ops: add specific + more complete tests for operations

Author: mdekstrand

src/lenskit/operations.py

@@ -54,7 +54,7 @@ def recommend(
     if items is not None and not isinstance(items, ItemList):
         items = ItemList(items)
     res = pipeline.run(node, query=query, n=n, items=items, _profile=profiler)
-    if not isinstance(res, ItemList):
+    if not isinstance(res, ItemList):  # pragma: nocover
         raise TypeError("recommender pipeline did not return an item list")
 
     return res
@@ -93,7 +93,7 @@ def score(
     if items is not None and not isinstance(items, ItemList):
         items = ItemList(items)
     res = pipeline.run(node, query=query, items=items, _profile=profiler)
-    if not isinstance(res, ItemList):
+    if not isinstance(res, ItemList):  # pragma: nocover
         raise TypeError("scorer pipeline did not return an item list")
 
     return res

tests/test_operations.py

@@ -0,0 +1,43 @@
+import numpy as np
+
+from pytest import fixture
+
+from lenskit import Pipeline, predict, recommend, score, topn_pipeline
+from lenskit.data import Dataset
+from lenskit.knn import ItemKNNScorer
+
+
+@fixture(scope="module")
+def pipeline(ml_ds: Dataset):
+    pipe = topn_pipeline(ItemKNNScorer(max_nbrs=20), predicts_ratings=True)
+    pipe.train(ml_ds)
+    yield pipe
+
+
+def test_simple_recommend(pipeline: Pipeline):
+    recs = recommend(pipeline, 10, n=10)
+    assert len(recs) == 10
+
+
+def test_simple_recommend_items(ml_ds: Dataset, pipeline: Pipeline):
+    items = ml_ds.items.ids()
+    recs = recommend(pipeline, 10, n=10, items=items)
+    assert len(recs) == 10
+
+
+def test_simple_score(ml_ds: Dataset, pipeline: Pipeline, rng: np.random.Generator):
+    items = rng.choice(ml_ds.items.ids(), 50, replace=False)
+    preds = score(pipeline, 10, items)
+    assert len(preds) == 50
+    scores = preds.scores()
+    assert scores is not None
+    assert np.any(np.isfinite(scores))
+
+
+def test_simple_predict(ml_ds: Dataset, pipeline: Pipeline, rng: np.random.Generator):
+    items = rng.choice(ml_ds.items.ids(), 50, replace=False)
+    preds = predict(pipeline, 10, items)
+    assert len(preds) == 50
+    scores = preds.scores()
+    assert scores is not None
+    assert np.all(np.isfinite(scores))