def get_stats(individuals, end=False):
"""
Generate the statistics for an evolutionary run. Save statistics to
utilities.trackers.stats_list. Print statistics. Save fitness plot
information.
:param individuals: A population of individuals for which to generate
statistics.
:param end: Boolean flag for indicating the end of an evolutionary run.
:return: Nothing.
"""
if hasattr(params['FITNESS_FUNCTION'], 'multi_objective'):
# Multiple objective optimisation is being used.
# Remove fitness stats from the stats dictionary.
stats.pop('best_fitness', None)
stats.pop('ave_fitness', None)
# Update stats.
get_moo_stats(individuals, end)
else:
# Single objective optimisation is being used.
get_soo_stats(individuals, end)
if params['SAVE_STATE'] and not params['DEBUG'] and \
stats['gen'] % params['SAVE_STATE_STEP'] == 0:
# Save the state of the current evolutionary run.
create_state(individuals)
def get_stats(individuals, end=False):
"""
Generate the statistics for an evolutionary run. Save statistics to
utilities.trackers.stats_list. Print statistics. Save fitness plot
information.
:param individuals: A population of individuals for which to generate
statistics.
:param end: Boolean flag for indicating the end of an evolutionary run.
:return: Nothing.
"""
if hasattr(params['FITNESS_FUNCTION'], 'multi_objective'):
# Multiple objective optimisation is being used.
# Remove fitness stats from the stats dictionary.
stats.pop('best_fitness', None)
stats.pop('ave_fitness', None)
# Update stats.
get_moo_stats(individuals, end)
else:
# Single objective optimisation is being used.
get_soo_stats(individuals, end)
if params['SAVE_STATE'] and not params['DEBUG'] and \
stats['gen'] % params['SAVE_STATE_STEP'] == 0:
# Save the state of the current evolutionary run.
create_state(individuals)
def get_stats(individuals, end=False):
"""
Generate the statistics for an evolutionary run. Save statistics to
utilities.trackers.stats_list. Print statistics. Save fitness plot
information.
:param individuals: A population of individuals for which to generate
statistics.
:param end: Boolean flag for indicating the end of an evolutionary run.
:return: Nothing.
"""
# Find the Pareto front from the population and convert in a
# *pareto_front* object
non_dominated, dominated = first_pareto_front(individuals)
pf_pop = ParetoFront(non_dominated)
# if not trackers.best_ever or pf_pop > trackers.best_ever:
# Save best individual in trackers.best_ever.
trackers.best_ever = pf_pop
if end or params['VERBOSE'] or not params['DEBUG']:
# Update all stats.
update_stats(individuals, end)
# Print statistics
if params['VERBOSE'] and not end:
print_generation_stats()
elif not params['SILENT']:
# Print simple display output.
perc = stats['gen'] / (params['GENERATIONS'] + 1) * 100
stdout.write("Evolution: %d%% complete\r" % perc)
stdout.flush()
# Generate test fitness on regression problems
if hasattr(params['FITNESS_FUNCTION'], "training_test") and end:
trackers.best_ever.training_fitness = copy(trackers.best_ever.fitness)
test_pf = []
for ind in trackers.best_ever.pf_solutions:
test_pf.append(params['FITNESS_FUNCTION'](ind, dist='test'))
trackers.best_ever.test_fitness = TestFitness(test_pf)
# params['FITNESS_FUNCTION'](trackers.best_ever, dist='test')
trackers.best_ever.fitness = trackers.best_ever.training_fitness
# Save stats to list.
if params['VERBOSE'] or (not params['DEBUG'] and not end):
trackers.stats_list.append(copy(stats))
# Save stats to file.
if not params['DEBUG']:
if stats['gen'] == 0:
save_stats_headers(stats)
save_stats_to_file(stats, end)
if params['SAVE_ALL']:
save_best_ind_to_file(stats, end, stats['gen'])
elif params['VERBOSE'] or end:
save_best_ind_to_file(stats, end, "best")
if end and not params['SILENT']:
print_final_stats()
if params['SAVE_STATE'] and not params['DEBUG'] and \
stats['gen'] % params['SAVE_STATE_STEP'] == 0:
# Save the state of the current evolutionary run.
create_state(individuals)
