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(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
tol = 1e-2
zeroThr = 1e-5
# input file and output file
inputFile = args.inputFile.format("data")
yaxis = decompTools.loadAxisInfo(args.inputFile.format("info"))
print "Starting Tensor Factorization with ID:{0}".format(exptID)
X = sptensor.loadTensor(inputFile)
np.random.seed(seed)
Y, ls = CP_ALS.cp_als(X, R, tol=tol, maxiters=iter)
ll = CP_APR.loglikelihood(X, Y)
# normalize the factors using the 1 norm and then sort in descending order
Y.normalize_sort(1)
Y = decompTools.zeroSmallFactors(Y, zeroThr=zeroThr)
Y.writeRawFile("results/als-raw-{0}.dat".format(exptID))
Youtfile = "results/als-db-{0}-{1}.csv".format(exptID, iter)
Ysqlfile = "results/als-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 client from /home/joyceho/workspace/tensor/{0} of del modified by coldel| insert into joyceho.tensor_factors;\n"
.format(Youtfile))
sqlOut.write(
"insert into joyceho.tensor_models values({0}, {1}, \'{2}\',\'{3}\', {4}, {5}, {6}, {7}, {8});\n"
tol = 1e-2
zeroThr = 1e-5
# input file and output file
inputFile = args.inputFile.format("data")
yaxis = decompTools.loadAxisInfo(args.inputFile.format("info"))
print "Starting Tensor Factorization with ID:{0}".format(exptID)
X = sptensor.loadTensor(inputFile)
np.random.seed(seed)
Y, ls = CP_ALS.cp_als(X, R, tol=tol, maxiters=iter)
ll = CP_APR.loglikelihood(X, Y)
# normalize the factors using the 1 norm and then sort in descending order
Y.normalize_sort(1)
Y = decompTools.zeroSmallFactors(Y, zeroThr=zeroThr)
Y.writeRawFile("results/als-raw-{0}.dat".format(exptID))
Youtfile = "results/als-db-{0}-{1}.csv".format(exptID, iter)
Ysqlfile = "results/als-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 client from /home/joyceho/workspace/tensor/{0} of del modified by coldel| insert into joyceho.tensor_factors;\n".format(Youtfile))
sqlOut.write("insert into joyceho.tensor_models values({0}, {1}, \'{2}\',\'{3}\', {4}, {5}, {6}, {7}, {8});\n".format(exptID, labelID, patientSet, exptDesc, iter, innerIter, ls, ll, 0))
sqlOut.close()
print "Completed Tensor Factorization with ID:{0}".format(exptID)
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
import ktensor
import numpy
import decompTools
caseX = ktensor.loadTensor("results/apr-raw-1.dat")
controlX = ktensor.loadTensor("results/apr-raw-2.dat")
allX = ktensor.loadTensor("results/apr-raw-200.dat")
## since they don't share the same axis, remove one of the factors
caseX.U = [caseX.U[1], caseX.U[2]]
caseX = decompTools.zeroSmallFactors(caseX, 1e-2)
controlX.U = [controlX.U[1], controlX.U[2]]
controlX = decompTools.zeroSmallFactors(controlX, 1e-2)
allX.U = [allX.U[1], allX.U[2]]
allX = decompTools.zeroSmallFactors(allX, 1e-2)
fms = caseX.greedy_fms(controlX)
numpy.savetxt("plots/case-control.csv", fms, delimiter=",")
allFMS = caseX.greedy_fms(allX)
numpy.savetxt("plots/case-all.csv", allFMS, delimiter=",")
import ktensor
import numpy;
import decompTools
caseX = ktensor.loadTensor("results/apr-raw-1.dat")
controlX = ktensor.loadTensor("results/apr-raw-2.dat")
allX = ktensor.loadTensor("results/apr-raw-200.dat")
## since they don't share the same axis, remove one of the factors
caseX.U = [caseX.U[1], caseX.U[2]]
caseX = decompTools.zeroSmallFactors(caseX, 1e-2)
controlX.U = [controlX.U[1], controlX.U[2]]
controlX = decompTools.zeroSmallFactors(controlX, 1e-2)
allX.U = [allX.U[1], allX.U[2]]
allX = decompTools.zeroSmallFactors(allX, 1e-2)
fms = caseX.greedy_fms(controlX)
numpy.savetxt("plots/case-control.csv", fms, delimiter=",")
allFMS = caseX.greedy_fms(allX)
numpy.savetxt("plots/case-all.csv", allFMS, delimiter=",")
