def __init__(self, trial_no, run_name, fitness, configuration, controller,
run_results_folder_path):
Thread.__init__(self)
self.name = '{} Trial {}'.format(run_name, trial_no)
self.run_name = run_name
self.trial_no = trial_no
self.fitness = fitness
self.configuration = configuration
self.controller = controller
self.run_results_folder_path = run_results_folder_path
self.status = 'Running' # TODO - BAD
# True if the user has selected to pause the trial
self.wait = False
self.graph_dictionary = {
'rerendering': False,
'graph_title': configuration.graph_title,
'graph_names': configuration.graph_names,
'all_graph_dicts': configuration.all_graph_dicts
}
for name in configuration.graph_names:
self.graph_dictionary['all_graph_dicts'][name]['generate'] = True
if configuration.plot_view != 'special':
self.plot_view = Plot_View
self.enable_traceback = configuration.enable_traceback
self.GEN = configuration.max_iter
self.generations_array = []
self.max_fitness = configuration.max_fitness
self.best_fitness_array = []
self.clf = None
self.gp_training_fitness = None
self.gp_training_set = None
if configuration.surrogate_type == "dummy":
self.surrogate_model = DummySurrogateModel(fitness, configuration,
self.controller)
else:
self.surrogate_model = ProperSurrogateModel(fitness,
configuration,
self.controller)
# Contains all the counter variables that may be used for visualization
self.counter_dictionary = {}
self.counter_dictionary['g'] = 1
self.counter_dictionary['fit'] = 1
# The last counter value to be visualized, 0 means none
self.latest_counter_plot = 0
self.controller.register_trial(self)
self.backup = self.create_backup_manager()
class Trial(Thread):
def __init__(self, trial_no, run_name, fitness, configuration, controller,
run_results_folder_path):
Thread.__init__(self)
self.name = '{} Trial {}'.format(run_name, trial_no)
self.run_name = run_name
self.trial_no = trial_no
self.fitness = fitness
self.configuration = configuration
self.controller = controller
self.run_results_folder_path = run_results_folder_path
self.status = 'Running' # TODO - BAD
# True if the user has selected to pause the trial
self.wait = False
self.graph_dictionary = {
'rerendering': False,
'graph_title': configuration.graph_title,
'graph_names': configuration.graph_names,
'all_graph_dicts': configuration.all_graph_dicts
}
for name in configuration.graph_names:
self.graph_dictionary['all_graph_dicts'][name]['generate'] = True
if configuration.plot_view != 'special':
self.plot_view = Plot_View
self.enable_traceback = configuration.enable_traceback
self.GEN = configuration.max_iter
self.generations_array = []
self.max_fitness = configuration.max_fitness
self.best_fitness_array = []
self.clf = None
self.gp_training_fitness = None
self.gp_training_set = None
if configuration.surrogate_type == "dummy":
self.surrogate_model = DummySurrogateModel(fitness, configuration,
self.controller)
else:
self.surrogate_model = ProperSurrogateModel(fitness,
configuration,
self.controller)
# Contains all the counter variables that may be used for visualization
self.counter_dictionary = {}
self.counter_dictionary['g'] = 1
self.counter_dictionary['fit'] = 1
# The last counter value to be visualized, 0 means none
self.latest_counter_plot = 0
self.controller.register_trial(self)
self.backup = self.create_backup_manager()
def initialise(self):
"""
Initialises the trial and returns True if everything went OK,
False otherwise.
"""
self.results_folder = self._create_results_folder()
if not self.results_folder:
# Results folder could not be created
return False
self.run_initialize()
self.best = None
self.fitness_evaluated = False
self.gp = None
self.model_failed = False
self.model_retrained = True
#TODO - maybe remove this boolean
self.new_best_over_iteration = False
self.M_best = self.fitness.worst_value
self.population = None
self.svc_training_labels = None
self.svc_training_set = None
self.initialize_population()
return True
def run(self):
self.start_time = datetime.now().strftime('%d-%m-%Y %H:%M:%S')
self.view_update()
logging.info('{} started'.format(self.get_name()))
logging.info('Run prepared... executing')
while self.counter_dictionary['g'] < self.GEN + 1:
logging.info('[{}] Generation {}'.format(
self.get_name(), self.counter_dictionary['g']))
# Roll population
first_pop = self.population.pop(0)
self.population.append(first_pop)
# Initialise termination check
self.check = False
if self.counter_dictionary['fit'] > self.max_fitness:
logging.info('Fitness counter exceeded the limit... exiting')
break
# Train surrogate model
while not self.surrogate_model.train(self.population):
logging.info('Re-initializing population')
self.initialize_population()
code, mean, variance = \
self.surrogate_model.model_particles(self.population)
self.post_model_filter(self.population, code, mean, variance)
#logging.info(self.population[0].fitness.values)
# Iteration of meta-heuristic
self.meta_iterate()
self.filter_population()
# TODO: termination condition
#TODO: This will be done after the presentation
'''
if self.counter_dictionary['g'] % self.configuration.M == 0:
if self.M_best == self.best:
#Return best?
logging.info('New best was found after M')
self.M_best = self.fitness.worst_value;
pass
else:
logging.info('Perturbing things')
#Return perturbation?
pass
'''
# Wait until the user unpauses the trial.
while self.wait:
time.sleep(0)
self.increment_counter('g')
self.view_update()
self.status = 'Finished'
self.view_update()
logging.info('{} finished'.format(self.get_name()))
def _create_results_folder(self):
"""
Creates a folder used for storing results.
Returns a results folder path or None if it could not be created.
"""
path = '{}/trial-{}'.format(self.run_results_folder_path,
self.trial_no)
try:
os.makedirs(path)
return path
except OSError, e:
# Folder already exists
return path
except Exception, e:
logging.error('Could not create folder: {}, aborting'.format(path),
exc_info=sys.exc_info())
return None
