def test_vocab_serialization(nlp):
"""Test that string information is retained across storage"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
q2_hash = 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=[0.4, 0.1])
adam_hash = mykb.add_alias(alias="adam",
entities=["Q2"],
probabilities=[0.9])
candidates = mykb.get_alias_candidates("adam")
assert len(candidates) == 1
assert candidates[0].entity == q2_hash
assert candidates[0].entity_ == "Q2"
assert candidates[0].alias == adam_hash
assert candidates[0].alias_ == "adam"
with make_tempdir() as d:
mykb.to_disk(d / "kb")
kb_new_vocab = KnowledgeBase(Vocab(), entity_vector_length=1)
kb_new_vocab.from_disk(d / "kb")
candidates = kb_new_vocab.get_alias_candidates("adam")
assert len(candidates) == 1
assert candidates[0].entity == q2_hash
assert candidates[0].entity_ == "Q2"
assert candidates[0].alias == adam_hash
assert candidates[0].alias_ == "adam"
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_kb_serialize_2(nlp):
v = [5, 6, 7, 8]
kb1 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb1.set_entities(["E1"], [1], [v])
assert kb1.get_vector("E1") == v
with make_tempdir() as d:
kb1.to_disk(d / "kb")
kb2 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb2.from_disk(d / "kb")
assert kb2.get_vector("E1") == v
def create_kb(kb_dir='sample_kb', nlp_dir='sample_nlp'):
nlp = spacy.load('en_core_web_lg')
text = 'Tennis champion Emerson was expected to win Wimbledon.'
doc = nlp(text)
file_path = 'entities.csv'
name_dict, desc_dict = load_entities(file_path)
sample_qid, sample_desc = list(desc_dict.items())[0]
sample_doc = nlp(sample_desc)
entity_vector_length = len(sample_doc.vector) # should be 300
kb = KnowledgeBase(vocab=nlp.vocab,
entity_vector_length=entity_vector_length)
for qid, desc in desc_dict.items():
desc_doc = nlp(desc)
desc_enc = desc_doc.vector
# NB: entity_vector could be any encoding
# freq is the count of times the word appears in the corpus
# not used in this tutorial
kb.add_entity(entity=qid, entity_vector=desc_enc, freq=42)
# add provided alias
for qid, name in name_dict.items():
# probabilities is P(entity|alias) = 1.0
# we assume that each alias only maps to one entity
kb.add_alias(alias=name, entities=[qid], probabilities=[1])
# add additional alias with equal probability
# this could be learned from data
qids = name_dict.keys()
probs = [0.3 for qid in qids]
kb.add_alias(alias='Emerson', entities=qids, probabilities=probs)
print(f'Entities in the KB: {kb.get_entity_strings()}')
print(f'Aliases in the KB: {kb.get_alias_strings()}')
print()
# questions here are:
# 1) what matching function is being used? - is this deterministic?
# 2) what threshold is being used to determine how many candidates are presented?
entities = [
c.entity_ for c in kb.get_alias_candidates('Roy Stanley Emerson')
]
print(f'Candidates for \'Roy Stanley Emerson\': {entities}')
entities = [c.entity_ for c in kb.get_alias_candidates('Emerson')]
print(f'Candidates for \'Emerson\': {entities}')
entities = [c.entity_ for c in kb.get_alias_candidates('Todd')]
print(f'Candidates for \'Todd\': {entities}')
kb.to_disk(kb_dir)
nlp.to_disk(nlp_dir)
def test_kb_serialize_vocab(nlp):
"""Test serialization of the KB and custom strings"""
entity = "MyFunnyID"
assert entity not in nlp.vocab.strings
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
assert not mykb.contains_entity(entity)
mykb.add_entity(entity, freq=342, entity_vector=[3])
assert mykb.contains_entity(entity)
assert entity in mykb.vocab.strings
with make_tempdir() as d:
# normal read-write behaviour
mykb.to_disk(d / "kb")
mykb_new = KnowledgeBase(Vocab(), entity_vector_length=1)
mykb_new.from_disk(d / "kb")
assert entity in mykb_new.vocab.strings
def test_save_and_load_knowledge_base():
nlp = Language()
kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
with make_tempdir() as d:
path = d / "kb"
try:
kb.to_disk(path)
except Exception as e:
pytest.fail(str(e))
try:
kb_loaded = KnowledgeBase(nlp.vocab, entity_vector_length=1)
kb_loaded.from_disk(path)
except Exception as e:
pytest.fail(str(e))
def test_issue6730(en_vocab):
"""Ensure that the KB does not accept empty strings, but otherwise IO works fine."""
from spacy.kb import KnowledgeBase
kb = KnowledgeBase(en_vocab, entity_vector_length=3)
kb.add_entity(entity="1", freq=148, entity_vector=[1, 2, 3])
with pytest.raises(ValueError):
kb.add_alias(alias="", entities=["1"], probabilities=[0.4])
assert kb.contains_alias("") is False
kb.add_alias(alias="x", entities=["1"], probabilities=[0.2])
kb.add_alias(alias="y", entities=["1"], probabilities=[0.1])
with make_tempdir() as tmp_dir:
kb.to_disk(tmp_dir)
kb.from_disk(tmp_dir)
assert kb.get_size_aliases() == 2
assert set(kb.get_alias_strings()) == {"x", "y"}
def test_kb_set_entities(nlp):
"""Test that set_entities entirely overwrites the previous set of entities"""
v = [5, 6, 7, 8]
v1 = [1, 1, 1, 0]
v2 = [2, 2, 2, 3]
kb1 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb1.set_entities(["E0"], [1], [v])
assert kb1.get_entity_strings() == ["E0"]
kb1.set_entities(["E1", "E2"], [1, 9], [v1, v2])
assert set(kb1.get_entity_strings()) == {"E1", "E2"}
assert kb1.get_vector("E1") == v1
assert kb1.get_vector("E2") == v2
with make_tempdir() as d:
kb1.to_disk(d / "kb")
kb2 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb2.from_disk(d / "kb")
assert set(kb2.get_entity_strings()) == {"E1", "E2"}
assert kb2.get_vector("E1") == v1
assert kb2.get_vector("E2") == v2
def test_issue4674():
"""Test that setting entities with overlapping identifiers does not mess up IO"""
nlp = English()
kb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
vector1 = [0.9, 1.1, 1.01]
vector2 = [1.8, 2.25, 2.01]
with pytest.warns(UserWarning):
kb.set_entities(
entity_list=["Q1", "Q1"],
freq_list=[32, 111],
vector_list=[vector1, vector2],
)
assert kb.get_size_entities() == 1
# dumping to file & loading back in
with make_tempdir() as d:
dir_path = ensure_path(d)
if not dir_path.exists():
dir_path.mkdir()
file_path = dir_path / "kb"
kb.to_disk(str(file_path))
kb2 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb2.from_disk(str(file_path))
assert kb2.get_size_entities() == 1
def test_kb_serialize(nlp):
"""Test serialization of the KB"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
with make_tempdir() as d:
# normal read-write behaviour
mykb.to_disk(d / "kb")
mykb.from_disk(d / "kb")
mykb.to_disk(d / "new" / "kb")
mykb.from_disk(d / "new" / "kb")
# allow overwriting an existing file
mykb.to_disk(d / "kb")
with pytest.raises(ValueError):
# can not read from an unknown file
mykb.from_disk(d / "unknown" / "kb")
