LazyStatic: Use dynamic thresholds

miplearn/components/lazy_static.py

@@ -11,6 +11,7 @@ from tqdm.auto import tqdm
 
 from miplearn import Classifier
 from miplearn.classifiers.counting import CountingClassifier
+from miplearn.classifiers.threshold import MinProbabilityThreshold, Threshold
 from miplearn.components.component import Component
 from miplearn.types import TrainingSample, Features, LearningSolveStats
 
@@ -36,13 +37,14 @@ class StaticLazyConstraintsComponent(Component):
     def __init__(
         self,
         classifier: Classifier = CountingClassifier(),
-        threshold: float = 0.05,
+        threshold: Threshold = MinProbabilityThreshold([0.50, 0.50]),
         violation_tolerance: float = -0.5,
     ) -> None:
         assert isinstance(classifier, Classifier)
-        self.threshold: float = threshold
         self.classifier_prototype: Classifier = classifier
+        self.threshold_prototype: Threshold = threshold
         self.classifiers: Dict[Hashable, Classifier] = {}
+        self.thresholds: Dict[Hashable, Threshold] = {}
         self.pool: Dict[str, LazyConstraint] = {}
         self.violation_tolerance: float = violation_tolerance
         self.enforced_cids: Set[str] = set()
@@ -156,9 +158,10 @@ class StaticLazyConstraintsComponent(Component):
         for category in x.keys():
             if category not in self.classifiers:
                 continue
-            clf = self.classifiers[category]
-            proba = clf.predict_proba(np.array(x[category]))
-            pred = list(proba[:, 1] > self.threshold)
+            npx = np.array(x[category])
+            proba = self.classifiers[category].predict_proba(npx)
+            thr = self.thresholds[category].predict(npx)
+            pred = list(proba[:, 1] > thr[1])
             for (i, is_selected) in enumerate(pred):
                 if is_selected:
                     enforced_cids += [category_to_cids[category][i]]
@@ -196,4 +199,6 @@ class StaticLazyConstraintsComponent(Component):
         for c in y.keys():
             assert c in x
             self.classifiers[c] = self.classifier_prototype.clone()
+            self.thresholds[c] = self.threshold_prototype.clone()
             self.classifiers[c].fit(x[c], y[c])
+            self.thresholds[c].fit(self.classifiers[c], x[c], y[c])