Make all before/solve callbacks receive same parameters

tests/components/steps/test_drop_redundant.py

@@ -80,7 +80,14 @@ def test_drop_redundant():
     component.classifiers = classifiers
 
     # LearningSolver calls before_solve
-    component.before_solve_mip(solver, instance, None)
+    component.before_solve_mip(
+        solver=solver,
+        instance=instance,
+        model=None,
+        stats={},
+        features=None,
+        training_data=None,
+    )
 
     # Should query list of constraints
     internal.get_constraint_ids.assert_called_once()
@@ -123,7 +130,14 @@ def test_drop_redundant():
 
     # LearningSolver calls after_solve
     training_data = {}
-    component.after_solve_mip(solver, instance, None, {}, training_data)
+    component.after_solve_mip(
+        solver=solver,
+        instance=instance,
+        model=None,
+        stats={},
+        features=None,
+        training_data=training_data,
+    )
 
     # Should query slack for all inequalities
     internal.get_inequality_slacks.assert_called_once()
@@ -147,7 +161,14 @@ def test_drop_redundant_with_check_feasibility():
     component.classifiers = classifiers
 
     # LearningSolver call before_solve
-    component.before_solve_mip(solver, instance, None)
+    component.before_solve_mip(
+        solver=solver,
+        instance=instance,
+        model=None,
+        stats={},
+        features=None,
+        training_data=None,
+    )
 
     # Assert constraints are extracted
     assert internal.extract_constraint.call_count == 2

tests/components/test_lazy_dynamic.py

@@ -86,7 +86,14 @@ def test_lazy_before():
     component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
     component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
 
-    component.before_solve_mip(solver, instances[0], models[0])
+    component.before_solve_mip(
+        solver=solver,
+        instance=instances[0],
+        model=models[0],
+        stats=None,
+        features=None,
+        training_data=None,
+    )
 
     # Should ask classifier likelihood of each constraint being violated
     expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])

tests/components/test_lazy_static.py

@@ -69,7 +69,14 @@ def test_usage_with_solver():
     )
 
     # LearningSolver calls before_solve
-    component.before_solve_mip(solver, instance, None)
+    component.before_solve_mip(
+        solver=solver,
+        instance=instance,
+        model=None,
+        stats=None,
+        features=None,
+        training_data=None,
+    )
 
     # Should ask if instance has static lazy constraints
     instance.has_static_lazy_constraints.assert_called_once()

tests/components/test_objective.py

@@ -157,11 +157,11 @@ def test_xy_sample_without_lp() -> None:
     assert y_actual == y_expected
 
 
-def test_usage():
+def test_usage() -> None:
     solver = LearningSolver(components=[ObjectiveValueComponent()])
     instance = get_knapsack_instance(GurobiPyomoSolver())
     solver.solve(instance)
     solver.fit([instance])
     stats = solver.solve(instance)
-    assert stats["Lower bound"] == stats["Objective: predicted LB"]
-    assert stats["Upper bound"] == stats["Objective: predicted UB"]
+    assert stats["Lower bound"] == stats["Objective: Predicted LB"]
+    assert stats["Upper bound"] == stats["Objective: Predicted UB"]

tests/components/test_primal.py

@@ -226,6 +226,6 @@ def test_usage():
     solver.solve(instance)
     solver.fit([instance])
     stats = solver.solve(instance)
-    assert stats["Primal: free"] == 0
-    assert stats["Primal: one"] + stats["Primal: zero"] == 10
+    assert stats["Primal: Free"] == 0
+    assert stats["Primal: One"] + stats["Primal: Zero"] == 10
     assert stats["Lower bound"] == stats["Warm start value"]