mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 18:06:29 +03:00
358cbb21e3
* candidate generator as separate part of EL config * update comment * ent instead of str as input for candidate generation * Span instead of str: correct type indication * fix types * unit test to create new candidate generator * fix replace_pipe argument passing * move error message, general cleanup * add vocab back to KB constructor * provide KB as callable from Vocab arg * rename to kb_loader, fix KB serialization as part of the EL pipe * fix typo * reformatting * cleanup * fix comment * fix wrongly duplicated code from merge conflict * rename dump to to_disk * from_disk instead of load_bulk * update test after recent removal of set_morphology in tagger * remove old doc
122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
from typing import Callable
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from spacy import util
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from spacy.lang.en import English
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from spacy.util import ensure_path, registry
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from spacy.kb import KnowledgeBase
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from ..util import make_tempdir
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def test_serialize_kb_disk(en_vocab):
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# baseline assertions
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kb1 = _get_dummy_kb(en_vocab)
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_check_kb(kb1)
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# dumping to file & loading back in
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with make_tempdir() as d:
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dir_path = ensure_path(d)
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if not dir_path.exists():
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dir_path.mkdir()
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file_path = dir_path / "kb"
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kb1.to_disk(str(file_path))
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kb2 = KnowledgeBase(vocab=en_vocab, entity_vector_length=3)
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kb2.from_disk(str(file_path))
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# final assertions
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_check_kb(kb2)
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def _get_dummy_kb(vocab):
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kb = KnowledgeBase(vocab, entity_vector_length=3)
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kb.add_entity(entity="Q53", freq=33, entity_vector=[0, 5, 3])
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kb.add_entity(entity="Q17", freq=2, entity_vector=[7, 1, 0])
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kb.add_entity(entity="Q007", freq=7, entity_vector=[0, 0, 7])
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kb.add_entity(entity="Q44", freq=342, entity_vector=[4, 4, 4])
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kb.add_alias(alias="double07", entities=["Q17", "Q007"], probabilities=[0.1, 0.9])
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kb.add_alias(
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alias="guy",
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entities=["Q53", "Q007", "Q17", "Q44"],
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probabilities=[0.3, 0.3, 0.2, 0.1],
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)
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kb.add_alias(alias="random", entities=["Q007"], probabilities=[1.0])
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return kb
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def _check_kb(kb):
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# check entities
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assert kb.get_size_entities() == 4
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for entity_string in ["Q53", "Q17", "Q007", "Q44"]:
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assert entity_string in kb.get_entity_strings()
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for entity_string in ["", "Q0"]:
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assert entity_string not in kb.get_entity_strings()
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# check aliases
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assert kb.get_size_aliases() == 3
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for alias_string in ["double07", "guy", "random"]:
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assert alias_string in kb.get_alias_strings()
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for alias_string in ["nothingness", "", "randomnoise"]:
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assert alias_string not in kb.get_alias_strings()
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# check candidates & probabilities
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candidates = sorted(kb.get_alias_candidates("double07"), key=lambda x: x.entity_)
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assert len(candidates) == 2
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assert candidates[0].entity_ == "Q007"
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assert 6.999 < candidates[0].entity_freq < 7.01
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assert candidates[0].entity_vector == [0, 0, 7]
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assert candidates[0].alias_ == "double07"
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assert 0.899 < candidates[0].prior_prob < 0.901
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assert candidates[1].entity_ == "Q17"
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assert 1.99 < candidates[1].entity_freq < 2.01
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assert candidates[1].entity_vector == [7, 1, 0]
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assert candidates[1].alias_ == "double07"
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assert 0.099 < candidates[1].prior_prob < 0.101
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def test_serialize_subclassed_kb():
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"""Check that IO of a custom KB works fine as part of an EL pipe."""
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class SubKnowledgeBase(KnowledgeBase):
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def __init__(self, vocab, entity_vector_length, custom_field):
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super().__init__(vocab, entity_vector_length)
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self.custom_field = custom_field
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@registry.assets.register("spacy.CustomKB.v1")
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def custom_kb(
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entity_vector_length: int, custom_field: int
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) -> Callable[["Vocab"], KnowledgeBase]:
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def custom_kb_factory(vocab):
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return SubKnowledgeBase(
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vocab=vocab,
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entity_vector_length=entity_vector_length,
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custom_field=custom_field,
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)
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return custom_kb_factory
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nlp = English()
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config = {
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"kb_loader": {
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"@assets": "spacy.CustomKB.v1",
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"entity_vector_length": 342,
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"custom_field": 666,
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}
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}
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entity_linker = nlp.add_pipe("entity_linker", config=config)
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assert type(entity_linker.kb) == SubKnowledgeBase
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assert entity_linker.kb.entity_vector_length == 342
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assert entity_linker.kb.custom_field == 666
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# Make sure the custom KB is serialized correctly
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with make_tempdir() as tmp_dir:
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nlp.to_disk(tmp_dir)
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nlp2 = util.load_model_from_path(tmp_dir)
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entity_linker2 = nlp2.get_pipe("entity_linker")
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assert type(entity_linker2.kb) == SubKnowledgeBase
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assert entity_linker2.kb.entity_vector_length == 342
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assert entity_linker2.kb.custom_field == 666
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