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sim_trajectory_compare_multidim.py
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sim_trajectory_compare_multidim.py
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# -*- coding: utf-8 -*-
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
gamma = 0.9
import logging
logging.basicConfig(level=logging.INFO,
format='%(message)s', filename='trajectory_dim_{0}.log'.format(state_dim))
simulator = Simulator(dim_state=state_dim, model_type='save')
### dim 1:
# 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': 200, 'trace': False, 'rbf_dim': (state_dim + 2)*2,
# 'verbose': True, 'model': 'linear', 'eps': 1e-5}
# 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': 200, 'trace': False, 'rbf_dim': [20], 'verbose': True, 'model': 'linear', 'eps': 1e-5}
# wm_ratiolearner_setting = {'mode': 'linear', 'rbf_ndims': [30], 'batch_size': 32, 'epoch': 3, 'lr': 0.01, 'verbose': True}
### dim 3:
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}
# rbf_dim1 = [1, 2, 3, 4, 5]
# rbf_dim2 = [1, 3, 5, 7, 9]
# rbf_dim1 = (np.array(rbf_dim1) * state_dim).tolist()
# rbf_dim2 = (np.array(rbf_dim2) * state_dim).tolist()
# palearner_setting = {'discrete_state': False, 'rbf_dim': rbf_dim1, 'cv_score': 'accuracy', 'verbose': True}
# pmlearner_setting = {'discrete_state': False, 'discrete_action': False, 'rbf_dim': rbf_dim1, 'cv_score': 'accuracy', 'verbose': True}
# qlearner_setting = {'epoch': 100, 'trace': False, 'rbf_dim': rbf_dim2, 'verbose': True, 'model': 'linear', 'eps': 1e-5}
# ratiolearner_setting = {'mode': 'linear', 'rbf_ndims': rbf_dim2, 'batch_size': 32, 'epoch': 3, 'lr': 0.01, 'verbose': True}
# wm_palearner_setting = {'discrete_state': False, 'rbf_dim': rbf_dim1, 'cv_score': 'accuracy', 'verbose': True}
# wm_qlearner_setting = {'epoch': 100, 'trace': False, 'rbf_dim': rbf_dim2, 'verbose': True, 'model': 'linear', 'eps': 1e-5}
# wm_ratiolearner_setting = {'mode': 'linear', 'rbf_ndims': rbf_dim2, 'batch_size': 32, 'epoch': 3, 'lr': 0.01, 'verbose': True}
# opeuc_truth = simulator.estimate_ope(target_policy, gamma=gamma, mc_s0_time=20000, max_time=500)
# print(opeuc_truth)
# opeuc_truth = 1.6755813213456041 ## dimension: 2
opeuc_truth = 1.8934765607096165 ## dimension: 3
# opeuc_truth = 2.3456954159828456 # dimension: 5
# logging.info("Truth: {0}".format(opeuc_truth))
sim_rep = 400
num_time = 20
num_trajectory_list = [20, 40, 80, 160, 320]
# num_trajectory_list = [320, 640]
seed_list = list(range(sim_rep))
num_trajectory_list_size = len(num_trajectory_list)
opeuc_error = np.zeros((num_trajectory_list_size, sim_rep))
opedr_error = np.zeros((num_trajectory_list_size, sim_rep))
reg_error = np.zeros((num_trajectory_list_size, sim_rep))
is_opedr_error = np.zeros((num_trajectory_list_size, sim_rep))
opedr_error_wm = np.zeros((num_trajectory_list_size, sim_rep))
reg_error_wm = np.zeros((num_trajectory_list_size, sim_rep))
is_opedr_error_wm = np.zeros((num_trajectory_list_size, sim_rep))
opeuc_cover = np.zeros((num_trajectory_list_size, sim_rep))
opedr_cover = np.zeros((num_trajectory_list_size, sim_rep))
reg_cover = np.zeros((num_trajectory_list_size, sim_rep))
is_opedr_cover = np.zeros((num_trajectory_list_size, sim_rep))
opedr_wm_cover = np.zeros((num_trajectory_list_size, sim_rep))
reg_wm_cover = np.zeros((num_trajectory_list_size, sim_rep))
is_opedr_wm_cover = np.zeros((num_trajectory_list_size, sim_rep))
for i, num_trajectory in enumerate(num_trajectory_list):
logging.info("Trajectory: {0} start.".format(num_trajectory))
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_error[i, k] = opeuc_est - opeuc_truth
opeuc_ci = compute_ci(num_trajectory, num_time, opeuc_obj.eif_arr)
opeuc_cover[i, k] = cover_truth(opeuc_ci, opeuc_truth)
## REG, IS, DRL
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
opedr_error[i, k] = opedr_est - opeuc_truth
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)
## REG, IS, DRL (with 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_est - opeuc_truth, opeuc_cover[i, k]))
logging.info("DRL: {0}, CI-Cover: {1}".format(opedr_est - opeuc_truth, opedr_cover[i, k]))
logging.info("REG: {0}, CI-Cover: {1}".format(opedr_baseline_est - opeuc_truth, reg_cover[i, k]))
logging.info("MIS: {0}, CI-Cover: {1}".format(is_est - opeuc_truth, is_opedr_cover[i, k]))
logging.info("DRL-WM: {0}, CI-Cover: {1}".format(opedr_est_wm - opeuc_truth, opedr_wm_cover[i, k]))
logging.info("REG-WM: {0}, CI-Cover: {1}".format(opedr_baseline_est_wm - opeuc_truth, reg_wm_cover[i, k]))
logging.info("MIS-WM: {0}, CI-Cover: {1}".format(is_est_wm - opeuc_truth, is_opedr_wm_cover[i, k]))
pass
pass
np.savetxt('trajectory-reg-{0}.csv'.format(state_dim), reg_error, delimiter=", ")
np.savetxt('trajectory-regwm-{0}.csv'.format(state_dim), reg_error_wm, delimiter=", ")
np.savetxt('trajectory-drl-{0}.csv'.format(state_dim), opedr_error, delimiter=", ")
np.savetxt('trajectory-drlwm-{0}.csv'.format(state_dim), opedr_error_wm, delimiter=", ")
np.savetxt('trajectory-is-{0}.csv'.format(state_dim), is_opedr_error, delimiter=", ")
np.savetxt('trajectory-iswm-{0}.csv'.format(state_dim), is_opedr_error_wm, delimiter=", ")
np.savetxt('trajectory-cope-{0}.csv'.format(state_dim), opeuc_error, delimiter=", ")
np.savetxt('trajectory-reg-cover-{0}.csv'.format(state_dim), reg_cover, delimiter=", ")
np.savetxt('trajectory-regwm-cover-{0}.csv'.format(state_dim), reg_wm_cover, delimiter=", ")
np.savetxt('trajectory-drl-cover-{0}.csv'.format(state_dim), opedr_cover, delimiter=", ")
np.savetxt('trajectory-drlwm-cover-{0}.csv'.format(state_dim), opedr_wm_cover, delimiter=", ")
np.savetxt('trajectory-is-cover-{0}.csv'.format(state_dim), is_opedr_cover, delimiter=", ")
np.savetxt('trajectory-iswm-cover-{0}.csv'.format(state_dim), is_opedr_wm_cover, delimiter=", ")
np.savetxt('trajectory-cope-cover-{0}.csv'.format(state_dim), opeuc_cover, delimiter=", ")