Only include static features in after-load

miplearn/components/dynamic_common.py

@@ -62,9 +62,9 @@ class DynamicConstraintsComponent(Component):
 
             # Features
             features = []
-            assert sample.after_lp is not None
-            assert sample.after_lp.instance is not None
-            features.extend(sample.after_lp.instance.to_list())
+            assert sample.after_load is not None
+            assert sample.after_load.instance is not None
+            features.extend(sample.after_load.instance.to_list())
             features.extend(instance.get_constraint_features(cid))
             for ci in features:
                 assert isinstance(ci, float), (

miplearn/components/primal.py

@@ -149,6 +149,7 @@ class PrimalSolutionComponent(Component):
         x: Dict = {}
         y: Dict = {}
         assert sample.after_load is not None
+        assert sample.after_load.instance is not None
         assert sample.after_load.variables is not None
         for (var_name, var) in sample.after_load.variables.items():
             # Initialize categories
@@ -160,14 +161,11 @@ class PrimalSolutionComponent(Component):
                 y[category] = []
 
             # Features
-            sf = sample.after_load
+            features = list(sample.after_load.instance.to_list())
+            features.extend(sample.after_load.variables[var_name].to_list())
             if sample.after_lp is not None:
-                sf = sample.after_lp
-            assert sf.instance is not None
-            features = list(sf.instance.to_list())
-            assert sf.variables is not None
-            assert sf.variables[var_name] is not None
-            features.extend(sf.variables[var_name].to_list())
+                assert sample.after_lp.variables is not None
+                features.extend(sample.after_lp.variables[var_name].to_list())
             x[category].append(features)
 
             # Labels

miplearn/features.py

@@ -82,7 +82,7 @@ class Constraint:
     category: Optional[Hashable] = None
     dual_value: Optional[float] = None
     lazy: bool = False
-    lhs: Dict[str, float] = lambda: {}  # type: ignore
+    lhs: Optional[Dict[str, float]] = None
     rhs: float = 0.0
     sa_rhs_down: Optional[float] = None
     sa_rhs_up: Optional[float] = None
@@ -136,10 +136,19 @@ class FeaturesExtractor:
     ) -> None:
         self.solver = internal_solver
 
-    def extract(self, instance: "Instance") -> Features:
+    def extract(
+        self,
+        instance: "Instance",
+        with_static: bool = True,
+    ) -> Features:
         features = Features()
-        features.variables = self.solver.get_variables()
-        features.constraints = self.solver.get_constraints()
+        features.variables = self.solver.get_variables(
+            with_static=with_static,
+        )
+        features.constraints = self.solver.get_constraints(
+            with_static=with_static,
+        )
+        if with_static:
             self._extract_user_features_vars(instance, features)
             self._extract_user_features_constrs(instance, features)
             self._extract_user_features_instance(instance, features)

miplearn/solvers/gurobi.py

@@ -84,6 +84,7 @@ class GurobiSolver(InternalSolver):
     @overrides
     def add_constraint(self, constr: Constraint, name: str) -> None:
         assert self.model is not None
+        assert constr.lhs is not None
         lhs = self.gp.quicksum(
             self._varname_to_var[varname] * coeff
             for (varname, coeff) in constr.lhs.items()
@@ -153,23 +154,36 @@ class GurobiSolver(InternalSolver):
         ]
 
     @overrides
-    def get_constraints(self) -> Dict[str, Constraint]:
+    def get_constraints(self, with_static: bool = True) -> Dict[str, Constraint]:
         model = self.model
         assert model is not None
         self._raise_if_callback()
         if self._dirty:
             model.update()
             self._dirty = False
+
         gp_constrs = model.getConstrs()
-        var_names = model.getAttr("varName", model.getVars())
         constr_names = model.getAttr("constrName", gp_constrs)
-        rhs = model.getAttr("rhs", gp_constrs)
-        sense = model.getAttr("sense", gp_constrs)
+        lhs: Optional[List[Dict]] = None
+        rhs = None
+        sense = None
         dual_value = None
         sa_rhs_up = None
         sa_rhs_down = None
         slack = None
         basis_status = None
+
+        if with_static:
+            var_names = model.getAttr("varName", model.getVars())
+            rhs = model.getAttr("rhs", gp_constrs)
+            sense = model.getAttr("sense", gp_constrs)
+            lhs = []
+            for (i, gp_constr) in enumerate(gp_constrs):
+                expr = model.getRow(gp_constr)
+                lhsi = {}
+                for j in range(expr.size()):
+                    lhsi[var_names[expr.getVar(j).index]] = expr.getCoeff(j)
+                lhs.append(lhsi)
         if self._has_lp_solution:
             dual_value = model.getAttr("pi", gp_constrs)
             sa_rhs_up = model.getAttr("saRhsUp", gp_constrs)
@@ -177,16 +191,20 @@ class GurobiSolver(InternalSolver):
             basis_status = model.getAttr("cbasis", gp_constrs)
         if self._has_lp_solution or self._has_mip_solution:
             slack = model.getAttr("slack", gp_constrs)
+
         constraints: Dict[str, Constraint] = {}
         for (i, gp_constr) in enumerate(gp_constrs):
-            expr = model.getRow(gp_constr)
-            lhs = {}
-            for j in range(expr.size()):
-                lhs[var_names[expr.getVar(j).index]] = expr.getCoeff(j)
             assert (
                 constr_names[i] not in constraints
             ), f"Duplicated constraint name detected: {constr_names[i]}"
-            constraint = Constraint(lhs=lhs, rhs=rhs[i], sense=sense[i])
+            constraint = Constraint()
+            if with_static:
+                assert lhs is not None
+                assert rhs is not None
+                assert sense is not None
+                constraint.lhs = lhs[i]
+                constraint.rhs = rhs[i]
+                constraint.sense = sense[i]
             if dual_value is not None:
                 assert sa_rhs_up is not None
                 assert sa_rhs_down is not None
@@ -247,13 +265,10 @@ class GurobiSolver(InternalSolver):
         ]
 
     @overrides
-    def get_variables(self) -> Dict[str, Variable]:
+    def get_variables(self, with_static: bool = True) -> Dict[str, Variable]:
         assert self.model is not None
         variables = {}
         gp_vars = self.model.getVars()
-        lb = self.model.getAttr("lb", gp_vars)
-        ub = self.model.getAttr("ub", gp_vars)
-        obj_coeff = self.model.getAttr("obj", gp_vars)
         names = self.model.getAttr("varName", gp_vars)
         values = None
         rc = None
@@ -264,6 +279,13 @@ class GurobiSolver(InternalSolver):
         sa_lb_up = None
         sa_lb_down = None
         vbasis = None
+        ub = None
+        lb = None
+        obj_coeff = None
+        if with_static:
+            lb = self.model.getAttr("lb", gp_vars)
+            ub = self.model.getAttr("ub", gp_vars)
+            obj_coeff = self.model.getAttr("obj", gp_vars)
         if self.model.solCount > 0:
             values = self.model.getAttr("x", gp_vars)
         if self._has_lp_solution:
@@ -281,12 +303,15 @@ class GurobiSolver(InternalSolver):
             assert (
                 names[i] not in variables
             ), f"Duplicated variable name detected: {names[i]}"
-            var = Variable(
-                lower_bound=lb[i],
-                upper_bound=ub[i],
-                obj_coeff=obj_coeff[i],
-                type=self._original_vtype[gp_var],
-            )
+            var = Variable()
+            if with_static:
+                assert lb is not None
+                assert ub is not None
+                assert obj_coeff is not None
+                var.lower_bound = lb[i]
+                var.upper_bound = ub[i]
+                var.obj_coeff = obj_coeff[i]
+                var.type = self._original_vtype[gp_var]
             if values is not None:
                 var.value = values[i]
             if rc is not None:
@@ -319,6 +344,7 @@ class GurobiSolver(InternalSolver):
 
     @overrides
     def is_constraint_satisfied(self, constr: Constraint, tol: float = 1e-6) -> bool:
+        assert constr.lhs is not None
         lhs = 0.0
         for (varname, coeff) in constr.lhs.items():
             var = self._varname_to_var[varname]

miplearn/solvers/internal.py

@@ -170,7 +170,7 @@ class InternalSolver(ABC, EnforceOverrides):
         raise NotImplementedError()
 
     @abstractmethod
-    def get_constraints(self) -> Dict[str, Constraint]:
+    def get_constraints(self, with_static: bool = True) -> Dict[str, Constraint]:
         pass
 
     @abstractmethod
@@ -237,7 +237,7 @@ class InternalSolver(ABC, EnforceOverrides):
         return False
 
     @abstractmethod
-    def get_variables(self) -> Dict[str, Variable]:
+    def get_variables(self, with_static: bool = True) -> Dict[str, Variable]:
         pass
 
     @abstractmethod

miplearn/solvers/learning.py

@@ -187,7 +187,10 @@ class LearningSolver:
             # Extract features (after-lp)
             # -------------------------------------------------------
             logger.info("Extracting features (after-lp)...")
-            features = FeaturesExtractor(self.internal_solver).extract(instance)
+            features = FeaturesExtractor(self.internal_solver).extract(
+                instance,
+                with_static=False,
+            )
             features.extra = {}
             features.lp_solve = lp_stats
             sample.after_lp = features
@@ -249,7 +252,10 @@ class LearningSolver:
         # Extract features (after-mip)
         # -------------------------------------------------------
         logger.info("Extracting features (after-mip)...")
-        features = FeaturesExtractor(self.internal_solver).extract(instance)
+        features = FeaturesExtractor(self.internal_solver).extract(
+            instance,
+            with_static=False,
+        )
         features.mip_solve = mip_stats
         features.extra = {}
         sample.after_mip = features

miplearn/solvers/pyomo/base.py

@@ -77,6 +77,7 @@ class BasePyomoSolver(InternalSolver):
     ) -> None:
         assert self.model is not None
         if isinstance(constr, Constraint):
+            assert constr.lhs is not None
             lhs = 0.0
             for (varname, coeff) in constr.lhs.items():
                 var = self._varname_to_var[varname]
@@ -127,7 +128,7 @@ class BasePyomoSolver(InternalSolver):
             self._pyomo_solver.update_var(var)
 
     @overrides
-    def get_constraints(self) -> Dict[str, Constraint]:
+    def get_constraints(self, with_static: bool = True) -> Dict[str, Constraint]:
         assert self.model is not None
 
         constraints = {}
@@ -136,11 +137,17 @@ class BasePyomoSolver(InternalSolver):
                 for idx in constr:
                     name = f"{constr.name}[{idx}]"
                     assert name not in constraints
-                    constraints[name] = self._parse_pyomo_constraint(constr[idx])
+                    constraints[name] = self._parse_pyomo_constraint(
+                        constr[idx],
+                        with_static=with_static,
+                    )
             else:
                 name = constr.name
                 assert name not in constraints
-                constraints[name] = self._parse_pyomo_constraint(constr)
+                constraints[name] = self._parse_pyomo_constraint(
+                    constr,
+                    with_static=with_static,
+                )
 
         return constraints
 
@@ -169,7 +176,7 @@ class BasePyomoSolver(InternalSolver):
         return solution
 
     @overrides
-    def get_variables(self) -> Dict[str, Variable]:
+    def get_variables(self, with_static: bool = True) -> Dict[str, Variable]:
         assert self.model is not None
         variables = {}
         for var in self.model.component_objects(pyomo.core.Var):
@@ -177,7 +184,10 @@ class BasePyomoSolver(InternalSolver):
                 varname = f"{var}[{idx}]"
                 if idx is None:
                     varname = str(var)
-                variables[varname] = self._parse_pyomo_variable(var[idx])
+                variables[varname] = self._parse_pyomo_variable(
+                    var[idx],
+                    with_static=with_static,
+                )
         return variables
 
     @overrides
@@ -201,6 +211,7 @@ class BasePyomoSolver(InternalSolver):
     @overrides
     def is_constraint_satisfied(self, constr: Constraint, tol: float = 1e-6) -> bool:
         lhs = 0.0
+        assert constr.lhs is not None
         for (varname, coeff) in constr.lhs.items():
             var = self._varname_to_var[varname]
             lhs += var.value * coeff
@@ -378,14 +389,20 @@ class BasePyomoSolver(InternalSolver):
     def _get_warm_start_regexp(self) -> Optional[str]:
         return None
 
-    def _parse_pyomo_variable(self, var: pyomo.core.Var) -> Variable:
+    def _parse_pyomo_variable(
+        self,
+        pyomo_var: pyomo.core.Var,
+        with_static: bool = True,
+    ) -> Variable:
         assert self.model is not None
+        variable = Variable()
 
+        if with_static:
             # Variable type
             vtype: Optional[str] = None
-        if var.domain == pyomo.core.Binary:
+            if pyomo_var.domain == pyomo.core.Binary:
                 vtype = "B"
-        elif var.domain in [
+            elif pyomo_var.domain in [
                 pyomo.core.Reals,
                 pyomo.core.NonNegativeReals,
                 pyomo.core.NonPositiveReals,
@@ -394,42 +411,43 @@ class BasePyomoSolver(InternalSolver):
             ]:
                 vtype = "C"
             if vtype is None:
-            raise Exception(f"unknown variable domain: {var.domain}")
+                raise Exception(f"unknown variable domain: {pyomo_var.domain}")
+            variable.type = vtype
 
             # Bounds
-        lb, ub = var.bounds
-
-        # Reduced costs
-        rc = None
-        if var in self.model.rc:
-            rc = self.model.rc[var]
+            lb, ub = pyomo_var.bounds
+            variable.upper_bound = float(ub)
+            variable.lower_bound = float(lb)
 
             # Objective coefficient
             obj_coeff = 0.0
-        if var.name in self._obj:
-            obj_coeff = self._obj[var.name]
-
-        return Variable(
-            value=var.value,
-            type=vtype,
-            lower_bound=float(lb),
-            upper_bound=float(ub),
-            obj_coeff=obj_coeff,
-            reduced_cost=rc,
-        )
+            if pyomo_var.name in self._obj:
+                obj_coeff = self._obj[pyomo_var.name]
+            variable.obj_coeff = obj_coeff
+
+        # Reduced costs
+        if pyomo_var in self.model.rc:
+            variable.reduced_cost = self.model.rc[pyomo_var]
+
+        variable.value = pyomo_var.value
+        return variable
 
     def _parse_pyomo_constraint(
         self,
         pyomo_constr: pyomo.core.Constraint,
+        with_static: bool = True,
     ) -> Constraint:
         assert self.model is not None
         constr = Constraint()
 
+        if with_static:
             # Extract RHS and sense
             has_ub = pyomo_constr.has_ub()
             has_lb = pyomo_constr.has_lb()
             assert (
-            (not has_lb) or (not has_ub) or pyomo_constr.upper() == pyomo_constr.lower()
+                (not has_lb)
+                or (not has_ub)
+                or pyomo_constr.upper() == pyomo_constr.lower()
             ), "range constraints not supported"
             if not has_ub:
                 constr.sense = ">"

miplearn/solvers/pyomo/gurobi.py

@@ -55,37 +55,6 @@ class GurobiPyomoSolver(BasePyomoSolver):
             gvar = self._pyomo_solver._pyomo_var_to_solver_var_map[var]
             gvar.setAttr(GRB.Attr.BranchPriority, int(round(priority)))
 
-    @overrides
-    def get_variables(self) -> Dict[str, Variable]:
-        variables = super().get_variables()
-        if self._has_lp_solution:
-            for (varname, var) in variables.items():
-                pvar = self._varname_to_var[varname]
-                gvar = self._pyomo_solver._pyomo_var_to_solver_var_map[pvar]
-                GurobiSolver._parse_gurobi_var_lp(gvar, var)
-
-        return variables
-
-    @overrides
-    def get_variable_attrs(self) -> List[str]:
-        return [
-            "basis_status",
-            "category",
-            "lower_bound",
-            "obj_coeff",
-            "reduced_cost",
-            "sa_lb_down",
-            "sa_lb_up",
-            "sa_obj_down",
-            "sa_obj_up",
-            "sa_ub_down",
-            "sa_ub_up",
-            "type",
-            "upper_bound",
-            "user_features",
-            "value",
-        ]
-
     @overrides
     def _extract_node_count(self, log: str) -> int:
         return max(1, int(self._pyomo_solver._solver_model.getAttr("NodeCount")))

miplearn/solvers/tests/__init__.py

@@ -279,63 +279,25 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
     assert isinstance(mip_stats.mip_wallclock_time, float)
     assert mip_stats.mip_wallclock_time > 0
 
-    # Fetch variables (after-load)
+    # Fetch variables (after-mip)
     assert_equals(
-        _round_variables(solver.get_variables()),
+        _round_variables(solver.get_variables(with_static=False)),
         _remove_unsupported_var_attrs(
             solver,
             {
-                "x[0]": Variable(
-                    lower_bound=0.0,
-                    obj_coeff=505.0,
-                    type="B",
-                    upper_bound=1.0,
-                    value=1.0,
-                ),
-                "x[1]": Variable(
-                    lower_bound=0.0,
-                    obj_coeff=352.0,
-                    type="B",
-                    upper_bound=1.0,
-                    value=0.0,
-                ),
-                "x[2]": Variable(
-                    lower_bound=0.0,
-                    obj_coeff=458.0,
-                    type="B",
-                    upper_bound=1.0,
-                    value=1.0,
-                ),
-                "x[3]": Variable(
-                    lower_bound=0.0,
-                    obj_coeff=220.0,
-                    type="B",
-                    upper_bound=1.0,
-                    value=1.0,
-                ),
-                "z": Variable(
-                    lower_bound=0.0,
-                    obj_coeff=0.0,
-                    type="C",
-                    upper_bound=67.0,
-                    value=61.0,
-                ),
+                "x[0]": Variable(value=1.0),
+                "x[1]": Variable(value=0.0),
+                "x[2]": Variable(value=1.0),
+                "x[3]": Variable(value=1.0),
+                "z": Variable(value=61.0),
             },
         ),
     )
 
     # Fetch constraints (after-mip)
     assert_equals(
-        _round_constraints(solver.get_constraints()),
-        {
-            "eq_capacity": Constraint(
-                lazy=False,
-                lhs={"x[0]": 23.0, "x[1]": 26.0, "x[2]": 20.0, "x[3]": 18.0, "z": -1.0},
-                rhs=0.0,
-                sense="=",
-                slack=0.0,
-            )
-        },
+        _round_constraints(solver.get_constraints(with_static=False)),
+        {"eq_capacity": Constraint(slack=0.0)},
     )
 
     # Build a new constraint

tests/components/test_dynamic_lazy.py

@@ -28,18 +28,18 @@ def training_instances() -> List[Instance]:
     instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]
     instances[0].samples = [
         Sample(
-            after_lp=Features(instance=InstanceFeatures()),
+            after_load=Features(instance=InstanceFeatures()),
             after_mip=Features(extra={"lazy_enforced": {"c1", "c2"}}),
         ),
         Sample(
-            after_lp=Features(instance=InstanceFeatures()),
+            after_load=Features(instance=InstanceFeatures()),
             after_mip=Features(extra={"lazy_enforced": {"c2", "c3"}}),
         ),
     ]
-    instances[0].samples[0].after_lp.instance.to_list = Mock(  # type: ignore
+    instances[0].samples[0].after_load.instance.to_list = Mock(  # type: ignore
         return_value=[5.0]
     )
-    instances[0].samples[1].after_lp.instance.to_list = Mock(  # type: ignore
+    instances[0].samples[1].after_load.instance.to_list = Mock(  # type: ignore
         return_value=[5.0]
     )
     instances[0].get_constraint_category = Mock(  # type: ignore
@@ -60,11 +60,11 @@ def training_instances() -> List[Instance]:
     )
     instances[1].samples = [
         Sample(
-            after_lp=Features(instance=InstanceFeatures()),
+            after_load=Features(instance=InstanceFeatures()),
             after_mip=Features(extra={"lazy_enforced": {"c3", "c4"}}),
         )
     ]
-    instances[1].samples[0].after_lp.instance.to_list = Mock(  # type: ignore
+    instances[1].samples[0].after_load.instance.to_list = Mock(  # type: ignore
         return_value=[8.0]
     )
     instances[1].get_constraint_category = Mock(  # type: ignore

tests/components/test_primal.py

@@ -27,6 +27,7 @@ from miplearn.solvers.tests import assert_equals
 def sample() -> Sample:
     sample = Sample(
         after_load=Features(
+            instance=InstanceFeatures(),
             variables={
                 "x[0]": Variable(category="default"),
                 "x[1]": Variable(category=None),
@@ -35,7 +36,6 @@ def sample() -> Sample:
             },
         ),
         after_lp=Features(
-            instance=InstanceFeatures(),
             variables={
                 "x[0]": Variable(),
                 "x[1]": Variable(),
@@ -52,7 +52,7 @@ def sample() -> Sample:
             }
         ),
     )
-    sample.after_lp.instance.to_list = Mock(return_value=[5.0])  # type: ignore
+    sample.after_load.instance.to_list = Mock(return_value=[5.0])  # type: ignore
     sample.after_lp.variables["x[0]"].to_list = Mock(  # type: ignore
         return_value=[0.0, 0.0]
     )