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a4b32b9552
* Handle missing reference values in scorer Handle missing values in reference doc during scoring where it is possible to detect an unset state for the attribute. If no reference docs contain annotation, `None` is returned instead of a score. `spacy evaluate` displays `-` for missing scores and the missing scores are saved as `None`/`null` in the metrics. Attributes without unset states: * `token.head`: relies on `token.dep` to recognize unset values * `doc.cats`: unable to handle missing annotation Additional changes: * add optional `has_annotation` check to `score_scans` to replace `doc.sents` hack * update `score_token_attr_per_feat` to handle missing and empty morph representations * fix bug in `Doc.has_annotation` for normalization of `IS_SENT_START` vs. `SENT_START` * Fix import * Update return types
264 lines
10 KiB
Cython
264 lines
10 KiB
Cython
# cython: infer_types=True, profile=True, binding=True
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from typing import Optional, Union, Dict
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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, validate_get_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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include_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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@property
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def label_data(self) -> Dict[str, Dict[str, Union[str, float, int, None]]]:
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"""A dictionary with all labels data."""
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return {"morph": self.cfg["labels_morph"], "pos": self.cfg["labels_pos"]}
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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, *, nlp=None, labels=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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nlp (Language): The current nlp object the component is part of.
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DOCS: https://nightly.spacy.io/api/morphologizer#initialize
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"""
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validate_get_examples(get_examples, "Morphologizer.initialize")
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if labels is not None:
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self.cfg["labels_morph"] = labels["morph"]
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self.cfg["labels_pos"] = labels["pos"]
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else:
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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 = str(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 = str(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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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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RETURNS (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(Errors.E910.format(name=self.name))
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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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def morph_key_getter(token, attr):
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return getattr(token, attr).key
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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", getter=morph_key_getter, **kwargs))
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results.update(Scorer.score_token_attr_per_feat(examples,
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"morph", getter=morph_key_getter, **kwargs))
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return results
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