Revert "Doe categorical (#243)"

This reverts commit 126393e.

bofire/data_models/constraints/nchoosek.py

@@ -93,7 +93,6 @@ def is_fulfilled(self, experiments: pd.DataFrame, tol: float = 1e-6) -> pd.Serie
         Returns:
             bool: True if fulfilled else False.
         """
-
         cols = self.features
         sums = (np.abs(experiments[cols]) > tol).sum(axis=1)
 

bofire/data_models/constraints/nonlinear.py

@@ -73,7 +73,7 @@ def jacobian(self, experiments: pd.DataFrame) -> pd.DataFrame:
 
 
 class NonlinearEqualityConstraint(NonlinearConstraint):
-    """Nonlinear equality constraint of the form 'expression == 0'.
+    """Nonlinear inequality constraint of the form 'expression <= 0'.
 
     Attributes:
         expression: Mathematical expression that can be evaluated by `pandas.eval`.
@@ -91,7 +91,7 @@ def __str__(self):
 
 
 class NonlinearInequalityConstraint(NonlinearConstraint):
-    """Nonlinear inequality constraint of the form 'expression <= 0'.
+    """Linear inequality constraint of the form 'expression == 0'.
 
     Attributes:
         expression: Mathematical expression that can be evaluated by `pandas.eval`.

bofire/data_models/domain/domain.py

@@ -179,7 +179,7 @@ def validate_linear_constraints(cls, v, values):
         # gather continuous inputs in dictionary
         continuous_inputs_dict = {}
         for f in values["inputs"]:
-            if isinstance(f, ContinuousInput):
+            if type(f) is ContinuousInput:
                 continuous_inputs_dict[f.key] = f
 
         # check if non continuous input features appear in linear constraints

bofire/data_models/strategies/doe.py

@@ -2,6 +2,8 @@
 
 from bofire.data_models.constraints.api import Constraint
 from bofire.data_models.features.api import (
+    CategoricalInput,
+    DiscreteInput,
     Feature,
     MolecularInput,
 )
@@ -20,19 +22,14 @@ class DoEStrategy(Strategy):
         ],
         str,
     ]
-    optimization_strategy: Literal[
-        "default", "exhaustive", "branch-and-bound", "partially-random", "relaxed"
-    ] = "default"
-
-    verbose: bool = False
 
     @classmethod
     def is_constraint_implemented(cls, my_type: Type[Constraint]) -> bool:
         return True
 
     @classmethod
     def is_feature_implemented(cls, my_type: Type[Feature]) -> bool:
-        if my_type in [MolecularInput]:
+        if my_type in [CategoricalInput, DiscreteInput, MolecularInput]:
             return False
         return True
 

bofire/data_models/strategies/samplers/polytope.py

@@ -15,10 +15,6 @@
     Feature,
 )
 from bofire.data_models.strategies.samplers.sampler import SamplerStrategy
-from bofire.strategies.doe.utils_features import (
-    RelaxableBinaryInput,
-    RelaxableDiscreteInput,
-)
 
 
 class PolytopeSampler(SamplerStrategy):
@@ -49,6 +45,4 @@ def is_feature_implemented(cls, my_type: Type[Feature]) -> bool:
             CategoricalInput,
             DiscreteInput,
             CategoricalDescriptorInput,
-            RelaxableBinaryInput,
-            RelaxableDiscreteInput,
         ]