mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 21:57:15 +03:00
89f974d4f5
* Remove backwards-compatible overwrite from Entity Linker This also adds a docstring about overwrite, since it wasn't present. * Fix docstring * Remove backward compat settings in Morphologizer This also needed a docstring added. For this component it's less clear what the right overwrite settings are. * Remove backward compat from sentencizer This was simple * Remove backward compat from senter Another simple one * Remove backward compat setting from tagger * Add docstrings * Update spacy/pipeline/morphologizer.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update docs --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
751 lines
30 KiB
Python
751 lines
30 KiB
Python
from typing import Optional, Iterable, Callable, Dict, Sequence, Union, List, Any
|
|
from typing import cast
|
|
from numpy import dtype
|
|
from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
|
|
from pathlib import Path
|
|
from itertools import islice
|
|
import srsly
|
|
import random
|
|
from thinc.api import CosineDistance, Model, Optimizer, Config
|
|
from thinc.api import set_dropout_rate
|
|
|
|
from ..kb import KnowledgeBase, Candidate
|
|
from ..ml import empty_kb
|
|
from ..tokens import Doc, Span
|
|
from .pipe import deserialize_config
|
|
from .trainable_pipe import TrainablePipe
|
|
from ..language import Language
|
|
from ..vocab import Vocab
|
|
from ..training import Example, validate_examples, validate_get_examples
|
|
from ..errors import Errors
|
|
from ..util import SimpleFrozenList, registry
|
|
from .. import util
|
|
from ..scorer import Scorer
|
|
|
|
|
|
ActivationsT = Dict[str, Union[List[Ragged], List[str]]]
|
|
|
|
KNOWLEDGE_BASE_IDS = "kb_ids"
|
|
|
|
default_model_config = """
|
|
[model]
|
|
@architectures = "spacy.EntityLinker.v2"
|
|
|
|
[model.tok2vec]
|
|
@architectures = "spacy.HashEmbedCNN.v2"
|
|
pretrained_vectors = null
|
|
width = 96
|
|
depth = 2
|
|
embed_size = 2000
|
|
window_size = 1
|
|
maxout_pieces = 3
|
|
subword_features = true
|
|
"""
|
|
DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
|
|
|
|
|
|
@Language.factory(
|
|
"entity_linker",
|
|
requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"],
|
|
assigns=["token.ent_kb_id"],
|
|
default_config={
|
|
"model": DEFAULT_NEL_MODEL,
|
|
"labels_discard": [],
|
|
"n_sents": 0,
|
|
"incl_prior": True,
|
|
"incl_context": True,
|
|
"entity_vector_length": 64,
|
|
"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
|
|
"get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
|
|
"overwrite": False,
|
|
"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
|
|
"use_gold_ents": True,
|
|
"candidates_batch_size": 1,
|
|
"threshold": None,
|
|
"save_activations": False,
|
|
},
|
|
default_score_weights={
|
|
"nel_micro_f": 1.0,
|
|
"nel_micro_r": None,
|
|
"nel_micro_p": None,
|
|
},
|
|
)
|
|
def make_entity_linker(
|
|
nlp: Language,
|
|
name: str,
|
|
model: Model,
|
|
*,
|
|
labels_discard: Iterable[str],
|
|
n_sents: int,
|
|
incl_prior: bool,
|
|
incl_context: bool,
|
|
entity_vector_length: int,
|
|
get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
|
|
get_candidates_batch: Callable[
|
|
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
|
|
],
|
|
overwrite: bool,
|
|
scorer: Optional[Callable],
|
|
use_gold_ents: bool,
|
|
candidates_batch_size: int,
|
|
threshold: Optional[float] = None,
|
|
save_activations: bool,
|
|
):
|
|
"""Construct an EntityLinker component.
|
|
|
|
model (Model[List[Doc], Floats2d]): A model that learns document vector
|
|
representations. Given a batch of Doc objects, it should return a single
|
|
array, with one row per item in the batch.
|
|
labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
|
|
n_sents (int): The number of neighbouring sentences to take into account.
|
|
incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
|
|
incl_context (bool): Whether or not to include the local context in the model.
|
|
entity_vector_length (int): Size of encoding vectors in the KB.
|
|
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
|
|
produces a list of candidates, given a certain knowledge base and a textual mention.
|
|
get_candidates_batch (
|
|
Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
|
|
): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
|
|
scorer (Optional[Callable]): The scoring method.
|
|
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
|
|
component must provide entity annotations.
|
|
candidates_batch_size (int): Size of batches for entity candidate generation.
|
|
threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
|
|
prediction is discarded. If None, predictions are not filtered by any threshold.
|
|
save_activations (bool): save model activations in Doc when annotating.
|
|
"""
|
|
|
|
if not model.attrs.get("include_span_maker", False):
|
|
try:
|
|
from spacy_legacy.components.entity_linker import EntityLinker_v1
|
|
except:
|
|
raise ImportError(
|
|
"In order to use v1 of the EntityLinker, you must use spacy-legacy>=3.0.12."
|
|
)
|
|
# The only difference in arguments here is that use_gold_ents and threshold aren't available.
|
|
return EntityLinker_v1(
|
|
nlp.vocab,
|
|
model,
|
|
name,
|
|
labels_discard=labels_discard,
|
|
n_sents=n_sents,
|
|
incl_prior=incl_prior,
|
|
incl_context=incl_context,
|
|
entity_vector_length=entity_vector_length,
|
|
get_candidates=get_candidates,
|
|
overwrite=overwrite,
|
|
scorer=scorer,
|
|
)
|
|
return EntityLinker(
|
|
nlp.vocab,
|
|
model,
|
|
name,
|
|
labels_discard=labels_discard,
|
|
n_sents=n_sents,
|
|
incl_prior=incl_prior,
|
|
incl_context=incl_context,
|
|
entity_vector_length=entity_vector_length,
|
|
get_candidates=get_candidates,
|
|
get_candidates_batch=get_candidates_batch,
|
|
overwrite=overwrite,
|
|
scorer=scorer,
|
|
use_gold_ents=use_gold_ents,
|
|
candidates_batch_size=candidates_batch_size,
|
|
threshold=threshold,
|
|
save_activations=save_activations,
|
|
)
|
|
|
|
|
|
def entity_linker_score(examples, **kwargs):
|
|
return Scorer.score_links(examples, negative_labels=[EntityLinker.NIL], **kwargs)
|
|
|
|
|
|
@registry.scorers("spacy.entity_linker_scorer.v1")
|
|
def make_entity_linker_scorer():
|
|
return entity_linker_score
|
|
|
|
|
|
class EntityLinker(TrainablePipe):
|
|
"""Pipeline component for named entity linking.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker
|
|
"""
|
|
|
|
NIL = "NIL" # string used to refer to a non-existing link
|
|
|
|
def __init__(
|
|
self,
|
|
vocab: Vocab,
|
|
model: Model,
|
|
name: str = "entity_linker",
|
|
*,
|
|
labels_discard: Iterable[str],
|
|
n_sents: int,
|
|
incl_prior: bool,
|
|
incl_context: bool,
|
|
entity_vector_length: int,
|
|
get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
|
|
get_candidates_batch: Callable[
|
|
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
|
|
],
|
|
overwrite: bool = False,
|
|
scorer: Optional[Callable] = entity_linker_score,
|
|
use_gold_ents: bool,
|
|
candidates_batch_size: int,
|
|
threshold: Optional[float] = None,
|
|
save_activations: bool = False,
|
|
) -> None:
|
|
"""Initialize an entity linker.
|
|
|
|
vocab (Vocab): The shared vocabulary.
|
|
model (thinc.api.Model): The Thinc Model powering the pipeline component.
|
|
name (str): The component instance name, used to add entries to the
|
|
losses during training.
|
|
labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
|
|
n_sents (int): The number of neighbouring sentences to take into account.
|
|
incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
|
|
incl_context (bool): Whether or not to include the local context in the model.
|
|
entity_vector_length (int): Size of encoding vectors in the KB.
|
|
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
|
|
produces a list of candidates, given a certain knowledge base and a textual mention.
|
|
get_candidates_batch (
|
|
Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
|
|
Iterable[Candidate]]
|
|
): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
|
|
overwrite (bool): Whether to overwrite existing non-empty annotations.
|
|
scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
|
|
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
|
|
component must provide entity annotations.
|
|
candidates_batch_size (int): Size of batches for entity candidate generation.
|
|
threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the
|
|
threshold, prediction is discarded. If None, predictions are not filtered by any threshold.
|
|
DOCS: https://spacy.io/api/entitylinker#init
|
|
"""
|
|
|
|
if threshold is not None and not (0 <= threshold <= 1):
|
|
raise ValueError(
|
|
Errors.E1043.format(
|
|
range_start=0,
|
|
range_end=1,
|
|
value=threshold,
|
|
)
|
|
)
|
|
|
|
self.vocab = vocab
|
|
self.model = model
|
|
self.name = name
|
|
self.labels_discard = list(labels_discard)
|
|
self.n_sents = n_sents
|
|
self.incl_prior = incl_prior
|
|
self.incl_context = incl_context
|
|
self.get_candidates = get_candidates
|
|
self.get_candidates_batch = get_candidates_batch
|
|
self.cfg: Dict[str, Any] = {"overwrite": overwrite}
|
|
self.distance = CosineDistance(normalize=False)
|
|
# how many neighbour sentences to take into account
|
|
# create an empty KB by default
|
|
self.kb = empty_kb(entity_vector_length)(self.vocab)
|
|
self.scorer = scorer
|
|
self.use_gold_ents = use_gold_ents
|
|
self.candidates_batch_size = candidates_batch_size
|
|
self.threshold = threshold
|
|
self.save_activations = save_activations
|
|
|
|
if candidates_batch_size < 1:
|
|
raise ValueError(Errors.E1044)
|
|
|
|
def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
|
|
"""Define the KB of this pipe by providing a function that will
|
|
create it using this object's vocab."""
|
|
if not callable(kb_loader):
|
|
raise ValueError(Errors.E885.format(arg_type=type(kb_loader)))
|
|
|
|
self.kb = kb_loader(self.vocab) # type: ignore
|
|
|
|
def validate_kb(self) -> None:
|
|
# Raise an error if the knowledge base is not initialized.
|
|
if self.kb is None:
|
|
raise ValueError(Errors.E1018.format(name=self.name))
|
|
if len(self.kb) == 0:
|
|
raise ValueError(Errors.E139.format(name=self.name))
|
|
|
|
def initialize(
|
|
self,
|
|
get_examples: Callable[[], Iterable[Example]],
|
|
*,
|
|
nlp: Optional[Language] = None,
|
|
kb_loader: Optional[Callable[[Vocab], KnowledgeBase]] = None,
|
|
):
|
|
"""Initialize the pipe for training, using a representative set
|
|
of data examples.
|
|
|
|
get_examples (Callable[[], Iterable[Example]]): Function that
|
|
returns a representative sample of gold-standard Example objects.
|
|
nlp (Language): The current nlp object the component is part of.
|
|
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab
|
|
instance. Note that providing this argument will overwrite all data accumulated in the current KB.
|
|
Use this only when loading a KB as-such from file.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#initialize
|
|
"""
|
|
validate_get_examples(get_examples, "EntityLinker.initialize")
|
|
if kb_loader is not None:
|
|
self.set_kb(kb_loader)
|
|
self.validate_kb()
|
|
nO = self.kb.entity_vector_length
|
|
doc_sample = []
|
|
vector_sample = []
|
|
for eg in islice(get_examples(), 10):
|
|
doc = eg.x
|
|
if self.use_gold_ents:
|
|
ents, _ = eg.get_aligned_ents_and_ner()
|
|
doc.ents = ents
|
|
doc_sample.append(doc)
|
|
vector_sample.append(self.model.ops.alloc1f(nO))
|
|
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
|
|
assert len(vector_sample) > 0, Errors.E923.format(name=self.name)
|
|
|
|
# XXX In order for size estimation to work, there has to be at least
|
|
# one entity. It's not used for training so it doesn't have to be real,
|
|
# so we add a fake one if none are present.
|
|
# We can't use Doc.has_annotation here because it can be True for docs
|
|
# that have been through an NER component but got no entities.
|
|
has_annotations = any([doc.ents for doc in doc_sample])
|
|
if not has_annotations:
|
|
doc = doc_sample[0]
|
|
ent = doc[0:1]
|
|
ent.label_ = "XXX"
|
|
doc.ents = (ent,)
|
|
|
|
self.model.initialize(
|
|
X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32")
|
|
)
|
|
|
|
if not has_annotations:
|
|
# Clean up dummy annotation
|
|
doc.ents = []
|
|
|
|
def batch_has_learnable_example(self, examples):
|
|
"""Check if a batch contains a learnable example.
|
|
|
|
If one isn't present, then the update step needs to be skipped.
|
|
"""
|
|
|
|
for eg in examples:
|
|
for ent in eg.predicted.ents:
|
|
candidates = list(self.get_candidates(self.kb, ent))
|
|
if candidates:
|
|
return True
|
|
|
|
return False
|
|
|
|
def update(
|
|
self,
|
|
examples: Iterable[Example],
|
|
*,
|
|
drop: float = 0.0,
|
|
sgd: Optional[Optimizer] = None,
|
|
losses: Optional[Dict[str, float]] = None,
|
|
) -> Dict[str, float]:
|
|
"""Learn from a batch of documents and gold-standard information,
|
|
updating the pipe's model. Delegates to predict and get_loss.
|
|
|
|
examples (Iterable[Example]): A batch of Example objects.
|
|
drop (float): The dropout rate.
|
|
sgd (thinc.api.Optimizer): The optimizer.
|
|
losses (Dict[str, float]): Optional record of the loss during training.
|
|
Updated using the component name as the key.
|
|
RETURNS (Dict[str, float]): The updated losses dictionary.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#update
|
|
"""
|
|
self.validate_kb()
|
|
if losses is None:
|
|
losses = {}
|
|
losses.setdefault(self.name, 0.0)
|
|
if not examples:
|
|
return losses
|
|
validate_examples(examples, "EntityLinker.update")
|
|
|
|
set_dropout_rate(self.model, drop)
|
|
docs = [eg.predicted for eg in examples]
|
|
# save to restore later
|
|
old_ents = [doc.ents for doc in docs]
|
|
|
|
for doc, ex in zip(docs, examples):
|
|
if self.use_gold_ents:
|
|
ents, _ = ex.get_aligned_ents_and_ner()
|
|
doc.ents = ents
|
|
else:
|
|
# only keep matching ents
|
|
doc.ents = ex.get_matching_ents()
|
|
|
|
# make sure we have something to learn from, if not, short-circuit
|
|
if not self.batch_has_learnable_example(examples):
|
|
return losses
|
|
|
|
sentence_encodings, bp_context = self.model.begin_update(docs)
|
|
|
|
# now restore the ents
|
|
for doc, old in zip(docs, old_ents):
|
|
doc.ents = old
|
|
|
|
loss, d_scores = self.get_loss(
|
|
sentence_encodings=sentence_encodings, examples=examples
|
|
)
|
|
bp_context(d_scores)
|
|
if sgd is not None:
|
|
self.finish_update(sgd)
|
|
losses[self.name] += loss
|
|
return losses
|
|
|
|
def get_loss(self, examples: Iterable[Example], sentence_encodings: Floats2d):
|
|
validate_examples(examples, "EntityLinker.get_loss")
|
|
entity_encodings = []
|
|
eidx = 0 # indices in gold entities to keep
|
|
keep_ents = [] # indices in sentence_encodings to keep
|
|
|
|
for eg in examples:
|
|
kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
|
|
|
|
for ent in eg.get_matching_ents():
|
|
kb_id = kb_ids[ent.start]
|
|
if kb_id:
|
|
entity_encoding = self.kb.get_vector(kb_id)
|
|
entity_encodings.append(entity_encoding)
|
|
keep_ents.append(eidx)
|
|
|
|
eidx += 1
|
|
entity_encodings = self.model.ops.asarray2f(entity_encodings, dtype="float32")
|
|
selected_encodings = sentence_encodings[keep_ents]
|
|
|
|
# if there are no matches, short circuit
|
|
if not keep_ents:
|
|
out = self.model.ops.alloc2f(*sentence_encodings.shape)
|
|
return 0, out
|
|
|
|
if selected_encodings.shape != entity_encodings.shape:
|
|
err = Errors.E147.format(
|
|
method="get_loss", msg="gold entities do not match up"
|
|
)
|
|
raise RuntimeError(err)
|
|
gradients = self.distance.get_grad(selected_encodings, entity_encodings)
|
|
# to match the input size, we need to give a zero gradient for items not in the kb
|
|
out = self.model.ops.alloc2f(*sentence_encodings.shape)
|
|
out[keep_ents] = gradients
|
|
|
|
loss = self.distance.get_loss(selected_encodings, entity_encodings)
|
|
loss = loss / len(entity_encodings)
|
|
return float(loss), out
|
|
|
|
def predict(self, docs: Iterable[Doc]) -> ActivationsT:
|
|
"""Apply the pipeline's model to a batch of docs, without modifying them.
|
|
Returns the KB IDs for each entity in each doc, including NIL if there is
|
|
no prediction.
|
|
|
|
docs (Iterable[Doc]): The documents to predict.
|
|
RETURNS (List[str]): The models prediction for each document.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#predict
|
|
"""
|
|
self.validate_kb()
|
|
entity_count = 0
|
|
final_kb_ids: List[str] = []
|
|
ops = self.model.ops
|
|
xp = ops.xp
|
|
docs_ents: List[Ragged] = []
|
|
docs_scores: List[Ragged] = []
|
|
if not docs:
|
|
return {
|
|
KNOWLEDGE_BASE_IDS: final_kb_ids,
|
|
"ents": docs_ents,
|
|
"scores": docs_scores,
|
|
}
|
|
if isinstance(docs, Doc):
|
|
docs = [docs]
|
|
for doc in docs:
|
|
doc_ents: List[Ints1d] = []
|
|
doc_scores: List[Floats1d] = []
|
|
if len(doc) == 0:
|
|
docs_scores.append(Ragged(ops.alloc1f(0), ops.alloc1i(0)))
|
|
docs_ents.append(Ragged(xp.zeros(0, dtype="uint64"), ops.alloc1i(0)))
|
|
continue
|
|
sentences = [s for s in doc.sents]
|
|
|
|
# Loop over entities in batches.
|
|
for ent_idx in range(0, len(doc.ents), self.candidates_batch_size):
|
|
ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size]
|
|
|
|
# Look up candidate entities.
|
|
valid_ent_idx = [
|
|
idx
|
|
for idx in range(len(ent_batch))
|
|
if ent_batch[idx].label_ not in self.labels_discard
|
|
]
|
|
|
|
batch_candidates = list(
|
|
self.get_candidates_batch(
|
|
self.kb, [ent_batch[idx] for idx in valid_ent_idx]
|
|
)
|
|
if self.candidates_batch_size > 1
|
|
else [
|
|
self.get_candidates(self.kb, ent_batch[idx])
|
|
for idx in valid_ent_idx
|
|
]
|
|
)
|
|
|
|
# Looping through each entity in batch (TODO: rewrite)
|
|
for j, ent in enumerate(ent_batch):
|
|
sent_index = sentences.index(ent.sent)
|
|
assert sent_index >= 0
|
|
|
|
if self.incl_context:
|
|
# get n_neighbour sentences, clipped to the length of the document
|
|
start_sentence = max(0, sent_index - self.n_sents)
|
|
end_sentence = min(
|
|
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
|
|
final_kb_ids.append(self.NIL)
|
|
self._add_activations(
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
scores=[0.0],
|
|
ents=[0],
|
|
)
|
|
else:
|
|
candidates = list(batch_candidates[j])
|
|
if not candidates:
|
|
# no prediction possible for this entity - setting to NIL
|
|
final_kb_ids.append(self.NIL)
|
|
self._add_activations(
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
scores=[0.0],
|
|
ents=[0],
|
|
)
|
|
elif len(candidates) == 1 and self.threshold is None:
|
|
# shortcut for efficiency reasons: take the 1 candidate
|
|
final_kb_ids.append(candidates[0].entity_)
|
|
self._add_activations(
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
scores=[1.0],
|
|
ents=[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
|
|
)
|
|
self._add_activations(
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
scores=scores,
|
|
ents=[c.entity for c in candidates],
|
|
)
|
|
self._add_doc_activations(
|
|
docs_scores=docs_scores,
|
|
docs_ents=docs_ents,
|
|
doc_scores=doc_scores,
|
|
doc_ents=doc_ents,
|
|
)
|
|
if not (len(final_kb_ids) == entity_count):
|
|
err = Errors.E147.format(
|
|
method="predict", msg="result variables not of equal length"
|
|
)
|
|
raise RuntimeError(err)
|
|
return {
|
|
KNOWLEDGE_BASE_IDS: final_kb_ids,
|
|
"ents": docs_ents,
|
|
"scores": docs_scores,
|
|
}
|
|
|
|
def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT) -> None:
|
|
"""Modify a batch of documents, using pre-computed scores.
|
|
|
|
docs (Iterable[Doc]): The documents to modify.
|
|
activations (ActivationsT): The activations used for setting annotations, produced
|
|
by EntityLinker.predict.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#set_annotations
|
|
"""
|
|
kb_ids = cast(List[str], activations[KNOWLEDGE_BASE_IDS])
|
|
count_ents = len([ent for doc in docs for ent in doc.ents])
|
|
if count_ents != len(kb_ids):
|
|
raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids)))
|
|
i = 0
|
|
overwrite = self.cfg["overwrite"]
|
|
for j, doc in enumerate(docs):
|
|
if self.save_activations:
|
|
doc.activations[self.name] = {}
|
|
for act_name, acts in activations.items():
|
|
if act_name != KNOWLEDGE_BASE_IDS:
|
|
# We only copy activations that are Ragged.
|
|
doc.activations[self.name][act_name] = cast(Ragged, acts[j])
|
|
|
|
for ent in doc.ents:
|
|
kb_id = kb_ids[i]
|
|
i += 1
|
|
for token in ent:
|
|
if token.ent_kb_id == 0 or overwrite:
|
|
token.ent_kb_id_ = kb_id
|
|
|
|
def to_bytes(self, *, exclude=tuple()):
|
|
"""Serialize the pipe to a bytestring.
|
|
|
|
exclude (Iterable[str]): String names of serialization fields to exclude.
|
|
RETURNS (bytes): The serialized object.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#to_bytes
|
|
"""
|
|
self._validate_serialization_attrs()
|
|
serialize = {}
|
|
if hasattr(self, "cfg") and self.cfg is not None:
|
|
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
|
|
serialize["vocab"] = lambda: self.vocab.to_bytes(exclude=exclude)
|
|
serialize["kb"] = self.kb.to_bytes
|
|
serialize["model"] = self.model.to_bytes
|
|
return util.to_bytes(serialize, exclude)
|
|
|
|
def from_bytes(self, bytes_data, *, exclude=tuple()):
|
|
"""Load the pipe from a bytestring.
|
|
|
|
exclude (Iterable[str]): String names of serialization fields to exclude.
|
|
RETURNS (TrainablePipe): The loaded object.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#from_bytes
|
|
"""
|
|
self._validate_serialization_attrs()
|
|
|
|
def load_model(b):
|
|
try:
|
|
self.model.from_bytes(b)
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149) from None
|
|
|
|
deserialize = {}
|
|
if hasattr(self, "cfg") and self.cfg is not None:
|
|
deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b))
|
|
deserialize["vocab"] = lambda b: self.vocab.from_bytes(b, exclude=exclude)
|
|
deserialize["kb"] = lambda b: self.kb.from_bytes(b)
|
|
deserialize["model"] = load_model
|
|
util.from_bytes(bytes_data, deserialize, exclude)
|
|
return self
|
|
|
|
def to_disk(
|
|
self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
|
|
) -> None:
|
|
"""Serialize the pipe to disk.
|
|
|
|
path (str / Path): Path to a directory.
|
|
exclude (Iterable[str]): String names of serialization fields to exclude.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#to_disk
|
|
"""
|
|
serialize = {}
|
|
serialize["vocab"] = lambda p: self.vocab.to_disk(p, exclude=exclude)
|
|
serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg)
|
|
serialize["kb"] = lambda p: self.kb.to_disk(p)
|
|
serialize["model"] = lambda p: self.model.to_disk(p)
|
|
util.to_disk(path, serialize, exclude)
|
|
|
|
def from_disk(
|
|
self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
|
|
) -> "EntityLinker":
|
|
"""Load the pipe from disk. Modifies the object in place and returns it.
|
|
|
|
path (str / Path): Path to a directory.
|
|
exclude (Iterable[str]): String names of serialization fields to exclude.
|
|
RETURNS (EntityLinker): The modified EntityLinker object.
|
|
|
|
DOCS: https://spacy.io/api/entitylinker#from_disk
|
|
"""
|
|
|
|
def load_model(p):
|
|
try:
|
|
with p.open("rb") as infile:
|
|
self.model.from_bytes(infile.read())
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149) from None
|
|
|
|
deserialize: Dict[str, Callable[[Any], Any]] = {}
|
|
deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p))
|
|
deserialize["vocab"] = lambda p: self.vocab.from_disk(p, exclude=exclude)
|
|
deserialize["kb"] = lambda p: self.kb.from_disk(p)
|
|
deserialize["model"] = load_model
|
|
util.from_disk(path, deserialize, exclude)
|
|
return self
|
|
|
|
def rehearse(self, examples, *, sgd=None, losses=None, **config):
|
|
raise NotImplementedError
|
|
|
|
def add_label(self, label):
|
|
raise NotImplementedError
|
|
|
|
def _add_doc_activations(
|
|
self,
|
|
*,
|
|
docs_scores: List[Ragged],
|
|
docs_ents: List[Ragged],
|
|
doc_scores: List[Floats1d],
|
|
doc_ents: List[Ints1d],
|
|
):
|
|
if not self.save_activations:
|
|
return
|
|
ops = self.model.ops
|
|
lengths = ops.asarray1i([s.shape[0] for s in doc_scores])
|
|
docs_scores.append(Ragged(ops.flatten(doc_scores), lengths))
|
|
docs_ents.append(Ragged(ops.flatten(doc_ents), lengths))
|
|
|
|
def _add_activations(
|
|
self,
|
|
*,
|
|
doc_scores: List[Floats1d],
|
|
doc_ents: List[Ints1d],
|
|
scores: Sequence[float],
|
|
ents: Sequence[int],
|
|
):
|
|
if not self.save_activations:
|
|
return
|
|
ops = self.model.ops
|
|
doc_scores.append(ops.asarray1f(scores))
|
|
doc_ents.append(ops.asarray1i(ents, dtype="uint64"))
|