Remove tuples from VariableFeatures

miplearn/features.py

@@ -30,22 +30,22 @@ class InstanceFeatures:
 
 @dataclass
 class VariableFeatures:
-    names: Optional[Tuple[str, ...]] = None
-    basis_status: Optional[Tuple[str, ...]] = None
-    categories: Optional[Tuple[Optional[Hashable], ...]] = None
-    lower_bounds: Optional[Tuple[float, ...]] = None
-    obj_coeffs: Optional[Tuple[float, ...]] = None
-    reduced_costs: Optional[Tuple[float, ...]] = None
-    sa_lb_down: Optional[Tuple[float, ...]] = None
-    sa_lb_up: Optional[Tuple[float, ...]] = None
-    sa_obj_down: Optional[Tuple[float, ...]] = None
-    sa_obj_up: Optional[Tuple[float, ...]] = None
-    sa_ub_down: Optional[Tuple[float, ...]] = None
-    sa_ub_up: Optional[Tuple[float, ...]] = None
-    types: Optional[Tuple[str, ...]] = None
-    upper_bounds: Optional[Tuple[float, ...]] = None
-    user_features: Optional[Tuple[Optional[Tuple[float, ...]], ...]] = None
-    values: Optional[Tuple[float, ...]] = None
+    names: Optional[List[str]] = None
+    basis_status: Optional[List[str]] = None
+    categories: Optional[List[Optional[Hashable]]] = None
+    lower_bounds: Optional[List[float]] = None
+    obj_coeffs: Optional[List[float]] = None
+    reduced_costs: Optional[List[float]] = None
+    sa_lb_down: Optional[List[float]] = None
+    sa_lb_up: Optional[List[float]] = None
+    sa_obj_down: Optional[List[float]] = None
+    sa_obj_up: Optional[List[float]] = None
+    sa_ub_down: Optional[List[float]] = None
+    sa_ub_up: Optional[List[float]] = None
+    types: Optional[List[str]] = None
+    upper_bounds: Optional[List[float]] = None
+    user_features: Optional[List[Optional[List[float]]]] = None
+    values: Optional[List[float]] = None
 
     # Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
     # approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
@@ -190,7 +190,7 @@ class FeaturesExtractor:
         assert features.variables is not None
         assert features.variables.names is not None
         categories: List[Hashable] = []
-        user_features: List[Optional[Tuple[float, ...]]] = []
+        user_features: List[Optional[List[float]]] = []
         for (i, var_name) in enumerate(features.variables.names):
             category: Hashable = instance.get_variable_category(var_name)
             user_features_i: Optional[List[float]] = None
@@ -217,9 +217,9 @@ class FeaturesExtractor:
             if user_features_i is None:
                 user_features.append(None)
             else:
-                user_features.append(tuple(user_features_i))
-        features.variables.categories = tuple(categories)
-        features.variables.user_features = tuple(user_features)
+                user_features.append(user_features_i)
+        features.variables.categories = categories
+        features.variables.user_features = user_features
 
     def _extract_user_features_constrs(
         self,

miplearn/solvers/gurobi.py

@@ -75,12 +75,12 @@ class GurobiSolver(InternalSolver):
         self._cname_to_constr: Dict[str, "gurobipy.Constr"] = {}
         self._gp_vars: Tuple["gurobipy.Var", ...] = tuple()
         self._gp_constrs: Tuple["gurobipy.Constr", ...] = tuple()
-        self._var_names: Tuple[str, ...] = tuple()
+        self._var_names: List[str] = []
         self._constr_names: Tuple[str, ...] = tuple()
-        self._var_types: Tuple[str, ...] = tuple()
-        self._var_lbs: Tuple[float, ...] = tuple()
-        self._var_ubs: Tuple[float, ...] = tuple()
-        self._var_obj_coeffs: Tuple[float, ...] = tuple()
+        self._var_types: List[str] = []
+        self._var_lbs: List[float] = []
+        self._var_ubs: List[float] = []
+        self._var_obj_coeffs: List[float] = []
 
         if self.lazy_cb_frequency == 1:
             self.lazy_cb_where = [self.gp.GRB.Callback.MIPSOL]
@@ -328,8 +328,8 @@ class GurobiSolver(InternalSolver):
             obj_coeffs = self._var_obj_coeffs
 
         if self._has_lp_solution:
-            reduced_costs = tuple(model.getAttr("rc", self._gp_vars))
-            basis_status = tuple(
+            reduced_costs = model.getAttr("rc", self._gp_vars)
+            basis_status = list(
                 map(
                     _parse_gurobi_vbasis,
                     model.getAttr("vbasis", self._gp_vars),
@@ -337,15 +337,15 @@ class GurobiSolver(InternalSolver):
             )
 
             if with_sa:
-                sa_obj_up = tuple(model.getAttr("saobjUp", self._gp_vars))
-                sa_obj_down = tuple(model.getAttr("saobjLow", self._gp_vars))
-                sa_ub_up = tuple(model.getAttr("saubUp", self._gp_vars))
-                sa_ub_down = tuple(model.getAttr("saubLow", self._gp_vars))
-                sa_lb_up = tuple(model.getAttr("salbUp", self._gp_vars))
-                sa_lb_down = tuple(model.getAttr("salbLow", self._gp_vars))
+                sa_obj_up = model.getAttr("saobjUp", self._gp_vars)
+                sa_obj_down = model.getAttr("saobjLow", self._gp_vars)
+                sa_ub_up = model.getAttr("saubUp", self._gp_vars)
+                sa_ub_down = model.getAttr("saubLow", self._gp_vars)
+                sa_lb_up = model.getAttr("salbUp", self._gp_vars)
+                sa_lb_down = model.getAttr("salbLow", self._gp_vars)
 
         if model.solCount > 0:
-            values = tuple(model.getAttr("x", self._gp_vars))
+            values = model.getAttr("x", self._gp_vars)
 
         return VariableFeatures(
             names=self._var_names,
@@ -600,12 +600,12 @@ class GurobiSolver(InternalSolver):
         self._cname_to_constr = cname_to_constr
         self._gp_vars = tuple(gp_vars)
         self._gp_constrs = tuple(gp_constrs)
-        self._var_names = tuple(var_names)
+        self._var_names = var_names
         self._constr_names = tuple(constr_names)
-        self._var_types = tuple(var_types)
-        self._var_lbs = tuple(var_lbs)
-        self._var_ubs = tuple(var_ubs)
-        self._var_obj_coeffs = tuple(var_obj_coeffs)
+        self._var_types = var_types
+        self._var_lbs = var_lbs
+        self._var_ubs = var_ubs
+        self._var_obj_coeffs = var_obj_coeffs
 
     def __getstate__(self) -> Dict:
         return {

miplearn/solvers/pyomo/base.py

@@ -337,33 +337,20 @@ class BasePyomoSolver(InternalSolver):
                 if self._has_lp_solution or self._has_mip_solution:
                     values.append(v.value)
 
-        types_t: Optional[Tuple[str, ...]] = None
-        upper_bounds_t: Optional[Tuple[float, ...]] = None
-        lower_bounds_t: Optional[Tuple[float, ...]] = None
-        obj_coeffs_t: Optional[Tuple[float, ...]] = None
-        reduced_costs_t: Optional[Tuple[float, ...]] = None
-        values_t: Optional[Tuple[float, ...]] = None
-
-        if with_static:
-            types_t = tuple(types)
-            upper_bounds_t = tuple(upper_bounds)
-            lower_bounds_t = tuple(lower_bounds)
-            obj_coeffs_t = tuple(obj_coeffs)
-
-        if self._has_lp_solution:
-            reduced_costs_t = tuple(reduced_costs)
-
-        if self._has_lp_solution or self._has_mip_solution:
-            values_t = tuple(values)
+        def _none_if_empty(obj: Any) -> Any:
+            if len(obj) == 0:
+                return None
+            else:
+                return obj
 
         return VariableFeatures(
-            names=tuple(names),
-            types=types_t,
-            upper_bounds=upper_bounds_t,
-            lower_bounds=lower_bounds_t,
-            obj_coeffs=obj_coeffs_t,
-            reduced_costs=reduced_costs_t,
-            values=values_t,
+            names=_none_if_empty(names),
+            types=_none_if_empty(types),
+            upper_bounds=_none_if_empty(upper_bounds),
+            lower_bounds=_none_if_empty(lower_bounds),
+            obj_coeffs=_none_if_empty(obj_coeffs),
+            reduced_costs=_none_if_empty(reduced_costs),
+            values=_none_if_empty(values),
         )
 
     @overrides

miplearn/solvers/tests/__init__.py

@@ -57,11 +57,11 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
     assert_equals(
         solver.get_variables(),
         VariableFeatures(
-            names=("x[0]", "x[1]", "x[2]", "x[3]", "z"),
-            lower_bounds=(0.0, 0.0, 0.0, 0.0, 0.0),
-            upper_bounds=(1.0, 1.0, 1.0, 1.0, 67.0),
-            types=("B", "B", "B", "B", "C"),
-            obj_coeffs=(505.0, 352.0, 458.0, 220.0, 0.0),
+            names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
+            lower_bounds=[0.0, 0.0, 0.0, 0.0, 0.0],
+            upper_bounds=[1.0, 1.0, 1.0, 1.0, 67.0],
+            types=["B", "B", "B", "B", "C"],
+            obj_coeffs=[505.0, 352.0, 458.0, 220.0, 0.0],
         ),
     )
 
@@ -100,16 +100,16 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
         _filter_attrs(
             solver.get_variable_attrs(),
             VariableFeatures(
-                names=("x[0]", "x[1]", "x[2]", "x[3]", "z"),
-                basis_status=("U", "B", "U", "L", "U"),
-                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),
-                values=(1.0, 0.923077, 1.0, 0.0, 67.0),
+                names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
+                basis_status=["U", "B", "U", "L", "U"],
+                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],
+                values=[1.0, 0.923077, 1.0, 0.0, 67.0],
             ),
         ),
     )
@@ -153,8 +153,8 @@ def run_basic_usage_tests(solver: InternalSolver) -> None:
         _filter_attrs(
             solver.get_variable_attrs(),
             VariableFeatures(
-                names=("x[0]", "x[1]", "x[2]", "x[3]", "z"),
-                values=(1.0, 0.0, 1.0, 1.0, 61.0),
+                names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
+                values=[1.0, 0.0, 1.0, 1.0, 61.0],
             ),
         ),
     )

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],