spaCy/spacy/pipeline/textcat.py

from typing import Iterable, Tuple, Optional, Dict, List, Callable, Any, Union
from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config
from thinc.types import Floats2d
import numpy
from itertools import islice

from .trainable_pipe import TrainablePipe
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 ..util import registry
from ..vocab import Vocab


ActivationsT = Dict[str, Floats2d]


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

[model.tok2vec]
@architectures = "spacy.Tok2Vec.v2"

[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
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.v2"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
depth = 2

[model.linear_model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
"""
DEFAULT_SINGLE_TEXTCAT_MODEL = Config().from_str(single_label_default_config)["model"]

single_label_bow_config = """
[model]
@architectures = "spacy.TextCatBOW.v2"
exclusive_classes = true
ngram_size = 1
no_output_layer = false
"""

single_label_cnn_config = """
[model]
@architectures = "spacy.TextCatCNN.v2"
exclusive_classes = true

[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v2"
pretrained_vectors = null
width = 96
depth = 4
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true
"""


@Language.factory(
    "textcat",
    assigns=["doc.cats"],
    default_config={
        "threshold": 0.5,
        "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
        "scorer": {"@scorers": "spacy.textcat_scorer.v1"},
        "save_activations": False,
    },
    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_textcat(
    nlp: Language,
    name: str,
    model: Model[List[Doc], List[Floats2d]],
    threshold: float,
    scorer: Optional[Callable],
    save_activations: bool,
) -> "TextCategorizer":
    """Create a TextCategorizer component. The text categorizer predicts categories
    over a whole document. It can learn one or more labels, and the labels are considered
    to be mutually exclusive (i.e. one true label per doc).

    model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
        scores for each category.
    threshold (float): Cutoff to consider a prediction "positive".
    scorer (Optional[Callable]): The scoring method.
    save_activations (bool): save model activations in Doc when annotating.
    """
    return TextCategorizer(
        nlp.vocab,
        model,
        name,
        threshold=threshold,
        scorer=scorer,
        save_activations=save_activations,
    )


def textcat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
    return Scorer.score_cats(
        examples,
        "cats",
        multi_label=False,
        **kwargs,
    )


@registry.scorers("spacy.textcat_scorer.v1")
def make_textcat_scorer():
    return textcat_score


class TextCategorizer(TrainablePipe):
    """Pipeline component for single-label text classification.

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

    def __init__(
        self,
        vocab: Vocab,
        model: Model,
        name: str = "textcat",
        *,
        threshold: float,
        scorer: Optional[Callable] = textcat_score,
        save_activations: bool = False,
    ) -> None:
        """Initialize a text categorizer for single-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".
        scorer (Optional[Callable]): The scoring method. Defaults to
                Scorer.score_cats for the attribute "cats".

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

    @property
    def support_missing_values(self):
        # There are no missing values as the textcat should always
        # predict exactly one label. All other labels are 0.0
        # Subclasses may override this property to change internal behaviour.
        return False

    @property
    def labels(self) -> Tuple[str]:
        """RETURNS (Tuple[str]): The labels currently added to the component.

        DOCS: https://spacy.io/api/textcategorizer#labels
        """
        return tuple(self.cfg["labels"])  # type: ignore[arg-type, return-value]

    @property
    def label_data(self) -> List[str]:
        """RETURNS (List[str]): Information about the component's labels.

        DOCS: https://spacy.io/api/textcategorizer#label_data
        """
        return self.labels  # type: ignore[return-value]

    def predict(self, docs: Iterable[Doc]) -> ActivationsT:
        """Apply the pipeline's model to a batch of docs, without modifying them.

        docs (Iterable[Doc]): The documents to predict.
        RETURNS: The models prediction for each document.

        DOCS: https://spacy.io/api/textcategorizer#predict
        """
        if not any(len(doc) for doc in docs):
            # Handle cases where there are no tokens in any docs.
            tensors = [doc.tensor for doc in docs]
            xp = self.model.ops.xp
            scores = xp.zeros((len(list(docs)), len(self.labels)))
            return {"probabilities": scores}
        scores = self.model.predict(docs)
        scores = self.model.ops.asarray(scores)
        return {"probabilities": scores}

    def set_annotations(self, docs: Iterable[Doc], activations: ActivationsT) -> None:
        """Modify a batch of Doc objects, using pre-computed scores.

        docs (Iterable[Doc]): The documents to modify.
        scores: The scores to set, produced by TextCategorizer.predict.

        DOCS: https://spacy.io/api/textcategorizer#set_annotations
        """
        probs = activations["probabilities"]
        for i, doc in enumerate(docs):
            if self.save_activations:
                doc.activations[self.name] = {}
                doc.activations[self.name]["probabilities"] = probs[i]
            for j, label in enumerate(self.labels):
                doc.cats[label] = float(probs[i, j])

    def update(
        self,
        examples: Iterable[Example],
        *,
        drop: float = 0.0,
        sgd: Optional[Optimizer] = None,
        losses: Optional[Dict[str, float]] = None,
    ) -> Dict[str, float]:
        """Learn from a batch of documents and gold-standard information,
        updating the pipe's model. Delegates to predict and get_loss.

        examples (Iterable[Example]): A batch of Example objects.
        drop (float): The dropout rate.
        sgd (thinc.api.Optimizer): The optimizer.
        losses (Dict[str, float]): Optional record of the loss during training.
            Updated using the component name as the key.
        RETURNS (Dict[str, float]): The updated losses dictionary.

        DOCS: https://spacy.io/api/textcategorizer#update
        """
        if losses is None:
            losses = {}
        losses.setdefault(self.name, 0.0)
        validate_examples(examples, "TextCategorizer.update")
        self._validate_categories(examples)
        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
        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.finish_update(sgd)
        losses[self.name] += loss
        return losses

    def rehearse(
        self,
        examples: Iterable[Example],
        *,
        drop: float = 0.0,
        sgd: Optional[Optimizer] = None,
        losses: Optional[Dict[str, float]] = None,
    ) -> Dict[str, float]:
        """Perform a "rehearsal" update from a batch of data. Rehearsal updates
        teach the current model to make predictions similar to an initial model,
        to try to address the "catastrophic forgetting" problem. This feature is
        experimental.

        examples (Iterable[Example]): A batch of Example objects.
        drop (float): The dropout rate.
        sgd (thinc.api.Optimizer): The optimizer.
        losses (Dict[str, float]): Optional record of the loss during training.
            Updated using the component name as the key.
        RETURNS (Dict[str, float]): The updated losses dictionary.

        DOCS: https://spacy.io/api/textcategorizer#rehearse
        """
        if losses is None:
            losses = {}
        losses.setdefault(self.name, 0.0)
        if self._rehearsal_model is None:
            return losses
        validate_examples(examples, "TextCategorizer.rehearse")
        self._validate_categories(examples)
        docs = [eg.predicted for eg in examples]
        if not any(len(doc) for doc in docs):
            # Handle cases where there are no tokens in any docs.
            return losses
        set_dropout_rate(self.model, drop)
        scores, bp_scores = self.model.begin_update(docs)
        target, _ = self._rehearsal_model.begin_update(docs)
        gradient = scores - target
        bp_scores(gradient)
        if sgd is not None:
            self.finish_update(sgd)
        losses[self.name] += (gradient**2).sum()
        return losses

    def _examples_to_truth(
        self, examples: Iterable[Example]
    ) -> Tuple[numpy.ndarray, numpy.ndarray]:
        nr_examples = len(list(examples))
        truths = numpy.zeros((nr_examples, len(self.labels)), dtype="f")
        not_missing = numpy.ones((nr_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]
                elif self.support_missing_values:
                    not_missing[i, j] = 0.0
        truths = self.model.ops.asarray(truths)  # type: ignore
        return truths, not_missing  # type: ignore

    def get_loss(self, examples: Iterable[Example], scores) -> Tuple[float, float]:
        """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.
        RETURNS (Tuple[float, float]): The loss and the gradient.

        DOCS: https://spacy.io/api/textcategorizer#get_loss
        """
        validate_examples(examples, "TextCategorizer.get_loss")
        self._validate_categories(examples)
        truths, not_missing = self._examples_to_truth(examples)
        not_missing = self.model.ops.asarray(not_missing)  # type: ignore
        d_scores = scores - truths
        d_scores *= not_missing
        mean_square_error = (d_scores**2).mean()
        return float(mean_square_error), d_scores

    def add_label(self, label: str) -> int:
        """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://spacy.io/api/textcategorizer#add_label
        """
        if not isinstance(label, str):
            raise ValueError(Errors.E187)
        if label in self.labels:
            return 0
        self._allow_extra_label()
        self.cfg["labels"].append(label)  # type: ignore[attr-defined]
        if self.model and "resize_output" in self.model.attrs:
            self.model = self.model.attrs["resize_output"](self.model, len(self.labels))
        self.vocab.strings.add(label)
        return 1

    def initialize(
        self,
        get_examples: Callable[[], Iterable[Example]],
        *,
        nlp: Optional[Language] = None,
        labels: Optional[Iterable[str]] = None,
        positive_label: Optional[str] = None,
    ) -> 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 (Optional[Iterable[str]]): 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.
        positive_label (Optional[str]): The positive label for a binary task with exclusive classes,
            `None` otherwise and by default.

        DOCS: https://spacy.io/api/textcategorizer#initialize
        """
        validate_get_examples(get_examples, "TextCategorizer.initialize")
        self._validate_categories(get_examples())
        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)
        if len(self.labels) < 2:
            raise ValueError(Errors.E867)
        if positive_label is not None:
            if positive_label not in self.labels:
                err = Errors.E920.format(pos_label=positive_label, labels=self.labels)
                raise ValueError(err)
            if len(self.labels) != 2:
                err = Errors.E919.format(pos_label=positive_label, labels=self.labels)
                raise ValueError(err)
        self.cfg["positive_label"] = positive_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 _validate_categories(self, examples: Iterable[Example]):
        """Check whether the provided examples all have single-label cats annotations."""
        for ex in examples:
            if list(ex.reference.cats.values()).count(1.0) > 1:
                raise ValueError(Errors.E895.format(value=ex.reference.cats))