Refactor: split _merge_hits_from_sources in vw_ensemble

annif/backend/vw_ensemble.py

@@ -67,21 +67,25 @@ def initialize(self):
             self._load_subject_freq()
         super().initialize()
 
+    def _calculate_scores(self, subj_id, subj_score_vector):
+        ex = self._format_example(subj_id, subj_score_vector)
+        raw_score = subj_score_vector.mean()
+        pred_score = (self._model.predict(ex) + 1.0) / 2.0
+        return raw_score, pred_score
+
     def _merge_hits_from_sources(self, hits_from_sources, project, params):
         score_vector = np.array([hits.vector
                                  for hits, _ in hits_from_sources])
+        discount_rate = self.params.get('discount_rate',
+                                        self.DEFAULT_DISCOUNT_RATE)
         result = np.zeros(score_vector.shape[1])
         for subj_id in range(score_vector.shape[1]):
-            if score_vector[:, subj_id].sum() > 0.0:
-                ex = self._format_example(
-                    subj_id,
-                    score_vector[:, subj_id])
-                discount_rate = self.params.get(
-                    'discount_rate', self.DEFAULT_DISCOUNT_RATE)
+            subj_score_vector = score_vector[:, subj_id]
+            if subj_score_vector.sum() > 0.0:
+                raw_score, pred_score = self._calculate_scores(
+                    subj_id, subj_score_vector)
                 raw_weight = 1.0 / \
                     ((discount_rate * self._subject_freq[subj_id]) + 1)
-                raw_score = score_vector[:, subj_id].mean()
-                pred_score = (self._model.predict(ex) + 1.0) / 2.0
                 result[subj_id] = (raw_weight * raw_score) + \
                     (1.0 - raw_weight) * pred_score
         return VectorSuggestionResult(result, project.subjects)