Plotting

dacapo/experiments/arraytypes/binary.py

@@ -1,4 +1,4 @@
-from arraytype import ArrayType
+from .arraytype import ArrayType
 
 import attr
 

dacapo/experiments/datasplits/datasets/arrays/cellmap_array_config.py

@@ -2,7 +2,7 @@
 
 from .array_config import ArrayConfig
 from .cellmap_array import CellMapArray
-from .array_config import ArrayConfig
+from .array import Array
 
 from typing import List
 
@@ -14,7 +14,7 @@ class CellMapArrayConfig(ArrayConfig):
 
     array_type = CellMapArray
 
-    source_array_config: ArrayConfig = attr.ib(
+    source_array_config: Array = attr.ib(
         metadata={
             "help_text": "The Array from which to pull annotated data. Is expected to contain a volume with uint64 voxels and no channel dimension"
         }

dacapo/experiments/validation_scores.py

@@ -1,6 +1,8 @@
 from .validation_iteration_scores import ValidationIterationScores
 from typing import List
 import attr
+import inspect
+import numpy as np
 
 
 @attr.s
@@ -29,31 +31,58 @@ def validated_until(self):
             return 0
         return max([score.iteration for score in self.iteration_scores]) + 1
 
+    def get_attribute_names(self, class_instance):
+
+        attributes = inspect.getmembers(
+            class_instance, lambda a: not(inspect.isroutine(a)))
+        names = [a[0] for a in attributes if not(
+            a[0].startswith('__') and a[0].endswith('__'))]
+        return names
 
-'''
     def get_score_names(self):
 
-        for scores in self.scores:
-            for parameters, sample_scores in scores.items():
-                return sample_scores['scores']['average'].keys()
+        if self.iteration_scores:
+            example_parameter_scores = self.iteration_scores[0].parameter_scores
+            score_class_instance = example_parameter_scores[0][1]
+            return self.get_attribute_names(score_class_instance)
+
+        raise RuntimeError("No scores were added, yet")
+
+    def get_postprocessor_parameter_names(self):
+
+        if self.iteration_scores:
+            example_parameter_scores = self.iteration_scores[0].parameter_scores
+            postprocessor_class_instance = example_parameter_scores[0][0]
+            return self.get_attribute_names(postprocessor_class_instance)
 
         raise RuntimeError("No scores were added, yet")
 
     def get_best(self, score_name=None, higher_is_better=True):
 
         names = self.get_score_names()
+        postprocessor_parameter_names = self.get_postprocessor_parameter_names()
 
         best_scores = {name: [] for name in names}
-        for iteration_scores in self.scores:
+        best_score_parameters = {name: []
+                                 for name in postprocessor_parameter_names}
+
+        for iteration_score in self.iteration_scores:
             ips = np.array([
-                parameter_scores['scores']['average'].get(score_name, np.nan)
-                for parameter_scores in iteration_scores.values()
+                getattr(parameter_score[1], score_name, np.nan)
+                for parameter_score in iteration_score.parameter_scores
             ], dtype=np.float32)
             ips[np.isnan(ips)] = -np.inf if higher_is_better else np.inf
             i = np.argmax(ips) if higher_is_better else np.argmin(ips)
+            best_score = iteration_score.parameter_scores[i]
+
             for name in names:
                 best_scores[name].append(
-                    list(iteration_scores.values())[i]['scores']['average'].get(name, 0)
+                    getattr(best_score[1], name)
                 )
-        return best_scores
-'''
+
+            for name in postprocessor_parameter_names:
+                best_score_parameters[name].append(
+                    getattr(best_score[0], name)
+                )
+
+        return (best_score_parameters, best_scores)