Enforce more overrides

miplearn/__init__.py

@@ -27,3 +27,6 @@ from .solvers.learning import LearningSolver
 from .solvers.pyomo.base import BasePyomoSolver
 from .solvers.pyomo.cplex import CplexPyomoSolver
 from .solvers.pyomo.gurobi import GurobiPyomoSolver
+
+# noinspection PyUnresolvedReferences
+from overrides import overrides

miplearn/components/component.py

@@ -5,6 +5,7 @@
 from typing import Any, List, TYPE_CHECKING, Tuple, Dict, Hashable
 
 import numpy as np
+from overrides import EnforceOverrides
 
 from miplearn.features import TrainingSample, Features
 from miplearn.instance.base import Instance
@@ -15,7 +16,7 @@ if TYPE_CHECKING:
 
 
 # noinspection PyMethodMayBeStatic
-class Component:
+class Component(EnforceOverrides):
     """
     A Component is an object which adds functionality to a LearningSolver.
 

miplearn/components/dynamic_common.py

@@ -5,6 +5,7 @@
 from typing import Dict, Hashable, List, Tuple, TYPE_CHECKING
 
 import numpy as np
+from overrides import overrides
 
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import Threshold
@@ -73,6 +74,7 @@ class DynamicConstraintsComponent(Component):
                     y[category] += [[True, False]]
         return x, y, cids
 
+    @overrides
     def sample_xy(
         self,
         instance: "Instance",
@@ -101,6 +103,7 @@ class DynamicConstraintsComponent(Component):
                     pred += [cids[category][i]]
         return pred
 
+    @overrides
     def fit(self, training_instances: List["Instance"]) -> None:
         collected_cids = set()
         for instance in training_instances:
@@ -114,6 +117,7 @@ class DynamicConstraintsComponent(Component):
         self.known_cids.extend(sorted(collected_cids))
         super().fit(training_instances)
 
+    @overrides
     def fit_xy(
         self,
         x: Dict[Hashable, np.ndarray],
@@ -127,6 +131,7 @@ class DynamicConstraintsComponent(Component):
             self.classifiers[category].fit(npx, npy)
             self.thresholds[category].fit(self.classifiers[category], npx, npy)
 
+    @overrides
     def sample_evaluate(
         self,
         instance: "Instance",

miplearn/components/dynamic_lazy.py

@@ -6,6 +6,7 @@ import logging
 from typing import Dict, List, TYPE_CHECKING, Hashable, Tuple, Any
 
 import numpy as np
+from overrides import overrides
 
 from miplearn.instance.base import Instance
 from miplearn.classifiers import Classifier
@@ -53,6 +54,7 @@ class DynamicLazyConstraintsComponent(Component):
             cobj = instance.build_lazy_constraint(model, cid)
             solver.internal_solver.add_constraint(cobj)
 
+    @overrides
     def before_solve_mip(
         self,
         solver: "LearningSolver",
@@ -68,6 +70,7 @@ class DynamicLazyConstraintsComponent(Component):
         logger.info("Enforcing %d lazy constraints..." % len(cids))
         self.enforce(cids, instance, model, solver)
 
+    @overrides
     def iteration_cb(
         self,
         solver: "LearningSolver",
@@ -89,6 +92,7 @@ class DynamicLazyConstraintsComponent(Component):
 
     # Delegate ML methods to self.dynamic
     # -------------------------------------------------------------------
+    @overrides
     def sample_xy(
         self,
         instance: "Instance",
@@ -103,9 +107,11 @@ class DynamicLazyConstraintsComponent(Component):
     ) -> List[Hashable]:
         return self.dynamic.sample_predict(instance, sample)
 
+    @overrides
     def fit(self, training_instances: List["Instance"]) -> None:
         self.dynamic.fit(training_instances)
 
+    @overrides
     def fit_xy(
         self,
         x: Dict[Hashable, np.ndarray],
@@ -113,6 +119,7 @@ class DynamicLazyConstraintsComponent(Component):
     ) -> None:
         self.dynamic.fit_xy(x, y)
 
+    @overrides
     def sample_evaluate(
         self,
         instance: "Instance",

miplearn/components/dynamic_user_cuts.py

@@ -6,6 +6,7 @@ import logging
 from typing import Any, TYPE_CHECKING, Hashable, Set, Tuple, Dict, List
 
 import numpy as np
+from overrides import overrides
 
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.counting import CountingClassifier
@@ -35,6 +36,7 @@ class UserCutsComponent(Component):
         self.enforced: Set[Hashable] = set()
         self.n_added_in_callback = 0
 
+    @overrides
     def before_solve_mip(
         self,
         solver: "LearningSolver",
@@ -55,6 +57,7 @@ class UserCutsComponent(Component):
             solver.internal_solver.add_constraint(cobj)
         stats["UserCuts: Added ahead-of-time"] = len(cids)
 
+    @overrides
     def user_cut_cb(
         self,
         solver: "LearningSolver",
@@ -78,6 +81,7 @@ class UserCutsComponent(Component):
         if len(cids) > 0:
             logger.debug(f"Added {len(cids)} violated user cuts")
 
+    @overrides
     def after_solve_mip(
         self,
         solver: "LearningSolver",
@@ -93,6 +97,7 @@ class UserCutsComponent(Component):
 
     # Delegate ML methods to self.dynamic
     # -------------------------------------------------------------------
+    @overrides
     def sample_xy(
         self,
         instance: "Instance",
@@ -107,9 +112,11 @@ class UserCutsComponent(Component):
     ) -> List[Hashable]:
         return self.dynamic.sample_predict(instance, sample)
 
+    @overrides
     def fit(self, training_instances: List["Instance"]) -> None:
         self.dynamic.fit(training_instances)
 
+    @overrides
     def fit_xy(
         self,
         x: Dict[Hashable, np.ndarray],
@@ -117,6 +124,7 @@ class UserCutsComponent(Component):
     ) -> None:
         self.dynamic.fit_xy(x, y)
 
+    @overrides
     def sample_evaluate(
         self,
         instance: "Instance",

miplearn/components/objective.py

@@ -6,6 +6,7 @@ import logging
 from typing import List, Dict, Any, TYPE_CHECKING, Tuple, Hashable
 
 import numpy as np
+from overrides import overrides
 from sklearn.linear_model import LinearRegression
 
 from miplearn.classifiers import Regressor
@@ -34,6 +35,7 @@ class ObjectiveValueComponent(Component):
         self.regressors: Dict[str, Regressor] = {}
         self.regressor_prototype = regressor
 
+    @overrides
     def before_solve_mip(
         self,
         solver: "LearningSolver",
@@ -49,6 +51,7 @@ class ObjectiveValueComponent(Component):
             logger.info(f"Predicted {c.lower()}: %.6e" % v)
             stats[f"Objective: Predicted {c.lower()}"] = v  # type: ignore
 
+    @overrides
     def fit_xy(
         self,
         x: Dict[Hashable, np.ndarray],
@@ -73,6 +76,7 @@ class ObjectiveValueComponent(Component):
                 logger.info(f"{c} regressor not fitted. Skipping.")
         return pred
 
+    @overrides
     def sample_xy(
         self,
         instance: Instance,
@@ -94,6 +98,7 @@ class ObjectiveValueComponent(Component):
             y["Upper bound"] = [[sample.upper_bound]]
         return x, y
 
+    @overrides
     def sample_evaluate(
         self,
         instance: Instance,

miplearn/components/primal.py

@@ -13,6 +13,7 @@ from typing import (
 )
 
 import numpy as np
+from overrides import overrides
 
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.adaptive import AdaptiveClassifier
@@ -58,6 +59,7 @@ class PrimalSolutionComponent(Component):
         self.threshold_prototype = threshold
         self.classifier_prototype = classifier
 
+    @overrides
     def before_solve_mip(
         self,
         solver: "LearningSolver",
@@ -137,6 +139,7 @@ class PrimalSolutionComponent(Component):
 
         return solution
 
+    @overrides
     def sample_xy(
         self,
         instance: Instance,
@@ -172,6 +175,7 @@ class PrimalSolutionComponent(Component):
                 y[category] += [[opt_value < 0.5, opt_value >= 0.5]]
         return x, y
 
+    @overrides
     def sample_evaluate(
         self,
         instance: Instance,
@@ -212,6 +216,7 @@ class PrimalSolutionComponent(Component):
             ),
         }
 
+    @overrides
     def fit_xy(
         self,
         x: Dict[Hashable, np.ndarray],

miplearn/components/static_lazy.py

@@ -6,6 +6,7 @@ import logging
 from typing import Dict, Tuple, List, Hashable, Any, TYPE_CHECKING, Set
 
 import numpy as np
+from overrides import overrides
 
 from miplearn.classifiers import Classifier
 from miplearn.classifiers.counting import CountingClassifier
@@ -49,6 +50,7 @@ class StaticLazyConstraintsComponent(Component):
         self.n_restored: int = 0
         self.n_iterations: int = 0
 
+    @overrides
     def before_solve_mip(
         self,
         solver: "LearningSolver",
@@ -84,6 +86,7 @@ class StaticLazyConstraintsComponent(Component):
         self.n_restored = 0
         self.n_iterations = 0
 
+    @overrides
     def after_solve_mip(
         self,
         solver: "LearningSolver",
@@ -97,6 +100,7 @@ class StaticLazyConstraintsComponent(Component):
         stats["LazyStatic: Restored"] = self.n_restored
         stats["LazyStatic: Iterations"] = self.n_iterations
 
+    @overrides
     def iteration_cb(
         self,
         solver: "LearningSolver",
@@ -108,6 +112,7 @@ class StaticLazyConstraintsComponent(Component):
         else:
             return self._check_and_add(solver)
 
+    @overrides
     def lazy_cb(
         self,
         solver: "LearningSolver",
@@ -170,6 +175,7 @@ class StaticLazyConstraintsComponent(Component):
                     enforced_cids += [category_to_cids[category][i]]
         return enforced_cids
 
+    @overrides
     def sample_xy(
         self,
         instance: "Instance",
@@ -195,6 +201,7 @@ class StaticLazyConstraintsComponent(Component):
                     y[category] += [[True, False]]
         return x, y
 
+    @overrides
     def fit_xy(
         self,
         x: Dict[Hashable, np.ndarray],