def run_simulations(num_sims,
mean_list,
variance,
step_sizes,
outfile_directory,
softmax_beta=None,
reordering_fn=None,
prior_mean=0,
forceActions=0):
'''
Runs num_sims bandit simulations with several different sample sizes (those in the list step_sizes).
Bandit uses the thompson_ng sampling policy.
'''
for i in range(num_sims):
for num_steps in step_sizes:
if forceActions != 0:
print("Forcing actions:", forceActions)
forced = make_forced_actions(len(mean_list), num_steps,
forceActions)
else:
forced = forced_actions()
cur_reward_file = get_rewards_filename(outfile_directory,
num_steps, i)
# Check if they've passed in one variance for everything or multiple variances
if not hasattr(variance, '__len__'):
# only one variance - turn into a list
variances = [variance] * len(mean_list)
else:
# multiple variances - pass straight through
variances = variance
generate_single_bandit.generate_normal_distribution_file(
mean_list, variances, num_steps, cur_reward_file)
if softmax_beta != None:
# reorder rewards
reordered_reward_file = get_reordered_rewards_filename(
outfile_directory, num_steps, i)
reorder_samples_in_rewards.reorder_rewards_by_quartile(
cur_reward_file, reordered_reward_file, reordering_fn,
softmax_beta)
else:
reordered_reward_file = cur_reward_file
cur_output_file = get_output_filename(outfile_directory, num_steps,
i)
models = [
ng_normal.NGNormal(mu=prior_mean, k=1, alpha=1, beta=1)
for _ in range(len(mean_list))
]
thompson_ng_policy.calculate_thompson_single_bandit(
reordered_reward_file,
num_actions=len(mean_list),
dest=cur_output_file,
models=models,
action_mode=thompson_ng_policy.ActionSelectionMode.
prob_is_best,
relearn=True,
forced=forced)
def run_simulations(num_sims, prob_per_arm, step_sizes, outfile_directory, successPrior = 1, failurePrior = 1, softmax_beta = None, \
reordering_fn = None, forceActions = 0, batch_size = 1, burn_in_size = 1):
'''
Runs num_sims bandit simulations with several different sample sizes (those in the list step_sizes).
Bandit uses the thompson_ng sampling policy.
'''
for i in range(num_sims):
# num_steps_prev = 0
for num_steps in step_sizes:
if forceActions != 0:
# print("Forcing actions:", forceActions)
forced = run_effect_size_simulations.make_forced_actions(len(prob_per_arm), num_steps, forceActions)
else:
forced = forced_actions()
cur_reward_file = get_rewards_filename(outfile_directory, num_steps, i)
generate_single_bandit.generate_file(np.array(prob_per_arm),
num_steps,
cur_reward_file)
if softmax_beta != None:
# reorder rewards
reordered_reward_file = get_reordered_rewards_filename(outfile_directory, num_steps, i)
reorder_samples_in_rewards.reorder_rewards_by_quartile(cur_reward_file,
reordered_reward_file,
reordering_fn,
softmax_beta)
else:
reordered_reward_file = cur_reward_file
cur_output_file = get_output_filename(outfile_directory, num_steps, i)
models = [beta_bernoulli.BetaBern(success=successPrior, failure=failurePrior) for _ in range(len(prob_per_arm))]
'''thompson_policy.calculate_thompson_single_bandit(reordered_reward_file,
num_actions=len(prob_per_arm),
dest= cur_output_file,
models=models,
action_mode=thompson_policy.ActionSelectionMode.prob_is_best,
relearn=True,
forced = forced,
batch_size = batch_size,
burn_in_size = burn_in_size)
'''
# num_steps_prev = num_steps
thompson_policy.old_two_phase_random_thompson_policy(reordered_reward_file,
num_actions=len(prob_per_arm),
dest= cur_output_file,
random_dur=0,
models=models,
random_start=0,
action_mode=thompson_policy.ActionSelectionMode.prob_is_best,
relearn=True,
forced = forced,
batch_size = batch_size,
burn_in_size = burn_in_size)
def run_simulations(num_sims, prob_per_arm, step_sizes, outfile_directory, successPrior = 1, failurePrior = 1, softmax_beta = None, \
reordering_fn = None, forceActions = 0, batch_size = 1, burn_in_size = 1, c = 0.1, resample = True):
'''
Runs num_sims bandit simulations with several different sample sizes (those in the list step_sizes).
Bandit uses the thompson_ng sampling policy.
'''
for i in range(num_sims):
# num_steps_prev = 0
for num_steps in step_sizes:
if forceActions != 0:
# print("Forcing actions:", forceActions)
forced = run_effect_size_simulations.make_forced_actions(
len(prob_per_arm), num_steps, forceActions)
else:
forced = forced_actions()
cur_reward_file = get_rewards_filename(outfile_directory,
num_steps, i)
generate_single_bandit.generate_file(np.array(prob_per_arm),
num_steps, cur_reward_file)
if softmax_beta != None:
# reorder rewards
reordered_reward_file = get_reordered_rewards_filename(
outfile_directory, num_steps, i)
reorder_samples_in_rewards.reorder_rewards_by_quartile(
cur_reward_file, reordered_reward_file, reordering_fn,
softmax_beta)
else:
reordered_reward_file = cur_reward_file
cur_output_file = get_output_filename(outfile_directory, num_steps,
i)
models = [
beta_bernoulli.BetaBern(success=successPrior,
failure=failurePrior)
for _ in range(len(prob_per_arm))
]
#if don't pass model, then will be Greedy
#thresh = 0.03
# thresh = 0.1 # for small effect, es = 0.1, 0.55 - 0.45 = 0.10
ppd.calculate_epsilon_single_bandit(reordered_reward_file,
models=models,
num_actions=len(prob_per_arm),
dest=cur_output_file,
forced=forced,
c=c,
resample=resample)
