Components: Switch from factory methods to prototype objects

tests/components/test_lazy_dynamic.py

@@ -22,6 +22,7 @@ def test_lazy_fit():
     instances[0].found_violated_lazy_constraints = ["a", "b"]
     instances[1].found_violated_lazy_constraints = ["b", "c"]
     classifier = Mock(spec=Classifier)
+    classifier.clone = lambda: Mock(spec=Classifier)
     component = DynamicLazyConstraintsComponent(classifier=classifier)
 
     component.fit(instances)

tests/components/test_objective.py

@@ -38,11 +38,13 @@ def test_x_y_predict() -> None:
     # Construct mock regressors
     lb_regressor = Mock(spec=Regressor)
     lb_regressor.predict = Mock(return_value=np.array([[5.0], [6.0]]))
+    lb_regressor.clone = lambda: lb_regressor
     ub_regressor = Mock(spec=Regressor)
     ub_regressor.predict = Mock(return_value=np.array([[3.0], [3.0]]))
+    ub_regressor.clone = lambda: ub_regressor
     comp = ObjectiveValueComponent(
-        lb_regressor=lambda: lb_regressor,
-        ub_regressor=lambda: ub_regressor,
+        lb_regressor=lb_regressor,
+        ub_regressor=ub_regressor,
     )
 
     # Should build x correctly
@@ -77,9 +79,10 @@ def test_obj_evaluate():
     instances, models = get_test_pyomo_instances()
     reg = Mock(spec=Regressor)
     reg.predict = Mock(return_value=np.array([[1000.0], [1000.0]]))
+    reg.clone = lambda: reg
     comp = ObjectiveValueComponent(
-        lb_regressor=lambda: reg,
-        ub_regressor=lambda: reg,
+        lb_regressor=reg,
+        ub_regressor=reg,
     )
     comp.fit(instances)
     ev = comp.evaluate(instances)

tests/components/test_primal.py

@@ -189,10 +189,11 @@ def test_predict() -> None:
 
 
 def test_fit_xy():
-    comp = PrimalSolutionComponent(
-        classifier=lambda: Mock(spec=Classifier),
-        threshold=lambda: Mock(spec=Threshold),
-    )
+    clf = Mock(spec=Classifier)
+    clf.clone = lambda: Mock(spec=Classifier)
+    thr = Mock(spec=Threshold)
+    thr.clone = lambda: Mock(spec=Threshold)
+    comp = PrimalSolutionComponent(classifier=clf, threshold=thr)
     x = {
         "type-a": np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]),
         "type-b": np.array([[7.0, 8.0, 9.0]]),