Add _gurobipy suffix to all build_model functions

miplearn/problems/__init__.py

@@ -11,7 +11,7 @@ from pyomo import environ as pe
 def _gurobipy_set_params(model: gp.Model, params: Optional[dict[str, Any]]) -> None:
     assert isinstance(model, gp.Model)
     if params is not None:
-        for (param_name, param_value) in params.items():
+        for param_name, param_value in params.items():
             setattr(model.params, param_name, param_value)
 
 
@@ -24,5 +24,5 @@ def _pyomo_set_params(
         solver == "gurobi_persistent"
     ), "setting parameters is only supported with gurobi_persistent"
     if solver == "gurobi_persistent" and params is not None:
-        for (param_name, param_value) in params.items():
+        for param_name, param_value in params.items():
             model.solver.set_gurobi_param(param_name, param_value)

miplearn/problems/binpack.py

@@ -109,7 +109,7 @@ class BinPackGenerator:
         return [_sample() for n in range(n_samples)]
 
 
-def build_binpack_model(data: Union[str, BinPackData]) -> GurobiModel:
+def build_binpack_model_gurobipy(data: Union[str, BinPackData]) -> GurobiModel:
     """Converts bin packing problem data into a concrete Gurobipy model."""
     if isinstance(data, str):
         data = read_pkl_gz(data)

miplearn/problems/multiknapsack.py

@@ -174,7 +174,9 @@ class MultiKnapsackGenerator:
         return [_sample() for _ in range(n_samples)]
 
 
-def build_multiknapsack_model(data: Union[str, MultiKnapsackData]) -> GurobiModel:
+def build_multiknapsack_model_gurobipy(
+    data: Union[str, MultiKnapsackData]
+) -> GurobiModel:
     """Converts multi-knapsack problem data into a concrete Gurobipy model."""
     if isinstance(data, str):
         data = read_pkl_gz(data)

miplearn/problems/pmedian.py

@@ -141,7 +141,7 @@ class PMedianGenerator:
         return [_sample() for _ in range(n_samples)]
 
 
-def build_pmedian_model(data: Union[str, PMedianData]) -> GurobiModel:
+def build_pmedian_model_gurobipy(data: Union[str, PMedianData]) -> GurobiModel:
     """Converts capacitated p-median data into a concrete Gurobipy model."""
     if isinstance(data, str):
         data = read_pkl_gz(data)

miplearn/problems/setpack.py

@@ -53,7 +53,7 @@ class SetPackGenerator:
         ]
 
 
-def build_setpack_model(data: Union[str, SetPackData]) -> GurobiModel:
+def build_setpack_model_gurobipy(data: Union[str, SetPackData]) -> GurobiModel:
     if isinstance(data, str):
         data = read_pkl_gz(data)
     assert isinstance(data, SetPackData)

miplearn/problems/stab.py

@@ -101,7 +101,7 @@ def build_stab_model_gurobipy(
     model.setObjective(quicksum(-data.weights[i] * x[i] for i in nodes))
 
     # Edge inequalities
-    for (i1, i2) in data.graph.edges:
+    for i1, i2 in data.graph.edges:
         model.addConstr(x[i1] + x[i2] <= 1)
 
     def cuts_separate(m: GurobiModel) -> List[Hashable]:
@@ -137,7 +137,7 @@ def build_stab_model_pyomo(
 
     # Edge inequalities
     model.edge_eqs = pe.ConstraintList()
-    for (i1, i2) in data.graph.edges:
+    for i1, i2 in data.graph.edges:
         model.edge_eqs.add(model.x[i1] + model.x[i2] <= 1)
 
     # Clique inequalities

miplearn/problems/tsp.py

@@ -119,7 +119,6 @@ def build_tsp_model_gurobipy(
     data: Union[str, TravelingSalesmanData],
     params: Optional[dict[str, Any]] = None,
 ) -> GurobiModel:
-
     model = gp.Model()
     _gurobipy_set_params(model, params)
 
@@ -173,7 +172,6 @@ def build_tsp_model_pyomo(
     solver: str = "gurobi_persistent",
     params: Optional[dict[str, Any]] = None,
 ) -> PyomoModel:
-
     model = pe.ConcreteModel()
     data = _tsp_read(data)
 

miplearn/problems/uc.py

@@ -112,7 +112,7 @@ class UnitCommitmentGenerator:
         return [_sample() for _ in range(n_samples)]
 
 
-def build_uc_model(data: Union[str, UnitCommitmentData]) -> GurobiModel:
+def build_uc_model_gurobipy(data: Union[str, UnitCommitmentData]) -> GurobiModel:
     """
     Models the unit commitment problem according to equations (1)-(5) of:
 

miplearn/problems/vertexcover.py

@@ -40,7 +40,9 @@ class MinWeightVertexCoverGenerator:
         ]
 
 
-def build_vertexcover_model(data: Union[str, MinWeightVertexCoverData]) -> GurobiModel:
+def build_vertexcover_model_gurobipy(
+    data: Union[str, MinWeightVertexCoverData]
+) -> GurobiModel:
     if isinstance(data, str):
         data = read_pkl_gz(data)
     assert isinstance(data, MinWeightVertexCoverData)
@@ -48,7 +50,7 @@ def build_vertexcover_model(data: Union[str, MinWeightVertexCoverData]) -> Gurob
     nodes = list(data.graph.nodes)
     x = model.addVars(nodes, vtype=GRB.BINARY, name="x")
     model.setObjective(quicksum(data.weights[i] * x[i] for i in nodes))
-    for (v1, v2) in data.graph.edges:
+    for v1, v2 in data.graph.edges:
         model.addConstr(x[v1] + x[v2] >= 1)
     model.update()
     return GurobiModel(model)