Always pass (n,)-shaped arrays to regressors instead of (n,1)

src/python/miplearn/components/objective.py

@@ -41,18 +41,22 @@ class ObjectiveValueComponent(Component):
         features = InstanceFeaturesExtractor().extract(training_instances)
         ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
         lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
+        assert ub.shape == (len(training_instances), 1)
+        assert lb.shape == (len(training_instances), 1)
         self.ub_regressor = deepcopy(self.regressor_prototype)
         self.lb_regressor = deepcopy(self.regressor_prototype)
         logger.debug("Fitting ub_regressor...")
-        self.ub_regressor.fit(features, ub)
+        self.ub_regressor.fit(features, ub.ravel())
         logger.debug("Fitting ub_regressor...")
-        self.lb_regressor.fit(features, lb)
+        self.lb_regressor.fit(features, lb.ravel())
         
     def predict(self, instances):
         features = InstanceFeaturesExtractor().extract(instances)
         lb = self.lb_regressor.predict(features)
         ub = self.ub_regressor.predict(features)
-        return np.hstack([lb, ub])
+        assert lb.shape == (len(instances),)
+        assert ub.shape == (len(instances),)
+        return np.array([lb, ub]).T
 
     def evaluate(self, instances):
         y_pred = self.predict(instances)

src/python/miplearn/components/tests/test_objective.py

@@ -23,7 +23,7 @@ def test_usage():
 def test_obj_evaluate():
     instances, models = get_training_instances_and_models()
     reg = Mock(spec=Regressor)
-    reg.predict = Mock(return_value=np.array([[1000.0], [1000.0]]))
+    reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
     comp = ObjectiveValueComponent(regressor=reg)
     comp.fit(instances)
     ev = comp.evaluate(instances)