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