Rename methods that use TrainingSample to _old

tests/components/test_component.py

@@ -10,7 +10,7 @@ from miplearn.instance.base import Instance
 
 
 def test_xy_instance() -> None:
-    def _sample_xy(features: Features, sample: str) -> Tuple[Dict, Dict]:
+    def _sample_xy_old(features: Features, sample: str) -> Tuple[Dict, Dict]:
         x = {
             "s1": {
                 "category_a": [
@@ -60,7 +60,7 @@ def test_xy_instance() -> None:
     instance_2 = Mock(spec=Instance)
     instance_2.training_data = ["s3"]
     instance_2.features = {}
-    comp.sample_xy = _sample_xy  # type: ignore
+    comp.sample_xy_old = _sample_xy_old  # type: ignore
     x_expected = {
         "category_a": [
             [1, 2, 3],

tests/components/test_dynamic_lazy.py

@@ -160,7 +160,7 @@ def test_sample_predict_evaluate(training_instances: List[Instance]) -> None:
         training_instances[0].training_data[0],
     )
     assert pred == ["c1", "c4"]
-    ev = comp.sample_evaluate(
+    ev = comp.sample_evaluate_old(
         training_instances[0],
         training_instances[0].training_data[0],
     )

tests/components/test_objective.py

@@ -33,7 +33,7 @@ def features() -> Features:
 
 
 @pytest.fixture
-def sample() -> TrainingSample:
+def sample_old() -> TrainingSample:
     return TrainingSample(
         lower_bound=1.0,
         upper_bound=2.0,
@@ -50,7 +50,7 @@ def sample_without_lp() -> TrainingSample:
 
 
 @pytest.fixture
-def sample_without_ub() -> TrainingSample:
+def sample_without_ub_old() -> TrainingSample:
     return TrainingSample(
         lower_bound=1.0,
         lp_value=3.0,
@@ -59,7 +59,7 @@ def sample_without_ub() -> TrainingSample:
 
 def test_sample_xy(
     instance: Instance,
-    sample: TrainingSample,
+    sample_old: TrainingSample,
 ) -> None:
     x_expected = {
         "Lower bound": [[1.0, 2.0, 3.0]],
@@ -69,7 +69,7 @@ def test_sample_xy(
         "Lower bound": [[1.0]],
         "Upper bound": [[2.0]],
     }
-    xy = ObjectiveValueComponent().sample_xy(instance, sample)
+    xy = ObjectiveValueComponent().sample_xy_old(instance, sample_old)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -88,7 +88,7 @@ def test_sample_xy_without_lp(
         "Lower bound": [[1.0]],
         "Upper bound": [[2.0]],
     }
-    xy = ObjectiveValueComponent().sample_xy(instance, sample_without_lp)
+    xy = ObjectiveValueComponent().sample_xy_old(instance, sample_without_lp)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -97,14 +97,14 @@ def test_sample_xy_without_lp(
 
 def test_sample_xy_without_ub(
     instance: Instance,
-    sample_without_ub: TrainingSample,
+    sample_without_ub_old: TrainingSample,
 ) -> None:
     x_expected = {
         "Lower bound": [[1.0, 2.0, 3.0]],
         "Upper bound": [[1.0, 2.0, 3.0]],
     }
     y_expected = {"Lower bound": [[1.0]]}
-    xy = ObjectiveValueComponent().sample_xy(instance, sample_without_ub)
+    xy = ObjectiveValueComponent().sample_xy_old(instance, sample_without_ub_old)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -179,9 +179,9 @@ def test_fit_xy_without_ub() -> None:
 
 def test_sample_predict(
     instance: Instance,
-    sample: TrainingSample,
+    sample_old: TrainingSample,
 ) -> None:
-    x, y = ObjectiveValueComponent().sample_xy(instance, sample)
+    x, y = ObjectiveValueComponent().sample_xy_old(instance, sample_old)
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Upper bound"] = Mock(spec=Regressor)
@@ -191,7 +191,7 @@ def test_sample_predict(
     comp.regressors["Upper bound"].predict = Mock(  # type: ignore
         side_effect=lambda _: np.array([[60.0]])
     )
-    pred = comp.sample_predict(instance, sample)
+    pred = comp.sample_predict(instance, sample_old)
     assert pred == {
         "Lower bound": 50.0,
         "Upper bound": 60.0,
@@ -208,15 +208,15 @@ def test_sample_predict(
 
 def test_sample_predict_without_ub(
     instance: Instance,
-    sample_without_ub: TrainingSample,
+    sample_without_ub_old: TrainingSample,
 ) -> None:
-    x, y = ObjectiveValueComponent().sample_xy(instance, sample_without_ub)
+    x, y = ObjectiveValueComponent().sample_xy_old(instance, sample_without_ub_old)
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Lower bound"].predict = Mock(  # type: ignore
         side_effect=lambda _: np.array([[50.0]])
     )
-    pred = comp.sample_predict(instance, sample_without_ub)
+    pred = comp.sample_predict(instance, sample_without_ub_old)
     assert pred == {
         "Lower bound": 50.0,
     }
@@ -226,13 +226,13 @@ def test_sample_predict_without_ub(
     )
 
 
-def test_sample_evaluate(instance: Instance, sample: TrainingSample) -> None:
+def test_sample_evaluate(instance: Instance, sample_old: TrainingSample) -> None:
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Lower bound"].predict = lambda _: np.array([[1.05]])  # type: ignore
     comp.regressors["Upper bound"] = Mock(spec=Regressor)
     comp.regressors["Upper bound"].predict = lambda _: np.array([[2.50]])  # type: ignore
-    ev = comp.sample_evaluate(instance, sample)
+    ev = comp.sample_evaluate_old(instance, sample_old)
     assert ev == {
         "Lower bound": {
             "Actual value": 1.0,

tests/components/test_primal.py

@@ -68,7 +68,7 @@ def test_xy() -> None:
             [True, False],
         ]
     }
-    xy = PrimalSolutionComponent().sample_xy(instance, sample)
+    xy = PrimalSolutionComponent().sample_xy_old(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -119,7 +119,7 @@ def test_xy_without_lp_solution() -> None:
             [True, False],
         ]
     }
-    xy = PrimalSolutionComponent().sample_xy(instance, sample)
+    xy = PrimalSolutionComponent().sample_xy_old(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -164,7 +164,7 @@ def test_predict() -> None:
             "x[2]": 0.9,
         }
     )
-    x, _ = PrimalSolutionComponent().sample_xy(instance, sample)
+    x, _ = PrimalSolutionComponent().sample_xy_old(instance, sample)
     comp = PrimalSolutionComponent()
     comp.classifiers = {"default": clf}
     comp.thresholds = {"default": thr}
@@ -253,7 +253,7 @@ def test_evaluate() -> None:
             "x[4]": 1.0,
         }
     )
-    ev = comp.sample_evaluate(instance, sample)
+    ev = comp.sample_evaluate_old(instance, sample)
     assert ev == {
         0: classifier_evaluation_dict(tp=1, fp=1, tn=3, fn=0),
         1: classifier_evaluation_dict(tp=2, fp=0, tn=1, fn=2),

tests/components/test_static_lazy.py

@@ -116,7 +116,7 @@ def test_usage_with_solver(instance: Instance) -> None:
     stats: LearningSolveStats = {}
 
     # LearningSolver calls before_solve_mip
-    component.before_solve_mip(
+    component.before_solve_mip_old(
         solver=solver,
         instance=instance,
         model=None,
@@ -154,7 +154,7 @@ def test_usage_with_solver(instance: Instance) -> None:
     internal.add_constraint.assert_not_called()
 
     # LearningSolver calls after_solve_mip
-    component.after_solve_mip(
+    component.after_solve_mip_old(
         solver=solver,
         instance=instance,
         model=None,
@@ -250,7 +250,7 @@ def test_sample_xy(
         "type-a": [[False, True], [False, True], [True, False]],
         "type-b": [[False, True]],
     }
-    xy = StaticLazyConstraintsComponent().sample_xy(instance, sample)
+    xy = StaticLazyConstraintsComponent().sample_xy_old(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected