def _load_experiment(ex_dir: pyrado.PathLike):
# Load the algorithm
algo = Algorithm.load_snapshot(ex_dir)
if not isinstance(algo, (NPDR, BayesSim)):
raise pyrado.TypeErr(given=algo, expected_type=(NPDR, BayesSim))
# Load the prior and the data
prior = pyrado.load("prior.pt", ex_dir)
data_real = pyrado.load("data_real.pt", ex_dir)
# Load the posteriors
posteriors = [
SBIBase.load_posterior(ex_dir, idx_round=i, verbose=True)
for i in range(algo.num_sbi_rounds)
]
posteriors = remove_none_from_list(
posteriors) # in case the algorithm terminated early
if data_real.shape[0] > len(posteriors):
print_cbt(
f"Found {data_real.shape[0]} data sets but {len(posteriors)} posteriors. Truncated the superfluous data.",
"y",
)
data_real = data_real[:len(posteriors), :]
# Artificially repeat the data (which was the same for every round) to later be able to use the same code
data_real = data_real.repeat(len(posteriors), 1)
assert data_real.shape[0] == len(posteriors)
return algo, prior, data_real, posteriors
env_sim, policy, kwout = load_experiment(ex_dir, args)
env_real = pyrado.load("env_real.pkl", ex_dir)
prior = kwout["prior"]
posterior = kwout["posterior"]
data_real = kwout["data_real"]
if args.mode.lower() == "evolution-round" and args.iter == -1:
args.iter = algo.curr_iter
print_cbt(
"Set the evaluation iteration to the latest iteration of the algorithm.",
"y")
# Load the sequence of posteriors if desired
if args.mode.lower() == "evolution-iter":
posterior = [
SBIBase.load_posterior(ex_dir, idx_iter=i, verbose=True)
for i in range(algo.max_iter)
]
posterior = remove_none_from_list(
posterior) # in case the algorithm terminated early
elif args.mode.lower() == "evolution-round":
posterior = [
SBIBase.load_posterior(ex_dir, idx_round=i, verbose=True)
for i in range(algo.num_sbi_rounds)
]
posterior = remove_none_from_list(
posterior) # in case the algorithm terminated early
if "evolution" in args.mode.lower(
) and data_real.shape[0] > len(posterior):
print_cbt(