def useHier(topX, regX, R, hierIters, hierInner, regIters, regInner,
tensorInfo):
topY1, top1stats, top1mstats = CP_APR.cp_apr(topX,
R,
maxiters=hierIters,
maxinner=hierInner)
# reduce them to probability and then just sort them
topY1.normalize_sort(1)
topY1 = pmdTools.zeroSmallFactors(topY1, 1e-4)
### Use the factors to populate the factors
Udiag = np.zeros((len(tensorInfo['diag']), R))
Umed = np.zeros((len(tensorInfo['med']), R))
### Patient factors stays the same
for idx, diag in enumerate(tensorInfo['diag']):
topDiagIdx = tensorInfo['diagHier'][diag]
diagCount = tensorInfo['diagHierCount'][topDiagIdx]
Udiag[idx, :] = topY1.U[1][topDiagIdx, :] / diagCount
for idx, med in enumerate(tensorInfo['med']):
topMedIdx = tensorInfo['medHier'][med]
medCount = tensorInfo['medHierCount'][topMedIdx]
Umed[idx, :] = topY1.U[2][topMedIdx, :] / medCount
Mtop = ktensor.ktensor(np.ones(R), [topY1.U[0].copy(), Udiag, Umed])
Y1, ystats, mstats = CP_APR.cp_apr(X1,
R,
Minit=Mtop,
maxiters=regIters,
maxinner=regInner)
return Y1, topY1, top1stats, top1mstats, ystats, mstats
def evaluatePredictionAUC_1(self,experCount):
run = 0
sumBaseAUC=0.0
sumCprAUC=0.0
MCPR, cpstats, mstats = CP_APR.cp_apr(self.X, self.R, maxiters=1, maxinner=7)
#MCPR.normalize_sort(1)
MCPR.redistribute(0)
## scale by summing across the rows
totWeight = np.sum(MCPR.U[0], axis=1)
zeroIdx = np.where(totWeight < 1e-100)[0]
if len(zeroIdx) > 0:
# for the zero ones we're going to evenly distribute
evenDist = np.repeat(1.0 / self.R, len(zeroIdx)*self.R)
MCPR.U[0][zeroIdx, :] = evenDist.reshape((len(zeroIdx), self.R))
totWeight = np.sum(MCPR.U[0], axis=1)
twMat = np.repeat(totWeight, self.R).reshape(self.X.shape[0], self.R)
MCPR.U[0] = MCPR.U[0] / twMat
rawXfile=self.data_dir+'experiment_runprecess/rawdataX_'+str(experCount)+'.csv'
rawYfile=self.data_dir+'experiment_runprecess/rawdataY_'+str(experCount)+'.csv'
cprXfile=self.data_dir+'experiment_runprecess/cprdataX_'+str(experCount)+'.csv'
cprYfile=self.data_dir+'experiment_runprecess/cprdataY_'+str(experCount)+'.csv'
np.savetxt(rawXfile,self.rawFeatures)
np.savetxt(rawYfile,self.Y)
np.savetxt(cprXfile, MCPR.U[0])
np.savetxt(cprYfile,self.Y)
def factorTensor(X):
# set the seed for the same initialization
np.random.seed(seed)
Y, iterStats, mstats = CP_APR.cp_apr(X, R, tol=tol, maxiters=outerIters, maxinner=innerIters)
Y.normalize_sort(1)
Y = decompTools.zeroSmallFactors(Y, zeroThr=zeroThr)
return Y
def factorTensor(X):
# set the seed for the same initialization
np.random.seed(seed)
Y, iterStats, mstats = CP_APR.cp_apr(X, R, tol=tol, maxiters=outerIters, maxinner=innerIters)
Y.normalize_sort(1)
Y = decompTools.zeroSmallFactors(Y, zeroThr=zeroThr)
return Y
def findFactors(self, trainX, zeroThr=1e-4):
""" Find the factor basis for this tensor """
M, cpstats, mstats = CP_APR.cp_apr(trainX, R=self.R, maxiters=self.outerIter, maxinner=self.innerIter)
M.normalize_sort(1)
# zero out the small factors
for n in range(M.ndims()):
zeroIdx = np.where(M.U[n] < zeroThr)
M.U[n][zeroIdx] = 0
return KLProjection.KLProjection(M.U, self.R)
def findFactors(self, trainX, zeroThr=1e-4):
""" Find the factor basis for this tensor """
M, cpstats, mstats = CP_APR.cp_apr(trainX, R=self.R, maxiters=self.outerIter, maxinner=self.innerIter)
M.normalize_sort(1)
# zero out the small factors
for n in range(M.ndims()):
zeroIdx = np.where(M.U[n] < zeroThr)
M.U[n][zeroIdx] = 0
return KLProjection.KLProjection(M.U, self.R)
def findFactors(X, R=100, outerIter=70, innerIter=10, zeroThr=1e-4):
""" Find the factor basis for this tensor """
M, cpstats, mstats = CP_APR.cp_apr(X,
R=R,
maxiters=outerIter,
maxinner=innerIter)
M.normalize_sort(1)
M = decompTools.zeroSmallFactors(M, zeroThr)
return KLProjection.KLProjection(M.U, R), M, mstats
def useHier(topX, regX, R, hierIters, hierInner, regIters, regInner, tensorInfo):
topY1, top1stats, top1mstats = CP_APR.cp_apr(topX, R, maxiters=hierIters, maxinner=hierInner)
# reduce them to probability and then just sort them
topY1.normalize_sort(1)
topY1 = pmdTools.zeroSmallFactors(topY1, 1e-4)
### Use the factors to populate the factors
Udiag = np.zeros((len(tensorInfo['diag']), R))
Umed = np.zeros((len(tensorInfo['med']), R))
### Patient factors stays the same
for idx, diag in enumerate(tensorInfo['diag']):
topDiagIdx = tensorInfo['diagHier'][diag]
diagCount = tensorInfo['diagHierCount'][topDiagIdx]
Udiag[idx,:] = topY1.U[1][topDiagIdx,:] / diagCount
for idx, med in enumerate(tensorInfo['med']):
topMedIdx = tensorInfo['medHier'][med]
medCount = tensorInfo['medHierCount'][topMedIdx]
Umed[idx,:] = topY1.U[2][topMedIdx,:] / medCount
Mtop = ktensor.ktensor(np.ones(R), [topY1.U[0].copy(), Udiag, Umed])
Y1, ystats, mstats = CP_APR.cp_apr(X1, R, Minit=Mtop, maxiters=regIters, maxinner=regInner)
return Y1, topY1, top1stats, top1mstats, ystats, mstats
def decomposeCountTensor(filename, R, outerIters=20, innerIters=10, convergeTol=1e-2, zeroTol=1e-4):
"""
Given a file, load the tensor data and then
From a file, load the tensor data and
then decompose using CP_APR with specified rank
Parameters:
filename - the file that stores the sparse tensor representation using numpy
R - the rank of the tensor
outerIters - the maximum number of outer iterations
innerIters - the maximum number of inner iterations
convergeTol - the convergence tolerance
zeroTol - the amount to zero out the factors
Output:
"""
X = sptensor.loadTensor(filename)
Y, iterStats, modelStats = CP_APR.cp_apr(X, R, tol=convergeTol, maxiters=outerIters, maxinner=innerIters)
# normalize the factors using the 1 norm and then sort in descending order
Y.normalize_sort(1)
Y = zeroSmallFactors(Y, zeroThr=zeroTol)
return Y, iterStats, modelStats
client = MongoClient()
db = client.gravel
exptDB = db.factor
## verify the experimentID is okay
if exptDB.find({"id": exptID}).count():
print "Experiment ID already exists, select another"
return
print "Starting Tensor Factorization with ID:{0}".format(exptID)
np.random.seed(seed)
## factorize using CP_APR (this is the original)
Y, iterStats, modelStats = CP_APR.cp_apr(X,
R,
tol=tol,
maxiters=outerIters,
maxinner=innerIters)
##
Y.writeRawFile("results/apr-raw-{0}.dat".format(exptID))
Youtfile = "results/apr-db-{0}-{1}.csv".format(exptID, iter)
Ysqlfile = "results/apr-sql-{0}.sql".format(exptID)
# save the decomposition into the format
Yout = decompTools.getDBOutput(Y, yaxis)
Yout = np.column_stack((np.repeat(exptID, Yout.shape[0]), Yout))
np.savetxt(Youtfile, Yout, fmt="%s", delimiter="|")
sqlOut = file(Ysqlfile, "w")
sqlOut.write(
compOut = []
for train, test in ttss:
if n != nSample:
n = n + 1
continue
else:
trainShape = list(X.shape)
train[0] = len(train)
trainX = predictionModel.tensorSubset(X, train, trainShape)
## Do the tensor factorization
np.random.seed(seed)
startTime = time.time()
M, cpstats, mstats = CP_APR.cp_apr(trainX,
R,
maxiters=outerIter,
maxinner=10)
M.normalize_sort(1)
# zero out the small factors
for n in range(1, 2):
zeroIdx = np.where(M.U[n] < zeroThr)
M.U[n][zeroIdx] = 0
elapsed = time.time() - startTime
compOut.append({
"expt": exptID,
"R": R,
"Outer": outerIter,
"Model": "Limestone",
"Comp": elapsed
})
## connection to mongo-db
client = MongoClient()
db = client.gravel
exptDB = db.factor
## verify the experimentID is okay
if exptDB.find({"id": exptID}).count():
print "Experiment ID already exists, select another"
return
print "Starting Tensor Factorization with ID:{0}".format(exptID)
np.random.seed(seed)
## factorize using CP_APR (this is the original)
Y, iterStats, modelStats = CP_APR.cp_apr(X, R, tol=tol, maxiters=outerIters, maxinner=innerIters)
##
Y.writeRawFile("results/apr-raw-{0}.dat".format(exptID))
Youtfile = "results/apr-db-{0}-{1}.csv".format(exptID, iter)
Ysqlfile = "results/apr-sql-{0}.sql".format(exptID)
# save the decomposition into the format
Yout = decompTools.getDBOutput(Y, yaxis)
Yout = np.column_stack((np.repeat(exptID, Yout.shape[0]), Yout))
np.savetxt(Youtfile, Yout, fmt="%s", delimiter="|")
sqlOut = file(Ysqlfile, "w")
sqlOut.write("load data local infile '/home/joyce/workspace//Health/analysis/tensor/{0}' into table tensor_factors fields terminated by '|' ;\n".format(Youtfile))
sqlOut.write("insert into tensor_models(expt_ID, label_ID, description, rank, iterations, inner_iterations, seed, least_squares, log_likelihood, kkt_violation) values({0}, {1}, \'{2}\', {3}, {4}, {5}, {6}, {7}, {8}, {9});\n".format(exptID, labelID, exptDesc, R, iter, innerIter, seed, mstats['LS'], mstats['LL'], mstats['KKT']))
startTime = time.time()
spntf = SP_NTF.SP_NTF(X,
R=R,
alpha=alpha,
maxinner=INNER_ITER,
maxiters=MAX_ITER)
Yinfo = spntf.computeDecomp(gamma=gamma)
## calculate all the request entries
marbleElapse = time.time() - startTime
marbleFMS, marbleFOS, marbleNNZ = calculateValues(
TM, spntf.M[SP_NTF.REG_LOCATION])
np.random.seed(seed)
startTime = time.time()
YCP, ycpstats, mstats = CP_APR.cp_apr(X,
R=R,
maxinner=INNER_ITER,
maxiters=MAX_ITER)
cpaprElapse = time.time() - startTime
cpaprFMS, cpaprFOS, cpaprNNZ = calculateValues(TM, YCP)
for n in range(YCP.ndims()):
YCP.U[n] = tensorTools.hardThresholdMatrix(YCP.U[n], gamma[n])
limestoneFMS, limestoneFOS, limestoneNNZ = calculateValues(TM, YCP)
sampleResult = {
"Order": ["Marble", "CPAPR", "Limestone"],
"FMS": [marbleFMS, cpaprFMS, limestoneFMS],
"FOS": [marbleFOS, cpaprFOS, limestoneFOS],
"CompTime": [marbleElapse, cpaprElapse, cpaprElapse],
"NNZ": [marbleNNZ, cpaprNNZ, limestoneNNZ]
}
return fms, fos, nnz
for sample in range(10):
seed = sample*1000
np.random.seed(seed)
## solve the solution
startTime = time.time()
spntf = SP_NTF.SP_NTF(X, R=R, alpha=alpha, maxinner=INNER_ITER, maxiters=MAX_ITER)
Yinfo = spntf.computeDecomp(gamma=gamma)
## calculate all the request entries
marbleElapse = time.time() - startTime
marbleFMS, marbleFOS, marbleNNZ = calculateValues(TM, spntf.M[SP_NTF.REG_LOCATION])
np.random.seed(seed)
startTime = time.time()
YCP, ycpstats, mstats = CP_APR.cp_apr(X, R=R, maxinner=INNER_ITER, maxiters=MAX_ITER)
cpaprElapse = time.time() - startTime
cpaprFMS, cpaprFOS, cpaprNNZ = calculateValues(TM, YCP)
for n in range(YCP.ndims()):
YCP.U[n] = tensorTools.hardThresholdMatrix(YCP.U[n], gamma[n])
limestoneFMS, limestoneFOS, limestoneNNZ = calculateValues(TM, YCP)
sampleResult = {
"Order": ["Marble", "CPAPR", "Limestone"],
"FMS":[marbleFMS, cpaprFMS, limestoneFMS],
"FOS":[marbleFOS, cpaprFOS, limestoneFOS],
"CompTime": [marbleElapse, cpaprElapse, cpaprElapse],
"NNZ": [marbleNNZ, cpaprNNZ, limestoneNNZ]
}
data[str(sample)] = sampleResult
import CP_APR
import ktensor
"""
Test file associated with the CP decomposition using APR
"""
""" Test factorization of sparse matrix """
subs = np.array([[0, 3, 1], [1, 0, 1], [1, 2, 1], [1, 3, 1], [3, 0, 0]])
vals = np.array([[1], [1], [1], [1], [3]])
siz = np.array([5, 5, 2]) # 5x5x2 tensor
X = sptensor.sptensor(subs, vals, siz)
U0 = np.array([[0.7689, 0.8843, 0.7487, 0.0900],
[0.1673, 0.5880, 0.8256, 0.1117],
[0.8620, 0.1548, 0.7900, 0.1363],
[0.9899, 0.1999, 0.3185, 0.6787],
[0.5144, 0.4070, 0.5341, 0.4952]])
U1 = np.array([[0.1897, 0.5606, 0.8790, 0.9900],
[0.4950, 0.9296, 0.9889, 0.5277],
[0.1476, 0.6967, 0.0006, 0.4795],
[0.0550, 0.5828, 0.8654, 0.8013],
[0.8507, 0.8154, 0.6126, 0.2278]])
U2 = np.array([[0.4981, 0.5747, 0.7386, 0.2467],
[0.9009, 0.8452, 0.5860, 0.6664]])
Minit = ktensor.ktensor(np.ones(4), [U0, U1, U2])
fms = Minit.fms(Minit)
Y, cpstats, modelStats = CP_APR.cp_apr(X, 4, Minit=Minit, maxiters=100)
Y.normalize_sort(1)
""" Test factorization of regular matrix """
X = tensor.tensor(range(1, 25), [3, 4, 2])
print CP_APR.cp_apr(X, 4)
import numpy as np; import CP_APR import ktensor import KLProjection """ Test file associated with the CP decomposition using APR """ """ Test factorization of sparse matrix """ subs = np.array([[0,3,1], [1,0,1], [1,2,1], [1,3,1], [3,0,0]]); vals = np.array([[1],[1],[1],[1],[3]]); siz = np.array([5,5,2]) # 5x5x2 tensor X = sptensor.sptensor(subs, vals, siz) U0 = np.array([[0.7689, 0.8843, 0.7487, 0.0900], [0.1673, 0.5880, 0.8256, 0.1117], [0.8620, 0.1548, 0.7900, 0.1363], [0.9899, 0.1999, 0.3185, 0.6787], [0.5144, 0.4070, 0.5341, 0.4952]]) U1 = np.array([[0.1897, 0.5606, 0.8790, 0.9900], [0.4950, 0.9296, 0.9889, 0.5277], [0.1476, 0.6967, 0.0006, 0.4795], [0.0550, 0.5828, 0.8654, 0.8013], [0.8507, 0.8154, 0.6126, 0.2278]]) U2 = np.array([[0.4981, 0.5747, 0.7386, 0.2467], [0.9009, 0.8452, 0.5860, 0.6664]]) Minit = ktensor.ktensor(np.ones(4), [U0, U1, U2]) fms = Minit.fms(Minit) Y, cpstats, modelStats = CP_APR.cp_apr(X,4, Minit=Minit, maxiters=100); Y.normalize_sort(1) subs2 = np.array([[0,3,1], [1,2,0]]) vals2 = np.array([[1], [1]]) siz2 = np.array([2,5,2]) Xhat = sptensor.sptensor(subs2, vals2, siz2) klproj = KLProjection.KLProjection(Y.U, 4) np.random.seed(10) klproj.projectSlice(Xhat, 0)
diagIdx = idx / 470
medIdx = idx % 470
return axisList[1][diagIdx] + axisList[2][medIdx]
for train, test in ttss:
if n != nSample:
n = n + 1
continue
else:
trainShape = list(X.shape)
train[0] = len(train)
trainX = predictionModel.tensorSubset(X, train, trainShape)
## Do the tensor factorization
np.random.seed(seed)
M, cpstats, mstats = CP_APR.cp_apr(trainX, R, maxiters=outerIter, maxinner=10)
M.normalize_sort(1)
M.writeRawFile(factorFile)
Yout = decompTools.getDBOutput(M, yaxis)
Yout = np.column_stack((np.repeat(exptID, Yout.shape[0]), Yout))
np.savetxt(Youtfile, Yout, fmt="%s", delimiter="|")
sqlOut = file(Ysqlfile, "w")
sqlOut.write("load data local infile '/home/joyce/workspace/Health/analysis/tensor/{0}' into table tensor_factors fields terminated by '|' ;\n".format(Youtfile))
sqlOut.write("insert into tensor_models(expt_ID, label_ID, description, rank, iterations, inner_iterations, seed, least_squares, log_likelihood, kkt_violation) values({0}, {1}, \'{2}\', {3}, {4}, {5}, {6}, {7}, {8}, {9});\n".format(exptID, labelID, exptDesc, R, outerIter, innerIter, seed, mstats['LS'], mstats['LL'], mstats['KKT']))
klp = KLProjection.KLProjection(M.U, M.R)
ptfFeat = klp.projectSlice(X, 0)
trainY = Y[train]
predModel.fit(ptfFeat[train, :], trainY)
ptfPred = predModel.predict_proba(ptfFeat[test,:])
fpr, tpr, thresholds = metrics.roc_curve(Y[test], ptfPred[:, 1], pos_label=1)
## calculate diagnosis-medication combination
diagMed = [[a, b] for a, b in itertools.product(yaxis[1], yaxis[2])]
def getDBEntry(featureName, m):
output = np.zeros((1, 4))
for r in range(R):
# get the nonzero indices
idx = np.flatnonzero(m[:, r])
tmp = np.column_stack((np.array(diagMed)[idx], np.repeat(r, len(idx)), m[idx, r]))
output = np.vstack((output, tmp))
output = np.delete(output, (0), axis=0)
output = np.column_stack((np.repeat(exptID, output.shape[0]), np.repeat(featureName, output.shape[0]), output))
return output
np.random.seed(seed)
M, cpstats, mstats = CP_APR.cp_apr(X, R, maxiters=iters, maxinner=innerIter)
M.normalize_sort(1)
## Threshold the values
for n in range(1,2):
zeroIdx = np.where(M.U[n] < modeThr)
M.U[n][zeroIdx] = 0
## Get the diagnosis-medication matrix
ptfMatrix = khatrirao.khatrirao(M.U[1], M.U[2])
dbOutput = getDBEntry("CP-APR", ptfMatrix)
flatX = sptenmat.sptenmat(X, [0]).tocsrmat() # matricize along the first mode
nmfModel = nimfa.mf(flatX, method="nmf", max_iter=iters, rank=R)
nmfResult = nimfa.mf_run(nmfModel)
nmfBasis = nmfResult.coef().transpose()
nmfBasis = preprocessing.normalize(nmfBasis, norm="l1", axis=0)
nmfBasis = nmfBasis.toarray()
flatX = sptenmat.sptenmat(xprime, [0]).tocsrmat() # matricize along the first mode
stats = np.zeros((1,6))
## NMF Timing
for k in range(samples):
startTime = time.time()
nmfModel = nimfa.mf(flatX, method="nmf", max_iter=iters, rank=R)
nmfResult = nimfa.mf_run(nmfModel)
elapsed = time.time() - startTime
stats = np.vstack((stats, np.array([R, iters, pn, k, "NMF", elapsed])))
## PCA Timing
for k in range(samples):
startTime = time.time()
pcaModel.fit(flatX)
elapsed = time.time() - startTime
stats = np.vstack((stats, np.array([R, iters, pn, k, "PCA", elapsed])))
## Tensor factorization timing
for k in range(samples):
startTime = time.time()
CP_APR.cp_apr(xprime, R, maxiters=iters)
elapsed = time.time() - startTime
stats = np.vstack((stats, np.array([R, iters, pn, k, "CP_APR", elapsed])))
stats = np.delete(stats, (0), axis=0)
outFile = "results/patient-cpu-{0}.csv".format(pn)
np.savetxt(outFile, stats, fmt="%s", delimiter="|")
print "load data local infile '/home/joyce/workspace/Health/analysis/tensor/{0}' into table comp_metrics fields terminated by '|' ;\n".format(outFile)
def findFactors(X, R=100, outerIter=70, innerIter=10, zeroThr=1e-4):
""" Find the factor basis for this tensor """
M, cpstats, mstats = CP_APR.cp_apr(X, R=R, maxiters=outerIter, maxinner=innerIter)
M.normalize_sort(1)
M = decompTools.zeroSmallFactors(M, zeroThr)
return KLProjection.KLProjection(M.U, R), M, mstats
