Add multi objective

aimclub · Oct 31, 2023 · a9fd28c · a9fd28c
1 parent 5161abd
commit a9fd28c
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Showing 2 changed files with 23 additions and 11 deletions.
diff --git a/golem/core/tuning/iopt_tuner.py b/golem/core/tuning/iopt_tuner.py
@@ -1,3 +1,4 @@
+from copy import deepcopy
 from dataclasses import dataclass, field
 from datetime import timedelta
 from random import choice
@@ -16,6 +17,7 @@
 from golem.core.optimisers.objective import ObjectiveEvaluate
 from golem.core.tuning.search_space import SearchSpace, get_node_operation_parameter_label, convert_parameters
 from golem.core.tuning.tuner_interface import BaseTuner, DomainGraphForTune
+from golem.utilities.data_structures import ensure_wrapped_in_sequence
 
 
 @dataclass
@@ -49,12 +51,13 @@ def from_parameters_dicts(float_parameters_dict: Optional[Dict[str, List]] = Non
 class GolemProblem(Problem, Generic[DomainGraphForTune]):
     def __init__(self, graph: DomainGraphForTune,
                  objective_evaluate: ObjectiveEvaluate,
-                 problem_parameters: IOptProblemParameters):
+                 problem_parameters: IOptProblemParameters,
+                 objectives_number: int = 1):
         super().__init__()
         self.objective_evaluate = objective_evaluate
         self.graph = graph
 
-        self.number_of_objectives = 1
+        self.number_of_objectives = objectives_number
         self.number_of_constraints = 0
 
         self.discrete_variable_names = problem_parameters.discrete_parameters_names
@@ -146,16 +149,18 @@ def tune(self, graph: DomainGraphForTune, show_progress: bool = True) -> DomainG
         no_parameters_to_optimize = (not problem_parameters.discrete_parameters_names and
                                      not problem_parameters.float_parameters_names)
         self.init_check(graph)
+        objectives_number = len(ensure_wrapped_in_sequence(self.init_metric))
+        is_multi_objective = objectives_number > 1
 
         if no_parameters_to_optimize:
             self._stop_tuning_with_message(f'Graph "{graph.graph_description}" has no parameters to optimize')
-            final_graph = graph
+            tuned_graphs = graph
         else:
             if initial_parameters:
                 initial_point = Point(**initial_parameters)
                 self.solver_parameters.start_point = initial_point
 
-            problem = GolemProblem(graph, self.objective_evaluate, problem_parameters)
+            problem = GolemProblem(graph, self.objective_evaluate, problem_parameters, objectives_number)
             solver = Solver(problem, parameters=self.solver_parameters)
 
             if show_progress:
@@ -164,14 +169,21 @@ def tune(self, graph: DomainGraphForTune, show_progress: bool = True) -> DomainG
 
             solver.solve()
             solution = solver.get_results()
-            best_point = solution.best_trials[0].point
-            best_parameters = problem.get_parameters_dict_from_iopt_point(best_point)
-            final_graph = self.set_arg_graph(graph, best_parameters)
-
-            self.was_tuned = True
+            if not is_multi_objective:
+                best_point = solution.best_trials[0].point
+                best_parameters = problem.get_parameters_dict_from_iopt_point(best_point)
+                tuned_graphs = self.set_arg_graph(graph, best_parameters)
+                self.was_tuned = True
+            else:
+                tuned_graphs = []
+                for best_trial in solution.best_trials:
+                    best_parameters = problem.get_parameters_dict_from_iopt_point(best_trial.point)
+                    tuned_graph = self.set_arg_graph(deepcopy(graph), best_parameters)
+                    tuned_graphs.append(tuned_graph)
+                    self.was_tuned = True
 
         # Validate if optimisation did well
-        graph = self.final_check(final_graph)
+        graph = self.final_check(tuned_graphs, is_multi_objective)
         final_graph = self.adapter.restore(graph)
         return final_graph
 

diff --git a/test/unit/tuning/test_tuning.py b/test/unit/tuning/test_tuning.py
@@ -114,7 +114,7 @@ def test_node_tuning(search_space, graph):
         assert tuner.init_metric >= tuner.obtained_metric
 
 
-@pytest.mark.parametrize('tuner_cls', [OptunaTuner])
+@pytest.mark.parametrize('tuner_cls', [OptunaTuner, IOptTuner])
 @pytest.mark.parametrize('init_graph, adapter, obj_eval',
                          [(mock_graph_with_params(), MockAdapter(),
                            MockObjectiveEvaluate(Objective({'sum_metric': ParamsSumMetric.get_value,