Correct item list collection merging and implement bulk analysis grouped summaries

lenskit/lenskit/data/collection.py

@@ -220,8 +220,8 @@ def add_from(self, other: ItemListCollection, **fields: ID):
         """
         for key, list in other:
             if fields:
-                fields = key._asdict() | fields
-                key = self._key_class(**fields)
+                cf = key._asdict() | fields
+                key = self._key_class(**cf)
             self._add(key, list)
 
     def _add(self, key: K, list: ItemList):

lenskit/lenskit/metrics/bulk.py

@@ -76,7 +76,7 @@ def global_metrics(self) -> pd.Series:
         """
         return self._global_metrics
 
-    def list_metrics(self, *, fill_missing=True) -> pd.DataFrame:
+    def list_metrics(self, fill_missing=True) -> pd.DataFrame:
         """
         Get the per-list scores of the results.  This is a data frame with one
         row per list (with the list key on the inded), and one metric per
@@ -91,7 +91,7 @@ def list_metrics(self, *, fill_missing=True) -> pd.DataFrame:
         """
         return self._list_metrics.fillna(self._defaults)
 
-    def list_summary(self) -> pd.DataFrame:
+    def list_summary(self, *keys: str) -> pd.DataFrame:
         """
         Sumamry statistics for the per-list metrics.  Each metric is on its own
         row, with columns reporting the following:
@@ -105,10 +105,19 @@ def list_summary(self) -> pd.DataFrame:
 
         Additional columns are added based on other options.  Missing metric
         values are filled with their defaults before computing statistics.
+
+        Args:
+            keys:
+                Identifiers for different conditions that should be reported
+                separately (grouping keys for the final result).
         """
         scores = self.list_metrics(fill_missing=True)
-        df = scores.agg(["mean", "median", "std"]).T
-        df.index.name = "metric"
+        if keys:
+            df = scores.groupby(list(keys)).agg(["mean", "median", "std"]).stack(level=0)
+            assert isinstance(df, pd.DataFrame)
+        else:
+            df = scores.agg(["mean", "median", "std"]).T
+            df.index.name = "metric"
         return df
 
     def merge_from(self, other: RunAnalysisResult):

lenskit/tests/data/test_collection.py

@@ -117,13 +117,16 @@ def test_lookup_projected():
 def test_add_from():
     ilc = ItemListCollection(["model", "user_id"])
 
-    ilc1 = ItemListCollection.from_dict({72: ItemList(["a", "b"])}, key="user_id")
+    ilc1 = ItemListCollection.from_dict({72: ItemList(["a", "b"]), 48: ItemList()}, key="user_id")
     ilc.add_from(ilc1, model="foo")
 
-    assert len(ilc) == 1
+    assert len(ilc) == 2
     il = ilc.lookup(("foo", 72))
     assert il is not None
     assert il.ids().tolist() == ["a", "b"]
+    il = ilc.lookup(("foo", 48))
+    assert il is not None
+    assert len(il) == 0
 
 
 def test_from_df(rng, ml_ratings: pd.DataFrame):

lenskit/tests/eval/test_bulk_metrics.py

@@ -43,3 +43,26 @@ def test_recs(demo_recs):
     print(stats)
     for m in bms.metrics:
         assert stats.loc[m.label, "mean"] == approx(scores[m.label].mean())
+
+
+def test_recs_multi(demo_recs):
+    split, recs = demo_recs
+
+    il2 = ItemListCollection(["rep", "user_id"])
+    il2.add_from(recs, rep=1)
+    il2.add_from(recs, rep=2)
+
+    bms = RunAnalysis()
+    bms.add_metric(ListLength())
+    bms.add_metric(Precision())
+    bms.add_metric(NDCG())
+    bms.add_metric(RBP)
+    bms.add_metric(RecipRank)
+
+    metrics = bms.compute(il2, split.test)
+    scores = metrics.list_metrics()
+    stats = metrics.list_summary("rep")
+    print(stats)
+    for m in bms.metrics:
+        assert stats.loc[(1, m.label), "mean"] == approx(scores.loc[1, m.label].mean())
+        assert stats.loc[(2, m.label), "mean"] == approx(scores.loc[2, m.label].mean())