Add penalty

ConceptLearner/DiscriminativeExplainer.py

@@ -17,6 +17,20 @@ from ontolearn.owlapy.model import OWLNamedIndividual, IRI, OWLObjectIntersectio
     OWLObjectSomeValuesFrom, OWLClass, OWLNothing
 import re
 
+class LinearPressureFitness():
+    """Linear parametric parsimony pressure."""
+
+    __slots__ = 'gain', 'penalty'
+
+
+    def __init__(self, gain: float = 2048.0, penalty: float = 1.0):
+        self.gain = gain
+        self.penalty = penalty
+
+    def apply(self, individual):
+        quality = individual.quality.values[0]
+        fitness = self.gain*quality - self.penalty*len(individual)
+        individual.fitness.values = (round(fitness, 5),)
 
 class DiscriminativeExplainer:
     """An abstract class that represents a model explainer for a Graph Neural Network (GNN).
@@ -98,7 +112,8 @@ class DiscriminativeExplainer:
                 debug: Optional[bool] = False,
                 max_runtime: Optional[int] = 60,
                 num_generations: Optional[int] = 600,
-                quality_func: Optional[AbstractScorer] = None):
+                quality_func: Optional[AbstractScorer] = None,
+                penalty=1.0):
         """Generates an explanation for the given label using the GNN and dataset.
 
         Args:
@@ -121,7 +136,8 @@ class DiscriminativeExplainer:
             max_runtime=max_runtime,
             num_generations=num_generations,
             quality_func=quality_func,
-            population_size=1000
+            population_size=1000,
+            fitness_func=LinearPressureFitness(penalty=penalty)
         )
 
         positive_examples = []