Rename Variables and Constraints; move to internal.py

miplearn/components/static_lazy.py

@@ -12,7 +12,8 @@ from miplearn.classifiers import Classifier
 from miplearn.classifiers.counting import CountingClassifier
 from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
 from miplearn.components.component import Component
-from miplearn.features import Sample, ConstraintFeatures
+from miplearn.features import Sample
+from miplearn.solvers.internal import Constraints
 from miplearn.instance.base import Instance
 from miplearn.types import LearningSolveStats
 
@@ -45,7 +46,7 @@ class StaticLazyConstraintsComponent(Component):
         self.threshold_prototype: Threshold = threshold
         self.classifiers: Dict[Hashable, Classifier] = {}
         self.thresholds: Dict[Hashable, Threshold] = {}
-        self.pool: ConstraintFeatures = ConstraintFeatures()
+        self.pool: Constraints = Constraints()
         self.violation_tolerance: float = violation_tolerance
         self.enforced_cids: Set[Hashable] = set()
         self.n_restored: int = 0
@@ -82,7 +83,7 @@ class StaticLazyConstraintsComponent(Component):
             logger.info("Instance does not have static lazy constraints. Skipping.")
         self.enforced_cids = set(self.sample_predict(sample))
         logger.info("Moving lazy constraints to the pool...")
-        constraints = ConstraintFeatures.from_sample(sample)
+        constraints = Constraints.from_sample(sample)
         assert constraints.lazy is not None
         assert constraints.names is not None
         selected = [

miplearn/features.py

@@ -4,9 +4,8 @@
 
 import collections
 import numbers
-from dataclasses import dataclass
 from math import log, isfinite
-from typing import TYPE_CHECKING, Dict, Optional, List, Hashable, Tuple, Any
+from typing import TYPE_CHECKING, Dict, Optional, List, Hashable, Any
 
 import numpy as np
 
@@ -15,76 +14,6 @@ if TYPE_CHECKING:
     from miplearn.instance.base import Instance
 
 
-@dataclass
-class VariableFeatures:
-    names: Optional[List[str]] = None
-    basis_status: Optional[List[str]] = None
-    lower_bounds: Optional[List[float]] = None
-    obj_coeffs: Optional[List[float]] = None
-    reduced_costs: Optional[List[float]] = None
-    sa_lb_down: Optional[List[float]] = None
-    sa_lb_up: Optional[List[float]] = None
-    sa_obj_down: Optional[List[float]] = None
-    sa_obj_up: Optional[List[float]] = None
-    sa_ub_down: Optional[List[float]] = None
-    sa_ub_up: Optional[List[float]] = None
-    types: Optional[List[str]] = None
-    upper_bounds: Optional[List[float]] = None
-    values: Optional[List[float]] = None
-
-
-@dataclass
-class ConstraintFeatures:
-    basis_status: Optional[List[str]] = None
-    dual_values: Optional[List[float]] = None
-    lazy: Optional[List[bool]] = None
-    lhs: Optional[List[List[Tuple[str, float]]]] = None
-    names: Optional[List[str]] = None
-    rhs: Optional[List[float]] = None
-    sa_rhs_down: Optional[List[float]] = None
-    sa_rhs_up: Optional[List[float]] = None
-    senses: Optional[List[str]] = None
-    slacks: Optional[List[float]] = None
-
-    @staticmethod
-    def from_sample(sample: "Sample") -> "ConstraintFeatures":
-        return ConstraintFeatures(
-            basis_status=sample.get("lp_constr_basis_status"),
-            dual_values=sample.get("lp_constr_dual_values"),
-            lazy=sample.get("constr_lazy"),
-            lhs=sample.get("constr_lhs"),
-            names=sample.get("constr_names"),
-            rhs=sample.get("constr_rhs"),
-            sa_rhs_down=sample.get("lp_constr_sa_rhs_down"),
-            sa_rhs_up=sample.get("lp_constr_sa_rhs_up"),
-            senses=sample.get("constr_senses"),
-            slacks=sample.get("lp_constr_slacks"),
-        )
-
-    def __getitem__(self, selected: List[bool]) -> "ConstraintFeatures":
-        return ConstraintFeatures(
-            basis_status=self._filter(self.basis_status, selected),
-            dual_values=self._filter(self.dual_values, selected),
-            names=self._filter(self.names, selected),
-            lazy=self._filter(self.lazy, selected),
-            lhs=self._filter(self.lhs, selected),
-            rhs=self._filter(self.rhs, selected),
-            sa_rhs_down=self._filter(self.sa_rhs_down, selected),
-            sa_rhs_up=self._filter(self.sa_rhs_up, selected),
-            senses=self._filter(self.senses, selected),
-            slacks=self._filter(self.slacks, selected),
-        )
-
-    def _filter(
-        self,
-        obj: Optional[List],
-        selected: List[bool],
-    ) -> Optional[List]:
-        if obj is None:
-            return None
-        return [obj[i] for (i, selected_i) in enumerate(selected) if selected_i]
-
-
 class Sample:
     def __init__(
         self,

miplearn/solvers/gurobi.py

@@ -10,7 +10,6 @@ from typing import List, Any, Dict, Optional, Hashable, Tuple, TYPE_CHECKING
 
 from overrides import overrides
 
-from miplearn.features import VariableFeatures, ConstraintFeatures
 from miplearn.instance.base import Instance
 from miplearn.solvers import _RedirectOutput
 from miplearn.solvers.internal import (
@@ -19,6 +18,8 @@ from miplearn.solvers.internal import (
     IterationCallback,
     LazyCallback,
     MIPSolveStats,
+    Variables,
+    Constraints,
 )
 from miplearn.solvers.pyomo.base import PyomoTestInstanceKnapsack
 from miplearn.types import (
@@ -91,7 +92,7 @@ class GurobiSolver(InternalSolver):
             ]
 
     @overrides
-    def add_constraints(self, cf: ConstraintFeatures) -> None:
+    def add_constraints(self, cf: Constraints) -> None:
         assert cf.names is not None
         assert cf.senses is not None
         assert cf.lhs is not None
@@ -120,7 +121,7 @@ class GurobiSolver(InternalSolver):
     @overrides
     def are_constraints_satisfied(
         self,
-        cf: ConstraintFeatures,
+        cf: Constraints,
         tol: float = 1e-5,
     ) -> List[bool]:
         assert cf.names is not None
@@ -196,7 +197,7 @@ class GurobiSolver(InternalSolver):
         with_static: bool = True,
         with_sa: bool = True,
         with_lhs: bool = True,
-    ) -> ConstraintFeatures:
+    ) -> Constraints:
         model = self.model
         assert model is not None
         assert model.numVars == len(self._gp_vars)
@@ -241,7 +242,7 @@ class GurobiSolver(InternalSolver):
         if self._has_lp_solution or self._has_mip_solution:
             slacks = model.getAttr("slack", gp_constrs)
 
-        return ConstraintFeatures(
+        return Constraints(
             basis_status=basis_status,
             dual_values=dual_value,
             lhs=lhs,
@@ -300,7 +301,7 @@ class GurobiSolver(InternalSolver):
         self,
         with_static: bool = True,
         with_sa: bool = True,
-    ) -> VariableFeatures:
+    ) -> Variables:
         model = self.model
         assert model is not None
 
@@ -347,7 +348,7 @@ class GurobiSolver(InternalSolver):
         if model.solCount > 0:
             values = model.getAttr("x", self._gp_vars)
 
-        return VariableFeatures(
+        return Variables(
             names=self._var_names,
             upper_bounds=upper_bounds,
             lower_bounds=lower_bounds,

miplearn/solvers/internal.py

@@ -5,9 +5,8 @@
 import logging
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Any, List, Optional, List
+from typing import Any, Optional, List, Tuple, TYPE_CHECKING
 
-from miplearn.features import VariableFeatures, ConstraintFeatures
 from miplearn.instance.base import Instance
 from miplearn.types import (
     IterationCallback,
@@ -18,6 +17,9 @@ from miplearn.types import (
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    from miplearn.features import Sample
+
 
 @dataclass
 class LPSolveStats:
@@ -44,20 +46,90 @@ class MIPSolveStats:
     mip_warm_start_value: Optional[float] = None
 
 
+@dataclass
+class Variables:
+    names: Optional[List[str]] = None
+    basis_status: Optional[List[str]] = None
+    lower_bounds: Optional[List[float]] = None
+    obj_coeffs: Optional[List[float]] = None
+    reduced_costs: Optional[List[float]] = None
+    sa_lb_down: Optional[List[float]] = None
+    sa_lb_up: Optional[List[float]] = None
+    sa_obj_down: Optional[List[float]] = None
+    sa_obj_up: Optional[List[float]] = None
+    sa_ub_down: Optional[List[float]] = None
+    sa_ub_up: Optional[List[float]] = None
+    types: Optional[List[str]] = None
+    upper_bounds: Optional[List[float]] = None
+    values: Optional[List[float]] = None
+
+
+@dataclass
+class Constraints:
+    basis_status: Optional[List[str]] = None
+    dual_values: Optional[List[float]] = None
+    lazy: Optional[List[bool]] = None
+    lhs: Optional[List[List[Tuple[str, float]]]] = None
+    names: Optional[List[str]] = None
+    rhs: Optional[List[float]] = None
+    sa_rhs_down: Optional[List[float]] = None
+    sa_rhs_up: Optional[List[float]] = None
+    senses: Optional[List[str]] = None
+    slacks: Optional[List[float]] = None
+
+    @staticmethod
+    def from_sample(sample: "Sample") -> "Constraints":
+        return Constraints(
+            basis_status=sample.get("lp_constr_basis_status"),
+            dual_values=sample.get("lp_constr_dual_values"),
+            lazy=sample.get("constr_lazy"),
+            lhs=sample.get("constr_lhs"),
+            names=sample.get("constr_names"),
+            rhs=sample.get("constr_rhs"),
+            sa_rhs_down=sample.get("lp_constr_sa_rhs_down"),
+            sa_rhs_up=sample.get("lp_constr_sa_rhs_up"),
+            senses=sample.get("constr_senses"),
+            slacks=sample.get("lp_constr_slacks"),
+        )
+
+    def __getitem__(self, selected: List[bool]) -> "Constraints":
+        return Constraints(
+            basis_status=self._filter(self.basis_status, selected),
+            dual_values=self._filter(self.dual_values, selected),
+            names=self._filter(self.names, selected),
+            lazy=self._filter(self.lazy, selected),
+            lhs=self._filter(self.lhs, selected),
+            rhs=self._filter(self.rhs, selected),
+            sa_rhs_down=self._filter(self.sa_rhs_down, selected),
+            sa_rhs_up=self._filter(self.sa_rhs_up, selected),
+            senses=self._filter(self.senses, selected),
+            slacks=self._filter(self.slacks, selected),
+        )
+
+    def _filter(
+        self,
+        obj: Optional[List],
+        selected: List[bool],
+    ) -> Optional[List]:
+        if obj is None:
+            return None
+        return [obj[i] for (i, selected_i) in enumerate(selected) if selected_i]
+
+
 class InternalSolver(ABC):
     """
     Abstract class representing the MIP solver used internally by LearningSolver.
     """
 
     @abstractmethod
-    def add_constraints(self, cf: ConstraintFeatures) -> None:
+    def add_constraints(self, cf: Constraints) -> None:
         """Adds the given constraints to the model."""
         pass
 
     @abstractmethod
     def are_constraints_satisfied(
         self,
-        cf: ConstraintFeatures,
+        cf: Constraints,
         tol: float = 1e-5,
     ) -> List[bool]:
         """
@@ -133,7 +205,7 @@ class InternalSolver(ABC):
         with_static: bool = True,
         with_sa: bool = True,
         with_lhs: bool = True,
-    ) -> ConstraintFeatures:
+    ) -> Constraints:
         pass
 
     @abstractmethod
@@ -149,7 +221,7 @@ class InternalSolver(ABC):
         self,
         with_static: bool = True,
         with_sa: bool = True,
-    ) -> VariableFeatures:
+    ) -> Variables:
         """
         Returns a description of the decision variables in the problem.
 

miplearn/solvers/pyomo/base.py

@@ -19,7 +19,6 @@ from pyomo.core.expr.numeric_expr import SumExpression, MonomialTermExpression
 from pyomo.opt import TerminationCondition
 from pyomo.opt.base.solvers import SolverFactory
 
-from miplearn.features import VariableFeatures, ConstraintFeatures
 from miplearn.instance.base import Instance
 from miplearn.solvers import _RedirectOutput, _none_if_empty
 from miplearn.solvers.internal import (
@@ -28,6 +27,8 @@ from miplearn.solvers.internal import (
     IterationCallback,
     LazyCallback,
     MIPSolveStats,
+    Variables,
+    Constraints,
 )
 from miplearn.types import (
     SolverParams,
@@ -79,7 +80,7 @@ class BasePyomoSolver(InternalSolver):
         self._has_mip_solution = False
 
     @overrides
-    def add_constraints(self, cf: ConstraintFeatures) -> None:
+    def add_constraints(self, cf: Constraints) -> None:
         assert cf.names is not None
         assert cf.senses is not None
         assert cf.lhs is not None
@@ -111,7 +112,7 @@ class BasePyomoSolver(InternalSolver):
     @overrides
     def are_constraints_satisfied(
         self,
-        cf: ConstraintFeatures,
+        cf: Constraints,
         tol: float = 1e-5,
     ) -> List[bool]:
         assert cf.names is not None
@@ -159,7 +160,7 @@ class BasePyomoSolver(InternalSolver):
         with_static: bool = True,
         with_sa: bool = True,
         with_lhs: bool = True,
-    ) -> ConstraintFeatures:
+    ) -> Constraints:
         model = self.model
         assert model is not None
 
@@ -233,7 +234,7 @@ class BasePyomoSolver(InternalSolver):
                 names.append(constr.name)
                 _parse_constraint(constr)
 
-        return ConstraintFeatures(
+        return Constraints(
             names=_none_if_empty(names),
             rhs=_none_if_empty(rhs),
             senses=_none_if_empty(senses),
@@ -271,7 +272,7 @@ class BasePyomoSolver(InternalSolver):
         self,
         with_static: bool = True,
         with_sa: bool = True,
-    ) -> VariableFeatures:
+    ) -> Variables:
         assert self.model is not None
 
         names: List[str] = []
@@ -326,7 +327,7 @@ class BasePyomoSolver(InternalSolver):
                 if self._has_lp_solution or self._has_mip_solution:
                     values.append(v.value)
 
-        return VariableFeatures(
+        return Variables(
             names=_none_if_empty(names),
             types=_none_if_empty(types),
             upper_bounds=_none_if_empty(upper_bounds),

miplearn/solvers/tests/__init__.py

@@ -6,8 +6,7 @@ from typing import Any, List
 
 import numpy as np
 
-from miplearn.features import VariableFeatures, ConstraintFeatures
+from miplearn.solvers.internal import InternalSolver, Variables, Constraints
-from miplearn.solvers.internal import InternalSolver
 
 inf = float("inf")
 
@@ -40,7 +39,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
     # Fetch variables (after-load)
     assert_equals(
         solver.get_variables(),
-        VariableFeatures(
+        Variables(
             names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
             lower_bounds=[0.0, 0.0, 0.0, 0.0, 0.0],
             upper_bounds=[1.0, 1.0, 1.0, 1.0, 67.0],
@@ -52,7 +51,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
     # Fetch constraints (after-load)
     assert_equals(
         solver.get_constraints(),
-        ConstraintFeatures(
+        Constraints(
             names=["eq_capacity"],
             rhs=[0.0],
             lhs=[
@@ -83,7 +82,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
         solver.get_variables(with_static=False),
         _filter_attrs(
             solver.get_variable_attrs(),
-            VariableFeatures(
+            Variables(
                 names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
                 basis_status=["U", "B", "U", "L", "U"],
                 reduced_costs=[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
@@ -103,7 +102,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
         solver.get_constraints(with_static=False),
         _filter_attrs(
             solver.get_constraint_attrs(),
-            ConstraintFeatures(
+            Constraints(
                 basis_status=["N"],
                 dual_values=[13.538462],
                 names=["eq_capacity"],
@@ -136,7 +135,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
         solver.get_variables(with_static=False),
         _filter_attrs(
             solver.get_variable_attrs(),
-            VariableFeatures(
+            Variables(
                 names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
                 values=[1.0, 0.0, 1.0, 1.0, 61.0],
             ),
@@ -148,7 +147,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
         solver.get_constraints(with_static=False),
         _filter_attrs(
             solver.get_constraint_attrs(),
-            ConstraintFeatures(
+            Constraints(
                 names=["eq_capacity"],
                 slacks=[0.0],
             ),
@@ -156,7 +155,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
     )
 
     # Build new constraint and verify that it is violated
-    cf = ConstraintFeatures(
+    cf = Constraints(
         names=["cut"],
         lhs=[[("x[0]", 1.0)]],
         rhs=[0.0],
@@ -170,7 +169,7 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
         solver.get_constraints(with_static=True),
         _filter_attrs(
             solver.get_constraint_attrs(),
-            ConstraintFeatures(
+            Constraints(
                 names=["eq_capacity", "cut"],
                 rhs=[0.0, 0.0],
                 lhs=[

tests/components/test_static_lazy.py

@@ -11,9 +11,9 @@ from numpy.testing import assert_array_equal
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import Threshold, MinProbabilityThreshold
 from miplearn.components.static_lazy import StaticLazyConstraintsComponent
-from miplearn.features import Sample, ConstraintFeatures
+from miplearn.features import Sample
 from miplearn.instance.base import Instance
-from miplearn.solvers.internal import InternalSolver
+from miplearn.solvers.internal import InternalSolver, Constraints
 from miplearn.solvers.learning import LearningSolver
 from miplearn.types import (
     LearningSolveStats,
@@ -118,7 +118,7 @@ def test_usage_with_solver(instance: Instance) -> None:
 
     # Should ask internal solver to verify if constraints in the pool are
     # satisfied and add the ones that are not
-    c = ConstraintFeatures.from_sample(sample)[[False, False, True, False, False]]
+    c = Constraints.from_sample(sample)[[False, False, True, False, False]]
     internal.are_constraints_satisfied.assert_called_once_with(c, tol=1.0)
     internal.are_constraints_satisfied.reset_mock()
     internal.add_constraints.assert_called_once_with(c)

tests/test_features.py

@@ -6,10 +6,9 @@ import numpy as np
 
 from miplearn.features import (
     FeaturesExtractor,
-    VariableFeatures,
-    ConstraintFeatures,
     Sample,
 )
+from miplearn.solvers.internal import Variables, Constraints
 from miplearn.solvers.gurobi import GurobiSolver
 from miplearn.solvers.tests import assert_equals
 
@@ -129,7 +128,7 @@ def test_knapsack() -> None:
 
 
 def test_constraint_getindex() -> None:
-    cf = ConstraintFeatures(
+    cf = Constraints(
         names=["c1", "c2", "c3"],
         rhs=[1.0, 2.0, 3.0],
         senses=["=", "<", ">"],
@@ -150,7 +149,7 @@ def test_constraint_getindex() -> None:
     )
     assert_equals(
         cf[[True, False, True]],
-        ConstraintFeatures(
+        Constraints(
             names=["c1", "c3"],
             rhs=[1.0, 3.0],
             senses=["=", ">"],
@@ -177,8 +176,8 @@ def test_assert_equals() -> None:
         np.array([[1.0, 2.0], [3.0, 4.0]]),
     )
     assert_equals(
-        VariableFeatures(values=np.array([1.0, 2.0])),  # type: ignore
+        Variables(values=np.array([1.0, 2.0])),  # type: ignore
-        VariableFeatures(values=np.array([1.0, 2.0])),  # type: ignore
+        Variables(values=np.array([1.0, 2.0])),  # type: ignore
     )
     assert_equals(np.array([True, True]), [True, True])
     assert_equals((1.0,), (1.0,))