Changeset 1145 for framspy/evolalg/dissimilarity

Timestamp:

07/07/21 14:38:57 (3 years ago)

Author:

Maciej Komosinski

Message:

Added niching and novelty search with limited (i.e., local) competition ("nearest neighbors" according to dissimilarity measure)

Location:

framspy/evolalg/dissimilarity

Files:

• dissimilarity.py (modified) (3 diffs)
• frams_dissimilarity.py (modified) (3 diffs)

framspy/evolalg/dissimilarity/dissimilarity.py

@@ -7 +7 @@
 class Dissimilarity(Step, ABC):
 
-    def __init__(self, reduction="mean", output_field="dissim", *args, **kwargs):
+    def __init__(self, reduction="mean", output_field="dissim", knn=None, *args, **kwargs):
         super(Dissimilarity, self).__init__(*args, **kwargs)
 
         self.output_field = output_field
         self.fn_reduce = None
-        if reduction == "mean":
+        self.knn = knn
+        if reduction == "mean": # TODO change this 'elif' sequence to dictionary?
             self.fn_reduce = np.mean
         elif reduction == "max":
@@ -20 +21 @@
         elif reduction == "sum":
             self.fn_reduce = np.sum
-        elif reduction == "none" or reduction == None:
+        elif reduction == "knn_mean":
+            self.fn_reduce = self.knn_mean
+        elif reduction == "none" or reduction is None:
             self.fn_reduce = None
         else:
-            raise ValueError("Unknown reduction type. Supported: mean, max, min, sum, none")
+            raise ValueError("Unknown reduction type. Supported: mean, max, min, sum, knn_mean, none")
 
     def reduce(self, dissim_matrix):
@@ -29 +32 @@
             return dissim_matrix
         return self.fn_reduce(dissim_matrix, axis=1)
+
+    def knn_mean(self, dissim_matrix,axis):
+        return np.mean(np.partition(dissim_matrix, self.knn)[:,:self.knn],axis=axis)

framspy/evolalg/dissimilarity/frams_dissimilarity.py

@@ -6 +6 @@
 from evolalg.dissimilarity.dissimilarity import Dissimilarity
 
+#TODO eliminate overlap with dissimilarity.py
+
 
 class FramsDissimilarity(FramsStep):
 
-    def __init__(self, frams_lib, reduction="mean", output_field="dissim", *args, **kwargs):
+    def __init__(self, frams_lib, reduction="mean", output_field="dissim", knn=None, *args, **kwargs):
         super(FramsDissimilarity, self).__init__(frams_lib, *args, **kwargs)
 
         self.output_field = output_field
         self.fn_reduce = None
+        self.knn = knn
         if reduction == "mean":
             self.fn_reduce = np.mean
@@ -22 +25 @@
         elif reduction == "sum":
             self.fn_reduce = np.sum
+        elif reduction == "knn_mean":
+            self.fn_reduce = self.knn_mean
         elif reduction == "none" or reduction is None:
             self.fn_reduce = None
         else:
-            raise ValueError("Unknown reduction type. Supported: mean, max, min, sum, none")
+            raise ValueError("Unknown reduction type. Supported: mean, max, min, sum, knn_mean, none")
 
     def reduce(self, dissim_matrix):
@@ -41 +46 @@
             setattr(ind, self.output_field, d)
         return population
+
+    def knn_mean(self, dissim_matrix,axis):
+        return np.mean(np.partition(dissim_matrix, self.knn)[:,:self.knn],axis=axis)