from itertools import islice from typing import Iterable, Tuple, Optional, Dict, List, Callable, Iterator, Any from thinc.api import get_array_module, Model, Optimizer, set_dropout_rate, Config from thinc.types import Floats2d import numpy from .pipe import Pipe from ..language import Language from ..training import Example, validate_examples 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.TextCatEnsemble.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 """ @Language.factory( "textcat", assigns=["doc.cats"], default_config={"threshold": 0.5, "model": DEFAULT_TEXTCAT_MODEL}, default_score_weights={ "cats_score": 1.0, "cats_score_desc": None, "cats_p": None, "cats_r": None, "cats_f": 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 ) -> "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 TextCategorizer(nlp.vocab, model, name, threshold=threshold) class TextCategorizer(Pipe): """Pipeline component for text classification. DOCS: https://nightly.spacy.io/api/textcategorizer """ def __init__( self, vocab: Vocab, model: Model, name: str = "textcat", *, threshold: float ) -> None: """Initialize a text categorizer. 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://nightly.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) @property def labels(self) -> Tuple[str]: """RETURNS (Tuple[str]): The labels currently added to the component. DOCS: https://nightly.spacy.io/api/textcategorizer#labels """ return tuple(self.cfg["labels"]) @labels.setter def labels(self, value: List[str]) -> None: # TODO: This really shouldn't be here. I had a look and I added it when # I added the labels property, but it's pretty nasty to have this, and # will lead to problems. self.cfg["labels"] = tuple(value) @property def label_data(self) -> List[str]: """RETURNS (List[str]): Information about the component's labels.""" return self.labels def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]: """Apply the pipe to a stream of documents. This usually happens under the hood when the nlp object is called on a text and all components are applied to the Doc. stream (Iterable[Doc]): A stream of documents. batch_size (int): The number of documents to buffer. YIELDS (Doc): Processed documents in order. DOCS: https://nightly.spacy.io/api/textcategorizer#pipe """ 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]): """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://nightly.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 = 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: """Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores. docs (Iterable[Doc]): The documents to modify. scores: The scores to set, produced by TextCategorizer.predict. DOCS: https://nightly.spacy.io/api/textcategorizer#set_annotations """ 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]: """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. set_annotations (bool): Whether or not to update the Example objects with the predictions. 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://nightly.spacy.io/api/textcategorizer#update """ if losses is None: losses = {} losses.setdefault(self.name, 0.0) validate_examples(examples, "TextCategorizer.update") 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 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, ) -> 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://nightly.spacy.io/api/textcategorizer#rehearse """ if losses is not None: losses.setdefault(self.name, 0.0) if self._rehearsal_model is None: return losses validate_examples(examples, "TextCategorizer.rehearse") 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(examples) gradient = scores - target bp_scores(gradient) if sgd is not None: self.finish_update(sgd) if losses is not None: losses[self.name] += (gradient ** 2).sum() return losses 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]: """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://nightly.spacy.io/api/textcategorizer#get_loss """ validate_examples(examples, "TextCategorizer.get_loss") 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: """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://nightly.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.labels = tuple(list(self.labels) + [label]) return 1 def initialize( self, get_examples: Callable[[], Iterable[Example]], *, nlp: Optional[Language] = None, labels: Optional[Dict] = None, positive_label: Optional[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://nightly.spacy.io/api/textcategorizer#initialize """ self._ensure_examples(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 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 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://nightly.spacy.io/api/textcategorizer#score """ validate_examples(examples, "TextCategorizer.score") return Scorer.score_cats( examples, "cats", labels=self.labels, multi_label=self.model.attrs["multi_label"], positive_label=self.cfg["positive_label"], threshold=self.cfg["threshold"], **kwargs, )