def cost(x, axis=None, samples=Ns):
"""upper bound on expected model output
Inputs:
x: list of model hyperparameters
axis: int, the index of y on which to find bound (all, by default)
samples: int, number of samples, for a non-deterministic OUQ model
Returns:
upper bound on expected value of model output
"""
# build a model, F(x|a), and tune "a" for optimal F.
toy_ = wrap(d=x[0], e=x[1])(toy) #NOTE: reduces nx by 2
#print('building model F(x|a)...')
model = WrapModel('model', model=toy_, nx=nx, ny=ny, rnd=False)
rnd = Ns if model.rnd else None
#print('building UQ objective of expected model output...')
b = ExpectedValue(model,
bnd,
constraint=scons,
cvalid=is_cons,
samples=rnd)
i = counter.count()
#print('solving for upper bound on expected model output...')
solver = b.upper_bound(axis=axis, id=i, **param)
if type(solver) is not tuple:
solver = (solver, ) #FIXME: save solver to DB (or pkl)
if axis is None:
results = tuple(-s.bestEnergy for s in solver) #NOTE: -1 for LUB
#print('[id: %s] %s' % (i, tuple(s.bestSolution for s in solver)))
else:
results = -solver[axis].bestEnergy #NOTE: -1 for LUB
#print('[id: %s] %s' % (i, solver[axis].bestSolution))
return results
def cost(x, axis=None, samples=Ns):
"""upper bound on expected model error, for surrogate and 'truth'
Inputs:
x: list of model hyperparameters
axis: int, the index of y on which to find bound (all, by default)
samples: int, number of samples, for a non-deterministic OUQ model
Returns:
upper bound on expected value of model error
"""
# CASE 0: |F(x|a) - F'(x|a')|, no G. Tune "a" for optimal F, a = x[-2:]
toy_ = wrap(d=x[0], e=x[1])(toy)
#print('building model F(x|a) of truth...')
model = WrapModel('model', model=toy_, nx=nx, ny=ny, rnd=False)
#print('building UQ model of model error...')
error = ErrorModel('error', model=truth, surrogate=model)
rnd = Ns if error.rnd else None
#print('building UQ objective of expected model error...')
b = ExpectedValue(error,
bnd,
constraint=scons,
cvalid=is_cons,
samples=rnd)
i = counter.count()
#print('solving for upper bound on expected model error...')
solver = b.upper_bound(axis=axis, id=i, **param)
if type(solver) is not tuple:
solver = (solver, ) #FIXME: save solver to DB (or pkl)
if axis is None:
results = tuple(-s.bestEnergy for s in solver) #NOTE: -1 for LUB
#print('[id: %s] %s' % (i, tuple(s.bestSolution for s in solver)))
else:
results = -solver[axis].bestEnergy #NOTE: -1 for LUB
#print('[id: %s] %s' % (i, solver[axis].bestSolution))
return results
# *) F(x|a) "is callable". Update "d" in G(d), then actively update G(x|A).
# CASE 0: |F(x|a) - F'(x|a')|, no G. Tune "a" for optimal F, a = x[-2:]
if __name__ == '__main__':
#from toys import cost5x3 as toy; nx = 5; ny = 3
#from toys import function5x3 as toy; nx = 5; ny = 3
#from toys import cost5x1 as toy; nx = 5; ny = 1
#from toys import function5x1 as toy; nx = 5; ny = 1
#from toys import cost5 as toy; nx = 5; ny = None
from toys import function5 as toy
nx = 5
ny = None
from toys import wrap
toy3 = wrap(d=10, e=10)(toy)
nx = nx - 2 #NOTE: reduces nx by 2
# update 'inner-loop' optimization parameters
from misc import param, npts, wlb, wub, is_cons, scons
from ouq import ExpectedValue
from mystic.monitors import VerboseLoggingMonitor, Monitor, VerboseMonitor
from mystic.termination import VTRChangeOverGeneration as VTRCOG
from mystic.termination import Or, VTR, ChangeOverGeneration as COG
param['opts']['termination'] = COG(1e-10, 100) #NOTE: short stop?
param['npop'] = 160 #NOTE: increase if results.txt is not monotonic
param['stepmon'] = VerboseLoggingMonitor(1,
20,
filename='log.txt',
label='output')