def simultpredparego(self, pool=None):
"""
Perform multi updates on ParEGO MOBO by varying the weighting function.
Args:
pool (int, optional): A multiprocessing.Pool instance.
Will be passed to functions for use, if specified.
Defaults to None.
Returns:
xalltemp (nparray) : Array of design variables updates.
yalltemp (nparray) : Array of objectives value updates.
metricall (nparray) : Array of metric of the updates.
"""
idxs = np.random.choice(11, self.multiupdate)
scalinfotemp = deepcopy(self.KrigScalarizedInfo)
xalltemp = self.Xall[:, :]
yalltemp = self.yall[:, :]
yprednext = np.zeros(shape=[len(self.kriglist)])
for ii, idx in enumerate(idxs):
print(f"update number {ii + 1}")
scalinfotemp['X'] = xalltemp
scalinfotemp['y'] = paregopre(yalltemp, idx)
krigtemp = Kriging(scalinfotemp,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
krigtemp.train(disp=False, pool=pool)
x_n, met_n = run_single_opt(krigtemp,
self.moboInfo,
krigconstlist=self.krigconstlist,
cheapconstlist=self.cheapconstlist,
pool=pool)
xnext = x_n
metricnext = met_n
for jj, krigobj in enumerate(self.kriglist):
yprednext[jj] = krigobj.predict(xnext, ['pred'])
if ii == 0:
xallnext = deepcopy(xnext)
yallnext = deepcopy(yprednext)
metricall = deepcopy(metricnext)
else:
xallnext = np.vstack((xallnext, xnext))
yallnext = np.vstack((yallnext, yprednext))
metricall = np.vstack((metricall, metricnext))
yalltemp = np.vstack((yalltemp, yprednext))
xalltemp = np.vstack((xalltemp, xnext))
return xallnext, yallnext, metricall
def run(self, disp=True, infeasible=None):
"""
Run multi objective unconstrained Bayesian optimization.
Args:
disp (bool): Display process or not. Defaults to True
Returns:
xupdate (nparray): Array of design variables updates.
yupdate (nparray): Array of objectives updates
metricall (nparray): Array of metric values of the updates.
"""
self.nup = 0 # Number of current iteration
self.Xall = self.kriglist[0].KrigInfo['X']
self.yall = np.zeros(shape=[
np.size(self.kriglist[0].KrigInfo["y"], axis=0),
len(self.kriglist)
])
for ii in range(np.size(self.yall, axis=1)):
self.yall[:, ii] = self.kriglist[ii].KrigInfo["y"][:, 0]
if infeasible is not None:
self.yall = np.delete(self.yall.copy(), infeasible, 0)
self.Xall = np.delete(self.Xall.copy(), infeasible, 0)
else:
pass
self.ypar, _ = searchpareto.paretopoint(self.yall)
print("Begin multi-objective Bayesian optimization process.")
if self.autoupdate and disp:
print(
f"Update no.: {self.nup+1}, F-count: {np.size(self.Xall,0)}, "
f"Maximum no. updates: {self.moboInfo['nup']+1}")
else:
pass
# If the optimizer is ParEGO, create a scalarized Kriging
if self.moboInfo['acquifunc'].lower() == 'parego':
self.KrigScalarizedInfo = deepcopy(self.kriglist[0].KrigInfo)
self.KrigScalarizedInfo['y'] = paregopre(self.yall)
self.scalkrig = Kriging(self.KrigScalarizedInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.scalkrig.train(disp=False)
else:
pass
# Perform update on design space
if self.moboInfo['acquifunc'].lower() == 'ehvi':
self.ehviupdate(disp)
elif self.moboInfo['acquifunc'].lower() == 'parego':
self.paregoupdate(disp)
else:
raise ValueError(self.moboInfo["acquifunc"],
" is not a valid acquisition function.")
# Finish optimization and return values
if disp:
print("Optimization finished, now creating the final outputs.")
if self.multiupdate == 0 or self.multiupdate == 1:
xupdate = self.Xall[-self.moboInfo['nup']:, :]
yupdate = self.yall[-self.moboInfo['nup']:, :]
supdate = self.spredall[-self.moboInfo['nup']:, :]
else:
xupdate = self.Xall[(-self.moboInfo['nup'] * self.multiupdate):, :]
yupdate = self.yall[(-self.moboInfo['nup'] * self.multiupdate):, :]
supdate = self.spredall[(-self.moboInfo['nup'] *
self.multiupdate):, :]
metricall = self.metricall
return xupdate, yupdate, supdate, metricall
def enrich(self, xnext):
"""
Evaluate and enrich experimental design.
Args:
xnext: Next design variable(s) to be evaluated.
Returns:
None
"""
# Evaluate new sample
if type(self.kriglist[0].KrigInfo['problem']) == str:
if np.ndim(xnext) == 1:
ynext = evaluate(xnext, self.kriglist[0].KrigInfo['problem'])
else:
ynext = np.zeros(shape=[np.size(xnext, 0), len(self.kriglist)])
for ii in range(np.size(xnext, 0)):
ynext[ii, :] = evaluate(
xnext[ii, :], self.kriglist[0].KrigInfo['problem'])
elif callable(self.kriglist[0].KrigInfo['problem']):
ynext = self.kriglist[0].KrigInfo['problem'](xnext)
else:
raise ValueError(
'KrigInfo["problem"] is not a string nor a callable function!')
if self.krigconstlist is not None:
for idx, constobj in enumerate(self.krigconstlist):
if type(constobj.KrigInfo['problem']) == str:
ynext_const = evaluate(xnext, constobj.KrigInfo['problem'])
elif callable(constobj.KrigInfo['problem']):
ynext_const = constobj.KrigInfo['problem'](xnext).reshape(
-1, 1)
else:
raise ValueError(
'KrigConstInfo["problem"] is not a string nor a callable function!'
)
constobj.KrigInfo['X'] = np.vstack(
(constobj.KrigInfo['X'], xnext))
constobj.KrigInfo['y'] = np.vstack(
(constobj.KrigInfo['y'], ynext_const))
constobj.standardize()
constobj.train(disp=False)
else:
pass
# Treatment for failed solutions, Reference : "Forrester, A. I., Sóbester, A., & Keane, A. J. (2006). Optimization with missing data.
# Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 462(2067), 935-945."
if np.isnan(ynext).any() is True:
for jj in range(len(self.kriglist)):
SSqr, y_hat = self.kriglist[jj].predict(
xnext, ['SSqr', 'pred'])
ynext[0, jj] = y_hat + SSqr
# Enrich experimental design
self.yall = np.vstack((self.yall, ynext))
self.Xall = np.vstack((self.Xall, xnext))
self.ypar, I = searchpareto.paretopoint(
self.yall) # Recompute non-dominated solutions
if self.moboInfo['acquifunc'] == 'ehvi':
for index, krigobj in enumerate(self.kriglist):
krigobj.KrigInfo['X'] = self.Xall
krigobj.KrigInfo['y'] = self.yall[:, index].reshape(-1, 1)
krigobj.standardize()
krigobj.train(disp=False)
elif self.moboInfo['acquifunc'] == 'parego':
self.KrigScalarizedInfo['X'] = self.Xall
self.KrigScalarizedInfo['y'] = paregopre(self.yall)
self.scalkrig = Kriging(self.KrigScalarizedInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.scalkrig.train(disp=False)
for index, krigobj in enumerate(self.kriglist):
krigobj.KrigInfo['X'] = self.Xall
krigobj.KrigInfo['y'] = self.yall[:, index].reshape(-1, 1)
krigobj.standardize()
krigobj.train(disp=False)
else:
raise ValueError(self.moboInfo["acquifunc"],
" is not a valid acquisition function.")
# Save data
if self.savedata:
I = I.astype(int)
Xbest = self.Xall[I, :]
sio.savemat(self.moboInfo["filename"], {
"xbest": Xbest,
"ybest": self.ypar
})
def _run(self, disp=True, infeasible=None, pool=None):
"""
Run multi objective unconstrained Bayesian optimization.
Args:
disp (bool, optional): Print progress. Defaults to True.
infeasible (np.ndarray, optional): Indices of infeasible
samples to delete. Defaults to None.
pool (int, optional): A multiprocessing.Pool instance.
Will be passed to functions for use, if specified.
Defaults to None.
Returns:
xalltemp (np.ndarray): [n_kb, n_dv] array of update design
variable values.
yalltemp (np.ndarray): [n_kb, n_obj] array of update
objective values.
salltemp (np.ndarray): [n_kb, n_obj] array of update
objective uncertainty values.
metricall (np.ndarray): [n_kb, 1] array of update metric
values.
"""
self.nup = 0 # Number of current iteration
self.Xall = self.kriglist[0].KrigInfo['X']
n_samp = self.kriglist[0].KrigInfo["nsamp"]
n_krig = len(self.kriglist)
self.yall = np.zeros([n_samp, n_krig])
for ii in range(n_krig):
self.yall[:, ii] = self.kriglist[ii].KrigInfo["y"][:, 0]
if infeasible is not None:
self.yall = np.delete(self.yall.copy(), infeasible, 0)
self.Xall = np.delete(self.Xall.copy(), infeasible, 0)
self.ypar, _ = searchpareto.paretopoint(self.yall)
print("Begin multi-objective Bayesian optimization process.")
if self.autoupdate and disp:
print(f"Update no.: {self.nup + 1}, F-count: {n_samp}, "
f"Maximum no. updates: {self.moboInfo['nup'] + 1}")
# If the optimizer is ParEGO, create a scalarized Kriging
if self.moboInfo['acquifunc'].lower() == 'parego':
self.KrigScalarizedInfo = deepcopy(self.kriglist[0].KrigInfo)
self.KrigScalarizedInfo['y'] = paregopre(self.yall)
self.scalkrig = Kriging(self.KrigScalarizedInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.scalkrig.train(disp=False, pool=pool)
# Perform update on design space
if self.moboInfo['acquifunc'].lower().startswith('ehvi'):
self.ehviupdate(disp, pool=pool)
elif self.moboInfo['acquifunc'].lower() == 'parego':
self.paregoupdate(disp, pool=pool)
else:
raise ValueError(f"{self.moboInfo['acquifunc']} is not a valid "
f"acquisition function.")
# Finish optimization and return values
if disp:
print("Optimization finished, now creating the final outputs.")
xupdate = self.Xall[(-self.moboInfo['nup'] * self.multiupdate):, :]
yupdate = self.yall[(-self.moboInfo['nup'] * self.multiupdate):, :]
supdate = self.spredall[(-self.moboInfo['nup'] * self.multiupdate):, :]
metricall = self.metricall
return xupdate, yupdate, supdate, metricall