Fix all tests

miplearn/classifiers/adaptive.py

@@ -90,6 +90,13 @@ class AdaptiveClassifier(Classifier):
         n_samples = x_train.shape[0]
         assert y_train.shape == (n_samples, 2)
 
+        # If almost all samples belong to the same class, return a fixed prediction and
+        # skip all the other steps.
+        if y_train[:, 0].mean() > 0.999 or y_train[:, 1].mean() > 0.999:
+            self.classifier = CountingClassifier()
+            self.classifier.fit(x_train, y_train)
+            return
+
         best_name, best_clf, best_score = None, None, -float("inf")
         for (name, specs) in self.candidates.items():
             if n_samples < specs.min_samples:

miplearn/components/lazy_dynamic.py

@@ -85,8 +85,10 @@ class DynamicLazyConstraintsComponent(Component):
             disable=not sys.stdout.isatty(),
         ):
             logger.debug("Training: %s" % (str(v)))
-            label = np.zeros(len(training_instances))
-            label[violation_to_instance_idx[v]] = 1.0
+            label = [[True, False] for i in training_instances]
+            for idx in violation_to_instance_idx[v]:
+                label[idx] = [False, True]
+            label = np.array(label, dtype=np.bool8)
             classifier.fit(features, label)
 
     def predict(self, instance):

miplearn/components/steps/convert_tight.py

@@ -116,9 +116,11 @@ class ConvertTightIneqsIntoEqsStep(Component):
                 if category not in y:
                     y[category] = []
                 if 0 <= slack <= self.slack_tolerance:
-                    y[category] += [[1]]
+                    y[category] += [[False, True]]
                 else:
-                    y[category] += [[0]]
+                    y[category] += [[True, False]]
+            for category in y.keys():
+                y[category] = np.array(y[category], dtype=np.bool8)
         return y
 
     def predict(self, x):