Refactor Random Strategy II (#352)

* integrate polytope sampling into random strategy

* rename universal to space filling

bofire/data_models/api.py

@@ -25,7 +25,6 @@
     AnyCondition,
     AnyLocalSearchConfig,
     AnyPredictive,
-    AnySampler,
     AnyStrategy,
     PredictiveStrategy,
     Strategy,
@@ -39,6 +38,7 @@
 
 data_model_list = [
     AnyAcquisitionFunction,
+    AnyCondition,
     AnyConstraint,
     AnyFeature,
     AnyKernel,

bofire/data_models/domain/features.py

@@ -209,6 +209,8 @@ def sample(
         Returns:
             pd.DataFrame: Dataframe containing the samples.
         """
+        if len(self) == 0:
+            return pd.DataFrame()
         if method == SamplingMethodEnum.UNIFORM:
             # we cannot just propagate the provided seed to
             # the sample methods as they would then sample

bofire/data_models/strategies/api.py

@@ -2,7 +2,6 @@
 
 from bofire.data_models.strategies.doe import DoEStrategy
 from bofire.data_models.strategies.factorial import FactorialStrategy
-from bofire.data_models.strategies.polytope import PolytopeSampler
 from bofire.data_models.strategies.predictives.botorch import LSRBO, BotorchStrategy
 from bofire.data_models.strategies.predictives.mobo import MoboStrategy
 from bofire.data_models.strategies.predictives.multiobjective import (
@@ -20,6 +19,7 @@
 )
 from bofire.data_models.strategies.random import RandomStrategy
 from bofire.data_models.strategies.shortest_path import ShortestPathStrategy
+from bofire.data_models.strategies.space_filling import SpaceFillingStrategy
 from bofire.data_models.strategies.stepwise.conditions import (  # noqa: F401
     AlwaysTrueCondition,
     CombiCondition,
@@ -30,9 +30,6 @@
     StepwiseStrategy,
 )
 from bofire.data_models.strategies.strategy import Strategy
-from bofire.data_models.strategies.universal_constraint import (
-    UniversalConstraintSampler,
-)
 
 AbstractStrategy = Union[
     Strategy,
@@ -49,8 +46,7 @@
     QehviStrategy,
     QnehviStrategy,
     QparegoStrategy,
-    PolytopeSampler,
-    UniversalConstraintSampler,
+    SpaceFillingStrategy,
     RandomStrategy,
     DoEStrategy,
     StepwiseStrategy,
@@ -70,8 +66,6 @@
     MoboStrategy,
 ]
 
-AnySampler = Union[PolytopeSampler, UniversalConstraintSampler]
-
 
 AnyCondition = Union[NumberOfExperimentsCondition, CombiCondition, AlwaysTrueCondition]
 

bofire/data_models/strategies/universal_constraint.py → bofire/data_models/strategies/space_filling.py

@@ -10,12 +10,17 @@
     NonlinearEqualityConstraint,
     NonlinearInequalityConstraint,
 )
-from bofire.data_models.features.api import ContinuousInput, ContinuousOutput, Feature
+from bofire.data_models.features.api import (
+    CategoricalOutput,
+    ContinuousInput,
+    ContinuousOutput,
+    Feature,
+)
 from bofire.data_models.strategies.strategy import Strategy
 
 
-class UniversalConstraintSampler(Strategy):
-    """Sampler that generates samples by optimization in IPOPT.
+class SpaceFillingStrategy(Strategy):
+    """Stratey that generates space filling samples by optimization in IPOPT.
 
     Attributes:
         domain (Domain): Domain defining the constrained input space
@@ -24,7 +29,7 @@ class UniversalConstraintSampler(Strategy):
         ipopt_options (dict, optional): Dictionary containing options for the IPOPT solver. Defaults to {"maxiter":200, "disp"=0}.
     """
 
-    type: Literal["UniversalConstraintSampler"] = "UniversalConstraintSampler"
+    type: Literal["SpaceFillingStrategy"] = "SpaceFillingStrategy"
     sampling_fraction: Annotated[float, Field(gt=0, lt=1)] = 0.3
     ipopt_options: dict = {"maxiter": 200, "disp": 0}
 
@@ -40,7 +45,4 @@ def is_constraint_implemented(cls, my_type: Type[Constraint]) -> bool:
 
     @classmethod
     def is_feature_implemented(cls, my_type: Type[Feature]) -> bool:
-        return my_type in [
-            ContinuousInput,
-            ContinuousOutput,
-        ]
+        return my_type in [ContinuousInput, ContinuousOutput, CategoricalOutput]

bofire/data_models/strategies/stepwise/stepwise.py

@@ -7,16 +7,20 @@
 from bofire.data_models.constraints.api import Constraint
 from bofire.data_models.features.api import Feature
 from bofire.data_models.strategies.doe import DoEStrategy
-from bofire.data_models.strategies.polytope import PolytopeSampler
+from bofire.data_models.strategies.factorial import FactorialStrategy
+from bofire.data_models.strategies.predictives.mobo import MoboStrategy
 from bofire.data_models.strategies.predictives.qehvi import QehviStrategy
 from bofire.data_models.strategies.predictives.qnehvi import QnehviStrategy
 from bofire.data_models.strategies.predictives.qparego import QparegoStrategy
 from bofire.data_models.strategies.predictives.sobo import (
     AdditiveSoboStrategy,
+    CustomSoboStrategy,
     MultiplicativeSoboStrategy,
     SoboStrategy,
 )
 from bofire.data_models.strategies.random import RandomStrategy
+from bofire.data_models.strategies.shortest_path import ShortestPathStrategy
+from bofire.data_models.strategies.space_filling import SpaceFillingStrategy
 from bofire.data_models.strategies.stepwise.conditions import (
     AlwaysTrueCondition,
     CombiCondition,
@@ -28,12 +32,16 @@
     SoboStrategy,
     AdditiveSoboStrategy,
     MultiplicativeSoboStrategy,
+    CustomSoboStrategy,
     QehviStrategy,
     QnehviStrategy,
     QparegoStrategy,
-    PolytopeSampler,
+    SpaceFillingStrategy,
     RandomStrategy,
     DoEStrategy,
+    FactorialStrategy,
+    MoboStrategy,
+    ShortestPathStrategy,
 ]
 
 AnyCondition = Union[NumberOfExperimentsCondition, CombiCondition, AlwaysTrueCondition]

bofire/strategies/api.py

@@ -1,6 +1,5 @@
 from bofire.strategies.doe_strategy import DoEStrategy
 from bofire.strategies.mapper import map
-from bofire.strategies.polytope import PolytopeSampler
 from bofire.strategies.predictives.botorch import BotorchStrategy
 from bofire.strategies.predictives.predictive import PredictiveStrategy
 from bofire.strategies.predictives.qehvi import QehviStrategy
@@ -14,6 +13,6 @@
 )
 from bofire.strategies.random import RandomStrategy
 from bofire.strategies.shortest_path import ShortestPathStrategy
+from bofire.strategies.space_filling import SpaceFillingStrategy
 from bofire.strategies.stepwise.stepwise import StepwiseStrategy
 from bofire.strategies.strategy import Strategy
-from bofire.strategies.universal_constraint import UniversalConstraintSampler

bofire/strategies/doe/design.py

@@ -17,9 +17,7 @@
 from bofire.data_models.domain.api import Domain
 from bofire.data_models.enum import SamplingMethodEnum
 from bofire.data_models.features.api import ContinuousInput, Input
-from bofire.data_models.strategies.api import (
-    PolytopeSampler as PolytopeSamplerDataModel,
-)
+from bofire.data_models.strategies.api import RandomStrategy as RandomStrategyDataModel
 from bofire.strategies.doe.objective import get_objective_class
 from bofire.strategies.doe.utils import (
     constraints_as_scipy_constraints,
@@ -28,7 +26,7 @@
     nchoosek_constraints_as_bounds,
 )
 from bofire.strategies.enum import OptimalityCriterionEnum
-from bofire.strategies.polytope import PolytopeSampler
+from bofire.strategies.random import RandomStrategy
 
 
 def find_local_max_ipopt_BaB(
@@ -454,9 +452,7 @@ def find_local_max_ipopt(
         x0 = sampling.values.flatten()
     else:
         if len(domain.constraints.get(NonlinearConstraint)) == 0:
-            sampler = PolytopeSampler(
-                data_model=PolytopeSamplerDataModel(domain=domain)
-            )
+            sampler = RandomStrategy(data_model=RandomStrategyDataModel(domain=domain))
             x0 = sampler.ask(n_experiments).to_numpy().flatten()
         else:
             warnings.warn(

bofire/strategies/doe/utils.py

@@ -18,10 +18,8 @@
 )
 from bofire.data_models.domain.domain import Domain
 from bofire.data_models.features.continuous import ContinuousInput
-from bofire.data_models.strategies.api import (
-    PolytopeSampler as PolytopeSamplerDataModel,
-)
-from bofire.strategies.polytope import PolytopeSampler
+from bofire.data_models.strategies.api import RandomStrategy as RandomStrategyDataModel
+from bofire.strategies.random import RandomStrategy
 
 
 def get_formula_from_string(
@@ -175,7 +173,7 @@ def n_zero_eigvals(
     )
     N = len(model_formula) + 3
 
-    sampler = PolytopeSampler(data_model=PolytopeSamplerDataModel(domain=domain))
+    sampler = RandomStrategy(data_model=RandomStrategyDataModel(domain=domain))
     X = sampler.ask(N)
     # compute eigenvalues of information matrix
     A = model_formula.get_model_matrix(X)

bofire/strategies/mapper.py

@@ -3,7 +3,6 @@
 import bofire.data_models.strategies.api as data_models
 from bofire.strategies.doe_strategy import DoEStrategy
 from bofire.strategies.factorial import FactorialStrategy
-from bofire.strategies.polytope import PolytopeSampler
 from bofire.strategies.predictives.botorch import BotorchStrategy
 from bofire.strategies.predictives.mobo import MoboStrategy
 from bofire.strategies.predictives.predictive import PredictiveStrategy
@@ -18,9 +17,9 @@
 )
 from bofire.strategies.random import RandomStrategy
 from bofire.strategies.shortest_path import ShortestPathStrategy
+from bofire.strategies.space_filling import SpaceFillingStrategy
 from bofire.strategies.stepwise.stepwise import StepwiseStrategy
 from bofire.strategies.strategy import Strategy
-from bofire.strategies.universal_constraint import UniversalConstraintSampler
 
 STRATEGY_MAP: Dict[Type[data_models.Strategy], Type[Strategy]] = {
     data_models.RandomStrategy: RandomStrategy,
@@ -31,8 +30,7 @@
     data_models.QehviStrategy: QehviStrategy,
     data_models.QnehviStrategy: QnehviStrategy,
     data_models.QparegoStrategy: QparegoStrategy,
-    data_models.PolytopeSampler: PolytopeSampler,
-    data_models.UniversalConstraintSampler: UniversalConstraintSampler,
+    data_models.SpaceFillingStrategy: SpaceFillingStrategy,
     data_models.DoEStrategy: DoEStrategy,
     data_models.StepwiseStrategy: StepwiseStrategy,
     data_models.FactorialStrategy: FactorialStrategy,