spaCy/spacy/pipeline/textcat_multilabel.py

from itertools import islice
from typing import Iterable, Optional, Dict, List, Callable, Any

from thinc.api import Model, Config
from thinc.types import Floats2d

from ..language import Language
from ..training import Example, validate_examples, validate_get_examples
from ..errors import Errors
from ..scorer import Scorer
from ..tokens import Doc
from ..vocab import Vocab
from .textcat import TextCategorizer


multi_label_default_config = """
[model]
@architectures = "spacy.TextCatEnsemble.v2"

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

[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v1"
width = 64
rows = [2000, 2000, 1000, 1000, 1000, 1000]
attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
include_static_vectors = false

[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
depth = 2

[model.linear_model]
@architectures = "spacy.TextCatBOW.v1"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
"""
DEFAULT_MULTI_TEXTCAT_MODEL = Config().from_str(multi_label_default_config)["model"]

multi_label_bow_config = """
[model]
@architectures = "spacy.TextCatBOW.v1"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
"""

multi_label_cnn_config = """
[model]
@architectures = "spacy.TextCatCNN.v1"
exclusive_classes = false

[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 96
depth = 4
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true
"""


@Language.factory(
    "textcat_multilabel",
    assigns=["doc.cats"],
    default_config={"threshold": 0.5, "model": DEFAULT_MULTI_TEXTCAT_MODEL},
    default_score_weights={
        "cats_score": 1.0,
        "cats_score_desc": None,
        "cats_micro_p": None,
        "cats_micro_r": None,
        "cats_micro_f": None,
        "cats_macro_p": None,
        "cats_macro_r": None,
        "cats_macro_f": None,
        "cats_macro_auc": None,
        "cats_f_per_type": None,
        "cats_macro_auc_per_type": None,
    },
)
def make_multilabel_textcat(
    nlp: Language, name: str, model: Model[List[Doc], List[Floats2d]], threshold: float
) -> "TextCategorizer":
    """Create a TextCategorizer compoment. The text categorizer predicts categories
    over a whole document. It can learn one or more labels, and the labels can
    be mutually exclusive (i.e. one true label per doc) or non-mutually exclusive
    (i.e. zero or more labels may be true per doc). The multi-label setting is
    controlled by the model instance that's provided.

    model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
        scores for each category.
    threshold (float): Cutoff to consider a prediction "positive".
    """
    return MultiLabel_TextCategorizer(nlp.vocab, model, name, threshold=threshold)


class MultiLabel_TextCategorizer(TextCategorizer):
    """Pipeline component for multi-label text classification.

    DOCS: https://spacy.io/api/multilabel_textcategorizer
    """

    def __init__(
        self,
        vocab: Vocab,
        model: Model,
        name: str = "textcat_multilabel",
        *,
        threshold: float,
    ) -> None:
        """Initialize a text categorizer for multi-label classification.

        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.
        threshold (float): Cutoff to consider a prediction "positive".

        DOCS: https://spacy.io/api/multilabel_textcategorizer#init
        """
        self.vocab = vocab
        self.model = model
        self.name = name
        self._rehearsal_model = None
        cfg = {"labels": [], "threshold": threshold}
        self.cfg = dict(cfg)

    def initialize(
        self,
        get_examples: Callable[[], Iterable[Example]],
        *,
        nlp: Optional[Language] = None,
        labels: Optional[Iterable[str]] = 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.
        nlp (Language): The current nlp object the component is part of.
        labels: The labels to add to the component, typically generated by the
            `init labels` command. If no labels are provided, the get_examples
            callback is used to extract the labels from the data.

        DOCS: https://spacy.io/api/multilabel_textcategorizer#initialize
        """
        validate_get_examples(get_examples, "MultiLabel_TextCategorizer.initialize")
        if labels is None:
            for example in get_examples():
                for cat in example.y.cats:
                    self.add_label(cat)
        else:
            for label in labels:
                self.add_label(label)
        subbatch = list(islice(get_examples(), 10))
        doc_sample = [eg.reference for eg in subbatch]
        label_sample, _ = self._examples_to_truth(subbatch)
        self._require_labels()
        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)

    def score(self, examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
        """Score a batch of examples.

        examples (Iterable[Example]): The examples to score.
        RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_cats.

        DOCS: https://spacy.io/api/multilabel_textcategorizer#score
        """
        validate_examples(examples, "MultiLabel_TextCategorizer.score")
        return Scorer.score_cats(
            examples,
            "cats",
            labels=self.labels,
            multi_label=True,
            threshold=self.cfg["threshold"],
            **kwargs,
        )

    def _validate_categories(self, examples: List[Example]):
        """This component allows any type of single- or multi-label annotations.
        This method overwrites the more strict one from 'textcat'."""
        pass