def summary(self):
lg.msg(logging.INFO, 'Statistics')
summary = []
for p in self.problems:
if not self.settings['prb'][p]['enabled']:
continue
for b in self.benchmarks:
_new_benchmark = True
gbest_ft = {}
bdp = {} # Bounds diff pct
other = {}
for o in self.optimizers:
if not self.settings['opt'][o]['enabled']:
continue
for j in self.jobs:
if not (j.pid == p and j.bid == b and j.oid == o):
continue
if _new_benchmark:
lg.msg(logging.INFO, 'Summary for problem {} benchmark {}'.format(p, b))
_new_benchmark = False
gbest_ft[j.oid] = {}
gbest_ft[j.oid] = j.gft
bdp[j.oid] = {}
other[j.oid] = {}
other[j.oid]['avg_comp_time_s'] = j.avg_comp_time_s
other[j.oid]['budget'] = j.budget_total
other[j.oid]['budget_rem'] = j.budget
if j.iter_last_imp:
other[j.oid]['avg_iter_last_imp'] = int(statistics.mean(j.iter_last_imp))
else:
other[j.oid]['avg_iter_last_imp'] = 'n/a'
if other[j.oid]['avg_iter_last_imp'] != 'n/a':
other[j.oid]['budget_no_imp_pct'] = round(((j.budget_total - other[j.oid]['avg_iter_last_imp']) / j.budget_total) * 100, 2)
else:
other[j.oid]['budget_no_imp_pct'] = 'n/a'
if j.imp_count:
other[j.oid]['avg_imp_count'] = int(statistics.mean(j.imp_count))
else:
other[j.oid]['avg_imp_count'] = 'n/a'
if j.bid != 'na':
bdp[j.oid] = [j.pid_cls.ilb, j.pid_lb_diff_pct, j.pid_cls.iub, j.pid_ub_diff_pct]
else:
bdp[j.oid] = ['na'] * 4
# Only proceed for compiled stats for valid problem/benchmark/optimizer
if not gbest_ft:
continue
stats_summary = Stats.get_summary(gbest_ft)
format_spec = "{:>30}" * 16
cols = ['Optimizer', 'Min Fitness', 'Max Fitness', 'Avg Fitness', 'StDev', 'Wilcoxon', 'LB', 'LB Diff %',
'UB', 'UB Diff %', 'Avg Cts', 'Budget', 'Budget Rem', 'Avg Iter Last Imp', 'Budget No Imp %',
'Avg Imp Count']
summary.append(cols)
lg.msg(logging.INFO, format_spec.format(*cols))
for k, v in stats_summary.items():
lg.msg(logging.INFO, format_spec.format(str(k),
str(round(v['minf'], 3)),
str(round(v['maxf'], 3)),
str(v['mean']),
str(v['stdev']),
str(v['wts']),
str(bdp[k][0]),
str(bdp[k][1]),
str(bdp[k][2]),
str(bdp[k][3]),
str(round(other[k]['avg_comp_time_s'], 3)),
other[k]['budget'],
other[k]['budget_rem'],
other[k]['avg_iter_last_imp'],
other[k]['budget_no_imp_pct'],
other[k]['avg_imp_count']))
summary.append([str(k), str(v['minf']), str(v['maxf']), str(v['mean']), str(v['stdev']), str(v['wts']),
str(bdp[k][0]), str(bdp[k][1]), str(bdp[k][2]), str(bdp[k][3]),
str(round(other[k]['avg_comp_time_s'], 3)), other[k]['budget'], other[k]['budget_rem'],
other[k]['avg_iter_last_imp'], other[k]['budget_no_imp_pct'], other[k]['avg_imp_count']])
# Summary per problem
Helper.write_to_csv(summary, self.results_path + '/' + p + ' ' + b + ' problem summary.csv')
# Fitness trend for all optimizers per problem
filename = self.results_path + '/' + p + ' ' + b + ' all optimizers gbest fitness trend'
self.vis.fitness_trend_all_optimizers(gbest_ft, filename)
Helper.write_to_csv(gbest_ft, filename + '.csv')
