mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 10:26:35 +03:00
7f5715a081
* setting KB in the EL constructor, similar to how the model is passed on * removing wikipedia example files - moved to projects * throw an error when nlp.update is called with 2 positional arguments * rewriting the config logic in create pipe to accomodate for other objects (e.g. KB) in the config * update config files with new parameters * avoid training pipeline components that don't have a model (like sentencizer) * various small fixes + UX improvements * small fixes * set thinc to 8.0.0a9 everywhere * remove outdated comment
319 lines
12 KiB
Python
319 lines
12 KiB
Python
import pytest
|
|
|
|
from spacy.kb import KnowledgeBase
|
|
|
|
from spacy import util
|
|
from spacy.lang.en import English
|
|
from spacy.pipeline import EntityRuler
|
|
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_candidate_generation(nlp):
|
|
"""Test correct candidate generation"""
|
|
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])
|
|
|
|
# test the size of the relevant candidates
|
|
assert len(mykb.get_candidates("douglas")) == 2
|
|
assert len(mykb.get_candidates("adam")) == 1
|
|
assert len(mykb.get_candidates("shrubbery")) == 0
|
|
|
|
# test the content of the candidates
|
|
assert mykb.get_candidates("adam")[0].entity_ == "Q2"
|
|
assert mykb.get_candidates("adam")[0].alias_ == "adam"
|
|
assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 12)
|
|
assert_almost_equal(mykb.get_candidates("adam")[0].prior_prob, 0.9)
|
|
|
|
|
|
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_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_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_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"""
|
|
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=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])
|
|
|
|
# set up pipeline with NER (Entity Ruler) and NEL (prior probability only, model not trained)
|
|
sentencizer = nlp.create_pipe("sentencizer")
|
|
nlp.add_pipe(sentencizer)
|
|
|
|
ruler = EntityRuler(nlp)
|
|
patterns = [
|
|
{"label": "GPE", "pattern": "Boston"},
|
|
{"label": "GPE", "pattern": "Denver"},
|
|
]
|
|
ruler.add_patterns(patterns)
|
|
nlp.add_pipe(ruler)
|
|
|
|
cfg = {"kb": mykb, "incl_prior": False}
|
|
el_pipe = nlp.create_pipe(name="entity_linker", config=cfg)
|
|
el_pipe.begin_training()
|
|
el_pipe.incl_context = False
|
|
el_pipe.incl_prior = True
|
|
nlp.add_pipe(el_pipe, last=True)
|
|
|
|
# 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}}}),
|
|
("Russ Cochran his reprints include EC Comics.", {"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}}),
|
|
("Russ Cochran has been publishing comic art.", {"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}}),
|
|
("Russ Cochran was a member of University of Kentucky's golf team.", {"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}}),
|
|
]
|
|
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()
|
|
nlp.add_pipe(nlp.create_pipe('sentencizer'))
|
|
|
|
# Add a custom component to recognize "Russ Cochran" as an entity for the example training data
|
|
ruler = EntityRuler(nlp)
|
|
patterns = [{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]}]
|
|
ruler.add_patterns(patterns)
|
|
nlp.add_pipe(ruler)
|
|
|
|
# Convert the texts to docs to make sure we have doc.ents set for the training examples
|
|
TRAIN_DOCS = []
|
|
for text, annotation in TRAIN_DATA:
|
|
doc = nlp(text)
|
|
annotation_clean = annotation
|
|
TRAIN_DOCS.append((doc, annotation_clean))
|
|
|
|
# create artificial KB - assign same prior weight to the two russ cochran's
|
|
# Q2146908 (Russ Cochran): American golfer
|
|
# Q7381115 (Russ Cochran): publisher
|
|
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
|
|
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])
|
|
|
|
# Create the Entity Linker component and add it to the pipeline
|
|
entity_linker = nlp.create_pipe("entity_linker", config={"kb": mykb})
|
|
nlp.add_pipe(entity_linker, last=True)
|
|
|
|
# train the NEL pipe
|
|
optimizer = nlp.begin_training()
|
|
for i in range(50):
|
|
losses = {}
|
|
nlp.update(TRAIN_DOCS, sgd=optimizer, losses=losses)
|
|
assert losses["entity_linker"] < 0.001
|
|
|
|
# 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)
|
|
predictions = []
|
|
for text, annotation in TRAIN_DATA:
|
|
doc2 = nlp2(text)
|
|
for ent in doc2.ents:
|
|
predictions.append(ent.kb_id_)
|
|
assert predictions == GOLD_entities
|