/
test_utils.py
524 lines (474 loc) · 21.7 KB
/
test_utils.py
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# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
# See AUTHORS.txt
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
# you can obtain one at http://mozilla.org/MPL/2.0/.
# SPDX-License-Identifier: MPL-2.0
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems.
from grid2op.tests.helper_path_test import *
PATH_ADN_CHRONICS_FOLDER = os.path.abspath(
os.path.join(PATH_CHRONICS, "test_multi_chronics")
)
PATH_PREVIOUS_RUNNER = os.path.join(data_test_dir, "runner_data")
from grid2op.Reward import L2RPNSandBoxScore
from grid2op.MakeEnv import make
from grid2op.dtypes import dt_float
from grid2op.Agent import DoNothingAgent, RecoPowerlineAgent
from grid2op.utils import EpisodeStatistics, ScoreL2RPN2020, ScoreICAPS2021
from grid2op.Parameters import Parameters
import warnings
warnings.simplefilter("error")
class TestEpisodeStatistics(HelperTests):
"""test teh grid2op.utils.EpisodeStatistics"""
def test_read(self):
"""test that i can read the data stored"""
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
with make("rte_case5_example", test=True) as env:
obs = env.reset()
stats = EpisodeStatistics(env)
aor_, ids_ = stats.get("a_or")
assert aor_.shape == (7930, 8)
assert np.max(ids_) == 19
assert ids_.shape == (7930, 1)
assert self.compare_vect(
np.mean(aor_, axis=0),
np.array(
[
351.6208,
153.674,
91.057,
80.47367,
351.93213,
89.18627,
89.18627,
74.77638,
],
dtype=dt_float,
),
)
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_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_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"))
)
class TestL2RPNSCORE(HelperTests):
"""test teh grid2op.utils.EpisodeStatistics"""
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_RP_NO_OVERFLOW
),
)
)
# delete them
scores.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_OVERFLOW
),
)
)
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_RP_NO_OVERFLOW
),
)
)
my_agent = DoNothingAgent(env.action_space)
my_scores, *_ = scores.get(my_agent)
assert np.max(np.abs(my_scores)) <= self.tol_one
# delete them
scores.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_OVERFLOW
),
)
)
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_RP_NO_OVERFLOW
),
)
)
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_rp
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
scores.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_OVERFLOW
),
)
)
def test_modif_max_step_increase(self):
"""test that i can modify the max step (and that if I increase it it does trigger a recomputation)"""
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_RP_NO_OVERFLOW
),
)
)
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 scores2._recomputed_dn
assert scores2._recomputed_no_ov_rp
# delete them
scores.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_OVERFLOW
),
)
)
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_RP_NO_OVERFLOW
),
)
)
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_rp
scores2 = ScoreL2RPN2020(env, nb_scenario=3, verbose=0, max_step=5)
assert not scores2._recomputed_dn
assert not scores2._recomputed_no_ov_rp
# delete them
scores.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_OVERFLOW
),
)
)
def test_reco_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_rp
# 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_RP_NO_OVERFLOW
),
)
)
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_rp
my_agent = RecoPowerlineAgent(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
scores.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_OVERFLOW
),
)
)
class TestICAPSSCORE(HelperTests):
"""test teh grid2op.utils.EpisodeStatistics"""
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_alarm"),
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_this, n_played, total_ts = scores.get(my_agent)
for (ep_score, op_score, alarm_score) in scores_this:
assert (
np.abs(ep_score - 30.0) <= self.tol_one
), f"wrong score for the episode: {ep_score} vs 30."
assert np.abs(op_score - 0.0) <= self.tol_one, (
f"wrong score for the operationnal cost: " f"{op_score} vs 0."
)
assert np.abs(alarm_score - 100.0) <= 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_RP_NO_OVERFLOW
),
)
)
# delete them
scores.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_OVERFLOW
),
)
)
if __name__ == "__main__":
unittest.main()