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b0228d8ea6
* chore: add cython-linter dev dependency * fix: lexeme.pyx * fix: morphology.pxd * fix: tokenizer.pxd * fix: vocab.pxd * fix: morphology.pxd (line length) * ci: add cython-lint * ci: fix cython-lint call * Fix kb/candidate.pyx. * Fix kb/kb.pyx. * Fix kb/kb_in_memory.pyx. * Fix kb. * Fix training/ partially. * Fix training/. Ignore trailing whitespaces and too long lines. * Fix ml/. * Fix matcher/. * Fix pipeline/. * Fix tokens/. * Fix build errors. Fix vocab.pyx. * Fix cython-lint install and run. * Fix lexeme.pyx, parts_of_speech.pxd, vectors.pyx. Temporarily disable cython-lint execution. * Fix attrs.pyx, lexeme.pyx, symbols.pxd, isort issues. * Make cython-lint install conditional. Fix tokenizer.pyx. * Fix remaining files. Reenable cython-lint check. * Readded parentheses. * Fix test_build_dependencies(). * Add explanatory comment to cython-lint execution. --------- Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
91 lines
2.6 KiB
Cython
91 lines
2.6 KiB
Cython
# cython: infer_types=True, profile=True
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from typing import Iterable
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from .kb cimport KnowledgeBase
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from ..tokens import Span
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cdef class Candidate:
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"""A `Candidate` object refers to a textual mention (`alias`) that may or
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may not be resolved to a specific `entity` from a Knowledge Base. This
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will be used as input for the entity linking algorithm which will
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disambiguate the various candidates to the correct one.
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Each candidate (alias, entity) pair is assigned a certain prior probability.
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DOCS: https://spacy.io/api/kb/#candidate-init
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"""
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def __init__(
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self,
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KnowledgeBase kb,
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entity_hash,
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entity_freq,
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entity_vector,
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alias_hash,
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prior_prob
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):
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self.kb = kb
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self.entity_hash = entity_hash
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self.entity_freq = entity_freq
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self.entity_vector = entity_vector
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self.alias_hash = alias_hash
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self.prior_prob = prior_prob
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@property
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def entity(self) -> int:
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"""RETURNS (uint64): hash of the entity's KB ID/name"""
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return self.entity_hash
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@property
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def entity_(self) -> str:
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"""RETURNS (str): ID/name of this entity in the KB"""
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return self.kb.vocab.strings[self.entity_hash]
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@property
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def alias(self) -> int:
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"""RETURNS (uint64): hash of the alias"""
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return self.alias_hash
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@property
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def alias_(self) -> str:
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"""RETURNS (str): ID of the original alias"""
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return self.kb.vocab.strings[self.alias_hash]
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@property
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def entity_freq(self) -> float:
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return self.entity_freq
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@property
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def entity_vector(self) -> Iterable[float]:
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return self.entity_vector
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@property
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def prior_prob(self) -> float:
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return self.prior_prob
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def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
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"""
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Return candidate entities for a given mention and fetching appropriate
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entries from the index.
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kb (KnowledgeBase): Knowledge base to query.
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mention (Span): Entity mention for which to identify candidates.
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RETURNS (Iterable[Candidate]): Identified candidates.
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"""
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return kb.get_candidates(mention)
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def get_candidates_batch(
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kb: KnowledgeBase, mentions: Iterable[Span]
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) -> Iterable[Iterable[Candidate]]:
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"""
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Return candidate entities for the given mentions and fetching appropriate entries
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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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"""
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return kb.get_candidates_batch(mentions)
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