Fix was_tuned (aimclub#36)

SoloWayG · Feb 21, 2023 · 1d6be04 · 1d6be04
1 parent 6b2fa54
commit 1d6be04
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diff --git a/golem/core/tuning/sequential.py b/golem/core/tuning/sequential.py
@@ -77,6 +77,8 @@ def tune(self, graph: DomainGraphForTune) -> DomainGraphForTune:
         # Validation is the optimization do well
         final_graph = self.final_check(graph)
 
+        self.was_tuned = True
+
         return self.adapter.restore(final_graph)
 
     def get_nodes_order(self, nodes_number: int) -> range:
@@ -126,6 +128,7 @@ def tune_node(self, graph: DomainGraphForTune, node_index: int) -> DomainGraphFo
                                 iterations_per_node=self.iterations,
                                 seconds_per_node=self.max_seconds,
                                 )
+            self.was_tuned = True
 
         # Validation is the optimization do well
         final_graph = self.final_check(graph)

diff --git a/golem/core/tuning/simultaneous.py b/golem/core/tuning/simultaneous.py
@@ -74,6 +74,8 @@ def tune(self, graph: DomainGraphForTune, show_progress: bool = True) -> DomainG
                     # Validation is the optimization do well
                     graph = self.final_check(tuned_graph)
 
+                    self.was_tuned = True
+
                 except Exception as ex:
                     self.log.warning(f'Exception {ex} occurred during tuning')
 

diff --git a/test/unit/optimizers/fitness/test_fitness.py b/test/unit/optimizers/fitness/test_fitness.py
@@ -6,6 +6,7 @@
 import pytest
 
 from golem.core.optimisers.fitness import null_fitness, SingleObjFitness, MultiObjFitness
+from golem.core.optimisers.objective.objective import to_fitness
 from golem.serializers import Serializer
 
 
@@ -125,3 +126,10 @@ def test_fitness_serialization(fitness):
     assert fitness.valid == reserialized.valid
     if fitness.valid:
         assert fitness == reserialized
+
+def test_universal_fitness_compare():
+    assert to_fitness([1., 1., 3.], multi_objective=False).dominates(to_fitness([1., 2., 3.], multi_objective=False))
+    assert to_fitness([1., 1., 3.], multi_objective=True).dominates(to_fitness([1., 2., 3.], multi_objective=True))
+
+    assert to_fitness([1., 1., 3.], multi_objective=False).dominates(to_fitness([1., 2., 1.], multi_objective=False))
+    assert not to_fitness([1., 1., 3.], multi_objective=True).dominates(to_fitness([1., 2., 1.], multi_objective=True))