def projectData(self, XHat, n, maxiters=10, maxinner=10):
## store off the old ones
origM = {REG_LOCATION: ktensor.copyTensor(self.M[REG_LOCATION]), AUG_LOCATION: ktensor.copyTensor(self.M[AUG_LOCATION])}
origX = self.X
self.X = XHat
## randomize the nth
self.M[REG_LOCATION].U[n] = np.random.rand(self.X.shape[n], self.R)
self.M[REG_LOCATION].lmbda = np.ones(self.R)
self.M[AUG_LOCATION].U[n] = np.random.rand(self.X.shape[n], 1)
self.M[AUG_LOCATION].lmbda = np.ones(1)
## renormalize
self.M[REG_LOCATION].normalize(1)
self.normalizeAugTensor()
lastLL = tensorTools.loglikelihood(self.X,self.M)
for iteration in range(maxiters):
xsubs = self.X.subs[:,n]
B, Pi, inI1, kktModeViolation1 = self.__solveSignalTensor(xsubs, self.M[AUG_LOCATION].U[n], n)
inI2, kktModeViolation2 = self.__solveAugmentedTensor(xsubs, B, Pi, n)
ll = tensorTools.loglikelihood(self.X,self.M)
if np.abs(lastLL - ll) < self.dlTol:
break
lastLL = ll
## scale by summing across the rows
totWeight = np.sum(self.M[REG_LOCATION].U[n], axis=1)
zeroIdx = np.where(totWeight < 1e-100)[0]
if len(zeroIdx) > 0:
evenDist = 1.0 / self.M[REG_LOCATION].R
self.M[REG_LOCATION].U[n][zeroIdx, :] = np.tile(evenDist, (len(zeroIdx), self.M[REG_LOCATION].R))
totWeight = np.sum(self.M[REG_LOCATION].U[n], axis=1)
twMat = np.repeat(totWeight, self.M[REG_LOCATION].R).reshape(self.X.shape[n], self.M[REG_LOCATION].R)
projMat = self.M[REG_LOCATION].U[n] / twMat
biasMat = self.M[AUG_LOCATION].U[n]
self.M = origM
self.X = origX
return projMat, biasMat
def computeDecomp(self, gamma=None, gradual=True):
## random initialize if not existing
if self.M[REG_LOCATION] == None and self.M[AUG_LOCATION] == None:
self.initialize()
## Kkeep track of the iteration information
iterInfo = OrderedDict(sorted({}.items(), key=lambda t:t[1]))
lastLL = tensorTools.loglikelihood(self.X, self.M)
## projection factor starts at 0 (unless there's no gradual)
xi = 0 if gradual else 1
## if nothing is set, we're just not going to do any hard-thresholding
if gamma == None:
gamma = list(np.repeat(0, self.N))
## for outer iterations
for iteration in range(self.maxIters):
startIter = time.time()
for n in range(self.N):
startMode = time.time()
## first we calculate the "augmented" tensor matricization
self.M[AUG_LOCATION].redistribute(n)
xsubs = self.X.subs[:,n]
B, Pi, inI1, kktModeViolation1 = self.__solveSignalTensor(xsubs, self.M[AUG_LOCATION].U[n], n)
## hard threshold based on the xi and gamma
thr = xi * gamma[n]
if (thr > 0):
self.M[REG_LOCATION].U[n] = tensorTools.hardThresholdMatrix(self.M[REG_LOCATION].U[n], thr)
# renormalize the mode
self.M[REG_LOCATION].normalize_mode(n, 1)
## recalculate B using the new matrix
B = np.dot(self.M[REG_LOCATION].U[n], np.diag(self.M[REG_LOCATION].lmbda))
elapsed1 = time.time() - startMode
# now that we are done, we can calculate the new 'unaugmented matricization'
inI2, kktModeViolation2 = self.__solveAugmentedTensor(xsubs, B, Pi, n)
elapsed2 = time.time() - startMode
ll = tensorTools.loglikelihood(self.X, self.M)
iterInfo[str((iteration, n))] = { "Time": [elapsed1, elapsed2],
"KKTViolation": [kktModeViolation1, kktModeViolation2],
"Iterations": [inI1, inI2],
"LL": ll}
if gradual:
xiTemp = 1-np.min([1, (np.absolute(lastLL - ll) / np.max(np.absolute([lastLL,ll])))])
if xiTemp > xi:
## take the mean of the two
xi = (xi + xiTemp) / 2
print("Iteration {0}: Xi = {1}, dll = {2}, time = {3}".format(iteration, xi, np.abs(lastLL - ll), time.time() - startIter))
if np.abs(lastLL - ll) < self.dlTol and xi >= 0.99:
break;
lastLL = ll
return iterInfo
def cp_apr(X,
R,
Minit=None,
tol=1e-4,
maxiters=1000,
maxinner=10,
epsilon=1e-10,
kappatol=1e-10,
kappa=1e-2):
"""
Compute nonnegative CP with alternative Poisson regression.
Code is the python implementation of cp_apr in the MATLAB Tensor Toolbox
Parameters
----------
X : input tensor of the class tensor or sptensor
R : the rank of the CP
Minit : the initial guess (in the form of a ktensor), if None random guess
tol : tolerance on the inner KKT violation
maxiters : maximum number of iterations
maxinner : maximum number of inner iterations
epsilon : parameter to avoid dividing by zero
kappatol : tolerance on complementary slackness
kappa : offset to fix complementary slackness
Returns
-------
M : the CP model as a ktensor
cpStats: the statistics for each inner iteration
modelStats: a dictionary item with the final statistics for this tensor factorization
"""
N = X.ndims()
## Random initialization
if Minit == None:
F = tensorTools.randomInit(X.shape, R)
Minit = ktensor.ktensor(np.ones(R), F)
nInnerIters = np.zeros(maxiters)
## Initialize M and Phi for iterations
M = Minit
M.normalize(1)
Phi = [[] for i in range(N)]
kktModeViolations = np.zeros(N)
kktViolations = -np.ones(maxiters)
nViolations = np.zeros(maxiters)
## statistics
cpStats = np.zeros(7)
for iteration in range(maxiters):
startIter = time.time()
isConverged = True
for n in range(N):
startMode = time.time()
## Make adjustments to M[n] entries that violate complementary slackness
if iteration > 0:
V = np.logical_and(Phi[n] > 1, M.U[n] < kappatol)
if np.count_nonzero(V) > 0:
nViolations[iteration] = nViolations[iteration] + 1
M.U[n][V > 0] = M.U[n][V > 0] + kappa
M, Phi[n], inner, kktModeViolations[
n], isConverged = __solveSubproblem(X, M, n, maxinner,
isConverged, epsilon, tol)
#print '****************************************'
#print M.U[0][1,:]
#print M.U[0].shape
#print '****************************************'
elapsed = time.time() - startMode
# only write the outer iterations for now
#cpStats = np.vstack((cpStats, np.array([iteration, n, inner, tensorTools.lsqrFit(X,M), tensorTools.loglikelihood(X,[M]), kktModeViolations[n], elapsed])))
kktViolations[iteration] = np.max(kktModeViolations)
elapsed = time.time() - startIter
#cpStats = np.vstack((cpStats, np.array([iter, -1, -1, kktViolations[iter], __loglikelihood(X,M), elapsed])))
print(
"Iteration {0}: Inner Its={1} with KKT violation={2}, nViolations={3}, and elapsed time={4}"
.format(iteration, nInnerIters[iteration],
kktViolations[iteration], nViolations[iteration], elapsed))
if isConverged:
break
cpStats = np.delete(cpStats, (0),
axis=0) # delete the first row which was superfluous
### Print the statistics
fit = tensorTools.lsqrFit(X, M)
ll = tensorTools.loglikelihood(X, [M])
print("Number of iterations = {0}".format(iteration))
print("Final least squares fit = {0}".format(fit))
print("Final log-likelihood = {0}".format(ll))
print("Final KKT Violation = {0}".format(kktViolations[iteration]))
print("Total inner iterations = {0}".format(np.sum(nInnerIters)))
modelStats = {
"Iters": iter,
"LS": fit,
"LL": ll,
"KKT": kktViolations[iteration]
}
return M, cpStats, modelStats
def cp_apr(X, R, Minit=None, tol=1e-4, maxiters=1000, maxinner=10,
epsilon=1e-10, kappatol=1e-10, kappa=1e-2):
"""
Compute nonnegative CP with alternative Poisson regression.
Code is the python implementation of cp_apr in the MATLAB Tensor Toolbox
Parameters
----------
X : input tensor of the class tensor or sptensor
R : the rank of the CP
Minit : the initial guess (in the form of a ktensor), if None random guess
tol : tolerance on the inner KKT violation
maxiters : maximum number of iterations
maxinner : maximum number of inner iterations
epsilon : parameter to avoid dividing by zero
kappatol : tolerance on complementary slackness
kappa : offset to fix complementary slackness
Returns
-------
M : the CP model as a ktensor
cpStats: the statistics for each inner iteration
modelStats: a dictionary item with the final statistics for this tensor factorization
"""
N = X.ndims()
## Random initialization
if Minit == None:
F = tensorTools.randomInit(X.shape, R)
Minit = ktensor.ktensor(np.ones(R), F);
nInnerIters = np.zeros(maxiters);
## Initialize M and Phi for iterations
M = Minit
M.normalize(1)
Phi = [[] for i in range(N)]
kktModeViolations = np.zeros(N)
kktViolations = -np.ones(maxiters)
nViolations = np.zeros(maxiters)
## statistics
cpStats = np.zeros(7)
for iteration in range(maxiters):
startIter = time.time()
isConverged = True;
for n in range(N):
startMode = time.time()
## Make adjustments to M[n] entries that violate complementary slackness
if iteration > 0:
V = np.logical_and(Phi[n] > 1, M.U[n] < kappatol)
if np.count_nonzero(V) > 0:
nViolations[iteration] = nViolations[iteration] + 1
M.U[n][V > 0] = M.U[n][V > 0] + kappa
M, Phi[n], inner, kktModeViolations[n], isConverged = __solveSubproblem(X, M, n, maxinner, isConverged, epsilon, tol)
elapsed = time.time() - startMode
# only write the outer iterations for now
cpStats = np.vstack((cpStats, np.array([iteration, n, inner, tensorTools.lsqrFit(X,M), tensorTools.loglikelihood(X,[M]), kktModeViolations[n], elapsed])))
kktViolations[iteration] = np.max(kktModeViolations);
elapsed = time.time()-startIter
#cpStats = np.vstack((cpStats, np.array([iter, -1, -1, kktViolations[iter], __loglikelihood(X,M), elapsed])))
print("Iteration {0}: Inner Its={1} with KKT violation={2}, nViolations={3}, and elapsed time={4}".format(iteration, nInnerIters[iteration], kktViolations[iteration], nViolations[iteration], elapsed));
if isConverged:
break;
cpStats = np.delete(cpStats, (0), axis=0) # delete the first row which was superfluous
### Print the statistics
fit = tensorTools.lsqrFit(X,M)
ll = tensorTools.loglikelihood(X,[M])
print("Number of iterations = {0}".format(iteration))
print("Final least squares fit = {0}".format(fit))
print("Final log-likelihood = {0}".format(ll))
print("Final KKT Violation = {0}".format(kktViolations[iteration]))
print("Total inner iterations = {0}".format(np.sum(nInnerIters)))
modelStats = {"Iters" : iter, "LS" : fit, "LL" : ll, "KKT" : kktViolations[iteration]}
return M, cpStats, modelStats
