spaCy/spacy/tests/serialize/test_serialize_kb.py

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")