Implement ENTMOOT in Bofire (#278)

* Convert features and constraints to entmoot

* EntingStrategy functional and datamodel

* Ask implemented for Enting with example

* Convert Enting to Predictive

* Implement multi objective and maximization

* Refactor, move files to appropriate directories

* Enting docstrings and test for domain conversion

* Multiple candidates generated in ask

* Implement new entmoot constraints

* Implement new entmoot Maximize objective

* Implement new entmoot EntingParams

* Implement entmoot.ConstraintList

* Create data models for Enting parameters

* Update field default factory for entingparams

Co-authored-by: Johannes P. Dürholt <[email protected]>

* Test serialization for enting

* Intial Enting tests, add to bofire api

* Add tests for functionality of Enting Stratey

* Update bofire/data_models/strategies/predictives/enting.py

Co-authored-by: Johannes P. Dürholt <[email protected]>

* Typing updates to enting

* Merge entmoot utils into enting

* Fix assumed ordering of features in tests

* Create entmoot extra

* Flatten Enting parameters

* Enting tests for parameter consistency

* Add specs test for entmoot

* Clarified behaviour of kappa_fantasy in enting

* Clean up batch proposals for enting

* Safer imports in test_enting

* Move enting helper methods

* Fix enting imports/installs; minor bugs in tests

* Fix import-dependent type hints

* Commit to trigger workflow

* Fix problematic imports in test_enting

* Add entmoot to tests that need it

* More changes to fix pipeline

* Add ignores for pyright

* Check if gurobi license is available; remove notebook

* Mark tests requiring Gurobi as slow

* Add EntingStrategy to types

* Add Enting to mapper

---------

Co-authored-by: Johannes P. Dürholt <[email protected]>

.github/workflows/test.yaml

@@ -39,7 +39,7 @@ jobs:
         shell: bash -l {0}
         run: |
           conda install -c conda-forge cyipopt
-          pip install ".[optimization,tests,cheminfo,xgb]"
+          pip install ".[optimization,tests,cheminfo,xgb,entmoot]"
       - name: Run tests, Python ${{ matrix.python-version }}
         shell: bash -l {0}
         run: pytest -ra --cov=bofire --cov-report term-missing tests
@@ -64,7 +64,7 @@ jobs:
           pip install --upgrade git+https://github.com/cornellius-gp/gpytorch.git
           export ALLOW_LATEST_GPYTORCH_LINOP=true
           pip install --upgrade git+https://github.com/pytorch/botorch.git
-          pip install ".[optimization,tests,cheminfo,xgb]"
+          pip install ".[optimization,tests,cheminfo,xgb,entmoot]"
       - name: Run tests, Python ${{ matrix.python-version }}
         shell: bash -l {0}
         run: pytest -ra --cov=bofire --cov-report term-missing tests
@@ -86,7 +86,7 @@ jobs:
         shell: bash -l {0}
         run: |
           conda install -c conda-forge cyipopt
-          pip install ".[optimization,tests,cheminfo,xgb]"
+          pip install ".[optimization,tests,cheminfo,xgb,entmoot]"
       - name: Run notebooks
         shell: bash -l {0}
         run: python scripts/run_tutorials.py -p "$(pwd)"

bofire/data_models/strategies/actual_strategy_type.py

@@ -2,6 +2,7 @@
 
 from bofire.data_models.strategies.doe import DoEStrategy
 from bofire.data_models.strategies.factorial import FactorialStrategy
+from bofire.data_models.strategies.predictives.enting import EntingStrategy
 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
@@ -24,6 +25,7 @@
     QehviStrategy,
     QnehviStrategy,
     QparegoStrategy,
+    EntingStrategy,
     SpaceFillingStrategy,
     RandomStrategy,
     DoEStrategy,

bofire/data_models/strategies/api.py

@@ -5,6 +5,7 @@
 from bofire.data_models.strategies.factorial import FactorialStrategy
 from bofire.data_models.strategies.meta_strategy_type import MetaStrategy
 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,
@@ -55,6 +56,7 @@
     QehviStrategy,
     QnehviStrategy,
     QparegoStrategy,
+    EntingStrategy,
     MoboStrategy,
 ]
 

bofire/data_models/strategies/predictives/enting.py

@@ -0,0 +1,78 @@
+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]

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_actual.py

@@ -3,6 +3,7 @@
 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.predictives.enting import EntingStrategy
 from bofire.strategies.predictives.mobo import MoboStrategy
 from bofire.strategies.predictives.qehvi import QehviStrategy
 from bofire.strategies.predictives.qnehvi import QnehviStrategy
@@ -27,6 +28,7 @@
     data_models.QehviStrategy: QehviStrategy,
     data_models.QnehviStrategy: QnehviStrategy,
     data_models.QparegoStrategy: QparegoStrategy,
+    data_models.EntingStrategy: EntingStrategy,
     data_models.SpaceFillingStrategy: SpaceFillingStrategy,
     data_models.DoEStrategy: DoEStrategy,
     data_models.FactorialStrategy: FactorialStrategy,