spaCy/spacy/pipeline/morphologizer.pyx

# cython: infer_types=True, profile=True, binding=True
from typing import Optional
import srsly
from thinc.api import SequenceCategoricalCrossentropy, Model, Config
from itertools import islice

from ..tokens.doc cimport Doc
from ..vocab cimport Vocab
from ..morphology cimport Morphology

from ..parts_of_speech import IDS as POS_IDS
from ..symbols import POS
from ..language import Language
from ..errors import Errors
from .pipe import deserialize_config
from .tagger import Tagger
from .. import util
from ..scorer import Scorer
from ..training import validate_examples


default_model_config = """
[model]
@architectures = "spacy.Tagger.v1"

[model.tok2vec]
@architectures = "spacy.Tok2Vec.v1"

[model.tok2vec.embed]
@architectures = "spacy.CharacterEmbed.v1"
width = 128
rows = 7000
nM = 64
nC = 8
also_use_static_vectors = false

[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = 128
depth = 4
window_size = 1
maxout_pieces = 3
"""

DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]


@Language.factory(
    "morphologizer",
    assigns=["token.morph", "token.pos"],
    default_config={"model": DEFAULT_MORPH_MODEL},
    scores=["pos_acc", "morph_acc", "morph_per_feat"],
    default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5},
)
def make_morphologizer(
    nlp: Language,
    model: Model,
    name: str,
):
    return Morphologizer(nlp.vocab, model, name)


class Morphologizer(Tagger):
    POS_FEAT = "POS"

    def __init__(
        self,
        vocab: Vocab,
        model: Model,
        name: str = "morphologizer",
        *,
        labels_morph: Optional[dict] = None,
        labels_pos: Optional[dict] = None,
    ):
        """Initialize a morphologizer.

        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_morph (dict): Mapping of morph + POS tags to morph labels.
        labels_pos (dict): Mapping of morph + POS tags to POS tags.

        DOCS: https://nightly.spacy.io/api/morphologizer#init
        """
        self.vocab = vocab
        self.model = model
        self.name = name
        self._rehearsal_model = None
        # to be able to set annotations without string operations on labels,
        # store mappings from morph+POS labels to token-level annotations:
        # 1) labels_morph stores a mapping from morph+POS->morph
        # 2) labels_pos stores a mapping from morph+POS->POS
        cfg = {"labels_morph": labels_morph or {}, "labels_pos": labels_pos or {}}
        self.cfg = dict(sorted(cfg.items()))
        # add mappings for empty morph
        self.cfg["labels_morph"][Morphology.EMPTY_MORPH] = Morphology.EMPTY_MORPH
        self.cfg["labels_pos"][Morphology.EMPTY_MORPH] = POS_IDS[""]

    @property
    def labels(self):
        """RETURNS (Tuple[str]): The labels currently added to the component."""
        return tuple(self.cfg["labels_morph"].keys())

    def add_label(self, label):
        """Add a new label to the pipe.

        label (str): The label to add.
        RETURNS (int): 0 if label is already present, otherwise 1.

        DOCS: https://nightly.spacy.io/api/morphologizer#add_label
        """
        if not isinstance(label, str):
            raise ValueError(Errors.E187)
        if label in self.labels:
            return 0
        self._allow_extra_label()
        # normalize label
        norm_label = self.vocab.morphology.normalize_features(label)
        # extract separate POS and morph tags
        label_dict = Morphology.feats_to_dict(label)
        pos = label_dict.get(self.POS_FEAT, "")
        if self.POS_FEAT in label_dict:
            label_dict.pop(self.POS_FEAT)
        # normalize morph string and add to morphology table
        norm_morph = self.vocab.strings[self.vocab.morphology.add(label_dict)]
        # add label mappings
        if norm_label not in self.cfg["labels_morph"]:
            self.cfg["labels_morph"][norm_label] = norm_morph
            self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
        return 1

    def begin_training(self, get_examples, *, pipeline=None, sgd=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.
        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://nightly.spacy.io/api/morphologizer#begin_training
        """
        self._ensure_examples(get_examples)
        # First, fetch all labels from the data
        for example in get_examples():
            for i, token in enumerate(example.reference):
                pos = token.pos_
                morph = token.morph_
                # create and add the combined morph+POS label
                morph_dict = Morphology.feats_to_dict(morph)
                if pos:
                    morph_dict[self.POS_FEAT] = pos
                norm_label = self.vocab.strings[self.vocab.morphology.add(morph_dict)]
                # add label->morph and label->POS mappings
                if norm_label not in self.cfg["labels_morph"]:
                    self.cfg["labels_morph"][norm_label] = morph
                    self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
        if len(self.labels) <= 1:
            raise ValueError(Errors.E143.format(name=self.name))
        doc_sample = []
        label_sample = []
        for example in islice(get_examples(), 10):
            gold_array = []
            for i, token in enumerate(example.reference):
                pos = token.pos_
                morph = token.morph_
                morph_dict = Morphology.feats_to_dict(morph)
                if pos:
                    morph_dict[self.POS_FEAT] = pos
                norm_label = self.vocab.strings[self.vocab.morphology.add(morph_dict)]
                gold_array.append([1.0 if label == norm_label else 0.0 for label in self.labels])
            doc_sample.append(example.x)
            label_sample.append(self.model.ops.asarray(gold_array, dtype="float32"))
        assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
        assert len(label_sample) > 0, Errors.E923.format(name=self.name)
        self.model.initialize(X=doc_sample, Y=label_sample)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd

    def set_annotations(self, docs, batch_tag_ids):
        """Modify a batch of documents, using pre-computed scores.

        docs (Iterable[Doc]): The documents to modify.
        batch_tag_ids: The IDs to set, produced by Morphologizer.predict.

        DOCS: https://nightly.spacy.io/api/morphologizer#set_annotations
        """
        if isinstance(docs, Doc):
            docs = [docs]
        cdef Doc doc
        cdef Vocab vocab = self.vocab
        for i, doc in enumerate(docs):
            doc_tag_ids = batch_tag_ids[i]
            if hasattr(doc_tag_ids, "get"):
                doc_tag_ids = doc_tag_ids.get()
            for j, tag_id in enumerate(doc_tag_ids):
                morph = self.labels[tag_id]
                doc.c[j].morph = self.vocab.morphology.add(self.cfg["labels_morph"][morph])
                doc.c[j].pos = self.cfg["labels_pos"][morph]

    def get_loss(self, examples, scores):
        """Find the loss and gradient of loss for the batch of documents and
        their predicted scores.

        examples (Iterable[Examples]): The batch of examples.
        scores: Scores representing the model's predictions.
        RETUTNRS (Tuple[float, float]): The loss and the gradient.

        DOCS: https://nightly.spacy.io/api/morphologizer#get_loss
        """
        validate_examples(examples, "Morphologizer.get_loss")
        loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
        truths = []
        for eg in examples:
            eg_truths = []
            pos_tags = eg.get_aligned("POS", as_string=True)
            morphs = eg.get_aligned("MORPH", as_string=True)
            for i in range(len(morphs)):
                pos = pos_tags[i]
                morph = morphs[i]
                # POS may align (same value for multiple tokens) when morph
                # doesn't, so if either is None, treat both as None here so that
                # truths doesn't end up with an unknown morph+POS combination
                if pos is None or morph is None:
                    pos = None
                    morph = None
                label_dict = Morphology.feats_to_dict(morph)
                if pos:
                    label_dict[self.POS_FEAT] = pos
                label = self.vocab.strings[self.vocab.morphology.add(label_dict)]
                eg_truths.append(label)
            truths.append(eg_truths)
        d_scores, loss = loss_func(scores, truths)
        if self.model.ops.xp.isnan(loss):
            raise ValueError("nan value when computing loss")
        return float(loss), d_scores

    def score(self, examples, **kwargs):
        """Score a batch of examples.

        examples (Iterable[Example]): The examples to score.
        RETURNS (Dict[str, Any]): The scores, produced by
            Scorer.score_token_attr for the attributes "pos" and "morph" and
            Scorer.score_token_attr_per_feat for the attribute "morph".

        DOCS: https://nightly.spacy.io/api/morphologizer#score
        """
        validate_examples(examples, "Morphologizer.score")
        results = {}
        results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
        results.update(Scorer.score_token_attr(examples, "morph", **kwargs))
        results.update(Scorer.score_token_attr_per_feat(examples,
            "morph", **kwargs))
        return results