Merge EL sentence crossing tests.

spacy/tests/pipeline/test_entity_linker.py

@@ -1,9 +1,9 @@
-from typing import Callable, Iterable, Dict, Any
+from typing import Callable, Iterable, Dict, Any, Tuple
 
 import pytest
 from numpy.testing import assert_equal
 
-from spacy import registry, util
+from spacy import registry, util, Language
 from spacy.attrs import ENT_KB_ID
 from spacy.compat import pickle
 from spacy.kb import Candidate, InMemoryLookupKB, get_candidates, KnowledgeBase
@@ -108,18 +108,24 @@ def test_issue7065():
 
 
 @pytest.mark.issue(7065)
-def test_issue7065_b():
+@pytest.mark.parametrize("entity_in_first_sentence", [True, False])
+def test_sentence_crossing_ents(entity_in_first_sentence: bool):
+    """Tests if NEL crashes if entities cross sentence boundaries and the first associated sentence doesn't have an
+    entity.
+    entity_in_prior_sentence (bool): Whether to include an entity in the first sentence associated with the
+    sentence-crossing entity.
+    """
     # Test that the NEL doesn't crash when an entity crosses a sentence boundary
     nlp = English()
     vector_length = 3
     nlp.add_pipe("sentencizer")
     text = "Mahler 's Symphony No. 8 was beautiful."
-    entities = [(0, 6, "PERSON"), (10, 24, "WORK")]
-    links = {
-        (0, 6): {"Q7304": 1.0, "Q270853": 0.0},
-        (10, 24): {"Q7304": 0.0, "Q270853": 1.0},
-    }
-    sent_starts = [1, -1, 0, 0, 0, 0, 0, 0, 0]
+    entities = [(10, 24, "WORK")]
+    links = {(10, 24): {"Q7304": 0.0, "Q270853": 1.0}}
+    if entity_in_first_sentence:
+        entities.append((0, 6, "PERSON"))
+        links[(0, 6)] = {"Q7304": 1.0, "Q270853": 0.0}
+    sent_starts = [1, -1, 0, 0, 0, 1, 0, 0, 0]
     doc = nlp(text)
     example = Example.from_dict(
         doc, {"entities": entities, "links": links, "sent_starts": sent_starts}
@@ -152,24 +158,8 @@ def test_issue7065_b():
         losses = {}
         nlp.update(train_examples, sgd=optimizer, losses=losses)
 
-    # Add a custom rule-based component to mimick NER
-    patterns = [
-        {"label": "PERSON", "pattern": [{"LOWER": "mahler"}]},
-        {
-            "label": "WORK",
-            "pattern": [
-                {"LOWER": "symphony"},
-                {"LOWER": "no"},
-                {"LOWER": "."},
-                {"LOWER": "8"},
-            ],
-        },
-    ]
-    ruler = nlp.add_pipe("entity_ruler", before="entity_linker")
-    ruler.add_patterns(patterns)
-    # test the trained model - this should not throw E148
-    doc = nlp(text)
-    assert doc
+    # This shouldn't crash.
+    entity_linker.predict([example.reference])
 
 
 def test_no_entities():
@@ -1219,47 +1209,3 @@ def test_span_maker_forward_with_empty():
     # just to get a model
     span_maker = build_span_maker()
     span_maker([doc1, doc2], False)
-
-
-def test_sentence_crossing_ents():
-    """Tests if NEL crashes if entities cross sentence boundaries and the first associated sentence doesn't have an
-    entity.
-    """
-    nlp = English()
-    vector_length = 3
-    nlp.add_pipe("sentencizer")
-    text = "Mahler 's Symphony No. 8 was beautiful."
-    entities = [(10, 24, "WORK")]
-    links = {
-        (10, 24): {"Q7304": 0.0, "Q270853": 1.0},
-    }
-    sent_starts = [1, -1, 0, 0, 0, 1, 0, 0, 0]
-    doc = nlp(text)
-    example = Example.from_dict(
-        doc, {"entities": entities, "links": links, "sent_starts": sent_starts}
-    )
-    assert len(list(example.reference.ents[0].sents)) == 2
-    train_examples = [example]
-
-    def create_kb(vocab):
-        # create artificial KB
-        mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
-        mykb.add_entity(entity="Q270853", freq=12, entity_vector=[9, 1, -7])
-        mykb.add_alias(
-            alias="No. 8",
-            entities=["Q270853"],
-            probabilities=[1.0],
-        )
-        return mykb
-
-    # Create the Entity Linker component and add it to the pipeline
-    entity_linker = nlp.add_pipe("entity_linker", last=True)
-    entity_linker.set_kb(create_kb)
-    # train the NEL pipe
-    optimizer = nlp.initialize(get_examples=lambda: train_examples)
-    for i in range(2):
-        losses = {}
-        nlp.update(train_examples, sgd=optimizer, losses=losses)
-
-    # This shouldn't crash.
-    entity_linker.predict([example.reference])