mirror of https://github.com/explosion/spaCy.git
493 lines
21 KiB
Python
493 lines
21 KiB
Python
from itertools import islice
|
|
from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List
|
|
from pathlib import Path
|
|
import srsly
|
|
import random
|
|
from thinc.api import CosineDistance, Model, Optimizer, Config
|
|
from thinc.api import set_dropout_rate
|
|
import warnings
|
|
|
|
from ..kb import KnowledgeBase, Candidate
|
|
from ..ml import empty_kb
|
|
from ..tokens import Doc
|
|
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, Warnings
|
|
from ..util import SimpleFrozenList
|
|
from .. import util
|
|
from ..scorer import Scorer
|
|
|
|
|
|
default_model_config = """
|
|
[model]
|
|
@architectures = "spacy.EntityLinker.v1"
|
|
|
|
[model.tok2vec]
|
|
@architectures = "spacy.HashEmbedCNN.v1"
|
|
pretrained_vectors = null
|
|
width = 96
|
|
depth = 2
|
|
embed_size = 300
|
|
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": [],
|
|
"incl_prior": True,
|
|
"incl_context": True,
|
|
"entity_vector_length": 64,
|
|
"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
|
|
},
|
|
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],
|
|
incl_prior: bool,
|
|
incl_context: bool,
|
|
entity_vector_length: int,
|
|
get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
|
|
):
|
|
"""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.
|
|
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.
|
|
"""
|
|
return EntityLinker(
|
|
nlp.vocab,
|
|
model,
|
|
name,
|
|
labels_discard=labels_discard,
|
|
incl_prior=incl_prior,
|
|
incl_context=incl_context,
|
|
entity_vector_length=entity_vector_length,
|
|
get_candidates=get_candidates,
|
|
)
|
|
|
|
|
|
class EntityLinker(TrainablePipe):
|
|
"""Pipeline component for named entity linking.
|
|
|
|
DOCS: https://nightly.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],
|
|
incl_prior: bool,
|
|
incl_context: bool,
|
|
entity_vector_length: int,
|
|
get_candidates: Callable[[KnowledgeBase, "Span"], Iterable[Candidate]],
|
|
) -> 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.
|
|
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.
|
|
|
|
DOCS: https://nightly.spacy.io/api/entitylinker#init
|
|
"""
|
|
self.vocab = vocab
|
|
self.model = model
|
|
self.name = name
|
|
cfg = {
|
|
"labels_discard": list(labels_discard),
|
|
"incl_prior": incl_prior,
|
|
"incl_context": incl_context,
|
|
"entity_vector_length": entity_vector_length,
|
|
}
|
|
self.get_candidates = get_candidates
|
|
self.cfg = dict(cfg)
|
|
self.distance = CosineDistance(normalize=False)
|
|
# how many neightbour sentences to take into account
|
|
self.n_sents = cfg.get("n_sents", 0)
|
|
# create an empty KB by default. If you want to load a predefined one, specify it in 'initialize'.
|
|
self.kb = empty_kb(entity_vector_length)(self.vocab)
|
|
|
|
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."""
|
|
self.kb = kb_loader(self.vocab)
|
|
self.cfg["entity_vector_length"] = self.kb.entity_vector_length
|
|
|
|
def validate_kb(self) -> None:
|
|
# Raise an error if the knowledge base is not initialized.
|
|
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: 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://nightly.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 example in islice(get_examples(), 10):
|
|
doc_sample.append(example.x)
|
|
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)
|
|
self.model.initialize(
|
|
X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32")
|
|
)
|
|
|
|
def update(
|
|
self,
|
|
examples: Iterable[Example],
|
|
*,
|
|
set_annotations: bool = False,
|
|
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.
|
|
set_annotations (bool): Whether or not to update the Example objects
|
|
with the predictions.
|
|
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://nightly.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")
|
|
sentence_docs = []
|
|
docs = [eg.predicted for eg in examples]
|
|
if set_annotations:
|
|
# This seems simpler than other ways to get that exact output -- but
|
|
# it does run the model twice :(
|
|
predictions = self.model.predict(docs)
|
|
for eg in examples:
|
|
sentences = [s for s in eg.reference.sents]
|
|
kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
|
|
for ent in eg.reference.ents:
|
|
# KB ID of the first token is the same as the whole span
|
|
kb_id = kb_ids[ent.start]
|
|
if kb_id:
|
|
try:
|
|
# find the sentence in the list of sentences.
|
|
sent_index = sentences.index(ent.sent)
|
|
except AttributeError:
|
|
# Catch the exception when ent.sent is None and provide a user-friendly warning
|
|
raise RuntimeError(Errors.E030) from None
|
|
# get n previous sentences, if there are any
|
|
start_sentence = max(0, sent_index - self.n_sents)
|
|
# get n posterior sentences, or as many < n as there are
|
|
end_sentence = min(len(sentences) - 1, sent_index + self.n_sents)
|
|
# get token positions
|
|
start_token = sentences[start_sentence].start
|
|
end_token = sentences[end_sentence].end
|
|
# append that span as a doc to training
|
|
sent_doc = eg.predicted[start_token:end_token].as_doc()
|
|
sentence_docs.append(sent_doc)
|
|
set_dropout_rate(self.model, drop)
|
|
if not sentence_docs:
|
|
warnings.warn(Warnings.W093.format(name="Entity Linker"))
|
|
return losses
|
|
sentence_encodings, bp_context = self.model.begin_update(sentence_docs)
|
|
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
|
|
if set_annotations:
|
|
self.set_annotations(docs, predictions)
|
|
return losses
|
|
|
|
def get_loss(self, examples: Iterable[Example], sentence_encodings):
|
|
validate_examples(examples, "EntityLinker.get_loss")
|
|
entity_encodings = []
|
|
for eg in examples:
|
|
kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
|
|
for ent in eg.reference.ents:
|
|
kb_id = kb_ids[ent.start]
|
|
if kb_id:
|
|
entity_encoding = self.kb.get_vector(kb_id)
|
|
entity_encodings.append(entity_encoding)
|
|
entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32")
|
|
if sentence_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(sentence_encodings, entity_encodings)
|
|
loss = self.distance.get_loss(sentence_encodings, entity_encodings)
|
|
loss = loss / len(entity_encodings)
|
|
return loss, gradients
|
|
|
|
def __call__(self, doc: Doc) -> Doc:
|
|
"""Apply the pipe to a Doc.
|
|
|
|
doc (Doc): The document to process.
|
|
RETURNS (Doc): The processed Doc.
|
|
|
|
DOCS: https://nightly.spacy.io/api/entitylinker#call
|
|
"""
|
|
kb_ids = self.predict([doc])
|
|
self.set_annotations([doc], kb_ids)
|
|
return doc
|
|
|
|
def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
|
|
"""Apply the pipe to a stream of documents. This usually happens under
|
|
the hood when the nlp object is called on a text and all components are
|
|
applied to the Doc.
|
|
|
|
stream (Iterable[Doc]): A stream of documents.
|
|
batch_size (int): The number of documents to buffer.
|
|
YIELDS (Doc): Processed documents in order.
|
|
|
|
DOCS: https://nightly.spacy.io/api/entitylinker#pipe
|
|
"""
|
|
for docs in util.minibatch(stream, size=batch_size):
|
|
kb_ids = self.predict(docs)
|
|
self.set_annotations(docs, kb_ids)
|
|
yield from docs
|
|
|
|
def predict(self, docs: Iterable[Doc]) -> List[str]:
|
|
"""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[int]): The models prediction for each document.
|
|
|
|
DOCS: https://nightly.spacy.io/api/entitylinker#predict
|
|
"""
|
|
self.validate_kb()
|
|
entity_count = 0
|
|
final_kb_ids = []
|
|
if not docs:
|
|
return final_kb_ids
|
|
if isinstance(docs, Doc):
|
|
docs = [docs]
|
|
for i, doc in enumerate(docs):
|
|
sentences = [s for s in doc.sents]
|
|
if len(doc) > 0:
|
|
# Looping through each sentence and each entity
|
|
# This may go wrong if there are entities across sentences - which shouldn't happen normally.
|
|
for sent_index, sent in enumerate(sentences):
|
|
if sent.ents:
|
|
# get n_neightbour 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)
|
|
xp = self.model.ops.xp
|
|
if self.cfg.get("incl_context"):
|
|
sentence_encoding = self.model.predict([sent_doc])[0]
|
|
sentence_encoding_t = sentence_encoding.T
|
|
sentence_norm = xp.linalg.norm(sentence_encoding_t)
|
|
for ent in sent.ents:
|
|
entity_count += 1
|
|
to_discard = self.cfg.get("labels_discard", [])
|
|
if to_discard and ent.label_ in to_discard:
|
|
# ignoring this entity - setting to NIL
|
|
final_kb_ids.append(self.NIL)
|
|
else:
|
|
candidates = self.get_candidates(self.kb, ent)
|
|
if not candidates:
|
|
# no prediction possible for this entity - setting to NIL
|
|
final_kb_ids.append(self.NIL)
|
|
elif len(candidates) == 1:
|
|
# shortcut for efficiency reasons: take the 1 candidate
|
|
# TODO: thresholding
|
|
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.cfg.get("incl_prior"):
|
|
prior_probs = xp.asarray(
|
|
[0.0 for _ in candidates]
|
|
)
|
|
scores = prior_probs
|
|
# add in similarity from the context
|
|
if self.cfg.get("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)
|
|
)
|
|
# TODO: thresholding
|
|
best_index = scores.argmax().item()
|
|
best_candidate = candidates[best_index]
|
|
final_kb_ids.append(best_candidate.entity_)
|
|
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 final_kb_ids
|
|
|
|
def set_annotations(self, docs: Iterable[Doc], kb_ids: List[str]) -> None:
|
|
"""Modify a batch of documents, using pre-computed scores.
|
|
|
|
docs (Iterable[Doc]): The documents to modify.
|
|
kb_ids (List[str]): The IDs to set, produced by EntityLinker.predict.
|
|
|
|
DOCS: https://nightly.spacy.io/api/entitylinker#set_annotations
|
|
"""
|
|
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
|
|
for doc in docs:
|
|
for ent in doc.ents:
|
|
kb_id = kb_ids[i]
|
|
i += 1
|
|
for token in ent:
|
|
token.ent_kb_id_ = kb_id
|
|
|
|
def score(self, examples, **kwargs):
|
|
"""Score a batch of examples.
|
|
|
|
examples (Iterable[Example]): The examples to score.
|
|
RETURNS (Dict[str, Any]): The scores.
|
|
|
|
DOCS TODO: https://nightly.spacy.io/api/entity_linker#score
|
|
"""
|
|
validate_examples(examples, "EntityLinker.score")
|
|
return Scorer.score_links(examples, negative_labels=[self.NIL])
|
|
|
|
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://nightly.spacy.io/api/entitylinker#to_disk
|
|
"""
|
|
serialize = {}
|
|
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://nightly.spacy.io/api/entitylinker#from_disk
|
|
"""
|
|
|
|
def load_model(p):
|
|
try:
|
|
self.model.from_bytes(p.open("rb").read())
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149) from None
|
|
|
|
deserialize = {}
|
|
deserialize["cfg"] = lambda p: self.cfg.update(deserialize_config(p))
|
|
deserialize["kb"] = lambda p: self.kb.from_disk(p)
|
|
deserialize["model"] = load_model
|
|
util.from_disk(path, deserialize, exclude)
|
|
for s in self.kb._added_strings:
|
|
self.vocab.strings.add(s)
|
|
return self
|
|
|
|
def rehearse(self, examples, *, sgd=None, losses=None, **config):
|
|
raise NotImplementedError
|
|
|
|
def add_label(self, label):
|
|
raise NotImplementedError
|