Changeset 1182 for framspy/evolalg/dissimilarity/frams_dissimilarity.py

Timestamp:

08/31/22 00:05:43 (3 years ago)

Author:

Maciej Komosinski

Message:

More concise code and less redundancy in dissimilarity classes, added support for archive of genotypes, added hard limit on the number of genotype chars

File:

• framspy/evolalg/dissimilarity/frams_dissimilarity.py (modified) (2 diffs)

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", knn=None, *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.fn_reduce = Dissimilarity.get_reduction_by_name(reduction)
         self.knn = knn
-        if reduction == "mean":
-            self.fn_reduce = np.mean
-        elif reduction == "max":
-            self.fn_reduce = np.max
-        elif reduction == "min":
-            self.fn_reduce = np.min
-        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, knn_mean, none")
 
-    def reduce(self, dissim_matrix):
-        if self.fn_reduce is None:
-            return dissim_matrix
-        return self.fn_reduce(dissim_matrix, axis=1)
 
     def call(self, population):
@@ -41 +21 @@
         if len(population) == 0:
             return []
-        dissim_matrix = self.frams.dissimilarity([_.genotype for _ in population])
-        dissim = self.reduce(dissim_matrix)
+        dissim_matrix = self.frams.dissimilarity([_.genotype for _ in population], 1)
+        dissim = Dissimilarity.reduce(dissim_matrix, self.fn_reduce, self.knn)
         for d,ind in zip(dissim, population):
             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)