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146 lines
5.3 KiB
Python
146 lines
5.3 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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import pytest
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from spacy.kb import KnowledgeBase
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from spacy.lang.en import English
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from spacy.pipeline import EntityRuler
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@pytest.fixture
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def nlp():
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return English()
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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=1)
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# adding entities
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mykb.add_entity(entity='Q1', prob=0.9, entity_vector=[1])
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mykb.add_entity(entity='Q2', prob=0.5, entity_vector=[2])
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mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
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# adding aliases
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mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.2])
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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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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', prob=0.9, entity_vector=[1])
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mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
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mykb.add_entity(entity='Q3', prob=0.5, 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(alias='douglas', entities=['Q2', 'Q342'], probabilities=[0.8, 0.2])
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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', prob=0.9, entity_vector=[1])
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mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
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mykb.add_entity(entity='Q3', prob=0.5, 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', prob=0.9, entity_vector=[1])
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mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
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mykb.add_entity(entity='Q3', prob=0.5, 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(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.3, 0.4, 0.1])
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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', prob=0.9, 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', prob=0.2, 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', prob=0.9, entity_vector=[1])
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mykb.add_entity(entity='Q2', prob=0.2, entity_vector=[2])
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mykb.add_entity(entity='Q3', prob=0.5, entity_vector=[3])
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# adding aliases
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mykb.add_alias(alias='douglas', entities=['Q2', 'Q3'], probabilities=[0.8, 0.2])
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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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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', prob=0.9, entity_vector=[1])
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mykb.add_entity(entity='Q2', prob=0.8, 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 = [{"label": "GPE", "pattern": "Boston"},
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{"label": "GPE", "pattern": "Denver"}]
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ruler.add_patterns(patterns)
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nlp.add_pipe(ruler)
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el_pipe = nlp.create_pipe(name='entity_linker', config={"context_width": 64})
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el_pipe.set_kb(mykb)
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el_pipe.begin_training()
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el_pipe.context_weight = 0
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el_pipe.prior_weight = 1
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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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