Changeset 1182 for framspy/evolalg/dissimilarity/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/dissimilarity.py (modified) (1 diff)

framspy/evolalg/dissimilarity/dissimilarity.py

@@ -11 +11 @@
 
         self.output_field = output_field
-        self.fn_reduce = None
+        self.fn_reduce = Dissimilarity.get_reduction_by_name(reduction)
         self.knn = knn
-        if reduction == "mean": # TODO change this 'elif' sequence to dictionary?
-            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
+
+
+    @staticmethod
+    def reduce(dissim_matrix, fn_reduce, knn):
+        if fn_reduce is None:
+            return dissim_matrix
+        elif fn_reduce is Dissimilarity.knn_mean:
+            return fn_reduce(dissim_matrix, 1, knn)
         else:
-            raise ValueError("Unknown reduction type. Supported: mean, max, min, sum, knn_mean, none")
+            return fn_reduce(dissim_matrix, axis=1)
 
-    def reduce(self, dissim_matrix):
-        if self.fn_reduce is None:
-            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)
+    @staticmethod
+    def knn_mean(dissim_matrix, axis, knn):
+        return np.mean(np.partition(dissim_matrix, knn)[:, :knn], axis=axis)
+
+
+    @staticmethod
+    def get_reduction_by_name(reduction: str):
+
+        if reduction not in REDUCTION_FUNCTION:
+            raise ValueError(f"Unknown reduction type '{reduction}'. Supported: {','.join(REDUCTION_FUNCTION.keys())}")
+
+        return REDUCTION_FUNCTION[reduction]
+
+
+
+REDUCTION_FUNCTION = {
+            "mean": np.mean,
+            "max": np.max,
+            "min": np.min,
+            "sum": np.sum,
+            "knn_mean": Dissimilarity.knn_mean,
+            "none": None
+        }