Changeset 1289 for framspy/evolalg/base/experiment_niching_abc.py

Timestamp:

01/15/24 05:43:37 (4 months ago)

Author:

Maciej Komosinski

Message:

fitness_set_negative_to_zero boolean (a.k.a. "only positive fitness", needed for novelty and niching diversity control) becomes a command-line flag instead of a hardcoded value

File:

• framspy/evolalg/base/experiment_niching_abc.py (modified) (4 diffs)

framspy/evolalg/base/experiment_niching_abc.py

@@ -24 +24 @@
     archive_size: int = None
 
-    def __init__(self, fit, normalize, popsize, hof_size, save_only_best=True, knn_niching=5, knn_nslc=10, archive_size=0) -> None:
+    def __init__(self, fit, normalize, popsize, hof_size, save_only_best, knn_niching, knn_nslc, archive_size) -> None:
         ExperimentABC.__init__(self,popsize=popsize, hof_size=hof_size, save_only_best=save_only_best)
         self.fit = fit
@@ -51 +51 @@
             divide_by = np.sum(dissim_matrix)
         else:
-            raise Exception(f"Wrong normalization method,", self.normalize)
+            raise ValueError("Wrong normalization method: '%s'" % self.normalize)
         if divide_by != 0:
             return dissim_matrix/divide_by
@@ -65 +65 @@
             dissim_list = np.mean(np.partition(
                 self.normalize_dissim(dissim_matrix), self.knn_niching)[:, :self.knn_niching], axis=1)
+
+        if Individual.fitness_set_negative_to_zero is False and ("niching" in self.fit or "novelty" in self.fit):
+                raise ValueError("Negative fitness values not tested in combination with niching or novelty. When using these techniques, verify formulas or consider using the flag -fitness_set_negative_to_zero") # once the formulas are verified/improved, the command-line flag and this conditional check can be removed.
 
         if "niching" in self.fit:
@@ -73 +76 @@
                 i.fitness = d
         else:
-            raise Exception("Wrong fit type: ", self.fit,
-                            f" choose the correct one or implement a new behavior.")
+            raise ValueError("Unsupported fit type: '%s'. Use the correct type or implement a new behavior." % self.fit)
         population_structures.update_archive(dissim_matrix, population_archive)