mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 11:29:13 +03:00
203 lines
7.1 KiB
Python
203 lines
7.1 KiB
Python
# coding: utf-8
|
|
from __future__ import unicode_literals
|
|
|
|
import pytest
|
|
|
|
from spacy.kb import KnowledgeBase
|
|
from spacy.lang.en import English
|
|
from spacy.pipeline import EntityRuler
|
|
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_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)
|
|
|
|
el_pipe = nlp.create_pipe(name="entity_linker")
|
|
el_pipe.set_kb(mykb)
|
|
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"
|