Implement ENTMOOT in Bofire

bofire/data_models/strategies/api.py

@@ -3,6 +3,7 @@
 from bofire.data_models.strategies.doe import DoEStrategy
 from bofire.data_models.strategies.factorial import FactorialStrategy
 from bofire.data_models.strategies.predictives.botorch import LSRBO, BotorchStrategy
+from bofire.data_models.strategies.predictives.enting import EntingStrategy
 from bofire.data_models.strategies.predictives.mobo import MoboStrategy
 from bofire.data_models.strategies.predictives.multiobjective import (
     MultiobjectiveStrategy,
@@ -47,6 +48,7 @@
     QnehviStrategy,
     QparegoStrategy,
     SpaceFillingStrategy,
+    EntingStrategy,
     RandomStrategy,
     DoEStrategy,
     StepwiseStrategy,
@@ -63,6 +65,7 @@
     QehviStrategy,
     QnehviStrategy,
     QparegoStrategy,
+    EntingStrategy,
     MoboStrategy,
 ]
 

bofire/data_models/strategies/predictives/enting.py

@@ -0,0 +1,116 @@
+from typing import Any, Dict, Literal, Type
+
+from pydantic import PositiveFloat, PositiveInt
+
+from bofire.data_models.constraints.api import (
+    Constraint,
+    LinearEqualityConstraint,
+    LinearInequalityConstraint,
+    NChooseKConstraint,
+)
+from bofire.data_models.features.api import (
+    CategoricalDescriptorInput,
+    CategoricalInput,
+    ContinuousInput,
+    ContinuousOutput,
+    DiscreteInput,
+    Feature,
+)
+from bofire.data_models.objectives.api import (
+    MaximizeObjective,
+    MinimizeObjective,
+    Objective,
+)
+from bofire.data_models.strategies.predictives.predictive import PredictiveStrategy
+
+
+class EntingStrategy(PredictiveStrategy):
+    type: Literal["EntingStrategy"] = "EntingStrategy"
+
+    # uncertainty model parameters
+    beta: PositiveFloat = 1.96
+    bound_coeff: PositiveFloat = 0.5
+    acq_sense: Literal["exploration", "penalty"] = "exploration"
+    dist_trafo: Literal["normal", "standard"] = "normal"
+    dist_metric: Literal["euclidean_squared", "l1", "l2"] = "euclidean_squared"
+    cat_metric: Literal["overlap", "of", "goodall4"] = "overlap"
+
+    # lightgbm training hyperparameters
+    # see https://lightgbm.readthedocs.io/en/latest/Parameters.html
+    num_boost_round: PositiveInt = 100
+    max_depth: PositiveInt = 3
+    min_data_in_leaf: PositiveInt = 1
+    min_data_per_group: PositiveInt = 1
+    verbose: Literal[-1, 0, 1, 2] = -1
+
+    # pyomo parameters
+    solver_name: str = "gurobi"
+    solver_verbose: bool = False
+    solver_params: Dict[str, Any] = {}
+
+    # kappa_fantasy determines a bound on the predicted value of an unseen point
+    # used for making batch predictions, y* = mean + kappa_fantasy * std
+    # for a both min and max problems, a positive value is 'pesimistic'
+    # and a negative value is 'optimistic'
+    # a value of zero implies future observations will be exactly the mean
+    kappa_fantasy: float = 1.96
+
+    @classmethod
+    def is_constraint_implemented(cls, my_type: Type[Constraint]) -> bool:
+        return my_type in [
+            LinearEqualityConstraint,
+            LinearInequalityConstraint,
+            NChooseKConstraint,
+        ]
+
+    @classmethod
+    def is_feature_implemented(cls, my_type: Type[Feature]) -> bool:
+        return my_type in [
+            CategoricalInput,
+            DiscreteInput,
+            CategoricalDescriptorInput,
+            ContinuousInput,
+            ContinuousOutput,
+        ]
+
+    @classmethod
+    def is_objective_implemented(cls, my_type: Type[Objective]) -> bool:
+        return my_type in [MinimizeObjective, MaximizeObjective]
+
+    def dump_enting_params(self) -> dict:
+        """Dump the model in the nested structure required for ENTMOOT.
+
+        Returns:
+            dict: the nested dictionary of entmoot params.
+        """
+        return {
+            "unc_params": {
+                "beta": self.beta,
+                "bound_coeff": self.bound_coeff,
+                "acq_sense": self.acq_sense,
+                "dist_trafo": self.dist_trafo,
+                "dist_metric": self.dist_metric,
+                "cat_metric": self.cat_metric,
+            },
+            "tree_train_params": {
+                "train_params": {
+                    "num_boost_round": self.num_boost_round,
+                    "max_depth": self.max_depth,
+                    "min_data_in_leaf": self.min_data_in_leaf,
+                    "min_data_per_group": self.min_data_per_group,
+                    "verbose": self.verbose,
+                },
+            },
+        }
+
+    def dump_solver_params(self) -> dict:
+        """Dump the solver parameters for pyomo.
+
+        Returns:
+            dict: the nested dictionary of solver params.
+        """
+        return {
+            "solver_name": self.solver_name,
+            "verbose": self.solver_verbose,
+            **self.solver_params,
+        }

bofire/strategies/api.py

@@ -1,6 +1,7 @@
 from bofire.strategies.doe_strategy import DoEStrategy
 from bofire.strategies.mapper import map
 from bofire.strategies.predictives.botorch import BotorchStrategy
+from bofire.strategies.predictives.enting import EntingStrategy
 from bofire.strategies.predictives.predictive import PredictiveStrategy
 from bofire.strategies.predictives.qehvi import QehviStrategy
 from bofire.strategies.predictives.qnehvi import QnehviStrategy

bofire/strategies/mapper.py

@@ -4,6 +4,7 @@
 from bofire.strategies.doe_strategy import DoEStrategy
 from bofire.strategies.factorial import FactorialStrategy
 from bofire.strategies.predictives.botorch import BotorchStrategy
+from bofire.strategies.predictives.enting import EntingStrategy
 from bofire.strategies.predictives.mobo import MoboStrategy
 from bofire.strategies.predictives.predictive import PredictiveStrategy
 from bofire.strategies.predictives.qehvi import QehviStrategy
@@ -34,6 +35,7 @@
     data_models.DoEStrategy: DoEStrategy,
     data_models.StepwiseStrategy: StepwiseStrategy,
     data_models.FactorialStrategy: FactorialStrategy,
+    data_models.EntingStrategy: EntingStrategy,
     data_models.MoboStrategy: MoboStrategy,
     data_models.ShortestPathStrategy: ShortestPathStrategy,
 }