Designed to work with model-based search on SH-based multi-fidelity algorithms.
Requires certain strict assumptions about fidelities and rung maps.
Returns True is in the warmstart phase and False under model-based search.
Source code in neps/optimizers/multi_fidelity/mf_bo.py
| def is_init_phase(self) -> bool:
"""Returns True is in the warmstart phase and False under model-based search."""
if self.modelling_type == "rung":
# build a model per rung or per fidelity
# in this case, the initial design checks if `init_size` number of
# configurations have finished at a rung or not and the highest such rung is
# chosen for model building at teh current iteration
if self._active_rung() is None:
return True
elif self.modelling_type == "joint":
# builds a model across all fidelities with the fidelity as a dimension
# in this case, calculate the total number of function evaluations spent
# and in vanilla BO fashion use that to compare with the initital design size
valid_perf_mask = self.observed_configs["perf"].notna()
rungs = self.observed_configs.loc[valid_perf_mask, "rung"]
total_resources = sum(self.rung_map[r] for r in rungs)
resources = total_resources / self.max_budget
if resources < self.init_size:
return True
else:
raise ValueError("Choice of modelling_type not in {{'rung', 'joint'}}")
return False
|
Samples configuration from policies or random.
Source code in neps/optimizers/multi_fidelity/mf_bo.py
| def sample_new_config(
self,
rung: int | None = None,
**kwargs: Any,
) -> SearchSpace:
"""Samples configuration from policies or random."""
if self.model_based and not self.is_init_phase():
incumbent = None
if self.modelling_type == "rung":
# `rung` should not be None when not in init phase
active_max_rung = self._active_rung()
fidelity = None
active_max_fidelity = self.rung_map[active_max_rung]
elif self.modelling_type == "joint":
fidelity = self.rung_map[rung]
active_max_fidelity = None
# IMPORTANT step for correct 2-step acquisition
incumbent = min(self.rung_histories[rung]["perf"])
else:
raise ValueError("Choice of modelling_type not in 'rung', 'joint'")
assert (
(fidelity is None and active_max_fidelity is not None)
or (active_max_fidelity is None and fidelity is not None)
or (active_max_fidelity is not None and fidelity is not None)
), "Either condition needs to be not None!"
config = self.model_policy.sample(
active_max_fidelity=active_max_fidelity,
fidelity=fidelity,
incumbent=incumbent,
**self.sampling_args,
)
elif self.sampling_policy is not None:
config = self.sampling_policy.sample(**self.sampling_args)
else:
config = sample_one_old(
self.pipeline_space,
patience=self.patience,
user_priors=self.use_priors,
ignore_fidelity=True,
)
return config
|