add interpoint to botorch (#426)

bofire/data_models/domain/domain.py

@@ -21,6 +21,7 @@
 from bofire.data_models.constraints.api import (
  AnyConstraint,
  ConstraintNotFulfilledError,
+ InterpointEqualityConstraint,
  LinearConstraint,
  NChooseKConstraint,
  ProductConstraint,
@@ -154,6 +155,9 @@ def validate_constraints(self):
  for f in c.features: # type: ignore
  if f not in keys:
  raise ValueError(f"feature {f} in constraint unknown ({keys})")
+ for c in self.constraints.get(InterpointEqualityConstraint):
+ if c.feature not in keys:
+ raise ValueError(f"feature {c.feature} not known.")
  return self
 
  @model_validator(mode="after")

bofire/data_models/strategies/predictives/botorch.py

@@ -7,13 +7,18 @@
 from bofire.data_models.base import BaseModel
 from bofire.data_models.constraints.api import (
  Constraint,
+ InterpointConstraint,
  LinearConstraint,
  NonlinearEqualityConstraint,
  NonlinearInequalityConstraint,
 )
 from bofire.data_models.domain.api import Domain, Outputs
 from bofire.data_models.enum import CategoricalEncodingEnum, CategoricalMethodEnum
-from bofire.data_models.features.api import CategoricalDescriptorInput, CategoricalInput
+from bofire.data_models.features.api import (
+ CategoricalDescriptorInput,
+ CategoricalInput,
+ ContinuousInput,
+)
 from bofire.data_models.outlier_detection.api import OutlierDetections
 from bofire.data_models.strategies.predictives.predictive import PredictiveStrategy
 from bofire.data_models.strategies.shortest_path import has_local_search_region
@@ -126,6 +131,16 @@ def is_constraint_implemented(cls, my_type: Type[Constraint]) -> bool:
  return False
  return True
 
+ @model_validator(mode="after")
+ def validate_interpoint_constraints(self):
+ if self.domain.constraints.get(InterpointConstraint) and len(
+ self.domain.inputs.get(ContinuousInput)
+ ) != len(self.domain.inputs):
+ raise ValueError(
+ "Interpoint constraints can only be used for pure continuous search spaces."
+ )
+ return self
+
  @model_validator(mode="after")
  def validate_surrogate_specs(self):
  """Ensures that a prediction model is specified for each output feature"""

bofire/data_models/strategies/predictives/qparego.py

@@ -3,6 +3,12 @@
 from pydantic import Field
 
 from bofire.data_models.acquisition_functions.api import qEI, qLogEI, qLogNEI, qNEI
+from bofire.data_models.constraints.api import (
+ Constraint,
+ InterpointConstraint,
+ NonlinearEqualityConstraint,
+ NonlinearInequalityConstraint,
+)
 from bofire.data_models.features.api import Feature
 from bofire.data_models.objectives.api import (
  CloseToTargetObjective,
@@ -40,3 +46,21 @@ def is_objective_implemented(cls, my_type: Type[Objective]) -> bool:
  ]:
  return False
  return True
+
+ @classmethod
+ def is_constraint_implemented(cls, my_type: Type[Constraint]) -> bool:
+ """Method to check if a specific constraint type is implemented for the strategy
+
+ Args:
+ my_type (Type[Constraint]): Constraint class
+
+ Returns:
+ bool: True if the constraint type is valid for the strategy chosen, False otherwise
+ """
+ if my_type in [
+ NonlinearInequalityConstraint,
+ NonlinearEqualityConstraint,
+ InterpointConstraint,
+ ]:
+ return False
+ return True

bofire/strategies/predictives/botorch.py

@@ -45,6 +45,7 @@
 from bofire.surrogates.botorch_surrogates import BotorchSurrogates
 from bofire.utils.torch_tools import (
  get_initial_conditions_generator,
+ get_interpoint_constraints,
  get_linear_constraints,
  get_nonlinear_constraints,
  tkwargs,
@@ -358,6 +359,9 @@ def _optimize_acqf_continuous(
  options=self._get_optimizer_options(), # type: ignore
  )
  else:
+ interpoints = get_interpoint_constraints(
+ domain=self.domain, n_candidates=candidate_count
+ )
  candidates, acqf_vals = optimize_acqf(
  acq_function=acqfs[0],
  bounds=bounds,
@@ -367,7 +371,8 @@ def _optimize_acqf_continuous(
  equality_constraints=get_linear_constraints(
  domain=self.domain,
  constraint=LinearEqualityConstraint, # type: ignore
- ),
+ )
+ + interpoints,
  inequality_constraints=get_linear_constraints(
  domain=self.domain,
  constraint=LinearInequalityConstraint, # type: ignore

bofire/utils/torch_tools.py

@@ -106,6 +106,8 @@ def get_interpoint_constraints(
  of a tensor with the feature indices, coefficients and a float for the rhs.
  """
  constraints = []
+ if n_candidates == 1:
+ return constraints
  for constraint in domain.constraints.get(InterpointEqualityConstraint):
  assert isinstance(constraint, InterpointEqualityConstraint)
  coefficients = torch.tensor([1.0, -1.0]).to(**tkwargs)

tests/bofire/data_models/specs/domain.py

@@ -1,3 +1,4 @@
+from bofire.data_models.constraints.api import InterpointEqualityConstraint
 from bofire.data_models.domain.api import Constraints, Domain, Inputs, Outputs
 from bofire.data_models.features.api import ContinuousInput, ContinuousOutput
 from tests.bofire.data_models.specs.features import specs as features
@@ -71,3 +72,25 @@
  error=ValueError,
  message="Feature keys are not unique",
 )
+
+specs.add_invalid(
+ Domain,
+ lambda: {
+ "inputs": Inputs(
+ features=[
+ features.valid(ContinuousInput).obj(key="i1"),
+ features.valid(ContinuousInput).obj(key="i2"),
+ ]
+ ),
+ "outputs": Outputs(
+ features=[
+ features.valid(ContinuousOutput).obj(key="o1"),
+ ]
+ ),
+ "constraints": Constraints(
+ constraints=[InterpointEqualityConstraint(feature="i3")]
+ ),
+ },
+ error=ValueError,
+ message="feature i3 not known.",
+)

tests/bofire/data_models/specs/strategies.py

@@ -1,6 +1,7 @@
 import bofire.data_models.strategies.api as strategies
 from bofire.data_models.acquisition_functions.api import qEI, qLogNEHVI, qPI
 from bofire.data_models.constraints.api import (
+ InterpointEqualityConstraint,
  LinearEqualityConstraint,
  LinearInequalityConstraint,
  NChooseKConstraint,
@@ -416,6 +417,29 @@
  message="LSR-BO only supported for linear constraints.",
 )
 
+specs.add_invalid(
+ strategies.SoboStrategy,
+ lambda: {
+ "domain": Domain(
+ inputs=Inputs(
+ features=[
+ ContinuousInput(
+ key=k, bounds=(0, 1), local_relative_bounds=(0.1, 0.1)
+ )
+ for k in ["a", "b", "c"]
+ ]
+ + [CategoricalInput(key="d", categories=["a", "b", "c"])]
+ ),
+ outputs=Outputs(features=[ContinuousOutput(key="alpha")]),
+ constraints=Constraints(
+ constraints=[InterpointEqualityConstraint(feature="a")]
+ ),
+ ).model_dump(),
+ },
+ error=ValueError,
+ message="Interpoint constraints can only be used for pure continuous search spaces.",
+)
+
 specs.add_valid(
  strategies.FractionalFactorialStrategy,
  lambda: {

tests/bofire/strategies/test_sobo.py

@@ -31,7 +31,10 @@
  qSR,
  qUCB,
 )
-from bofire.data_models.constraints.api import NChooseKConstraint
+from bofire.data_models.constraints.api import (
+ InterpointEqualityConstraint,
+ NChooseKConstraint,
+)
 from bofire.data_models.domain.api import Domain, Inputs, Outputs
 from bofire.data_models.features.api import ContinuousInput, ContinuousOutput
 from bofire.data_models.objectives.api import (
@@ -478,3 +481,14 @@ def test_sobo_get_optimizer_options():
  strategy_data = data_models.SoboStrategy(domain=domain, maxiter=500, batch_limit=4)
  strategy = SoboStrategy(data_model=strategy_data)
  assert strategy._get_optimizer_options() == {"maxiter": 500, "batch_limit": 1}
+
+
+def test_sobo_interpoint():
+ bench = Himmelblau()
+ experiments = bench.f(bench.domain.inputs.sample(4), return_complete=True)
+ domain = bench._domain
+ domain.constraints.constraints.append(InterpointEqualityConstraint(feature="x_1"))
+ strategy_data = data_models.SoboStrategy(domain=domain)
+ strategy = SoboStrategy(data_model=strategy_data)
+ strategy.tell(experiments)
+ strategy.ask(2)

tests/bofire/utils/test_torch_tools.py

@@ -352,6 +352,8 @@ def test_get_interpoint_equality_constraints():
  dtype=torch.int64,
  ),
  )
+ constraints = get_interpoint_constraints(domain=domain, n_candidates=1)
+ assert len(constraints) == 0
 
 
 def test_get_linear_constraints():