def load_forecasts(env, chronic_idx, config):
chronics_dir, chronics, chronics_sorted = get_sorted_chronics(env=env)
case_chronics = env.chronics_handler.path
chronic_name = chronics_sorted[chronic_idx]
chronic_dir = os.path.join(case_chronics, chronic_name)
prods_file = [
file for file in os.listdir(chronic_dir) if is_prods_file(file)
]
loads_file = [
file for file in os.listdir(chronic_dir) if is_loads_file(file)
]
assert len(prods_file) == 1 and len(loads_file) == 1
prods = read_bz2_to_dataframe(os.path.join(chronic_dir, prods_file[0]),
sep=";")
loads = read_bz2_to_dataframe(os.path.join(chronic_dir, loads_file[0]),
sep=";")
gen_org_to_grid_name = config["names_chronics_to_grid"]["prods"]
load_org_to_grid_name = config["names_chronics_to_grid"]["loads"]
prods = prods.rename(columns=gen_org_to_grid_name)
prods = prods.reindex(sorted(prods.columns,
key=lambda x: x.split("_")[-1]),
axis=1)
loads = loads.rename(columns=load_org_to_grid_name)
loads = loads.reindex(sorted(loads.columns,
key=lambda x: x.split("_")[-1]),
axis=1)
return prods.values, loads.values
if save_dir:
agent_name_ = agent_name.replace(" ", "-").lower()
file_name = (f"{agent_name_}-chronic-" +
"{:05}".format(chronic_idx) + "-")
fig_mu.savefig(os.path.join(save_dir, file_name + "mus"))
fig_frac.savefig(
os.path.join(save_dir, file_name + "fraction-obj"))
plt.close(fig_mu)
plt.close(fig_frac)
@staticmethod
def _runner(case, env, agent, n_chronics, n_steps,
done_chronic_indices=()):
chronics_dir, chronics, chronics_sorted = get_sorted_chronics(env=env)
pprint("Chronics:", chronics_dir)
np.random.seed(0)
env.seed(0)
chronic_data = []
for chronic_idx, chronic in enumerate(chronics_sorted):
if len(chronic_data) >= n_chronics > 0:
break
if chronic_idx in done_chronic_indices:
continue
chronic_org_idx = chronics.index(chronic)
env.chronics_handler.tell_id(chronic_org_idx - 1) # Set chronic id
def _runner(self,
env,
agent,
do_chronics=(),
n_chronics=-1,
n_steps=-1,
verbose=False):
self.print_experiment("Performance")
agent.print_agent(default=verbose)
agent_name = agent.name.replace(" ", "-").lower()
self.collector._load_chronics(agent_name=agent_name)
chronics_dir, chronics, chronics_sorted = get_sorted_chronics(env=env)
pprint("Chronics:", chronics_dir)
if len(self.collector.chronic_ids):
pprint(
" - Done chronics:",
", ".join(
map(lambda x: str(x), sorted(self.collector.chronic_ids))),
)
if len(do_chronics):
pprint(
" - To do chronics:",
", ".join(map(lambda x: str(x), sorted(do_chronics))),
)
done_chronic_ids = []
for chronic_idx, chronic_name in enumerate(chronics_sorted):
if len(done_chronic_ids) >= n_chronics >= 0:
break
# If chronic already done
if chronic_idx in self.collector.chronic_ids:
continue
# Environment specific filtering
if "rte_case5" in env.name:
if chronic_idx not in do_chronics:
continue
elif "l2rpn_2019" in env.name:
if chronic_idx not in do_chronics:
continue
elif "l2rpn_wcci_2020" in env.name:
if chronic_idx not in do_chronics:
continue
chronic_org_idx = chronics.index(chronic_name)
env.chronics_handler.tell_id(chronic_org_idx - 1) # Set chronic id
obs = env.reset()
agent.reset(obs=obs)
chronic_len = env.chronics_handler.real_data.data.max_iter
chronic_path_name = "/".join(
os.path.normpath(env.chronics_handler.get_id()).split(
os.sep)[-3:])
augmentation_info = os.path.join(env.chronics_handler.get_id(),
"augmentation.json")
ps = None
if os.path.isfile(augmentation_info):
with open(augmentation_info, "r") as f:
ps = json.load(f)
pprint(" - Chronic:", chronic_path_name)
# if ps:
# p = ps["p"]
# min_p = ps["min_p"]
# max_p = ps["max_p"]
# targets = ps["targets"]
#
# pprint(" - Augmentation:", ps["augmentation"])
# pprint(
# " - Rate:",
# "p = {:.2f} ~ [{:.2f}, {:.2f}]".format(p, min_p, max_p),
# )
# if targets:
# pprint(" - Targets:", str(targets))
t = 0
done = False
reward = np.nan
"""
Collect data.
"""
while not done and not (t >= n_steps > 0):
action = agent.act(obs, reward=reward, done=done)
obs_next, reward, done, info = env.step(action)
self.collector._add(obs, action, reward, done)
semi_action = False
if agent.semi_agent is not None:
semi_action = agent.semi_agent.semi_action
dist, dist_status, dist_status = agent.distance_to_ref_topology(
obs_next.topo_vect, obs_next.line_status)
self.collector._add_plus(dist, dist_status, dist_status,
semi_action)
t = env.chronics_handler.real_data.data.current_index
if t % 200 == 0:
semi_sum = np.sum(self.collector.semi_actions)
pprint(" - Step:", t, semi_sum)
if done:
semi_sum = np.sum(self.collector.semi_actions)
pprint(" - Length:", f"{t}/{chronic_len}", semi_sum)
obs = obs_next
self.collector.obses.append(obs.to_vect())
self.collector.total_return = compute_returns(
self.collector.rewards)[0]
self.collector.duration = t
self.collector.chronic_len = chronic_len
self.collector.chronic_name = chronic_name
done_chronic_ids.append(chronic_idx)
self.collector._save_chronic(agent_name, chronic_idx, verbose)
self.collector.reset()
def collect(self,
env,
agent,
do_chronics=(),
n_chronics=-1,
n_steps=-1,
verbose=False):
self.print_collector("Collecting")
agent.print_agent(default=verbose)
agent_name = agent.name.replace(" ", "-").lower()
self._load_chronics(agent_name=agent_name)
chronics_dir, chronics, chronics_sorted = get_sorted_chronics(env=env)
pprint("Chronics:", chronics_dir)
if len(self.chronic_ids):
pprint(
" - Done chronics:",
", ".join(map(lambda x: str(x), sorted(self.chronic_ids))),
)
if len(do_chronics):
pprint(
" - To do chronics:",
", ".join(map(lambda x: str(x), sorted(do_chronics))),
)
done_chronic_ids = []
for chronic_idx, chronic_name in enumerate(chronics_sorted):
if len(done_chronic_ids) >= n_chronics > 0:
break
# If chronic already done
if chronic_idx in self.chronic_ids:
continue
# Environment specific filtering
if "rte_case5" in env.name:
if chronic_idx not in do_chronics:
continue
elif "l2rpn_2019" in env.name:
if chronic_idx not in do_chronics:
continue
elif "l2rpn_wcci_2020" in env.name:
if chronic_idx not in do_chronics:
continue
chronic_org_idx = chronics.index(chronic_name)
env.chronics_handler.tell_id(chronic_org_idx - 1) # Set chronic id
obs = env.reset()
agent.reset(obs=obs)
chronic_len = env.chronics_handler.real_data.data.max_iter
chronic_path_name = "/".join(
os.path.normpath(env.chronics_handler.get_id()).split(
os.sep)[-3:])
pprint(" - Chronic:", chronic_path_name)
t = 0
done = False
reward = np.nan
"""
Collect data.
"""
while not done and not (t >= n_steps > 0):
action = agent.act(obs, reward=reward, done=done)
obs_next, reward, done, info = env.step(action)
self._add(obs, action, reward, done)
t = env.chronics_handler.real_data.data.current_index
if t % 50 == 0:
pprint(" - Step:", t)
if done:
pprint(" - Length:", f"{t}/{chronic_len}")
obs = obs_next
self.obses.append(obs.to_vect())
done_chronic_ids.append(chronic_idx)
self._save_chronic(agent_name, chronic_idx, verbose)
self.reset()