def get_soo_stats(individuals, end):
"""
Generate the statistics for an evolutionary run with a single objective.
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.
"""
# Get best individual.
best = max(individuals)
if not trackers.best_ever or best > trackers.best_ever:
# Save best individual in trackers.best_ever.
trackers.best_ever = best
if end or params['VERBOSE'] or not params['DEBUG']:
# Update all stats.
update_stats(individuals, end)
# Save fitness plot information
if params['SAVE_PLOTS'] and not params['DEBUG']:
if not end:
trackers.best_fitness_list.append(trackers.best_ever.fitness)
if params['VERBOSE'] or end:
save_plot_from_data(trackers.best_fitness_list, "best_fitness")
# 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:
# Save training fitness.
trackers.best_ever.training_fitness = copy(trackers.best_ever.fitness)
# Evaluate test fitness.
trackers.best_ever.test_fitness = params['FITNESS_FUNCTION'](
trackers.best_ever, dist='test')
# Set main fitness as training fitness.
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, trackers.best_ever, end, stats['gen'])
elif params['VERBOSE'] or end:
save_best_ind_to_file(stats, trackers.best_ever, end)
if end and not params['SILENT']:
print_final_stats()
def get_moo_stats(individuals, end):
"""
Generate the statistics for an evolutionary run with multiple objectives.
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.
"""
# Compute the pareto front metrics for the population.
pareto = compute_pareto_metrics(individuals)
# Save first front in trackers. Sort arbitrarily along first objective.
trackers.best_ever = sorted(pareto.fronts[0], key=lambda x: x.fitness[0])
# Store stats about pareto fronts.
stats['pareto_fronts'] = len(pareto.fronts)
stats['first_front'] = len(pareto.fronts[0])
if end or params['VERBOSE'] or not params['DEBUG']:
# Update all stats.
update_stats(individuals, end)
# Save fitness plot information
if params['SAVE_PLOTS'] and not params['DEBUG']:
# Initialise empty array for fitnesses for all inds on first pareto
# front.
all_arr = [[] for _ in range(params['FITNESS_FUNCTION'].num_obj)]
# Generate array of fitness values.
fitness_array = [ind.fitness for ind in trackers.best_ever]
# Add paired fitnesses to array for graphing.
for fit in fitness_array:
for o in range(params['FITNESS_FUNCTION'].num_obj):
all_arr[o].append(fit[o])
if not end:
trackers.first_pareto_list.append(all_arr)
# Append empty array to best fitness list.
trackers.best_fitness_list.append([])
# Get best fitness for each objective.
for o, ff in \
enumerate(params['FITNESS_FUNCTION'].fitness_functions):
# Get sorted list of all fitness values for objective "o"
fits = sorted(all_arr[o], reverse=ff.maximise)
# Append best fitness to trackers list.
trackers.best_fitness_list[-1].append(fits[0])
if params['VERBOSE'] or end:
# Plot best fitness for each objective.
for o, ff in \
enumerate(params['FITNESS_FUNCTION'].fitness_functions):
to_plot = [i[o] for i in trackers.best_fitness_list]
# Plot fitness data for objective o.
plotname = ff.__class__.__name__ + str(o)
save_plot_from_data(to_plot, plotname)
# TODO: PonyGE2 can currently only plot moo problems with 2 objectives.
# Check that the number of fitness objectives is not greater than 2
if params['FITNESS_FUNCTION'].num_obj > 2:
s = "stats.stats.get_moo_stats\n" \
"Warning: Plotting of more than 2 simultaneous " \
"objectives is not yet enabled in PonyGE2."
print(s)
else:
save_pareto_fitness_plot()
# Print statistics
if params['VERBOSE'] and not end:
print_generation_stats()
print_first_front_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:
for ind in trackers.best_ever:
# Iterate over all individuals in the first front.
# Save training fitness.
ind.training_fitness = copy(ind.fitness)
# Evaluate test fitness.
ind.test_fitness = params['FITNESS_FUNCTION'](ind, dist='test')
# Set main fitness as training fitness.
ind.fitness = ind.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_first_front_to_file(stats, end, stats['gen'])
elif params['VERBOSE'] or end:
save_first_front_to_file(stats, end)
if end and not params['SILENT']:
print_final_moo_stats()
def get_soo_stats(individuals, end):
"""
Generate the statistics for an evolutionary run with a single objective.
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.
"""
# Get best individual.
best = max(individuals)
if not trackers.best_ever or best > trackers.best_ever:
# Save best individual in trackers.best_ever.
trackers.best_ever = best
if end or params['VERBOSE'] or not params['DEBUG']:
# Update all stats.
update_stats(individuals, end)
# Save fitness plot information
if params['SAVE_PLOTS'] and not params['DEBUG']:
if not end:
trackers.best_fitness_list.append(trackers.best_ever.fitness)
if params['VERBOSE'] or end:
save_plot_from_data(trackers.best_fitness_list, "best_fitness")
# 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:
# Save training fitness.
trackers.best_ever.training_fitness = copy(trackers.best_ever.fitness)
# Evaluate test fitness.
trackers.best_ever.test_fitness = params['FITNESS_FUNCTION'](
trackers.best_ever, dist='test')
# Set main fitness as training fitness.
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, trackers.best_ever, end, stats['gen'])
elif params['VERBOSE'] or end:
save_best_ind_to_file(stats, trackers.best_ever, end)
if end and not params['SILENT']:
print_final_stats()
def get_moo_stats(individuals, end):
"""
Generate the statistics for an evolutionary run with multiple objectives.
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.
"""
# Compute the pareto front metrics for the population.
pareto = compute_pareto_metrics(individuals)
# Save first front in trackers. Sort arbitrarily along first objective.
trackers.best_ever = sorted(pareto.fronts[0], key=lambda x: x.fitness[0])
# Store stats about pareto fronts.
stats['pareto_fronts'] = len(pareto.fronts)
stats['first_front'] = len(pareto.fronts[0])
if end or params['VERBOSE'] or not params['DEBUG']:
# Update all stats.
update_stats(individuals, end)
# Save fitness plot information
if params['SAVE_PLOTS'] and not params['DEBUG']:
# Initialise empty array for fitnesses for all inds on first pareto
# front.
all_arr = [[] for _ in range(params['FITNESS_FUNCTION'].num_obj)]
# Generate array of fitness values.
fitness_array = [ind.fitness for ind in trackers.best_ever]
# Add paired fitnesses to array for graphing.
for fit in fitness_array:
for o in range(params['FITNESS_FUNCTION'].num_obj):
all_arr[o].append(fit[o])
if not end:
trackers.first_pareto_list.append(all_arr)
# Append empty array to best fitness list.
trackers.best_fitness_list.append([])
# Get best fitness for each objective.
for o, ff in \
enumerate(params['FITNESS_FUNCTION'].fitness_functions):
# Get sorted list of all fitness values for objective "o"
fits = sorted(all_arr[o], reverse=ff.maximise)
# Append best fitness to trackers list.
trackers.best_fitness_list[-1].append(fits[0])
if params['VERBOSE'] or end:
# Plot best fitness for each objective.
for o, ff in \
enumerate(params['FITNESS_FUNCTION'].fitness_functions):
to_plot = [i[o] for i in trackers.best_fitness_list]
# Plot fitness data for objective o.
plotname = ff.__class__.__name__ + str(o)
save_plot_from_data(to_plot, plotname)
# TODO: PonyGE2 can currently only plot moo problems with 2 objectives.
# Check that the number of fitness objectives is not greater than 2
if params['FITNESS_FUNCTION'].num_obj > 2:
s = "stats.stats.get_moo_stats\n" \
"Warning: Plotting of more than 2 simultaneous " \
"objectives is not yet enabled in PonyGE2."
print(s)
else:
save_pareto_fitness_plot()
# Print statistics
if params['VERBOSE'] and not end:
print_generation_stats()
print_first_front_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:
for ind in trackers.best_ever:
# Iterate over all individuals in the first front.
# Save training fitness.
ind.training_fitness = copy(ind.fitness)
# Evaluate test fitness.
ind.test_fitness = params['FITNESS_FUNCTION'](ind, dist='test')
# Set main fitness as training fitness.
ind.fitness = ind.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_first_front_to_file(stats, end, stats['gen'])
elif params['VERBOSE'] or end:
save_first_front_to_file(stats, end)
if end and not params['SILENT']:
print_final_moo_stats()
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)
def __get_stats__(individuals, end):
# Get best individual.
best = max(individuals)
if not trackers.best_ever or best > trackers.best_ever:
# Save best individual in trackers.best_ever.
trackers.best_ever = best
if end or params['VERBOSE'] or not params['DEBUG']:
# Update all stats.
update_stats(individuals, end)
# Save fitness plot information
if params['SAVE_PLOTS'] and not params['DEBUG']:
if not end:
trackers.best_fitness_list.append(trackers.best_ever.fitness)
if params['VERBOSE'] or end:
save_plot_from_data(trackers.best_fitness_list, "best_fitness")
# 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:
# Save training fitness.
trackers.best_ever.training_fitness = copy(trackers.best_ever.fitness)
# Evaluate test fitness.
trackers.best_ever.test_fitness = params['FITNESS_FUNCTION'](
trackers.best_ever, dist='test')
# Set main fitness as training fitness.
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, trackers.best_ever, end, stats['gen'])
elif params['VERBOSE'] or end:
save_best_ind_to_file(stats, trackers.best_ever, end)
# if end and not params['SILENT']:
# print_final_stats()
return stats
