# 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.
import os
import numpy as np
import json
import copy
import tempfile
from grid2op.dtypes import dt_float
from grid2op.Reward import L2RPNSandBoxScore, _AlarmScore
from grid2op.utils.underlying_statistics import EpisodeStatistics
from grid2op.utils.l2rpn_2020_scores import ScoreL2RPN2020
from grid2op.Episode import EpisodeData
[docs]class ScoreICAPS2021(ScoreL2RPN2020):
"""
This class allows to compute the same score as the one computed for the ICAPS 2021 competitions.
It uses some "EpisodeStatistics" of the environment to compute these scores. These statistics, if not available
are computed at the initialization.
When using it a second time these information are reused.
This scores is the combination of the `ScoreL2RPN2020` score and some extra scores based on the alarm feature.
Examples
---------
This class can be used as follow:
.. code-block:: python
import grid2op
from grid2op.utils import ScoreICAPS2021
from grid2op.Agent import DoNothingAgent
env = grid2op.make("l2rpn_case14_sandbox")
nb_scenario = 2
my_score = ScoreICAPS2021(env,
nb_scenario=nb_scenario,
env_seeds=[0 for _ in range(nb_scenario)],
agent_seeds=[0 for _ in range(nb_scenario)]
)
my_agent = DoNothingAgent(env.action_space)
print(my_score.get(my_agent))
Notes
-------
To prevent overfitting, we strongly recommend you to use the :func:`grid2op.Environment.Environment.train_val_split`
and use this function on the built validation set only.
Also note than computing the statistics, and evaluating an agent on a whole dataset of multiple GB can take a
really long time and a lot of memory. This fact, again, plea in favor of using this function only on
a validation set.
We also strongly recommend to set the seeds of your agent (agent_seeds)
and of the environment (env_seeds) if you want to use this feature. Reproducibility is really important if you
want to make progress.
.. warning::
The triggering (or not) of the recomputation of the statistics is not perfect for now.
We recommend you to use always
the same seeds (`env_seeds` and `agent_seeds` key word argument of this functions)
and the same parameters (`env.parameters`) when using a given environments.
You might need to clean it manually if you change
one of theses things by calling :func:`ScoreL2RPN2020.clear_all()` function .
"""
NAME_DN = "icaps2021_dn"
# NAME_DN_NO_OVERFLOW = "icaps2021_no_overflow"
NAME_RP_NO_OVERWLOW = "icaps2021_no_overflow_reco"
[docs] def __init__(
self,
env,
env_seeds=None,
agent_seeds=None,
nb_scenario=16,
min_losses_ratio=0.8,
verbose=0,
max_step=-1,
nb_process_stats=1,
scale_alarm_score=100.0,
weight_op_score=0.7,
weight_alarm_score=0.3,
add_nb_highres_sim=False,
):
ScoreL2RPN2020.__init__(
self,
env=env,
env_seeds=env_seeds,
agent_seeds=agent_seeds,
nb_scenario=nb_scenario,
min_losses_ratio=min_losses_ratio,
verbose=verbose,
max_step=max_step,
nb_process_stats=nb_process_stats,
scores_func={
"grid_operational_cost": L2RPNSandBoxScore,
"alarm_cost": _AlarmScore,
},
score_names=["grid_operational_cost_scores", "alarm_cost_scores"],
add_nb_highres_sim=add_nb_highres_sim,
)
self.scale_alarm_score = scale_alarm_score
self.weight_op_score = weight_op_score
self.weight_alarm_score = weight_alarm_score
def _compute_episode_score(
self,
ep_id, # the ID here, which is an integer and is not the ID from chronics balblabla
meta,
other_rewards,
dn_metadata,
no_ov_metadata,
score_file_to_use="grid_operational_cost_scores",
):
"""
Performs the rescaling of the score given the information stored in the "statistics" of this
environment.
This computes the score for a single episode. The loop to compute the score for all the
episodes is the same as for l2rpn_2020_scores and is then reused.
"""
# compute the operational score
op_score, n_played, total_ts = super()._compute_episode_score(
ep_id,
meta,
other_rewards,
dn_metadata,
no_ov_metadata,
# score_file_to_use should match the
# L2RPNSandBoxScore key in
# self.scores_func
score_file_to_use=score_file_to_use
)
# should match underlying_statistics.run_env `dict_kwg["other_rewards"][XXX] = ...`
# XXX is right now f"{EpisodeStatistics.KEY_SCORE}_{nm}" [this should match the XXX]
alarm_score_nm = "alarm_cost_scores"
real_nm = EpisodeStatistics._nm_score_from_attr_name(alarm_score_nm)
key_score_file = f"{EpisodeStatistics.KEY_SCORE}_{real_nm}"
alarm_score = float(other_rewards[-1][key_score_file])
alarm_score = self.scale_alarm_score * alarm_score
ep_score = (
self.weight_op_score * op_score + self.weight_alarm_score * alarm_score
)
return (ep_score, op_score, alarm_score), n_played, total_ts
if __name__ == "__main__":
import grid2op
from lightsim2grid import LightSimBackend
from grid2op.Agent import RandomAgent, DoNothingAgent
env = grid2op.make("l2rpn_case14_sandbox", backend=LightSimBackend())
nb_scenario = 2
my_score = ScoreICAPS2021(
env,
nb_scenario=nb_scenario,
env_seeds=[0 for _ in range(nb_scenario)],
agent_seeds=[0 for _ in range(nb_scenario)],
)
my_agent = RandomAgent(env.action_space)
my_agent = DoNothingAgent(env.action_space)
print(my_score.get(my_agent))