StaticLazy: Refactor

miplearn/components/component.py

@@ -151,8 +151,8 @@ class Component:
 
     def fit_xy(
         self,
-        x: Dict[str, np.ndarray],
-        y: Dict[str, np.ndarray],
+        x: Dict[Hashable, np.ndarray],
+        y: Dict[Hashable, np.ndarray],
     ) -> None:
         """
         Given two dictionaries x and y, mapping the name of the category to matrices

miplearn/components/lazy_static.py

@@ -4,7 +4,7 @@
 
 import logging
 import sys
-from typing import Dict, Tuple, Optional
+from typing import Dict, Tuple, Optional, List, Hashable, Any, TYPE_CHECKING, Set
 
 import numpy as np
 from tqdm.auto import tqdm
@@ -12,203 +12,163 @@ from tqdm.auto import tqdm
 from miplearn import Classifier
 from miplearn.classifiers.counting import CountingClassifier
 from miplearn.components.component import Component
-from miplearn.types import TrainingSample, Features
+from miplearn.types import TrainingSample, Features, LearningSolveStats
 
 logger = logging.getLogger(__name__)
 
 
+if TYPE_CHECKING:
+    from miplearn.solvers.learning import LearningSolver, Instance
+
+
 class LazyConstraint:
-    def __init__(self, cid, obj):
+    def __init__(self, cid: str, obj: Any) -> None:
         self.cid = cid
         self.obj = obj
 
 
 class StaticLazyConstraintsComponent(Component):
+    """
+    Component that decides which of the constraints tagged as lazy should
+    be kept in the formulation, and which should be removed.
+    """
+
     def __init__(
         self,
-        classifier=CountingClassifier(),
-        threshold=0.05,
-        use_two_phase_gap=True,
-        large_gap=1e-2,
-        violation_tolerance=-0.5,
-    ):
+        classifier: Classifier = CountingClassifier(),
+        threshold: float = 0.05,
+        violation_tolerance: float = -0.5,
+    ) -> None:
         assert isinstance(classifier, Classifier)
-        self.threshold = threshold
-        self.classifier_prototype = classifier
-        self.classifiers = {}
-        self.pool = []
-        self.original_gap = None
-        self.large_gap = large_gap
-        self.is_gap_large = False
-        self.use_two_phase_gap = use_two_phase_gap
-        self.violation_tolerance = violation_tolerance
+        self.threshold: float = threshold
+        self.classifier_prototype: Classifier = classifier
+        self.classifiers: Dict[Hashable, Classifier] = {}
+        self.pool: Dict[str, LazyConstraint] = {}
+        self.violation_tolerance: float = violation_tolerance
+        self.enforced_cids: Set[str] = set()
+        self.n_restored: int = 0
+        self.n_iterations: int = 0
 
     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)
-            self.original_gap = solver.gap_tolerance
-            self.is_gap_large = True
-            solver.internal_solver.set_gap_tolerance(self.large_gap)
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+        stats: LearningSolveStats,
+        features: Features,
+        training_data: TrainingSample,
+    ) -> None:
+        assert solver.internal_solver is not None
+        if not features["Instance"]["Lazy constraint count"] == 0:
+            logger.info("Instance does not have static lazy constraints. Skipping.")
+        logger.info("Predicting required lazy constraints...")
+        self.enforced_cids = set(self.sample_predict(features, training_data))
+        logger.info("Moving lazy constraints to the pool...")
+        self.pool = {}
+        for (cid, cdict) in features["Constraints"].items():
+            if cdict["Lazy"] and cid not in self.enforced_cids:
+                self.pool[cid] = LazyConstraint(
+                    cid=cid,
+                    obj=solver.internal_solver.extract_constraint(cid),
+                )
+        logger.info(
+            f"{len(self.enforced_cids)} lazy constraints kept; "
+            f"{len(self.pool)} moved to the pool"
+        )
+        stats["LazyStatic: Removed"] = len(self.pool)
+        stats["LazyStatic: Kept"] = len(self.enforced_cids)
+        stats["LazyStatic: Restored"] = 0
+        self.n_restored = 0
+        self.n_iterations = 0
 
-        instance.found_violated_lazy_constraints = []
-        if instance.has_static_lazy_constraints():
-            self._extract_and_predict_static(solver, instance)
+    def after_solve_mip(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+        stats: LearningSolveStats,
+        features: Features,
+        training_data: TrainingSample,
+    ) -> None:
+        training_data["LazyStatic: Enforced"] = self.enforced_cids
+        stats["LazyStatic: Restored"] = self.n_restored
+        stats["LazyStatic: Iterations"] = self.n_iterations
 
-    def iteration_cb(self, solver, instance, model):
+    def iteration_cb(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+    ) -> bool:
         if solver.use_lazy_cb:
             return False
         else:
-            should_repeat = self._check_and_add(instance, solver)
-            if should_repeat:
-                return True
-            else:
-                if self.is_gap_large:
-                    logger.info("Restoring gap tolerance to %f", self.original_gap)
-                    solver.internal_solver.set_gap_tolerance(self.original_gap)
-                    self.is_gap_large = False
-                    return True
-                else:
-                    return False
+            return self._check_and_add(solver)
 
-    def lazy_cb(self, solver, instance, model):
-        self._check_and_add(instance, solver)
+    def lazy_cb(
+        self,
+        solver: "LearningSolver",
+        instance: "Instance",
+        model: Any,
+    ) -> None:
+        self._check_and_add(solver)
 
-    def _check_and_add(self, instance, solver):
-        logger.debug("Finding violated lazy constraints...")
-        constraints_to_add = []
-        for c in self.pool:
+    def _check_and_add(self, solver: "LearningSolver") -> bool:
+        assert solver.internal_solver is not None
+        logger.info("Finding violated lazy constraints...")
+        enforced: List[LazyConstraint] = []
+        for (cid, c) in self.pool.items():
             if not solver.internal_solver.is_constraint_satisfied(
-                c.obj, tol=self.violation_tolerance
+                c.obj,
+                tol=self.violation_tolerance,
             ):
-                constraints_to_add.append(c)
-        for c in constraints_to_add:
-            self.pool.remove(c)
+                enforced.append(c)
+        logger.info(f"{len(enforced)} violations found")
+        for c in enforced:
+            del self.pool[c.cid]
             solver.internal_solver.add_constraint(c.obj)
-            instance.found_violated_lazy_constraints += [c.cid]
-        if len(constraints_to_add) > 0:
-            logger.info(
-                "%8d lazy constraints added %8d in the pool"
-                % (len(constraints_to_add), len(self.pool))
-            )
+            self.enforced_cids.add(c.cid)
+            self.n_restored += 1
+        logger.info(
+            f"{len(enforced)} constraints restored; {len(self.pool)} in the pool"
+        )
+        if len(enforced) > 0:
+            self.n_iterations += 1
             return True
         else:
             return False
 
-    def fit(self, training_instances):
-        training_instances = [
-            t
-            for t in training_instances
-            if hasattr(t, "found_violated_lazy_constraints")
-        ]
-
-        logger.debug("Extracting x and y...")
-        x = self.x(training_instances)
-        y = self.y(training_instances)
-
-        logger.debug("Fitting...")
-        for category in tqdm(
-            x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
-        ):
-            if category not in self.classifiers:
-                self.classifiers[category] = self.classifier_prototype.clone()
-            self.classifiers[category].fit(x[category], y[category])
-
-    def predict(self, instance):
-        pass
-
-    def evaluate(self, instances):
-        pass
-
-    def _extract_and_predict_static(self, solver, instance):
-        x = {}
-        constraints = {}
-        logger.info("Extracting lazy constraints...")
-        for cid in solver.internal_solver.get_constraint_ids():
-            if instance.is_constraint_lazy(cid):
-                category = instance.get_constraint_category(cid)
-                if category not in x:
-                    x[category] = []
-                    constraints[category] = []
-                x[category] += [instance.get_constraint_features(cid)]
-                c = LazyConstraint(
-                    cid=cid,
-                    obj=solver.internal_solver.extract_constraint(cid),
-                )
-                constraints[category] += [c]
-                self.pool.append(c)
-        logger.info("%8d lazy constraints extracted" % len(self.pool))
-        logger.info("Predicting required lazy constraints...")
-        n_added = 0
-        for (category, x_values) in x.items():
+    def sample_predict(
+        self,
+        features: Features,
+        sample: TrainingSample,
+    ) -> List[str]:
+        x, y = self.sample_xy(features, sample)
+        category_to_cids: Dict[Hashable, List[str]] = {}
+        for (cid, cdict) in features["Constraints"].items():
+            if "Category" not in cdict or cdict["Category"] is None:
+                continue
+            category = cdict["Category"]
+            if category not in category_to_cids:
+                category_to_cids[category] = []
+            category_to_cids[category] += [cid]
+        enforced_cids: List[str] = []
+        for category in x.keys():
             if category not in self.classifiers:
                 continue
-            if isinstance(x_values[0], np.ndarray):
-                x[category] = np.array(x_values)
-            proba = self.classifiers[category].predict_proba(x[category])
-            for i in range(len(proba)):
-                if proba[i][1] > self.threshold:
-                    n_added += 1
-                    c = constraints[category][i]
-                    self.pool.remove(c)
-                    solver.internal_solver.add_constraint(c.obj)
-                    instance.found_violated_lazy_constraints += [c.cid]
-        logger.info(
-            "%8d lazy constraints added %8d in the pool"
-            % (
-                n_added,
-                len(self.pool),
-            )
-        )
-
-    def _collect_constraints(self, train_instances):
-        constraints = {}
-        for instance in train_instances:
-            for cid in instance.found_violated_lazy_constraints:
-                category = instance.get_constraint_category(cid)
-                if category not in constraints:
-                    constraints[category] = set()
-                constraints[category].add(cid)
-        for (category, cids) in constraints.items():
-            constraints[category] = sorted(list(cids))
-        return constraints
-
-    def x(self, train_instances):
-        result = {}
-        constraints = self._collect_constraints(train_instances)
-        for (category, cids) in constraints.items():
-            result[category] = []
-            for instance in train_instances:
-                for cid in cids:
-                    result[category].append(instance.get_constraint_features(cid))
-        return result
-
-    def y(self, train_instances):
-        result = {}
-        constraints = self._collect_constraints(train_instances)
-        for (category, cids) in constraints.items():
-            result[category] = []
-            for instance in train_instances:
-                for cid in cids:
-                    if cid in instance.found_violated_lazy_constraints:
-                        result[category].append([0, 1])
-                    else:
-                        result[category].append([1, 0])
-        return result
+            clf = self.classifiers[category]
+            proba = clf.predict_proba(np.array(x[category]))
+            pred = list(proba[:, 1] > self.threshold)
+            for (i, is_selected) in enumerate(pred):
+                if is_selected:
+                    enforced_cids += [category_to_cids[category][i]]
+        return enforced_cids
 
     @staticmethod
     def sample_xy(
         features: Features,
         sample: TrainingSample,
-    ) -> Tuple[Dict, Dict]:
+    ) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
         x: Dict = {}
         y: Dict = {}
         for (cid, cfeatures) in features["Constraints"].items():
@@ -227,3 +187,13 @@ class StaticLazyConstraintsComponent(Component):
                 else:
                     y[category] += [[True, False]]
         return x, y
+
+    def fit_xy(
+        self,
+        x: Dict[Hashable, np.ndarray],
+        y: Dict[Hashable, np.ndarray],
+    ) -> None:
+        for c in y.keys():
+            assert c in x
+            self.classifiers[c] = self.classifier_prototype.clone()
+            self.classifiers[c].fit(x[c], y[c])

miplearn/components/objective.py

@@ -58,8 +58,8 @@ class ObjectiveValueComponent(Component):
 
     def fit_xy(
         self,
-        x: Dict[str, np.ndarray],
-        y: Dict[str, np.ndarray],
+        x: Dict[Hashable, np.ndarray],
+        y: Dict[Hashable, np.ndarray],
     ) -> None:
         for c in ["Upper bound", "Lower bound"]:
             if c in y:
@@ -84,9 +84,9 @@ class ObjectiveValueComponent(Component):
     def sample_xy(
         features: Features,
         sample: TrainingSample,
-    ) -> Tuple[Dict[str, List[List[float]]], Dict[str, List[List[float]]]]:
-        x: Dict[str, List[List[float]]] = {}
-        y: Dict[str, List[List[float]]] = {}
+    ) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
+        x: Dict[Hashable, List[List[float]]] = {}
+        y: Dict[Hashable, List[List[float]]] = {}
         f = list(features["Instance"]["User features"])
         if "LP value" in sample and sample["LP value"] is not None:
             f += [sample["LP value"]]

miplearn/components/primal.py

@@ -148,7 +148,7 @@ class PrimalSolutionComponent(Component):
     def sample_xy(
         features: Features,
         sample: TrainingSample,
-    ) -> Tuple[Dict, Dict]:
+    ) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
         x: Dict = {}
         y: Dict = {}
         solution: Optional[Solution] = None
@@ -227,8 +227,8 @@ class PrimalSolutionComponent(Component):
 
     def fit_xy(
         self,
-        x: Dict[str, np.ndarray],
-        y: Dict[str, np.ndarray],
+        x: Dict[Hashable, np.ndarray],
+        y: Dict[Hashable, np.ndarray],
     ) -> None:
         for category in x.keys():
             clf = self.classifier_prototype.clone()

miplearn/features.py

@@ -20,11 +20,12 @@ class FeaturesExtractor:
         self.solver = internal_solver
 
     def extract(self, instance: "Instance") -> Features:
-        return {
-            "Instance": self._extract_instance(instance),
-            "Constraints": self._extract_constraints(instance),
+        features: Features = {
             "Variables": self._extract_variables(instance),
+            "Constraints": self._extract_constraints(instance),
         }
+        features["Instance"] = self._extract_instance(instance, features)
+        return features
 
     def _extract_variables(self, instance: "Instance") -> Dict:
         variables = self.solver.get_empty_solution()
@@ -92,7 +93,10 @@ class FeaturesExtractor:
         return constraints
 
     @staticmethod
-    def _extract_instance(instance: "Instance") -> InstanceFeatures:
+    def _extract_instance(
+        instance: "Instance",
+        features: Features,
+    ) -> InstanceFeatures:
         user_features = instance.get_instance_features()
         assert isinstance(user_features, list), (
             f"Instance features must be a list. "
@@ -103,4 +107,11 @@ class FeaturesExtractor:
                 f"Instance features must be a list of numbers. "
                 f"Found {type(v).__name__} instead."
             )
-        return {"User features": user_features}
+        lazy_count = 0
+        for (cid, cdict) in features["Constraints"].items():
+            if cdict["Lazy"]:
+                lazy_count += 1
+        return {
+            "User features": user_features,
+            "Lazy constraint count": lazy_count,
+        }

miplearn/types.py

@@ -69,6 +69,10 @@ LearningSolveStats = TypedDict(
         "Upper bound": Optional[float],
         "Wallclock time": float,
         "Warm start value": Optional[float],
+        "LazyStatic: Removed": int,
+        "LazyStatic: Kept": int,
+        "LazyStatic: Restored": int,
+        "LazyStatic: Iterations": int,
     },
     total=False,
 )
@@ -77,6 +81,7 @@ InstanceFeatures = TypedDict(
     "InstanceFeatures",
     {
         "User features": List[float],
+        "Lazy constraint count": int,
     },
     total=False,
 )