def dataImputation(fname, y, rsMethodName, eval_fname):
rsIndex = ResponseSurfaces.getEnumValue(rsMethodName)
# write script
f = tempfile.SpooledTemporaryFile(mode="wt")
if platform.system() == 'Windows':
import win32api
fname = win32api.GetShortPathName(fname)
f.write('load %s\n' % fname) # load data
cmd = 'rscreate'
f.write('%s\n' % cmd)
f.write('%d\n' % y) # select output
f.write('%d\n' % rsIndex) # select response surface
cmd = 'rseval'
f.write('%s\n' % cmd)
f.write('y\n') # data taken from register
f.write('%s\n' % eval_fname)
f.write('y\n') # do fuzzy evaluation
f.write('y\n') # write data to file
f.write('quit\n')
f.seek(0)
# invoke psuade
out, error = Common.invokePsuade(f)
f.close()
if error:
return None
outfile = 'eval_sample'
assert(os.path.exists(outfile))
return outfile
def run(self):
self.functionSignal.connect(self.functionHandle)
self.textDialogShowSignal.connect(self.textDialog.show)
self.textDialogCloseSignal.connect(self.textDialog.close)
self.textDialogInsertSignal.connect(self.textDialog.textedit.insertPlainText)
self.textDialogEnsureVisibleSignal.connect(
self.textDialog.textedit.ensureCursorVisible
)
self.showErrorSignal.connect(self.textDialog.showError)
out, error = Common.invokePsuade(
self.fileHandle,
textDialog=self.textDialog,
dialogShowSignal=self.textDialogShowSignal,
dialogCloseSignal=self.textDialogCloseSignal,
textInsertSignal=self.textDialogInsertSignal,
ensureVisibleSignal=self.textDialogEnsureVisibleSignal,
showErrorSignal=self.showErrorSignal,
printOutputToScreen=True,
plotOuuValuesSignal=self.plotValuesSignal,
)
if out is None:
return
self.fileHandle.close()
self.functionSignal.emit(out, error)
self.finishedSignal.emit()
def rseval(rsdata, pdata, cdata, rstypes):
# rsdata: SampleData containing the RS training data
# pdata: SampleData containing the sample representing the prior over uncertain variables
# cdata: SampleData containing the candidate set
# rstypes: dictionary with output index as key and string denote RS types as value; possible values: ['MARS',
# 'linear', 'quadratic', 'cubic']
# convert the data into psuade files
cfile = os.path.join(dname, 'CandidateSet')
pfile = os.path.join(dname, 'PriorSample')
rsfile = os.path.join(dname, 'RSTrainData')
cfile = writeSample(cfile, cdata)
pfile = writeSample(pfile, pdata)
y = 1
rsfile = RSAnalyzer.writeRSdata(rsfile, y, rsdata)
cmd = 'odoeu_rseval'
inputNames = rsdata.getInputNames()
priorNames = pdata.getInputNames()
priorIndices = []
for name in priorNames:
priorIndices.append(inputNames.index(name) + 1)
rs_list = ['MARS', 'linear', 'quadratic', 'cubic', 'GP3']
# extract the indices of RS types for outputs
rs_idx = [ResponseSurfaces.getEnumValue(s) for s in rs_list]
rsdict = dict(zip(rs_list, rs_idx))
# write script
f = tempfile.SpooledTemporaryFile(mode='wt')
if platform.system() == 'Windows':
import win32api
cfile = win32api.GetShortPathName(cfile)
pfile = win32api.GetShortPathName(pfile)
rsfile = win32api.GetShortPathName(rsfile)
f.write('load %s\n' % rsfile)
f.write('%s\n' % cmd)
f.write('y\n')
for i in priorIndices:
f.write(
'%d\n' % i
) # specify random variables, should be consistent with vars in prior
f.write('0\n') # 0 to proceed
f.write('%s\n' %
pfile) # file containing the prior sample (psuade sample format)
f.write('%s\n' %
cfile) # file containing the candidate set (psuade sample format)
for rs in rstypes: # for each output, specify RS index
f.write('%d\n' % rsdict[rstypes[rs]])
f.write('quit\n')
f.seek(0)
# invoke psuade
out, error = Common.invokePsuade(f)
if error:
return None
outfile = 'odoeu_rseval.out'
assert (os.path.exists(outfile))
return outfile
def odoeu(cdata,
cfile,
pdata,
rsdata,
rstypes,
opt,
nd,
max_iters=100,
edata=None):
# cdata: SampleData containing the original candidate set
# cfile: PSUADE sample file containing the original candidates with the mean/std of the selected output
# cdata: SampleData containing the candidate set
# pdata: SampleData containing the sample representing the prior over uncertain variables
# rsdata: SampleData containing the RS training data
# rstypes: dictionary with output index as key and string denote RS types as value; possible values: ['MARS',
# 'linear', 'quadratic', 'cubic']
# nd: int denoting design size
# max_iters: int denoting maximum number of iterations for the optimization routine [default: 100]
# edata: SampleData containing the evaluation set
# parse params
opts = ['G', 'I', 'D', 'A']
assert (opt in opts)
optdict = dict(zip(opts, range(1, len(opts) + 1)))
opt_index = optdict[opt]
cmd = 'odoeu_optns'
# TO DO for Pedro: check in GUI?
# maximum iterations should be in range [100, 1000]
assert (99 < max_iters < 1001)
# initialize constants
ncand = cdata.getNumSamples()
nOutputs = rsdata.getNumOutputs()
# extract the indices of random variables
inputNames = rsdata.getInputNames()
priorNames = pdata.getInputNames()
priorIndices = []
for name in priorNames:
priorIndices.append(inputNames.index(name) + 1)
rs_list = ['MARS', 'linear', 'quadratic', 'cubic', 'GP3']
# TO DO for Pedro: check in GUI?
# this checks to see if user is trying to pass an unsupported RS
for rs in rstypes:
assert (rstypes[rs] in rs_list)
assert (len(rstypes) == nOutputs)
# extract the indices of RS types for outputs
rs_idx = [ResponseSurfaces.getEnumValue(s) for s in rs_list]
rsdict = dict(zip(rs_list, rs_idx))
# convert the data into psuade files
pfile = os.path.join(dname, 'PriorSample')
rsfile = os.path.join(dname, 'RSTrainData')
pfile = writeSample(pfile, pdata)
efile = os.path.join(dname, 'EvaluationSet')
if edata is None:
efile = writeSample(efile, cdata)
else:
efile = writeSample(efile, edata)
y = 1
rsfile = RSAnalyzer.writeRSdata(rsfile, y, rsdata)
# write script
f = tempfile.SpooledTemporaryFile(mode='wt')
if platform.system() == 'Windows':
import win32api
pfile = win32api.GetShortPathName(pfile)
rsfile = win32api.GetShortPathName(rsfile)
efile = win32api.GetShortPathName(efile)
f.write('%s\n' % cmd)
f.write('y\n')
f.write('%d\n' % opt_index) # choose G, I, D, A
f.write('%d\n' % ncand) # size of the candidate set
f.write('%d\n' % nd) # design size
f.write(
'%d\n' %
max_iters) # max number of iterations, must be greater or equal to 100
f.write('n\n') # no initial guess
f.write('%s\n' %
rsfile) # file containing RS training data (psuade format)
for i in priorIndices:
f.write(
'%d\n' % i
) # specify random variables, should be consistent with vars in prior
f.write('0\n') # 0 to proceed
f.write('%s\n' %
pfile) # file containing the prior sample (psuade sample format)
f.write('%s\n' %
cfile) # file containing the candidate set (psuade sample format)
f.write('%s\n' % efile
) # ... evaluate the optimality values on the (same) candidate set
for rs in rstypes: # for each output, specify RS index
f.write('%d\n' % rsdict[rstypes[rs]])
# TO DO: as we add more RS, may need to port more code from RSAnalyzer.py to handle different RS types
f.write('quit\n')
f.seek(0)
# invoke psuade
out, error = Common.invokePsuade(f)
if error:
return None
# parse output
best_indices = None
best_optval = None
m = re.findall(cmd + r' best selection = (\d+ \d+)', out)
if m:
best_indices = [i for i in m[0].split()]
s = r'\s*'.join([i for i in best_indices])
best_optvals = re.findall(s + r'\s*===> output = (\S*)', out)
best_indices = [int(i) for i in best_indices]
best_optval = float(best_optvals[0])
return best_indices, best_optval
def compress(fname):
N = 0 # number of samples in x3sample['file']
with open(fname) as f: ### TO DO for Jeremy: check sample size in GUI
header = f.readline()
header = header.split()
N = int(header[0])
nInputs = int(header[1])
Nmin = 100 # psuade minimum for genhistogram
if N < Nmin:
warn = 'OUU: In function compress(), "x3sample file" requires '
warn = warn + 'at least %d samples.' % Nmin
Common.showError(warn)
return {N: fname} # return original sample file
outfiles = {}
nbins_max = 20
nscenarios_max = 1501
for nbins in xrange(2, nbins_max):
# write script to invoke scenario compression
f = tempfile.SpooledTemporaryFile()
if platform.system() == 'Windows':
import win32api
fname = win32api.GetShortPathName(fname)
f.write('read_std %s\n' % fname)
f.write('genhistogram\n')
for x in xrange(nInputs):
f.write('%d\n' % nbins)
f.write('quit\n')
f.seek(0)
# invoke psuade
out, error = Common.invokePsuade(f)
f.close()
if error:
return None
# check output file
sfile = 'psuade_pdfhist_sample'
if os.path.exists(sfile):
Ns = 0 # number of samples in psuade_pdfhist_sample
with open(sfile) as f:
header = f.readline()
header = header.split()
Ns = int(header[0])
sfile_ = Common.getLocalFileName(OUU.dname, fname,
'.compressed' + str(Ns))
if os.path.exists(sfile_):
os.remove(sfile_)
os.rename(sfile, sfile_)
sfile = sfile_
else:
error = 'OUU: %s does not exist.' % sfile
Common.showError(error, out)
return None
# append scenario file to data structure
if len(outfiles) > 1 and Ns > min(N, nscenarios_max):
return outfiles
else:
outfiles[Ns] = (sfile, nbins)
return outfiles