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_uniform_random(num_sims,
mean_list,
variance,
steps_before_switch,
steps_after_switch,
outfile_directory,
forceActions=0,
switch_to_best_if_nonsignificant=True):
'''
Runs num_sims bandit simulations with several different sample sizes (those in the list step_sizes).
Samples uniformly at random.
'''
for i in range(num_sims):
if forceActions != 0:
print("Forcing actions:", forceActions)
forced = make_forced_actions(len(mean_list), steps_before_switch,
forceActions)
else:
forced = forced_actions()
cur_reward_file = get_rewards_filename(
outfile_directory, steps_before_switch + steps_after_switch, 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, steps_before_switch + steps_after_switch,
cur_reward_file)
#
cur_output_file = get_output_filename(
outfile_directory, steps_before_switch + steps_after_switch, i)
models = [
ng_normal.NGNormal(mu=0, k=1, alpha=1, beta=1)
for _ in range(len(mean_list))
]
thompson_ng_policy.calculate_thompson_switch_to_fixed_policy(
cur_reward_file,
num_actions=len(mean_list),
dest=cur_output_file,
num_actions_before_switch=steps_before_switch,
models=models,
switch_to_best_if_nonsignificant=switch_to_best_if_nonsignificant,
epsilon=1.0,
action_mode=thompson_ng_policy.ActionSelectionMode.prob_is_best,
forced=forced)
