Make sample_ method accept instance

tests/components/test_objective.py

@@ -7,7 +7,7 @@ from unittest.mock import Mock
 import pytest
 from numpy.testing import assert_array_equal
 
-from miplearn import GurobiPyomoSolver, LearningSolver, Regressor
+from miplearn import GurobiPyomoSolver, LearningSolver, Regressor, Instance
 from miplearn.components.objective import ObjectiveValueComponent
 from miplearn.features import TrainingSample, InstanceFeatures, Features
 from tests.fixtures.knapsack import get_knapsack_instance
@@ -15,6 +15,13 @@ from tests.fixtures.knapsack import get_knapsack_instance
 import numpy as np
 
 
+@pytest.fixture
+def instance(features: Features) -> Instance:
+    instance = Mock(spec=Instance)
+    instance.features = features
+    return instance
+
+
 @pytest.fixture
 def features() -> Features:
     return Features(
@@ -50,7 +57,7 @@ def sample_without_ub() -> TrainingSample:
 
 
 def test_sample_xy(
-    features: Features,
+    instance: Instance,
     sample: TrainingSample,
 ) -> None:
     x_expected = {
@@ -61,7 +68,7 @@ def test_sample_xy(
         "Lower bound": [[1.0]],
         "Upper bound": [[2.0]],
     }
-    xy = ObjectiveValueComponent.sample_xy(features, sample)
+    xy = ObjectiveValueComponent.sample_xy(instance, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -69,7 +76,7 @@ def test_sample_xy(
 
 
 def test_sample_xy_without_lp(
-    features: Features,
+    instance: Instance,
     sample_without_lp: TrainingSample,
 ) -> None:
     x_expected = {
@@ -80,7 +87,7 @@ def test_sample_xy_without_lp(
         "Lower bound": [[1.0]],
         "Upper bound": [[2.0]],
     }
-    xy = ObjectiveValueComponent.sample_xy(features, 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
@@ -88,7 +95,7 @@ def test_sample_xy_without_lp(
 
 
 def test_sample_xy_without_ub(
-    features: Features,
+    instance: Instance,
     sample_without_ub: TrainingSample,
 ) -> None:
     x_expected = {
@@ -96,7 +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(features, 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
@@ -170,10 +177,10 @@ def test_fit_xy_without_ub() -> None:
 
 
 def test_sample_predict(
-    features: Features,
+    instance: Instance,
     sample: TrainingSample,
 ) -> None:
-    x, y = ObjectiveValueComponent.sample_xy(features, sample)
+    x, y = ObjectiveValueComponent.sample_xy(instance, sample)
     comp = ObjectiveValueComponent()
     comp.regressors["Lower bound"] = Mock(spec=Regressor)
     comp.regressors["Upper bound"] = Mock(spec=Regressor)
@@ -183,7 +190,7 @@ def test_sample_predict(
     comp.regressors["Upper bound"].predict = Mock(  # type: ignore
         side_effect=lambda _: np.array([[60.0]])
     )
-    pred = comp.sample_predict(features, sample)
+    pred = comp.sample_predict(instance, sample)
     assert pred == {
         "Lower bound": 50.0,
         "Upper bound": 60.0,
@@ -199,16 +206,16 @@ def test_sample_predict(
 
 
 def test_sample_predict_without_ub(
-    features: Features,
+    instance: Instance,
     sample_without_ub: TrainingSample,
 ) -> None:
-    x, y = ObjectiveValueComponent.sample_xy(features, 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
         side_effect=lambda _: np.array([[50.0]])
     )
-    pred = comp.sample_predict(features, sample_without_ub)
+    pred = comp.sample_predict(instance, sample_without_ub)
     assert pred == {
         "Lower bound": 50.0,
     }
@@ -218,13 +225,13 @@ def test_sample_predict_without_ub(
     )
 
 
-def test_sample_evaluate(features: Features, sample: TrainingSample) -> None:
+def test_sample_evaluate(instance: Instance, sample: 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(features, sample)
+    ev = comp.sample_evaluate(instance, sample)
     assert ev == {
         "Lower bound": {
             "Actual value": 1.0,