Change in highs api and status handling in both cases (#682)

* Change in highs api and status handling in both cases

* Use a mapping dictionary with the tuples

* Small change

* Added new test to take into account that a time limit can be reached without solution. Test available for HiGHS and CBC for now

* Wrong mapping

---------

Co-authored-by: pchtsp <pchtsp@gmail.com>

pulp/apis/highs_api.py

@@ -1,5 +1,6 @@
 # PuLP : Python LP Modeler
 # Version 2.4
+from math import inf
 
 # Copyright (c) 2002-2005, Jean-Sebastien Roy (js@jeannot.org)
 # Modifications Copyright (c) 2007- Stuart Anthony Mitchell (s.mitchell@auckland.ac.nz)
@@ -186,12 +187,12 @@ def actualSolve(self, lp):
         elif model_status.lower() == "infeasible":  # infeasible
             status, status_sol = (
                 constants.LpStatusInfeasible,
-                constants.LpSolutionNoSolutionFound,
+                constants.LpSolutionInfeasible,
             )
         elif model_status.lower() == "unbounded":  # unbounded
             status, status_sol = (
                 constants.LpStatusUnbounded,
-                constants.LpSolutionNoSolutionFound,
+                constants.LpSolutionUnbounded,
             )
         else:  # no solution
             status, status_sol = (
@@ -390,47 +391,108 @@ def buildSolverModel(self, lp):
 
         def findSolutionValues(self, lp):
             status = lp.solverModel.getModelStatus()
+            obj_value = lp.solverModel.getObjectiveValue()
+
             solution = lp.solverModel.getSolution()
             HighsModelStatus = highspy.HighsModelStatus
+
             status_dict = {
-                HighsModelStatus.kNotset: constants.LpStatusNotSolved,
-                HighsModelStatus.kLoadError: constants.LpStatusNotSolved,
-                HighsModelStatus.kModelError: constants.LpStatusNotSolved,
-                HighsModelStatus.kPresolveError: constants.LpStatusNotSolved,
-                HighsModelStatus.kSolveError: constants.LpStatusNotSolved,
-                HighsModelStatus.kPostsolveError: constants.LpStatusNotSolved,
-                HighsModelStatus.kModelEmpty: constants.LpStatusNotSolved,
-                HighsModelStatus.kOptimal: constants.LpStatusOptimal,
-                HighsModelStatus.kInfeasible: constants.LpStatusInfeasible,
-                HighsModelStatus.kUnboundedOrInfeasible: constants.LpStatusInfeasible,
-                HighsModelStatus.kUnbounded: constants.LpStatusUnbounded,
-                HighsModelStatus.kObjectiveBound: constants.LpStatusNotSolved,
-                HighsModelStatus.kObjectiveTarget: constants.LpStatusNotSolved,
-                HighsModelStatus.kTimeLimit: constants.LpStatusNotSolved,
-                HighsModelStatus.kIterationLimit: constants.LpStatusNotSolved,
-                HighsModelStatus.kUnknown: constants.LpStatusNotSolved,
+                HighsModelStatus.kNotset: (
+                    constants.LpStatusNotSolved,
+                    constants.LpSolutionNoSolutionFound,
+                ),
+                HighsModelStatus.kLoadError: (
+                    constants.LpStatusNotSolved,
+                    constants.LpSolutionNoSolutionFound,
+                ),
+                HighsModelStatus.kModelError: (
+                    constants.LpStatusNotSolved,
+                    constants.LpSolutionNoSolutionFound,
+                ),
+                HighsModelStatus.kPresolveError: (
+                    constants.LpStatusNotSolved,
+                    constants.LpSolutionNoSolutionFound,
+                ),
+                HighsModelStatus.kSolveError: (
+                    constants.LpStatusNotSolved,
+                    constants.LpSolutionNoSolutionFound,
+                ),
+                HighsModelStatus.kPostsolveError: (
+                    constants.LpStatusNotSolved,
+                    constants.LpSolutionNoSolutionFound,
+                ),
+                HighsModelStatus.kModelEmpty: (
+                    constants.LpStatusNotSolved,
+                    constants.LpSolutionNoSolutionFound,
+                ),
+                HighsModelStatus.kOptimal: (
+                    constants.LpStatusOptimal,
+                    constants.LpSolutionOptimal,
+                ),
+                HighsModelStatus.kInfeasible: (
+                    constants.LpStatusInfeasible,
+                    constants.LpSolutionInfeasible,
+                ),
+                HighsModelStatus.kUnboundedOrInfeasible: (
+                    constants.LpStatusInfeasible,
+                    constants.LpSolutionInfeasible,
+                ),
+                HighsModelStatus.kUnbounded: (
+                    constants.LpStatusUnbounded,
+                    constants.LpSolutionUnbounded,
+                ),
+                HighsModelStatus.kObjectiveBound: (
+                    constants.LpStatusOptimal,
+                    constants.LpSolutionIntegerFeasible,
+                ),
+                HighsModelStatus.kObjectiveTarget: (
+                    constants.LpStatusOptimal,
+                    constants.LpSolutionIntegerFeasible,
+                ),
+                HighsModelStatus.kTimeLimit: (
+                    constants.LpStatusOptimal,
+                    constants.LpSolutionIntegerFeasible,
+                ),
+                HighsModelStatus.kIterationLimit: (
+                    constants.LpStatusOptimal,
+                    constants.LpSolutionIntegerFeasible,
+                ),
+                HighsModelStatus.kUnknown: (
+                    constants.LpStatusNotSolved,
+                    constants.LpSolutionNoSolutionFound,
+                ),
             }
 
             col_values = list(solution.col_value)
+
+            # Assign values to the variables as with lp.assignVarsVals()
             for var in lp.variables():
                 var.varValue = col_values[var.index]
 
-            return status_dict[status]
+            if obj_value == float(inf) and status in (
+                HighsModelStatus.kTimeLimit,
+                HighsModelStatus.kIterationLimit,
+            ):
+                return constants.LpStatusNotSolved, constants.LpSolutionNoSolutionFound
+            else:
+                return status_dict[status]
 
         def actualSolve(self, lp):
             self.createAndConfigureSolver(lp)
             self.buildSolverModel(lp)
             self.callSolver(lp)
 
-            solutionStatus = self.findSolutionValues(lp)
+            status, sol_status = self.findSolutionValues(lp)
 
             for var in lp.variables():
                 var.modified = False
 
             for constraint in lp.constraints.values():
                 constraint.modifier = False
 
-            return solutionStatus
+            lp.assignStatus(status, sol_status)
+
+            return status
 
         def actualResolve(self, lp, **kwargs):
             raise PulpSolverError("HiGHS: Resolving is not supported")

pulp/tests/test_pulp.py

@@ -1291,7 +1291,8 @@ def test_measuring_solving_time(self):
                 return
             prob = create_bin_packing_problem(bins=bins, seed=99)
             self.solver.timeLimit = time_limit
-            prob.solve(self.solver)
+            status = prob.solve(self.solver)
+
             delta = 20
             reported_time = prob.solutionTime
             if self.solver.name in ["PULP_CBC_CMD", "COIN_CMD"]:
@@ -1304,9 +1305,27 @@ def test_measuring_solving_time(self):
                 msg=f"optimization time for solver {self.solver.name}",
             )
             self.assertTrue(prob.objective.value() is not None)
+            self.assertEqual(status, const.LpStatusOptimal)
             for v in prob.variables():
                 self.assertTrue(v.varValue is not None)
 
+        @gurobi_test
+        def test_time_limit_no_solution(self):
+            print("\t Test time limit with no solution")
+
+            time_limit = 1
+            solver_settings = dict(HiGHS=50, PULP_CBC_CMD=30, COIN_CMD=30)
+            bins = solver_settings.get(self.solver.name)
+            if bins is None:
+                # not all solvers have timeLimit support
+                return
+            prob = create_bin_packing_problem(bins=bins, seed=99)
+            self.solver.timeLimit = time_limit
+            status = prob.solve(self.solver)
+            self.assertEqual(prob.status, const.LpStatusNotSolved)
+            self.assertEqual(status, const.LpStatusNotSolved)
+            self.assertEqual(prob.sol_status, const.LpSolutionNoSolutionFound)
+
         def test_invalid_var_names(self):
             prob = LpProblem(self._testMethodName, const.LpMinimize)
             x = LpVariable("a")