mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 18:07:26 +03:00
145 lines
4.6 KiB
Python
145 lines
4.6 KiB
Python
from typing import Callable
|
|
|
|
from spacy import util
|
|
from spacy.util import ensure_path, registry, load_model_from_config
|
|
from spacy.kb import KnowledgeBase
|
|
from spacy.vocab import Vocab
|
|
from thinc.api import Config
|
|
|
|
from ..util import make_tempdir
|
|
from numpy import zeros
|
|
|
|
|
|
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 _get_dummy_kb(vocab):
|
|
kb = KnowledgeBase(vocab, entity_vector_length=3)
|
|
kb.add_entity(entity="Q53", freq=33, entity_vector=[0, 5, 3])
|
|
kb.add_entity(entity="Q17", freq=2, entity_vector=[7, 1, 0])
|
|
kb.add_entity(entity="Q007", freq=7, entity_vector=[0, 0, 7])
|
|
kb.add_entity(entity="Q44", freq=342, entity_vector=[4, 4, 4])
|
|
|
|
kb.add_alias(alias="double07", entities=["Q17", "Q007"], probabilities=[0.1, 0.9])
|
|
kb.add_alias(
|
|
alias="guy",
|
|
entities=["Q53", "Q007", "Q17", "Q44"],
|
|
probabilities=[0.3, 0.3, 0.2, 0.1],
|
|
)
|
|
kb.add_alias(alias="random", entities=["Q007"], probabilities=[1.0])
|
|
|
|
return kb
|
|
|
|
|
|
def _check_kb(kb):
|
|
# check entities
|
|
assert kb.get_size_entities() == 4
|
|
for entity_string in ["Q53", "Q17", "Q007", "Q44"]:
|
|
assert entity_string in kb.get_entity_strings()
|
|
for entity_string in ["", "Q0"]:
|
|
assert entity_string not in kb.get_entity_strings()
|
|
|
|
# check aliases
|
|
assert kb.get_size_aliases() == 3
|
|
for alias_string in ["double07", "guy", "random"]:
|
|
assert alias_string in kb.get_alias_strings()
|
|
for alias_string in ["nothingness", "", "randomnoise"]:
|
|
assert alias_string not in kb.get_alias_strings()
|
|
|
|
# check candidates & probabilities
|
|
candidates = sorted(kb.get_alias_candidates("double07"), key=lambda x: x.entity_)
|
|
assert len(candidates) == 2
|
|
|
|
assert candidates[0].entity_ == "Q007"
|
|
assert 6.999 < candidates[0].entity_freq < 7.01
|
|
assert candidates[0].entity_vector == [0, 0, 7]
|
|
assert candidates[0].alias_ == "double07"
|
|
assert 0.899 < candidates[0].prior_prob < 0.901
|
|
|
|
assert candidates[1].entity_ == "Q17"
|
|
assert 1.99 < candidates[1].entity_freq < 2.01
|
|
assert candidates[1].entity_vector == [7, 1, 0]
|
|
assert candidates[1].alias_ == "double07"
|
|
assert 0.099 < candidates[1].prior_prob < 0.101
|
|
|
|
|
|
def test_serialize_subclassed_kb():
|
|
"""Check that IO of a custom KB works fine as part of an EL pipe."""
|
|
|
|
config_string = """
|
|
[nlp]
|
|
lang = "en"
|
|
pipeline = ["entity_linker"]
|
|
|
|
[components]
|
|
|
|
[components.entity_linker]
|
|
factory = "entity_linker"
|
|
|
|
[initialize]
|
|
|
|
[initialize.components]
|
|
|
|
[initialize.components.entity_linker]
|
|
|
|
[initialize.components.entity_linker.kb_loader]
|
|
@misc = "spacy.CustomKB.v1"
|
|
entity_vector_length = 342
|
|
custom_field = 666
|
|
"""
|
|
|
|
class SubKnowledgeBase(KnowledgeBase):
|
|
def __init__(self, vocab, entity_vector_length, custom_field):
|
|
super().__init__(vocab, entity_vector_length)
|
|
self.custom_field = custom_field
|
|
|
|
@registry.misc("spacy.CustomKB.v1")
|
|
def custom_kb(
|
|
entity_vector_length: int, custom_field: int
|
|
) -> Callable[[Vocab], KnowledgeBase]:
|
|
def custom_kb_factory(vocab):
|
|
kb = SubKnowledgeBase(
|
|
vocab=vocab,
|
|
entity_vector_length=entity_vector_length,
|
|
custom_field=custom_field,
|
|
)
|
|
kb.add_entity("random_entity", 0.0, zeros(entity_vector_length))
|
|
return kb
|
|
|
|
return custom_kb_factory
|
|
|
|
config = Config().from_str(config_string)
|
|
nlp = load_model_from_config(config, auto_fill=True)
|
|
nlp.initialize()
|
|
|
|
entity_linker = nlp.get_pipe("entity_linker")
|
|
assert type(entity_linker.kb) == SubKnowledgeBase
|
|
assert entity_linker.kb.entity_vector_length == 342
|
|
assert entity_linker.kb.custom_field == 666
|
|
|
|
# Make sure the custom KB is serialized correctly
|
|
with make_tempdir() as tmp_dir:
|
|
nlp.to_disk(tmp_dir)
|
|
nlp2 = util.load_model_from_path(tmp_dir)
|
|
entity_linker2 = nlp2.get_pipe("entity_linker")
|
|
# After IO, the KB is the standard one
|
|
assert type(entity_linker2.kb) == KnowledgeBase
|
|
assert entity_linker2.kb.entity_vector_length == 342
|
|
assert not hasattr(entity_linker2.kb, "custom_field")
|