mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 10:26:35 +03:00
146 lines
5.3 KiB
Python
146 lines
5.3 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 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=1)
|
|
|
|
# adding entities
|
|
mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
|
|
mykb.add_entity(entity='Q2', prob=0.5, entity_vector=[2])
|
|
mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
|
|
|
|
# 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)
|
|
|
|
|
|
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', prob=0.9, entity_vector=[1])
|
|
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
|
mykb.add_entity(entity='Q3', prob=0.5, 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', prob=0.9, entity_vector=[1])
|
|
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
|
mykb.add_entity(entity='Q3', prob=0.5, 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', prob=0.9, entity_vector=[1])
|
|
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
|
mykb.add_entity(entity='Q3', prob=0.5, 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', prob=0.9, 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', prob=0.2, 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', prob=0.9, entity_vector=[1])
|
|
mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
|
|
mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
|
|
|
|
# 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 relevant candidates
|
|
assert(len(mykb.get_candidates('douglas')) == 2)
|
|
assert(len(mykb.get_candidates('adam')) == 1)
|
|
assert(len(mykb.get_candidates('shrubbery')) == 0)
|
|
|
|
|
|
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', prob=0.9, entity_vector=[1])
|
|
mykb.add_entity(entity='Q2', prob=0.8, 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', config={"context_width": 64})
|
|
el_pipe.set_kb(mykb)
|
|
el_pipe.begin_training()
|
|
el_pipe.context_weight = 0
|
|
el_pipe.prior_weight = 1
|
|
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
|