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spaCy/spacy/tests/pipeline/test_entity_linker.py
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from typing import Callable, Iterable
import pytest
from numpy.testing import assert_equal
from spacy.attrs import ENT_KB_ID
from spacy.compat import pickle
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
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")
@pytest.mark.issue(9137)
def test_kb_serialize_2(nlp):
v = [5, 6, 7, 8]
kb1 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb1.set_entities(["E1"], [1], [v])
assert kb1.get_vector("E1") == v
with make_tempdir() as d:
kb1.to_disk(d / "kb")
kb2 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb2.from_disk(d / "kb")
assert kb2.get_vector("E1") == v
def test_kb_set_entities(nlp):
"""Test that set_entities entirely overwrites the previous set of entities"""
v = [5, 6, 7, 8]
v1 = [1, 1, 1, 0]
v2 = [2, 2, 2, 3]
kb1 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb1.set_entities(["E0"], [1], [v])
assert kb1.get_entity_strings() == ["E0"]
kb1.set_entities(["E1", "E2"], [1, 9], [v1, v2])
assert set(kb1.get_entity_strings()) == {"E1", "E2"}
assert kb1.get_vector("E1") == v1
assert kb1.get_vector("E2") == v2
with make_tempdir() as d:
kb1.to_disk(d / "kb")
kb2 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb2.from_disk(d / "kb")
assert set(kb2.get_entity_strings()) == {"E1", "E2"}
assert kb2.get_vector("E1") == v1
assert kb2.get_vector("E2") == v2
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("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_nel_nsents(nlp):
"""Test that n_sents can be set through the configuration"""
entity_linker = nlp.add_pipe("entity_linker", config={})
assert entity_linker.n_sents == 0
entity_linker = nlp.replace_pipe(
"entity_linker", "entity_linker", config={"n_sents": 2}
)
assert entity_linker.n_sents == 2
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"
assert kb_new_vocab.get_vector("Q2") == [2]
assert_almost_equal(kb_new_vocab.get_prior_prob("Q2", "douglas"), 0.4)
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
@pytest.mark.filterwarnings("ignore:\\[W036")
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)
@pytest.mark.filterwarnings("ignore:\\[W036")
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
# 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
# Make sure that running pipe twice, or comparing to call, always amounts to the same predictions
texts = [
"Russ Cochran captured his first major title with his son as caddie.",
"Russ Cochran his reprints include EC Comics.",
"Russ Cochran has been publishing comic art.",
"Russ Cochran was a member of University of Kentucky's golf team.",
]
batch_deps_1 = [doc.to_array([ENT_KB_ID]) for doc in nlp.pipe(texts)]
batch_deps_2 = [doc.to_array([ENT_KB_ID]) for doc in nlp.pipe(texts)]
no_batch_deps = [doc.to_array([ENT_KB_ID]) for doc in [nlp(text) for text in texts]]
assert_equal(batch_deps_1, batch_deps_2)
assert_equal(batch_deps_1, no_batch_deps)
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
@pytest.mark.xfail(reason="Needs fixing")
def test_kb_pickle():
# Test that the KB can be pickled
nlp = English()
kb_1 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb_1.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
assert not kb_1.contains_alias("Russ Cochran")
kb_1.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
assert kb_1.contains_alias("Russ Cochran")
data = pickle.dumps(kb_1)
kb_2 = pickle.loads(data)
assert kb_2.contains_alias("Russ Cochran")
@pytest.mark.xfail(reason="Needs fixing")
def test_nel_pickle():
# Test that a pipeline with an EL component can be pickled
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=3)
kb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
kb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
return kb
nlp_1 = English()
nlp_1.add_pipe("ner")
entity_linker_1 = nlp_1.add_pipe("entity_linker", last=True)
entity_linker_1.set_kb(create_kb)
assert nlp_1.pipe_names == ["ner", "entity_linker"]
assert entity_linker_1.kb.contains_alias("Russ Cochran")
data = pickle.dumps(nlp_1)
nlp_2 = pickle.loads(data)
assert nlp_2.pipe_names == ["ner", "entity_linker"]
entity_linker_2 = nlp_2.get_pipe("entity_linker")
assert entity_linker_2.kb.contains_alias("Russ Cochran")
def test_kb_to_bytes():
# Test that the KB's to_bytes method works correctly
nlp = English()
kb_1 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb_1.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
kb_1.add_entity(entity="Q66", freq=9, entity_vector=[1, 2, 3])
kb_1.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
kb_1.add_alias(alias="Boeing", entities=["Q66"], probabilities=[0.5])
kb_1.add_alias(
alias="Randomness", entities=["Q66", "Q2146908"], probabilities=[0.1, 0.2]
)
assert kb_1.contains_alias("Russ Cochran")
kb_bytes = kb_1.to_bytes()
kb_2 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
assert not kb_2.contains_alias("Russ Cochran")
kb_2 = kb_2.from_bytes(kb_bytes)
# check that both KBs are exactly the same
assert kb_1.get_size_entities() == kb_2.get_size_entities()
assert kb_1.entity_vector_length == kb_2.entity_vector_length
assert kb_1.get_entity_strings() == kb_2.get_entity_strings()
assert kb_1.get_vector("Q2146908") == kb_2.get_vector("Q2146908")
assert kb_1.get_vector("Q66") == kb_2.get_vector("Q66")
assert kb_2.contains_alias("Russ Cochran")
assert kb_1.get_size_aliases() == kb_2.get_size_aliases()
assert kb_1.get_alias_strings() == kb_2.get_alias_strings()
assert len(kb_1.get_alias_candidates("Russ Cochran")) == len(
kb_2.get_alias_candidates("Russ Cochran")
)
assert len(kb_1.get_alias_candidates("Randomness")) == len(
kb_2.get_alias_candidates("Randomness")
)
def test_nel_to_bytes():
# Test that a pipeline with an EL component can be converted to bytes
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=3)
kb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
kb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
return kb
nlp_1 = English()
nlp_1.add_pipe("ner")
entity_linker_1 = nlp_1.add_pipe("entity_linker", last=True)
entity_linker_1.set_kb(create_kb)
assert entity_linker_1.kb.contains_alias("Russ Cochran")
assert nlp_1.pipe_names == ["ner", "entity_linker"]
nlp_bytes = nlp_1.to_bytes()
nlp_2 = English()
nlp_2.add_pipe("ner")
nlp_2.add_pipe("entity_linker", last=True)
assert nlp_2.pipe_names == ["ner", "entity_linker"]
assert not nlp_2.get_pipe("entity_linker").kb.contains_alias("Russ Cochran")
nlp_2 = nlp_2.from_bytes(nlp_bytes)
kb_2 = nlp_2.get_pipe("entity_linker").kb
assert kb_2.contains_alias("Russ Cochran")
assert kb_2.get_vector("Q2146908") == [6, -4, 3]
assert_almost_equal(
kb_2.get_prior_prob(entity="Q2146908", alias="Russ Cochran"), 0.8
)
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
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