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]

tests/__init__.py

@@ -1,26 +1,3 @@
 #  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 miplearn.problems.knapsack import KnapsackInstance
-from miplearn.solvers.learning import LearningSolver
-
-
-def get_test_pyomo_instances():
-    instances = [
-        KnapsackInstance(
-            weights=[23.0, 26.0, 20.0, 18.0],
-            prices=[505.0, 352.0, 458.0, 220.0],
-            capacity=67.0,
-        ),
-        KnapsackInstance(
-            weights=[25.0, 30.0, 22.0, 18.0],
-            prices=[500.0, 365.0, 420.0, 150.0],
-            capacity=70.0,
-        ),
-    ]
-    models = [instance.to_model() for instance in instances]
-    solver = LearningSolver()
-    for i in range(len(instances)):
-        solver.solve(instances[i], models[i])
-    return instances, models

tests/components/test_lazy_dynamic.py

@@ -12,7 +12,7 @@ from miplearn.classifiers import Classifier
 from miplearn.components.lazy_dynamic import DynamicLazyConstraintsComponent
 from miplearn.solvers.internal import InternalSolver
 from miplearn.solvers.learning import LearningSolver
-from .. import get_test_pyomo_instances
+from tests.fixtures.knapsack import get_test_pyomo_instances
 
 E = 0.1
 

tests/components/test_objective.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 cast
 from unittest.mock import Mock
 
@@ -10,7 +11,7 @@ from numpy.testing import assert_array_equal
 from miplearn.instance import Instance
 from miplearn.classifiers import Regressor
 from miplearn.components.objective import ObjectiveValueComponent
-from .. import get_test_pyomo_instances
+from tests.fixtures.knapsack import get_test_pyomo_instances
 
 
 def test_x_y_predict() -> None:

tests/components/test_primal.py

@@ -11,7 +11,6 @@ from miplearn import Classifier
 from miplearn.classifiers.threshold import Threshold, MinPrecisionThreshold
 from miplearn.components.primal import PrimalSolutionComponent
 from miplearn.instance import Instance
-from tests import get_test_pyomo_instances
 
 
 def test_x_y_fit() -> None:

tests/fixtures/knapsack.py

@@ -0,0 +1,45 @@
+#  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 miplearn import BasePyomoSolver, GurobiSolver, InternalSolver, Instance
+from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
+from miplearn.solvers.learning import LearningSolver
+from tests.solvers import _is_subclass_or_instance
+
+
+def get_test_pyomo_instances():
+    instances = [
+        KnapsackInstance(
+            weights=[23.0, 26.0, 20.0, 18.0],
+            prices=[505.0, 352.0, 458.0, 220.0],
+            capacity=67.0,
+        ),
+        KnapsackInstance(
+            weights=[25.0, 30.0, 22.0, 18.0],
+            prices=[500.0, 365.0, 420.0, 150.0],
+            capacity=70.0,
+        ),
+    ]
+    models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for i in range(len(instances)):
+        solver.solve(instances[i], models[i])
+    return instances, models
+
+
+def get_knapsack_instance(solver: InternalSolver) -> Instance:
+    if _is_subclass_or_instance(solver, BasePyomoSolver):
+        return KnapsackInstance(
+            weights=[23.0, 26.0, 20.0, 18.0],
+            prices=[505.0, 352.0, 458.0, 220.0],
+            capacity=67.0,
+        )
+    elif _is_subclass_or_instance(solver, GurobiSolver):
+        return GurobiKnapsackInstance(
+            weights=[23.0, 26.0, 20.0, 18.0],
+            prices=[505.0, 352.0, 458.0, 220.0],
+            capacity=67.0,
+        )
+    else:
+        assert False

tests/solvers/__init__.py

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from inspect import isclass
-from typing import List, Callable
+from typing import List, Callable, Any
 
 from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
 from miplearn.solvers.gurobi import GurobiSolver
@@ -13,7 +13,7 @@ from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
 from miplearn.solvers.pyomo.xpress import XpressPyomoSolver
 
 
-def _is_subclass_or_instance(obj, parent_class):
+def _is_subclass_or_instance(obj: Any, parent_class: Any) -> bool:
     return isinstance(obj, parent_class) or (
         isclass(obj) and issubclass(obj, parent_class)
     )
@@ -35,5 +35,5 @@ def _get_knapsack_instance(solver):
     assert False
 
 
-def _get_internal_solvers() -> List[Callable[[], InternalSolver]]:
+def get_internal_solvers() -> List[Callable[[], InternalSolver]]:
     return [GurobiPyomoSolver, GurobiSolver, XpressPyomoSolver]

tests/solvers/test_internal_solver.py

@@ -13,7 +13,7 @@ from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.pyomo.base import BasePyomoSolver
 from . import (
     _get_knapsack_instance,
-    _get_internal_solvers,
+    get_internal_solvers,
 )
 from ..fixtures.infeasible import get_infeasible_instance
 
@@ -32,7 +32,7 @@ def test_redirect_output():
 
 
 def test_internal_solver_warm_starts():
-    for solver_class in _get_internal_solvers():
+    for solver_class in get_internal_solvers():
         logger.info("Solver: %s" % solver_class)
         instance = _get_knapsack_instance(solver_class)
         model = instance.to_model()
@@ -83,7 +83,7 @@ def test_internal_solver_warm_starts():
 
 
 def test_internal_solver():
-    for solver_class in _get_internal_solvers():
+    for solver_class in get_internal_solvers():
         logger.info("Solver: %s" % solver_class)
 
         instance = _get_knapsack_instance(solver_class)
@@ -175,7 +175,7 @@ def test_internal_solver():
 
 
 def test_relax():
-    for solver_class in _get_internal_solvers():
+    for solver_class in get_internal_solvers():
         instance = _get_knapsack_instance(solver_class)
         solver = solver_class()
         solver.set_instance(instance)
@@ -185,7 +185,7 @@ def test_relax():
 
 
 def test_infeasible_instance():
-    for solver_class in _get_internal_solvers():
+    for solver_class in get_internal_solvers():
         instance = get_infeasible_instance(solver_class)
         solver = solver_class()
         solver.set_instance(instance)
@@ -203,7 +203,7 @@ def test_infeasible_instance():
 
 
 def test_iteration_cb():
-    for solver_class in _get_internal_solvers():
+    for solver_class in get_internal_solvers():
         logger.info("Solver: %s" % solver_class)
         instance = _get_knapsack_instance(solver_class)
         solver = solver_class()

tests/solvers/test_learning_solver.py

@@ -10,14 +10,14 @@ import os
 
 from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.learning import LearningSolver
-from . import _get_knapsack_instance, _get_internal_solvers
+from . import _get_knapsack_instance, get_internal_solvers
 
 logger = logging.getLogger(__name__)
 
 
 def test_learning_solver():
     for mode in ["exact", "heuristic"]:
-        for internal_solver in _get_internal_solvers():
+        for internal_solver in get_internal_solvers():
             logger.info("Solver: %s" % internal_solver)
             instance = _get_knapsack_instance(internal_solver)
             solver = LearningSolver(
@@ -26,6 +26,9 @@ def test_learning_solver():
             )
 
             solver.solve(instance)
+
+            assert hasattr(instance, "model_features")
+
             data = instance.training_data[0]
             assert data["Solution"]["x"][0] == 1.0
             assert data["Solution"]["x"][1] == 0.0
@@ -49,7 +52,7 @@ def test_learning_solver():
 
 
 def test_solve_without_lp():
-    for internal_solver in _get_internal_solvers():
+    for internal_solver in get_internal_solvers():
         logger.info("Solver: %s" % internal_solver)
         instance = _get_knapsack_instance(internal_solver)
         solver = LearningSolver(
@@ -62,7 +65,7 @@ def test_solve_without_lp():
 
 
 def test_parallel_solve():
-    for internal_solver in _get_internal_solvers():
+    for internal_solver in get_internal_solvers():
         instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
         solver = LearningSolver(solver=internal_solver)
         results = solver.parallel_solve(instances, n_jobs=3)
@@ -73,7 +76,7 @@ def test_parallel_solve():
 
 
 def test_solve_fit_from_disk():
-    for internal_solver in _get_internal_solvers():
+    for internal_solver in get_internal_solvers():
         # Create instances and pickle them
         filenames = []
         for k in range(3):

tests/test_features.py

@@ -0,0 +1,23 @@
+#  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 miplearn.features import ModelFeaturesExtractor
+from tests.fixtures.knapsack import get_knapsack_instance
+from tests.solvers import get_internal_solvers
+
+
+def test_knapsack() -> None:
+    for solver_factory in get_internal_solvers():
+        # Initialize model, instance and internal solver
+        solver = solver_factory()
+        instance = get_knapsack_instance(solver)
+        model = instance.to_model()
+        solver.set_instance(instance, model)
+
+        # Extract all model features
+        extractor = ModelFeaturesExtractor(solver)
+        features = extractor.extract()
+
+        # Test constraint features
+        print(solver, features)
+        assert features["ConstraintRHS"]["eq_capacity"] == 67.0