Added an error when using formulations that don't support jump

src/omlt/base/var.py

@@ -67,23 +67,6 @@ def tanh(self):
 
 
 class OmltVar(ABC):
-    # @abstractmethod
-    # def construct(self, data):
-    #     """Construct the variable."""
-
-    # @abstractmethod
-    # def is_constructed(self):
-    #     """Return True if the variable has been constructed."""
-
-    # @abstractmethod
-    # def fix(self, value, *, skip_validation=False):
-    #     """Fix the value of the variable."""
-
-    # @property
-    # @abstractmethod
-    # def ctype(self):
-    #     """Return the type of the variable."""
-
     @property
     @abstractmethod
     def name(self):
@@ -112,17 +95,6 @@ def __init__(self, binary=False):  # noqa: ARG002, FBT002
     def is_indexed(self):
         return False
 
-    # Bound-setting interface for scalar variables:
-    # @property
-    # @abstractmethod
-    # def bounds(self):
-    #     """Return a tuple with the lower and upper bounds."""
-
-    # @bounds.setter
-    # @abstractmethod
-    # def bounds(self, val):
-    #     """Set lower and upper bounds to the given tuple."""
-
     @property
     @abstractmethod
     def lb(self):
@@ -213,20 +185,6 @@ class OmltIndexed(OmltVar):
     def is_indexed(self):
         return True
 
-    # @property
-    # @abstractmethod
-    # def index_set(self):
-    #     """Return the index set for the variable."""
-
-    # # Bound-setting interface for indexed variables:
-    # @abstractmethod
-    # def setub(self, value):
-    #     """Set upper bounds on all component variables."""
-
-    # @abstractmethod
-    # def setlb(self, value):
-    #     """Set lower bounds on all component variables."""
-
     # Interface: act as a dict for the sub-variables.
     @abstractmethod
     def __getitem__(self, item):

src/omlt/block.py

@@ -94,6 +94,12 @@ def build_formulation(self, formulation, lang=None):
         if lang is not None:
             self._format = lang
 
+        if self._format != DEFAULT_MODELING_LANGUAGE and formulation.pyomo_only:
+            msg = (
+                f"OMLT does not support building {formulation} with modeling languages"
+                " other than Pyomo."
+            )
+
         self._setup_inputs_outputs(
             input_indexes=list(formulation.input_indexes),
             output_indexes=list(formulation.output_indexes),

src/omlt/formulation.py

@@ -55,6 +55,16 @@ def _build_formulation(self):
         corresponding mathematical formulation of the model.
         """
 
+    @property
+    @abc.abstractmethod
+    def pyomo_only(self):
+        """Pyomo Only.
+
+        Return True if this formulation can only be built on a Pyomo
+        block, and False if it can be built on blocks using other
+        modeling languages.
+        """
+
 
 class _PyomoFormulation(_PyomoFormulationInterface):
     """Pyomo Formulation.

src/omlt/gbt/gbt_formulation.py

@@ -62,6 +62,9 @@ def _build_formulation(self):
             output_vars=self.block.scaled_outputs,
         )
 
+    @property
+    def pyomo_only(self):
+        return True
 
 def add_formulation_to_block(block, model_definition, input_vars, output_vars):  # noqa: C901, PLR0912, PLR0915
     r"""Adds the gradient-boosted trees formulation to the given Pyomo block.

src/omlt/linear_tree/lt_formulation.py

@@ -109,6 +109,10 @@ def _build_formulation(self):
             include_leaf_equalities=True,
         )
 
+    @property
+    def pyomo_only(self):
+        return True
+
 
 class LinearTreeHybridBigMFormulation(_PyomoFormulation):
     r"""Class to add a Linear Tree Hybrid Big-M formulation to OmltBlock.
@@ -165,6 +169,10 @@ def output_indexes(self):
         """The indexes of the formulation output."""
         return list(range(self.model_definition.n_outputs))
 
+    @property
+    def pyomo_only(self):
+        return True
+
     def _build_formulation(self):
         """Build formulation.
 

src/omlt/neuralnet/nn_formulation.py

@@ -140,6 +140,10 @@ def output_indexes(self):
             raise ValueError(MULTI_OUTPUTS_UNSUPPORTED)
         return network_outputs[0].output_indexes
 
+    @property
+    def pyomo_only(self):
+        return False
+
 
 def _build_neural_network_formulation(  # noqa: C901,PLR0912,PLR0915
     block, network_structure, layer_constraints, activation_constraints
@@ -325,6 +329,10 @@ def _supported_default_activation_constraints(self):
             "relu": ComplementarityReLUActivation(),
         }
 
+    @property
+    def pyomo_only(self):
+        return True
+
 
 class ReducedSpaceNNFormulation(_PyomoFormulation):
     """Reduced Space Neural Network Formulation.
@@ -475,6 +483,10 @@ def output_indexes(self):
             raise ValueError(MULTI_OUTPUTS_UNSUPPORTED)
         return network_outputs[0].output_indexes
 
+    @property
+    def pyomo_only(self):
+        return False
+
 
 class ReducedSpaceSmoothNNFormulation(ReducedSpaceNNFormulation):
     """Reduced Space Smooth Neural Network Formulation.
@@ -659,3 +671,7 @@ def output_indexes(self):
         if len(network_outputs) != 1:
             raise ValueError(MULTI_OUTPUTS_UNSUPPORTED)
         return network_outputs[0].output_indexes
+
+    @property
+    def pyomo_only(self):
+        return False