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Start refactoring of Candidate classes.
This commit is contained in:
Raphael Mitsch
parent
7e6888dcd4
commit
75aee55bc3
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from .kb cimport KnowledgeBase
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from libcpp.vector cimport vector
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from ..typedefs cimport hash_t
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# Object used by the Entity Linker that summarizes one entity-alias candidate combination.
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cdef class Candidate:
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cdef readonly KnowledgeBase kb
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cdef hash_t entity_hash
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cdef float entity_freq
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cdef vector[float] entity_vector
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cdef hash_t alias_hash
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cdef float prior_prob
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127
spacy/kb/candidate.py
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127
spacy/kb/candidate.py
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@ -0,0 +1,127 @@
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import abc
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from typing import List, Union, Optional
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from spacy import Errors
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from ..tokens import Span
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class Candidate(abc.ABC):
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"""A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved
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to a specific `entity_id` from a Knowledge Base. This will be used as input for the entity_id linking
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algorithm which will disambiguate the various candidates to the correct one.
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Each candidate (alias, entity_id) 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, mention: str, entity_id: Union[int, str], entity_vector: List[float]
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):
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"""Create new instance of `Candidate`. Note: has to be a sub-class, otherwise error will be raised.
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mention (str): Mention text for this candidate.
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entity_id (Union[int, str]): Unique ID of entity_id.
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"""
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self.mention = mention
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self.entity = entity_id
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self.entity_vector = entity_vector
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@property
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def entity_id(self) -> Union[int, str]:
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"""RETURNS (Union[int, str]): Entity ID."""
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return self.entity
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def entity_(self) -> Union[int, str]:
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"""RETURNS (Union[int, str]): Entity ID (for backwards compatibility)."""
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return self.entity
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@property
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def mention(self) -> str:
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"""RETURNS (str): Mention."""
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return self.mention
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@property
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def entity_vector(self) -> List[float]:
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"""RETURNS (List[float]): Entity vector."""
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return self.entity_vector
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class InMemoryLookupKBCandidate(Candidate):
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"""`Candidate` for InMemoryLookupKBCandidate."""
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# todo how to resolve circular import issue? -> replace with callable for hash?
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def __init__(
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self,
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kb: KnowledgeBase,
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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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"""
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prior_prob (float): Prior probability of entity_id for this mention - i.e. the probability that, independent of the
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context, this mention resolves to this entity_id in the corpus used to build the knowledge base. In cases in
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which this isn't always possible (e.g.: the corpus to analyse contains mentions that the KB corpus doesn't)
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it might be better to eschew this information and always supply the same value.
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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_id'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_id 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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"""RETURNS (List[float]): Entity vector."""
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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 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_all(
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kb: KnowledgeBase, mentions: Generator[Iterable[Span], None, None]
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) -> Iterator[Iterable[Iterable[Candidate]]]:
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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 (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_all(mentions)
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@ -1,76 +0,0 @@
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# cython: infer_types=True, profile=True
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from typing import Iterable, Generator, Iterator
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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 may not be resolved
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to a specific `entity` from a Knowledge Base. This will be used as input for the entity linking
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algorithm which will 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__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
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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 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_all(
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kb: KnowledgeBase, mentions: Generator[Iterable[Span], None, None]
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) -> Iterator[Iterable[Iterable[Candidate]]]:
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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 (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_all(mentions)
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@ -532,9 +532,12 @@ class EntityLinker(TrainablePipe):
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else:
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random.shuffle(candidates)
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# set all prior probabilities to 0 if incl_prior=False
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prior_probs = xp.asarray([c.prior_prob for c in candidates])
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if not self.incl_prior:
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prior_probs = xp.asarray([0.0 for _ in candidates])
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prior_probs = xp.asarray(
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[
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0.0 if self.incl_prior else c.prior_prob
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for c in candidates
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]
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)
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scores = prior_probs
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# add in similarity from the context
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if self.incl_context:
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