Make all before/solve callbacks receive same parameters

miplearn/components/component.py

@@ -28,18 +28,35 @@ class Component:
         solver: "LearningSolver",
         instance: Instance,
         model: Any,
+        stats: LearningSolveStats,
+        features: Features,
+        training_data: TrainingSample,
     ) -> None:
         """
         Method called by LearningSolver before the root LP relaxation is solved.
 
         Parameters
         ----------
-        solver
+        solver: LearningSolver
             The solver calling this method.
-        instance
+        instance: Instance
             The instance being solved.
         model
             The concrete optimization model being solved.
+        stats: LearningSolveStats
+            A dictionary containing statistics about the solution process, such as
+            number of nodes explored and running time. Components are free to add
+            their own statistics here. For example, PrimalSolutionComponent adds
+            statistics regarding the number of predicted variables. All statistics in
+            this dictionary are exported to the benchmark CSV file.
+        features: Features
+            Features describing the model.
+        training_data: TrainingSample
+            A dictionary containing data that may be useful for training machine
+            learning models and accelerating the solution process. Components are
+            free to add their own training data here. For example,
+            PrimalSolutionComponent adds the current primal solution. The data must
+            be pickable.
         """
         return
 
@@ -49,31 +66,12 @@ class Component:
         instance: Instance,
         model: Any,
         stats: LearningSolveStats,
+        features: Features,
         training_data: TrainingSample,
     ) -> None:
         """
         Method called by LearningSolver after the root LP relaxation is solved.
-
+        See before_solve_lp for a description of the pameters.
-        Parameters
-        ----------
-        solver: LearningSolver
-            The solver calling this method.
-        instance: Instance
-            The instance being solved.
-        model: Any
-            The concrete optimization model being solved.
-        stats: LearningSolveStats
-            A dictionary containing statistics about the solution process, such as
-            number of nodes explored and running time. Components are free to add
-            their own statistics here. For example, PrimalSolutionComponent adds
-            statistics regarding the number of predicted variables. All statistics in
-            this dictionary are exported to the benchmark CSV file.
-        training_data: TrainingSample
-            A dictionary containing data that may be useful for training machine
-            learning models and accelerating the solution process. Components are
-            free to add their own training data here. For example,
-            PrimalSolutionComponent adds the current primal solution. The data must
-            be pickable.
         """
         return
 
@@ -82,18 +80,13 @@ class Component:
         solver: "LearningSolver",
         instance: Instance,
         model: Any,
+        stats: LearningSolveStats,
+        features: Features,
+        training_data: TrainingSample,
     ) -> None:
         """
         Method called by LearningSolver before the MIP is solved.
-
+        See before_solve_lp for a description of the pameters.
-        Parameters
-        ----------
-        solver
-            The solver calling this method.
-        instance
-            The instance being solved.
-        model
-            The concrete optimization model being solved.
         """
         return
 
@@ -103,31 +96,12 @@ class Component:
         instance: Instance,
         model: Any,
         stats: LearningSolveStats,
+        features: Features,
         training_data: TrainingSample,
     ) -> None:
         """
         Method called by LearningSolver after the MIP is solved.
-
+        See before_solve_lp for a description of the pameters.
-        Parameters
-        ----------
-        solver: LearningSolver
-            The solver calling this method.
-        instance: Instance
-            The instance being solved.
-        model: Any
-            The concrete optimization model being solved.
-        stats: LearningSolveStats
-            A dictionary containing statistics about the solution process, such as
-            number of nodes explored and running time. Components are free to add
-            their own statistics here. For example, PrimalSolutionComponent adds
-            statistics regarding the number of predicted variables. All statistics in
-            this dictionary are exported to the benchmark CSV file.
-        training_data: TrainingSample
-            A dictionary containing data that may be useful for training machine
-            learning models and accelerating the solution process. Components are
-            free to add their own training data here. For example,
-            PrimalSolutionComponent adds the current primal solution. The data must
-            be pickable.
         """
         return
 

miplearn/components/cuts.py

@@ -33,7 +33,15 @@ class UserCutsComponent(Component):
         self.classifier_prototype: Classifier = classifier
         self.classifiers: Dict[Any, Classifier] = {}
 
-    def before_solve_mip(self, solver, instance, model):
+    def before_solve_mip(
+        self,
+        solver,
+        instance,
+        model,
+        stats,
+        features,
+        training_data,
+    ):
         instance.found_violated_user_cuts = []
         logger.info("Predicting violated user cuts...")
         violations = self.predict(instance)
@@ -42,16 +50,6 @@ class UserCutsComponent(Component):
             cut = instance.build_user_cut(model, v)
             solver.internal_solver.add_constraint(cut)
 
-    def after_solve_mip(
-        self,
-        solver,
-        instance,
-        model,
-        results,
-        training_data,
-    ):
-        pass
-
     def fit(self, training_instances):
         logger.debug("Fitting...")
         features = InstanceFeaturesExtractor().extract(training_instances)

miplearn/components/lazy_dynamic.py

@@ -33,7 +33,15 @@ class DynamicLazyConstraintsComponent(Component):
         self.classifier_prototype: Classifier = classifier
         self.classifiers: Dict[Any, Classifier] = {}
 
-    def before_solve_mip(self, solver, instance, model):
+    def before_solve_mip(
+        self,
+        solver,
+        instance,
+        model,
+        stats,
+        features,
+        training_data,
+    ):
         instance.found_violated_lazy_constraints = []
         logger.info("Predicting violated lazy constraints...")
         violations = self.predict(instance)
@@ -54,16 +62,6 @@ class DynamicLazyConstraintsComponent(Component):
             solver.internal_solver.add_constraint(cut)
         return True
 
-    def after_solve_mip(
-        self,
-        solver,
-        instance,
-        model,
-        stats,
-        training_data,
-    ):
-        pass
-
     def fit(self, training_instances):
         logger.debug("Fitting...")
         features = InstanceFeaturesExtractor().extract(training_instances)

miplearn/components/lazy_static.py

@@ -43,7 +43,15 @@ class StaticLazyConstraintsComponent(Component):
         self.use_two_phase_gap = use_two_phase_gap
         self.violation_tolerance = violation_tolerance
 
-    def before_solve_mip(self, solver, instance, model):
+    def before_solve_mip(
+        self,
+        solver,
+        instance,
+        model,
+        stats,
+        features,
+        training_data,
+    ):
         self.pool = []
         if not solver.use_lazy_cb and self.use_two_phase_gap:
             logger.info("Increasing gap tolerance to %f", self.large_gap)
@@ -55,16 +63,6 @@ class StaticLazyConstraintsComponent(Component):
         if instance.has_static_lazy_constraints():
             self._extract_and_predict_static(solver, instance)
 
-    def after_solve_mip(
-        self,
-        solver,
-        instance,
-        model,
-        stats,
-        training_data,
-    ):
-        pass
-
     def iteration_cb(self, solver, instance, model):
         if solver.use_lazy_cb:
             return False

miplearn/components/objective.py

@@ -52,32 +52,20 @@ class ObjectiveValueComponent(Component):
         solver: "LearningSolver",
         instance: Instance,
         model: Any,
+        stats: LearningSolveStats,
+        features: Features,
+        training_data: TrainingSample,
     ) -> None:
         if self.ub_regressor is not None:
             logger.info("Predicting optimal value...")
             pred = self.predict([instance])
-            self._predicted_lb = pred["Upper bound"][0]
+            predicted_lb = pred["Upper bound"][0]
-            self._predicted_ub = pred["Lower bound"][0]
+            predicted_ub = pred["Lower bound"][0]
-            logger.info(
+            logger.info("Predicted LB=%.2f, UB=%.2f" % (predicted_lb, predicted_ub))
-                "Predicted values: lb=%.2f, ub=%.2f"
+            if predicted_ub is not None:
-                % (
+                stats["Objective: Predicted UB"] = predicted_ub
-                    self._predicted_lb,
+            if predicted_lb is not None:
-                    self._predicted_ub,
+                stats["Objective: Predicted LB"] = predicted_lb
-                )
-            )
-
-    def after_solve_mip(
-        self,
-        solver: "LearningSolver",
-        instance: Instance,
-        model: Any,
-        stats: LearningSolveStats,
-        training_data: TrainingSample,
-    ) -> None:
-        if self._predicted_ub is not None:
-            stats["Objective: predicted UB"] = self._predicted_ub
-        if self._predicted_lb is not None:
-            stats["Objective: predicted LB"] = self._predicted_lb
 
     def fit(self, training_instances: Union[List[str], List[Instance]]) -> None:
         self.lb_regressor = self.lb_regressor_prototype.clone()

miplearn/components/primal.py

@@ -62,9 +62,16 @@ class PrimalSolutionComponent(Component):
         self.thresholds: Dict[Hashable, Threshold] = {}
         self.threshold_prototype = threshold
         self.classifier_prototype = classifier
-        self.stats: Dict[str, float] = {}
 
-    def before_solve_mip(self, solver, instance, model):
+    def before_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: Instance,
+        model: Any,
+        stats: LearningSolveStats,
+        features: Features,
+        training_data: TrainingSample,
+    ) -> None:
         if len(self.thresholds) > 0:
             logger.info("Predicting MIP solution...")
             solution = self.predict(
@@ -72,41 +79,32 @@ class PrimalSolutionComponent(Component):
                 instance.training_data[-1],
             )
 
-            # Collect prediction statistics
+            # Update statistics
-            self.stats["Primal: Free"] = 0
+            stats["Primal: Free"] = 0
-            self.stats["Primal: Zero"] = 0
+            stats["Primal: Zero"] = 0
-            self.stats["Primal: One"] = 0
+            stats["Primal: One"] = 0
             for (var, var_dict) in solution.items():
                 for (idx, value) in var_dict.items():
                     if value is None:
-                        self.stats["Primal: Free"] += 1
+                        stats["Primal: Free"] += 1
                     else:
                         if value < 0.5:
-                            self.stats["Primal: Zero"] += 1
+                            stats["Primal: Zero"] += 1
                         else:
-                            self.stats["Primal: One"] += 1
+                            stats["Primal: One"] += 1
             logger.info(
-                f"Predicted: free: {self.stats['Primal: Free']}, "
+                f"Predicted: free: {stats['Primal: Free']}, "
-                f"zero: {self.stats['Primal: zero']}, "
+                f"zero: {stats['Primal: Zero']}, "
-                f"one: {self.stats['Primal: One']}"
+                f"one: {stats['Primal: One']}"
             )
 
             # Provide solution to the solver
+            assert solver.internal_solver is not None
             if self.mode == "heuristic":
                 solver.internal_solver.fix(solution)
             else:
                 solver.internal_solver.set_warm_start(solution)
 
-    def after_solve_mip(
-        self,
-        solver: "LearningSolver",
-        instance: Instance,
-        model: Any,
-        stats: LearningSolveStats,
-        training_data: TrainingSample,
-    ) -> None:
-        stats.update(self.stats)
-
     def fit_xy(
         self,
         x: Dict[str, np.ndarray],

miplearn/components/steps/convert_tight.py

@@ -45,8 +45,23 @@ class ConvertTightIneqsIntoEqsStep(Component):
         self.check_optimality = check_optimality
         self.converted = []
         self.original_sense = {}
+        self.n_restored = 0
+        self.n_infeasible_iterations = 0
+        self.n_suboptimal_iterations = 0
+
+    def before_solve_mip(
+        self,
+        solver,
+        instance,
+        model,
+        stats,
+        features,
+        training_data,
+    ):
+        self.n_restored = 0
+        self.n_infeasible_iterations = 0
+        self.n_suboptimal_iterations = 0
 
-    def before_solve_mip(self, solver, instance, _):
         logger.info("Predicting tight LP constraints...")
         x, constraints = DropRedundantInequalitiesStep.x(
             instance,
@@ -54,11 +69,8 @@ class ConvertTightIneqsIntoEqsStep(Component):
         )
         y = self.predict(x)
 
-        self.n_converted = 0
+        n_converted = 0
-        self.n_restored = 0
+        n_kept = 0
-        self.n_kept = 0
-        self.n_infeasible_iterations = 0
-        self.n_suboptimal_iterations = 0
         for category in y.keys():
             for i in range(len(y[category])):
                 if y[category][i][0] == 1:
@@ -67,11 +79,13 @@ class ConvertTightIneqsIntoEqsStep(Component):
                     self.original_sense[cid] = s
                     solver.internal_solver.set_constraint_sense(cid, "=")
                     self.converted += [cid]
-                    self.n_converted += 1
+                    n_converted += 1
                 else:
-                    self.n_kept += 1
+                    n_kept += 1
+        stats["ConvertTight: Kept"] = n_kept
+        stats["ConvertTight: Converted"] = n_converted
 
-        logger.info(f"Converted {self.n_converted} inequalities")
+        logger.info(f"Converted {n_converted} inequalities")
 
     def after_solve_mip(
         self,
@@ -79,12 +93,11 @@ class ConvertTightIneqsIntoEqsStep(Component):
         instance,
         model,
         stats,
+        features,
         training_data,
     ):
         if "slacks" not in training_data.keys():
             training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
-        stats["ConvertTight: Kept"] = self.n_kept
-        stats["ConvertTight: Converted"] = self.n_converted
         stats["ConvertTight: Restored"] = self.n_restored
         stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
         stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations

miplearn/components/steps/drop_redundant.py

@@ -46,12 +46,20 @@ class DropRedundantInequalitiesStep(Component):
         self.violation_tolerance = violation_tolerance
         self.max_iterations = max_iterations
         self.current_iteration = 0
-        self.total_dropped = 0
+        self.n_iterations = 0
-        self.total_restored = 0
+        self.n_restored = 0
-        self.total_kept = 0
-        self.total_iterations = 0
 
-    def before_solve_mip(self, solver, instance, _):
+    def before_solve_mip(
+        self,
+        solver,
+        instance,
+        model,
+        stats,
+        features,
+        training_data,
+    ):
+        self.n_iterations = 0
+        self.n_restored = 0
         self.current_iteration = 0
 
         logger.info("Predicting redundant LP constraints...")
@@ -62,10 +70,8 @@ class DropRedundantInequalitiesStep(Component):
         y = self.predict(x)
 
         self.pool = []
-        self.total_dropped = 0
+        n_dropped = 0
-        self.total_restored = 0
+        n_kept = 0
-        self.total_kept = 0
-        self.total_iterations = 0
         for category in y.keys():
             for i in range(len(y[category])):
                 if y[category][i][1] == 1:
@@ -75,10 +81,12 @@ class DropRedundantInequalitiesStep(Component):
                         obj=solver.internal_solver.extract_constraint(cid),
                     )
                     self.pool += [c]
-                    self.total_dropped += 1
+                    n_dropped += 1
                 else:
-                    self.total_kept += 1
+                    n_kept += 1
-        logger.info(f"Extracted {self.total_dropped} predicted constraints")
+        stats["DropRedundant: Kept"] = n_kept
+        stats["DropRedundant: Dropped"] = n_dropped
+        logger.info(f"Extracted {n_dropped} predicted constraints")
 
     def after_solve_mip(
         self,
@@ -86,18 +94,13 @@ class DropRedundantInequalitiesStep(Component):
         instance,
         model,
         stats,
+        features,
         training_data,
     ):
         if "slacks" not in training_data.keys():
             training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
-        stats.update(
+        stats["DropRedundant: Iterations"] = self.n_iterations
-            {
+        stats["DropRedundant: Restored"] = self.n_restored
-                "DropRedundant: Kept": self.total_kept,
-                "DropRedundant: Dropped": self.total_dropped,
-                "DropRedundant: Restored": self.total_restored,
-                "DropRedundant: Iterations": self.total_iterations,
-            }
-        )
 
     def fit(self, training_instances, n_jobs=1):
         x, y = self.x_y(training_instances, n_jobs=n_jobs)
@@ -234,12 +237,12 @@ class DropRedundantInequalitiesStep(Component):
             self.pool.remove(c)
             solver.internal_solver.add_constraint(c.obj)
         if len(constraints_to_add) > 0:
-            self.total_restored += len(constraints_to_add)
+            self.n_restored += len(constraints_to_add)
             logger.info(
                 "%8d constraints %8d in the pool"
                 % (len(constraints_to_add), len(self.pool))
             )
-            self.total_iterations += 1
+            self.n_iterations += 1
             return True
         else:
             return False

miplearn/components/steps/relax_integrality.py

@@ -14,16 +14,14 @@ class RelaxIntegralityStep(Component):
     Component that relaxes all integrality constraints before the problem is solved.
     """
 
-    def before_solve_mip(self, solver, instance, _):
+    def before_solve_mip(
-        logger.info("Relaxing integrality...")
-        solver.internal_solver.relax()
-
-    def after_solve_mip(
         self,
         solver,
         instance,
         model,
         stats,
+        features,
         training_data,
     ):
-        return
+        logger.info("Relaxing integrality...")
+        solver.internal_solver.relax()

miplearn/solvers/learning.py

@@ -178,11 +178,20 @@ class LearningSolver:
         extractor = FeaturesExtractor(self.internal_solver)
         instance.features = extractor.extract(instance)
 
+        callback_args = (
+            self,
+            instance,
+            model,
+            stats,
+            instance.features,
+            training_sample,
+        )
+
         # Solve root LP relaxation
         if self.solve_lp:
             logger.debug("Running before_solve_lp callbacks...")
             for component in self.components.values():
-                component.before_solve_lp(self, instance, model)
+                component.before_solve_lp(*callback_args)
 
             logger.info("Solving root LP relaxation...")
             lp_stats = self.internal_solver.solve_lp(tee=tee)
@@ -193,7 +202,7 @@ class LearningSolver:
 
             logger.debug("Running after_solve_lp callbacks...")
             for component in self.components.values():
-                component.after_solve_lp(self, instance, model, stats, training_sample)
+                component.after_solve_lp(*callback_args)
         else:
             training_sample["LP solution"] = self.internal_solver.get_empty_solution()
             training_sample["LP value"] = 0.0
@@ -222,7 +231,7 @@ class LearningSolver:
         # Before-solve callbacks
         logger.debug("Running before_solve_mip callbacks...")
         for component in self.components.values():
-            component.before_solve_mip(self, instance, model)
+            component.before_solve_mip(*callback_args)
 
         # Solve MIP
         logger.info("Solving MIP...")
@@ -250,7 +259,7 @@ class LearningSolver:
         # After-solve callbacks
         logger.debug("Calling after_solve_mip callbacks...")
         for component in self.components.values():
-            component.after_solve_mip(self, instance, model, stats, training_sample)
+            component.after_solve_mip(*callback_args)
 
         # Write to file, if necessary
         if not discard_output and filename is not None:

miplearn/types.py

@@ -59,11 +59,11 @@ LearningSolveStats = TypedDict(
         "MIP log": str,
         "Mode": str,
         "Nodes": Optional[int],
-        "Objective: predicted LB": float,
+        "Objective: Predicted LB": float,
-        "Objective: predicted UB": float,
+        "Objective: Predicted UB": float,
-        "Primal: free": int,
+        "Primal: Free": int,
-        "Primal: one": int,
+        "Primal: One": int,
-        "Primal: zero": int,
+        "Primal: Zero": int,
         "Sense": str,
         "Solver": str,
         "Upper bound": Optional[float],

tests/components/steps/test_drop_redundant.py

@@ -80,7 +80,14 @@ def test_drop_redundant():
     component.classifiers = classifiers
 
     # LearningSolver calls before_solve
-    component.before_solve_mip(solver, instance, None)
+    component.before_solve_mip(
+        solver=solver,
+        instance=instance,
+        model=None,
+        stats={},
+        features=None,
+        training_data=None,
+    )
 
     # Should query list of constraints
     internal.get_constraint_ids.assert_called_once()
@@ -123,7 +130,14 @@ def test_drop_redundant():
 
     # LearningSolver calls after_solve
     training_data = {}
-    component.after_solve_mip(solver, instance, None, {}, training_data)
+    component.after_solve_mip(
+        solver=solver,
+        instance=instance,
+        model=None,
+        stats={},
+        features=None,
+        training_data=training_data,
+    )
 
     # Should query slack for all inequalities
     internal.get_inequality_slacks.assert_called_once()
@@ -147,7 +161,14 @@ def test_drop_redundant_with_check_feasibility():
     component.classifiers = classifiers
 
     # LearningSolver call before_solve
-    component.before_solve_mip(solver, instance, None)
+    component.before_solve_mip(
+        solver=solver,
+        instance=instance,
+        model=None,
+        stats={},
+        features=None,
+        training_data=None,
+    )
 
     # Assert constraints are extracted
     assert internal.extract_constraint.call_count == 2

tests/components/test_lazy_dynamic.py

@@ -86,7 +86,14 @@ def test_lazy_before():
     component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
     component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
 
-    component.before_solve_mip(solver, instances[0], models[0])
+    component.before_solve_mip(
+        solver=solver,
+        instance=instances[0],
+        model=models[0],
+        stats=None,
+        features=None,
+        training_data=None,
+    )
 
     # Should ask classifier likelihood of each constraint being violated
     expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])

tests/components/test_lazy_static.py

@@ -69,7 +69,14 @@ def test_usage_with_solver():
     )
 
     # LearningSolver calls before_solve
-    component.before_solve_mip(solver, instance, None)
+    component.before_solve_mip(
+        solver=solver,
+        instance=instance,
+        model=None,
+        stats=None,
+        features=None,
+        training_data=None,
+    )
 
     # Should ask if instance has static lazy constraints
     instance.has_static_lazy_constraints.assert_called_once()

tests/components/test_objective.py

@@ -157,11 +157,11 @@ def test_xy_sample_without_lp() -> None:
     assert y_actual == y_expected
 
 
-def test_usage():
+def test_usage() -> None:
     solver = LearningSolver(components=[ObjectiveValueComponent()])
     instance = get_knapsack_instance(GurobiPyomoSolver())
     solver.solve(instance)
     solver.fit([instance])
     stats = solver.solve(instance)
-    assert stats["Lower bound"] == stats["Objective: predicted LB"]
+    assert stats["Lower bound"] == stats["Objective: Predicted LB"]
-    assert stats["Upper bound"] == stats["Objective: predicted UB"]
+    assert stats["Upper bound"] == stats["Objective: Predicted UB"]

tests/components/test_primal.py

@@ -226,6 +226,6 @@ def test_usage():
     solver.solve(instance)
     solver.fit([instance])
     stats = solver.solve(instance)
-    assert stats["Primal: free"] == 0
+    assert stats["Primal: Free"] == 0
-    assert stats["Primal: one"] + stats["Primal: zero"] == 10
+    assert stats["Primal: One"] + stats["Primal: Zero"] == 10
     assert stats["Lower bound"] == stats["Warm start value"]

tests/solvers/test_learning_solver.py

@@ -133,7 +133,7 @@ def test_simulate_perfect():
             simulate_perfect=True,
         )
         stats = solver.solve(tmp.name)
-        assert stats["Lower bound"] == stats["Objective: predicted LB"]
+        assert stats["Lower bound"] == stats["Objective: Predicted LB"]
 
 
 def test_gap():