Remove tuples from VariableFeatures

tests/components/test_primal.py

@@ -29,8 +29,8 @@ def sample() -> Sample:
         after_load=Features(
             instance=InstanceFeatures(),
             variables=VariableFeatures(
-                names=("x[0]", "x[1]", "x[2]", "x[3]"),
-                categories=("default", None, "default", "default"),
+                names=["x[0]", "x[1]", "x[2]", "x[3]"],
+                categories=["default", None, "default", "default"],
             ),
         ),
         after_lp=Features(
@@ -38,8 +38,8 @@ def sample() -> Sample:
         ),
         after_mip=Features(
             variables=VariableFeatures(
-                names=("x[0]", "x[1]", "x[2]", "x[3]"),
-                values=(0.0, 1.0, 1.0, 0.0),
+                names=["x[0]", "x[1]", "x[2]", "x[3]"],
+                values=[0.0, 1.0, 1.0, 0.0],
             )
         ),
     )

tests/problems/test_tsp.py

@@ -44,7 +44,7 @@ def test_instance() -> None:
     features = instance.samples[0].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)
+    assert features.variables.values == [1.0, 0.0, 1.0, 1.0, 0.0, 1.0]
     assert features.mip_solve is not None
     assert features.mip_solve.mip_lower_bound == 4.0
     assert features.mip_solve.mip_upper_bound == 4.0
@@ -75,7 +75,7 @@ def test_subtour() -> None:
     assert lazy_enforced is not None
     assert len(lazy_enforced) > 0
     assert features.variables is not None
-    assert features.variables.values == (
+    assert features.variables.values == [
         1.0,
         0.0,
         0.0,
@@ -91,6 +91,6 @@ def test_subtour() -> None:
         0.0,
         1.0,
         1.0,
-    )
+    ]
     solver.fit([instance])
     solver.solve(instance)

tests/solvers/test_learning_solver.py

@@ -41,7 +41,7 @@ def test_learning_solver(
             after_mip = sample.after_mip
             assert after_mip is not None
             assert after_mip.variables is not None
-            assert after_mip.variables.values == (1.0, 0.0, 1.0, 1.0, 61.0)
+            assert after_mip.variables.values == [1.0, 0.0, 1.0, 1.0, 61.0]
             assert after_mip.mip_solve is not None
             assert after_mip.mip_solve.mip_lower_bound == 1183.0
             assert after_mip.mip_solve.mip_upper_bound == 1183.0
@@ -51,7 +51,7 @@ def test_learning_solver(
             after_lp = sample.after_lp
             assert after_lp is not None
             assert after_lp.variables is not None
-            assert _round(after_lp.variables.values) == (1.0, 0.923077, 1.0, 0.0, 67.0)
+            assert _round(after_lp.variables.values) == [1.0, 0.923077, 1.0, 0.0, 67.0]
             assert after_lp.lp_solve is not None
             assert after_lp.lp_solve.lp_value is not None
             assert round(after_lp.lp_solve.lp_value, 3) == 1287.923

tests/test_features.py

@@ -31,28 +31,28 @@ def test_knapsack() -> None:
     assert_equals(
         _round(features.variables),
         VariableFeatures(
-            names=("x[0]", "x[1]", "x[2]", "x[3]", "z"),
-            basis_status=("U", "B", "U", "L", "U"),
-            categories=("default", "default", "default", "default", None),
-            lower_bounds=(0.0, 0.0, 0.0, 0.0, 0.0),
-            obj_coeffs=(505.0, 352.0, 458.0, 220.0, 0.0),
-            reduced_costs=(193.615385, 0.0, 187.230769, -23.692308, 13.538462),
-            sa_lb_down=(-inf, -inf, -inf, -0.111111, -inf),
-            sa_lb_up=(1.0, 0.923077, 1.0, 1.0, 67.0),
-            sa_obj_down=(311.384615, 317.777778, 270.769231, -inf, -13.538462),
-            sa_obj_up=(inf, 570.869565, inf, 243.692308, inf),
-            sa_ub_down=(0.913043, 0.923077, 0.9, 0.0, 43.0),
-            sa_ub_up=(2.043478, inf, 2.2, inf, 69.0),
-            types=("B", "B", "B", "B", "C"),
-            upper_bounds=(1.0, 1.0, 1.0, 1.0, 67.0),
-            user_features=(
-                (23.0, 505.0),
-                (26.0, 352.0),
-                (20.0, 458.0),
-                (18.0, 220.0),
+            names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
+            basis_status=["U", "B", "U", "L", "U"],
+            categories=["default", "default", "default", "default", None],
+            lower_bounds=[0.0, 0.0, 0.0, 0.0, 0.0],
+            obj_coeffs=[505.0, 352.0, 458.0, 220.0, 0.0],
+            reduced_costs=[193.615385, 0.0, 187.230769, -23.692308, 13.538462],
+            sa_lb_down=[-inf, -inf, -inf, -0.111111, -inf],
+            sa_lb_up=[1.0, 0.923077, 1.0, 1.0, 67.0],
+            sa_obj_down=[311.384615, 317.777778, 270.769231, -inf, -13.538462],
+            sa_obj_up=[inf, 570.869565, inf, 243.692308, inf],
+            sa_ub_down=[0.913043, 0.923077, 0.9, 0.0, 43.0],
+            sa_ub_up=[2.043478, inf, 2.2, inf, 69.0],
+            types=["B", "B", "B", "B", "C"],
+            upper_bounds=[1.0, 1.0, 1.0, 1.0, 67.0],
+            user_features=[
+                [23.0, 505.0],
+                [26.0, 352.0],
+                [20.0, 458.0],
+                [18.0, 220.0],
                 None,
-            ),
-            values=(1.0, 0.923077, 1.0, 0.0, 67.0),
+            ],
+            values=[1.0, 0.923077, 1.0, 0.0, 67.0],
             alvarez_2017=[
                 [1.0, 0.32899, 0.0, 0.0, 1.0, 1.0, 5.265874, 46.051702],
                 [1.0, 0.229316, 0.0, 0.076923, 1.0, 1.0, 3.532875, 5.388476],