v0.0.16 Wrapped TabuSearch, removed custom naming for variables

Author: CameleoGrey

examples/object_oriented/nqueens/persistence/CotwinBuilderNQueens.py

@@ -21,7 +21,7 @@ def build_cotwin(self, domain_model, is_already_initialized):
         for i in range( len(queens) ):
             queen_id = i
             column_id = i
-            planning_row_id = GJInteger("queen_{}_row_id".format(queen_id), 0, n-1, False, queens[i].row.row_id, None)
+            planning_row_id = GJInteger(0, n-1, False, queens[i].row.row_id, None)
             cot_queen = CotQueen( queen_id, planning_row_id, column_id )
             cot_queens.append( cot_queen )
 

examples/object_oriented/nqueens/scripts/solve_nqueens.py

@@ -19,16 +19,16 @@
 if __name__ == "__main__":
 
     # build domain model
-    domain_builder = DomainBuilderNQueens(256, random_seed=45)
+    domain_builder = DomainBuilderNQueens(10000, random_seed=45)
     cotwin_builder = CotwinBuilderNQueens()
 
     #termination_strategy = StepsLimit(step_count_limit=1000)
     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=128, tabu_entity_rate=0.0, 
-                       mutation_rate_multiplier=None, move_probas=None, 
-                       migration_frequency=10, termination_strategy=termination_strategy)
+    agent = TabuSearch(neighbours_count=20, tabu_entity_rate=0.0, 
+                       mutation_rate_multiplier=None, move_probas=[0, 1, 0, 0, 0, 0], 
+                       migration_frequency=1, termination_strategy=termination_strategy)
 
     solver = Solver(domain_builder, cotwin_builder, agent, 
                     n_jobs=10, parallelization_backend="processing", #processing, threading 

greyjack/Cargo.lock

@@ -1,6 +1,6 @@
 [package]
 name = "greyjack"
-version = "0.0.15"
+version = "0.0.16"
 edition = "2021"
 
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

greyjack/Cargo.toml

@@ -27,12 +27,14 @@ def _build_metaheuristic_base(self):
         semantic_groups_dict = self.score_requester.variables_manager.semantic_groups_map.copy()
         discrete_ids = self.score_requester.variables_manager.discrete_ids.copy()
 
-        self.metaheuristic_base = TabuSearchBase(
+        self.metaheuristic_base = TabuSearchBase.new(
+            self.cotwin.score_calculator.score_variant,
+            self.score_requester.variables_manager,
             self.neighbours_count,
             self.tabu_entity_rate,
+            semantic_groups_dict,
             self.mutation_rate_multiplier,
             self.move_probas.copy() if self.move_probas else None,
-            semantic_groups_dict,
             discrete_ids,
         )
 

greyjack/greyjack/agents/TabuSearch.py

@@ -183,7 +183,7 @@ def init_population(self):
 
     def step_plain(self):
         new_population = []
-        samples = self.metaheuristic_base.sample_candidates_plain(self.population, self.agent_top_individual, self.score_requester.variables_manager)
+        samples = self.metaheuristic_base.sample_candidates_plain(self.population, self.agent_top_individual)
         scores = self.score_requester.request_score_plain(samples)
         if self.score_precision is not None:
             for score in scores:
@@ -196,7 +196,7 @@ def step_plain(self):
 
     def step_incremental(self):
         new_population = []
-        sample, deltas = self.metaheuristic_base.sample_candidates_incremental(self.population, self.agent_top_individual, self.score_requester.variables_manager)
+        sample, deltas = self.metaheuristic_base.sample_candidates_incremental(self.population, self.agent_top_individual)
         scores = self.score_requester.request_score_incremental(sample, deltas)
         if self.score_precision is not None:
             for score in scores: