Merge pull request #6238 from svlandeg/feature/kb-vocab-test

spacy/pipeline/trainable_pipe.pyx

@@ -243,7 +243,7 @@ cdef class TrainablePipe(Pipe):
     def _validate_serialization_attrs(self):
         """Check that the pipe implements the required attributes. If a subclass
         implements a custom __init__ method but doesn't set these attributes,
-        the currently default to None, so we need to perform additonal checks.
+        they currently default to None, so we need to perform additonal checks.
         """
         if not hasattr(self, "vocab") or self.vocab is None:
             raise ValueError(Errors.E899.format(name=util.get_object_name(self)))

spacy/tests/pipeline/test_entity_linker.py

@@ -5,6 +5,7 @@ from spacy.kb import KnowledgeBase, get_candidates, Candidate
 from spacy.vocab import Vocab
 
 from spacy import util, registry
+from spacy.ml import load_kb
 from spacy.scorer import Scorer
 from spacy.training import Example
 from spacy.lang.en import English
@@ -215,7 +216,7 @@ def test_el_pipe_configuration(nlp):
         return kb
 
     # run an EL pipe without a trained context encoder, to check the candidate generation step only
-    entity_linker = nlp.add_pipe("entity_linker", config={"incl_context": False},)
+    entity_linker = nlp.add_pipe("entity_linker", config={"incl_context": False})
     entity_linker.set_kb(create_kb)
     # With the default get_candidates function, matching is case-sensitive
     text = "Douglas and douglas are not the same."
@@ -496,6 +497,32 @@ def test_overfitting_IO():
         assert predictions == GOLD_entities
 
 
+def test_kb_serialization():
+    # Test that the KB can be used in a pipeline with a different vocab
+    vector_length = 3
+    with make_tempdir() as tmp_dir:
+        kb_dir = tmp_dir / "kb"
+        nlp1 = English()
+        assert "Q2146908" not in nlp1.vocab.strings
+        mykb = KnowledgeBase(nlp1.vocab, entity_vector_length=vector_length)
+        mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
+        mykb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
+        assert "Q2146908" in nlp1.vocab.strings
+        mykb.to_disk(kb_dir)
+
+        nlp2 = English()
+        assert "RandomWord" not in nlp2.vocab.strings
+        nlp2.vocab.strings.add("RandomWord")
+        assert "RandomWord" in nlp2.vocab.strings
+        assert "Q2146908" not in nlp2.vocab.strings
+
+        # Create the Entity Linker component with the KB from file, and check the final vocab
+        entity_linker = nlp2.add_pipe("entity_linker", last=True)
+        entity_linker.set_kb(load_kb(kb_dir))
+        assert "Q2146908" in nlp2.vocab.strings
+        assert "RandomWord" in nlp2.vocab.strings
+
+
 def test_scorer_links():
     train_examples = []
     nlp = English()