def test_donothing_noov_80(self):
"""test that do nothing has a score of 80.0 if it is run with "no overflow disconnection" """
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
# I cannot decrease the max step: it must be above the number of steps the do nothing does
scores = ScoreL2RPN2020(env,
nb_scenario=2,
verbose=0,
max_step=130)
assert scores._recomputed_dn
assert scores._recomputed_no_ov
# the statistics have been properly computed
assert os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN)))
assert os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
my_agent = DoNothingAgent(env.action_space)
my_scores, *_ = scores.get(my_agent)
assert np.max(
np.abs(my_scores
)) <= self.tol_one, "error for the first do nothing"
param = Parameters()
param.NO_OVERFLOW_DISCONNECTION = True
with make("rte_case5_example", test=True, param=param) as env:
scores2 = ScoreL2RPN2020(env,
nb_scenario=2,
verbose=0,
max_step=130)
assert not scores2._recomputed_dn
assert not scores2._recomputed_no_ov
my_agent = DoNothingAgent(env.action_space)
my_scores, *_ = scores2.get(my_agent)
assert np.max(
np.abs(np.array(my_scores) - 80.0)) <= self.tol_one
# delete them
stats_0 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_DN)
stats_1 = EpisodeStatistics(env,
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)
stats_0.clear_all()
stats_1.clear_all()
assert not os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN)))
assert not os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
def test_modif_nb_scenario(self):
"""
test that i can modify the nb_scenario and it properly recomputes it when it increased and not
when it decreases
"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
scores = ScoreL2RPN2020(env,
nb_scenario=2,
verbose=0,
max_step=5)
# the statistics have been properly computed
assert os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN)))
assert os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
my_agent = DoNothingAgent(env.action_space)
my_scores, *_ = scores.get(my_agent)
assert np.max(
np.abs(my_scores
)) <= self.tol_one, "error for the first do nothing"
scores2 = ScoreL2RPN2020(env,
nb_scenario=4,
verbose=0,
max_step=5)
assert scores2._recomputed_dn
assert scores2._recomputed_no_ov
scores2 = ScoreL2RPN2020(env,
nb_scenario=3,
verbose=0,
max_step=5)
assert not scores2._recomputed_dn
assert not scores2._recomputed_no_ov
# delete them
stats_0 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_DN)
stats_1 = EpisodeStatistics(env,
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)
stats_0.clear_all()
stats_1.clear_all()
assert not os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN)))
assert not os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
def test_compute_without_score(self):
"""test that i can compute and erase the results afterwards"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
stats = EpisodeStatistics(env, "test")
stats.compute(nb_scenario=2, max_step=10, pbar=False)
# i can access it
prods, ids_ = stats.get("prod_p")
assert prods.shape == (22, 2), "error on the prods shape"
assert ids_.shape == (22, 1), "error on the ids shape"
with self.assertRaises(RuntimeError):
scores, ids_ = stats.get("scores")
# i can clear everything
stats.clear_all()
assert not os.path.exists(os.path.join(env.get_path_env(), stats.get_name_dir("test")))
def test_compute_with_score(self):
"""test that i can compute and erase the results afterwards"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
stats = EpisodeStatistics(env, "test")
stats.compute(nb_scenario=2, max_step=10, pbar=False, scores_func=L2RPNSandBoxScore)
# i can access it
scores, ids_ = stats.get(EpisodeStatistics.SCORES)
assert scores.shape == (20,), "error on the score shape"
assert ids_.shape == (20, 1), "error on the ids shape"
scores, ids_ = stats.get("scores")
assert scores.shape == (20,), "error on the score shape"
assert ids_.shape == (20, 1), "error on the ids shape"
# i can clear everything
stats.clear_all()
assert not os.path.exists(os.path.join(env.get_path_env(), stats.get_name_dir("test")))
def test_compute_erase(self):
"""test that i can compute and erase the results afterwards"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
stats = EpisodeStatistics(env, "test")
stats.compute(nb_scenario=1, max_step=10, pbar=False)
# the file have been created
assert os.path.exists(os.path.join(env.get_path_env(), stats.get_name_dir("test")))
# i can access it
aor_, ids_ = stats.get("a_or")
assert aor_.shape == (11, 8)
# i can clear the data of individual episode
stats.clear_episode_data()
assert not os.path.exists(os.path.join(env.get_path_env(), stats.get_name_dir("test"), "00"))
# i can clear everything
stats.clear_all()
assert not os.path.exists(os.path.join(env.get_path_env(), stats.get_name_dir("test")))
def test_donothing_0(self):
"""test that do nothing has a score of 0.00"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
scores = ScoreL2RPN2020(env,
nb_scenario=4,
verbose=0,
max_step=20)
# the statistics have been properly computed
assert os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN)))
assert os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
my_agent = DoNothingAgent(env.action_space)
my_scores, *_ = scores.get(my_agent)
assert np.max(np.abs(my_scores)) <= self.tol_one
# delete them
stats_0 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_DN)
stats_1 = EpisodeStatistics(env,
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)
stats_0.clear_all()
stats_1.clear_all()
assert not os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN)))
assert not os.path.exists(
os.path.join(
env.get_path_env(),
EpisodeStatistics.get_name_dir(
ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
def test_can_compute(self):
"""test that i can initialize the score and then delete the statistics"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
scores = ScoreL2RPN2020(env, nb_scenario=4, verbose=0, max_step=50)
# the statistics have been properly computed
assert os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN)))
assert os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
# delete them
stats_0 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_DN)
stats_1 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)
stats_2 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_RP_NO_OVERWLOW)
stats_0.clear_all()
stats_1.clear_all()
stats_2.clear_all()
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN)))
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_RP_NO_OVERWLOW)))
def test_modif_max_step_decrease(self):
"""
test that i can modify the max step by decreaseing it (and in that case it does not trigger a recomputation
of the statistics)
"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
scores = ScoreL2RPN2020(env, nb_scenario=2, verbose=0, max_step=15)
# the statistics have been properly computed
assert os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN)))
assert os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
my_agent = DoNothingAgent(env.action_space)
my_scores, *_ = scores.get(my_agent)
assert np.max(np.abs(my_scores)) <= self.tol_one, "error for the first do nothing"
scores2 = ScoreL2RPN2020(env, nb_scenario=2, verbose=0, max_step=10)
assert not scores2._recomputed_dn
assert not scores2._recomputed_no_ov
my_agent = DoNothingAgent(env.action_space)
my_scores2, *_ = scores2.get(my_agent)
assert np.max(np.abs(my_scores2)) <= self.tol_one, "error for the second do nothing"
# delete them
stats_0 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_DN)
stats_1 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)
stats_2 = EpisodeStatistics(env, ScoreL2RPN2020.NAME_RP_NO_OVERWLOW)
stats_0.clear_all()
stats_1.clear_all()
stats_2.clear_all()
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN)))
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_DN_NO_OVERWLOW)))
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreL2RPN2020.NAME_RP_NO_OVERWLOW)))
def test_can_compute(self):
"""test that i can initialize the score and then delete the statistics"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make(os.path.join(PATH_DATA_TEST, "l2rpn_neurips_2020_track1_with_alert"), test=True) as env:
scores = ScoreICAPS2021(env,
nb_scenario=2,
verbose=0,
max_step=50,
env_seeds=[1, 2], # with these seeds do nothing goes till the end
agent_seeds=[3, 4])
my_agent = DoNothingAgent(env.action_space)
scores, n_played, total_ts = scores.get(my_agent)
for (ep_score, op_score, alarm_score) in scores:
assert np.abs(ep_score - 30.) <= self.tol_one, f"wrong score for the episode: {ep_score} vs 30."
assert np.abs(op_score - 0.) <= self.tol_one, f"wrong score for the operationnal cost: " \
f"{op_score} vs 0."
assert np.abs(alarm_score - 100.) <= self.tol_one, f"wrong score for the alarm: " \
f"{alarm_score} vs 100."
# the statistics have been properly computed
assert os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreICAPS2021.NAME_DN)))
assert os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreICAPS2021.NAME_DN_NO_OVERWLOW)))
# delete them
stats_0 = EpisodeStatistics(env, ScoreICAPS2021.NAME_DN)
stats_1 = EpisodeStatistics(env, ScoreICAPS2021.NAME_DN_NO_OVERWLOW)
stats_2 = EpisodeStatistics(env, ScoreICAPS2021.NAME_RP_NO_OVERWLOW)
stats_0.clear_all()
stats_1.clear_all()
stats_2.clear_all()
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreICAPS2021.NAME_DN)))
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreICAPS2021.NAME_DN_NO_OVERWLOW)))
assert not os.path.exists(os.path.join(env.get_path_env(),
EpisodeStatistics.get_name_dir(ScoreICAPS2021.NAME_RP_NO_OVERWLOW)))