def paregoupdate(self, disp=True, pool=None):
"""
Update MOBO using ParEGO algorithm.
Args:
disp (bool, optional): Print progress. Defaults to True.
pool (int, optional): A multiprocessing.Pool instance.
Will be passed to functions for use, if specified.
Defaults to None.
Returns:
None
"""
while self.nup < self.moboInfo['nup']:
# Perform update(s)
if self.multiupdate < 0:
raise ValueError(
"Number of multiple update must be greater or "
"equal to 0")
elif self.multiupdate in (0, 1):
x_n, met_n = run_single_opt(self.scalkrig,
self.moboInfo,
krigconstlist=self.krigconstlist,
cheapconstlist=self.cheapconstlist,
pool=pool)
xnext = x_n
metricnext = met_n
yprednext = np.zeros(shape=[2])
for ii, krigobj in enumerate(self.kriglist):
yprednext[ii] = krigobj.predict(xnext, ['pred'])
else:
xnext, yprednext, metricnext = self.simultpredparego(pool=pool)
if self.nup == 0:
self.metricall = metricnext.reshape(-1, 1)
else:
self.metricall = np.vstack((self.metricall, metricnext))
# Break Loop if auto is false
if not self.autoupdate:
self.Xall = np.vstack((self.Xall, xnext))
self.yall = np.vstack((self.yall, yprednext))
break
# Evaluate and enrich experimental design
self.enrich(xnext, pool=pool)
# Update number of iterations
self.nup += 1
# Show optimization progress
if disp:
print(f"Update no.: {self.nup+1}, "
f"F-count: {np.size(self.Xall, 0)}, "
f"Maximum no. updates: {self.moboInfo['nup']+1}")
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 paregoupdate(self, disp):
"""
Update MOBO using ParEGO algorithm.
Args:
disp (bool): Display process or not.
Returns:
None
"""
while self.nup < self.moboInfo['nup']:
# Perform update(s)
if self.multiupdate < 0:
raise ValueError(
"Number of multiple update must be greater or equal to 0")
elif self.multiupdate == 0 or self.multiupdate == 1:
xnext, metricnext = run_single_opt(self.scalkrig,
self.moboInfo,
self.krigconstlist,
self.cheapconstlist)
yprednext = np.zeros(shape=[2])
for ii, krigobj in enumerate(self.kriglist):
yprednext[ii] = krigobj.predict(xnext, ['pred'])
else:
xnext, yprednext, metricnext = self.simultpredparego()
if self.nup == 0:
self.metricall = metricnext
else:
self.metricall = np.vstack((self.metricall, metricnext))
# Break Loop if auto is false
if self.autoupdate is False:
self.Xall = np.vstack((self.Xall, xnext))
self.yall = np.vstack((self.yall, yprednext))
break
else:
pass
# Evaluate and enrich experimental design
self.enrich(xnext)
# Update number of iterations
self.nup += 1
# Show optimization progress
if disp:
print(
f"Update no.: {self.nup+1}, F-count: {np.size(self.Xall, 0)}, "
f"Maximum no. updates: {self.moboInfo['nup']+1}")
def run(self, disp=True):
"""
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
"""
self.nup = 0 # Number of current iteration
self.Xall = self.krigobj.KrigInfo['X']
self.yall = self.krigobj.KrigInfo['y']
self.yhist = np.array([np.min(self.yall)])
self.istall = 0
print("Begin single-objective Bayesian optimization process.")
while self.nup < self.soboInfo['nup']:
if self.autoupdate and disp:
print(f"Update no.: {self.nup + 1}, F-count: {np.size(self.Xall, 0)}, "
f"Best f(x): {self.yhist[self.nup]}, Stall counter: {self.istall}")
else:
pass
# Find next suggested point
self.xnext, self.metricnext = run_single_opt(self.krigobj,self.soboInfo,self.krigconstlist,self.cheapconstlist)
# Break Loop if autoupdate is False
if self.autoupdate is False:
break
else:
pass
# Evaluate response for next decision variable
if type(self.krigobj.KrigInfo['problem']) == str:
self.ynext = evaluate(self.xnext,self.krigobj.KrigInfo['problem'])
elif callable(self.krigobj.KrigInfo['problem']):
self.ynext = self.krigobj.KrigInfo['problem'](self.xnext)
# 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(self.ynext).any() is True:
SSqr, y_hat = self.krigobj.predict(self.xnext, ['SSqr', 'pred'])
self.ynext = y_hat + SSqr
# Enrich experimental design
self.krigobj.KrigInfo['X'] = np.vstack((self.krigobj.KrigInfo['X'], self.xnext))
self.krigobj.KrigInfo['y'] = np.vstack((self.krigobj.KrigInfo['y'], self.ynext))
# Re-train Kriging model
self.krigobj.standardize()
self.krigobj.train(disp=False)
if self.nup == 0:
self.xupdate = deepcopy(self.xnext)
self.yupdate = deepcopy(self.ynext)
else:
self.xupdate = np.vstack((self.xupdate,self.xnext))
self.yupdate = np.vstack((self.yupdate,self.ynext))
self.nup += 1
self.yhist = np.vstack((self.yhist, np.min(self.krigobj.KrigInfo['y'])))
# Check stall iteration
if self.yhist[self.nup,0] == self.yhist[self.nup-1,0]:
self.istall += 1
if self.istall == self.soboInfo['stalliteration']:
break
else:
pass
else:
self.istall = 0
print("Optimization finished, now creating the final outputs.")
y_opt = np.min(self.krigobj.KrigInfo['y'])
min_pos = np.argmin(self.krigobj.KrigInfo['y'])
x_opt = self.krigobj.KrigInfo['X'][min_pos,:]
if self.autoupdate:
return x_opt,y_opt
else:
return self.xnext,self.ynext
def _run(self, disp=True, pool=None):
"""Run multi objective unconstrained Bayesian optimization.
Args:
disp (bool, optional): Print progress. Defaults to True.
pool (int, optional): A multiprocessing.Pool instance.
Will be passed to functions for use, if specified.
Defaults to None.
Returns:
xupdate (np.ndarray): Matrix of updated samples after
optimization.
yupdate (np.ndarray): Response matrix of updated sample
solutions after optimization.
"""
self.nup = 0 # Number of current iteration
self.Xall = self.krigobj.KrigInfo["X"]
self.yall = self.krigobj.KrigInfo["y"]
self.yhist = np.array([np.min(self.yall)])
self.istall = 0
print("Begin single-objective Bayesian optimization process.")
while self.nup < self.soboInfo["nup"]:
if self.autoupdate and disp:
print(
f"Update no.: {self.nup + 1}, F-count: {np.size(self.Xall, 0)}, "
f"Best f(x): {self.yhist[self.nup]}, Stall counter: {self.istall}"
)
else:
pass
# Find next suggested point
x_n, metric_n = run_single_opt(self.krigobj,
self.soboInfo,
krigconstlist=self.krigconstlist,
cheapconstlist=self.cheapconstlist,
pool=pool)
self.xnext = x_n
self.metricnext = metric_n
# Break Loop if autoupdate is False
if self.autoupdate is False:
break
else:
pass
# Evaluate response for next decision variable
obj_krig_problem = self.krigobj.KrigInfo["problem"]
if isinstance(obj_krig_problem, str):
self.ynext = evaluate(self.xnext, obj_krig_problem)
elif callable(obj_krig_problem):
self.ynext = obj_krig_problem(self.xnext)
else:
raise ValueError('KrigInfo["problem"] is not a string nor a '
'callable function!')
# 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(self.ynext).any():
SSqr, y_hat = self.krigobj.predict(self.xnext,
["SSqr", "pred"])
self.ynext = y_hat + SSqr
# Enrich experimental design
self.krigobj.KrigInfo["X"] = np.vstack(
(self.krigobj.KrigInfo["X"], self.xnext))
self.krigobj.KrigInfo["y"] = np.vstack(
(self.krigobj.KrigInfo["y"], self.ynext))
# Re-train Kriging model
self.krigobj.standardize()
self.krigobj.train(disp=False, pool=pool)
if self.nup == 0:
self.xupdate = deepcopy(self.xnext)
self.yupdate = deepcopy(self.ynext)
else:
self.xupdate = np.vstack((self.xupdate, self.xnext))
self.yupdate = np.vstack((self.yupdate, self.ynext))
self.nup += 1
self.yhist = np.vstack(
(self.yhist, np.min(self.krigobj.KrigInfo["y"])))
# Check stall iteration
if self.yhist[self.nup, 0] == self.yhist[self.nup - 1, 0]:
self.istall += 1
if self.istall == self.soboInfo["stalliteration"]:
break
else:
self.istall = 0
print("Optimization finished, now creating the final outputs.")
y_opt = np.min(self.krigobj.KrigInfo["y"])
min_pos = np.argmin(self.krigobj.KrigInfo["y"])
x_opt = self.krigobj.KrigInfo["X"][min_pos, :]
if self.autoupdate:
return x_opt, y_opt
else:
return self.xnext, self.ynext