Make sample_xy an instance method

miplearn/components/lazy_static.py

@@ -171,8 +171,8 @@ class StaticLazyConstraintsComponent(Component):
                     enforced_cids += [category_to_cids[category][i]]
         return enforced_cids
 
-    @staticmethod
     def sample_xy(
+        self,
         instance: "Instance",
         sample: TrainingSample,
     ) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:

miplearn/components/objective.py

@@ -73,8 +73,8 @@ class ObjectiveValueComponent(Component):
                 logger.info(f"{c} regressor not fitted. Skipping.")
         return pred
 
-    @staticmethod
     def sample_xy(
+        self,
         instance: Instance,
         sample: TrainingSample,
     ) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:

tests/components/test_lazy_static.py

@@ -245,7 +245,7 @@ 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(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected

tests/components/test_objective.py

@@ -68,7 +68,7 @@ def test_sample_xy(
         "Lower bound": [[1.0]],
         "Upper bound": [[2.0]],
     }
-    xy = ObjectiveValueComponent.sample_xy(instance, sample)
+    xy = ObjectiveValueComponent().sample_xy(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -87,7 +87,7 @@ 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(instance, sample_without_lp)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -103,7 +103,7 @@ def test_sample_xy_without_ub(
         "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(instance, sample_without_ub)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -180,7 +180,7 @@ def test_sample_predict(
     instance: Instance,
     sample: TrainingSample,
 ) -> None:
-    x, y = ObjectiveValueComponent.sample_xy(instance, sample)
+    x, y = ObjectiveValueComponent().sample_xy(instance, sample)
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Upper bound"] = Mock(spec=Regressor)
@@ -209,7 +209,7 @@ def test_sample_predict_without_ub(
     instance: Instance,
     sample_without_ub: TrainingSample,
 ) -> None:
-    x, y = ObjectiveValueComponent.sample_xy(instance, sample_without_ub)
+    x, y = ObjectiveValueComponent().sample_xy(instance, sample_without_ub)
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Lower bound"].predict = Mock(  # type: ignore

tests/components/test_user_cuts.py

@@ -0,0 +1,31 @@
+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+from typing import Any, List
+
+import pytest
+from networkx import Graph
+import networkx as nx
+from scipy.stats import randint
+
+from miplearn import Instance
+from miplearn.problems.stab import MaxWeightStableSetGenerator
+
+
+class GurobiStableSetProblem(Instance):
+    def __init__(self, graph: Graph) -> None:
+        super().__init__()
+        self.graph = graph
+
+    def to_model(self) -> Any:
+        pass
+
+
+@pytest.fixture
+def instance() -> Instance:
+    graph = nx.generators.random_graphs.binomial_graph(50, 0.5)
+    return GurobiStableSetProblem(graph)
+
+
+def test_usage(instance: Instance) -> None:
+    pass