spaCy/spacy/pipeline/textcat.py

from typing import Iterable, Tuple, Optional, Dict, List, Callable, Iterator, Any
from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config
import numpy

from .pipe import Pipe
from ..language import Language
from ..gold import Example
from ..errors import Errors
from ..scorer import Scorer
from .. import util
from ..tokens import Doc
from ..vocab import Vocab


default_model_config = """
[model]
@architectures = "spacy.TextCat.v1"
exclusive_classes = false
pretrained_vectors = null
width = 64
conv_depth = 2
embed_size = 2000
window_size = 1
ngram_size = 1
dropout = null
"""
DEFAULT_TEXTCAT_MODEL = Config().from_str(default_model_config)["model"]

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

cnn_model_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
dropout = null
"""


@Language.factory(
    "textcat",
    assigns=["doc.cats"],
    default_config={"labels": [], "model": DEFAULT_TEXTCAT_MODEL},
)
def make_textcat(
    nlp: Language, name: str, model: Model, labels: Iterable[str]
) -> "TextCategorizer":
    return TextCategorizer(nlp.vocab, model, name, labels=labels)


class TextCategorizer(Pipe):
    """Pipeline component for text classification.

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

    def __init__(
        self,
        vocab: Vocab,
        model: Model,
        name: str = "textcat",
        *,
        labels: Iterable[str],
    ) -> None:
        self.vocab = vocab
        self.model = model
        self.name = name
        self._rehearsal_model = None
        cfg = {"labels": labels}
        self.cfg = dict(cfg)

    @property
    def labels(self) -> Tuple[str]:
        return tuple(self.cfg.setdefault("labels", []))

    def require_labels(self) -> None:
        """Raise an error if the component's model has no labels defined."""
        if not self.labels:
            raise ValueError(Errors.E143.format(name=self.name))

    @labels.setter
    def labels(self, value: Iterable[str]) -> None:
        self.cfg["labels"] = tuple(value)

    def pipe(self, stream: Iterator[str], batch_size: int = 128) -> Iterator[Doc]:
        for docs in util.minibatch(stream, size=batch_size):
            scores = self.predict(docs)
            self.set_annotations(docs, scores)
            yield from docs

    def predict(self, docs: Iterable[Doc]):
        tensors = [doc.tensor for doc in docs]
        if not any(len(doc) for doc in docs):
            # Handle cases where there are no tokens in any docs.
            xp = get_array_module(tensors)
            scores = xp.zeros((len(docs), len(self.labels)))
            return scores
        scores = self.model.predict(docs)
        scores = self.model.ops.asarray(scores)
        return scores

    def set_annotations(self, docs: Iterable[Doc], scores) -> None:
        for i, doc in enumerate(docs):
            for j, label in enumerate(self.labels):
                doc.cats[label] = float(scores[i, j])

    def update(
        self,
        examples: Iterable[Example],
        *,
        drop: float = 0.0,
        set_annotations: bool = False,
        sgd: Optional[Optimizer] = None,
        losses: Optional[Dict[str, float]] = None,
    ) -> Dict[str, float]:
        if losses is None:
            losses = {}
        losses.setdefault(self.name, 0.0)
        try:
            if not any(len(eg.predicted) if eg.predicted else 0 for eg in examples):
                # Handle cases where there are no tokens in any docs.
                return losses
        except AttributeError:
            types = set([type(eg) for eg in examples])
            raise TypeError(
                Errors.E978.format(name="TextCategorizer", method="update", types=types)
            )
        set_dropout_rate(self.model, drop)
        scores, bp_scores = self.model.begin_update([eg.predicted for eg in examples])
        loss, d_scores = self.get_loss(examples, scores)
        bp_scores(d_scores)
        if sgd is not None:
            self.model.finish_update(sgd)
        losses[self.name] += loss
        if set_annotations:
            docs = [eg.predicted for eg in examples]
            self.set_annotations(docs, scores=scores)
        return losses

    def rehearse(
        self,
        examples: Iterable[Example],
        drop: float = 0.0,
        sgd: Optional[Optimizer] = None,
        losses: Optional[Dict[str, float]] = None,
    ) -> None:
        if self._rehearsal_model is None:
            return
        try:
            docs = [eg.predicted for eg in examples]
        except AttributeError:
            types = set([type(eg) for eg in examples])
            err = Errors.E978.format(
                name="TextCategorizer", method="rehearse", types=types
            )
            raise TypeError(err)
        if not any(len(doc) for doc in docs):
            # Handle cases where there are no tokens in any docs.
            return
        set_dropout_rate(self.model, drop)
        scores, bp_scores = self.model.begin_update(docs)
        target = self._rehearsal_model(examples)
        gradient = scores - target
        bp_scores(gradient)
        if sgd is not None:
            self.model.finish_update(sgd)
        if losses is not None:
            losses.setdefault(self.name, 0.0)
            losses[self.name] += (gradient ** 2).sum()

    def _examples_to_truth(
        self, examples: List[Example]
    ) -> Tuple[numpy.ndarray, numpy.ndarray]:
        truths = numpy.zeros((len(examples), len(self.labels)), dtype="f")
        not_missing = numpy.ones((len(examples), len(self.labels)), dtype="f")
        for i, eg in enumerate(examples):
            for j, label in enumerate(self.labels):
                if label in eg.reference.cats:
                    truths[i, j] = eg.reference.cats[label]
                else:
                    not_missing[i, j] = 0.0
        truths = self.model.ops.asarray(truths)
        return truths, not_missing

    def get_loss(self, examples: Iterable[Example], scores) -> Tuple[float, float]:
        truths, not_missing = self._examples_to_truth(examples)
        not_missing = self.model.ops.asarray(not_missing)
        d_scores = (scores - truths) / scores.shape[0]
        d_scores *= not_missing
        mean_square_error = (d_scores ** 2).sum(axis=1).mean()
        return float(mean_square_error), d_scores

    def add_label(self, label: str) -> int:
        if not isinstance(label, str):
            raise ValueError(Errors.E187)
        if label in self.labels:
            return 0
        if self.model.has_dim("nO"):
            # This functionality was available previously, but was broken.
            # The problem is that we resize the last layer, but the last layer
            # is actually just an ensemble. We're not resizing the child layers
            # - a huge problem.
            raise ValueError(Errors.E116)
            # smaller = self.model._layers[-1]
            # larger = Linear(len(self.labels)+1, smaller.nI)
            # copy_array(larger.W[:smaller.nO], smaller.W)
            # copy_array(larger.b[:smaller.nO], smaller.b)
            # self.model._layers[-1] = larger
        self.labels = tuple(list(self.labels) + [label])
        return 1

    def begin_training(
        self,
        get_examples: Callable = lambda: [],
        pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
        sgd: Optional[Optimizer] = None,
    ) -> Optimizer:
        # TODO: begin_training is not guaranteed to see all data / labels ?
        examples = list(get_examples())
        for example in examples:
            try:
                y = example.y
            except AttributeError:
                err = Errors.E978.format(
                    name="TextCategorizer", method="update", types=type(example)
                )
                raise TypeError(err)
            for cat in y.cats:
                self.add_label(cat)
        self.require_labels()
        docs = [Doc(Vocab(), words=["hello"])]
        truths, _ = self._examples_to_truth(examples)
        self.set_output(len(self.labels))
        util.link_vectors_to_models(self.vocab)
        self.model.initialize(X=docs, Y=truths)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd

    def score(
        self,
        examples: Iterable[Example],
        positive_label: Optional[str] = None,
        **kwargs,
    ) -> Dict[str, Any]:
        return Scorer.score_cats(
            examples,
            "cats",
            labels=self.labels,
            multi_label=self.model.attrs["multi_label"],
            positive_label=positive_label,
            **kwargs,
        )