v0.2.0 API for pure (explicit) math models (classic solvers-like)

Author: CameleoGrey

examples/object_oriented/nqueens/persistence/DomainBuilderNQueens.py

@@ -36,7 +36,11 @@ def build_domain_from_scratch(self):
         return chess_field
 
     def build_from_solution(self, solution, initial_domain=None):
-        raise Exception("build_from_solution() not implemented for DomainBuilderNQueens")
+        domain = self.build_domain_from_scratch()
+        for i, row_id in enumerate( solution.variable_values_dict.values() ):
+            domain.queens[i].row.row_id = row_id
+        return domain
+
 
     def build_from_domain(self, domain):
         return super().build_from_domain(domain)

examples/object_oriented/nqueens/scripts/solve_nqueens.py

@@ -12,15 +12,15 @@
 from examples.object_oriented.nqueens.persistence.CotwinBuilderNQueens import CotwinBuilderNQueens
 from greyjack.agents.termination_strategies import *
 from greyjack.agents import *
-from greyjack.Solver import Solver
+from greyjack.SolverOOP import SolverOOP
 from greyjack.agents.base.LoggingLevel import LoggingLevel
 from greyjack.agents.base.ParallelizationBackend import ParallelizationBackend
 from greyjack.agents import *
 
 if __name__ == "__main__":
 
     # build domain model
-    domain_builder = DomainBuilderNQueens(1024, random_seed=45)
+    domain_builder = DomainBuilderNQueens(16, random_seed=45)
     cotwin_builder = CotwinBuilderNQueens(use_incremental_score_calculator=True)
 
     #termination_strategy = StepsLimit(step_count_limit=1000)
@@ -40,11 +40,14 @@
                                mutation_rate_multiplier=None, move_probas=[0, 1, 0, 0, 0, 0], 
                                migration_frequency=10, compare_to_global_frequency=10, termination_strategy=termination_strategy)"""
 
-    solver = Solver(domain_builder, cotwin_builder, agent, 
+    solver = SolverOOP(domain_builder, cotwin_builder, agent, 
                     ParallelizationBackend.Multiprocessing, LoggingLevel.FreshOnly,
                     n_jobs=10, score_precision=[0])
     solution = solver.solve()
     #print( "Cotwin solution looks that: " )
     #print( solution )
 
+    domain = domain_builder.build_from_solution(solution)
+    print(domain)
+
     print( "done" )

examples/object_oriented/nqueens/scripts/solve_nqueens_ortools.py

@@ -23,7 +23,7 @@ def all_in_one_constraint(self, planning_entity_dfs, problem_fact_dfs, delta_dfs
         # with huge performance boost on large datasets
 
         # from experiments: common performance up to ~2 times lower (depends on dataset size)
-        # than in fully Rust version of Solver. Need to keep in mind the opportunity of
+        # than in fully Rust version of SolverOOP. Need to keep in mind the opportunity of
         # rewriting constraints on Rust in Python version for production (if it's neccessary). 
 
         path_stops_df = planning_entity_dfs["path_stops"]

examples/object_oriented/tsp/score/IncrementalScoreCalculatorTSP.py

@@ -14,7 +14,7 @@
 from greyjack.agents.termination_strategies import *
 from greyjack.agents.base.LoggingLevel import LoggingLevel
 from greyjack.agents.base.ParallelizationBackend import ParallelizationBackend
-from greyjack import Solver
+from greyjack import SolverOOP
 from greyjack.agents import *
 
 if __name__ == "__main__":
@@ -53,7 +53,7 @@
                                mutation_rate_multiplier=None, move_probas=[0, 0.2, 0.2, 0.2, 0.2, 0.2], 
                                migration_frequency=10, termination_strategy=termination_strategy)"""
 
-    solver = Solver(domain_builder, cotwin_builder, agent, 
+    solver = SolverOOP(domain_builder, cotwin_builder, agent, 
                     ParallelizationBackend.Multiprocessing, LoggingLevel.Info,
                     n_jobs=10, score_precision=[0, 0])
     solution = solver.solve()

examples/object_oriented/tsp/scripts/solve_tsp.py

@@ -15,7 +15,7 @@
 from greyjack.agents.base.ParallelizationBackend import ParallelizationBackend
 from greyjack.agents.base.LoggingLevel import LoggingLevel
 from greyjack.agents.termination_strategies import *
-from greyjack.Solver import Solver
+from greyjack.SolverOOP import SolverOOP
 from greyjack.agents import *
 
 if __name__ == "__main__":
@@ -33,18 +33,18 @@
     #file_path = Path(data_dir_path, data_dir_path, "belgium", "basic", "air", "belgium-n1000-k40.vrp") #optimum: ~57.7; first_fit: ~195.3; RoutingModel: from 67.3 to 74 (depends on time)
     # multi-depot with timewindows
     #file_path = Path(data_dir_path, "belgium", "multidepot-timewindowed", "air", "belgium-tw-d2-n50-k10.vrp") # optimum: ~15.98; first_fit: ~27.89
-    file_path = Path(data_dir_path, "belgium", "multidepot-timewindowed", "air", "belgium-tw-d5-n500-k20.vrp") # optimum: ~43.3; first_fit: ~124.884
+    #file_path = Path(data_dir_path, "belgium", "multidepot-timewindowed", "air", "belgium-tw-d5-n500-k20.vrp") # optimum: ~43.3; first_fit: ~124.884
     #file_path = Path(data_dir_path, "belgium", "multidepot-timewindowed", "air", "belgium-tw-d8-n1000-k40.vrp") # optimum: ~58.1; first_fit: ~154.565
-    #file_path = Path(data_dir_path, "belgium", "multidepot-timewindowed", "air", "belgium-tw-d10-n2750-k55.vrp") # optimum: ~111; first_fit: ~380.9
+    file_path = Path(data_dir_path, "belgium", "multidepot-timewindowed", "air", "belgium-tw-d10-n2750-k55.vrp") # optimum: ~111; first_fit: ~380.9
 
     domain_builder = DomainBuilder(file_path)
     cotwin_builder = CotwinBuilder(use_incremental_score_calculator=True, use_greed_init=True)
 
     #termination_strategy = StepsLimit(step_count_limit=1000)
-    #termination_strategy = TimeSpentLimit(time_seconds_limit=60)
-    termination_strategy = ScoreNoImprovement(time_seconds_limit=15)
+    termination_strategy = TimeSpentLimit(time_seconds_limit=60)
+    #termination_strategy = ScoreNoImprovement(time_seconds_limit=15)
     #termination_strategy = ScoreLimit(score_to_compare=[0])
-    agent = TabuSearch(neighbours_count=1280, tabu_entity_rate=0.8, 
+    agent = TabuSearch(neighbours_count=128, tabu_entity_rate=0.8, 
                        mutation_rate_multiplier=None, move_probas=[0.5, 0.5, 0.0, 0.0, 0.0, 0.0],
                        migration_frequency=10, termination_strategy=termination_strategy)
     """agent = GeneticAlgorithm(population_size=128, crossover_probability=0.5, p_best_rate=0.05,
@@ -57,8 +57,8 @@
                                mutation_rate_multiplier=None, move_probas=[0.5, 0.5, 0.0, 0.0, 0.0, 0.0], 
                                migration_frequency=10, termination_strategy=termination_strategy)"""
 
-    solver = Solver(domain_builder, cotwin_builder, agent, 
-                    ParallelizationBackend.Multiprocessing, LoggingLevel.FreshOnly,
+    solver = SolverOOP(domain_builder, cotwin_builder, agent, 
+                    ParallelizationBackend.Multiprocessing, LoggingLevel.Info,
                     n_jobs=10, score_precision=[0, 0, 0])
     solution = solver.solve()
 
@@ -76,7 +76,7 @@
     agent = LateAcceptance(late_acceptance_size=64, tabu_entity_rate=0.05, 
                            mutation_rate_multiplier=None, move_probas=None, 
                            termination_strategy=termination_strategy)
-    solver = Solver(domain_builder, cotwin_builder, agent, 
+    solver = SolverOOP(domain_builder, cotwin_builder, agent, 
                     ParallelizationBackend.Multiprocessing, LoggingLevel.FreshOnly,
                     n_jobs=10, score_precision=[0, 0, 0], 
                     initial_solution=solution)
@@ -100,7 +100,7 @@
     agent = TabuSearch(neighbours_count=128, tabu_entity_rate=0.0, 
                        mutation_rate_multiplier=None, move_probas=[0.5, 0.5, 0.0, 0.0, 0.0, 0.0],
                        migration_frequency=10, termination_strategy=termination_strategy)
-    solver = Solver(domain_builder, cotwin_builder, agent, 
+    solver = SolverOOP(domain_builder, cotwin_builder, agent, 
                     ParallelizationBackend.Multiprocessing, LoggingLevel.FreshOnly,
                     n_jobs=10, score_precision=[0, 0, 0], 
                     initial_solution=domain)