Changes from review

models/deepscore.py

@@ -120,31 +120,6 @@ def from_measurements(measurements, provenance=None, **kwargs):
 
         return score
 
-    def evaluate_scores(self):
-        """Evaluate the ML/DL score for the associated measurement, using the 
-        appropriate algorithm.
-        """
-        # consider doing cases or another solution, for now just if-else block
-
-        algo = Provenance.get( self.provenance_id ).parameters['algorithm']
-
-        if algo == 'random':
-            self.score = np.random.default_rng().random()
-            self.algorithm = algo
-
-        elif algo == 'allperfect':
-            self.score = 1.0
-            self.algorithm = algo
-
-        elif algo in DeepscoreAlgorithmConverter.dict_inverse:
-            raise NotImplementedError(f"algorithm {algo} isn't yet implemented")
-
-        else:
-            raise ValueError(f"{algo} is not a valid ML algorithm.")
-
-        return None
-
-
     def get_upstreams(self, session=None):
         """Get the measurements that was used to make this deepscore. """
         with SmartSession(session) as session:

pipeline/data_store.py

@@ -1668,19 +1668,12 @@ def get_scores(self, provenance=None, reload=False, session=None):
 
         # sort the scores so the list order matches measurements
         if scores is not None and len(scores) > 0:
+
             if len(scores) != len(self.measurements):
                 raise RuntimeError(f"get_scores found {len(scores)} scores for {len(self.measurements)} measurements")
+
             m_ids = [str(m.id) for m in self.measurements]
-            sorted_scores = [None]*len(self.scores)
-            for score in scores:
-                idx = m_ids.index(str(score.measurements_id))
-                sorted_scores[idx] = score
-
-            if not all(scores):  # check if any None still remain
-                raise RuntimeError(f"get_scores did not replace all None values when sorting list")
-
-            scores = sorted_scores
-            self.scores = scores  # update the order in ds to be sorted too.
+            scores.sort( key=lambda x: m_ids.index( str(x.measurements_id) ) )
 
         return scores