from typing import Callable, Iterable import pytest from spacy.kb import KnowledgeBase, get_candidates, Candidate from spacy.vocab import Vocab from spacy import util, registry from spacy.scorer import Scorer from spacy.training import Example from spacy.lang.en import English from spacy.tests.util import make_tempdir from spacy.tokens import Span @pytest.fixture def nlp(): return English() def assert_almost_equal(a, b): delta = 0.0001 assert a - delta <= b <= a + delta def test_kb_valid_entities(nlp): """Test the valid construction of a KB with 3 entities and two aliases""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3) # adding entities mykb.add_entity(entity="Q1", freq=19, entity_vector=[8, 4, 3]) mykb.add_entity(entity="Q2", freq=5, entity_vector=[2, 1, 0]) mykb.add_entity(entity="Q3", freq=25, entity_vector=[-1, -6, 5]) # adding aliases mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.2]) mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9]) # test the size of the corresponding KB assert mykb.get_size_entities() == 3 assert mykb.get_size_aliases() == 2 # test retrieval of the entity vectors assert mykb.get_vector("Q1") == [8, 4, 3] assert mykb.get_vector("Q2") == [2, 1, 0] assert mykb.get_vector("Q3") == [-1, -6, 5] # test retrieval of prior probabilities assert_almost_equal(mykb.get_prior_prob(entity="Q2", alias="douglas"), 0.8) assert_almost_equal(mykb.get_prior_prob(entity="Q3", alias="douglas"), 0.2) assert_almost_equal(mykb.get_prior_prob(entity="Q342", alias="douglas"), 0.0) assert_almost_equal(mykb.get_prior_prob(entity="Q3", alias="douglassssss"), 0.0) def test_kb_invalid_entities(nlp): """Test the invalid construction of a KB with an alias linked to a non-existing entity""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) # adding entities mykb.add_entity(entity="Q1", freq=19, entity_vector=[1]) mykb.add_entity(entity="Q2", freq=5, entity_vector=[2]) mykb.add_entity(entity="Q3", freq=25, entity_vector=[3]) # adding aliases - should fail because one of the given IDs is not valid with pytest.raises(ValueError): mykb.add_alias( alias="douglas", entities=["Q2", "Q342"], probabilities=[0.8, 0.2] ) def test_kb_invalid_probabilities(nlp): """Test the invalid construction of a KB with wrong prior probabilities""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) # adding entities mykb.add_entity(entity="Q1", freq=19, entity_vector=[1]) mykb.add_entity(entity="Q2", freq=5, entity_vector=[2]) mykb.add_entity(entity="Q3", freq=25, entity_vector=[3]) # adding aliases - should fail because the sum of the probabilities exceeds 1 with pytest.raises(ValueError): mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.4]) def test_kb_invalid_combination(nlp): """Test the invalid construction of a KB with non-matching entity and probability lists""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) # adding entities mykb.add_entity(entity="Q1", freq=19, entity_vector=[1]) mykb.add_entity(entity="Q2", freq=5, entity_vector=[2]) mykb.add_entity(entity="Q3", freq=25, entity_vector=[3]) # adding aliases - should fail because the entities and probabilities vectors are not of equal length with pytest.raises(ValueError): mykb.add_alias( alias="douglas", entities=["Q2", "Q3"], probabilities=[0.3, 0.4, 0.1] ) def test_kb_invalid_entity_vector(nlp): """Test the invalid construction of a KB with non-matching entity vector lengths""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3) # adding entities mykb.add_entity(entity="Q1", freq=19, entity_vector=[1, 2, 3]) # this should fail because the kb's expected entity vector length is 3 with pytest.raises(ValueError): mykb.add_entity(entity="Q2", freq=5, entity_vector=[2]) def test_kb_default(nlp): """Test that the default (empty) KB is loaded upon construction""" entity_linker = nlp.add_pipe("entity_linker", config={}) assert len(entity_linker.kb) == 0 assert entity_linker.kb.get_size_entities() == 0 assert entity_linker.kb.get_size_aliases() == 0 # 64 is the default value from pipeline.entity_linker assert entity_linker.kb.entity_vector_length == 64 def test_kb_custom_length(nlp): """Test that the default (empty) KB can be configured with a custom entity length""" entity_linker = nlp.add_pipe( "entity_linker", config={"entity_vector_length": 35} ) assert len(entity_linker.kb) == 0 assert entity_linker.kb.get_size_entities() == 0 assert entity_linker.kb.get_size_aliases() == 0 assert entity_linker.kb.entity_vector_length == 35 def test_kb_initialize_empty(nlp): """Test that the EL can't initialize without examples""" entity_linker = nlp.add_pipe("entity_linker") with pytest.raises(TypeError): entity_linker.initialize(lambda: []) def test_kb_serialize(nlp): """Test serialization of the KB""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) with make_tempdir() as d: # normal read-write behaviour mykb.to_disk(d / "kb") mykb.from_disk(d / "kb") mykb.to_disk(d / "new" / "kb") mykb.from_disk(d / "new" / "kb") # allow overwriting an existing file mykb.to_disk(d / "kb") with pytest.raises(ValueError): # can not read from an unknown file mykb.from_disk(d / "unknown" / "kb") def test_kb_serialize_vocab(nlp): """Test serialization of the KB and custom strings""" entity = "MyFunnyID" assert entity not in nlp.vocab.strings mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) assert not mykb.contains_entity(entity) mykb.add_entity(entity, freq=342, entity_vector=[3]) assert mykb.contains_entity(entity) assert entity in mykb.vocab.strings with make_tempdir() as d: # normal read-write behaviour mykb.to_disk(d / "kb") mykb_new = KnowledgeBase(Vocab(), entity_vector_length=1) mykb_new.from_disk(d / "kb") assert entity in mykb_new.vocab.strings def test_candidate_generation(nlp): """Test correct candidate generation""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) doc = nlp("douglas adam Adam shrubbery") douglas_ent = doc[0:1] adam_ent = doc[1:2] Adam_ent = doc[2:3] shrubbery_ent = doc[3:4] # adding entities mykb.add_entity(entity="Q1", freq=27, entity_vector=[1]) mykb.add_entity(entity="Q2", freq=12, entity_vector=[2]) mykb.add_entity(entity="Q3", freq=5, entity_vector=[3]) # adding aliases mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1]) mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9]) # test the size of the relevant candidates assert len(get_candidates(mykb, douglas_ent)) == 2 assert len(get_candidates(mykb, adam_ent)) == 1 assert len(get_candidates(mykb, Adam_ent)) == 0 # default case sensitive assert len(get_candidates(mykb, shrubbery_ent)) == 0 # test the content of the candidates assert get_candidates(mykb, adam_ent)[0].entity_ == "Q2" assert get_candidates(mykb, adam_ent)[0].alias_ == "adam" assert_almost_equal(get_candidates(mykb, adam_ent)[0].entity_freq, 12) assert_almost_equal(get_candidates(mykb, adam_ent)[0].prior_prob, 0.9) def test_el_pipe_configuration(nlp): """Test correct candidate generation as part of the EL pipe""" nlp.add_pipe("sentencizer") pattern = {"label": "PERSON", "pattern": [{"LOWER": "douglas"}]} ruler = nlp.add_pipe("entity_ruler") ruler.add_patterns([pattern]) def create_kb(vocab): kb = KnowledgeBase(vocab, entity_vector_length=1) kb.add_entity(entity="Q2", freq=12, entity_vector=[2]) kb.add_entity(entity="Q3", freq=5, entity_vector=[3]) kb.add_alias( alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1] ) 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.set_kb(create_kb) # With the default get_candidates function, matching is case-sensitive text = "Douglas and douglas are not the same." doc = nlp(text) assert doc[0].ent_kb_id_ == "NIL" assert doc[1].ent_kb_id_ == "" assert doc[2].ent_kb_id_ == "Q2" def get_lowercased_candidates(kb, span): return kb.get_alias_candidates(span.text.lower()) @registry.misc.register("spacy.LowercaseCandidateGenerator.v1") def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]: return get_lowercased_candidates # replace the pipe with a new one with with a different candidate generator entity_linker = nlp.replace_pipe( "entity_linker", "entity_linker", config={ "incl_context": False, "get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"}, }, ) entity_linker.set_kb(create_kb) doc = nlp(text) assert doc[0].ent_kb_id_ == "Q2" assert doc[1].ent_kb_id_ == "" assert doc[2].ent_kb_id_ == "Q2" def test_vocab_serialization(nlp): """Test that string information is retained across storage""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) # adding entities mykb.add_entity(entity="Q1", freq=27, entity_vector=[1]) q2_hash = mykb.add_entity(entity="Q2", freq=12, entity_vector=[2]) mykb.add_entity(entity="Q3", freq=5, entity_vector=[3]) # adding aliases mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.4, 0.1]) adam_hash = mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9]) candidates = mykb.get_alias_candidates("adam") assert len(candidates) == 1 assert candidates[0].entity == q2_hash assert candidates[0].entity_ == "Q2" assert candidates[0].alias == adam_hash assert candidates[0].alias_ == "adam" with make_tempdir() as d: mykb.to_disk(d / "kb") kb_new_vocab = KnowledgeBase(Vocab(), entity_vector_length=1) kb_new_vocab.from_disk(d / "kb") candidates = kb_new_vocab.get_alias_candidates("adam") assert len(candidates) == 1 assert candidates[0].entity == q2_hash assert candidates[0].entity_ == "Q2" assert candidates[0].alias == adam_hash assert candidates[0].alias_ == "adam" def test_append_alias(nlp): """Test that we can append additional alias-entity pairs""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) # adding entities mykb.add_entity(entity="Q1", freq=27, entity_vector=[1]) mykb.add_entity(entity="Q2", freq=12, entity_vector=[2]) mykb.add_entity(entity="Q3", freq=5, entity_vector=[3]) # adding aliases mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.4, 0.1]) mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9]) # test the size of the relevant candidates assert len(mykb.get_alias_candidates("douglas")) == 2 # append an alias mykb.append_alias(alias="douglas", entity="Q1", prior_prob=0.2) # test the size of the relevant candidates has been incremented assert len(mykb.get_alias_candidates("douglas")) == 3 # append the same alias-entity pair again should not work (will throw a warning) with pytest.warns(UserWarning): mykb.append_alias(alias="douglas", entity="Q1", prior_prob=0.3) # test the size of the relevant candidates remained unchanged assert len(mykb.get_alias_candidates("douglas")) == 3 def test_append_invalid_alias(nlp): """Test that append an alias will throw an error if prior probs are exceeding 1""" mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1) # adding entities mykb.add_entity(entity="Q1", freq=27, entity_vector=[1]) mykb.add_entity(entity="Q2", freq=12, entity_vector=[2]) mykb.add_entity(entity="Q3", freq=5, entity_vector=[3]) # adding aliases mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1]) mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9]) # append an alias - should fail because the entities and probabilities vectors are not of equal length with pytest.raises(ValueError): mykb.append_alias(alias="douglas", entity="Q1", prior_prob=0.2) def test_preserving_links_asdoc(nlp): """Test that Span.as_doc preserves the existing entity links""" vector_length = 1 def create_kb(vocab): mykb = KnowledgeBase(vocab, entity_vector_length=vector_length) # adding entities mykb.add_entity(entity="Q1", freq=19, entity_vector=[1]) mykb.add_entity(entity="Q2", freq=8, entity_vector=[1]) # adding aliases mykb.add_alias(alias="Boston", entities=["Q1"], probabilities=[0.7]) mykb.add_alias(alias="Denver", entities=["Q2"], probabilities=[0.6]) return mykb # set up pipeline with NER (Entity Ruler) and NEL (prior probability only, model not trained) nlp.add_pipe("sentencizer") patterns = [ {"label": "GPE", "pattern": "Boston"}, {"label": "GPE", "pattern": "Denver"}, ] ruler = nlp.add_pipe("entity_ruler") ruler.add_patterns(patterns) config = {"incl_prior": False} entity_linker = nlp.add_pipe("entity_linker", config=config, last=True) entity_linker.set_kb(create_kb) nlp.initialize() assert entity_linker.model.get_dim("nO") == vector_length # test whether the entity links are preserved by the `as_doc()` function text = "She lives in Boston. He lives in Denver." doc = nlp(text) for ent in doc.ents: orig_text = ent.text orig_kb_id = ent.kb_id_ sent_doc = ent.sent.as_doc() for s_ent in sent_doc.ents: if s_ent.text == orig_text: assert s_ent.kb_id_ == orig_kb_id def test_preserving_links_ents(nlp): """Test that doc.ents preserves KB annotations""" text = "She lives in Boston. He lives in Denver." doc = nlp(text) assert len(list(doc.ents)) == 0 boston_ent = Span(doc, 3, 4, label="LOC", kb_id="Q1") doc.ents = [boston_ent] assert len(list(doc.ents)) == 1 assert list(doc.ents)[0].label_ == "LOC" assert list(doc.ents)[0].kb_id_ == "Q1" def test_preserving_links_ents_2(nlp): """Test that doc.ents preserves KB annotations""" text = "She lives in Boston. He lives in Denver." doc = nlp(text) assert len(list(doc.ents)) == 0 loc = doc.vocab.strings.add("LOC") q1 = doc.vocab.strings.add("Q1") doc.ents = [(loc, q1, 3, 4)] assert len(list(doc.ents)) == 1 assert list(doc.ents)[0].label_ == "LOC" assert list(doc.ents)[0].kb_id_ == "Q1" # fmt: off TRAIN_DATA = [ ("Russ Cochran captured his first major title with his son as caddie.", {"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}, "entities": [(0, 12, "PERSON")], "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}), ("Russ Cochran his reprints include EC Comics.", {"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}, "entities": [(0, 12, "PERSON")], "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0]}), ("Russ Cochran has been publishing comic art.", {"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}, "entities": [(0, 12, "PERSON")], "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0]}), ("Russ Cochran was a member of University of Kentucky's golf team.", {"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}, "entities": [(0, 12, "PERSON"), (43, 51, "LOC")], "sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}) ] GOLD_entities = ["Q2146908", "Q7381115", "Q7381115", "Q2146908"] # fmt: on def test_overfitting_IO(): # Simple test to try and quickly overfit the NEL component - ensuring the ML models work correctly nlp = English() vector_length = 3 assert "Q2146908" not in nlp.vocab.strings # Convert the texts to docs to make sure we have doc.ents set for the training examples train_examples = [] for text, annotation in TRAIN_DATA: doc = nlp(text) train_examples.append(Example.from_dict(doc, annotation)) def create_kb(vocab): # create artificial KB - assign same prior weight to the two russ cochran's # Q2146908 (Russ Cochran): American golfer # Q7381115 (Russ Cochran): publisher mykb = KnowledgeBase(vocab, entity_vector_length=vector_length) mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3]) mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7]) mykb.add_alias( alias="Russ Cochran", entities=["Q2146908", "Q7381115"], probabilities=[0.5, 0.5], ) 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) assert "Q2146908" in entity_linker.vocab.strings assert "Q2146908" in entity_linker.kb.vocab.strings assert "Q2146908" in entity_linker.kb._added_strings # train the NEL pipe optimizer = nlp.initialize(get_examples=lambda: train_examples) assert entity_linker.model.get_dim("nO") == vector_length assert entity_linker.model.get_dim("nO") == entity_linker.kb.entity_vector_length for i in range(50): losses = {} nlp.update(train_examples, sgd=optimizer, losses=losses) assert losses["entity_linker"] < 0.001 # adding additional components that are required for the entity_linker nlp.add_pipe("sentencizer", first=True) # Add a custom component to recognize "Russ Cochran" as an entity for the example training data patterns = [ {"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]} ] ruler = nlp.add_pipe("entity_ruler", before="entity_linker") ruler.add_patterns(patterns) # test the trained model predictions = [] for text, annotation in TRAIN_DATA: doc = nlp(text) for ent in doc.ents: predictions.append(ent.kb_id_) assert predictions == GOLD_entities # Also test the results are still the same after IO with make_tempdir() as tmp_dir: nlp.to_disk(tmp_dir) nlp2 = util.load_model_from_path(tmp_dir) assert nlp2.pipe_names == nlp.pipe_names assert "Q2146908" in nlp2.vocab.strings entity_linker2 = nlp2.get_pipe("entity_linker") assert "Q2146908" in entity_linker2.vocab.strings assert "Q2146908" in entity_linker2.kb.vocab.strings predictions = [] for text, annotation in TRAIN_DATA: doc2 = nlp2(text) for ent in doc2.ents: predictions.append(ent.kb_id_) assert predictions == GOLD_entities def test_scorer_links(): train_examples = [] nlp = English() ref1 = nlp("Julia lives in London happily.") ref1.ents = [ Span(ref1, 0, 1, label="PERSON", kb_id="Q2"), Span(ref1, 3, 4, label="LOC", kb_id="Q3"), ] pred1 = nlp("Julia lives in London happily.") pred1.ents = [ Span(pred1, 0, 1, label="PERSON", kb_id="Q70"), Span(pred1, 3, 4, label="LOC", kb_id="Q3"), ] train_examples.append(Example(pred1, ref1)) ref2 = nlp("She loves London.") ref2.ents = [ Span(ref2, 0, 1, label="PERSON", kb_id="Q2"), Span(ref2, 2, 3, label="LOC", kb_id="Q13"), ] pred2 = nlp("She loves London.") pred2.ents = [ Span(pred2, 0, 1, label="PERSON", kb_id="Q2"), Span(pred2, 2, 3, label="LOC", kb_id="NIL"), ] train_examples.append(Example(pred2, ref2)) ref3 = nlp("London is great.") ref3.ents = [Span(ref3, 0, 1, label="LOC", kb_id="NIL")] pred3 = nlp("London is great.") pred3.ents = [Span(pred3, 0, 1, label="LOC", kb_id="NIL")] train_examples.append(Example(pred3, ref3)) scores = Scorer().score_links(train_examples, negative_labels=["NIL"]) assert scores["nel_f_per_type"]["PERSON"]["p"] == 1 / 2 assert scores["nel_f_per_type"]["PERSON"]["r"] == 1 / 2 assert scores["nel_f_per_type"]["LOC"]["p"] == 1 / 1 assert scores["nel_f_per_type"]["LOC"]["r"] == 1 / 2 assert scores["nel_micro_p"] == 2 / 3 assert scores["nel_micro_r"] == 2 / 4