def _build_observer_options(self, budget):
# type: (int) -> ObserverOptions
opts = {
'result_folder':
'"%s/on_%s_budget%04dxD"' %
(self._algorithm_name, self._suite_name, budget),
'algorithm_name':
self._algorithm_name
}
return ObserverOptions(opts)
# see available choices via cocoex.known_suite_names
budget = 2 # maxfevals = budget x dimension ### INCREASE budget WHEN THE DATA CHAIN IS STABLE ###
max_runs = 1e9 # number of (almost) independent trials per problem instance
number_of_batches = 1 # allows to run everything in several batches
current_batch = 1 # 1..number_of_batches
##############################################################################
# By default we call SOLVER(fun, x0), but the INTERFACE CAN BE ADAPTED TO EACH SOLVER ABOVE
SOLVER = random_search
# SOLVER = optimize.fmin_cobyla
# SOLVER = my_solver # SOLVER = fmin_slsqp # SOLVER = cma.fmin
suite_instance = "" # "year:2016"
suite_options = "" # "dimensions: 2,3,5,10,20 " # if 40 is not desired
# for more suite options, see http://numbbo.github.io/coco-doc/C/#suite-parameters
observer_options = ObserverOptions({ # is (inherited from) a dictionary
'algorithm_info':
'"A SIMPLE RANDOM SEARCH ALGORITHM"', # CHANGE/INCOMMENT THIS!
# 'algorithm_name': '', # default already provided from SOLVER name
# 'result_folder': '', # default already provided from several global vars
})
######################### END CHANGE HERE ####################################
# ===============================================
# run (main)
# ===============================================
def main(budget=budget,
max_runs=max_runs,
current_batch=current_batch,
number_of_batches=number_of_batches):
"""Initialize suite and observer, then benchmark solver by calling
``batch_loop(SOLVER, suite, observer, budget,...``
# see available choices via cocoex.known_suite_names
budget = 2 # maxfevals = budget x dimension ### INCREASE budget WHEN THE DATA CHAIN IS STABLE ###
max_runs = 1e9 # number of (almost) independent trials per problem instance
number_of_batches = 1 # allows to run everything in several batches
current_batch = 1 # 1..number_of_batches
##############################################################################
# By default we call SOLVER(fun, x0), but the INTERFACE CAN BE ADAPTED TO EACH SOLVER ABOVE
# SOLVER = random_search
# SOLVER = optimize.fmin_cobyla
SOLVER = IBEA.myIBEA # SOLVER = fmin_slsqp # SOLVER = cma.fmin
suite_instance = "" # "year:2016"
suite_options = "dimensions: 2" # "dimensions: 2,3,5,10,20 " # if 40 is not desired
# for more suite options, see http://numbbo.github.io/coco-doc/C/#suite-parameters
observer_options = ObserverOptions({ # is (inherited from) a dictionary
'algorithm_info': '"An Adaptive IBEA algorithm"', # CHANGE/INCOMMENT THIS!
# 'algorithm_name': '', # default already provided from SOLVER name
# 'result_folder': '', # default already provided from several global vars
})
######################### END CHANGE HERE ####################################
# ===============================================
# run (main)
# ===============================================
def main(budget=budget,
max_runs=max_runs,
current_batch=current_batch,
number_of_batches=number_of_batches):
"""Initialize suite and observer, then benchmark solver by calling
``batch_loop(SOLVER, suite, observer, budget,...``
"""
suite = Suite(suite_name, suite_instance, suite_options)