def testUncentre(self):
shape = (50, 10)
r = 5
k = 100
X, U, s, V = SparseUtils.generateSparseLowRank(shape, r, k, verbose=True)
rowInds, colInds = X.nonzero()
Y = X.copy()
inds = X.nonzero()
X, mu1 = SparseUtils.centerRows(X)
X, mu2 = SparseUtils.centerCols(X, inds=inds)
cX = X.copy()
Y2 = SparseUtils.uncenter(X, mu1, mu2)
nptst.assert_array_almost_equal(Y.todense(), Y2.todense(), 3)
#We try softImpute on a centered matrix and check if the results are the same
lmbdas = numpy.array([0.1])
softImpute = SoftImpute(lmbdas)
Z = softImpute.learnModel(cX, fullMatrices=False)
Z = softImpute.predict([Z], cX.nonzero())[0]
error1 = MCEvaluator.rootMeanSqError(cX, Z)
X = SparseUtils.uncenter(cX, mu1, mu2)
Z2 = SparseUtils.uncenter(Z, mu1, mu2)
error2 = MCEvaluator.rootMeanSqError(X, Z2)
self.assertAlmostEquals(error1, error2)
def testCentreRows(self):
shape = (50, 10)
r = 5
k = 100
X, U, s, V = SparseUtils.generateSparseLowRank(shape, r, k, verbose=True)
rowInds, colInds = X.nonzero()
for i in range(rowInds.shape[0]):
self.assertEquals(X[rowInds[i], colInds[i]], numpy.array(X[X.nonzero()]).ravel()[i])
mu2 = numpy.array(X.sum(1)).ravel()
numNnz = numpy.zeros(X.shape[0])
for i in range(X.shape[0]):
for j in range(X.shape[1]):
if X[i,j]!=0:
numNnz[i] += 1
mu2 /= numNnz
mu2[numNnz==0] = 0
X, mu = SparseUtils.centerRows(X)
nptst.assert_array_almost_equal(numpy.array(X.mean(1)).ravel(), numpy.zeros(X.shape[0]))
nptst.assert_array_almost_equal(mu, mu2)
def testCentreRows(self):
shape = (50, 10)
r = 5
k = 100
X, U, s, V = SparseUtils.generateSparseLowRank(shape,
r,
k,
verbose=True)
rowInds, colInds = X.nonzero()
for i in range(rowInds.shape[0]):
self.assertEquals(X[rowInds[i], colInds[i]],
numpy.array(X[X.nonzero()]).ravel()[i])
mu2 = numpy.array(X.sum(1)).ravel()
numNnz = numpy.zeros(X.shape[0])
for i in range(X.shape[0]):
for j in range(X.shape[1]):
if X[i, j] != 0:
numNnz[i] += 1
mu2 /= numNnz
mu2[numNnz == 0] = 0
X, mu = SparseUtils.centerRows(X)
nptst.assert_array_almost_equal(
numpy.array(X.mean(1)).ravel(), numpy.zeros(X.shape[0]))
nptst.assert_array_almost_equal(mu, mu2)
def centerMatrix(self, X):
"""
Center a test matrix given we have already centered the training one.
"""
logging.debug("Centering test matrix of size: " + str(X.shape))
tempRowInds, tempColInds = X.nonzero()
X, muRows = SparseUtils.centerRows(X, self.muRows)
X.eliminate_zeros()
X.prune()
return X
def next(self):
X = next(self.iterator)
logging.debug("Centering train matrix of size: " + str(X.shape) + " and dtype " + str(X.dtype))
tempRowInds, tempColInds = X.nonzero()
X, self.muRows = SparseUtils.centerRows(X)
X.eliminate_zeros()
X.prune()
gc.collect()
self.i += 1
return X
def testCentreRows2(self):
shape = (50, 10)
r = 5
k = 100
#Test if centering rows changes the RMSE
X, U, s, V = SparseUtils.generateSparseLowRank(shape, r, k, verbose=True)
Y = X.copy()
Y.data = numpy.random.rand(X.nnz)
error = ((X.data - Y.data)**2).sum()
X, mu = SparseUtils.centerRows(X)
Y, mu = SparseUtils.centerRows(Y, mu)
error2 = ((X.data - Y.data)**2).sum()
self.assertAlmostEquals(error, error2)
error3 = numpy.linalg.norm(X.todense()- Y.todense())**2
self.assertAlmostEquals(error2, error3)
def testCentreRows2(self):
shape = (50, 10)
r = 5
k = 100
#Test if centering rows changes the RMSE
X, U, s, V = SparseUtils.generateSparseLowRank(shape,
r,
k,
verbose=True)
Y = X.copy()
Y.data = numpy.random.rand(X.nnz)
error = ((X.data - Y.data)**2).sum()
X, mu = SparseUtils.centerRows(X)
Y, mu = SparseUtils.centerRows(Y, mu)
error2 = ((X.data - Y.data)**2).sum()
self.assertAlmostEquals(error, error2)
error3 = numpy.linalg.norm(X.todense() - Y.todense())**2
self.assertAlmostEquals(error2, error3)
def testUncentre(self):
shape = (50, 10)
r = 5
k = 100
X, U, s, V = SparseUtils.generateSparseLowRank(shape,
r,
k,
verbose=True)
rowInds, colInds = X.nonzero()
Y = X.copy()
inds = X.nonzero()
X, mu1 = SparseUtils.centerRows(X)
X, mu2 = SparseUtils.centerCols(X, inds=inds)
cX = X.copy()
Y2 = SparseUtils.uncenter(X, mu1, mu2)
nptst.assert_array_almost_equal(Y.todense(), Y2.todense(), 3)
#We try softImpute on a centered matrix and check if the results are the same
lmbdas = numpy.array([0.1])
softImpute = SoftImpute(lmbdas)
Z = softImpute.learnModel(cX, fullMatrices=False)
Z = softImpute.predict([Z], cX.nonzero())[0]
error1 = MCEvaluator.rootMeanSqError(cX, Z)
X = SparseUtils.uncenter(cX, mu1, mu2)
Z2 = SparseUtils.uncenter(Z, mu1, mu2)
error2 = MCEvaluator.rootMeanSqError(X, Z2)
self.assertAlmostEquals(error1, error2)
