def run_bo_cost(target_f, pbounds, n_iter=30, init_params=None, acq_type='div', cost_max = 0.0):
print
bo_target_f = bo_target(target_f)
# cost = virtual_cost
bo = BayesianOptimization(bo_target_f, pbounds, if_cost=True, cost_function=virtual_cost, cost_max=cost_max)
init_points = 2
if init_params is not None:
bo.init_with_params(init_params)
bo.maximize(n_iter=n_iter-len(init_params),acq_type=acq_type)
else:
bo.maximize(init_points=init_points, n_iter=n_iter-init_points,acq_type=acq_type)
return [accum_cost(bo), best_value(bo.space.Y), bo]
def multi_exps():
n_iter = 25
n_runs = 20
report_interval = 5
init_points = 1
_bo_target_f = bo_target(t_f)
_bo = BayesianOptimization(_bo_target_f, pbounds)
# _init_params = _bo.space.random_points(init_points)
_init_params = [_bo.space.random_points(init_points) for _ in range(n_runs)]
# x_bo_cost, y_bo_cost, bo_cost = run_bo_cost(branin, pbounds, n_iter, _init_params)
# x_bo, y_bo, bo = run_bo(branin, pbounds, n_iter, _init_params)
bo_runs = [run_bo(t_f, pbounds, n_iter, _init_params[i]) for i in range(n_runs)]
bo_cost_runs = [run_bo_cost(t_f, pbounds, n_iter, _init_params[i], acq_type='div') for i in range(n_runs)]
bo_cost_my_runs = [run_bo_cost(t_f, pbounds, n_iter, _init_params[i], acq_type='others') for i in range(n_runs)]
plot_start = init_points
def gen_line(runs):
x_max = max([max(run[0]) for run in runs])
x_start = max([min(run[0]) for run in runs])
xs = np.arange(x_start, x_max, report_interval)
def cal_max_avg(x):
max_indexes = [len(list(filter(lambda li: li <= x, run[0]))) for run in runs]
max_cals = [run[1][max_index - 1] for max_index, run in zip(max_indexes, runs)]
return np.average(max_cals)
ys = np.array(list(map(cal_max_avg, xs)))
return xs, ys
bo_xs, bo_ys = gen_line(bo_runs)
bo_cost_xs, bo_cost_ys = gen_line(bo_cost_runs)
bo_cost_my_xs, bo_cost_my_ys = gen_line(bo_cost_my_runs)
plt.plot(bo_xs, bo_ys, marker="|", label="bo")
plt.plot(bo_cost_xs, bo_cost_ys, marker="|", label="bo_cost_aware")
plt.plot(bo_cost_my_xs, bo_cost_my_ys, marker="|", label="bo_cost_aware_my")
#plt.plot(bo_xs, bo_ys, label="bo")
#plt.plot(bo_cost_xs, bo_cost_ys, label="bo_cost_aware")
plt.legend()
plt.savefig('./cost_aware_BO_compare_parabola.png')
plt.show()
def main():
n_iter = 20
n_runs = 1
x = []
y = []
x_cost_div = []
y_cost_div = []
x_cost_my = []
y_cost_my = []
for i in range(n_runs):
print('\nrun {}\n'.format(i + 1))
init_points = 1
_bo_target_f = bo_target(t_f)
_bo = BayesianOptimization(_bo_target_f, pbounds)
_init_params = _bo.space.random_points(init_points)
#x_ex, y_ex = run_ex(t_f, n_iter)
x_bo, y_bo, bo = run_bo(t_f,
pbounds,
n_iter,
_init_params,
cost_max=1000.0)
x_bo_cost_div, y_bo_cost_div, bo_cost_div = run_bo_cost(
t_f, pbounds, n_iter, _init_params, 'div', cost_max=1000.0)
x_bo_cost_divca, y_bo_cost_divca, bo_cost_divca = run_bo_cost(
t_f, pbounds, n_iter, _init_params, 'divca', cost_max=1000.0)
x_bo_cost_my, y_bo_cost_my, bo_cost_my = run_bo_cost(t_f,
pbounds,
n_iter,
_init_params,
'switch',
cost_max=1000.0)
'''
x.append(i+1)
y.append(y_bo[-1])
x_cost_div.append(i+1)
y_cost_div.append(y_bo_cost_div[-1])
x_cost_my.append(i+1)
y_cost_my.append(y_bo_cost_my[-1])
for j,_ in enumerate(x_bo):
if abs(y_bo[j] + 0.397887) <= 0.01:
x.append(i+1)
y.append(x_bo[j])
break
for j,_ in enumerate(x_bo_cost_div):
if abs(y_bo_cost_div[j] + 0.397887) <= 0.01:
x_cost_div.append(i+1)
y_cost_div.append(x_bo[j])
break
for j,_ in enumerate(x_bo_cost_my):
if abs(y_bo_cost_my[j] + 0.397887) <= 0.01:
x_cost_my.append(i+1)
y_cost_my.append(x_bo[j])
break
'''
plot_start = init_points
#plt.plot(x_ex[0:], y_ex[0:], marker="1", label="ex_search")
plt.plot(x_bo[0:], y_bo[0:], label="bo")
# plt.scatter(x_bo[plot_start:], bo.space.Y[plot_start:], marker="x", label="bo_tria")
plt.plot(x_bo_cost_div[0:],
y_bo_cost_div[0:],
label="bo_cost_aware_div")
plt.plot(x_bo_cost_divca[0:],
y_bo_cost_divca[0:],
label="bo_cost_aware_divca")
plt.plot(x_bo_cost_my[0:], y_bo_cost_my[0:], label="bo_cost_aware_my")
# plt.scatter(x_bo_cost[plot_start:], bo_cost.space.Y[plot_start:], marker="o", label="bo_cost_aware_trial")
# plt.ylim(-5, 0)
plt.legend()
plt.savefig('./result/svm_cost_aware_BO_compare_tf_{}.png'.format(i))
plt.close()
'''
def main():
n_iter = 50
n_runs = 10
x=[]
y=[]
x_cost_div=[]
y_cost_div=[]
x_cost_divca=[]
y_cost_divca=[]
x_cost_my=[]
y_cost_my=[]
for i in range(n_runs):
print('\nrun {}\n'.format(i+1))
init_points = 2
_bo_target_f = bo_target(t_f)
_bo = BayesianOptimization(_bo_target_f, pbounds)
_init_params = _bo.space.random_points(init_points)
#x_ex, y_ex = run_ex(t_f, n_iter)
x_bo, y_bo, bo = run_bo(t_f, pbounds, n_iter,_init_params, cost_max=4500.0)
x_bo_cost_div, y_bo_cost_div, bo_cost_div = run_bo_cost(t_f, pbounds, n_iter, _init_params, 'div', cost_max=4500.0)
x_bo_cost_divca, y_bo_cost_divca, bo_cost_divca = run_bo_cost(t_f, pbounds, n_iter, _init_params, 'divca', cost_max=4500.0)
x_bo_cost_my, y_bo_cost_my, bo_cost_my = run_bo_cost(t_f, pbounds, n_iter, _init_params, 'switch', cost_max=4500.0)
'''
x.append(i+1)
y.append(y_bo[-1])
x_cost_div.append(i+1)
y_cost_div.append(y_bo_cost_div[-1])
x_cost_my.append(i+1)
y_cost_my.append(y_bo_cost_my[-1])
'''
for j,_ in enumerate(x_bo):
if abs(y_bo[j]) <= 0.41:
x.append(i+1)
y.append(x_bo[j])
break
for j,_ in enumerate(x_bo_cost_div):
if abs(y_bo_cost_div[j]) <= 0.41:
x_cost_div.append(i+1)
y_cost_div.append(x_bo_cost_div[j])
break
for j,_ in enumerate(x_bo_cost_divca):
if abs(y_bo_cost_divca[j]) <= 0.41:
x_cost_divca.append(i+1)
y_cost_divca.append(x_bo_cost_divca[j])
break
for j,_ in enumerate(x_bo_cost_my):
if abs(y_bo_cost_my[j]) <= 0.41:
x_cost_my.append(i+1)
y_cost_my.append(x_bo_cost_my[j])
break
'''
plot_start = init_points
#plt.plot(x_ex[0:], y_ex[0:], marker="1", label="ex_search")
plt.plot(x_bo[0:], y_bo[0:], label="bo")
# plt.scatter(x_bo[plot_start:], bo.space.Y[plot_start:], marker="x", label="bo_tria")
plt.plot(x_bo_cost_div[0:], y_bo_cost_div[0:], label="bo_cost_aware_div")
plt.plot(x_bo_cost_divca[0:], y_bo_cost_divca[0:], label="bo_cost_aware_divca")
plt.plot(x_bo_cost_my[0:], y_bo_cost_my[0:], label="bo_cost_aware_my")
# plt.scatter(x_bo_cost[plot_start:], bo_cost.space.Y[plot_start:], marker="o", label="bo_cost_aware_trial")
# plt.ylim(-5, 0)
plt.legend()
plt.savefig('./result/branin_cost_aware_BO_compare_tf_{}.png'.format(i))
plt.close()
x1 = [i for i in range(len(x_bo)-1)]
x2 = x1
y1 = [x_bo_cost_my[i+1]-x_bo_cost_my[i] for i in range(len(x_bo)-1)]
y2 = [x_bo[i+1]-x_bo[i] for i in range(len(x_bo)-1)]
plt.plot(x1, y1, marker='+', label='bo_cost_aware')
plt.plot(x2, y2, marker='x', label='bo')
plt.legend()
plt.savefig('./result/branin_cost_aware_BO_compare_cost_{}.png'.format(i))
plt.close()
#plt.show()
'''
plt.plot(x[0:], y[0:], marker="x", label="bo")
# plt.scatter(x_bo[plot_start:], bo.space.Y[plot_start:], marker="x", label="bo_tria")
plt.plot(x_cost_div[0:], y_cost_div[0:], marker="+", label="bo_cost_aware_div")
plt.plot(x_cost_divca[0:], y_cost_divca[0:], marker="|", label="bo_cost_aware_divca")
plt.plot(x_cost_my[0:], y_cost_my[0:], marker="^", label="bo_cost_aware_my")
plt.legend()
plt.savefig('./result/branin_cost_aware_BO_compare_when_close_to_max_target_value.png')
plt.close()