Replace instance.samples by instance.get/push_sample

miplearn/components/component.py

@@ -187,13 +187,14 @@ class Component:
         instances: List[Instance],
         n_jobs: int = 1,
     ) -> None:
+
         # Part I: Pre-fit
         def _pre_sample_xy(instance: Instance) -> Dict:
             pre_instance: Dict = {}
             for (cidx, comp) in enumerate(components):
                 pre_instance[cidx] = []
             instance.load()
-            for sample in instance.samples:
+            for sample in instance.get_samples():
                 for (cidx, comp) in enumerate(components):
                     pre_instance[cidx].append(comp.pre_sample_xy(instance, sample))
             instance.free()
@@ -219,7 +220,7 @@ class Component:
                 x_instance[cidx] = {}
                 y_instance[cidx] = {}
             instance.load()
-            for sample in instance.samples:
+            for sample in instance.get_samples():
                 for (cidx, comp) in enumerate(components):
                     x = x_instance[cidx]
                     y = y_instance[cidx]

miplearn/instance/base.py

@@ -28,7 +28,7 @@ class Instance(ABC):
     """
 
     def __init__(self) -> None:
-        self.samples: List[Sample] = []
+        self._samples: List[Sample] = []
 
     @abstractmethod
     def to_model(self) -> Any:
@@ -189,3 +189,9 @@ class Instance(ABC):
         Save any pending changes made to the instance to the underlying data store.
         """
         pass
+
+    def get_samples(self) -> List[Sample]:
+        return self._samples
+
+    def push_sample(self, sample: Sample) -> None:
+        self._samples.append(sample)

miplearn/instance/picklegz.py

@@ -10,6 +10,7 @@ from typing import Optional, Any, List, Hashable, cast, IO, TYPE_CHECKING, Dict
 
 from overrides import overrides
 
+from miplearn.features import Sample
 from miplearn.instance.base import Instance
 
 if TYPE_CHECKING:
@@ -120,18 +121,26 @@ class PickleGzInstance(Instance):
             obj = read_pickle_gz(self.filename)
             assert isinstance(obj, Instance)
             self.instance = obj
-            self.samples = self.instance.samples
 
     @overrides
     def free(self) -> None:
         self.instance = None  # type: ignore
-        self.samples = None  # type: ignore
         gc.collect()
 
     @overrides
     def flush(self) -> None:
         write_pickle_gz(self.instance, self.filename)
 
+    @overrides
+    def get_samples(self) -> List[Sample]:
+        assert self.instance is not None
+        return self.instance.get_samples()
+
+    @overrides
+    def push_sample(self, sample: Sample) -> None:
+        assert self.instance is not None
+        self.instance.push_sample(sample)
+
 
 def write_pickle_gz(obj: Any, filename: str) -> None:
     os.makedirs(os.path.dirname(filename), exist_ok=True)

miplearn/solvers/learning.py

@@ -150,7 +150,7 @@ class LearningSolver:
         # Initialize training sample
         # -------------------------------------------------------
         sample = Sample()
-        instance.samples.append(sample)
+        instance.push_sample(sample)
 
         # Initialize stats
         # -------------------------------------------------------

tests/components/test_dynamic_lazy.py

@@ -25,7 +25,7 @@ E = 0.1
 @pytest.fixture
 def training_instances() -> List[Instance]:
     instances = [cast(Instance, Mock(spec=Instance)) for _ in range(2)]
-    instances[0].samples = [
+    samples_0 = [
         Sample(
             after_load=Features(instance=InstanceFeatures()),
             after_mip=Features(extra={"lazy_enforced": {"c1", "c2"}}),
@@ -35,12 +35,9 @@ def training_instances() -> List[Instance]:
             after_mip=Features(extra={"lazy_enforced": {"c2", "c3"}}),
         ),
     ]
-    instances[0].samples[0].after_load.instance.to_list = Mock(  # type: ignore
+    samples_0[0].after_load.instance.to_list = Mock(return_value=[5.0])  # type: ignore
-        return_value=[5.0]
+    samples_0[1].after_load.instance.to_list = Mock(return_value=[5.0])  # type: ignore
-    )
+    instances[0].get_samples = Mock(return_value=samples_0)  # type: ignore
-    instances[0].samples[1].after_load.instance.to_list = Mock(  # type: ignore
-        return_value=[5.0]
-    )
     instances[0].get_constraint_categories = Mock(  # type: ignore
         return_value={
             "c1": "type-a",
@@ -57,15 +54,14 @@ def training_instances() -> List[Instance]:
             "c4": [3.0, 4.0],
         }
     )
-    instances[1].samples = [
+    samples_1 = [
         Sample(
             after_load=Features(instance=InstanceFeatures()),
             after_mip=Features(extra={"lazy_enforced": {"c3", "c4"}}),
         )
     ]
-    instances[1].samples[0].after_load.instance.to_list = Mock(  # type: ignore
+    samples_1[0].after_load.instance.to_list = Mock(return_value=[8.0])  # type: ignore
-        return_value=[8.0]
+    instances[1].get_samples = Mock(return_value=samples_1)  # type: ignore
-    )
     instances[1].get_constraint_categories = Mock(  # type: ignore
         return_value={
             "c1": None,
@@ -97,7 +93,7 @@ def test_sample_xy(training_instances: List[Instance]) -> None:
     }
     x_actual, y_actual = comp.sample_xy(
         training_instances[0],
-        training_instances[0].samples[0],
+        training_instances[0].get_samples()[0],
     )
     assert_equals(x_actual, x_expected)
     assert_equals(y_actual, y_expected)
@@ -184,12 +180,12 @@ def test_sample_predict_evaluate(training_instances: List[Instance]) -> None:
     )
     pred = comp.sample_predict(
         training_instances[0],
-        training_instances[0].samples[0],
+        training_instances[0].get_samples()[0],
     )
     assert pred == ["c1", "c4"]
     ev = comp.sample_evaluate(
         training_instances[0],
-        training_instances[0].samples[0],
+        training_instances[0].get_samples()[0],
     )
     assert ev == {
         "type-a": classifier_evaluation_dict(tp=1, fp=0, tn=0, fn=1),

tests/components/test_dynamic_user_cuts.py

@@ -80,7 +80,7 @@ def test_usage(
     solver: LearningSolver,
 ) -> None:
     stats_before = solver.solve(stab_instance)
-    sample = stab_instance.samples[0]
+    sample = stab_instance.get_samples()[0]
     assert sample.after_mip is not None
     assert sample.after_mip.extra is not None
     assert len(sample.after_mip.extra["user_cuts_enforced"]) > 0

tests/components/test_static_lazy.py

@@ -70,7 +70,7 @@ def sample() -> Sample:
 @pytest.fixture
 def instance(sample: Sample) -> Instance:
     instance = Mock(spec=Instance)
-    instance.samples = [sample]
+    instance.get_samples = Mock(return_value=[sample])  # type: ignore
     instance.has_static_lazy_constraints = Mock(return_value=True)
     return instance
 
@@ -111,7 +111,7 @@ def test_usage_with_solver(instance: Instance) -> None:
     )
 
     stats: LearningSolveStats = {}
-    sample = instance.samples[0]
+    sample = instance.get_samples()[0]
     assert sample.after_load is not None
     assert sample.after_mip is not None
     assert sample.after_mip.extra is not None

tests/problems/test_tsp.py

@@ -39,9 +39,10 @@ def test_instance() -> None:
     instance = TravelingSalesmanInstance(n_cities, distances)
     solver = LearningSolver()
     solver.solve(instance)
-    assert len(instance.samples) == 1
+    assert len(instance.get_samples()) == 1
-    assert instance.samples[0].after_mip is not None
+    sample = instance.get_samples()[0]
-    features = instance.samples[0].after_mip
+    assert sample.after_mip is not None
+    features = sample.after_mip
     assert features is not None
     assert features.variables is not None
     assert features.variables.values == [1.0, 0.0, 1.0, 1.0, 0.0, 1.0]
@@ -66,9 +67,10 @@ def test_subtour() -> None:
     instance = TravelingSalesmanInstance(n_cities, distances)
     solver = LearningSolver()
     solver.solve(instance)
-    assert len(instance.samples) == 1
+    assert len(instance.get_samples()) == 1
-    assert instance.samples[0].after_mip is not None
+    sample = instance.get_samples()[0]
-    features = instance.samples[0].after_mip
+    assert sample.after_mip is not None
+    features = sample.after_mip
     assert features.extra is not None
     assert "lazy_enforced" in features.extra
     lazy_enforced = features.extra["lazy_enforced"]

tests/solvers/test_learning_solver.py

@@ -35,8 +35,8 @@ def test_learning_solver(
             )
 
             solver.solve(instance)
-            assert len(instance.samples) > 0
+            assert len(instance.get_samples()) > 0
-            sample = instance.samples[0]
+            sample = instance.get_samples()[0]
 
             after_mip = sample.after_mip
             assert after_mip is not None
@@ -90,7 +90,7 @@ def test_parallel_solve(
         results = solver.parallel_solve(instances, n_jobs=3)
         assert len(results) == 10
         for instance in instances:
-            assert len(instance.samples) == 1
+            assert len(instance.get_samples()) == 1
 
 
 def test_solve_fit_from_disk(
@@ -109,13 +109,13 @@ def test_solve_fit_from_disk(
         solver = LearningSolver(solver=internal_solver)
         solver.solve(instances[0])
         instance_loaded = read_pickle_gz(cast(PickleGzInstance, instances[0]).filename)
-        assert len(instance_loaded.samples) > 0
+        assert len(instance_loaded.get_samples()) > 0
 
         # Test: parallel_solve
         solver.parallel_solve(instances)
         for instance in instances:
             instance_loaded = read_pickle_gz(cast(PickleGzInstance, instance).filename)
-            assert len(instance_loaded.samples) > 0
+            assert len(instance_loaded.get_samples()) > 0
 
         # Delete temporary files
         for instance in instances: