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https://github.com/explosion/spaCy.git
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590 lines
22 KiB
Python
590 lines
22 KiB
Python
from typing import Callable, Iterable
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import pytest
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from numpy.testing import assert_equal
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from spacy.attrs import ENT_KB_ID
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from spacy.kb import KnowledgeBase, get_candidates, Candidate
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from spacy.vocab import Vocab
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from spacy import util, registry
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from spacy.ml import load_kb
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from spacy.scorer import Scorer
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from spacy.training import Example
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from spacy.lang.en import English
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from spacy.tests.util import make_tempdir
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from spacy.tokens import Span
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@pytest.fixture
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def nlp():
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return English()
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def assert_almost_equal(a, b):
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delta = 0.0001
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assert a - delta <= b <= a + delta
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def test_kb_valid_entities(nlp):
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"""Test the valid construction of a KB with 3 entities and two aliases"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
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# adding entities
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mykb.add_entity(entity="Q1", freq=19, entity_vector=[8, 4, 3])
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mykb.add_entity(entity="Q2", freq=5, entity_vector=[2, 1, 0])
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mykb.add_entity(entity="Q3", freq=25, entity_vector=[-1, -6, 5])
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# adding aliases
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mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.2])
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mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
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# test the size of the corresponding KB
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assert mykb.get_size_entities() == 3
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assert mykb.get_size_aliases() == 2
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# test retrieval of the entity vectors
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assert mykb.get_vector("Q1") == [8, 4, 3]
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assert mykb.get_vector("Q2") == [2, 1, 0]
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assert mykb.get_vector("Q3") == [-1, -6, 5]
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# test retrieval of prior probabilities
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assert_almost_equal(mykb.get_prior_prob(entity="Q2", alias="douglas"), 0.8)
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assert_almost_equal(mykb.get_prior_prob(entity="Q3", alias="douglas"), 0.2)
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assert_almost_equal(mykb.get_prior_prob(entity="Q342", alias="douglas"), 0.0)
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assert_almost_equal(mykb.get_prior_prob(entity="Q3", alias="douglassssss"), 0.0)
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def test_kb_invalid_entities(nlp):
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"""Test the invalid construction of a KB with an alias linked to a non-existing entity"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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# adding entities
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mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
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mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
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mykb.add_entity(entity="Q3", freq=25, entity_vector=[3])
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# adding aliases - should fail because one of the given IDs is not valid
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with pytest.raises(ValueError):
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mykb.add_alias(
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alias="douglas", entities=["Q2", "Q342"], probabilities=[0.8, 0.2]
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)
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def test_kb_invalid_probabilities(nlp):
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"""Test the invalid construction of a KB with wrong prior probabilities"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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# adding entities
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mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
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mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
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mykb.add_entity(entity="Q3", freq=25, entity_vector=[3])
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# adding aliases - should fail because the sum of the probabilities exceeds 1
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with pytest.raises(ValueError):
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mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.4])
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def test_kb_invalid_combination(nlp):
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"""Test the invalid construction of a KB with non-matching entity and probability lists"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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# adding entities
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mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
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mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
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mykb.add_entity(entity="Q3", freq=25, entity_vector=[3])
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# adding aliases - should fail because the entities and probabilities vectors are not of equal length
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with pytest.raises(ValueError):
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mykb.add_alias(
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alias="douglas", entities=["Q2", "Q3"], probabilities=[0.3, 0.4, 0.1]
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)
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def test_kb_invalid_entity_vector(nlp):
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"""Test the invalid construction of a KB with non-matching entity vector lengths"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
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# adding entities
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mykb.add_entity(entity="Q1", freq=19, entity_vector=[1, 2, 3])
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# this should fail because the kb's expected entity vector length is 3
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with pytest.raises(ValueError):
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mykb.add_entity(entity="Q2", freq=5, entity_vector=[2])
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def test_kb_default(nlp):
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"""Test that the default (empty) KB is loaded upon construction"""
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entity_linker = nlp.add_pipe("entity_linker", config={})
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assert len(entity_linker.kb) == 0
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assert entity_linker.kb.get_size_entities() == 0
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assert entity_linker.kb.get_size_aliases() == 0
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# 64 is the default value from pipeline.entity_linker
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assert entity_linker.kb.entity_vector_length == 64
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def test_kb_custom_length(nlp):
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"""Test that the default (empty) KB can be configured with a custom entity length"""
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entity_linker = nlp.add_pipe("entity_linker", config={"entity_vector_length": 35})
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assert len(entity_linker.kb) == 0
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assert entity_linker.kb.get_size_entities() == 0
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assert entity_linker.kb.get_size_aliases() == 0
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assert entity_linker.kb.entity_vector_length == 35
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def test_kb_initialize_empty(nlp):
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"""Test that the EL can't initialize without examples"""
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entity_linker = nlp.add_pipe("entity_linker")
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with pytest.raises(TypeError):
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entity_linker.initialize(lambda: [])
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def test_kb_serialize(nlp):
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"""Test serialization of the KB"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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with make_tempdir() as d:
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# normal read-write behaviour
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mykb.to_disk(d / "kb")
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mykb.from_disk(d / "kb")
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mykb.to_disk(d / "new" / "kb")
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mykb.from_disk(d / "new" / "kb")
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# allow overwriting an existing file
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mykb.to_disk(d / "kb")
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with pytest.raises(ValueError):
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# can not read from an unknown file
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mykb.from_disk(d / "unknown" / "kb")
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def test_kb_serialize_vocab(nlp):
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"""Test serialization of the KB and custom strings"""
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entity = "MyFunnyID"
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assert entity not in nlp.vocab.strings
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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assert not mykb.contains_entity(entity)
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mykb.add_entity(entity, freq=342, entity_vector=[3])
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assert mykb.contains_entity(entity)
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assert entity in mykb.vocab.strings
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with make_tempdir() as d:
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# normal read-write behaviour
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mykb.to_disk(d / "kb")
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mykb_new = KnowledgeBase(Vocab(), entity_vector_length=1)
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mykb_new.from_disk(d / "kb")
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assert entity in mykb_new.vocab.strings
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def test_candidate_generation(nlp):
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"""Test correct candidate generation"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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doc = nlp("douglas adam Adam shrubbery")
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douglas_ent = doc[0:1]
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adam_ent = doc[1:2]
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Adam_ent = doc[2:3]
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shrubbery_ent = doc[3:4]
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# adding entities
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mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
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mykb.add_entity(entity="Q2", freq=12, entity_vector=[2])
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mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
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# adding aliases
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mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1])
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mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
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# test the size of the relevant candidates
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assert len(get_candidates(mykb, douglas_ent)) == 2
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assert len(get_candidates(mykb, adam_ent)) == 1
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assert len(get_candidates(mykb, Adam_ent)) == 0 # default case sensitive
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assert len(get_candidates(mykb, shrubbery_ent)) == 0
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# test the content of the candidates
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assert get_candidates(mykb, adam_ent)[0].entity_ == "Q2"
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assert get_candidates(mykb, adam_ent)[0].alias_ == "adam"
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assert_almost_equal(get_candidates(mykb, adam_ent)[0].entity_freq, 12)
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assert_almost_equal(get_candidates(mykb, adam_ent)[0].prior_prob, 0.9)
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def test_el_pipe_configuration(nlp):
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"""Test correct candidate generation as part of the EL pipe"""
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nlp.add_pipe("sentencizer")
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pattern = {"label": "PERSON", "pattern": [{"LOWER": "douglas"}]}
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns([pattern])
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def create_kb(vocab):
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kb = KnowledgeBase(vocab, entity_vector_length=1)
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kb.add_entity(entity="Q2", freq=12, entity_vector=[2])
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kb.add_entity(entity="Q3", freq=5, entity_vector=[3])
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kb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1])
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return kb
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# run an EL pipe without a trained context encoder, to check the candidate generation step only
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entity_linker = nlp.add_pipe("entity_linker", config={"incl_context": False})
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entity_linker.set_kb(create_kb)
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# With the default get_candidates function, matching is case-sensitive
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text = "Douglas and douglas are not the same."
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doc = nlp(text)
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assert doc[0].ent_kb_id_ == "NIL"
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assert doc[1].ent_kb_id_ == ""
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assert doc[2].ent_kb_id_ == "Q2"
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def get_lowercased_candidates(kb, span):
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return kb.get_alias_candidates(span.text.lower())
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@registry.misc("spacy.LowercaseCandidateGenerator.v1")
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def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
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return get_lowercased_candidates
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# replace the pipe with a new one with with a different candidate generator
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entity_linker = nlp.replace_pipe(
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"entity_linker",
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"entity_linker",
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config={
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"incl_context": False,
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"get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"},
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},
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)
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entity_linker.set_kb(create_kb)
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doc = nlp(text)
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assert doc[0].ent_kb_id_ == "Q2"
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assert doc[1].ent_kb_id_ == ""
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assert doc[2].ent_kb_id_ == "Q2"
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def test_nel_nsents(nlp):
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"""Test that n_sents can be set through the configuration"""
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entity_linker = nlp.add_pipe("entity_linker", config={})
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assert entity_linker.n_sents == 0
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entity_linker = nlp.replace_pipe("entity_linker", "entity_linker", config={"n_sents": 2})
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assert entity_linker.n_sents == 2
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def test_vocab_serialization(nlp):
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"""Test that string information is retained across storage"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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# adding entities
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mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
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q2_hash = mykb.add_entity(entity="Q2", freq=12, entity_vector=[2])
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mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
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# adding aliases
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mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.4, 0.1])
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adam_hash = mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
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candidates = mykb.get_alias_candidates("adam")
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assert len(candidates) == 1
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assert candidates[0].entity == q2_hash
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assert candidates[0].entity_ == "Q2"
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assert candidates[0].alias == adam_hash
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assert candidates[0].alias_ == "adam"
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with make_tempdir() as d:
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mykb.to_disk(d / "kb")
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kb_new_vocab = KnowledgeBase(Vocab(), entity_vector_length=1)
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kb_new_vocab.from_disk(d / "kb")
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candidates = kb_new_vocab.get_alias_candidates("adam")
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assert len(candidates) == 1
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assert candidates[0].entity == q2_hash
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assert candidates[0].entity_ == "Q2"
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assert candidates[0].alias == adam_hash
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assert candidates[0].alias_ == "adam"
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def test_append_alias(nlp):
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"""Test that we can append additional alias-entity pairs"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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# adding entities
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mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
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mykb.add_entity(entity="Q2", freq=12, entity_vector=[2])
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mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
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# adding aliases
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mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.4, 0.1])
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mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
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# test the size of the relevant candidates
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assert len(mykb.get_alias_candidates("douglas")) == 2
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# append an alias
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mykb.append_alias(alias="douglas", entity="Q1", prior_prob=0.2)
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# test the size of the relevant candidates has been incremented
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assert len(mykb.get_alias_candidates("douglas")) == 3
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# append the same alias-entity pair again should not work (will throw a warning)
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with pytest.warns(UserWarning):
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mykb.append_alias(alias="douglas", entity="Q1", prior_prob=0.3)
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# test the size of the relevant candidates remained unchanged
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assert len(mykb.get_alias_candidates("douglas")) == 3
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def test_append_invalid_alias(nlp):
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"""Test that append an alias will throw an error if prior probs are exceeding 1"""
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
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# adding entities
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mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
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mykb.add_entity(entity="Q2", freq=12, entity_vector=[2])
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mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
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# adding aliases
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mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1])
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mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
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# append an alias - should fail because the entities and probabilities vectors are not of equal length
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with pytest.raises(ValueError):
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mykb.append_alias(alias="douglas", entity="Q1", prior_prob=0.2)
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def test_preserving_links_asdoc(nlp):
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"""Test that Span.as_doc preserves the existing entity links"""
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vector_length = 1
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def create_kb(vocab):
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mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
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# adding entities
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mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
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mykb.add_entity(entity="Q2", freq=8, entity_vector=[1])
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# adding aliases
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mykb.add_alias(alias="Boston", entities=["Q1"], probabilities=[0.7])
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mykb.add_alias(alias="Denver", entities=["Q2"], probabilities=[0.6])
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return mykb
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# set up pipeline with NER (Entity Ruler) and NEL (prior probability only, model not trained)
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nlp.add_pipe("sentencizer")
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patterns = [
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{"label": "GPE", "pattern": "Boston"},
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{"label": "GPE", "pattern": "Denver"},
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]
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ruler = nlp.add_pipe("entity_ruler")
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ruler.add_patterns(patterns)
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config = {"incl_prior": False}
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entity_linker = nlp.add_pipe("entity_linker", config=config, last=True)
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entity_linker.set_kb(create_kb)
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nlp.initialize()
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assert entity_linker.model.get_dim("nO") == vector_length
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# test whether the entity links are preserved by the `as_doc()` function
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text = "She lives in Boston. He lives in Denver."
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doc = nlp(text)
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for ent in doc.ents:
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orig_text = ent.text
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orig_kb_id = ent.kb_id_
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sent_doc = ent.sent.as_doc()
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for s_ent in sent_doc.ents:
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if s_ent.text == orig_text:
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assert s_ent.kb_id_ == orig_kb_id
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def test_preserving_links_ents(nlp):
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"""Test that doc.ents preserves KB annotations"""
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text = "She lives in Boston. He lives in Denver."
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doc = nlp(text)
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assert len(list(doc.ents)) == 0
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boston_ent = Span(doc, 3, 4, label="LOC", kb_id="Q1")
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doc.ents = [boston_ent]
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assert len(list(doc.ents)) == 1
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assert list(doc.ents)[0].label_ == "LOC"
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assert list(doc.ents)[0].kb_id_ == "Q1"
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def test_preserving_links_ents_2(nlp):
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"""Test that doc.ents preserves KB annotations"""
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text = "She lives in Boston. He lives in Denver."
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doc = nlp(text)
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assert len(list(doc.ents)) == 0
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loc = doc.vocab.strings.add("LOC")
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q1 = doc.vocab.strings.add("Q1")
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doc.ents = [(loc, q1, 3, 4)]
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assert len(list(doc.ents)) == 1
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assert list(doc.ents)[0].label_ == "LOC"
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assert list(doc.ents)[0].kb_id_ == "Q1"
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# fmt: off
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TRAIN_DATA = [
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("Russ Cochran captured his first major title with his son as caddie.",
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{"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}},
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"entities": [(0, 12, "PERSON")],
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"sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}),
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("Russ Cochran his reprints include EC Comics.",
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{"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}},
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"entities": [(0, 12, "PERSON")],
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"sent_starts": [1, -1, 0, 0, 0, 0, 0, 0]}),
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("Russ Cochran has been publishing comic art.",
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{"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}},
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"entities": [(0, 12, "PERSON")],
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"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]})
|
|
]
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|
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()
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|
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):
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|
# 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
|
|
|
|
|
|
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
|