default=10)
args = parser.parse_args()
inputFile = args.inputFile
nSample = args.sample
exptID = args.expt + nSample
totSamples = args.bootstrap
testSize = args.testSize
seed = 10
innerIter = 10
outerIter = args.iter
R = args.rank
zeroThr = 1e-4
X = sptensor.loadTensor(inputFile.format("data"))
yaxis = decompTools.loadAxisInfo(inputFile.format("info"))
tensorInfo = shelve.open(inputFile.format("info"), "r")
Y = np.array(tensorInfo["class"], dtype='int')
tensorInfo.close()
diagMed = [[a, b] for a, b in itertools.product(yaxis[1], yaxis[2])]
predFile = "results/pred-metric-{0}-{1}.csv".format(exptID, nSample)
ttss = StratifiedShuffleSplit(Y,
n_iter=totSamples,
test_size=testSize,
random_state=seed)
print "Starting Tensor Prediction with ID:{0}".format(exptID)
n = 0
parser.add_argument("-re", "--rankend", type=int, help="End of rank increments", default=200)
args = parser.parse_args()
# Experimental sections
iter = args.iterations
innerIter = 10
tol = 1e-2
zeroThr = 1e-5
seed = args.seed
R = np.concatenate(([5, 10], np.arange(args.rankstart, args.rankend+25, 25)))
exptID = args.expt
labelID = args.label
exptDesc = args.description
inputFile = args.inputFile.format("data")
yaxis = decompTools.loadAxisInfo(args.inputFile.format("info"))
sqlOutfile = "results/rank-sql-{0}.sql".format(exptID)
rawFilePattern = "results/rank-raw-{0}-{1}.dat"
dbOutPattern = "results/rank-db-{0}-{1}.dat"
dbTimePattern = "results/rank-time-db-{0}-{1}.dat"
print "Starting Tensor Rank Experiment with ID:{0}".format(exptID)
sqlOut = file(sqlOutfile, "w")
for r in R:
np.random.seed(seed)
Y, ystats, mstats = decompTools.decomposeCountTensor(inputFile, R=r, outerIters=iter, innerIters=innerIter, convergeTol=tol, zeroTol=zeroThr)
Y.writeRawFile(rawFilePattern.format(exptID, r)) # output the raw file
# output the saved db file
dbYFile = dbOutPattern.format(exptID, r)
"""
factors = pf.shape[1] # the number of columns
rows = pf.shape[0]
idx = np.flatnonzero(pf[:, 0])
dbOut = np.column_stack((axis[idx], np.repeat(0, len(idx)), pf[idx, 0]))
for col in range(1, factors):
idx = np.flatnonzero(pf[:, col])
dbOut = np.vstack(
(dbOut,
np.column_stack((axis[idx], np.repeat(col, len(idx)), pf[idx,
col]))))
return dbOut
refX = sptensor.loadTensor(inputFile.format(0, "data"))
refAxis = decompTools.loadAxisInfo(inputFile.format(0, "info"))
## Find the factors for the first one
klp, M, mstats = findFactors(refX, R=rank, outerIter=outerIter, innerIter=10)
## Store off the factors to be loaded into a database
M.writeRawFile(MrawFile)
Mout = decompTools.getDBOutput(M, refAxis)
Mout = np.column_stack((np.repeat(exptID, Mout.shape[0]), Mout))
np.savetxt(Moutfile, Yout, fmt="%s", delimiter="|")
sqlOut = file(Ysqlfile, "w")
## write the factors and the models into the database
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(
PF : the factor matrix where rows is patients and the column are factor values
axis : the axis label of patients PIDs
"""
factors = pf.shape[1] # the number of columns
rows = pf.shape[0]
idx = np.flatnonzero(pf[:, 0])
dbOut = np.column_stack((axis[idx], np.repeat(0, len(idx)), pf[idx, 0]))
for col in range(1, factors):
idx = np.flatnonzero(pf[:, col])
dbOut = np.vstack((dbOut, np.column_stack((axis[idx], np.repeat(col, len(idx)), pf[idx, col]))))
return dbOut
refX = sptensor.loadTensor(inputFile.format(0, "data"))
refAxis = decompTools.loadAxisInfo(inputFile.format(0, "info"))
## Find the factors for the first one
klp, M, mstats = findFactors(refX, R=rank, outerIter=outerIter, innerIter=10)
## Store off the factors to be loaded into a database
M.writeRawFile(MrawFile)
Mout = decompTools.getDBOutput(M, refAxis)
Mout = np.column_stack((np.repeat(exptID, Mout.shape[0]), Mout))
np.savetxt(Moutfile, Yout, fmt="%s", delimiter="|")
sqlOut = file(Ysqlfile, "w")
## write the factors and the models into the database
sqlOut.write(
"load client from /home/joyceho/workspace/tensor/{0} of del modified by coldel| insert into joyceho.tensor_factors;\n".format(
Youtfile
)
parser.add_argument("-s", "--seed", type=int, help="random seed", default=0)
parser.add_argument("-i", "--iterations", type=int, help="Number of outer interations", default=70)
args = parser.parse_args()
R = args.rank
seed = args.seed
iters = args.iterations
filename = args.inputFile
exptID = args.expt
innerIter = 10
patThresh = 1e-50
modeThr = 1e-2
X = sptensor.loadTensor(filename.format("data"))
yaxis = decompTools.loadAxisInfo(filename.format("info"))
## 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
