remove deprecated toulbarsolver adapted for LNS, better use the autom…

…atic wrapped solver that you can build through 'to_lns_toulbar'
g-poveda · Oct 28, 2024 · dbaf7f0 · dbaf7f0
1 parent 8b34e70
commit dbaf7f0
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Showing 2 changed files with 10 additions and 107 deletions.
diff --git a/discrete_optimization/coloring/solvers/toulbar.py b/discrete_optimization/coloring/solvers/toulbar.py
@@ -31,7 +31,10 @@
 
 from discrete_optimization.generic_tools.do_solver import SolverDO, WarmstartMixin
 from discrete_optimization.generic_tools.lns_tools import ConstraintHandler
-from discrete_optimization.generic_tools.toulbar_tools import ToulbarSolver
+from discrete_optimization.generic_tools.toulbar_tools import (
+    ToulbarSolver,
+    to_lns_toulbar,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -272,72 +275,7 @@ def set_warm_start(self, solution: ColoringSolution) -> None:
             self.model.CFN.wcsp.setBestValue(i, solution.colors[i - 1])
 
 
-class ToulbarColoringSolverForLns(ToulbarColoringSolver):
-    depth: int = 0
-
-    def init_model(self, **kwargs: Any) -> None:
-        super().init_model(**kwargs)
-        self.model.SolveFirst()
-        self.depth = self.model.Depth()
-        self.model.Store()
-        self.initub = self.model.GetUB()
-
-    def solve_2(self, time_limit: Optional[int] = 20, **kwargs: Any) -> ResultStorage:
-        try:
-            solution = self.model.SolveNext(showSolutions=1, timeLimit=time_limit)
-            logger.info(f"=== Solution === \n {solution}")
-            logger.info(
-                f"Best solution = {solution[1]}, Bound = {self.model.GetDDualBound()}"
-            )
-            self.model.Restore(self.depth)
-            self.model.Store()
-            if solution is not None:
-                sol = ColoringSolution(
-                    problem=self.problem,
-                    colors=solution[0][1 : 1 + self.problem.number_of_nodes],
-                )
-                fit = self.aggreg_from_sol(sol)
-                return self.create_result_storage(
-                    [(sol, fit)],
-                )
-            else:
-                return self.create_result_storage()
-        except:
-            self.model.Restore(self.depth)
-            self.model.Store()
-            logger.info("Solve failed in given time")
-            return self.create_result_storage()
-
-    def solve(self, time_limit: Optional[int] = 20, **kwargs: Any) -> ResultStorage:
-        try:
-            solution = self.model.SolveNext(showSolutions=1, timeLimit=time_limit)
-            logger.info(f"=== Solution === \n {solution}")
-            logger.info(
-                f"Best solution = {solution[1]}, Bound = {self.model.GetDDualBound()}"
-            )
-            # Reinit for next iteration.
-            self.model.Restore(self.depth)
-            self.model.Store()
-            self.model.SetUB(self.initub)
-            if solution is not None:
-                sol = ColoringSolution(
-                    problem=self.problem,
-                    colors=solution[0][1 : 1 + self.problem.number_of_nodes],
-                )
-                fit = self.aggreg_from_sol(sol)
-                return self.create_result_storage(
-                    [(sol, fit)],
-                )
-            else:
-                self.model.ClearPropagationQueues()
-                return self.create_result_storage()
-        except:
-            self.model.ClearPropagationQueues()
-            self.model.Restore(self.depth)
-            self.model.Store()
-            self.model.SetUB(self.initub)
-            logger.info("Solve failed in given time")
-            return self.create_result_storage()
+ToulbarColoringSolverForLns = to_lns_toulbar(ToulbarColoringSolver)
 
 
 class ColoringConstraintHandlerToulbar(ConstraintHandler):

diff --git a/discrete_optimization/maximum_independent_set/solvers/toulbar.py b/discrete_optimization/maximum_independent_set/solvers/toulbar.py
@@ -9,11 +9,11 @@
 from typing import Any, Iterable, Optional
 
 from discrete_optimization.generic_tools.do_problem import Solution
-from discrete_optimization.generic_tools.hyperparameters.hyperparameter import (
-    CategoricalHyperparameter,
-)
 from discrete_optimization.generic_tools.lns_tools import ConstraintHandler
-from discrete_optimization.generic_tools.toulbar_tools import ToulbarSolver
+from discrete_optimization.generic_tools.toulbar_tools import (
+    ToulbarSolver,
+    to_lns_toulbar,
+)
 
 try:
     import pytoulbar2
@@ -71,42 +71,7 @@ def set_warm_start(self, solution: MisSolution) -> None:
             self.model.CFN.wcsp.setBestValue(i, solution.chosen[i])
 
 
-class ToulbarMisSolverForLns(ToulbarMisSolver):
-    depth: int = 0
-
-    def init_model(self, **kwargs: Any) -> None:
-        super().init_model(**kwargs)
-        self.model.SolveFirst()
-        self.depth = self.model.Depth()
-        self.model.Store()
-        self.initub = self.model.GetUB()
-
-    def solve(self, time_limit: Optional[int] = 20, **kwargs: Any) -> ResultStorage:
-        try:
-            solution = self.model.SolveNext(showSolutions=1, timeLimit=time_limit)
-            logger.info(f"=== Solution === \n {solution}")
-            logger.info(
-                f"Best solution = {solution[1]}, Bound = {self.model.GetDDualBound()}"
-            )
-            # Reinit for next iteration.
-            self.model.Restore(self.depth)
-            self.model.Store()
-            self.model.SetUB(self.initub)
-            if solution is not None:
-                sol = MisSolution(problem=self.problem, chosen=solution[0])
-                fit = self.aggreg_from_sol(sol)
-                return self.create_result_storage(
-                    [(sol, fit)],
-                )
-            else:
-                return self.create_result_storage()
-        except:
-            self.model.ClearPropagationQueues()
-            self.model.Restore(self.depth)
-            self.model.Store()
-            self.model.SetUB(self.initub)
-            logger.info("Solve failed in given time")
-            return self.create_result_storage()
+ToulbarMisSolverForLns = to_lns_toulbar(ToulbarMisSolver)
 
 
 class MisConstraintHandlerToulbar(ConstraintHandler):