shape[0], ks.shape[0]))
#Now compute errors manually
for j, k in enumerate(ks):
iterativeSoftImpute.setK(k)
for i, rho in enumerate(rhos):
iterativeSoftImpute.setRho(rho)
ZIter = iterativeSoftImpute.learnModel(iter([X]))
indList = [X.nonzero()]
outIterator = iterativeSoftImpute.predict(ZIter, indList)
Xhat = outIterator.next()
meanTestErrors2[i, j] = MCEvaluator.rootMeanSqError(X, Xhat)
nptst.assert_array_almost_equal(meanTestErrors, meanTestErrors2, 2)
