def __init__(self, worker_id, data, response_surface, maxeval, nsamples,
exp_design=None, sampling_method=None, extra=None, extra_vals=None):
# Check stopping criterion
self.start_time = time.time()
if maxeval < 0: # Time budget
self.maxeval = np.inf
self.time_budget = np.abs(maxeval)
else:
self.maxeval = maxeval
self.time_budget = np.inf
# Import problem information
self.worker_id = worker_id
self.data = data
self.fhat = response_surface
if self.fhat is None:
self.fhat = RBFInterpolant(kernel=CubicKernel, tail=LinearTail, maxp=maxeval)
self.fhat.reset() # Just to be sure!
self.nsamples = nsamples
self.extra = extra
self.extra_vals = extra_vals
# Default to generate sampling points using Symmetric Latin Hypercube
self.design = exp_design
if self.design is None:
if self.data.dim > 50:
self.design = LatinHypercube(data.dim, data.dim+1)
else:
self.design = SymmetricLatinHypercube(data.dim, 2*(data.dim+1))
self.xrange = np.asarray(data.xup - data.xlow)
# algorithm parameters
self.sigma_min = 0.005
self.sigma_max = 0.2
self.sigma_init = 0.2
self.failtol = max(5, data.dim)
self.succtol = 3
self.numeval = 0
self.status = 0
self.sigma = 0
self.resubmitter = RetryStrategy()
self.xbest = None
self.fbest = np.inf
self.fbest_old = None
# Set up search procedures and initialize
self.sampling = sampling_method
if self.sampling is None:
self.sampling = CandidateDYCORS(data)
self.check_input()
# Start with first experimental design
self.sample_initial()
def __init__(self, max_evals, opt_prob):
check_opt_prob(opt_prob)
if not isinstance(max_evals, int) and max_evals > 0:
raise ValueError("max_evals must be an integer >= exp_des.num_pts")
self.opt_prob = opt_prob
self.max_evals = max_evals
self.retry = RetryStrategy()
for _ in range(max_evals): # Generate the random points
x = np.random.uniform(low=opt_prob.lb, high=opt_prob.ub)
proposal = self.propose_eval(x)
self.retry.rput(proposal)
def __init__(self,
worker_id,
data,
response_surface,
maxeval,
nsamples,
exp_design=None,
sampling_method=None,
extra=None):
"""Initialize the optimization strategy.
:param worker_id: Start ID in a multistart setting
:param data: Problem parameter data structure
:param response_surface: Surrogate model object
:param maxeval: Function evaluation budget
:param nsamples: Number of simultaneous fevals allowed
:param exp_design: Experimental design
:param sampling_method: Sampling method for finding
points to evaluate
:param extra: Points to be added to the experimental design
"""
self.worker_id = worker_id
self.data = data
self.fhat = response_surface
if self.fhat is None:
self.fhat = RBFInterpolant(surftype=CubicRBFSurface, maxp=maxeval)
self.maxeval = maxeval
self.nsamples = nsamples
self.extra = extra
# Default to generate sampling points using Symmetric Latin Hypercube
self.design = exp_design
if self.design is None:
if self.data.dim > 50:
self.design = LatinHypercube(data.dim, data.dim + 1)
else:
self.design = SymmetricLatinHypercube(data.dim,
2 * (data.dim + 1))
self.xrange = np.asarray(data.xup - data.xlow)
# algorithm parameters
self.sigma_min = 0.005
self.sigma_max = 0.2
self.sigma_init = 0.2
self.failtol = max(5, data.dim)
self.succtol = 3
self.numeval = 0
self.status = 0
self.sigma = 0
self.resubmitter = RetryStrategy()
self.xbest = None
self.fbest = np.inf
self.fbest_old = None
# Set up search procedures and initialize
self.sampling = sampling_method
if self.sampling is None:
self.sampling = CandidateDYCORS(data)
self.check_input()
# Start with first experimental design
self.sample_initial()
def __init__(self,
worker_id,
data,
response_surface,
maxeval,
nsamples,
exp_design=None,
sampling_method=None,
archiving_method=None,
extra=None,
extra_vals=None,
store_sim=False):
# Check stopping criterion
self.start_time = time.time()
if maxeval < 0: # Time budget
self.maxeval = np.inf
self.time_budget = np.abs(maxeval)
else:
self.maxeval = maxeval
self.time_budget = np.inf
# Import problem information
self.worker_id = worker_id
self.data = data
self.fhat = []
if response_surface is None:
for i in range(self.data.nobj):
self.fhat.append(
RBFInterpolant(kernel=CubicKernel,
tail=LinearTail,
maxp=maxeval)) #MOPLS ONLY
else:
for i in range(self.data.nobj):
response_surface.reset() # Just to be sure!
self.fhat.append(deepcopy(response_surface)) #MOPLS ONLY
self.ncenters = nsamples
self.nsamples = 1
self.numinit = None
self.extra = extra
self.extra_vals = extra_vals
self.store_sim = store_sim
# Default to generate sampling points using Symmetric Latin Hypercube
self.design = exp_design
if self.design is None:
if self.data.dim > 50:
self.design = LatinHypercube(data.dim, data.dim + 1)
else:
self.design = SymmetricLatinHypercube(data.dim,
2 * (data.dim + 1))
self.xrange = np.asarray(data.xup - data.xlow)
# algorithm parameters
self.sigma_min = 0.005
self.sigma_max = 0.2
self.sigma_init = 0.2
self.failtol = max(5, data.dim)
self.failcount = 0
self.contol = 5
self.numeval = 0
self.status = 0
self.sigma = 0
self.resubmitter = RetryStrategy()
self.xbest = None
self.fbest = None
self.fbest_old = None
self.improvement_prev = 1
# population of centers and long-term archive
self.nd_archives = []
self.new_pop = []
self.sim_res = []
if archiving_method is None:
self.memory_archive = NonDominatedArchive(200)
else:
self.memory_archive = archiving_method
self.evals = []
self.maxfit = min(200, 20 * self.data.dim)
self.d_thresh = 1.0
# Set up search procedures and initialize
self.sampling = sampling_method
if self.sampling is None:
self.sampling = EvolutionaryAlgorithm(data)
self.check_input()
# Start with first experimental design
self.sample_initial()
