nb_init=0,
eval_init=False)
bo_args['acquisition_weight'] = 20
# ======================== /
# Create runners
# ======================== /
df = pd.DataFrame()
runner_dict = {}
for trial in range(NB_TRIALS):
for opt, init, itt in zip(NB_OPT_VEC, nb_init_vec, nb_iter_vec):
bo_args['nb_iter'] = itt * len(h_config) + init
bo_args['initial_design_numdata'] = init
runner = op.ParallelRunner(cstvec * opt,
op.MethodBO,
optimizer_args=bo_args,
share_init=True,
method='shared')
runner_dict[(opt, trial)] = [runner, itt]
#%% RUN OPTIMISER
# ======================== /
# Init runners
# ======================== /
print('Init runners')
t = time.time()
Batch = ut.Batch(inst)
for run, itt in runner_dict.values():
bo_args = run.optimizer_args
x_init = ut.gen_params_on_subspace(bo_args,
nb_ignore=12,
domain = np.array([(-NB_DELTA, NB_DELTA) for i in range(nb_params)])
domain += np.array([x_sol,x_sol]).transpose()
bo_args = ut.gen_default_argsbo(f=lambda x: .5,
domain=domain,
nb_init=NB_INIT,
eval_init=False)
bo_args['nb_iter'] = NB_ITER + NB_INIT
bo_args['acquisition_weight'] = 5
bo_args['acquisition_type'] = 'EI'
bo_args['initial_design_type'] = 'latin'
# ======================== /
# Init runner classes with different methods
# ======================== /
runner = op.ParallelRunner([cst],
op.MethodBO,
optimizer_args = bo_args,
share_init = True,
method = 'independent')
# ========================= /
# And initilization:
# ========================= /
Batch = ut.Batch(inst)
runner.next_evaluation_circuits()
print('Circs to init: {}'.format(len(runner.circs_to_exec)))
Batch.submit(runner)
Batch.execute()
runner._last_results_obj = Batch._last_results_obj
runner.init_optimisers()
# ======================== /
# Init runner (parallel runner is overkill here)
# ======================== /
df = pd.DataFrame()
np.random.seed(int(time.time()))
for trial in range(NB_TRIALS):
this_data = []
for opt, init, itt in zip(NB_OPT_VEC, nb_init_vec, nb_iter_vec):
bo_args['nb_iter'] = itt
bo_args['initial_design_numdata'] = init
runner = op.ParallelRunner([xy_cost]*opt,
op.MethodBO,
optimizer_args = bo_args,
share_init = False,
method = 'shared')
# # ========================= /
# # And initilization:
# ========================= /
runner.next_evaluation_circuits()
Batch.submit_exec_res(runner)
runner.init_optimisers()
# Run optimizer step by step
for ii in range(itt):
runner.next_evaluation_circuits()
Batch.submit_exec_res(runner)
bo_args['nb_iter'] = NB_ITER * len(cost_list) + NB_INIT
bo_args['acquisition_weight'] = 4
# ======================== /
# Init runner classes with different methods
# ======================== /
opt_bo = op.MethodBO
opt_spsa = op.MethodSPSA
bo_args_list = [copy.deepcopy(bo_args) for ii in range(len(cost_list))]
runner = op.ParallelRunner(cost_list,
opt_bo,
optimizer_args=bo_args_list,
share_init=True,
method='independent')
x_new = [[x_sol, x_sol], [x_sol]]
# ========================= /
# Testing circ generation and output formainting
# ========================= /
if len(runner.optim_list) == 2:
Batch = ut.Batch(inst)
runner.next_evaluation_circuits()
print(runner.method)
print(runner._last_x_new)
print('---------------------------------------------')
runner._gen_circuits_from_params(x_new, inplace=True)