def main():
""" Main function. """
# Get problem parameters
worker_manager = SyntheticWorkerManager(NUM_WORKERS,
time_distro='halfnormal')
_, func_caller, mutation_op, init_points, init_vals = get_opt_problem(
NN_TYPE)
print('Best init value: %0.5f' % (max(init_vals)))
for method in METHODS:
print(
'Method: %s =========================================================='
% (method))
worker_manager.reset()
# Iterate through each method
if method in ['GA', 'randGA']:
is_rand = method == 'randGA'
options, reporter = get_options_and_reporter(
method, init_points, init_vals)
ga_optimiser.ga_optimise_from_args(func_caller,
worker_manager,
CAPITAL,
'asy',
mutation_op,
is_rand=is_rand,
options=options,
reporter=reporter)
# Wrap up method
print('')
def _ga_maximise(self,
obj,
num_evals,
mutation_op,
init_pool,
init_pool_vals=None,
expects_inputs_to_be_iterable=True):
""" Maximise with genetic algorithms.
if expects_inputs_as_list is True it means the function expects the inputs to
be iterable by default.
"""
# Prep necessary variables
obj, options, reporter, mutation_op = self._get_ga_optimiser_args(
obj, num_evals, mutation_op, init_pool, init_pool_vals,
expects_inputs_to_be_iterable)
worker_manager = SyntheticWorkerManager(1, time_distro='const')
func_caller = FunctionCaller(obj, self)
opt_val, opt_pt, _ = ga_optimise_from_args(func_caller,
worker_manager,
num_evals,
'asy',
mutation_op,
options=options,
reporter=reporter)
return opt_val, opt_pt
def main():
""" Main function. """
# Obtain the search space
nn_domain = get_nn_domain_from_constraints('cnn', MAX_NUM_LAYERS,
MIN_NUM_LAYERS, MAX_MASS,
MIN_MASS, MAX_IN_DEGREE,
MAX_OUT_DEGREE, MAX_NUM_EDGES,
MAX_NUM_UNITS_PER_LAYER,
MIN_NUM_UNITS_PER_LAYER)
# Obtain a worker manager: A worker manager (defined in opt/worker_manager.py) is used
# to manage (possibly) multiple workers. For a synthetic experiment, we will use a
# synthetic worker manager with 1 worker.
worker_manager = SyntheticWorkerManager(1)
# Obtain a function caller: A function_caller is used to evaluate a function defined on
# neural network architectures. Here, we have obtained a function_caller from a
# synthetic function, but for real experiments, you might have to write your own caller.
# See the MLP/CNN demos for an example.
func_caller = FunctionCaller(cnn_syn_func1, nn_domain)
# Finally, specify the budget. In this case, it will be just the number of evaluations.
budget = 20
# Run nasbot
opt_val, opt_nn, _ = nasbot.nasbot(func_caller, worker_manager, budget)
# Print the optimal value and visualise the best network.
print '\nOptimum value found: ', opt_val
print 'Optimal network visualised in syn_opt_network.eps.'
visualise_nn(opt_nn, 'syn_opt_network')
def test_rand_optimisation_synchronous(self):
""" Tests optimisation of a single point. """
self.report('Testing NNRandomBandit with 4 synchronous workers.')
worker_manager = SyntheticWorkerManager(4, time_distro='halfnormal')
func_caller = FunctionCaller(cnn_syn_func1, self.cnn_domain)
options, options_clone, reporter, _, _ = self._get_optimiser_args('cnn')
opt_val, opt_pt, history = nasbot.nnrandbandit_from_func_caller(
func_caller, worker_manager, 5, 'syn',
options=options, reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options, options_clone)
self.report('')
def test_instantiation(self):
""" Test Creation of NNGPBandit object. """
self.report('Testing Random Optimiser instantiation.')
func_caller = FunctionCaller(cnn_syn_func1, self.cnn_domain)
worker_manager = SyntheticWorkerManager(1, time_distro='const')
optimiser = nasbot.NNRandomBandit(func_caller,
worker_manager, reporter='silent')
for attr in dir(optimiser):
if not attr.startswith('_'):
self.report('optimiser.%s = %s'%(attr, str(getattr(optimiser, attr))),
'test_result')
def test_instantiation(self):
""" Test creation of object. """
self.report('Testing GA Optimiser instantiation.')
func_caller = FunctionCaller(cnn_syn_func1, self.nn_domain)
worker_manager = SyntheticWorkerManager(1, time_distro='const')
optimiser = ga_optimiser.GAOptimiser(func_caller,
worker_manager,
self.cnn_mutation_op,
reporter='silent')
self.report('Instantiated GAOptimiser object.')
for attr in dir(optimiser):
if not attr.startswith('_'):
self.report(
'optimiser.%s = %s' %
(attr, str(getattr(optimiser, attr))), 'test_result')
def test_nasbot_optimisation_asynchronous(self):
""" Tests optimisation of a single point. """
self.report('Testing NASBOT with 4 asynchronous workers.')
worker_manager = SyntheticWorkerManager(4, time_distro='halfnormal')
func_caller = FunctionCaller(mlp_syn_func1, self.mlp_domain)
tp_comp = get_tp_comp('mlp-reg')
options, options_clone, reporter, _, _ = self._get_optimiser_args(
'mlp-reg')
opt_val, opt_pt, history = nasbot.nasbot(func_caller,
worker_manager,
5,
tp_comp,
options=options,
reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options,
options_clone)
self.report('')
def test_nasbot_optimisation_single(self):
""" Tests optimisation with a single worker. """
self.report('Testing NASBOT with a single worker.')
worker_manager = SyntheticWorkerManager(1, time_distro='const')
func_caller = FunctionCaller(cnn_syn_func1, self.cnn_domain)
tp_comp = get_tp_comp('cnn')
options, options_clone, reporter, _, _ = self._get_optimiser_args(
'cnn')
opt_val, opt_pt, history = nasbot.nasbot(func_caller,
worker_manager,
10,
tp_comp,
options=options,
reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options,
options_clone)
self.report('')
def test_optimisation_asynchronous(self):
""" Test optimisation. """
self.report('Testing GA Optimiser with four workers asynchronously.')
worker_manager = SyntheticWorkerManager(4, time_distro='halfnormal')
func_caller = FunctionCaller(mlp_syn_func1, self.nn_domain)
options, options_clone, reporter, _, mutation_op = self._get_optimiser_args(
'mlp')
opt_val, opt_pt, history = ga_optimiser.ga_optimise_from_args(
func_caller,
worker_manager,
20,
'asy',
mutation_op,
options=options,
reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options,
options_clone)
self.report('')
def test_ga_optimisation_single(self):
""" Test optimisation. """
self.report('Testing GA Optimiser with just one worker.')
worker_manager = SyntheticWorkerManager(1, time_distro='const')
func_caller = FunctionCaller(cnn_syn_func1, self.nn_domain)
options, options_clone, reporter, _, mutation_op = self._get_optimiser_args(
'cnn')
opt_val, opt_pt, history = ga_optimiser.ga_optimise_from_args(
func_caller,
worker_manager,
20,
'asy',
mutation_op,
options=options,
reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options,
options_clone)
self.report('')
def setUp(self):
""" Set up. """
self.func_caller = get_syn_function_caller_from_name('Hartmann6')
self.worker_manager_1 = SyntheticWorkerManager(1)
self.worker_manager_4 = SyntheticWorkerManager(4, time_distro='halfnormal')