mirror of
https://github.com/explosion/spaCy.git
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224 lines
6.8 KiB
Python
224 lines
6.8 KiB
Python
from typing import Iterable, Optional, Dict, List, Callable, Any, Union
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from thinc.types import Floats2d
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from thinc.api import Model, Config
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from itertools import islice
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from ..language import Language
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from ..training import Example, validate_get_examples
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from ..errors import Errors
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from ..scorer import Scorer
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from ..tokens import Doc
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from ..util import registry
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from ..vocab import Vocab
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from .textcat import TextCategorizer
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multi_label_default_config = """
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[model]
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@architectures = "spacy.TextCatEnsemble.v2"
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[model.tok2vec]
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@architectures = "spacy.Tok2Vec.v2"
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[model.tok2vec.embed]
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@architectures = "spacy.MultiHashEmbed.v2"
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width = 64
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rows = [2000, 2000, 500, 1000, 500]
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attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
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include_static_vectors = false
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[model.tok2vec.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v2"
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width = ${model.tok2vec.embed.width}
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window_size = 1
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maxout_pieces = 3
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depth = 2
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[model.linear_model]
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@architectures = "spacy.TextCatBOW.v2"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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"""
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DEFAULT_MULTI_TEXTCAT_MODEL = Config().from_str(multi_label_default_config)["model"]
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multi_label_bow_config = """
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[model]
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@architectures = "spacy.TextCatBOW.v2"
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exclusive_classes = false
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ngram_size = 1
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no_output_layer = false
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"""
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multi_label_cnn_config = """
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[model]
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@architectures = "spacy.TextCatCNN.v2"
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exclusive_classes = false
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v2"
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pretrained_vectors = null
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width = 96
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depth = 4
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embed_size = 2000
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window_size = 1
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maxout_pieces = 3
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subword_features = true
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"""
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@Language.factory(
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"textcat_multilabel",
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assigns=["doc.cats"],
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default_config={
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"threshold": 0.5,
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"model": DEFAULT_MULTI_TEXTCAT_MODEL,
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"scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v1"},
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"save_activations": False,
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},
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default_score_weights={
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"cats_score": 1.0,
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"cats_score_desc": None,
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"cats_micro_p": None,
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"cats_micro_r": None,
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"cats_micro_f": None,
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"cats_macro_p": None,
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"cats_macro_r": None,
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"cats_macro_f": None,
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"cats_macro_auc": None,
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"cats_f_per_type": None,
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},
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)
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def make_multilabel_textcat(
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nlp: Language,
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name: str,
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model: Model[List[Doc], List[Floats2d]],
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threshold: float,
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scorer: Optional[Callable],
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save_activations: bool,
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) -> "MultiLabel_TextCategorizer":
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"""Create a TextCategorizer component. The text categorizer predicts categories
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over a whole document. It can learn one or more labels, and the labels are considered
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to be non-mutually exclusive, which means that there can be zero or more labels
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per doc).
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model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
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scores for each category.
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threshold (float): Cutoff to consider a prediction "positive".
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scorer (Optional[Callable]): The scoring method.
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"""
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return MultiLabel_TextCategorizer(
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nlp.vocab,
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model,
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name,
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threshold=threshold,
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scorer=scorer,
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save_activations=save_activations,
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)
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def textcat_multilabel_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
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return Scorer.score_cats(
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examples,
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"cats",
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multi_label=True,
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**kwargs,
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)
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@registry.scorers("spacy.textcat_multilabel_scorer.v1")
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def make_textcat_multilabel_scorer():
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return textcat_multilabel_score
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class MultiLabel_TextCategorizer(TextCategorizer):
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"""Pipeline component for multi-label text classification.
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DOCS: https://spacy.io/api/textcategorizer
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"""
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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 = "textcat_multilabel",
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*,
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threshold: float,
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scorer: Optional[Callable] = textcat_multilabel_score,
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save_activations: bool = False,
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) -> None:
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"""Initialize a text categorizer for multi-label classification.
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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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threshold (float): Cutoff to consider a prediction "positive".
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<<<<<<< HEAD
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save_activations (bool): save model activations in Doc when annotating.
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=======
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scorer (Optional[Callable]): The scoring method.
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>>>>>>> upstream/master
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DOCS: https://spacy.io/api/textcategorizer#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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cfg = {"labels": [], "threshold": threshold}
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self.cfg = dict(cfg)
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self.scorer = scorer
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self.save_activations = save_activations
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@property
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def support_missing_values(self):
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return True
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def initialize( # type: ignore[override]
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self,
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get_examples: Callable[[], Iterable[Example]],
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*,
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nlp: Optional[Language] = None,
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labels: Optional[Iterable[str]] = None,
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):
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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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labels: The labels to add to the component, typically generated by the
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`init labels` command. If no labels are provided, the get_examples
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callback is used to extract the labels from the data.
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DOCS: https://spacy.io/api/textcategorizer#initialize
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"""
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validate_get_examples(get_examples, "MultiLabel_TextCategorizer.initialize")
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if labels is None:
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for example in get_examples():
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for cat in example.y.cats:
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self.add_label(cat)
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else:
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for label in labels:
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self.add_label(label)
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subbatch = list(islice(get_examples(), 10))
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self._validate_categories(subbatch)
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doc_sample = [eg.reference for eg in subbatch]
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label_sample, _ = self._examples_to_truth(subbatch)
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self._require_labels()
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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 _validate_categories(self, examples: Iterable[Example]):
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"""This component allows any type of single- or multi-label annotations.
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This method overwrites the more strict one from 'textcat'."""
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# check that annotation values are valid
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for ex in examples:
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for val in ex.reference.cats.values():
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if not (val == 1.0 or val == 0.0):
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raise ValueError(Errors.E851.format(val=val))
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