def onpol_sim(worker, subj_list, output_prefix, model_path):
params = OptML.get_variables(worker.get_params())
worker.set_params(params)
config = tf.ConfigProto(device_count={'GPU': 0})
with tf.Session(config=config) as sess:
DLogger.logger().debug("loading mode....")
load_model(sess, model_path)
DLogger.logger().debug("finished loading mode.")
for s in range(len(subj_list)):
DLogger.logger().debug("parameters {}".format(sess.run(params)))
for c in subj_list[s]:
DLogger.logger().debug("subject {} trial {}".format(
c['id'], c['block']))
choices = c['choices']
output_path = output_prefix + 'sim_' + id_generator(
size=7) + '/'
if not os.path.exists(output_path):
os.makedirs(output_path)
c2 = c.copy()
c2['option'] = {}
c2['N'] = c['id']
pdc = pd.DataFrame(c2, index=[0])
pdc.to_csv(output_path + 'config.csv', index=False)
_, _ = Simulator.simulate_env(sess,
worker,
output_path,
choices,
bandit_evn(c['prop0'],
c['prop1'],
init_state=None,
init_action=-1,
init_reward=0),
greedy=False)
# tf.reset_default_graph()
# worker = GQL.get_instance(2, 2, {})
# group_log_sigma2, group_mu, ind_log_sigma2, ind_mu = OptMIX.get_variables(worker.get_params())
# worker.set_params(group_mu)
# simulate_oscci_mix_QL('gql-mix-opt', 'GQL', worker)
#
# tf.reset_default_graph()
# worker = GQL.get_instance(2, 10, {})
# group_log_sigma2, group_mu, ind_log_sigma2, ind_mu = OptMIX.get_variables(worker.get_params())
# worker.set_params(group_mu)
# simulate_oscci_mix_QL('gql10d-mix-opt', 'GQL10D', worker)
############ ML graphs #############################
tf.reset_default_graph()
worker = QL.get_instance_without_pser(2, 0.1, 0.2)
worker.set_params(OptML.get_variables(worker.get_params()))
simulate_mix_QL('ql-ml-opt', 'QL', worker)
tf.reset_default_graph()
worker = QL.get_instance_with_pser(2, 0.1, 0.2, 0.2)
worker.set_params(OptML.get_variables(worker.get_params()))
simulate_mix_QL('qlp-ml-opt', 'QLP', worker)
tf.reset_default_graph()
worker = GQL.get_instance(2, 2, {})
worker.set_params(OptML.get_variables(worker.get_params()))
simulate_mix_QL('gql-ml-opt', 'GQL', worker)
tf.reset_default_graph()
worker = GQL.get_instance(2, 1, {})
worker.set_params(OptML.get_variables(worker.get_params()))
def test_and_save(sess, test, output_folder):
return OptML.test_and_save("", output_folder, None, sess, test, worker)