Add first model feature (constraint RHS)

miplearn/features.py

@@ -0,0 +1,26 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+from typing import TYPE_CHECKING
+
+from miplearn.types import ModelFeatures
+
+if TYPE_CHECKING:
+    from miplearn import InternalSolver
+
+
+class ModelFeaturesExtractor:
+    def __init__(
+        self,
+        internal_solver: "InternalSolver",
+    ) -> None:
+        self.internal_solver = internal_solver
+
+    def extract(self) -> ModelFeatures:
+        rhs = {}
+        for cid in self.internal_solver.get_constraint_ids():
+            rhs[cid] = self.internal_solver.get_constraint_rhs(cid)
+        return {
+            "ConstraintRHS": rhs,
+        }

miplearn/instance.py

@@ -9,7 +9,7 @@ from typing import Any, List, Optional, Hashable
 
 import numpy as np
 
-from miplearn.types import TrainingSample, VarIndex
+from miplearn.types import TrainingSample, VarIndex, ModelFeatures
 
 
 class Instance(ABC):
@@ -24,8 +24,9 @@ class Instance(ABC):
     features, which can be provided as inputs to machine learning models.
     """
 
-    def __init__(self):
+    def __init__(self) -> None:
         self.training_data: List[TrainingSample] = []
+        self.model_features: ModelFeatures = {}
 
     @abstractmethod
     def to_model(self) -> Any:

miplearn/problems/knapsack.py

@@ -1,6 +1,7 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
+from typing import List
 
 import numpy as np
 import pyomo.environ as pe
@@ -24,7 +25,6 @@ class ChallengeA:
         n_training_instances=500,
         n_test_instances=50,
     ):
-
         np.random.seed(seed)
         self.gen = MultiKnapsackGenerator(
             n=randint(low=250, high=251),
@@ -241,7 +241,12 @@ class KnapsackInstance(Instance):
     Simpler (one-dimensional) Knapsack Problem, used for testing.
     """
 
-    def __init__(self, weights, prices, capacity):
+    def __init__(
+        self,
+        weights: List[float],
+        prices: List[float],
+        capacity: float,
+    ) -> None:
         super().__init__()
         self.weights = weights
         self.prices = prices
@@ -282,7 +287,12 @@ class GurobiKnapsackInstance(KnapsackInstance):
     instead of Pyomo, used for testing.
     """
 
-    def __init__(self, weights, prices, capacity):
+    def __init__(
+        self,
+        weights: List[float],
+        prices: List[float],
+        capacity: float,
+    ) -> None:
         super().__init__(weights, prices, capacity)
 
     def to_model(self):

miplearn/solvers/gurobi.py

@@ -335,6 +335,10 @@ class GurobiSolver(InternalSolver):
         self.model.update()
         return [c.ConstrName for c in self.model.getConstrs()]
 
+    def get_constraint_rhs(self, cid: str) -> float:
+        assert self.model is not None
+        return self.model.getConstrByName(cid).rhs
+
     def extract_constraint(self, cid):
         self._raise_if_callback()
         constr = self.model.getConstrByName(cid)

miplearn/solvers/internal.py

@@ -155,6 +155,13 @@ class InternalSolver(ABC):
         """
         pass
 
+    @abstractmethod
+    def get_constraint_rhs(self, cid: str) -> float:
+        """
+        Returns the right-hand side of a given constraint.
+        """
+        pass
+
     @abstractmethod
     def add_constraint(self, cobj: Constraint) -> None:
         """

miplearn/solvers/learning.py

@@ -16,11 +16,12 @@ from miplearn.components.cuts import UserCutsComponent
 from miplearn.components.lazy_dynamic import DynamicLazyConstraintsComponent
 from miplearn.components.objective import ObjectiveValueComponent
 from miplearn.components.primal import PrimalSolutionComponent
+from miplearn.features import ModelFeaturesExtractor
 from miplearn.instance import Instance
 from miplearn.solvers import _RedirectOutput
 from miplearn.solvers.internal import InternalSolver
 from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
-from miplearn.types import MIPSolveStats, TrainingSample, LearningSolveStats
+from miplearn.types import TrainingSample, LearningSolveStats
 
 logger = logging.getLogger(__name__)
 
@@ -164,6 +165,10 @@ class LearningSolver:
         assert isinstance(self.internal_solver, InternalSolver)
         self.internal_solver.set_instance(instance, model)
 
+        # Extract model features
+        extractor = ModelFeaturesExtractor(self.internal_solver)
+        instance.model_features = extractor.extract()
+
         # Solve linear relaxation
         if self.solve_lp_first:
             logger.info("Solving LP relaxation...")

miplearn/solvers/pyomo/base.py

@@ -212,7 +212,11 @@ class BasePyomoSolver(InternalSolver):
         assert self.model is not None
         self._cname_to_constr = {}
         for constr in self.model.component_objects(Constraint):
-            self._cname_to_constr[constr.name] = constr
+            if isinstance(constr, pe.ConstraintList):
+                for idx in constr:
+                    self._cname_to_constr[f"{constr.name}[{idx}]"] = constr[idx]
+            else:
+                self._cname_to_constr[constr.name] = constr
 
     def fix(self, solution):
         count_total, count_fixed = 0, 0
@@ -302,6 +306,13 @@ class BasePyomoSolver(InternalSolver):
         else:
             return "="
 
+    def get_constraint_rhs(self, cid: str) -> float:
+        cobj = self._cname_to_constr[cid]
+        if cobj.has_ub:
+            return cobj.upper()
+        else:
+            return cobj.lower()
+
     def set_constraint_sense(self, cid: str, sense: str) -> None:
         raise Exception("Not implemented")
 

miplearn/types.py

@@ -71,6 +71,14 @@ LearningSolveStats = TypedDict(
     total=False,
 )
 
+ModelFeatures = TypedDict(
+    "ModelFeatures",
+    {
+        "ConstraintRHS": Dict[str, float],
+    },
+    total=False,
+)
+
 IterationCallback = Callable[[], bool]
 
 LazyCallback = Callable[[Any, Any], None]