Turn WeightedSuggestion to WeightedSuggestionsBatch

annif/backend/ensemble.py

@@ -42,22 +42,18 @@ def _suggest_with_sources(self, texts, sources):
                 self._normalize_hits(hits, source_project) for hits in hit_sets
             ]
             hit_sets_from_sources.append(
-                [
-                    annif.suggestion.WeightedSuggestion(
-                        hits=norm_hits, weight=weight, subjects=source_project.subjects
-                    )
-                    for norm_hits in norm_hit_sets
-                ]
+                annif.suggestion.WeightedSuggestionsBatch(
+                    hit_sets=norm_hit_sets,
+                    weight=weight,
+                    subjects=source_project.subjects,
+                )
             )
         return hit_sets_from_sources
 
     def _merge_hit_sets_from_sources(self, hit_sets_from_sources, params):
-        """Hook for merging hits from sources. Can be overridden by
+        """Hook for merging hit sets from sources. Can be overridden by
         subclasses."""
-        return [
-            annif.util.merge_hits(hits, len(self.project.subjects))
-            for hits in hit_sets_from_sources
-        ]
+        return annif.util.merge_hits(hit_sets_from_sources, len(self.project.subjects))
 
     def _suggest_batch(self, texts, params):
         sources = annif.util.parse_sources(params["sources"])
@@ -133,16 +129,16 @@ def _objective(self, trial):
             weighted_hits = []
             for project_id, hits in srchits.items():
                 weighted_hits.append(
-                    annif.suggestion.WeightedSuggestion(
-                        hits=hits,
+                    annif.suggestion.WeightedSuggestionsBatch(
+                        hit_sets=[hits],
                         weight=weights[project_id],
                         subjects=self._backend.project.subjects,
                     )
                 )
             batch.evaluate(
                 annif.util.merge_hits(
                     weighted_hits, len(self._backend.project.subjects)
-                ),
+                )[0],
                 goldsubj,
             )
         results = batch.results(metrics=[self._metric])

annif/backend/nn_ensemble.py

@@ -137,9 +137,9 @@ def _merge_hit_sets_from_sources(self, hit_sets_from_sources, params):
                     np.sqrt(hits.as_vector(len(subjects)))
                     * weight
                     * len(hit_sets_from_sources)
-                    for hits, weight, subjects in hits_from_sources
+                    for hits in proj_hit_set
                 ]
-                for hits_from_sources in hit_sets_from_sources
+                for proj_hit_set, weight, subjects in hit_sets_from_sources
             ],
             dtype=np.float32,
         ).transpose(1, 2, 0)

annif/suggestion.py

@@ -7,8 +7,8 @@
 import numpy as np
 
 SubjectSuggestion = collections.namedtuple("SubjectSuggestion", "subject_id score")
-WeightedSuggestion = collections.namedtuple(
-    "WeightedSuggestion", "hits weight subjects"
+WeightedSuggestionsBatch = collections.namedtuple(
+    "WeightedSuggestionsBatch", "hit_sets weight subjects"
 )
 
 

annif/util.py

@@ -54,15 +54,20 @@ def cleanup_uri(uri):
     return uri
 
 
-def merge_hits(weighted_hits, size):
-    """Merge hits from multiple sources. Input is a sequence of WeightedSuggestion
-    objects. The size parameter determines the length of the subject vector.
-    Returns an SuggestionResult object."""
-
-    weights = [whit.weight for whit in weighted_hits]
-    scores = [whit.hits.as_vector(size) for whit in weighted_hits]
-    result = np.average(scores, axis=0, weights=weights)
-    return VectorSuggestionResult(result)
+def merge_hits(weighted_hits_batches, size):
+    """Merge hit sets from multiple sources. Input is a sequence of
+    WeightedSuggestionsBatch objects. The size parameter determines the length of the
+    subject vector. Returns a list of SuggestionResult objects."""
+
+    weights = [batch.weight for batch in weighted_hits_batches]
+    score_vectors = np.array(
+        [
+            [whits.as_vector(size) for whits in batch.hit_sets]
+            for batch in weighted_hits_batches
+        ]
+    )
+    results = np.average(score_vectors, axis=0, weights=weights)
+    return [VectorSuggestionResult(res) for res in results]
 
 
 def parse_sources(sourcedef):