final_iter = [get_initial_state(seed) for seed in final_iter]
tmp_results = []
for initial_states in final_iter:
np.random.seed(1024)
result = evaluate_single_state(nimc,
initial_states,
nimc.k,
mult=10000)
tmp_results.append(result)
initial_states = final_iter[np.argmax(tmp_results)]
np.random.seed(1024)
result = evaluate_single_state(nimc,
initial_states,
nimc.k,
mult=250000)
print(result)
results.append(result)
print({
'results': results,
'num_queries': num_queries,
'original_results': original_results
})
savepklz(
{
'results': results,
'num_queries': num_queries,
'original_results': original_results
}, '../data/PlasmaLab_%s_exp%d.pklz' % (model, exp_id))
os.system('rm ' + tmp_model_name + ' ' + tmp_spec_name)
model = 'Slplatoon'
exp_id = int(sys.argv[1])
if __name__ == '__main__':
budget = 800000
outputs = []
rhomaxs = [0.95, 0.9, 0.8, 0.6, 0.4, 0.16, 0.01]
# run an experiment for each rho_max configuration
for rhomax in rhomaxs:
filename = 'data/HooVer_%s_budget%d_rhomax%f_exp%d.pklz'%(model, budget, rhomax, exp_id)
os.system('cd ../; python3 check.py --nRuns 1 --model %s --budget %d --rho_max %f --output %s --seed %d'%(model, budget, rhomax, filename, exp_id*1024))
outputs.append(loadpklz('../'+filename))
results = []
optimal_xs = [o['optimal_xs'][0] for o in outputs]
original_results = [o['optimal_values'][0] for o in outputs]
num_nodes = [o['num_nodes'][0] for o in outputs]
# Monte-Carlo estimation of the hitting probability (using 250k simulations)
nimc = models.__dict__[model]()
for initial_states in optimal_xs:
initial_states = initial_states.tolist()
np.random.seed(1024)
result = evaluate_single_state(nimc, initial_states, nimc.k, mult=250000)
results.append(result)
print(result)
savepklz({'results':results, 'rhomaxs':rhomaxs, 'original_results':original_results}, '../data/HooVer_%s_rhomax_exp%d.pklz'%(model, exp_id))
num=20,
base=2).astype('int') for start, end in _budgets
]
results = []
original_results = []
print(ss)
for s, budgets in zip(ss, _budgets):
s_results = []
for budget in budgets:
filename = 'data/HooVer_%s_nqueries_regret_budget%d_s%lf_exp%d.pklz' % (
model, budget, s, exp_id)
os.system(
'cd ../; python3 check.py --nRuns 1 --sigma 1e-5 --model %s --args %lf --budget %d --output %s --seed %d'
% (model, s, budget, filename, exp_id * 1024))
nimc = models.__dict__[model](s=s)
initial_states = loadpklz('../' + filename)['optimal_xs'][0]
original_results.append(
loadpklz('../' + filename)['optimal_values'][0])
result = nimc.get_prob(initial_states)
s_results.append(result)
results.append(s_results)
savepklz(
{
'results': results,
'ss': ss,
'budgets': _budgets,
'original_results': original_results
}, '../data/HooVer_%s_nqueries_regret_exp%d.pklz' % (model, exp_id))
# from IPython import embed; embed()
model = 'Slplatoon'
exp_id = int(sys.argv[1])
if __name__ == '__main__':
budget = 800000
outputs = []
bss = [10, 20, 50, 100, 400, 1600, 6400, 25600]
# run an experiment for each batch_size configuration
for bs in bss[::-1]:
filename = 'data/HooVer_%s_budget%d_bs%d_exp%d.pklz'%(model, budget, bs, exp_id)
os.system('cd ../; python3 check.py --nRuns 1 --model %s --budget %d --batch_size %d --output %s --seed %d'%(model, budget, bs, filename, exp_id*1024))
outputs.append(loadpklz('../'+filename))
results = []
optimal_xs = [o['optimal_xs'][0] for o in outputs]
original_results = [o['optimal_values'][0] for o in outputs]
num_nodes = [o['num_nodes'][0] for o in outputs]
# Monte-Carlo estimation of the hitting probability (using 250k simulations)
nimc = models.__dict__[model]()
for initial_states in optimal_xs:
initial_states = initial_states.tolist()
np.random.seed(1024)
result = evaluate_single_state(nimc, initial_states, nimc.k, mult=250000)
results.append(result)
print(result)
savepklz({'results':results, 'num_nodes': num_nodes, 'bss':bss, 'original_results':original_results}, '../data/HooVer_%s_bs_exp%d.pklz'%(model, exp_id))
return np.mean(reward)
if os.path.exists('oracle.pklz'):
data = loadpklz('oracle.pklz')
args = data['args']
mu = data['mu']
else:
args = np.meshgrid(range(cmab.nArms), range(cmab.nZ), range(cmab.nU))
args = np.stack([arg.reshape(-1) for arg in args]).T
args = [args[i] for i in range(args.shape[0])]
with Pool(processes=25) as pool:
mu = list(tqdm.tqdm(pool.imap(func, args), total=len(args)))
savepklz({'mu': mu, 'args': args}, 'oracle.pklz')
# import ipdb; ipdb.set_trace()
# func(args[0])
args = np.stack(args)
mu = np.array(mu)
def print_zu(z, u):
idx_zu = np.logical_and(args[:, 1] == z, args[:, 2] == u)
args_zu = args[idx_zu, :]
mu_zu = mu[idx_zu]
print('z=%d, u=%d' % (z, u))
print(args_zu[:, 0])
print(mu_zu)
print('-----')
try:
# call hoover.estimate_max_probability with the model and parameters
optimal_x, optimal_value, depth, memory_usage, n_nodes =\
hoover.estimate_max_probability(nimc, args.nHOOs, rho_max, sigma, budget_for_each_HOO, args.batch_size)
except AttributeError as e:
print(e)
continue
end_time = time.time()
running_time = end_time - start_time
running_times.append(running_time)
memory_usages.append(memory_usage/1024.0/1024.0)
optimal_values.append(optimal_value)
optimal_xs.append(optimal_x)
depths.append(depth)
num_nodes.append(n_nodes)
# Get the real number of queries from the model object.
# It may be a little bit different from the budget.
n_queries = nimc.cnt_queries
print('budget: ' + str(args.budget))
print('running time (s): %.2f +/- %.3f'%(np.mean(running_times), np.std(running_times)))
print('memory usage (MB): %.2f +/- %.3f'%(np.mean(memory_usages), np.std(memory_usages)))
print('optimal_values: %.4f +/- %.5f'%(np.mean(optimal_values), np.std(optimal_values)))
print('optimal_xs: '+str(optimal_xs))
print('depth: ' + str(depths))
print('number of nodes: ' + str(num_nodes))
print('actual n_queries: '+str(n_queries))
savepklz({'budget':n_queries, 'running_times':running_times, 'memory_usages':memory_usages, 'optimal_values':optimal_values, 'optimal_xs':optimal_xs, 'depths':depths, 'num_nodes':num_nodes}, args.output)
% (model, epsilon, delta, budget, s, exp_id), 'w') as f:
f.write(output)
with open(
'../data/PlasmaLab_%s_nqueries_regret_epsilon%lf_delta%lf_budget%d_s%lf_exp%d.txt'
% (model, epsilon, delta, budget, s, exp_id), 'r') as f:
output = f.readlines()
# Strips the newline character
output = [line.strip() for line in output]
seeds = output[1:-6]
final_iter = [
int(line.split(' ')[3]) for line in seeds[-budget + 10::]
]
final_iter = set(final_iter)
original_results.append(float(output[-2].split('|')[2]))
final_iter = [get_initial_state(seed) for seed in final_iter]
tmp_results = []
print(final_iter)
for initial_states in final_iter:
tmp_results.append(nimc.get_prob(initial_states))
s_results.append(np.max(tmp_results))
results.append(s_results)
savepklz({
'results': results,
'ss': ss,
'budgets': _budgets
}, '../data/PlasmaLab_%s_nqueries_regret_exp%d.pklz' % (model, exp_id))
os.system('rm ' + tmp_model_name + ' ' + tmp_spec_name)
model = 'ConceptualModel'
exp_id = int(sys.argv[1])
if __name__ == '__main__':
budget = 400000
results = []
original_results = []
ss = np.array([1.7, 2.6, 2.9, 3.2, 3.6]) * 1e-4
print(ss)
for s in ss:
filename = 'data/HooVer_%s_budget%d_s%lf_exp%d.pklz' % (model, budget,
s, exp_id)
os.system(
'cd ../; python3 check.py --nRuns 1 --sigma 1e-5 --model %s --args %lf --budget %d --output %s --seed %d'
% (model, s, budget, filename, exp_id * 1024))
nimc = models.__dict__[model]()
initial_states = loadpklz('../' + filename)['optimal_xs'][0]
original_results.append(
loadpklz('../' + filename)['optimal_values'][0])
result = nimc.get_prob(initial_states)
results.append(result)
savepklz(
{
'results': results,
'ss': ss,
'original_results': original_results
}, '../data/HooVer_%s_exp%d.pklz' % (model, exp_id))
# from IPython import embed; embed()
