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sim_time_compare_multidim.py
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sim_time_compare_multidim.py
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import numpy as np
from scipy.special import expit
from opeuc import opeuc_run
from opedr import opedr_run
from simulator_save import Simulator
from policy import target_policy
from utilize_ci import compute_ci, cover_truth
state_dim = 3
simulator = Simulator(dim_state=state_dim, model_type='save')
import logging
logging.basicConfig(level=logging.INFO,
format='%(message)s', filename='time_dim_{0}.log'.format(state_dim))
palearner_setting = {'discrete_state': False, 'rbf_dim': state_dim, 'cv_score': 'accuracy', 'verbose': True}
pmlearner_setting = {'discrete_state': False, 'discrete_action': False, 'rbf_dim': state_dim + 1, 'cv_score': 'accuracy', 'verbose': True}
qlearner_setting = {'epoch': 500, 'trace': False, 'rbf_dim': (state_dim + 2)*6, 'verbose': True, 'model': 'linear', 'eps': 1e-8}
ratiolearner_setting = {'mode': 'linear', 'rbf_ndims': state_dim * 3, 'batch_size': 32, 'epoch': 3, 'lr': 0.01, 'verbose': True}
wm_palearner_setting = {'discrete_state': False, 'rbf_dim': 5, 'cv_score': 'accuracy', 'verbose': True}
wm_qlearner_setting = {'epoch': 500, 'trace': False, 'rbf_dim': (state_dim + 3)*6, 'verbose': True, 'model': 'linear', 'eps': 1e-8}
wm_ratiolearner_setting = {'mode': 'linear', 'rbf_ndims': 6, 'batch_size': 32, 'epoch': 3, 'lr': 0.01, 'verbose': True}
# opeuc_truth = simulator.estimate_ope(target_policy, gamma=gamma, mc_s0_time=500, max_time=500)
# opeuc_truth = 1.4498987620607517
opeuc_truth = 1.8934765607096165 ## dimension: 3
gamma = 0.9
sim_rep = 400
# num_time_list = [60, 100, 140, 180, 220]
num_time_list = [20, 40, 80, 160, 320]
num_trajectory = 80
seed_list = list(range(sim_rep))
num_time_list_size = len(num_time_list)
reg_error = np.zeros((num_time_list_size, sim_rep))
reg_error_wm = np.zeros((num_time_list_size, sim_rep))
opedr_error = np.zeros((num_time_list_size, sim_rep))
opedr_error_wm = np.zeros((num_time_list_size, sim_rep))
is_opedr_error = np.zeros((num_time_list_size, sim_rep))
is_opedr_error_wm = np.zeros((num_time_list_size, sim_rep))
opeuc_error = np.zeros((num_time_list_size, sim_rep))
opeuc_cover = np.zeros((num_time_list_size, sim_rep))
opedr_cover = np.zeros((num_time_list_size, sim_rep))
reg_cover = np.zeros((num_time_list_size, sim_rep))
is_opedr_cover = np.zeros((num_time_list_size, sim_rep))
opedr_wm_cover = np.zeros((num_time_list_size, sim_rep))
reg_wm_cover = np.zeros((num_time_list_size, sim_rep))
is_opedr_wm_cover = np.zeros((num_time_list_size, sim_rep))
for i, num_time in enumerate(num_time_list):
logging.info("Time: {0} start.".format(num_time))
for k, r in enumerate(seed_list):
if r % 10 == 0:
print("Remain: ", 100 * (sim_rep - r) / sim_rep, "%.")
pass
simulator.sample_trajectory(num_trajectory, num_time, seed=r)
simulator.trajectory2iid()
sim_iid_dataset = simulator.iid_dataset
s0 = sim_iid_dataset['s0']
iid_dataset = []
iid_dataset.append(sim_iid_dataset['state'])
iid_dataset.append(sim_iid_dataset['action'])
iid_dataset.append(sim_iid_dataset['mediator'])
iid_dataset.append(sim_iid_dataset['reward'])
iid_dataset.append(sim_iid_dataset['next_state'])
## trl:
opeuc_est = 0.0
opeuc_baseline_est = 0.0
opeuc_obj = opeuc_run(s0, iid_dataset, target_policy, palearner_setting=palearner_setting, pmlearner_setting=pmlearner_setting,
qlearner_setting = qlearner_setting, ratiolearner_setting=ratiolearner_setting)
opeuc_est = opeuc_obj.opeuc
opeuc_baseline_est = opeuc_obj.intercept
err = opeuc_est - opeuc_truth
opeuc_error[i, k] = err
opeuc_ci = compute_ci(num_trajectory, num_time, opeuc_obj.eif_arr)
opeuc_cover[i, k] = cover_truth(opeuc_ci, opeuc_truth)
## dr
opedr_est = 0.0
opedr_baseline_est = 0.0
is_est = 0.0
opedr_obj = opedr_run(s0, iid_dataset, target_policy, gamma=gamma, palearner_setting=palearner_setting,
qlearner_setting=qlearner_setting, ratiolearner_setting=ratiolearner_setting, matrix_based_ope=True)
opedr_est = opedr_obj.opedr
opedr_baseline_est = opedr_obj.intercept
is_est = opedr_obj.weight_reward
err = opedr_est - opeuc_truth
opedr_error[i, k] = err
reg_error[i, k] = opedr_baseline_est - opeuc_truth
is_opedr_error[i, k] = is_est - opeuc_truth
opedr_ci = compute_ci(num_trajectory, num_time, opedr_obj.eif_arr)
opedr_cover[i, k] = cover_truth(opedr_ci, opeuc_truth)
reg_ci = compute_ci(num_trajectory, 1, opedr_obj.intercept_arr)
reg_cover[i, k] = cover_truth(reg_ci, opeuc_truth)
is_ci = compute_ci(num_trajectory, num_time, opedr_obj.is_arr)
is_opedr_cover[i, k] = cover_truth(is_ci, opeuc_truth)
## dr + mediator
mediator_mat = iid_dataset[2].reshape(num_trajectory, num_time)
mediator_mat = np.hstack([mediator_mat, np.random.binomial(n=1, p=0.5, size=s0.shape[0]).reshape(-1, 1)])
mediator_mat1 = mediator_mat[:, 0:num_time]
mediator_mat1 = mediator_mat1.reshape(-1, 1)
mediator_mat2 = mediator_mat[:, 1:]
mediator_mat2 = mediator_mat2.reshape(-1, 1)
s0 = np.hstack([s0, mediator_mat[:, 0].reshape(-1, 1)])
iid_dataset[0] = np.hstack([iid_dataset[0], mediator_mat1])
iid_dataset[4] = np.hstack([iid_dataset[4], mediator_mat2])
opedr_obj2 = opedr_run(s0, iid_dataset, target_policy, gamma=gamma, palearner_setting=wm_palearner_setting,
qlearner_setting=wm_qlearner_setting, ratiolearner_setting=wm_ratiolearner_setting,
matrix_based_ope=True)
opedr_est_wm = opedr_obj2.opedr
opedr_baseline_est_wm = opedr_obj2.intercept
is_est_wm = opedr_obj2.weight_reward
opedr_error_wm[i, k] = opedr_est_wm - opeuc_truth
reg_error_wm[i, k] = opedr_baseline_est_wm - opeuc_truth
is_opedr_error_wm[i, k] = is_est_wm - opeuc_truth
opedr_wm_ci = compute_ci(num_trajectory, num_time, opedr_obj2.eif_arr)
opedr_wm_cover[i, k] = cover_truth(opedr_ci, opeuc_truth)
reg_wm_ci = compute_ci(num_trajectory, 1, opedr_obj2.intercept_arr)
reg_wm_cover[i, k] = cover_truth(reg_ci, opeuc_truth)
is_wm_ci = compute_ci(num_trajectory, num_time, opedr_obj2.is_arr)
is_opedr_wm_cover[i, k] = cover_truth(is_ci, opeuc_truth)
logging.info("COPE: {0}, CI-Cover: {1}".format(opeuc_error[i, k], opeuc_cover[i, k]))
logging.info("DRL: {0}, CI-Cover: {1}".format(opedr_error[i, k], opedr_cover[i, k]))
logging.info("REG: {0}, CI-Cover: {1}".format(reg_error[i, k], reg_cover[i, k]))
logging.info("MIS: {0}, CI-Cover: {1}".format(is_opedr_error[i, k], is_opedr_cover[i, k]))
logging.info("DRL-WM: {0}, CI-Cover: {1}".format(opedr_error_wm[i, k], opedr_wm_cover[i, k]))
logging.info("REG-WM: {0}, CI-Cover: {1}".format(reg_error_wm[i, k], reg_wm_cover[i, k]))
logging.info("MIS-WM: {0}, CI-Cover: {1}".format(is_opedr_error_wm[i, k], is_opedr_wm_cover[i, k]))
pass
pass
np.savetxt('time-reg-{0}.csv'.format(state_dim), reg_error, delimiter=", ")
np.savetxt('time-regwm-{0}.csv'.format(state_dim), reg_error_wm, delimiter=", ")
np.savetxt('time-drl-{0}.csv'.format(state_dim), opedr_error, delimiter=", ")
np.savetxt('time-drlwm-{0}.csv'.format(state_dim), opedr_error_wm, delimiter=", ")
np.savetxt('time-is-{0}.csv'.format(state_dim), is_opedr_error, delimiter=", ")
np.savetxt('time-iswm-{0}.csv'.format(state_dim), is_opedr_error_wm, delimiter=", ")
np.savetxt('time-cope-{0}.csv'.format(state_dim), opeuc_error, delimiter=", ")
np.savetxt('time-reg-cover-{0}.csv'.format(state_dim), reg_cover, delimiter=", ")
np.savetxt('time-regwm-cover-{0}.csv'.format(state_dim), reg_wm_cover, delimiter=", ")
np.savetxt('time-drl-cover-{0}.csv'.format(state_dim), opedr_cover, delimiter=", ")
np.savetxt('time-drlwm-cover-{0}.csv'.format(state_dim), opedr_wm_cover, delimiter=", ")
np.savetxt('time-is-cover-{0}.csv'.format(state_dim), is_opedr_cover, delimiter=", ")
np.savetxt('time-iswm-cover-{0}.csv'.format(state_dim), is_opedr_wm_cover, delimiter=", ")
np.savetxt('time-cope-cover-{0}.csv'.format(state_dim), opeuc_cover, delimiter=", ")