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https://github.com/explosion/spaCy.git
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325 lines
12 KiB
Python
325 lines
12 KiB
Python
import pytest
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from spacy.kb import KnowledgeBase
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from spacy import util
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from spacy.lang.en import English
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from spacy.pipeline import EntityRuler
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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_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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# 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(mykb.get_candidates("douglas")) == 2
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assert len(mykb.get_candidates("adam")) == 1
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assert len(mykb.get_candidates("shrubbery")) == 0
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# test the content of the candidates
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assert mykb.get_candidates("adam")[0].entity_ == "Q2"
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assert mykb.get_candidates("adam")[0].alias_ == "adam"
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assert_almost_equal(mykb.get_candidates("adam")[0].entity_freq, 12)
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assert_almost_equal(mykb.get_candidates("adam")[0].prior_prob, 0.9)
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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_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_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_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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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=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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# set up pipeline with NER (Entity Ruler) and NEL (prior probability only, model not trained)
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sentencizer = nlp.create_pipe("sentencizer")
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nlp.add_pipe(sentencizer)
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ruler = EntityRuler(nlp)
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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.add_patterns(patterns)
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nlp.add_pipe(ruler)
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cfg = {"kb": mykb, "incl_prior": False}
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el_pipe = nlp.create_pipe(name="entity_linker", config=cfg)
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el_pipe.begin_training()
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el_pipe.incl_context = False
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el_pipe.incl_prior = True
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nlp.add_pipe(el_pipe, last=True)
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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.", {"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}}),
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("Russ Cochran his reprints include EC Comics.", {"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}}),
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("Russ Cochran has been publishing comic art.", {"links": {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}}),
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("Russ Cochran was a member of University of Kentucky's golf team.", {"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}}),
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]
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GOLD_entities = ["Q2146908", "Q7381115", "Q7381115", "Q2146908"]
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# fmt: on
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def test_overfitting_IO():
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# Simple test to try and quickly overfit the NEL component - ensuring the ML models work correctly
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nlp = English()
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nlp.add_pipe(nlp.create_pipe("sentencizer"))
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# Add a custom component to recognize "Russ Cochran" as an entity for the example training data
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ruler = EntityRuler(nlp)
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patterns = [
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{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]}
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]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler)
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# Convert the texts to docs to make sure we have doc.ents set for the training examples
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TRAIN_DOCS = []
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for text, annotation in TRAIN_DATA:
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doc = nlp(text)
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annotation_clean = annotation
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TRAIN_DOCS.append((doc, annotation_clean))
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# create artificial KB - assign same prior weight to the two russ cochran's
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# Q2146908 (Russ Cochran): American golfer
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# Q7381115 (Russ Cochran): publisher
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mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
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mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
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mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
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mykb.add_alias(
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alias="Russ Cochran",
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entities=["Q2146908", "Q7381115"],
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probabilities=[0.5, 0.5],
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)
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# Create the Entity Linker component and add it to the pipeline
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entity_linker = nlp.create_pipe("entity_linker", config={"kb": mykb})
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nlp.add_pipe(entity_linker, last=True)
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# train the NEL pipe
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optimizer = nlp.begin_training()
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for i in range(50):
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losses = {}
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nlp.update(TRAIN_DOCS, sgd=optimizer, losses=losses)
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assert losses["entity_linker"] < 0.001
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# test the trained model
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predictions = []
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for text, annotation in TRAIN_DATA:
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doc = nlp(text)
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for ent in doc.ents:
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predictions.append(ent.kb_id_)
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assert predictions == GOLD_entities
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# Also test the results are still the same after IO
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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predictions = []
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for text, annotation in TRAIN_DATA:
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doc2 = nlp2(text)
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for ent in doc2.ents:
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predictions.append(ent.kb_id_)
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assert predictions == GOLD_entities
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