def findMin(self, inffunc, meanfunc, covfunc, likfunc, x, y):
hypInArray = self.convert_to_array(meanfunc, covfunc, likfunc)
opt = cg(self.nlml, hypInArray, self.dnlml, (inffunc, meanfunc, covfunc, likfunc, x, y), maxiter=100, disp=False, full_output=True)
optimalHyp = opt[0]
funcValue = opt[1]
warnFlag = opt[4]
if warnFlag == 1:
print "Maximum number of iterations exceeded."
elif warnFlag == 2:
print "Gradient and/or function calls not changing."
if self.searchConfig:
searchRange = self.searchConfig.meanRange + self.searchConfig.covRange + self.searchConfig.likRange
if not (self.searchConfig.max_trails or self.searchConfig.min_threshold):
raise Exception('Specify at least one of the stop conditions')
while True:
self.trailsCounter += 1 # increase counter
for i in xrange(hypInArray.shape[0]): # random init of hyp
hypInArray[i]= np.random.uniform(low=searchRange[i][0], high=searchRange[i][1])
# value this time is better than optiaml min value
thisopt = cg(self.nlml, hypInArray, self.dnlml, (inffunc, meanfunc, covfunc, likfunc, x, y), maxiter=100, disp=False, full_output=True)
if thisopt[1] < funcValue:
funcValue = thisopt[1]
optimalHyp = thisopt[0]
if self.searchConfig.max_trails and self.trailsCounter > self.searchConfig.max_trails: # if exceed max_trails
return optimalHyp, funcValue
if self.searchConfig.min_threshold and funcValue <= self.searchConfig.min_threshold: # reach provided mininal
return optimalHyp, funcValue
return optimalHyp, funcValue
def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions to minimize the negative log marginal liklihood. This is REALLY inefficient!
x = convert_to_array(hyp)
if Flag == 'CG':
aa = cg(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fx = aa[1]; funcCalls = aa[2]; gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gvals = dnlml(x,F,hyp,varargin)
return convert_to_class(x,hyp), fx, gvals, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=True, full_output=True)
x = aa[0]; fvals = aa[1]; gvals = aa[2]; Bopt = aa[3]; funcCalls = aa[4]; gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
return convert_to_class(x,hyp), fvals, gvals, funcCalls
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
def findMin(self, x, y):
meanfunc = self.model.meanfunc
covfunc = self.model.covfunc
likfunc = self.model.likfunc
inffunc = self.model.inffunc
hypInArray = self._convert_to_array()
try:
opt = cg(self._nlml, hypInArray, self._dnlml, maxiter=100, disp=False, full_output=True)
optimalHyp = deepcopy(opt[0])
funcValue = opt[1]
warnFlag = opt[4]
if warnFlag == 1:
print "Maximum number of iterations exceeded."
elif warnFlag == 2:
print "Gradient and/or function calls not changing."
except:
self.errorCounter += 1
if not self.searchConfig:
raise Exception("Can not use conjugate gradient. Try other hyparameters")
self.trailsCounter += 1
if self.searchConfig:
searchRange = self.searchConfig.meanRange + self.searchConfig.covRange + self.searchConfig.likRange
if not (self.searchConfig.num_restarts or self.searchConfig.min_threshold):
raise Exception('Specify at least one of the stop conditions')
while True:
self.trailsCounter += 1 # increase counter
for i in xrange(hypInArray.shape[0]): # random init of hyp
hypInArray[i]= np.random.uniform(low=searchRange[i][0], high=searchRange[i][1])
# value this time is better than optiaml min value
try:
thisopt = cg(self._nlml, hypInArray, self._dnlml, maxiter=100, disp=False, full_output=True)
if thisopt[1] < funcValue:
funcValue = thisopt[1]
optimalHyp = thisopt[0]
except:
self.errorCounter += 1
if self.searchConfig.num_restarts and self.errorCounter > self.searchConfig.num_restarts/2:
print "[CG] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter)
raise Exception("Over half of the trails failed for conjugate gradient")
if self.searchConfig.num_restarts and self.trailsCounter > self.searchConfig.num_restarts-1: # if exceed num_restarts
print "[CG] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
if self.searchConfig.min_threshold and funcValue <= self.searchConfig.min_threshold: # reach provided mininal
print "[CG] %d out of %d trails failed during optimization" % (self.errorCounter, self.trailsCounter)
return optimalHyp, funcValue
return optimalHyp, funcValue
def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions to minimize the negative log marginal liklihood. This is REALLY inefficient!
x = convert_to_array(hyp) # Converts the hyperparameter class to an array
if Flag == 'CG':
aa = cg(nlml,
x,
dnlml, (F, hyp, varargin),
maxiter=100,
disp=True,
full_output=True)
x = aa[0]
fx = aa[1]
funcCalls = aa[2]
gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gvals = dnlml(x, F, hyp, varargin)
return convert_to_class(x, hyp), fx, gvals, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml,
x,
dnlml, (F, hyp, varargin),
maxiter=100,
disp=False,
full_output=True)
x = aa[0]
fvals = aa[1]
gvals = aa[2]
Bopt = aa[3]
funcCalls = aa[4]
gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
return convert_to_class(x, hyp), fvals, gvals, funcCalls
elif Flag == 'SCG':
# Use SCG
aa = scg(x, nlml, dnlml, (F, hyp, varargin), niters=40)
x = aa[0]
fvals = aa[1]
gvals = dnlml(x, F, hyp, varargin)
return convert_to_class(x, hyp), fvals, gvals
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions, sgc.py, or minimize.py to
# minimize the negative log marginal liklihood.
x = convert_to_array(hyp) # convert the hyperparameter class to an array
if Flag == 'CG':
aa = cg(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fopt = aa[1]; funcCalls = aa[2]; gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gopt = dnlml(x,F,hyp,varargin)
return convert_to_class(x,hyp), fopt, gopt, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml, x, dnlml, (F,hyp,varargin), maxiter=100, disp=False, full_output=True)
x = aa[0]; fopt = aa[1]; gopt = aa[2]; Bopt = aa[3]; funcCalls = aa[4]; gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
if isinstance(fopt, ndarray):
fopt = fopt[0]
return convert_to_class(x,hyp), fopt, gopt, funcCalls
elif Flag == 'SCG':
# use sgc.py
aa = scg(x, nlml, dnlml, (F,hyp,varargin), niters = 100)
hyp = convert_to_class(aa[0],hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0],F,hyp,varargin)
return hyp, fopt, gopt, len(aa[1])
elif Flag == 'Minimize':
# use minimize.py
aa = run(x, nlml, dnlml, (F,hyp,varargin), maxnumfuneval=-100)
hyp = convert_to_class(aa[0],hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0],F,hyp,varargin)
return hyp, fopt, gopt, len(aa[1])
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
def gp_train(gp, X, y, R=None, w=None, Flag = None):
''' gp_train() returns the learnt hyperparameters.
Following chapter 5.4.1 in Rasmussen and Williams: GPs for ML (2006).
The original version (MATLAB implementation) of used optimizer minimize.m
is copyright (C) 1999 - 2006, Carl Edward Rasmussen.
The used python versions are in scipy.optimize
Input R and w is needed for XGP regression! '''
# Build the parameter list that we will optimize
theta = np.concatenate((gp['meantheta'],gp['covtheta']))
if Flag == 'CG':
aa = cg(nlml, theta, dnlml, [gp,X,y,R,w], maxiter=100, disp=False, full_output=True)
theta = aa[0]; fvals = aa[1]; funcCalls = aa[2]; gradcalls = aa[3]
gvals = dnlml(theta, gp, X, y, R, w)
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
mt = len(gp['meantheta'])
gp['meantheta'] = theta[:mt]
gp['covtheta'] = theta[mt:]
return gp, fvals, gvals, funcCalls
elif Flag == 'BFGS':
# Use BFGS
#aa = bfgs(nlml, theta, dnlml, [gp,X,y,R,w], maxiter=100, disp=False, full_output=True)
aa = bfgs(nlml, theta, dnlml, [gp,X,y,R,w], maxiter=100, disp=True, full_output=True)
theta = aa[0]; fvals = aa[1]; gvals = aa[2]; Bopt = aa[3]; funcCalls = aa[4]; gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
mt = len(gp['meantheta'])
gp['meantheta'] = theta[:mt]
gp['covtheta'] = theta[mt:]
return gp, fvals, gvals, funcCalls
elif Flag == 'SCG':
theta, listF = scg.scg(theta, nlml, dnlml, [gp,X,y,R,w], niters = 100)
mt = len(gp['meantheta'])
gp['meantheta'] = theta[:mt]
gp['covtheta'] = theta[mt:]
return gp, listF
else:
raise Exception("Need to specify a method for optimization in gp_train")
def findMin(self, x, y, numIters=100):
meanfunc = self.model.meanfunc
covfunc = self.model.covfunc
likfunc = self.model.likfunc
inffunc = self.model.inffunc
hypInArray = self._convert_to_array()
try:
opt = cg(self._nlml,
hypInArray,
self._dnlml,
maxiter=numIters,
disp=False,
full_output=True)
optimalHyp = deepcopy(opt[0])
funcValue = opt[1]
warnFlag = opt[4]
if warnFlag == 1:
print("Maximum number of iterations exceeded.")
elif warnFlag == 2:
print("Gradient and/or function calls not changing.")
except:
self.errorCounter += 1
if not self.searchConfig:
raise Exception(
"Can not learn hyperparamters using conjugate gradient.")
self.trailsCounter += 1
if self.searchConfig:
searchRange = self.searchConfig.meanRange + self.searchConfig.covRange + self.searchConfig.likRange
if not (self.searchConfig.num_restarts
or self.searchConfig.min_threshold):
raise Exception('Specify at least one of the stop conditions')
while True:
self.trailsCounter += 1 # increase counter
for i in range(hypInArray.shape[0]): # random init of hyp
hypInArray[i] = np.random.uniform(low=searchRange[i][0],
high=searchRange[i][1])
# value this time is better than optiaml min value
try:
thisopt = cg(self._nlml,
hypInArray,
self._dnlml,
maxiter=100,
disp=False,
full_output=True)
if thisopt[1] < funcValue:
funcValue = thisopt[1]
optimalHyp = thisopt[0]
except:
self.errorCounter += 1
if self.searchConfig.num_restarts and self.errorCounter > old_div(
self.searchConfig.num_restarts, 2):
print(
"[CG] %d out of %d trails failed during optimization" %
(self.errorCounter, self.trailsCounter))
raise Exception(
"Over half of the trails failed for conjugate gradient"
)
if self.searchConfig.num_restarts and self.trailsCounter > self.searchConfig.num_restarts - 1: # if exceed num_restarts
print(
"[CG] %d out of %d trails failed during optimization" %
(self.errorCounter, self.trailsCounter))
return optimalHyp, funcValue
if self.searchConfig.min_threshold and funcValue <= self.searchConfig.min_threshold: # reach provided mininal
print(
"[CG] %d out of %d trails failed during optimization" %
(self.errorCounter, self.trailsCounter))
return optimalHyp, funcValue
return optimalHyp, funcValue
def check_grad(self, N=1):
Ws = [np.random.rand(self.crf.n_W) for _ in xrange(N)]
return [cg(self.obj, self.grad, W) for W in Ws]
def min_wrapper(hyp, F, Flag, *varargin):
# Utilize scipy.optimize functions, sgc.py, or minimize.py to
# minimize the negative log marginal liklihood.
x = convert_to_array(hyp) # convert the hyperparameter class to an array
if Flag == 'CG':
aa = cg(nlml,
x,
dnlml, (F, hyp, varargin),
maxiter=100,
disp=False,
full_output=True)
x = aa[0]
fopt = aa[1]
funcCalls = aa[2]
gradcalls = aa[3]
if aa[4] == 1:
print "Maximum number of iterations exceeded."
elif aa[4] == 2:
print "Gradient and/or function calls not changing."
gopt = dnlml(x, F, hyp, varargin)
return convert_to_class(x, hyp), fopt, gopt, funcCalls
elif Flag == 'BFGS':
# Use BFGS
aa = bfgs(nlml,
x,
dnlml, (F, hyp, varargin),
maxiter=100,
disp=False,
full_output=True)
x = aa[0]
fopt = aa[1]
gopt = aa[2]
Bopt = aa[3]
funcCalls = aa[4]
gradcalls = aa[5]
if aa[6] == 1:
print "Maximum number of iterations exceeded."
elif aa[6] == 2:
print "Gradient and/or function calls not changing."
if isinstance(fopt, ndarray):
fopt = fopt[0]
return convert_to_class(x, hyp), fopt, gopt, funcCalls
elif Flag == 'SCG':
# use sgc.py
aa = scg(x, nlml, dnlml, (F, hyp, varargin), niters=100)
hyp = convert_to_class(aa[0], hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0], F, hyp, varargin)
return hyp, fopt, gopt, len(aa[1])
elif Flag == 'Minimize':
# use minimize.py
aa = run(x, nlml, dnlml, (F, hyp, varargin), maxnumfuneval=-100)
hyp = convert_to_class(aa[0], hyp)
fopt = aa[1][-1]
gopt = dnlml(aa[0], F, hyp, varargin)
return hyp, fopt, gopt, len(aa[1])
else:
raise Exception('Incorrect usage of optimization flag in min_wrapper')
