Remove limit on nb of solutions in parameters for milp and cp

discrete_optimization/generic_tools/cp_tools.py

@@ -87,8 +87,6 @@ class ParametersCP:
     """
 
     intermediate_solution: bool
-    all_solutions: bool
-    nr_solutions: int
     free_search: bool
     multiprocess: bool
     nb_process: int
@@ -97,8 +95,6 @@ class ParametersCP:
     def __init__(
         self,
         intermediate_solution: bool,
-        all_solutions: bool,
-        nr_solutions: int,
         free_search: bool = False,
         multiprocess: bool = False,
         nb_process: int = 1,
@@ -107,12 +103,8 @@ def __init__(
         """
 
         :param intermediate_solution: retrieve intermediate solutions
-        :param all_solutions: returns all solutions found by the cp solver
-        :param nr_solutions: the requested number of solutions
         """
         self.intermediate_solution = intermediate_solution
-        self.all_solutions = all_solutions
-        self.nr_solutions = nr_solutions
         self.free_search = free_search
         self.multiprocess = multiprocess
         self.nb_process = nb_process
@@ -122,8 +114,6 @@ def __init__(
     def default() -> "ParametersCP":
         return ParametersCP(
             intermediate_solution=True,
-            all_solutions=False,
-            nr_solutions=1000,
             free_search=False,
             optimisation_level=1,
         )
@@ -132,8 +122,6 @@ def default() -> "ParametersCP":
     def default_cpsat() -> "ParametersCP":
         return ParametersCP(
             intermediate_solution=True,
-            all_solutions=False,
-            nr_solutions=1000,
             free_search=False,
             multiprocess=True,
             nb_process=6,
@@ -144,25 +132,19 @@ def default_cpsat() -> "ParametersCP":
     def default_fast_lns() -> "ParametersCP":
         return ParametersCP(
             intermediate_solution=True,
-            all_solutions=False,
-            nr_solutions=1000,
             free_search=False,
         )
 
     @staticmethod
     def default_free() -> "ParametersCP":
         return ParametersCP(
             intermediate_solution=True,
-            all_solutions=False,
-            nr_solutions=1000,
             free_search=True,
         )
 
     def copy(self) -> "ParametersCP":
         return ParametersCP(
             intermediate_solution=self.intermediate_solution,
-            all_solutions=self.all_solutions,
-            nr_solutions=self.nr_solutions,
             free_search=self.free_search,
             multiprocess=self.multiprocess,
             nb_process=self.nb_process,

discrete_optimization/generic_tools/lp_tools.py

@@ -63,14 +63,12 @@ def __init__(
         mip_gap_abs: float,
         mip_gap: float,
         retrieve_all_solution: bool,
-        n_solutions_max: int,
         pool_search_mode: int = 0,
     ):
         self.pool_solutions = pool_solutions
         self.mip_gap_abs = mip_gap_abs
         self.mip_gap = mip_gap
         self.retrieve_all_solution = retrieve_all_solution
-        self.n_solutions_max = n_solutions_max
         self.pool_search_mode = pool_search_mode
 
     @staticmethod
@@ -80,7 +78,6 @@ def default() -> "ParametersMilp":
             mip_gap_abs=0.0000001,
             mip_gap=0.000001,
             retrieve_all_solution=True,
-            n_solutions_max=10000,
         )
 
 
@@ -101,7 +98,7 @@ def retrieve_solutions(self, parameters_milp: ParametersMilp) -> ResultStorage:
 
         """
         if parameters_milp.retrieve_all_solution:
-            n_solutions = min(parameters_milp.n_solutions_max, self.nb_solutions)
+            n_solutions = self.nb_solutions
         else:
             n_solutions = 1
         list_solution_fits: List[Tuple[Solution, Union[float, TupleFitness]]] = []
@@ -238,7 +235,6 @@ def prepare_model(
         self.model.sol_pool_size = parameters_milp.pool_solutions
         if time_limit is not None:
             self.model.max_seconds = time_limit
-        self.model.max_solutions = parameters_milp.n_solutions_max
 
     def optimize_model(
         self,
@@ -475,7 +471,7 @@ def solve(
             self.model.time_limit = time_limit
         self.model.parameters.mip.tolerances.mipgap = parameters_milp.mip_gap
         listener = None
-        if parameters_milp.retrieve_all_solution or parameters_milp.n_solutions_max > 1:
+        if parameters_milp.retrieve_all_solution:
 
             class SolutionStorage(SolutionListener):
                 def __init__(self):

discrete_optimization/rcpsp/solver/cp_solvers.py

@@ -1842,6 +1842,8 @@ def solve(
         self,
         parameters_cp: Optional[ParametersCP] = None,
         time_limit: Optional[float] = 100.0,
+        nr_solutions: int = 1,
+        all_solutions: bool = False,
         **args,
     ):
         """Solve the CP problem with minizinc
@@ -1850,6 +1852,8 @@ def solve(
             parameters_cp: parameters specific to CP solvers
             time_limit: the solve process stops after this time limit (in seconds).
                 If None, no time limit is applied.
+            nr_solutions: of not `all_solutions`, the solve stops after finding `nr_solutions`
+            all_solutions: if True, do not stop when reaching `nr_solutions`
             **args: passed to init_model()
 
         Returns:
@@ -1866,10 +1870,8 @@ def solve(
             timeout = timedelta(seconds=time_limit)
         result = self.instance.solve(
             timeout=timeout,
-            nr_solutions=parameters_cp.nr_solutions
-            if not parameters_cp.all_solutions
-            else None,
-            all_solutions=parameters_cp.all_solutions,
+            nr_solutions=nr_solutions if not all_solutions else None,
+            all_solutions=all_solutions,
             intermediate_solutions=intermediate_solutions,
         )
         logger.debug(result.status)

discrete_optimization/rcpsp/solver/rcpsp_lp_lns_solver.py

@@ -430,7 +430,6 @@ def __init__(
             mip_gap_abs=0.001,
             mip_gap=0.001,
             retrieve_all_solution=True,
-            n_solutions_max=100,
         )
         self.constraint_handler = ConstraintHandlerStartTimeIntervalMRCPSP(
             problem=problem, fraction_to_fix=0.6, minus_delta=5, plus_delta=5

discrete_optimization/rcpsp/solver/rcpsp_pile.py

@@ -83,10 +83,9 @@ def __init__(
             }
         if problem.is_rcpsp_multimode() or problem.is_varying_resource():
             solver = CP_MRCPSP_MZN_MODES(problem, cp_solver_name=CPSolverName.CHUFFED)
-            params_cp = ParametersCP.default()
-            params_cp.nr_solutions = 1
-            params_cp.all_solutions = False
-            result_storage = solver.solve(parameters_cp=params_cp, time_limit=1)
+            result_storage = solver.solve(
+                nr_solutions=1, all_solutions=False, time_limit=1
+            )
             one_mode_setting = result_storage[0]
             self.modes_dict = {}
             for i in range(len(one_mode_setting)):
@@ -285,10 +284,9 @@ def __init__(
             }
         if problem.is_rcpsp_multimode() or problem.is_varying_resource():
             solver = CP_MRCPSP_MZN_MODES(problem, cp_solver_name=CPSolverName.CHUFFED)
-            params_cp = ParametersCP.default()
-            params_cp.nr_solutions = 1
-            params_cp.all_solutions = False
-            result_storage = solver.solve(parameters_cp=params_cp, time_limit=1)
+            result_storage = solver.solve(
+                nr_solutions=1, all_solutions=False, time_limit=1
+            )
             one_mode_setting = result_storage[0]
             self.modes_dict = {}
             for i in range(len(one_mode_setting)):

discrete_optimization/rcpsp_multiskill/multiskill_to_rcpsp.py

@@ -44,11 +44,8 @@ def construct_rcpsp_by_worker_type(
         check_resource_compliance: bool = True,
         one_worker_type_per_task: bool = False,
     ):
-        params_cp = ParametersCP(
-            intermediate_solution=True,
-            all_solutions=False,
-            nr_solutions=100,
-        )
+        params_cp = ParametersCP.default()
+        params_cp.intermediate_solution = True
         solver = PrecomputeEmployeesForTasks(
             ms_rcpsp_model=self.multiskill_model, cp_solver_name=CPSolverName.CHUFFED
         )
@@ -96,6 +93,8 @@ def construct_rcpsp_by_worker_type(
             results = solver.solve(
                 parameters_cp=params_cp,
                 time_limit=30,
+                all_solutions=False,
+                nr_solutions=100,
             )
             best_overskill_results = min(
                 results, key=lambda x: x.overskill_type

discrete_optimization/rcpsp_multiskill/solvers/cp_solvers.py

@@ -2213,6 +2213,8 @@ def solve(
         self,
         parameters_cp: Optional[ParametersCP] = None,
         time_limit: Optional[float] = 100.0,
+        nr_solutions: int = 100,
+        all_solutions: bool = False,
         **args,
     ):
         """Solve the CP problem with minizinc
@@ -2221,6 +2223,8 @@ def solve(
             parameters_cp: parameters specific to CP solvers
             time_limit: the solve process stops after this time limit (in seconds).
                 If None, no time limit is applied.
+            nr_solutions: of not `all_solutions`, the solve stops after finding `nr_solutions`
+            all_solutions: if True, do not stop when reaching `nr_solutions`
             **args: passed to init_model()
 
         Returns:
@@ -2237,8 +2241,9 @@ def solve(
             timeout = timedelta(seconds=time_limit)
         result = self.instance.solve(
             timeout=timeout,
-            nr_solutions=parameters_cp.nr_solutions,
             intermediate_solutions=intermediate_solutions,
+            nr_solutions=nr_solutions,
+            all_solutions=all_solutions,
         )
         logger.debug(result.status)
         return self.retrieve_solutions(result=result, parameters_cp=parameters_cp)

examples/rcpsp/rcpsp_cpsat_solver_example.py

@@ -30,7 +30,6 @@ def solve_makespan_with_cp_sat(problem: RCPSPModel):
     solver = CPSatRCPSPSolver(problem)
     solver.init_model()
     parameters_cp = ParametersCP.default()
-    parameters_cp.nr_solutions = 1
     parameters_cp.nb_process = 8
     result_storage = solver.solve(
         callbacks=[
@@ -51,7 +50,6 @@ def solve_resource_with_cp_sat(problem: RCPSPModel):
     solver = CPSatRCPSPSolverCumulativeResource(problem)
     solver.init_model(weight_on_used_resource=100, weight_on_makespan=1)
     parameters_cp = ParametersCP.default()
-    parameters_cp.nr_solutions = 1
     parameters_cp.nb_process = 8
     result_storage = solver.solve(
         callbacks=[

tests/facility/test_facility_lp_lns.py

@@ -50,7 +50,6 @@ def test_facility_lns():
         mip_gap=0.0001,
         mip_gap_abs=0.001,
         retrieve_all_solution=True,
-        n_solutions_max=1000,
     )
     solver = LP_Facility_Solver_PyMip(
         facility_problem,

tests/knapsack/test_knapsack_lp_lns_solver.py

@@ -37,7 +37,6 @@ def test_knapsack_lns():
         mip_gap=0.0001,
         mip_gap_abs=0.001,
         retrieve_all_solution=True,
-        n_solutions_max=1000,
     )
     solver = LPKnapsack(
         model,

tests/rcpsp/solver/test_rcpsp_cp.py

@@ -73,7 +73,6 @@ def test_cp_sm(optimisation_level):
     solver = CP_RCPSP_MZN(rcpsp_problem, cp_solver_name=CPSolverName.CHUFFED)
     solver.init_model(output_type=True)
     parameters_cp = ParametersCP.default()
-    parameters_cp.nr_solutions = 1
     parameters_cp.optimisation_level = optimisation_level
     result_storage = solver.solve(parameters_cp=parameters_cp, time_limit=100)
     solution, fit = result_storage.get_best_solution_fit()
@@ -104,7 +103,6 @@ def test_cp_rcp(optimisation_level):
     solver = CP_RCPSP_MZN(rcpsp_problem, cp_solver_name=CPSolverName.CHUFFED)
     solver.init_model(output_type=True)
     parameters_cp = ParametersCP.default()
-    parameters_cp.nr_solutions = 1
     parameters_cp.optimisation_level = optimisation_level
     result_storage = solver.solve(parameters_cp=parameters_cp, time_limit=20)
     solution, fit = result_storage.get_best_solution_fit()
@@ -192,8 +190,6 @@ def test_ortools_with_cb(caplog, random_seed):
     file = [f for f in files_available if model in f][0]
     rcpsp_problem = parse_file(file)
     solver = CPSatRCPSPSolver(problem=rcpsp_problem)
-    parameters_cp = ParametersCP.default()
-    parameters_cp.nr_solutions = 1
 
     class VariablePrinterCallback(Callback):
         def __init__(self) -> None:
@@ -211,9 +207,7 @@ def on_step_end(self, step: int, res: ResultStorage, solver: CPSatRCPSPSolver):
     callbacks = [VariablePrinterCallback(), TimerStopper(2)]
 
     with caplog.at_level(logging.DEBUG):
-        result_storage = solver.solve(
-            callbacks=callbacks, parameters_cp=parameters_cp, time_limit=10
-        )
+        result_storage = solver.solve(callbacks=callbacks, time_limit=10)
 
     assert "Solution #1" in caplog.text
     assert (

tests/rcpsp/solver/test_rcpsp_find_modes.py

@@ -12,10 +12,7 @@ def test_find_modes():
     file = [f for f in files_available if "j1010_1.mm" in f][0]
     rcpsp_problem = parse_file(file)
     solver = CP_MRCPSP_MZN_MODES(rcpsp_problem, cp_solver_name=CPSolverName.CHUFFED)
-    params_cp = ParametersCP.default()
-    params_cp.nr_solutions = float("inf")
-    params_cp.all_solutions = True
-    result_storage = solver.solve(parameters_cp=params_cp)
+    result_storage = solver.solve(all_solutions=True)
     assert len(result_storage) == 12744
 
 

tests/rcpsp/solver/test_rcpsp_lp_lns.py

@@ -49,7 +49,6 @@ def test_lns_sm():
         mip_gap_abs=0.001,
         mip_gap=0.001,
         retrieve_all_solution=True,
-        n_solutions_max=100,
     )
     params_objective_function = get_default_objective_setup(problem=rcpsp_problem)
     constraint_handler = ConstraintHandlerStartTimeInterval(
@@ -105,7 +104,6 @@ def test_lns_mm():
         mip_gap_abs=0.001,
         mip_gap=0.001,
         retrieve_all_solution=True,
-        n_solutions_max=100,
     )
     constraint_handler = ConstraintHandlerFixStartTime(
         problem=rcpsp_problem, fraction_fix_start_time=0.3

tests/rcpsp_multiskill/test_rcpsp_ms_lp_lns.py

@@ -64,7 +64,6 @@ def test_multiskill_imopse():
         mip_gap_abs=0.001,
         mip_gap=0.001,
         retrieve_all_solution=True,
-        n_solutions_max=100,
     )
     constraint_handler = ConstraintHandlerStartTimeIntervalMRCPSP(
         problem=model, fraction_to_fix=0.95, minus_delta=5, plus_delta=5