mirror of
https://github.com/explosion/spaCy.git
synced 2025-07-13 01:32:32 +03:00
Convert batched into doc-wise batched candidate generation.
This commit is contained in:
Raphael Mitsch
parent
2ce6aadda2
commit
7c28424f47
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@ -946,11 +946,10 @@ class Errors(metaclass=ErrorsWithCodes):
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"case pass an empty list for the previously not specified argument to avoid this error.")
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E1043 = ("Expected None or a value in range [{range_start}, {range_end}] for entity linker threshold, but got "
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"{value}.")
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E1044 = ("Expected `candidates_batch_size` to be >= 1, but got: {value}")
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E1045 = ("Encountered {parent} subclass without `{parent}.{method}` "
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E1044 = ("Encountered {parent} subclass without `{parent}.{method}` "
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"method in '{name}'. If you want to use this method, make "
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"sure it's overwritten on the subclass.")
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E1046 = ("{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default "
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E1045 = ("{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default "
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"knowledge base, use `InMemoryLookupKB`.")
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@ -1,3 +1,3 @@
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from .kb import KnowledgeBase
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from .kb_in_memory import InMemoryLookupKB
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from .candidate import Candidate, get_candidates, get_candidates_batch
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from .candidate import Candidate, get_candidates, get_candidates_all
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@ -1,6 +1,6 @@
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# cython: infer_types=True, profile=True
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from typing import Iterable
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from typing import Iterable, Generator
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from .kb cimport KnowledgeBase
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from ..tokens import Span
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@ -64,11 +64,13 @@ def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
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return kb.get_candidates(mention)
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def get_candidates_batch(kb: KnowledgeBase, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]:
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def get_candidates_all(
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kb: KnowledgeBase, mentions: Generator[Iterable[Span], None, None]
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) -> Generator[Iterable[Iterable[Candidate]], None, None]:
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"""
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Return candidate entities for the given mentions and fetching appropriate entries from the index.
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kb (KnowledgeBase): Knowledge base to query.
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mention (Iterable[Span]): Entity mentions for which to identify candidates.
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RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
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mention (Generator[Iterable[Span]]): Entity mentions per document for which to identify candidates.
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RETURNS (Generator[Iterable[Iterable[Candidate]]]): Identified candidates per document.
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"""
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return kb.get_candidates_batch(mentions)
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return kb.get_candidates_all(mentions)
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@ -1,7 +1,7 @@
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# cython: infer_types=True, profile=True
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from pathlib import Path
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from typing import Iterable, Tuple, Union
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from typing import Iterable, Tuple, Union, Generator
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from cymem.cymem cimport Pool
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from .candidate import Candidate
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@ -23,22 +23,24 @@ cdef class KnowledgeBase:
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# Make sure abstract KB is not instantiated.
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if self.__class__ == KnowledgeBase:
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raise TypeError(
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Errors.E1046.format(cls_name=self.__class__.__name__)
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Errors.E1045.format(cls_name=self.__class__.__name__)
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)
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self.vocab = vocab
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self.entity_vector_length = entity_vector_length
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self.mem = Pool()
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def get_candidates_batch(self, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]:
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def get_candidates_all(self, mentions: Generator[Iterable[Span]]) -> Generator[Iterable[Iterable[Candidate]]]:
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"""
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Return candidate entities for specified texts. Each candidate defines the entity, the original alias,
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and the prior probability of that alias resolving to that entity.
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If no candidate is found for a given text, an empty list is returned.
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mentions (Iterable[Span]): Mentions for which to get candidates.
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RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
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mentions (Generator[Iterable[Span]]): Mentions per documents for which to get candidates.
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RETURNS (Generator[Iterable[Iterable[Candidate]]]): Identified candidates per document.
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"""
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return [self.get_candidates(span) for span in mentions]
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for doc_mentions in mentions:
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yield [self.get_candidates(span) for span in doc_mentions]
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def get_candidates(self, mention: Span) -> Iterable[Candidate]:
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"""
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@ -49,7 +51,7 @@ cdef class KnowledgeBase:
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RETURNS (Iterable[Candidate]): Identified candidates.
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"""
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raise NotImplementedError(
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Errors.E1045.format(parent="KnowledgeBase", method="get_candidates", name=self.__name__)
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Errors.E1044.format(parent="KnowledgeBase", method="get_candidates", name=self.__name__)
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)
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def get_vectors(self, entities: Iterable[str]) -> Iterable[Iterable[float]]:
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@ -67,7 +69,7 @@ cdef class KnowledgeBase:
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RETURNS (Iterable[float]): Vector for specified entity.
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"""
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raise NotImplementedError(
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Errors.E1045.format(parent="KnowledgeBase", method="get_vector", name=self.__name__)
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Errors.E1044.format(parent="KnowledgeBase", method="get_vector", name=self.__name__)
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)
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def to_bytes(self, **kwargs) -> bytes:
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@ -75,7 +77,7 @@ cdef class KnowledgeBase:
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RETURNS (bytes): Current state as binary string.
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"""
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raise NotImplementedError(
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Errors.E1045.format(parent="KnowledgeBase", method="to_bytes", name=self.__name__)
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Errors.E1044.format(parent="KnowledgeBase", method="to_bytes", name=self.__name__)
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)
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def from_bytes(self, bytes_data: bytes, *, exclude: Tuple[str] = tuple()):
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@ -84,7 +86,7 @@ cdef class KnowledgeBase:
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exclude (Tuple[str]): Properties to exclude when restoring KB.
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"""
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raise NotImplementedError(
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Errors.E1045.format(parent="KnowledgeBase", method="from_bytes", name=self.__name__)
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Errors.E1044.format(parent="KnowledgeBase", method="from_bytes", name=self.__name__)
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)
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def to_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
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@ -94,7 +96,7 @@ cdef class KnowledgeBase:
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exclude (Iterable[str]): List of components to exclude.
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"""
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raise NotImplementedError(
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Errors.E1045.format(parent="KnowledgeBase", method="to_disk", name=self.__name__)
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Errors.E1044.format(parent="KnowledgeBase", method="to_disk", name=self.__name__)
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)
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def from_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
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@ -104,5 +106,5 @@ cdef class KnowledgeBase:
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exclude (Iterable[str]): List of components to exclude.
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"""
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raise NotImplementedError(
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Errors.E1045.format(parent="KnowledgeBase", method="from_disk", name=self.__name__)
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Errors.E1044.format(parent="KnowledgeBase", method="from_disk", name=self.__name__)
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)
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@ -1,12 +1,12 @@
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from pathlib import Path
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from typing import Optional, Callable, Iterable, List, Tuple
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from typing import Optional, Callable, Iterable, List, Tuple, Generator
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from thinc.types import Floats2d
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from thinc.api import chain, list2ragged, reduce_mean, residual
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from thinc.api import Model, Maxout, Linear, tuplify, Ragged
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from ...util import registry
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from ...kb import KnowledgeBase, InMemoryLookupKB
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from ...kb import Candidate, get_candidates, get_candidates_batch
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from ...kb import Candidate, get_candidates, get_candidates_all
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from ...vocab import Vocab
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from ...tokens import Span, Doc
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from ..extract_spans import extract_spans
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@ -105,8 +105,8 @@ def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
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return get_candidates
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@registry.misc("spacy.CandidateBatchGenerator.v1")
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def create_candidates_batch() -> Callable[
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[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
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@registry.misc("spacy.CandidateAllGenerator.v1")
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def create_candidates_all() -> Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]], Generator[Iterable[Iterable[Candidate]], None, None]
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]:
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return get_candidates_batch
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return get_candidates_all
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@ -1,4 +1,4 @@
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from typing import Optional, Iterable, Callable, Dict, Union, List, Any
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from typing import Optional, Iterable, Callable, Dict, Union, List, Any, Generator
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from thinc.types import Floats2d
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from pathlib import Path
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from itertools import islice
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@ -53,11 +53,11 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
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"incl_context": True,
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"entity_vector_length": 64,
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"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
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"get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
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"get_candidates_all": {"@misc": "spacy.CandidateAllGenerator.v1"},
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"overwrite": True,
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"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
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"use_gold_ents": True,
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"candidates_batch_size": 1,
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"candidates_doc_mode": False,
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"threshold": None,
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},
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default_score_weights={
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@ -77,13 +77,14 @@ def make_entity_linker(
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incl_context: bool,
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entity_vector_length: int,
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get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
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get_candidates_batch: Callable[
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[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
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get_candidates_all: Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]],
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Generator[Iterable[Iterable[Candidate]], None, None]
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],
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overwrite: bool,
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scorer: Optional[Callable],
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use_gold_ents: bool,
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candidates_batch_size: int,
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candidates_doc_mode: bool,
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threshold: Optional[float] = None,
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):
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"""Construct an EntityLinker component.
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@ -98,13 +99,18 @@ def make_entity_linker(
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entity_vector_length (int): Size of encoding vectors in the KB.
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get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
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produces a list of candidates, given a certain knowledge base and a textual mention.
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get_candidates_batch (
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Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
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): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
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get_candidates_all (
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Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]],
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Generator[Iterable[Iterable[Candidate]], None, None]
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]): Function that produces a list of candidates per document, given a certain knowledge base and several textual
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documents with textual mentions.
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scorer (Optional[Callable]): The scoring method.
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use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
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component must provide entity annotations.
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candidates_batch_size (int): Size of batches for entity candidate generation.
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candidates_doc_mode (bool): Whether or not to operate candidate generation in doc mode, i.e. to provide a generator
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yielding entities per document (candidate generator callable is called only once in this case). If False,
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the candidate generator is called once per entity.
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threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
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prediction is discarded. If None, predictions are not filtered by any threshold.
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"""
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@ -134,11 +140,11 @@ def make_entity_linker(
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incl_context=incl_context,
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entity_vector_length=entity_vector_length,
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get_candidates=get_candidates,
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get_candidates_batch=get_candidates_batch,
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get_candidates_all=get_candidates_all,
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overwrite=overwrite,
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scorer=scorer,
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use_gold_ents=use_gold_ents,
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candidates_batch_size=candidates_batch_size,
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candidates_doc_mode=candidates_doc_mode,
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threshold=threshold,
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)
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@ -172,13 +178,14 @@ class EntityLinker(TrainablePipe):
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incl_context: bool,
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entity_vector_length: int,
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get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
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get_candidates_batch: Callable[
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[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
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get_candidates_all: Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]],
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Generator[Iterable[Iterable[Candidate]], None, None]
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],
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overwrite: bool = BACKWARD_OVERWRITE,
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scorer: Optional[Callable] = entity_linker_score,
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use_gold_ents: bool,
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candidates_batch_size: int,
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candidates_doc_mode: bool,
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threshold: Optional[float] = None,
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) -> None:
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"""Initialize an entity linker.
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@ -194,14 +201,18 @@ class EntityLinker(TrainablePipe):
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entity_vector_length (int): Size of encoding vectors in the KB.
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get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
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produces a list of candidates, given a certain knowledge base and a textual mention.
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get_candidates_batch (
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Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
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Iterable[Candidate]]
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): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
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get_candidates_all (
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Callable[
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[KnowledgeBase, Generator[Iterable[Span], None, None]],
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Generator[Iterable[Iterable[Candidate]], None, None]
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]): Function that produces a list of candidates per document, given a certain knowledge base and several textual
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documents with textual mentions.
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scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
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use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
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component must provide entity annotations.
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candidates_batch_size (int): Size of batches for entity candidate generation.
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candidates_doc_mode (bool): Whether or not to operate candidate generation in doc mode, i.e. to provide a generator
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yielding entities per document (candidate generator callable is called only once in this case). If False,
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the candidate generator is called once per entity.
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threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the
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threshold, prediction is discarded. If None, predictions are not filtered by any threshold.
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DOCS: https://spacy.io/api/entitylinker#init
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@ -224,7 +235,7 @@ class EntityLinker(TrainablePipe):
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self.incl_prior = incl_prior
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self.incl_context = incl_context
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self.get_candidates = get_candidates
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self.get_candidates_batch = get_candidates_batch
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self.get_candidates_all = get_candidates_all
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self.cfg: Dict[str, Any] = {"overwrite": overwrite}
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self.distance = CosineDistance(normalize=False)
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# how many neighbour sentences to take into account
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@ -232,12 +243,9 @@ class EntityLinker(TrainablePipe):
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self.kb = empty_kb(entity_vector_length)(self.vocab)
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self.scorer = scorer
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self.use_gold_ents = use_gold_ents
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self.candidates_batch_size = candidates_batch_size
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self.candidates_doc_mode = candidates_doc_mode
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self.threshold = threshold
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if candidates_batch_size < 1:
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raise ValueError(Errors.E1044)
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def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
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"""Define the KB of this pipe by providing a function that will
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create it using this object's vocab."""
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@ -440,96 +448,98 @@ class EntityLinker(TrainablePipe):
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return final_kb_ids
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if isinstance(docs, Doc):
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docs = [docs]
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for i, doc in enumerate(docs):
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# Determine which entities are to be ignored due to labels_discard.
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valid_ent_idx_per_doc = (
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[
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idx
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for idx in range(len(doc.ents))
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if doc.ents[idx].label_ not in self.labels_discard
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]
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for doc in docs if len(doc.ents)
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)
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# Call candidate generator.
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if self.candidates_doc_mode:
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all_ent_cands = self.get_candidates_all(
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self.kb,
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([doc.ents[idx] for idx in next(valid_ent_idx_per_doc)] for doc in docs if len(doc.ents))
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)
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else:
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# Alternative: collect entities the old-fashioned way - by retrieving entities individually.
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all_ent_cands = (
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[self.get_candidates(self.kb, doc.ents[idx]) for idx in next(valid_ent_idx_per_doc)]
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for doc in docs if len(doc.ents)
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)
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for doc_idx, doc in enumerate(docs):
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if len(doc) == 0:
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continue
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sentences = [s for s in doc.sents]
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doc_ent_cands = list(next(all_ent_cands)) if len(doc.ents) else []
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# Loop over entities in batches.
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for ent_idx in range(0, len(doc.ents), self.candidates_batch_size):
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ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size]
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# Looping over candidate entities for this doc. (TODO: rewrite)
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for ent_cand_idx, ent in enumerate(doc.ents):
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sent_index = sentences.index(ent.sent)
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assert sent_index >= 0
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# Look up candidate entities.
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valid_ent_idx = [
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idx
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for idx in range(len(ent_batch))
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if ent_batch[idx].label_ not in self.labels_discard
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]
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batch_candidates = list(
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self.get_candidates_batch(
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self.kb, [ent_batch[idx] for idx in valid_ent_idx]
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if self.incl_context:
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# get n_neighbour sentences, clipped to the length of the document
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start_sentence = max(0, sent_index - self.n_sents)
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end_sentence = min(
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len(sentences) - 1, sent_index + self.n_sents
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)
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if self.candidates_batch_size > 1
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else [
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self.get_candidates(self.kb, ent_batch[idx])
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for idx in valid_ent_idx
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]
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)
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# Looping through each entity in batch (TODO: rewrite)
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for j, ent in enumerate(ent_batch):
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sent_index = sentences.index(ent.sent)
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assert sent_index >= 0
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if self.incl_context:
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# get n_neighbour sentences, clipped to the length of the document
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start_sentence = max(0, sent_index - self.n_sents)
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end_sentence = min(
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len(sentences) - 1, sent_index + self.n_sents
|
||||
)
|
||||
start_token = sentences[start_sentence].start
|
||||
end_token = sentences[end_sentence].end
|
||||
sent_doc = doc[start_token:end_token].as_doc()
|
||||
# currently, the context is the same for each entity in a sentence (should be refined)
|
||||
sentence_encoding = self.model.predict([sent_doc])[0]
|
||||
sentence_encoding_t = sentence_encoding.T
|
||||
sentence_norm = xp.linalg.norm(sentence_encoding_t)
|
||||
entity_count += 1
|
||||
if ent.label_ in self.labels_discard:
|
||||
# ignoring this entity - setting to NIL
|
||||
start_token = sentences[start_sentence].start
|
||||
end_token = sentences[end_sentence].end
|
||||
sent_doc = doc[start_token:end_token].as_doc()
|
||||
# currently, the context is the same for each entity in a sentence (should be refined)
|
||||
sentence_encoding = self.model.predict([sent_doc])[0]
|
||||
sentence_encoding_t = sentence_encoding.T
|
||||
sentence_norm = xp.linalg.norm(sentence_encoding_t)
|
||||
entity_count += 1
|
||||
if ent.label_ in self.labels_discard:
|
||||
# ignoring this entity - setting to NIL
|
||||
final_kb_ids.append(self.NIL)
|
||||
else:
|
||||
candidates = list(doc_ent_cands[ent_cand_idx])
|
||||
if not candidates:
|
||||
# no prediction possible for this entity - setting to NIL
|
||||
final_kb_ids.append(self.NIL)
|
||||
elif len(candidates) == 1 and self.threshold is None:
|
||||
# shortcut for efficiency reasons: take the 1 candidate
|
||||
final_kb_ids.append(candidates[0].entity_)
|
||||
else:
|
||||
candidates = list(batch_candidates[j])
|
||||
if not candidates:
|
||||
# no prediction possible for this entity - setting to NIL
|
||||
final_kb_ids.append(self.NIL)
|
||||
elif len(candidates) == 1 and self.threshold is None:
|
||||
# shortcut for efficiency reasons: take the 1 candidate
|
||||
final_kb_ids.append(candidates[0].entity_)
|
||||
else:
|
||||
random.shuffle(candidates)
|
||||
# set all prior probabilities to 0 if incl_prior=False
|
||||
prior_probs = xp.asarray([c.prior_prob for c in candidates])
|
||||
if not self.incl_prior:
|
||||
prior_probs = xp.asarray([0.0 for _ in candidates])
|
||||
scores = prior_probs
|
||||
# add in similarity from the context
|
||||
if self.incl_context:
|
||||
entity_encodings = xp.asarray(
|
||||
[c.entity_vector for c in candidates]
|
||||
)
|
||||
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
|
||||
if len(entity_encodings) != len(prior_probs):
|
||||
raise RuntimeError(
|
||||
Errors.E147.format(
|
||||
method="predict",
|
||||
msg="vectors not of equal length",
|
||||
)
|
||||
)
|
||||
# cosine similarity
|
||||
sims = xp.dot(entity_encodings, sentence_encoding_t) / (
|
||||
sentence_norm * entity_norm
|
||||
)
|
||||
if sims.shape != prior_probs.shape:
|
||||
raise ValueError(Errors.E161)
|
||||
scores = prior_probs + sims - (prior_probs * sims)
|
||||
final_kb_ids.append(
|
||||
candidates[scores.argmax().item()].entity_
|
||||
if self.threshold is None
|
||||
or scores.max() >= self.threshold
|
||||
else EntityLinker.NIL
|
||||
random.shuffle(candidates)
|
||||
# set all prior probabilities to 0 if incl_prior=False
|
||||
prior_probs = xp.asarray([c.prior_prob for c in candidates])
|
||||
if not self.incl_prior:
|
||||
prior_probs = xp.asarray([0.0 for _ in candidates])
|
||||
scores = prior_probs
|
||||
# add in similarity from the context
|
||||
if self.incl_context:
|
||||
entity_encodings = xp.asarray(
|
||||
[c.entity_vector for c in candidates]
|
||||
)
|
||||
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
|
||||
if len(entity_encodings) != len(prior_probs):
|
||||
raise RuntimeError(
|
||||
Errors.E147.format(
|
||||
method="predict",
|
||||
msg="vectors not of equal length",
|
||||
)
|
||||
)
|
||||
# cosine similarity
|
||||
sims = xp.dot(entity_encodings, sentence_encoding_t) / (
|
||||
sentence_norm * entity_norm
|
||||
)
|
||||
if sims.shape != prior_probs.shape:
|
||||
raise ValueError(Errors.E161)
|
||||
scores = prior_probs + sims - (prior_probs * sims)
|
||||
final_kb_ids.append(
|
||||
candidates[scores.argmax().item()].entity_
|
||||
if self.threshold is None
|
||||
or scores.max() >= self.threshold
|
||||
else EntityLinker.NIL
|
||||
)
|
||||
|
||||
if not (len(final_kb_ids) == entity_count):
|
||||
err = Errors.E147.format(
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from typing import Callable, Iterable, Dict, Any
|
||||
from typing import Callable, Iterable, Dict, Any, Generator
|
||||
|
||||
import pytest
|
||||
from numpy.testing import assert_equal
|
||||
|
|
@ -497,11 +497,14 @@ def test_el_pipe_configuration(nlp):
|
|||
assert doc[1].ent_kb_id_ == ""
|
||||
assert doc[2].ent_kb_id_ == "Q2"
|
||||
|
||||
# Replace the pipe with a new one with with a different candidate generator.
|
||||
|
||||
def get_lowercased_candidates(kb, span):
|
||||
return kb.get_alias_candidates(span.text.lower())
|
||||
|
||||
def get_lowercased_candidates_batch(kb, spans):
|
||||
return [get_lowercased_candidates(kb, span) for span in spans]
|
||||
def get_lowercased_candidates_all(kb, spans_per_doc):
|
||||
for doc_spans in spans_per_doc:
|
||||
yield [get_lowercased_candidates(kb, span) for span in doc_spans]
|
||||
|
||||
@registry.misc("spacy.LowercaseCandidateGenerator.v1")
|
||||
def create_candidates() -> Callable[
|
||||
|
|
@ -509,29 +512,39 @@ def test_el_pipe_configuration(nlp):
|
|||
]:
|
||||
return get_lowercased_candidates
|
||||
|
||||
@registry.misc("spacy.LowercaseCandidateBatchGenerator.v1")
|
||||
@registry.misc("spacy.LowercaseCandidateAllGenerator.v1")
|
||||
def create_candidates_batch() -> Callable[
|
||||
[InMemoryLookupKB, Iterable["Span"]], Iterable[Iterable[Candidate]]
|
||||
[InMemoryLookupKB, Generator[Iterable["Span"], None, None]],
|
||||
Generator[Iterable[Iterable[Candidate]], None, None]
|
||||
]:
|
||||
return get_lowercased_candidates_batch
|
||||
return get_lowercased_candidates_all
|
||||
|
||||
# replace the pipe with a new one with with a different candidate generator
|
||||
entity_linker = nlp.replace_pipe(
|
||||
"entity_linker",
|
||||
"entity_linker",
|
||||
config={
|
||||
"incl_context": False,
|
||||
"get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"},
|
||||
"get_candidates_batch": {
|
||||
"@misc": "spacy.LowercaseCandidateBatchGenerator.v1"
|
||||
def test_reconfigured_el(candidates_doc_mode: bool, doc_text: str) -> None:
|
||||
"""Test reconfigured EL for correct results.
|
||||
candidates_doc_mode (bool): candidates_doc_mode in pipe config.
|
||||
doc_text (str): Text to infer.
|
||||
"""
|
||||
_entity_linker = nlp.replace_pipe(
|
||||
"entity_linker",
|
||||
"entity_linker",
|
||||
config={
|
||||
"incl_context": False,
|
||||
"candidates_doc_mode": candidates_doc_mode,
|
||||
"get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"},
|
||||
"get_candidates_all": {
|
||||
"@misc": "spacy.LowercaseCandidateAllGenerator.v1"
|
||||
},
|
||||
},
|
||||
},
|
||||
)
|
||||
entity_linker.set_kb(create_kb)
|
||||
doc = nlp(text)
|
||||
assert doc[0].ent_kb_id_ == "Q2"
|
||||
assert doc[1].ent_kb_id_ == ""
|
||||
assert doc[2].ent_kb_id_ == "Q2"
|
||||
)
|
||||
_entity_linker.set_kb(create_kb)
|
||||
_doc = nlp(doc_text)
|
||||
assert _doc[0].ent_kb_id_ == "Q2"
|
||||
assert _doc[1].ent_kb_id_ == ""
|
||||
assert _doc[2].ent_kb_id_ == "Q2"
|
||||
|
||||
# Test individual and doc-wise candidate generation.
|
||||
test_reconfigured_el(False, text)
|
||||
test_reconfigured_el(True, text)
|
||||
|
||||
|
||||
def test_nel_nsents(nlp):
|
||||
|
|
@ -670,6 +683,7 @@ def test_preserving_links_asdoc(nlp):
|
|||
assert s_ent.kb_id_ == orig_kb_id
|
||||
|
||||
|
||||
|
||||
def test_preserving_links_ents(nlp):
|
||||
"""Test that doc.ents preserves KB annotations"""
|
||||
text = "She lives in Boston. He lives in Denver."
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user