spaCy/spacy/tests/pipeline/test_entity_linker.py
Sofie Van Landeghem 0ba1b5eebc CLI scripts for entity linking (wikipedia & generic) (#4091)
* document token ent_kb_id

* document span kb_id

* update pipeline documentation

* prior and context weights as bool's instead

* entitylinker api documentation

* drop for both models

* finish entitylinker documentation

* small fixes

* documentation for KB

* candidate documentation

* links to api pages in code

* small fix

* frequency examples as counts for consistency

* consistent documentation about tensors returned by predict

* add entity linking to usage 101

* add entity linking infobox and KB section to 101

* entity-linking in linguistic features

* small typo corrections

* training example and docs for entity_linker

* predefined nlp and kb

* revert back to similarity encodings for simplicity (for now)

* set prior probabilities to 0 when excluded

* code clean up

* bugfix: deleting kb ID from tokens when entities were removed

* refactor train el example to use either model or vocab

* pretrain_kb example for example kb generation

* add to training docs for KB + EL example scripts

* small fixes

* error numbering

* ensure the language of vocab and nlp stay consistent across serialization

* equality with =

* avoid conflict in errors file

* add error 151

* final adjustements to the train scripts - consistency

* update of goldparse documentation

* small corrections

* push commit

* turn kb_creator into CLI script (wip)

* proper parameters for training entity vectors

* wikidata pipeline split up into two executable scripts

* remove context_width

* move wikidata scripts in bin directory, remove old dummy script

* refine KB script with logs and preprocessing options

* small edits

* small improvements to logging of EL CLI script
2019-08-13 15:38:59 +02:00

174 lines
6.3 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
@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