mirror of
https://github.com/explosion/spaCy.git
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260 lines
9.9 KiB
Cython
260 lines
9.9 KiB
Cython
# cython: infer_types=True, profile=True, binding=True
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from typing import Optional
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import srsly
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from thinc.api import SequenceCategoricalCrossentropy, Model, Config
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from itertools import islice
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from ..tokens.doc cimport Doc
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from ..vocab cimport Vocab
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from ..morphology cimport Morphology
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from ..parts_of_speech import IDS as POS_IDS
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from ..symbols import POS
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from ..language import Language
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from ..errors import Errors
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from .pipe import deserialize_config
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from .tagger import Tagger
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from .. import util
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from ..scorer import Scorer
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from ..training import validate_examples
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default_model_config = """
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[model]
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@architectures = "spacy.Tagger.v1"
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[model.tok2vec]
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@architectures = "spacy.Tok2Vec.v1"
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[model.tok2vec.embed]
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@architectures = "spacy.CharacterEmbed.v1"
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width = 128
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rows = 7000
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nM = 64
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nC = 8
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also_use_static_vectors = false
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[model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v1"
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width = 128
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depth = 4
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window_size = 1
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maxout_pieces = 3
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"""
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DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"morphologizer",
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assigns=["token.morph", "token.pos"],
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default_config={"model": DEFAULT_MORPH_MODEL},
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default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
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)
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def make_morphologizer(
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nlp: Language,
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model: Model,
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name: str,
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):
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return Morphologizer(nlp.vocab, model, name)
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class Morphologizer(Tagger):
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POS_FEAT = "POS"
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def __init__(
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self,
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vocab: Vocab,
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model: Model,
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name: str = "morphologizer",
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*,
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labels_morph: Optional[dict] = None,
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labels_pos: Optional[dict] = None,
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):
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"""Initialize a morphologizer.
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vocab (Vocab): The shared vocabulary.
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model (thinc.api.Model): The Thinc Model powering the pipeline component.
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name (str): The component instance name, used to add entries to the
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losses during training.
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labels_morph (dict): Mapping of morph + POS tags to morph labels.
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labels_pos (dict): Mapping of morph + POS tags to POS tags.
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DOCS: https://nightly.spacy.io/api/morphologizer#init
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"""
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self.vocab = vocab
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self.model = model
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self.name = name
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self._rehearsal_model = None
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# to be able to set annotations without string operations on labels,
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# store mappings from morph+POS labels to token-level annotations:
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# 1) labels_morph stores a mapping from morph+POS->morph
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# 2) labels_pos stores a mapping from morph+POS->POS
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cfg = {"labels_morph": labels_morph or {}, "labels_pos": labels_pos or {}}
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self.cfg = dict(sorted(cfg.items()))
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# add mappings for empty morph
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self.cfg["labels_morph"][Morphology.EMPTY_MORPH] = Morphology.EMPTY_MORPH
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self.cfg["labels_pos"][Morphology.EMPTY_MORPH] = POS_IDS[""]
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@property
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def labels(self):
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"""RETURNS (Tuple[str]): The labels currently added to the component."""
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return tuple(self.cfg["labels_morph"].keys())
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def add_label(self, label):
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"""Add a new label to the pipe.
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label (str): The label to add.
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RETURNS (int): 0 if label is already present, otherwise 1.
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DOCS: https://nightly.spacy.io/api/morphologizer#add_label
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"""
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if not isinstance(label, str):
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raise ValueError(Errors.E187)
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if label in self.labels:
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return 0
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self._allow_extra_label()
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# normalize label
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norm_label = self.vocab.morphology.normalize_features(label)
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# extract separate POS and morph tags
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label_dict = Morphology.feats_to_dict(label)
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pos = label_dict.get(self.POS_FEAT, "")
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if self.POS_FEAT in label_dict:
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label_dict.pop(self.POS_FEAT)
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# normalize morph string and add to morphology table
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norm_morph = self.vocab.strings[self.vocab.morphology.add(label_dict)]
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# add label mappings
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if norm_label not in self.cfg["labels_morph"]:
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self.cfg["labels_morph"][norm_label] = norm_morph
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self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
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return 1
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def initialize(self, get_examples, *, pipeline=None, sgd=None):
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"""Initialize the pipe for training, using a representative set
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of data examples.
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get_examples (Callable[[], Iterable[Example]]): Function that
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returns a representative sample of gold-standard Example objects.
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pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
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components that this component is part of. Corresponds to
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nlp.pipeline.
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sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
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create_optimizer if it doesn't exist.
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RETURNS (thinc.api.Optimizer): The optimizer.
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DOCS: https://nightly.spacy.io/api/morphologizer#initialize
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"""
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self._ensure_examples(get_examples)
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# First, fetch all labels from the data
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for example in get_examples():
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for i, token in enumerate(example.reference):
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pos = token.pos_
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morph = token.morph_
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# create and add the combined morph+POS label
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morph_dict = Morphology.feats_to_dict(morph)
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if pos:
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morph_dict[self.POS_FEAT] = pos
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norm_label = self.vocab.strings[self.vocab.morphology.add(morph_dict)]
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# add label->morph and label->POS mappings
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if norm_label not in self.cfg["labels_morph"]:
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self.cfg["labels_morph"][norm_label] = morph
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self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
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if len(self.labels) <= 1:
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raise ValueError(Errors.E143.format(name=self.name))
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doc_sample = []
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label_sample = []
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for example in islice(get_examples(), 10):
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gold_array = []
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for i, token in enumerate(example.reference):
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pos = token.pos_
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morph = token.morph_
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morph_dict = Morphology.feats_to_dict(morph)
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if pos:
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morph_dict[self.POS_FEAT] = pos
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norm_label = self.vocab.strings[self.vocab.morphology.add(morph_dict)]
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gold_array.append([1.0 if label == norm_label else 0.0 for label in self.labels])
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doc_sample.append(example.x)
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label_sample.append(self.model.ops.asarray(gold_array, dtype="float32"))
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assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
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assert len(label_sample) > 0, Errors.E923.format(name=self.name)
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self.model.initialize(X=doc_sample, Y=label_sample)
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if sgd is None:
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sgd = self.create_optimizer()
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return sgd
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def set_annotations(self, docs, batch_tag_ids):
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"""Modify a batch of documents, using pre-computed scores.
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docs (Iterable[Doc]): The documents to modify.
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batch_tag_ids: The IDs to set, produced by Morphologizer.predict.
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DOCS: https://nightly.spacy.io/api/morphologizer#set_annotations
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"""
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if isinstance(docs, Doc):
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docs = [docs]
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cdef Doc doc
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cdef Vocab vocab = self.vocab
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for i, doc in enumerate(docs):
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doc_tag_ids = batch_tag_ids[i]
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if hasattr(doc_tag_ids, "get"):
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doc_tag_ids = doc_tag_ids.get()
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for j, tag_id in enumerate(doc_tag_ids):
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morph = self.labels[tag_id]
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doc.c[j].morph = self.vocab.morphology.add(self.cfg["labels_morph"][morph])
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doc.c[j].pos = self.cfg["labels_pos"][morph]
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def get_loss(self, examples, scores):
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"""Find the loss and gradient of loss for the batch of documents and
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their predicted scores.
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examples (Iterable[Examples]): The batch of examples.
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scores: Scores representing the model's predictions.
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RETUTNRS (Tuple[float, float]): The loss and the gradient.
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DOCS: https://nightly.spacy.io/api/morphologizer#get_loss
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"""
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validate_examples(examples, "Morphologizer.get_loss")
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loss_func = SequenceCategoricalCrossentropy(names=self.labels, normalize=False)
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truths = []
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for eg in examples:
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eg_truths = []
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pos_tags = eg.get_aligned("POS", as_string=True)
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morphs = eg.get_aligned("MORPH", as_string=True)
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for i in range(len(morphs)):
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pos = pos_tags[i]
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morph = morphs[i]
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# POS may align (same value for multiple tokens) when morph
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# doesn't, so if either is None, treat both as None here so that
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# truths doesn't end up with an unknown morph+POS combination
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if pos is None or morph is None:
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pos = None
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morph = None
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label_dict = Morphology.feats_to_dict(morph)
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if pos:
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label_dict[self.POS_FEAT] = pos
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label = self.vocab.strings[self.vocab.morphology.add(label_dict)]
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eg_truths.append(label)
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truths.append(eg_truths)
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d_scores, loss = loss_func(scores, truths)
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if self.model.ops.xp.isnan(loss):
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raise ValueError("nan value when computing loss")
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return float(loss), d_scores
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def score(self, examples, **kwargs):
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"""Score a batch of examples.
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examples (Iterable[Example]): The examples to score.
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RETURNS (Dict[str, Any]): The scores, produced by
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Scorer.score_token_attr for the attributes "pos" and "morph" and
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Scorer.score_token_attr_per_feat for the attribute "morph".
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DOCS: https://nightly.spacy.io/api/morphologizer#score
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"""
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validate_examples(examples, "Morphologizer.score")
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results = {}
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results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
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results.update(Scorer.score_token_attr(examples, "morph", **kwargs))
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results.update(Scorer.score_token_attr_per_feat(examples,
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"morph", **kwargs))
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return results
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