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_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 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 load(self, output_dir):
kb_path = os.path.join(output_dir, "kb")
vocab_path = os.path.join(output_dir, "vocab")
kb_info_path = os.path.join(output_dir, "kb_info.txt")
print("Loading vocab from", vocab_path)
print("Loading KB from", kb_path)
print("Loading KB info from", kb_info_path)
with open(kb_info_path, "r") as file:
# The first line is the entity_vector_length
entity_vector_length = int(file.readline().strip())
vocab = Vocab().from_disk(vocab_path)
kb = KnowledgeBase(vocab=vocab,
entity_vector_length=entity_vector_length)
kb.from_disk(kb_path)
self.kb = kb
return self.kb
def test_serialize_kb_disk(en_vocab):
# baseline assertions
kb1 = _get_dummy_kb(en_vocab)
_check_kb(kb1)
# 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"
kb1.to_disk(str(file_path))
kb2 = KnowledgeBase(vocab=en_vocab, entity_vector_length=3)
kb2.from_disk(str(file_path))
# final assertions
_check_kb(kb2)
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")
def create_kb(vocab):
entity_vector_length = 300
kb = KnowledgeBase(vocab=vocab,
entity_vector_length=entity_vector_length)
kb.from_disk(kb_folder)
return kb
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
kb.from_disk(kb_dir)
return kb