mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 04:08:09 +03:00
260 lines
9.9 KiB
Cython
260 lines
9.9 KiB
Cython
# 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},
|
|
default_score_weights={"pos_acc": 0.5, "morph_acc": 0.5, "morph_per_feat": None},
|
|
)
|
|
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
|