def kpcaopt(self,w,KPCAkernel,orig_X,out='default'):
# Calculate PLS coeff
if KPCAkernel != "gaussian" and KPCAkernel != "precomputed":
if KPCAkernel.lower() == 'poly' or KPCAkernel.lower() == 'polynomial':
_drm = drm(self.n_princomp, kernel='poly', gamma=10**w[0], coef0=10**w[1], degree=np.round(w[2]))
elif KPCAkernel.lower() == 'sigmoid':
_drm = drm(self.n_princomp, kernel='sigmoid', gamma=10**w[0], coef0=10**w[1])
elif KPCAkernel.lower() == 'rbf':
_drm = drm(self.n_princomp, kernel='rbf', gamma=10**w[0])
elif KPCAkernel.lower() == 'linear' or KPCAkernel.lower() == 'cosine':
_drm = drm(self.n_princomp)
self.KrigInfo["nvar"] = self.n_princomp
if self.standardization is True:
self.KrigInfo["X_norm"] = deepcopy(orig_X)
_drm.fit(self.KrigInfo["X_norm"].copy())
transformed = _drm.transform(self.KrigInfo["X_norm"].copy())
self.KrigInfo["lb2"] = (np.min(transformed, axis=0)) # Create lowerbound for transformed X
self.KrigInfo["ub2"] = (np.max(transformed, axis=0)) # Create upperbound for transformed X
self.KrigInfo["X_norm"] = standardize(transformed, self.KrigInfo['y'],
type=self.standtype.lower(),
range=np.vstack((self.KrigInfo["lb2"], self.KrigInfo["ub2"])))
self.KrigInfo['idx'] = polytruncation(self.KrigInfo["TrendOrder"], self.KrigInfo["nvar"], 1)
else:
self.KrigInfo["X"] = deepcopy(orig_X)
_drm.fit(self.KrigInfo["X"].copy())
transformed = _drm.transform(self.KrigInfo["X"].copy())
self.KrigInfo["X"] = transformed
self.KrigInfo['idx'] = polytruncation(self.KrigInfo["TrendOrder"], self.KrigInfo["nvar"], 1)
else:
n_features = np.size(orig_X,1)
self.KrigInfo["nvar"] = self.n_princomp
_drm = drm(self.n_princomp,kernel='precomputed')
k_mat = customkernel(orig_X,orig_X,w,n_features,type='gaussian')
if self.standardization is True:
self.KrigInfo["X_norm"] = deepcopy(orig_X)
transformed = _drm.fit_transform(k_mat)
self.KrigInfo["lb2"] = (np.min(transformed, axis=0)) # Create lowerbound for transformed X
self.KrigInfo["ub2"] = (np.max(transformed, axis=0)) # Create upperbound for transformed X
self.KrigInfo["X_norm"] = standardize(transformed, self.KrigInfo['y'],
type=self.standtype.lower(),
range=np.vstack((self.KrigInfo["lb2"], self.KrigInfo["ub2"])))
self.KrigInfo['idx'] = polytruncation(self.KrigInfo["TrendOrder"], self.KrigInfo["nvar"], 1)
else:
pass
if out == 'default':
self.KrigInfo["kernel"] = ["iso_gaussian"]
Kriging.train(self, disp=False)
else:
self.KrigInfo["kernel"] = ["gaussian"]
Kriging.train(self, disp=False, pre_theta=self.KrigInfo['Theta'])
loocverr, _ = Kriging.loocvcalc(self, drm=_drm)
if out == 'default':
return loocverr
elif out == 'all':
return _drm, loocverr
def generate_kriging():
# Sampling
nsample = 20
nvar = 2
nobj = 2
lb = -1 * np.ones(shape=[nvar])
ub = 1 * np.ones(shape=[nvar])
sampoption = "halton"
samplenorm, sample = sampling(sampoption,
nvar,
nsample,
result="real",
upbound=ub,
lobound=lb)
X = sample
# Evaluate sample
global y
y = evaluate(X, "schaffer")
# Initialize KrigInfo
global KrigInfo1
KrigInfo1 = initkriginfo("single")
# Set KrigInfo
KrigInfo1["X"] = X
KrigInfo1["y"] = y[:, 0].reshape(-1, 1)
KrigInfo1["problem"] = "schaffer"
KrigInfo1["nrestart"] = 5
KrigInfo1["ub"] = ub
KrigInfo1["lb"] = lb
KrigInfo1["optimizer"] = "lbfgsb"
# Initialize KrigInfo
global KrigInfo2
KrigInfo2 = deepcopy(KrigInfo1)
KrigInfo2['y'] = y[:, 1].reshape(-1, 1)
# Run Kriging
krigobj1 = Kriging(KrigInfo1,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
krigobj1.train(parallel=False)
loocverr1, _ = krigobj1.loocvcalc()
print("LOOCV error of Kriging model: ", loocverr1, "%")
krigobj2 = Kriging(KrigInfo2,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
krigobj2.train(parallel=False)
loocverr2, _ = krigobj2.loocvcalc()
print("LOOCV error of Kriging model: ", loocverr2, "%")
return krigobj1, krigobj2
def generate_kriging():
# Initialization
KrigInfo = dict()
kernel = ["gaussian"]
# Sampling
nsample = 40
nvar = 2
ub = np.array([5, 5])
lb = np.array([-5, -5])
nup = 3
sampoption = "halton"
samplenorm, sample = sampling(sampoption,
nvar,
nsample,
result="real",
upbound=ub,
lobound=lb)
X = sample
# Evaluate sample
y1 = evaluate(X, "styblinski")
# Initialize KrigInfo
KrigInfo = initkriginfo("single")
# Set KrigInfo
KrigInfo["X"] = X
KrigInfo["y"] = y1
KrigInfo["nvar"] = nvar
KrigInfo["problem"] = "styblinski"
KrigInfo["nsamp"] = nsample
KrigInfo["nrestart"] = 7
KrigInfo["ub"] = ub
KrigInfo["lb"] = lb
KrigInfo["kernel"] = kernel
KrigInfo["TrendOrder"] = 0
KrigInfo["nugget"] = -6
# KrigInfo["n_princomp"] = 1
KrigInfo["kernel"] = ["gaussian"]
KrigInfo["nkernel"] = len(KrigInfo["kernel"])
KrigInfo["optimizer"] = "lbfgsb"
# Run Kriging
t = time.time()
krigobj = Kriging(KrigInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
krigobj.train(parallel=False)
loocverr, _ = krigobj.loocvcalc()
elapsed = time.time() - t
print("elapsed time for train Kriging model: ", elapsed, "s")
print("LOOCV error of Kriging model: ", loocverr, "%")
return krigobj
def simultpredparego(self):
"""
Perform multi updates on ParEGO MOBO by varying the weighting function.
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)
xnext, metricnext = run_single_opt(krigtemp, self.moboInfo,
self.krigconstlist,
self.cheapconstlist)
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 generate_kriging():
# Sampling
nsample = 10
nvar = 2
lb = np.array([-5, -5])
ub = np.array([5, 5])
sampoption = "halton"
samplenorm, sample = sampling(sampoption,
nvar,
nsample,
result="real",
upbound=ub,
lobound=lb)
X = sample
# Evaluate sample
global y
y = evaluate(X, "styblinski")
# Initialize KrigInfo
global KrigInfo
KrigInfo = initkriginfo("single")
# Set KrigInfo
KrigInfo["X"] = X
KrigInfo["y"] = y
KrigInfo["problem"] = "styblinski"
KrigInfo["nrestart"] = 5
KrigInfo["ub"] = ub
KrigInfo["lb"] = lb
KrigInfo["optimizer"] = "lbfgsb"
# Run Kriging
krigobj = Kriging(KrigInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
krigobj.train(parallel=False)
loocverr, _ = krigobj.loocvcalc()
print("LOOCV error of Kriging model: ", loocverr, "%")
return krigobj
class Problem:
def __init__(self, X, y):
self.X = X
self.y = y
def createkrig(self):
# define variables
lb = np.array([0.1, 3, 3.1])
ub = np.array([0.9, 11.41, 60.95])
# Set kriging Info
KrigMultiInfo1 = initkriginfo("single")
KrigMultiInfo1["X"] = self.X
KrigMultiInfo1["y"] = self.y[:, 0].reshape(-1, 1)
KrigMultiInfo1["nrestart"] = 7
KrigMultiInfo1["ub"] = ub
KrigMultiInfo1["lb"] = lb
KrigMultiInfo1["optimizer"] = "lbfgsb"
KrigMultiInfo2 = deepcopy(KrigMultiInfo1)
KrigMultiInfo2['y'] = self.y[:, 1].reshape(-1, 1)
# train kriging
self.krigobj1 = Kriging(KrigMultiInfo1,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigobj1.train(parallel=False)
loocverr1, _ = self.krigobj1.loocvcalc()
print(f"LOOCV Error Kriging 1: {loocverr1}% (MAPE)")
self.krigobj2 = Kriging(KrigMultiInfo2,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigobj2.train(parallel=False)
loocverr2, _ = self.krigobj2.loocvcalc()
print(f"LOOCV Error Kriging 2: {loocverr2}% (MAPE)")
self.kriglist = [self.krigobj1, self.krigobj2]
def update_sample(self):
moboInfo = dict()
moboInfo["nup"] = 1
moboInfo["nrestart"] = 10
moboInfo["acquifunc"] = "ehvi"
moboInfo["acquifuncopt"] = "lbfgsb"
cheapconstlist = [self.geomconst]
mobo = MOBO(moboInfo,
self.kriglist,
autoupdate=False,
multiupdate=5,
savedata=False,
chpconst=cheapconstlist)
xupdate, yupdate, metricall = mobo.run(disp=True)
return xupdate, yupdate, metricall
def geomconst(self, vars):
# constraint 'geomconst' should have input of the design variables
vars = np.array(vars)
stat = foongconst(vars)
return stat
class MOBO:
"""
Perform multi-objective Bayesian Optimization
Args:
moboInfo (dict): Dictionary containing necessary information for multi-objective Bayesian optimization.
kriglist (list): List of Kriging object.
autoupdate (bool): True or False, depends on your decision to evaluate your function automatically or not.
multiupdate (int): Number of suggested samples returned for each iteration.
expconst (list): List of constraints Kriging object.
chpconst (list): List of cheap constraint function.
Returns:
xupdate (nparray): Array of design variables updates.
yupdate (nparray): Array of objectives updates
metricall (nparray): Array of metric values of the updates.
"""
def __init__(self,
moboInfo,
kriglist,
autoupdate=True,
multiupdate=0,
savedata=True,
expconst=None,
chpconst=None):
"""
Initialize MOBO class
Args:
moboInfo (dict): Dictionary containing necessary information for multi-objective Bayesian optimization.
kriglist (list): List of Kriging object.
autoupdate (bool): True or False, depends on your decision to evaluate your function automatically or not.
multiupdate (int): Number of suggested samples returned for each iteration.
savedata (bool): Save data for each iteration or not. Defaults to True.
expconst (list): List of constraints Kriging object.
chpconst (list): List of cheap constraint function.
"""
self.moboInfo = moboinfocheck(moboInfo, autoupdate)
self.kriglist = kriglist
self.krignum = len(self.kriglist)
self.autoupdate = autoupdate
self.multiupdate = multiupdate
self.savedata = savedata
self.krigconstlist = expconst
self.cheapconstlist = chpconst
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
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]
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']:, :]
else:
xupdate = self.Xall[(-self.moboInfo['nup'] * self.multiupdate):, :]
yupdate = self.yall[(-self.moboInfo['nup'] * self.multiupdate):, :]
metricall = self.metricall
return xupdate, yupdate, metricall
def ehviupdate(self, disp):
"""
Update MOBO using EHVI algorithm.
Args:
disp (bool): Display process or not.
Returns:
None
"""
while self.nup < self.moboInfo['nup']:
# Iteratively update the reference point for hypervolume computation if EHVI is used as the acquisition function
if self.moboInfo['refpointtype'].lower() == 'dynamic':
self.moboInfo['refpoint'] = np.max(
self.yall,
0) + (np.max(self.yall, 0) - np.min(self.yall, 0)) * 2
# 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_multi_opt(self.kriglist, self.moboInfo,
self.ypar,
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.simultpredehvi(disp)
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 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 simultpredehvi(self, disp=False):
"""
Perform multi updates on EHVI MOBO using Kriging believer method.
Returns:
xalltemp (nparray) : Array of design variables updates.
yalltemp (nparray) : Array of objectives value updates.
metricall (nparray) : Array of metric of the updates.
"""
krigtemp = [0] * len(self.kriglist)
for index, obj in enumerate(self.kriglist):
krigtemp[index] = deepcopy(obj)
yprednext = np.zeros(shape=[len(krigtemp)])
ypartemp = self.ypar
yall = self.yall
for ii in range(self.multiupdate):
if disp:
print(f"update number {ii+1}")
else:
pass
xnext, metrictemp = run_multi_opt(krigtemp, self.moboInfo,
ypartemp, self.krigconstlist,
self.cheapconstlist)
bound = np.vstack(
(-np.ones(shape=[1, krigtemp[0].KrigInfo["nvar"]]),
np.ones(shape=[1, krigtemp[0].KrigInfo["nvar"]])))
for jj in range(len(krigtemp)):
yprednext[jj] = krigtemp[jj].predict(xnext, 'pred')
krigtemp[jj].KrigInfo['X'] = np.vstack(
(krigtemp[jj].KrigInfo['X'], xnext))
krigtemp[jj].KrigInfo['y'] = np.vstack(
(krigtemp[jj].KrigInfo['y'], yprednext[jj]))
krigtemp[jj].standardize()
krigtemp[jj].KrigInfo["F"] = compute_regression_mat(
krigtemp[jj].KrigInfo["idx"],
krigtemp[jj].KrigInfo["X_norm"], bound,
np.ones(shape=[krigtemp[jj].KrigInfo["nvar"]]))
krigtemp[jj].KrigInfo = likelihood(
krigtemp[jj].KrigInfo['Theta'],
krigtemp[jj].KrigInfo,
mode='all',
trainvar=krigtemp[jj].trainvar)
if ii == 0:
xalltemp = deepcopy(xnext)
yalltemp = deepcopy(yprednext)
metricall = deepcopy(metrictemp)
else:
xalltemp = np.vstack((xalltemp, xnext))
yalltemp = np.vstack((yalltemp, yprednext))
metricall = np.vstack((metricall, metrictemp))
yall = np.vstack((yall, yprednext))
ypartemp, _ = searchpareto.paretopoint(yall)
return xalltemp, yalltemp, metricall
def simultpredparego(self):
"""
Perform multi updates on ParEGO MOBO by varying the weighting function.
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)
xnext, metricnext = run_single_opt(krigtemp, self.moboInfo,
self.krigconstlist,
self.cheapconstlist)
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 enrich(self, xnext):
"""
Evaluate and enrich experimental design.
Args:
xnext: Next design variable(s) to be evaluated.
Returns:
None
"""
# Evaluate new sample
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'])
# 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
})
class Fillgap:
def __init__(self, init_samp):
self.init_samp = init_samp
self.nsamp = np.size(self.init_samp, axis=0)
self.cd = np.zeros(shape=[self.nsamp, 1])
self.noise = np.zeros(shape=[self.nsamp, 1])
self.cl = np.zeros(shape=[self.nsamp, 1])
self.solnlist = [self.cd, self.noise, self.cl]
def createkrig(self):
df = pd.read_csv('../innout/opt_data(1).csv',
sep=',',
index_col='Name')
data = df.values
X = data[:, 0:6].astype(float)
y = data[:, 7:9].astype(float)
self.y = y
cldat = data[:, 6].astype(float)
# define variables
lb = np.min(X, axis=0)
ub = np.max(X, axis=0)
# Set Const Kriging
KrigConstInfo = initkriginfo("single")
KrigConstInfo["X"] = X
KrigConstInfo["y"] = cldat.reshape(-1, 1)
KrigConstInfo["nrestart"] = 5
KrigConstInfo["ub"] = ub
KrigConstInfo["lb"] = lb
KrigConstInfo["optimizer"] = "lbfgsb"
KrigConstInfo['limit'] = 0.15
# Set Kriging Info
KrigMultiInfo1 = initkriginfo("single")
KrigMultiInfo1["X"] = X
KrigMultiInfo1["y"] = y[:, 0].reshape(-1, 1)
KrigMultiInfo1["nrestart"] = 7
KrigMultiInfo1["ub"] = ub
KrigMultiInfo1["lb"] = lb
KrigMultiInfo1["optimizer"] = "slsqp"
KrigMultiInfo2 = deepcopy(KrigMultiInfo1)
KrigMultiInfo2['y'] = y[:, 1].reshape(-1, 1)
self.krigobj1 = Kriging(KrigMultiInfo1,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigobj1.train(parallel=False)
loocverr1, _ = self.krigobj1.loocvcalc()
self.krigobj2 = Kriging(KrigMultiInfo2,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigobj2.train(parallel=False)
loocverr2, _ = self.krigobj2.loocvcalc()
self.krigconst = Kriging(KrigConstInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigconst.train(parallel=False)
loocverr3, _ = self.krigconst.loocvcalc()
self.kriglist = [self.krigobj1, self.krigobj2, self.krigconst]
def evaluate(self):
for idx, krigobj in enumerate(self.kriglist):
self.solnlist[idx] = krigobj.predict(self.init_samp, ['pred'])
return self.solnlist
class Problem:
def __init__(self, X, y, cldat):
self.X = X
self.y = y
self.cldat = cldat
def createkrig(self):
# define variables
lb = np.min(self.X, axis=0)
ub = np.max(self.X, axis=0)
# Set Const Kriging
KrigConstInfo = initkriginfo("single")
KrigConstInfo["X"] = self.X
KrigConstInfo["y"] = self.cldat.reshape(-1, 1)
KrigConstInfo["nrestart"] = 5
KrigConstInfo["ub"] = ub
KrigConstInfo["lb"] = lb
KrigConstInfo["optimizer"] = "lbfgsb"
KrigConstInfo['limittype'] = '>='
KrigConstInfo['limit'] = 0.15
# Set Kriging Info
KrigMultiInfo1 = initkriginfo("single")
KrigMultiInfo1["X"] = self.X
KrigMultiInfo1["y"] = self.y[:, 0].reshape(-1, 1)
KrigMultiInfo1["nrestart"] = 7
KrigMultiInfo1["ub"] = ub
KrigMultiInfo1["lb"] = lb
KrigMultiInfo1["optimizer"] = "lbfgsb"
KrigMultiInfo2 = deepcopy(KrigMultiInfo1)
KrigMultiInfo2['y'] = self.y[:, 1].reshape(-1, 1)
self.krigobj1 = Kriging(KrigMultiInfo1,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigobj1.train(parallel=False)
loocverr1, _ = self.krigobj1.loocvcalc()
self.krigobj2 = Kriging(KrigMultiInfo2,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigobj2.train(parallel=False)
loocverr2, _ = self.krigobj2.loocvcalc()
self.krigconst = Kriging(KrigConstInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigconst.train(parallel=False)
loocverr3, _ = self.krigconst.loocvcalc()
self.kriglist = [self.krigobj1, self.krigobj2]
self.expconst = [self.krigconst]
def update_sample(self):
moboInfo = dict()
moboInfo["nup"] = 1
moboInfo["nrestart"] = 10
moboInfo["acquifunc"] = "ehvi"
moboInfo["acquifuncopt"] = "lbfgsb"
moboInfo["refpoint"] = np.array([0.06, 83])
cheapconstlist = [self.geomconst]
mobo = MOBO(moboInfo,
self.kriglist,
autoupdate=False,
multiupdate=5,
savedata=False,
expconst=self.expconst,
chpconst=cheapconstlist)
xupdate, yupdate, metricall = mobo.run(disp=True)
return xupdate, yupdate, metricall
def geomconst(self, vars):
# constraint 'geomconst' should have input of the design variables
vars = np.array(vars)
tip_angle = sweepdiffcheck.sweep_diff(vars[2], vars[4], 0.00165529)
stat = sweepdiffcheck.min_angle_violated(tip_angle, 7)
return stat
class Hispeedplane(Problem):
def __init__(self):
super(Hispeedplane, self).__init__(6, 2, 2)
self.types[:] = [
Real(5.04167e-02, 9.9583e-02),
Real(-4.425e-03, 4.425e-03),
Real(-6.3833e+01, 7.3833e+01),
Real(-1.475e+01, 1.475e+01),
Real(4.29167e-02, 1.094167e-01),
Real(2.067e-02, 9.933e-02)
]
self.constraints[:] = ">=0.15"
self.krigconst = object
self.krigobj1 = object
self.krigobj2 = object
self.createkrig()
def createkrig(self):
df = pd.read_csv('../innout/opt_data.csv', sep=',', index_col='Name')
data = df.values
X = data[:, 0:6].astype(float)
y = data[:, 7:9].astype(float)
cldat = data[:, 6].astype(float)
# define variables
lb = np.min(X, axis=0)
ub = np.max(X, axis=0)
# Set Const Kriging
KrigConstInfo = initkriginfo("single")
KrigConstInfo["X"] = X
KrigConstInfo["y"] = cldat.reshape(-1, 1)
KrigConstInfo["nrestart"] = 5
KrigConstInfo["ub"] = ub
KrigConstInfo["lb"] = lb
KrigConstInfo["optimizer"] = "lbfgsb"
KrigConstInfo['limit'] = 0.15
# Set Kriging Info
KrigMultiInfo1 = initkriginfo("single")
KrigMultiInfo1["X"] = X
KrigMultiInfo1["y"] = y[:, 0].reshape(-1, 1)
KrigMultiInfo1["nrestart"] = 7
KrigMultiInfo1["ub"] = ub
KrigMultiInfo1["lb"] = lb
KrigMultiInfo1["optimizer"] = "slsqp"
KrigMultiInfo2 = deepcopy(KrigMultiInfo1)
KrigMultiInfo2['y'] = y[:, 1].reshape(-1, 1)
self.krigobj1 = Kriging(KrigMultiInfo1,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigobj1.train(parallel=False)
loocverr1, _ = self.krigobj1.loocvcalc()
self.krigobj2 = Kriging(KrigMultiInfo2,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigobj2.train(parallel=False)
loocverr2, _ = self.krigobj2.loocvcalc()
self.krigconst = Kriging(KrigConstInfo,
standardization=True,
standtype='default',
normy=False,
trainvar=False)
self.krigconst.train(parallel=False)
loocverr3, _ = self.krigconst.loocvcalc()
def geomconst(self, vars):
# This constraint function should return 1 if the constraint is satisfied and 0 if not.
vars = np.array(vars)
tip_angle = sweepdiffcheck.sweep_diff(vars[2], vars[4], 0.00165529)
stat = sweepdiffcheck.min_angle_violated(tip_angle, 7)
return stat # return 1 or 0, 1 is larger than 0.15 then the constraint is satisfied
def evaluate(self, solution):
vars = np.array(solution.variables)
solution.objectives[:] = [
self.krigobj1.predict(vars, 'pred'),
self.krigobj2.predict(vars, 'pred')
]
solution.constraints[:] = [
self.krigconst.predict(vars, 'pred'),
self.geomconst(vars)
]
