Use np.ndarray for constraint methods in Instance

tests/components/test_dynamic_lazy.py

@@ -11,7 +11,7 @@ from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import MinProbabilityThreshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.dynamic_lazy import DynamicLazyConstraintsComponent
-from miplearn.features.sample import Sample, MemorySample
+from miplearn.features.sample import MemorySample
 from miplearn.instance.base import Instance
 from miplearn.solvers.tests import assert_equals
 
@@ -24,71 +24,71 @@ def training_instances() -> List[Instance]:
     samples_0 = [
         MemorySample(
             {
-                "mip_constr_lazy_enforced": {"c1", "c2"},
-                "static_instance_features": [5.0],
+                "mip_constr_lazy_enforced": {b"c1", b"c2"},
+                "static_instance_features": np.array([5.0]),
             },
         ),
         MemorySample(
             {
-                "mip_constr_lazy_enforced": {"c2", "c3"},
-                "static_instance_features": [5.0],
+                "mip_constr_lazy_enforced": {b"c2", b"c3"},
+                "static_instance_features": np.array([5.0]),
             },
         ),
     ]
     instances[0].get_samples = Mock(return_value=samples_0)  # type: ignore
     instances[0].get_constraint_categories = Mock(  # type: ignore
-        return_value={
-            "c1": "type-a",
-            "c2": "type-a",
-            "c3": "type-b",
-            "c4": "type-b",
-        }
+        return_value=np.array(["type-a", "type-a", "type-b", "type-b"], dtype="S")
     )
     instances[0].get_constraint_features = Mock(  # type: ignore
-        return_value={
-            "c1": [1.0, 2.0, 3.0],
-            "c2": [4.0, 5.0, 6.0],
-            "c3": [1.0, 2.0],
-            "c4": [3.0, 4.0],
-        }
+        return_value=np.array(
+            [
+                [1.0, 2.0, 3.0],
+                [4.0, 5.0, 6.0],
+                [1.0, 2.0, 0.0],
+                [3.0, 4.0, 0.0],
+            ]
+        )
+    )
+    instances[0].are_constraints_lazy = Mock(  # type: ignore
+        return_value=np.zeros(4, dtype=bool)
     )
     samples_1 = [
         MemorySample(
             {
-                "mip_constr_lazy_enforced": {"c3", "c4"},
-                "static_instance_features": [8.0],
+                "mip_constr_lazy_enforced": {b"c3", b"c4"},
+                "static_instance_features": np.array([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,
-            "c2": "type-a",
-            "c3": "type-b",
-            "c4": "type-b",
-        }
+        return_value=np.array(["", "type-a", "type-b", "type-b"], dtype="S")
     )
     instances[1].get_constraint_features = Mock(  # type: ignore
-        return_value={
-            "c2": [7.0, 8.0, 9.0],
-            "c3": [5.0, 6.0],
-            "c4": [7.0, 8.0],
-        }
+        return_value=np.array(
+            [
+                [7.0, 8.0, 9.0],
+                [5.0, 6.0, 0.0],
+                [7.0, 8.0, 0.0],
+            ]
+        )
+    )
+    instances[1].are_constraints_lazy = Mock(  # type: ignore
+        return_value=np.zeros(4, dtype=bool)
     )
     return instances
 
 
 def test_sample_xy(training_instances: List[Instance]) -> None:
     comp = DynamicLazyConstraintsComponent()
-    comp.pre_fit([{"c1", "c2", "c3", "c4"}])
+    comp.pre_fit([{b"c1", b"c2", b"c3", b"c4"}])
     x_expected = {
-        "type-a": [[5.0, 1.0, 2.0, 3.0], [5.0, 4.0, 5.0, 6.0]],
-        "type-b": [[5.0, 1.0, 2.0], [5.0, 3.0, 4.0]],
+        b"type-a": np.array([[5.0, 1.0, 2.0, 3.0], [5.0, 4.0, 5.0, 6.0]]),
+        b"type-b": np.array([[5.0, 1.0, 2.0, 0.0], [5.0, 3.0, 4.0, 0.0]]),
     }
     y_expected = {
-        "type-a": [[False, True], [False, True]],
-        "type-b": [[True, False], [True, False]],
+        b"type-a": np.array([[False, True], [False, True]]),
+        b"type-b": np.array([[True, False], [True, False]]),
     }
     x_actual, y_actual = comp.sample_xy(
         training_instances[0],
@@ -98,95 +98,26 @@ def test_sample_xy(training_instances: List[Instance]) -> None:
     assert_equals(y_actual, y_expected)
 
 
-# def test_fit(training_instances: List[Instance]) -> None:
-#     clf = Mock(spec=Classifier)
-#     clf.clone = Mock(side_effect=lambda: Mock(spec=Classifier))
-#     comp = DynamicLazyConstraintsComponent(classifier=clf)
-#     comp.fit(training_instances)
-#     assert clf.clone.call_count == 2
-#
-#     assert "type-a" in comp.classifiers
-#     clf_a = comp.classifiers["type-a"]
-#     assert clf_a.fit.call_count == 1  # type: ignore
-#     assert_array_equal(
-#         clf_a.fit.call_args[0][0],  # type: ignore
-#         np.array(
-#             [
-#                 [5.0, 1.0, 2.0, 3.0],
-#                 [5.0, 4.0, 5.0, 6.0],
-#                 [5.0, 1.0, 2.0, 3.0],
-#                 [5.0, 4.0, 5.0, 6.0],
-#                 [8.0, 7.0, 8.0, 9.0],
-#             ]
-#         ),
-#     )
-#     assert_array_equal(
-#         clf_a.fit.call_args[0][1],  # type: ignore
-#         np.array(
-#             [
-#                 [False, True],
-#                 [False, True],
-#                 [True, False],
-#                 [False, True],
-#                 [True, False],
-#             ]
-#         ),
-#     )
-#
-#     assert "type-b" in comp.classifiers
-#     clf_b = comp.classifiers["type-b"]
-#     assert clf_b.fit.call_count == 1  # type: ignore
-#     assert_array_equal(
-#         clf_b.fit.call_args[0][0],  # type: ignore
-#         np.array(
-#             [
-#                 [5.0, 1.0, 2.0],
-#                 [5.0, 3.0, 4.0],
-#                 [5.0, 1.0, 2.0],
-#                 [5.0, 3.0, 4.0],
-#                 [8.0, 5.0, 6.0],
-#                 [8.0, 7.0, 8.0],
-#             ]
-#         ),
-#     )
-#     assert_array_equal(
-#         clf_b.fit.call_args[0][1],  # type: ignore
-#         np.array(
-#             [
-#                 [True, False],
-#                 [True, False],
-#                 [False, True],
-#                 [True, False],
-#                 [False, True],
-#                 [False, True],
-#             ]
-#         ),
-#     )
-
-
 def test_sample_predict_evaluate(training_instances: List[Instance]) -> None:
     comp = DynamicLazyConstraintsComponent()
-    comp.known_cids.extend(["c1", "c2", "c3", "c4"])
-    comp.thresholds["type-a"] = MinProbabilityThreshold([0.5, 0.5])
-    comp.thresholds["type-b"] = MinProbabilityThreshold([0.5, 0.5])
-    comp.classifiers["type-a"] = Mock(spec=Classifier)
-    comp.classifiers["type-b"] = Mock(spec=Classifier)
-    comp.classifiers["type-a"].predict_proba = Mock(  # type: ignore
+    comp.known_cids.extend([b"c1", b"c2", b"c3", b"c4"])
+    comp.thresholds[b"type-a"] = MinProbabilityThreshold([0.5, 0.5])
+    comp.thresholds[b"type-b"] = MinProbabilityThreshold([0.5, 0.5])
+    comp.classifiers[b"type-a"] = Mock(spec=Classifier)
+    comp.classifiers[b"type-b"] = Mock(spec=Classifier)
+    comp.classifiers[b"type-a"].predict_proba = Mock(  # type: ignore
         side_effect=lambda _: np.array([[0.1, 0.9], [0.8, 0.2]])
     )
-    comp.classifiers["type-b"].predict_proba = Mock(  # type: ignore
+    comp.classifiers[b"type-b"].predict_proba = Mock(  # type: ignore
         side_effect=lambda _: np.array([[0.9, 0.1], [0.1, 0.9]])
     )
     pred = comp.sample_predict(
         training_instances[0],
         training_instances[0].get_samples()[0],
     )
-    assert pred == ["c1", "c4"]
+    assert pred == [b"c1", b"c4"]
     ev = comp.sample_evaluate(
         training_instances[0],
         training_instances[0].get_samples()[0],
     )
-    assert ev == {
-        "type-a": classifier_evaluation_dict(tp=1, fp=0, tn=0, fn=1),
-        "type-b": classifier_evaluation_dict(tp=0, fp=1, tn=1, fn=0),
-    }
+    assert ev == classifier_evaluation_dict(tp=1, fp=1, tn=1, fn=1)