Remove obsolete methods

tests/components/test_component.py

@@ -9,8 +9,8 @@ from miplearn.features import Features
 from miplearn.instance.base import Instance
 
 
-def test_xy_instance_old() -> None:
-    def _sample_xy_old(features: Features, sample: str) -> Tuple[Dict, Dict]:
+def test_xy_instance() -> None:
+    def _sample_xy(features: Features, sample: str) -> Tuple[Dict, Dict]:
         x = {
             "s1": {
                 "category_a": [
@@ -55,12 +55,10 @@ def test_xy_instance_old() -> None:
 
     comp = Component()
     instance_1 = Mock(spec=Instance)
-    instance_1.training_data = ["s1", "s2"]
-    instance_1.features = {}
+    instance_1.samples = ["s1", "s2"]
     instance_2 = Mock(spec=Instance)
-    instance_2.training_data = ["s3"]
-    instance_2.features = {}
-    comp.sample_xy_old = _sample_xy_old  # type: ignore
+    instance_2.samples = ["s3"]
+    comp.sample_xy = _sample_xy  # type: ignore
     x_expected = {
         "category_a": [
             [1, 2, 3],
@@ -96,6 +94,6 @@ def test_xy_instance_old() -> None:
             [11],
         ],
     }
-    x_actual, y_actual = comp.xy_instances_old([instance_1, instance_2])
+    x_actual, y_actual = comp.xy_instances([instance_1, instance_2])
     assert x_actual == x_expected
     assert y_actual == y_expected

tests/components/test_dynamic_lazy.py

@@ -13,7 +13,6 @@ from miplearn.classifiers.threshold import MinProbabilityThreshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
 from miplearn.features import (
-    TrainingSample,
     Features,
     InstanceFeatures,
     Sample,
@@ -24,60 +23,6 @@ from miplearn.solvers.tests import assert_equals
 E = 0.1
 
 
-@pytest.fixture
-def training_instances_old() -> List[Instance]:
-    instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]
-    instances[0].features = Features(
-        instance=InstanceFeatures(
-            user_features=[50.0],
-        ),
-    )
-    instances[0].training_data = [
-        TrainingSample(lazy_enforced={"c1", "c2"}),
-        TrainingSample(lazy_enforced={"c2", "c3"}),
-    ]
-    instances[0].get_constraint_category = Mock(  # type: ignore
-        side_effect=lambda cid: {
-            "c1": "type-a",
-            "c2": "type-a",
-            "c3": "type-b",
-            "c4": "type-b",
-        }[cid]
-    )
-    instances[0].get_constraint_features = Mock(  # type: ignore
-        side_effect=lambda cid: {
-            "c1": [1.0, 2.0, 3.0],
-            "c2": [4.0, 5.0, 6.0],
-            "c3": [1.0, 2.0],
-            "c4": [3.0, 4.0],
-        }[cid]
-    )
-    instances[1].features = Features(
-        instance=InstanceFeatures(
-            user_features=[80.0],
-        ),
-    )
-    instances[1].training_data = [
-        TrainingSample(lazy_enforced={"c3", "c4"}),
-    ]
-    instances[1].get_constraint_category = Mock(  # type: ignore
-        side_effect=lambda cid: {
-            "c1": None,
-            "c2": "type-a",
-            "c3": "type-b",
-            "c4": "type-b",
-        }[cid]
-    )
-    instances[1].get_constraint_features = Mock(  # type: ignore
-        side_effect=lambda cid: {
-            "c2": [7.0, 8.0, 9.0],
-            "c3": [5.0, 6.0],
-            "c4": [7.0, 8.0],
-        }[cid]
-    )
-    return instances
-
-
 @pytest.fixture
 def training_instances() -> List[Instance]:
     instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]

tests/components/test_dynamic_user_cuts.py

@@ -12,7 +12,7 @@ from gurobipy import GRB
 from networkx import Graph
 from overrides import overrides
 
-from miplearn import InternalSolver
+from miplearn.solvers.learning import InternalSolver
 from miplearn.components.dynamic_user_cuts import UserCutsComponent
 from miplearn.instance.base import Instance
 from miplearn.solvers.gurobi import GurobiSolver