def aggregate(filepath):
f = open(filepath, 'rb')
all_results = pickle.load(f)
f.close()
n = len(all_results)
d = len(all_results[0]['lnr_costs'])
var = np.zeros((n, d - 1))
opt_var = np.zeros((n, d - 1))
lamb = np.zeros((n, d - 1))
params = []
title = None
for t, result in enumerate(all_results):
params.append(result['param'])
title = result['title']
var[t, :] = result['variations']
opt_var[t, :] = result['opt_variations']
lamb[t, :] = result['lambdas']
var_mean, var_std = statistics.mean_sem(var.T)
opt_var_mean, opt_var_std = statistics.mean_sem(opt_var.T)
lamb_max = np.max(lamb, axis=1)
lamb_mean, lamb_std = statistics.mean_sem(lamb.T)
results = {
'var': [var_mean, var_std],
'opt_var': [opt_var_mean, opt_var_std],
'lamb': [lamb_max, lamb_mean, lamb_std]
}
return params, results
def aggregate_save(direc, prefix, all_results):
n = len(all_results)
d = len(all_results[0]['lnr_costs'])
var = np.zeros((n, d - 1))
opt_var = np.zeros((n, d - 1))
lamb = np.zeros((n, d - 1))
params = []
title = None
for t, result in enumerate(all_results):
params.append(result['param'])
title = result['title']
var[t, :] = result['variations']
opt_var[t, :] = result['opt_variations']
lamb[t, :] = result['lambdas']
var_mean, var_std = statistics.mean_sem(var.T)
opt_var_mean, opt_var_std = statistics.mean_sem(opt_var.T)
lamb_max = np.max(lamb, axis=1)
lamb_mean, lamb_std = statistics.mean_sem(lamb.T)
# Dynamic Regret
plt.subplot(311)
plt.title("Variations")
plt.errorbar(params, var_mean, yerr=var_std, label='lnr variations')
plt.errorbar(params,
opt_var_mean,
yerr=opt_var_std,
label='opt variations')
plt.xscale('log')
plt.legend()
plt.subplot(312)
plt.title("Max Lambda Ratio")
plt.xscale('log')
plt.plot(params, lamb_max)
plt.subplot(313)
plt.title("Mean Lambda Ratio")
plt.errorbar(params, lamb_mean, yerr=lamb_std)
plt.xscale('log')
plt.tight_layout()
filepath = os.path.join(direc, prefix) + '_lambda.pdf'
plt.savefig(filepath)
plt.close()
plt.cla()
plt.clf()
def aggregate(self, all_results):
n = len(all_results)
d = self.iters
lnr_costs = np.zeros((n, d))
opt_costs = np.zeros((n, d))
diff_costs = np.zeros((n, d))
lnr_batch_costs = np.zeros((n, d))
opt_batch_costs = np.zeros((n, d))
static_regret = np.zeros((n, d))
for t, result in enumerate(all_results):
lnr_costs[t, :] = result['lnr_costs']
opt_costs[t, :] = result['opt_costs']
diff_costs[t, :] = result['lnr_costs'] - result['opt_costs']
lnr_batch_costs[t, :] = result['lnr_batch_costs']
opt_batch_costs[t, :] = result['opt_batch_costs']
static_regret[t, :] = result['static_regret']
lnr_mean, lnr_std = statistics.mean_sem(lnr_costs)
opt_mean, opt_std = statistics.mean_sem(opt_costs)
diff_mean, diff_std = statistics.mean_sem(diff_costs)
lnr_batch_mean, lnr_batch_std = statistics.mean_sem(lnr_batch_costs)
opt_batch_mean, opt_batch_std = statistics.mean_sem(opt_batch_costs)
static_regret_mean, static_regret_sem = statistics.mean_sem(
static_regret)
x_axis = np.arange(len(lnr_mean))
# Dynamic Regret
plt.subplot(211)
plt.title("Actual loss")
plt.errorbar(x_axis, lnr_mean, yerr=lnr_std, label='lnr costs')
plt.errorbar(x_axis, opt_mean, yerr=opt_std, label='opt costs')
plt.legend()
plt.subplot(212)
plt.title("Difference")
plt.errorbar(x_axis, diff_mean, yerr=diff_std)
plt.tight_layout()
filepath = os.path.join(self.dir, self.prefix) + '.pdf'
plt.savefig(filepath)
plt.close()
plt.cla()
plt.clf()
# Static Regret
plt.subplot(211)
plt.title("Batch loss")
plt.errorbar(x_axis, lnr_batch_mean, yerr=lnr_std, label='lnr costs')
plt.errorbar(x_axis, opt_batch_mean, yerr=opt_std, label='opt costs')
plt.legend()
plt.subplot(212)
plt.title("Static Regret")
plt.errorbar(x_axis, static_regret_mean, yerr=diff_std)
plt.tight_layout()
filepath = os.path.join(self.dir, self.prefix) + '_batch.pdf'
plt.savefig(filepath)
plt.close()
plt.cla()
plt.clf()
def aggregate(filepath):
f = open(filepath, 'rb')
all_results = pickle.load(f)
f.close()
n = len(all_results)
d = len(all_results[0]['lnr_costs'])
lnr_costs = np.zeros((n, d))
opt_costs = np.zeros((n, d))
diff_costs = np.zeros((n, d))
static_regret = np.zeros((n, d))
rewards = np.zeros((n, d))
sup_rewards = np.zeros((n, d))
variations = np.zeros((n, d - 1))
opt_variations = np.zeros((n, d - 1))
lambdas = np.zeros((n, d - 1))
betas = np.zeros((n, d - 1))
for t, result in enumerate(all_results):
lnr_costs[t, :] = result['lnr_costs']
opt_costs[t, :] = result['opt_costs']
# lnr_costs[t, :] = np.cumsum(result['lnr_costs']) / (np.arange(d) + 1)
# opt_costs[t, :] = np.cumsum(result['opt_costs']) / (np.arange(d) + 1)
rewards[t, :] = result['rewards']
sup_rewards[t, :] = result['sup_rewards']
variations[t, :] = result['variations']
opt_variations[t, :] = result['opt_variations']
lambdas[t, :] = result['lambdas']
betas[t, :] = result['betas']
static_regret[t, :] = result['static_regret']
diff_costs = lnr_costs - opt_costs
lnr_mean, lnr_std = statistics.mean_sem(lnr_costs)
opt_mean, opt_std = statistics.mean_sem(opt_costs)
diff_mean, diff_std = statistics.mean_sem(diff_costs)
sr_mean, sr_std = statistics.mean_sem(static_regret)
var_mean, var_std = statistics.mean_sem(variations)
opt_var_mean, opt_var_std = statistics.mean_sem(opt_variations)
lam_mean, lam_std = statistics.mean_sem(lambdas)
beta_mean, beta_std = statistics.mean_sem(betas)
reward_mean, reward_std = statistics.mean_sem(rewards)
sup_reward_mean, sup_reward_std = statistics.mean_sem(sup_rewards)
x_axis = np.arange(len(lnr_mean))
results = {
"dyn_regret": (diff_mean, diff_std),
"static_regret": (sr_mean, sr_std),
"reward": (reward_mean, reward_std),
"sup_reward": (sup_reward_mean, sup_reward_std),
"var": (var_mean, var_std),
"opt_var": (opt_var_mean, opt_var_std),
"lam": (lam_mean, lam_std),
'beta': (beta_mean, beta_std)
}
return results
