spaCy/spacy/pipeline/entity_linker.py

from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List, Tuple
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 ..tokens import Doc
from .pipe import Pipe, deserialize_config
from ..language import Language
from ..vocab import Vocab
from ..gold import Example, validate_examples
from ..errors import Errors, Warnings
from ..util import SimpleFrozenList
from .. import util


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={
        "kb_loader": {"@assets": "spacy.EmptyKB.v1", "entity_vector_length": 64},
        "model": DEFAULT_NEL_MODEL,
        "labels_discard": [],
        "incl_prior": True,
        "incl_context": True,
        "get_candidates": {"@assets": "spacy.CandidateGenerator.v1"},
    },
)
def make_entity_linker(
    nlp: Language,
    name: str,
    model: Model,
    kb_loader: Callable[[Vocab], KnowledgeBase],
    *,
    labels_discard: Iterable[str],
    incl_prior: bool,
    incl_context: bool,
    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.
    kb (KnowledgeBase): The knowledge-base to link entities to.
    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.
    """
    return EntityLinker(
        nlp.vocab,
        model,
        name,
        kb_loader=kb_loader,
        labels_discard=labels_discard,
        incl_prior=incl_prior,
        incl_context=incl_context,
        get_candidates=get_candidates,
    )


class EntityLinker(Pipe):
    """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",
        *,
        kb_loader: Callable[[Vocab], KnowledgeBase],
        labels_discard: Iterable[str],
        incl_prior: bool,
        incl_context: bool,
        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.
        kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
        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.

        DOCS: https://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,
        }
        self.kb = kb_loader(self.vocab)
        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)

    def require_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 begin_training(
        self,
        get_examples: Callable[[], Iterable[Example]],
        *,
        pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
        sgd: Optional[Optimizer] = None,
    ) -> Optimizer:
        """Initialize the pipe for training, using data examples if available.

        get_examples (Callable[[], Iterable[Example]]): Optional function that
            returns gold-standard Example objects.
        pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
            components that this component is part of. Corresponds to
            nlp.pipeline.
        sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
            create_optimizer if it doesn't exist.
        RETURNS (thinc.api.Optimizer): The optimizer.

        DOCS: https://spacy.io/api/entitylinker#begin_training
        """
        self.require_kb()
        nO = self.kb.entity_vector_length
        self.set_output(nO)
        self.model.initialize()
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd

    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://spacy.io/api/entitylinker#update
        """
        self.require_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.predicted.sents]
            kb_ids = eg.get_aligned("ENT_KB_ID", as_string=True)
            for ent in eg.predicted.ents:
                kb_id = kb_ids[
                    ent.start
                ]  # KB ID of the first token is the same as the whole span
                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.model.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.predicted.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://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://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://spacy.io/api/entitylinker#predict
        """
        self.require_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://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 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["cfg"] = lambda p: srsly.write_json(p, self.cfg)
        serialize["vocab"] = lambda p: self.vocab.to_disk(p)
        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:
                self.model.from_bytes(p.open("rb").read())
            except AttributeError:
                raise ValueError(Errors.E149) from None

        deserialize = {}
        deserialize["vocab"] = lambda p: self.vocab.from_disk(p)
        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)
        return self

    def rehearse(self, examples, *, sgd=None, losses=None, **config):
        raise NotImplementedError

    def add_label(self, label):
        raise NotImplementedError