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make ngopt faster #1329

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make ngopt faster #1329

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434847e
make ngopt faster
teytaud Jan 12, 2022
be22cfc
black
teytaud Jan 12, 2022
d4b373c
fix
teytaud Jan 12, 2022
d1ccebc
fix
teytaud Jan 12, 2022
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2 changes: 1 addition & 1 deletion nevergrad/optimization/oneshot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,7 +175,7 @@ def _internal_ask(self) -> tp.ArrayLike:
point = self.parametrization.sample().get_standardized_data(reference=self.parametrization)
else:
raise ValueError("Unkwnown sampler {self.sampler}")
self._opposable_data = scale * point
self._opposable_data = scale * point # type: ignore
return self._opposable_data # type: ignore

def _internal_provide_recommendation(self) -> tp.Optional[tp.ArrayLike]:
Expand Down
22 changes: 13 additions & 9 deletions nevergrad/optimization/optimizerlib.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2779,9 +2779,9 @@ def _select_optimizer_cls(self) -> base.OptCls:
if (
self.budget > 5000 * self.dimension
): # Asymptotically let us trust NGOpt36 and its subtle restart.
return NGOpt36
return NGOpt36._select_optimizer_cls(self) # type: ignore
if self.dimension < 5: # Low dimension: let us hit the bounds.
return NGOpt21
return NGOpt21._select_optimizer_cls(self) # type: ignore
if self.dimension < 10: # Moderate dimension: reasonable restart + bet and run.
num = 1 + int(np.sqrt(8.0 * (8 * self.budget) // (self.dimension * 1000)))
return ConfPortfolio(optimizers=[NGOpt14] * num, warmup_ratio=0.7)
Expand All @@ -2793,7 +2793,7 @@ def _select_optimizer_cls(self) -> base.OptCls:
)
# We need a special case for dim < 30 ---> let's see later.
# Otherwise, let us go back to normal life: NGOpt16 which rocks in many cases, possibly Cobyla.
return NGOpt16
return NGOpt16._select_optimizer_cls(self) # type: ignore
elif ( # This might be specific of high-precision cases.
self.budget is not None
and self.fully_continuous
Expand All @@ -2803,7 +2803,11 @@ def _select_optimizer_cls(self) -> base.OptCls:
and self.budget > 50 * self.dimension
and p.helpers.Normalizer(self.parametrization).fully_bounded
):
return NGOpt8 if self.dimension < 3 else NGOpt15
return (
NGOpt8._select_optimizer_cls(self) # type: ignore
if self.dimension < 3
else NGOpt15._select_optimizer_cls(self) # type: ignore
)
else:
return super()._select_optimizer_cls()

Expand Down Expand Up @@ -2851,9 +2855,9 @@ def _select_optimizer_cls(self) -> base.OptCls:
if (
self.budget > 5000 * self.dimension
): # Asymptotically let us trust NGOpt36 and its subtle restart.
return NGOpt36
return NGOpt36._select_optimizer_cls(self) # type: ignore
if self.dimension < 5: # Low dimension: let us hit the bounds.
return NGOpt21
return NGOpt21._select_optimizer_cls(self) # type: ignore
if self.dimension < 10: # Moderate dimension: reasonable restart + bet and run.
num = 1 + int(np.sqrt(8.0 * (8 * self.budget) // (self.dimension * 1000)))
return ConfPortfolio(optimizers=[NGOpt14] * num, warmup_ratio=0.7)
Expand All @@ -2867,7 +2871,7 @@ def _select_optimizer_cls(self) -> base.OptCls:
return CmaFmin2
# We need a special case for dim < 30 ---> let's see later.
# Otherwise, let us go back to normal life: NGOpt16 which rocks in many cases, possibly Cobyla.
return NGOpt16
return NGOpt16._select_optimizer_cls(self) # type: ignore
elif ( # This might be specific of high-precision cases.
self.budget is not None
and self.fully_continuous
Expand All @@ -2878,12 +2882,12 @@ def _select_optimizer_cls(self) -> base.OptCls:
and p.helpers.Normalizer(self.parametrization).fully_bounded
):
if self.dimension < 3:
return NGOpt8
return NGOpt8._select_optimizer_cls(self) # type: ignore
if self.dimension <= 20 and self.num_workers == 1:
MetaModelFmin2 = ParametrizedMetaModel(multivariate_optimizer=CmaFmin2)
MetaModelFmin2.no_parallelization = True
return MetaModelFmin2
return NGOpt15
return NGOpt15._select_optimizer_cls(self) # type: ignore
else:
return super()._select_optimizer_cls()

Expand Down