DEHB = dehbs[args.version]
# Initializing DEHB object
dehb = DEHB(cs=cs,
dimensions=dimensions,
f=f,
strategy=args.strategy,
mutation_factor=args.mutation_factor,
crossover_prob=args.crossover_prob,
eta=args.eta,
min_budget=min_budget,
max_budget=max_budget,
generations=args.gens)
# Helper DE object for vector to config mapping
de = DE(cs=cs, b=b, f=f)
if args.runs is None: # for a single run
if not args.fix_seed:
np.random.seed(0)
# Running DE iterations
traj, runtime, history = dehb.run(iterations=args.iter,
verbose=args.verbose)
valid_scores, test_scores = calc_regrets(history)
save_json(valid_scores, test_scores, runtime, output_path, args.run_id)
else: # for multiple runs
for run_id, _ in enumerate(range(args.runs), start=args.run_start):
if not args.fix_seed:
np.random.seed(run_id)
'adult': (9, 243),
'higgs': (9, 243),
'letter': (3, 81),
'mnist': (9, 243),
'optdigits': (1, 27) ,
'poker': (81, 2187),
}
min_budget, max_budget = budgets[args.dataset]
# Initializing DE object
if args. async is None:
de = DE(cs=cs,
dimensions=dimensions,
f=f,
pop_size=args.pop_size,
mutation_factor=args.mutation_factor,
crossover_prob=args.crossover_prob,
strategy=args.strategy,
budget=max_budget)
else:
de = AsyncDE(cs=cs,
dimensions=dimensions,
f=f,
pop_size=args.pop_size,
mutation_factor=args.mutation_factor,
crossover_prob=args.crossover_prob,
strategy=args.strategy,
budget=max_budget,
async_strategy=args. async)
if args.runs is None: # for a single run
# Initializing DEHB object
dehb = DEHB(cs=cs,
dimensions=dimensions,
f=f,
strategy=args.strategy,
mutation_factor=args.mutation_factor,
crossover_prob=args.crossover_prob,
eta=args.eta,
min_budget=min_budget,
max_budget=max_budget,
generations=args.gens)
# Initializing DE object
de = DE(cs=cs,
dimensions=dimensions,
f=f,
pop_size=10,
mutation_factor=args.mutation_factor,
crossover_prob=args.crossover_prob,
strategy=args.strategy,
budget=args.max_budget)
if args.runs is None: # for a single run
if not args.fix_seed:
np.random.seed(args.run_id)
# Running DE iterations
traj, runtime, history = dehb.run(iterations=args.iter,
verbose=args.verbose)
fh = open(os.path.join(output_path, 'run_{}.json'.format(args.run_id)),
'w')
json.dump(calc_test_scores(runtime, history), fh)
fh.close()
else: # for multiple runs