# cython: infer_types=True # cython: profile=True import numpy import srsly import random from thinc.layers import chain, Linear, Maxout, Softmax, LayerNorm, list2array from thinc.initializers import zero_init from thinc.loss import CosineDistance from thinc.util import to_categorical, get_array_module from thinc.model import set_dropout_rate from ..tokens.doc cimport Doc from ..syntax.nn_parser cimport Parser from ..syntax.ner cimport BiluoPushDown from ..syntax.arc_eager cimport ArcEager from ..morphology cimport Morphology from ..vocab cimport Vocab from .functions import merge_subtokens from ..language import Language, component from ..syntax import nonproj from ..gold import Example from ..attrs import POS, ID from ..util import link_vectors_to_models, create_default_optimizer from ..parts_of_speech import X from ..kb import KnowledgeBase from ..ml.component_models import Tok2Vec, build_tagger_model from ..ml.component_models import build_text_classifier from ..ml.component_models import build_simple_cnn_text_classifier from ..ml.component_models import build_bow_text_classifier, build_nel_encoder from ..ml.component_models import masked_language_model from ..errors import Errors, TempErrors, user_warning, Warnings from .. import util def _load_cfg(path): if path.exists(): return srsly.read_json(path) else: return {} class Pipe(object): """This class is not instantiated directly. Components inherit from it, and it defines the interface that components should follow to function as components in a spaCy analysis pipeline. """ name = None @classmethod def Model(cls, *shape, **kwargs): """Initialize a model for the pipe.""" raise NotImplementedError @classmethod def from_nlp(cls, nlp, **cfg): return cls(nlp.vocab, **cfg) def _get_doc(self, example): """ Use this method if the `example` can be both a Doc or an Example """ if isinstance(example, Doc): return example return example.doc def __init__(self, vocab, model=True, **cfg): """Create a new pipe instance.""" raise NotImplementedError def __call__(self, example): """Apply the pipe to one document. The document is modified in-place, and returned. Both __call__ and pipe should delegate to the `predict()` and `set_annotations()` methods. """ self.require_model() doc = self._get_doc(example) predictions = self.predict([doc]) if isinstance(predictions, tuple) and len(predictions) == 2: scores, tensors = predictions self.set_annotations([doc], scores, tensors=tensors) else: self.set_annotations([doc], predictions) if isinstance(example, Example): example.doc = doc return example return doc def require_model(self): """Raise an error if the component's model is not initialized.""" if getattr(self, "model", None) in (None, True, False): raise ValueError(Errors.E109.format(name=self.name)) def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False): """Apply the pipe to a stream of documents. Both __call__ and pipe should delegate to the `predict()` and `set_annotations()` methods. """ for examples in util.minibatch(stream, size=batch_size): docs = [self._get_doc(ex) for ex in examples] predictions = self.predict(docs) if isinstance(predictions, tuple) and len(tuple) == 2: scores, tensors = predictions self.set_annotations(docs, scores, tensors=tensors) else: self.set_annotations(docs, predictions) if as_example: annotated_examples = [] for ex, doc in zip(examples, docs): ex.doc = doc annotated_examples.append(ex) yield from annotated_examples else: yield from docs def predict(self, docs): """Apply the pipeline's model to a batch of docs, without modifying them. """ self.require_model() raise NotImplementedError def set_annotations(self, docs, scores, tensors=None): """Modify a batch of documents, using pre-computed scores.""" raise NotImplementedError def update(self, examples, set_annotations=False, drop=0.0, sgd=None, losses=None): """Learn from a batch of documents and gold-standard information, updating the pipe's model. Delegates to predict() and get_loss(). """ if set_annotations: docs = (self._get_doc(ex) for ex in examples) docs = list(self.pipe(docs)) def rehearse(self, examples, sgd=None, losses=None, **config): pass def get_loss(self, examples, scores): """Find the loss and gradient of loss for the batch of examples (with embedded docs) and their predicted scores.""" raise NotImplementedError def add_label(self, label): """Add an output label, to be predicted by the model. It's possible to extend pretrained models with new labels, but care should be taken to avoid the "catastrophic forgetting" problem. """ raise NotImplementedError def create_optimizer(self): return create_default_optimizer() def begin_training( self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs ): """Initialize the pipe for training, using data exampes if available. If no model has been initialized yet, the model is added.""" if self.model is True: self.model = self.Model(**self.cfg) if hasattr(self, "vocab"): link_vectors_to_models(self.vocab) self.model.initialize() if sgd is None: sgd = self.create_optimizer() return sgd def get_gradients(self): """Get non-zero gradients of the model's parameters, as a dictionary keyed by the parameter ID. The values are (weights, gradients) tuples. """ gradients = {} if self.model in (None, True, False): return gradients queue = [self.model] seen = set() for node in queue: if node.id in seen: continue seen.add(node.id) if hasattr(node, "_mem") and node._mem.gradient.any(): gradients[node.id] = [node._mem.weights, node._mem.gradient] if hasattr(node, "_layers"): queue.extend(node._layers) return gradients def use_params(self, params): """Modify the pipe's model, to use the given parameter values.""" with self.model.use_params(params): yield def to_bytes(self, exclude=tuple(), **kwargs): """Serialize the pipe to a bytestring. exclude (list): String names of serialization fields to exclude. RETURNS (bytes): The serialized object. """ serialize = {} serialize["cfg"] = lambda: srsly.json_dumps(self.cfg) if self.model not in (True, False, None): serialize["model"] = self.model.to_bytes if hasattr(self, "vocab"): serialize["vocab"] = self.vocab.to_bytes exclude = util.get_serialization_exclude(serialize, exclude, kwargs) return util.to_bytes(serialize, exclude) def from_bytes(self, bytes_data, exclude=tuple(), **kwargs): """Load the pipe from a bytestring.""" def load_model(b): # TODO: Remove this once we don't have to handle previous models if self.cfg.get("pretrained_dims") and "pretrained_vectors" not in self.cfg: self.cfg["pretrained_vectors"] = self.vocab.vectors if self.model is True: self.model = self.Model(**self.cfg) try: self.model.from_bytes(b) except AttributeError: raise ValueError(Errors.E149) deserialize = {} deserialize["cfg"] = lambda b: self.cfg.update(srsly.json_loads(b)) if hasattr(self, "vocab"): deserialize["vocab"] = lambda b: self.vocab.from_bytes(b) deserialize["model"] = load_model exclude = util.get_serialization_exclude(deserialize, exclude, kwargs) util.from_bytes(bytes_data, deserialize, exclude) return self def to_disk(self, path, exclude=tuple(), **kwargs): """Serialize the pipe to disk.""" serialize = {} serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg) serialize["vocab"] = lambda p: self.vocab.to_disk(p) if self.model not in (None, True, False): serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes()) exclude = util.get_serialization_exclude(serialize, exclude, kwargs) util.to_disk(path, serialize, exclude) def from_disk(self, path, exclude=tuple(), **kwargs): """Load the pipe from disk.""" def load_model(p): # TODO: Remove this once we don't have to handle previous models if self.cfg.get("pretrained_dims") and "pretrained_vectors" not in self.cfg: self.cfg["pretrained_vectors"] = self.vocab.vectors if self.model is True: self.model = self.Model(**self.cfg) try: self.model.from_bytes(p.open("rb").read()) except AttributeError: raise ValueError(Errors.E149) deserialize = {} deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p)) deserialize["vocab"] = lambda p: self.vocab.from_disk(p) deserialize["model"] = load_model exclude = util.get_serialization_exclude(deserialize, exclude, kwargs) util.from_disk(path, deserialize, exclude) return self @component("tensorizer", assigns=["doc.tensor"]) class Tensorizer(Pipe): """Pre-train position-sensitive vectors for tokens.""" @classmethod def Model(cls, output_size=300, **cfg): """Create a new statistical model for the class. width (int): Output size of the model. embed_size (int): Number of vectors in the embedding table. **cfg: Config parameters. RETURNS (Model): A `thinc.model.Model` or similar instance. """ input_size = util.env_opt("token_vector_width", cfg.get("input_size", 96)) return Linear(output_size, input_size, init_W=zero_init) def __init__(self, vocab, model=True, **cfg): """Construct a new statistical model. Weights are not allocated on initialisation. vocab (Vocab): A `Vocab` instance. The model must share the same `Vocab` instance with the `Doc` objects it will process. model (Model): A `Model` instance or `True` to allocate one later. **cfg: Config parameters. EXAMPLE: >>> from spacy.pipeline import TokenVectorEncoder >>> tok2vec = TokenVectorEncoder(nlp.vocab) >>> tok2vec.model = tok2vec.Model(128, 5000) """ self.vocab = vocab self.model = model self.input_models = [] self.cfg = dict(cfg) def __call__(self, example): """Add context-sensitive vectors to a `Doc`, e.g. from a CNN or LSTM model. Vectors are set to the `Doc.tensor` attribute. docs (Doc or iterable): One or more documents to add vectors to. RETURNS (dict or None): Intermediate computations. """ doc = self._get_doc(example) tokvecses = self.predict([doc]) self.set_annotations([doc], tokvecses) if isinstance(example, Example): example.doc = doc return example return doc def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False): """Process `Doc` objects as a stream. stream (iterator): A sequence of `Doc` or `Example` objects to process. batch_size (int): Number of `Doc` or `Example` objects to group. YIELDS (iterator): A sequence of `Doc` or `Example` objects, in order of input. """ for examples in util.minibatch(stream, size=batch_size): docs = [self._get_doc(ex) for ex in examples] tensors = self.predict(docs) self.set_annotations(docs, tensors) if as_example: annotated_examples = [] for ex, doc in zip(examples, docs): ex.doc = doc annotated_examples.append(ex) yield from annotated_examples else: yield from docs def predict(self, docs): """Return a single tensor for a batch of documents. docs (iterable): A sequence of `Doc` objects. RETURNS (object): Vector representations for each token in the docs. """ self.require_model() inputs = self.model.ops.flatten([doc.tensor for doc in docs]) outputs = self.model(inputs) return self.model.ops.unflatten(outputs, [len(d) for d in docs]) def set_annotations(self, docs, tensors): """Set the tensor attribute for a batch of documents. docs (iterable): A sequence of `Doc` objects. tensors (object): Vector representation for each token in the docs. """ for doc, tensor in zip(docs, tensors): if tensor.shape[0] != len(doc): raise ValueError(Errors.E076.format(rows=tensor.shape[0], words=len(doc))) doc.tensor = tensor def update(self, examples, state=None, drop=0.0, set_annotations=False, sgd=None, losses=None): """Update the model. docs (iterable): A batch of `Doc` objects. golds (iterable): A batch of `GoldParse` objects. drop (float): The dropout rate. sgd (callable): An optimizer. RETURNS (dict): Results from the update. """ self.require_model() examples = Example.to_example_objects(examples) inputs = [] bp_inputs = [] set_dropout_rate(self.model, drop) for tok2vec in self.input_models: set_dropout_rate(tok2vec, drop) tensor, bp_tensor = tok2vec.begin_update([ex.doc for ex in examples]) inputs.append(tensor) bp_inputs.append(bp_tensor) inputs = self.model.ops.xp.hstack(inputs) scores, bp_scores = self.model.begin_update(inputs) loss, d_scores = self.get_loss(examples, scores) d_inputs = bp_scores(d_scores, sgd=sgd) d_inputs = self.model.ops.xp.split(d_inputs, len(self.input_models), axis=1) for d_input, bp_input in zip(d_inputs, bp_inputs): bp_input(d_input) if sgd is not None: for tok2vec in self.input_models: tok2vec.finish_update(sgd) self.model.finish_update(sgd) if losses is not None: losses.setdefault(self.name, 0.0) losses[self.name] += loss return loss def get_loss(self, examples, prediction): examples = Example.to_example_objects(examples) ids = self.model.ops.flatten([ex.doc.to_array(ID).ravel() for ex in examples]) target = self.vocab.vectors.data[ids] d_scores = (prediction - target) / prediction.shape[0] loss = (d_scores ** 2).sum() return loss, d_scores def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs): """Allocate models, pre-process training data and acquire an optimizer. get_examples (iterable): Gold-standard training data. pipeline (list): The pipeline the model is part of. """ if pipeline is not None: for name, model in pipeline: if getattr(model, "tok2vec", None): self.input_models.append(model.tok2vec) if self.model is True: self.model = self.Model(**self.cfg) self.model.initialize() link_vectors_to_models(self.vocab) if sgd is None: sgd = self.create_optimizer() return sgd @component("tagger", assigns=["token.tag", "token.pos"]) class Tagger(Pipe): """Pipeline component for part-of-speech tagging. DOCS: https://spacy.io/api/tagger """ def __init__(self, vocab, model=True, **cfg): self.vocab = vocab self.model = model self._rehearsal_model = None self.cfg = dict(sorted(cfg.items())) @property def labels(self): return tuple(self.vocab.morphology.tag_names) @property def tok2vec(self): if self.model in (None, True, False): return None else: return chain(self.model.get_ref("tok2vec"), list2array()) def __call__(self, example): doc = self._get_doc(example) tags = self.predict([doc]) self.set_annotations([doc], tags) if isinstance(example, Example): example.doc = doc return example return doc def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False): for examples in util.minibatch(stream, size=batch_size): docs = [self._get_doc(ex) for ex in examples] tag_ids = self.predict(docs) assert len(docs) == len(examples) assert len(tag_ids) == len(examples) self.set_annotations(docs, tag_ids) if as_example: annotated_examples = [] for ex, doc in zip(examples, docs): ex.doc = doc annotated_examples.append(ex) yield from annotated_examples else: yield from docs def predict(self, docs): self.require_model() if not any(len(doc) for doc in docs): # Handle cases where there are no tokens in any docs. n_labels = len(self.labels) guesses = [self.model.ops.alloc((0, n_labels)) for doc in docs] assert len(guesses) == len(docs) return guesses scores = self.model.predict(docs) assert len(scores) == len(docs), (len(scores), len(docs)) guesses = self._scores2guesses(scores) assert len(guesses) == len(docs) return guesses def _scores2guesses(self, scores): guesses = [] for doc_scores in scores: doc_guesses = doc_scores.argmax(axis=1) if not isinstance(doc_guesses, numpy.ndarray): doc_guesses = doc_guesses.get() guesses.append(doc_guesses) return guesses def set_annotations(self, docs, batch_tag_ids): if isinstance(docs, Doc): docs = [docs] cdef Doc doc cdef int idx = 0 cdef Vocab vocab = self.vocab assign_morphology = self.cfg.get("set_morphology", True) for i, doc in enumerate(docs): doc_tag_ids = batch_tag_ids[i] if hasattr(doc_tag_ids, "get"): doc_tag_ids = doc_tag_ids.get() for j, tag_id in enumerate(doc_tag_ids): # Don't clobber preset POS tags if doc.c[j].tag == 0: if doc.c[j].pos == 0 and assign_morphology: # Don't clobber preset lemmas lemma = doc.c[j].lemma vocab.morphology.assign_tag_id(&doc.c[j], tag_id) if lemma != 0 and lemma != doc.c[j].lex.orth: doc.c[j].lemma = lemma else: doc.c[j].tag = self.vocab.strings[self.labels[tag_id]] idx += 1 doc.is_tagged = True def update(self, examples, drop=0., sgd=None, losses=None, set_annotations=False): self.require_model() examples = Example.to_example_objects(examples) if losses is not None and self.name not in losses: losses[self.name] = 0. if not any(len(ex.doc) if ex.doc else 0 for ex in examples): # Handle cases where there are no tokens in any docs. return set_dropout_rate(self.model, drop) tag_scores, bp_tag_scores = self.model.begin_update([ex.doc for ex in examples]) loss, d_tag_scores = self.get_loss(examples, tag_scores) bp_tag_scores(d_tag_scores) if sgd not in (None, False): self.model.finish_update(sgd) if losses is not None: losses[self.name] += loss if set_annotations: docs = [ex.doc for ex in examples] self.set_annotations(docs, self._scores2guesses(tag_scores)) def rehearse(self, examples, drop=0., sgd=None, losses=None): """Perform a 'rehearsal' update, where we try to match the output of an initial model. """ if self._rehearsal_model is None: return examples = Example.to_example_objects(examples) docs = [ex.doc for ex in examples] 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) guesses, backprop = self.model.begin_update(docs) target = self._rehearsal_model(examples) gradient = guesses - target backprop(gradient) self.model.finish_update(sgd) if losses is not None: losses.setdefault(self.name, 0.0) losses[self.name] += (gradient**2).sum() def get_loss(self, examples, scores): scores = self.model.ops.flatten(scores) tag_index = {tag: i for i, tag in enumerate(self.labels)} cdef int idx = 0 correct = numpy.zeros((scores.shape[0],), dtype="i") guesses = scores.argmax(axis=1) known_labels = numpy.ones((scores.shape[0], 1), dtype="f") for ex in examples: gold = ex.gold for tag in gold.tags: if tag is None: correct[idx] = guesses[idx] elif tag in tag_index: correct[idx] = tag_index[tag] else: correct[idx] = 0 known_labels[idx] = 0. idx += 1 correct = self.model.ops.xp.array(correct, dtype="i") d_scores = scores - to_categorical(correct, n_classes=scores.shape[1]) d_scores *= self.model.ops.asarray(known_labels) loss = (d_scores**2).sum() docs = [ex.doc for ex in examples] d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs]) return float(loss), d_scores def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs): lemma_tables = ["lemma_rules", "lemma_index", "lemma_exc", "lemma_lookup"] if not any(table in self.vocab.lookups for table in lemma_tables): user_warning(Warnings.W022) orig_tag_map = dict(self.vocab.morphology.tag_map) new_tag_map = {} for example in get_examples(): for tag in example.token_annotation.tags: if tag in orig_tag_map: new_tag_map[tag] = orig_tag_map[tag] else: new_tag_map[tag] = {POS: X} cdef Vocab vocab = self.vocab if new_tag_map: vocab.morphology = Morphology(vocab.strings, new_tag_map, vocab.morphology.lemmatizer, exc=vocab.morphology.exc) self.cfg["pretrained_vectors"] = kwargs.get("pretrained_vectors") if self.model is True: for hp in ["token_vector_width", "conv_depth"]: if hp in kwargs: self.cfg[hp] = kwargs[hp] self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg) # Get batch of example docs, example outputs to call begin_training(). # This lets the model infer shapes. n_tags = self.vocab.morphology.n_tags for node in self.model.walk(): # TODO: softmax hack ? if node.name == "softmax" and node.has_dim("nO") is None: node.set_dim("nO", n_tags) link_vectors_to_models(self.vocab) self.model.initialize() if sgd is None: sgd = self.create_optimizer() return sgd @classmethod def Model(cls, n_tags=None, **cfg): if cfg.get("pretrained_dims") and not cfg.get("pretrained_vectors"): raise ValueError(TempErrors.T008) if "tok2vec" in cfg: tok2vec = cfg["tok2vec"] else: config = { "width": cfg.get("token_vector_width", 96), "embed_size": cfg.get("embed_size", 2000), "pretrained_vectors": cfg.get("pretrained_vectors", None), "window_size": cfg.get("window_size", 1), "cnn_maxout_pieces": cfg.get("cnn_maxout_pieces", 3), "subword_features": cfg.get("subword_features", True), "char_embed": cfg.get("char_embed", False), "conv_depth": cfg.get("conv_depth", 4), "bilstm_depth": cfg.get("bilstm_depth", 0), } tok2vec = Tok2Vec(**config) return build_tagger_model(n_tags, tok2vec) def add_label(self, label, values=None): if not isinstance(label, str): raise ValueError(Errors.E187) if label in self.labels: return 0 if self.model not in (True, False, None): # Here's how the model resizing will work, once the # neuron-to-tag mapping is no longer controlled by # the Morphology class, which sorts the tag names. # The sorting makes adding labels difficult. # smaller = self.model._layers[-1] # larger = Softmax(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 raise ValueError(TempErrors.T003) tag_map = dict(self.vocab.morphology.tag_map) if values is None: values = {POS: "X"} tag_map[label] = values self.vocab.morphology = Morphology( self.vocab.strings, tag_map=tag_map, lemmatizer=self.vocab.morphology.lemmatizer, exc=self.vocab.morphology.exc) return 1 def use_params(self, params): with self.model.use_params(params): yield def to_bytes(self, exclude=tuple(), **kwargs): serialize = {} if self.model not in (None, True, False): serialize["model"] = self.model.to_bytes serialize["vocab"] = self.vocab.to_bytes serialize["cfg"] = lambda: srsly.json_dumps(self.cfg) tag_map = dict(sorted(self.vocab.morphology.tag_map.items())) serialize["tag_map"] = lambda: srsly.msgpack_dumps(tag_map) exclude = util.get_serialization_exclude(serialize, exclude, kwargs) return util.to_bytes(serialize, exclude) def from_bytes(self, bytes_data, exclude=tuple(), **kwargs): def load_model(b): # TODO: Remove this once we don't have to handle previous models if self.cfg.get("pretrained_dims") and "pretrained_vectors" not in self.cfg: self.cfg["pretrained_vectors"] = self.vocab.vectors if self.model is True: token_vector_width = util.env_opt( "token_vector_width", self.cfg.get("token_vector_width", 96)) self.model = self.Model(**self.cfg) try: self.model.from_bytes(b) except AttributeError: raise ValueError(Errors.E149) def load_tag_map(b): tag_map = srsly.msgpack_loads(b) self.vocab.morphology = Morphology( self.vocab.strings, tag_map=tag_map, lemmatizer=self.vocab.morphology.lemmatizer, exc=self.vocab.morphology.exc) deserialize = { "vocab": lambda b: self.vocab.from_bytes(b), "tag_map": load_tag_map, "cfg": lambda b: self.cfg.update(srsly.json_loads(b)), "model": lambda b: load_model(b), } exclude = util.get_serialization_exclude(deserialize, exclude, kwargs) util.from_bytes(bytes_data, deserialize, exclude) return self def to_disk(self, path, exclude=tuple(), **kwargs): tag_map = dict(sorted(self.vocab.morphology.tag_map.items())) serialize = { "vocab": lambda p: self.vocab.to_disk(p), "tag_map": lambda p: srsly.write_msgpack(p, tag_map), "model": lambda p: p.open("wb").write(self.model.to_bytes()), "cfg": lambda p: srsly.write_json(p, self.cfg) } exclude = util.get_serialization_exclude(serialize, exclude, kwargs) util.to_disk(path, serialize, exclude) def from_disk(self, path, exclude=tuple(), **kwargs): def load_model(p): # TODO: Remove this once we don't have to handle previous models if self.cfg.get("pretrained_dims") and "pretrained_vectors" not in self.cfg: self.cfg["pretrained_vectors"] = self.vocab.vectors if self.model is True: self.model = self.Model(**self.cfg) with p.open("rb") as file_: try: self.model.from_bytes(file_.read()) except AttributeError: raise ValueError(Errors.E149) def load_tag_map(p): tag_map = srsly.read_msgpack(p) self.vocab.morphology = Morphology( self.vocab.strings, tag_map=tag_map, lemmatizer=self.vocab.morphology.lemmatizer, exc=self.vocab.morphology.exc) deserialize = { "cfg": lambda p: self.cfg.update(_load_cfg(p)), "vocab": lambda p: self.vocab.from_disk(p), "tag_map": load_tag_map, "model": load_model, } exclude = util.get_serialization_exclude(deserialize, exclude, kwargs) util.from_disk(path, deserialize, exclude) return self @component("sentrec", assigns=["token.is_sent_start"]) class SentenceRecognizer(Tagger): """Pipeline component for sentence segmentation. DOCS: https://spacy.io/api/sentencerecognizer """ def __init__(self, vocab, model=True, **cfg): self.vocab = vocab self.model = model self._rehearsal_model = None self.cfg = dict(sorted(cfg.items())) self.cfg.setdefault("cnn_maxout_pieces", 2) self.cfg.setdefault("subword_features", True) self.cfg.setdefault("token_vector_width", 12) self.cfg.setdefault("conv_depth", 1) self.cfg.setdefault("pretrained_vectors", None) @property def labels(self): # labels are numbered by index internally, so this matches GoldParse # and Example where the sentence-initial tag is 1 and other positions # are 0 return tuple(["I", "S"]) def set_annotations(self, docs, batch_tag_ids, **_): if isinstance(docs, Doc): docs = [docs] cdef Doc doc for i, doc in enumerate(docs): doc_tag_ids = batch_tag_ids[i] if hasattr(doc_tag_ids, "get"): doc_tag_ids = doc_tag_ids.get() for j, tag_id in enumerate(doc_tag_ids): # Don't clobber existing sentence boundaries if doc.c[j].sent_start == 0: if tag_id == 1: doc.c[j].sent_start = 1 else: doc.c[j].sent_start = -1 def update(self, examples, drop=0., sgd=None, losses=None): self.require_model() examples = Example.to_example_objects(examples) if losses is not None and self.name not in losses: losses[self.name] = 0. if not any(len(ex.doc) if ex.doc else 0 for ex in examples): # Handle cases where there are no tokens in any docs. return set_dropout_rate(self.model, drop) tag_scores, bp_tag_scores = self.model.begin_update([ex.doc for ex in examples]) loss, d_tag_scores = self.get_loss(examples, tag_scores) bp_tag_scores(d_tag_scores) if sgd is not None: self.model.finish_update(sgd) if losses is not None: losses[self.name] += loss def get_loss(self, examples, scores): scores = self.model.ops.flatten(scores) tag_index = range(len(self.labels)) cdef int idx = 0 correct = numpy.zeros((scores.shape[0],), dtype="i") guesses = scores.argmax(axis=1) known_labels = numpy.ones((scores.shape[0], 1), dtype="f") for ex in examples: gold = ex.gold for sent_start in gold.sent_starts: if sent_start is None: correct[idx] = guesses[idx] elif sent_start in tag_index: correct[idx] = sent_start else: correct[idx] = 0 known_labels[idx] = 0. idx += 1 correct = self.model.ops.xp.array(correct, dtype="i") d_scores = scores - to_categorical(correct, n_classes=scores.shape[1]) d_scores *= self.model.ops.asarray(known_labels) loss = (d_scores**2).sum() docs = [ex.doc for ex in examples] d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs]) return float(loss), d_scores def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs): cdef Vocab vocab = self.vocab if self.model is True: for hp in ["token_vector_width", "conv_depth"]: if hp in kwargs: self.cfg[hp] = kwargs[hp] self.model = self.Model(len(self.labels), **self.cfg) if sgd is None: sgd = self.create_optimizer() self.model.initialize() return sgd @classmethod def Model(cls, n_tags, **cfg): return build_tagger_model(n_tags, **cfg) def add_label(self, label, values=None): raise NotImplementedError def use_params(self, params): with self.model.use_params(params): yield def to_bytes(self, exclude=tuple(), **kwargs): serialize = {} if self.model not in (None, True, False): serialize["model"] = self.model.to_bytes serialize["vocab"] = self.vocab.to_bytes serialize["cfg"] = lambda: srsly.json_dumps(self.cfg) exclude = util.get_serialization_exclude(serialize, exclude, kwargs) return util.to_bytes(serialize, exclude) def from_bytes(self, bytes_data, exclude=tuple(), **kwargs): def load_model(b): if self.model is True: self.model = self.Model(len(self.labels), **self.cfg) try: self.model.from_bytes(b) except AttributeError: raise ValueError(Errors.E149) deserialize = { "vocab": lambda b: self.vocab.from_bytes(b), "cfg": lambda b: self.cfg.update(srsly.json_loads(b)), "model": lambda b: load_model(b), } exclude = util.get_serialization_exclude(deserialize, exclude, kwargs) util.from_bytes(bytes_data, deserialize, exclude) return self def to_disk(self, path, exclude=tuple(), **kwargs): serialize = { "vocab": lambda p: self.vocab.to_disk(p), "model": lambda p: p.open("wb").write(self.model.to_bytes()), "cfg": lambda p: srsly.write_json(p, self.cfg) } exclude = util.get_serialization_exclude(serialize, exclude, kwargs) util.to_disk(path, serialize, exclude) def from_disk(self, path, exclude=tuple(), **kwargs): def load_model(p): if self.model is True: self.model = self.Model(len(self.labels), **self.cfg) with p.open("rb") as file_: try: self.model.from_bytes(file_.read()) except AttributeError: raise ValueError(Errors.E149) deserialize = { "cfg": lambda p: self.cfg.update(_load_cfg(p)), "vocab": lambda p: self.vocab.from_disk(p), "model": load_model, } exclude = util.get_serialization_exclude(deserialize, exclude, kwargs) util.from_disk(path, deserialize, exclude) return self @component("nn_labeller") class MultitaskObjective(Tagger): """Experimental: Assist training of a parser or tagger, by training a side-objective. """ def __init__(self, vocab, model=True, target='dep_tag_offset', **cfg): self.vocab = vocab self.model = model if target == "dep": self.make_label = self.make_dep elif target == "tag": self.make_label = self.make_tag elif target == "ent": self.make_label = self.make_ent elif target == "dep_tag_offset": self.make_label = self.make_dep_tag_offset elif target == "ent_tag": self.make_label = self.make_ent_tag elif target == "sent_start": self.make_label = self.make_sent_start elif hasattr(target, "__call__"): self.make_label = target else: raise ValueError(Errors.E016) self.cfg = dict(cfg) self.cfg.setdefault("cnn_maxout_pieces", 2) @property def labels(self): return self.cfg.setdefault("labels", {}) @labels.setter def labels(self, value): self.cfg["labels"] = value def set_annotations(self, docs, dep_ids, tensors=None): pass def begin_training(self, get_examples=lambda: [], pipeline=None, tok2vec=None, sgd=None, **kwargs): gold_examples = nonproj.preprocess_training_data(get_examples()) # for raw_text, doc_annot in gold_tuples: for example in gold_examples: for i in range(len(example.token_annotation.ids)): label = self.make_label(i, example.token_annotation) if label is not None and label not in self.labels: self.labels[label] = len(self.labels) if self.model is True: token_vector_width = util.env_opt("token_vector_width") self.model = self.Model(len(self.labels), tok2vec=tok2vec) link_vectors_to_models(self.vocab) self.model.initialize() if sgd is None: sgd = self.create_optimizer() return sgd @classmethod def Model(cls, n_tags, tok2vec=None, **cfg): token_vector_width = util.env_opt("token_vector_width", 96) model = chain( tok2vec, Maxout(nO=token_vector_width*2, nI=token_vector_width, nP=3, dropout=0.0), LayerNorm(token_vector_width*2), Softmax(nO=n_tags, nI=token_vector_width*2) ) return model def predict(self, docs): self.require_model() tokvecs = self.model.tok2vec(docs) scores = self.model.softmax(tokvecs) return tokvecs, scores def get_loss(self, examples, scores): cdef int idx = 0 correct = numpy.zeros((scores.shape[0],), dtype="i") guesses = scores.argmax(axis=1) golds = [ex.gold for ex in examples] docs = [ex.doc for ex in examples] for i, gold in enumerate(golds): for j in range(len(docs[i])): # Handels alignment for tokenization differences token_annotation = gold.get_token_annotation() label = self.make_label(j, token_annotation) if label is None or label not in self.labels: correct[idx] = guesses[idx] else: correct[idx] = self.labels[label] idx += 1 correct = self.model.ops.xp.array(correct, dtype="i") d_scores = scores - to_categorical(correct, n_classes=scores.shape[1]) loss = (d_scores**2).sum() return float(loss), d_scores @staticmethod def make_dep(i, token_annotation): if token_annotation.deps[i] is None or token_annotation.heads[i] is None: return None return token_annotation.deps[i] @staticmethod def make_tag(i, token_annotation): return token_annotation.tags[i] @staticmethod def make_ent(i, token_annotation): if token_annotation.entities is None: return None return token_annotation.entities[i] @staticmethod def make_dep_tag_offset(i, token_annotation): if token_annotation.deps[i] is None or token_annotation.heads[i] is None: return None offset = token_annotation.heads[i] - i offset = min(offset, 2) offset = max(offset, -2) return f"{token_annotation.deps[i]}-{token_annotation.tags[i]}:{offset}" @staticmethod def make_ent_tag(i, token_annotation): if token_annotation.entities is None or token_annotation.entities[i] is None: return None else: return f"{token_annotation.tags[i]}-{token_annotation.entities[i]}" @staticmethod def make_sent_start(target, token_annotation, cache=True, _cache={}): """A multi-task objective for representing sentence boundaries, using BILU scheme. (O is impossible) The implementation of this method uses an internal cache that relies on the identity of the heads array, to avoid requiring a new piece of gold data. You can pass cache=False if you know the cache will do the wrong thing. """ words = token_annotation.words heads = token_annotation.heads assert len(words) == len(heads) assert target < len(words), (target, len(words)) if cache: if id(heads) in _cache: return _cache[id(heads)][target] else: for key in list(_cache.keys()): _cache.pop(key) sent_tags = ["I-SENT"] * len(words) _cache[id(heads)] = sent_tags else: sent_tags = ["I-SENT"] * len(words) def _find_root(child): seen = set([child]) while child is not None and heads[child] != child: seen.add(child) child = heads[child] return child sentences = {} for i in range(len(words)): root = _find_root(i) if root is None: sent_tags[i] = None else: sentences.setdefault(root, []).append(i) for root, span in sorted(sentences.items()): if len(span) == 1: sent_tags[span[0]] = "U-SENT" else: sent_tags[span[0]] = "B-SENT" sent_tags[span[-1]] = "L-SENT" return sent_tags[target] class ClozeMultitask(Pipe): @classmethod def Model(cls, vocab, tok2vec, **cfg): output_size = vocab.vectors.data.shape[1] output_layer = chain( Maxout(nO=output_size, nI=tok2vec.get_dim("nO"), nP=3, normalize=True, dropout=0.0), Linear(nO=output_size, nI=output_size, init_W=zero_init) ) model = chain(tok2vec, output_layer) model = masked_language_model(vocab, model) return model def __init__(self, vocab, model=True, **cfg): self.vocab = vocab self.model = model self.cfg = cfg self.distance = CosineDistance(ignore_zeros=True, normalize=False) def set_annotations(self, docs, dep_ids, tensors=None): pass def begin_training(self, get_examples=lambda: [], pipeline=None, tok2vec=None, sgd=None, **kwargs): link_vectors_to_models(self.vocab) if self.model is True: self.model = self.Model(self.vocab, tok2vec) X = self.model.ops.alloc((5, self.model.get_ref("tok2vec").get_dim("nO"))) self.model.initialize() self.model.output_layer.begin_training(X) if sgd is None: sgd = self.create_optimizer() return sgd def predict(self, docs): self.require_model() tokvecs = self.model.tok2vec(docs) vectors = self.model.output_layer(tokvecs) return tokvecs, vectors def get_loss(self, examples, vectors, prediction): # The simplest way to implement this would be to vstack the # token.vector values, but that's a bit inefficient, especially on GPU. # Instead we fetch the index into the vectors table for each of our tokens, # and look them up all at once. This prevents data copying. ids = self.model.ops.flatten([ex.doc.to_array(ID).ravel() for ex in examples]) target = vectors[ids] gradient = self.distance.get_grad(prediction, target) loss = self.distance.get_loss(prediction, target) return loss, gradient def update(self, examples, drop=0., set_annotations=False, sgd=None, losses=None): pass def rehearse(self, examples, drop=0., sgd=None, losses=None): self.require_model() examples = Example.to_example_objects(examples) if losses is not None and self.name not in losses: losses[self.name] = 0. set_dropout_rate(self.model, drop) predictions, bp_predictions = self.model.begin_update([ex.doc for ex in examples]) loss, d_predictions = self.get_loss(examples, self.vocab.vectors.data, predictions) bp_predictions(d_predictions) if sgd is not None: self.model.finish_update(sgd) if losses is not None: losses[self.name] += loss @component("textcat", assigns=["doc.cats"]) class TextCategorizer(Pipe): """Pipeline component for text classification. DOCS: https://spacy.io/api/textcategorizer """ @classmethod def Model(cls, nr_class=1, exclusive_classes=None, **cfg): if nr_class == 1: exclusive_classes = False if exclusive_classes is None: raise ValueError( "TextCategorizer Model must specify 'exclusive_classes'. " "This setting determines whether the model will output " "scores that sum to 1 for each example. If only one class " "is true for each example, you should set exclusive_classes=True. " "For 'multi_label' classification, set exclusive_classes=False." ) if "embed_size" not in cfg: cfg["embed_size"] = util.env_opt("embed_size", 2000) if "token_vector_width" not in cfg: cfg["token_vector_width"] = util.env_opt("token_vector_width", 96) if cfg.get("architecture") == "bow": return build_bow_text_classifier(nr_class, exclusive_classes, **cfg) else: if "tok2vec" in cfg: tok2vec = cfg["tok2vec"] else: config = { "width": cfg.get("token_vector_width", 96), "embed_size": cfg.get("embed_size", 2000), "pretrained_vectors": cfg.get("pretrained_vectors", None), "window_size": cfg.get("window_size", 1), "cnn_maxout_pieces": cfg.get("cnn_maxout_pieces", 3), "subword_features": cfg.get("subword_features", True), "char_embed": cfg.get("char_embed", False), "conv_depth": cfg.get("conv_depth", 4), "bilstm_depth": cfg.get("bilstm_depth", 0), } tok2vec = Tok2Vec(**config) return build_simple_cnn_text_classifier( tok2vec, nr_class, exclusive_classes, **cfg ) @property def tok2vec(self): if self.model in (None, True, False): return None else: return self.model.tok2vec def __init__(self, vocab, model=True, **cfg): self.vocab = vocab self.model = model self._rehearsal_model = None self.cfg = dict(cfg) if "exclusive_classes" not in cfg: self.cfg["exclusive_classes"] = True @property def labels(self): return tuple(self.cfg.setdefault("labels", [])) def require_labels(self): """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): self.cfg["labels"] = tuple(value) def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False): for examples in util.minibatch(stream, size=batch_size): docs = [self._get_doc(ex) for ex in examples] scores, tensors = self.predict(docs) self.set_annotations(docs, scores, tensors=tensors) if as_example: annotated_examples = [] for ex, doc in zip(examples, docs): ex.doc = doc annotated_examples.append(ex) yield from annotated_examples else: yield from docs def predict(self, docs): self.require_model() 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, tensors scores = self.model.predict(docs) scores = self.model.ops.asarray(scores) return scores, tensors def set_annotations(self, docs, scores, tensors=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, state=None, drop=0., set_annotations=False, sgd=None, losses=None): self.require_model() examples = Example.to_example_objects(examples) if not any(len(ex.doc) if ex.doc else 0 for ex in examples): # Handle cases where there are no tokens in any docs. return set_dropout_rate(self.model, drop) scores, bp_scores = self.model.begin_update([ex.doc for ex in examples]) loss, d_scores = self.get_loss(examples, scores) bp_scores(d_scores) if sgd is not None: self.model.finish_update(sgd) if losses is not None: losses.setdefault(self.name, 0.0) losses[self.name] += loss if set_annotations: docs = [ex.doc for ex in examples] self.set_annotations(docs, scores=scores) def rehearse(self, examples, drop=0., sgd=None, losses=None): if self._rehearsal_model is None: return examples = Example.to_example_objects(examples) docs=[ex.doc for ex in examples] 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 get_loss(self, examples, scores): golds = [ex.gold for ex in examples] truths = numpy.zeros((len(golds), len(self.labels)), dtype="f") not_missing = numpy.ones((len(golds), len(self.labels)), dtype="f") for i, gold in enumerate(golds): for j, label in enumerate(self.labels): if label in gold.cats: truths[i, j] = gold.cats[label] else: not_missing[i, j] = 0. truths = self.model.ops.asarray(truths) 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): if not isinstance(label, str): raise ValueError(Errors.E187) if label in self.labels: return 0 if self.model not in (None, True, False): # 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=lambda: [], pipeline=None, sgd=None, **kwargs): for example in get_examples(): for cat in example.doc_annotation.cats: self.add_label(cat) if self.model is True: self.cfg.update(kwargs) self.require_labels() self.model = self.Model(len(self.labels), **self.cfg) link_vectors_to_models(self.vocab) if sgd is None: sgd = self.create_optimizer() # TODO: use get_examples instead docs = [Doc(Vocab(), words=["hello"])] self.model.initialize(X=docs) return sgd cdef class DependencyParser(Parser): """Pipeline component for dependency parsing. DOCS: https://spacy.io/api/dependencyparser """ # cdef classes can't have decorators, so we're defining this here name = "parser" factory = "parser" assigns = ["token.dep", "token.is_sent_start", "doc.sents"] requires = [] TransitionSystem = ArcEager @property def postprocesses(self): output = [nonproj.deprojectivize] if self.cfg.get("learn_tokens") is True: output.append(merge_subtokens) return tuple(output) def add_multitask_objective(self, target): if target == "cloze": cloze = ClozeMultitask(self.vocab) self._multitasks.append(cloze) else: labeller = MultitaskObjective(self.vocab, target=target) self._multitasks.append(labeller) def init_multitask_objectives(self, get_examples, pipeline, sgd=None, **cfg): for labeller in self._multitasks: tok2vec = self.model.tok2vec labeller.begin_training(get_examples, pipeline=pipeline, tok2vec=tok2vec, sgd=sgd) def __reduce__(self): return (DependencyParser, (self.vocab, self.moves, self.model), None, None) @property def labels(self): labels = set() # Get the labels from the model by looking at the available moves for move in self.move_names: if "-" in move: label = move.split("-")[1] if "||" in label: label = label.split("||")[1] labels.add(label) return tuple(sorted(labels)) cdef class EntityRecognizer(Parser): """Pipeline component for named entity recognition. DOCS: https://spacy.io/api/entityrecognizer """ name = "ner" factory = "ner" assigns = ["doc.ents", "token.ent_iob", "token.ent_type"] requires = [] TransitionSystem = BiluoPushDown nr_feature = 6 def add_multitask_objective(self, target): if target == "cloze": cloze = ClozeMultitask(self.vocab) self._multitasks.append(cloze) else: labeller = MultitaskObjective(self.vocab, target=target) self._multitasks.append(labeller) def init_multitask_objectives(self, get_examples, pipeline, sgd=None, **cfg): for labeller in self._multitasks: tok2vec = self.model.tok2vec labeller.begin_training(get_examples, pipeline=pipeline, tok2vec=tok2vec) def __reduce__(self): return (EntityRecognizer, (self.vocab, self.moves, self.model), None, None) @property def labels(self): # Get the labels from the model by looking at the available moves, e.g. # B-PERSON, I-PERSON, L-PERSON, U-PERSON labels = set(move.split("-")[1] for move in self.move_names if move[0] in ("B", "I", "L", "U")) return tuple(sorted(labels)) @component( "entity_linker", requires=["doc.ents", "doc.sents", "token.ent_iob", "token.ent_type"], assigns=["token.ent_kb_id"] ) class EntityLinker(Pipe): """Pipeline component for named entity linking. DOCS: https://spacy.io/api/entitylinker """ NIL = "NIL" # string used to refer to a non-existing link @classmethod def Model(cls, **cfg): embed_width = cfg.get("embed_width", 300) hidden_width = cfg.get("hidden_width", 128) type_to_int = cfg.get("type_to_int", dict()) model = build_nel_encoder(embed_width=embed_width, hidden_width=hidden_width, ner_types=len(type_to_int), **cfg) return model def __init__(self, vocab, **cfg): self.vocab = vocab self.model = True self.kb = None self.cfg = dict(cfg) self.distance = CosineDistance(normalize=False) def set_kb(self, kb): self.kb = kb def require_model(self): # Raise an error if the component's model is not initialized. if getattr(self, "model", None) in (None, True, False): raise ValueError(Errors.E109.format(name=self.name)) def require_kb(self): # Raise an error if the knowledge base is not initialized. if getattr(self, "kb", None) in (None, True, False): raise ValueError(Errors.E139.format(name=self.name)) def begin_training(self, get_examples=lambda: [], pipeline=None, sgd=None, **kwargs): self.require_kb() self.cfg["entity_width"] = self.kb.entity_vector_length if self.model is True: self.model = self.Model(**self.cfg) self.model.initialize() if sgd is None: sgd = self.create_optimizer() return sgd def update(self, examples, state=None, set_annotations=False, drop=0.0, sgd=None, losses=None): self.require_model() self.require_kb() if losses is not None: losses.setdefault(self.name, 0.0) if not examples: return 0 examples = Example.to_example_objects(examples) sentence_docs = [] docs = [ex.doc for ex in examples] if set_annotations: # This seems simpler than other ways to get that exact output -- but # it does run the model twice :( predictions = self.model.predict(docs) golds = [ex.gold for ex in examples] for doc, gold in zip(docs, golds): ents_by_offset = dict() for ent in doc.ents: ents_by_offset[(ent.start_char, ent.end_char)] = ent for entity, kb_dict in gold.links.items(): start, end = entity mention = doc.text[start:end] # the gold annotations should link to proper entities - if this fails, the dataset is likely corrupt if not (start, end) in ents_by_offset: raise RuntimeError(Errors.E188) ent = ents_by_offset[(start, end)] for kb_id, value in kb_dict.items(): # Currently only training on the positive instances - we assume there is at least 1 per doc/gold if value: try: sentence_docs.append(ent.sent.as_doc()) except AttributeError: # Catch the exception when ent.sent is None and provide a user-friendly warning raise RuntimeError(Errors.E030) set_dropout_rate(self.model, drop) sentence_encodings, bp_context = self.model.begin_update(sentence_docs) loss, d_scores = self.get_similarity_loss(scores=sentence_encodings, golds=golds) bp_context(d_scores) if sgd is not None: self.model.finish_update(sgd) if losses is not None: losses[self.name] += loss if set_annotations: self.set_annotations(docs, predictions) return loss def get_similarity_loss(self, golds, scores): entity_encodings = [] for gold in golds: for entity, kb_dict in gold.links.items(): for kb_id, value in kb_dict.items(): # this loss function assumes we're only using positive examples if value: entity_encoding = self.kb.get_vector(kb_id) entity_encodings.append(entity_encoding) entity_encodings = self.model.ops.asarray(entity_encodings, dtype="float32") if scores.shape != entity_encodings.shape: raise RuntimeError(Errors.E147.format(method="get_similarity_loss", msg="gold entities do not match up")) gradients = self.distance.get_grad(scores, entity_encodings) loss = self.distance.get_loss(scores, entity_encodings) loss = loss / len(entity_encodings) return loss, gradients def get_loss(self, examples, scores): cats = [] for ex in examples: for entity, kb_dict in ex.gold.links.items(): for kb_id, value in kb_dict.items(): cats.append([value]) cats = self.model.ops.asarray(cats, dtype="float32") if len(scores) != len(cats): raise RuntimeError(Errors.E147.format(method="get_loss", msg="gold entities do not match up")) d_scores = (scores - cats) loss = (d_scores ** 2).sum() loss = loss / len(cats) return loss, d_scores def __call__(self, example): doc = self._get_doc(example) kb_ids, tensors = self.predict([doc]) self.set_annotations([doc], kb_ids, tensors=tensors) if isinstance(example, Example): example.doc = doc return example return doc def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False): for examples in util.minibatch(stream, size=batch_size): docs = [self._get_doc(ex) for ex in examples] kb_ids, tensors = self.predict(docs) self.set_annotations(docs, kb_ids, tensors=tensors) if as_example: annotated_examples = [] for ex, doc in zip(examples, docs): ex.doc = doc annotated_examples.append(ex) yield from annotated_examples else: yield from docs def predict(self, docs): """ Return the KB IDs for each entity in each doc, including NIL if there is no prediction """ self.require_model() self.require_kb() entity_count = 0 final_kb_ids = [] final_tensors = [] if not docs: return final_kb_ids, final_tensors if isinstance(docs, Doc): docs = [docs] for i, doc in enumerate(docs): if len(doc) > 0: # Looping through each sentence and each entity # This may go wrong if there are entities across sentences - because they might not get a KB ID for sent in doc.sents: sent_doc = sent.as_doc() # currently, the context is the same for each entity in a sentence (should be refined) sentence_encoding = self.model.predict([sent_doc])[0] xp = get_array_module(sentence_encoding) sentence_encoding_t = sentence_encoding.T sentence_norm = xp.linalg.norm(sentence_encoding_t) for ent in sent_doc.ents: entity_count += 1 to_discard = self.cfg.get("labels_discard", []) if to_discard and ent.label_ in to_discard: # ignoring this entity - setting to NIL final_kb_ids.append(self.NIL) final_tensors.append(sentence_encoding) else: candidates = self.kb.get_candidates(ent.text) if not candidates: # no prediction possible for this entity - setting to NIL final_kb_ids.append(self.NIL) final_tensors.append(sentence_encoding) elif len(candidates) == 1: # shortcut for efficiency reasons: take the 1 candidate # TODO: thresholding final_kb_ids.append(candidates[0].entity_) final_tensors.append(sentence_encoding) else: random.shuffle(candidates) # this will set all prior probabilities to 0 if they should be excluded from the model prior_probs = xp.asarray([c.prior_prob for c in candidates]) if not self.cfg.get("incl_prior", True): prior_probs = xp.asarray([0.0 for c in candidates]) scores = prior_probs # add in similarity from the context if self.cfg.get("incl_context", True): entity_encodings = xp.asarray([c.entity_vector for c in candidates]) entity_norm = xp.linalg.norm(entity_encodings, axis=1) if len(entity_encodings) != len(prior_probs): raise RuntimeError(Errors.E147.format(method="predict", msg="vectors not of equal length")) # cosine similarity sims = xp.dot(entity_encodings, sentence_encoding_t) / (sentence_norm * entity_norm) if sims.shape != prior_probs.shape: raise ValueError(Errors.E161) scores = prior_probs + sims - (prior_probs*sims) # TODO: thresholding best_index = scores.argmax() best_candidate = candidates[best_index] final_kb_ids.append(best_candidate.entity_) final_tensors.append(sentence_encoding) if not (len(final_tensors) == len(final_kb_ids) == entity_count): raise RuntimeError(Errors.E147.format(method="predict", msg="result variables not of equal length")) return final_kb_ids, final_tensors def set_annotations(self, docs, kb_ids, tensors=None): count_ents = len([ent for doc in docs for ent in doc.ents]) if count_ents != len(kb_ids): raise ValueError(Errors.E148.format(ents=count_ents, ids=len(kb_ids))) i=0 for doc in docs: for ent in doc.ents: kb_id = kb_ids[i] i += 1 for token in ent: token.ent_kb_id_ = kb_id def to_disk(self, path, exclude=tuple(), **kwargs): serialize = {} serialize["cfg"] = lambda p: srsly.write_json(p, self.cfg) serialize["vocab"] = lambda p: self.vocab.to_disk(p) serialize["kb"] = lambda p: self.kb.dump(p) if self.model not in (None, True, False): serialize["model"] = lambda p: p.open("wb").write(self.model.to_bytes()) exclude = util.get_serialization_exclude(serialize, exclude, kwargs) util.to_disk(path, serialize, exclude) def from_disk(self, path, exclude=tuple(), **kwargs): def load_model(p): if self.model is True: self.model = self.Model(**self.cfg) try: self.model.from_bytes(p.open("rb").read()) except AttributeError: raise ValueError(Errors.E149) def load_kb(p): kb = KnowledgeBase(vocab=self.vocab, entity_vector_length=self.cfg["entity_width"]) kb.load_bulk(p) self.set_kb(kb) deserialize = {} deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p)) deserialize["vocab"] = lambda p: self.vocab.from_disk(p) deserialize["kb"] = load_kb deserialize["model"] = load_model exclude = util.get_serialization_exclude(deserialize, exclude, kwargs) util.from_disk(path, deserialize, exclude) return self def rehearse(self, examples, sgd=None, losses=None, **config): raise NotImplementedError def add_label(self, label): raise NotImplementedError @component("sentencizer", assigns=["token.is_sent_start", "doc.sents"]) class Sentencizer(Pipe): """Segment the Doc into sentences using a rule-based strategy. DOCS: https://spacy.io/api/sentencizer """ default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹', '।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄', '᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿', '‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶', '꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒', '﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀', '𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼', '𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐', '𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂', '𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈'] def __init__(self, punct_chars=None, **kwargs): """Initialize the sentencizer. punct_chars (list): Punctuation characters to split on. Will be serialized with the nlp object. RETURNS (Sentencizer): The sentencizer component. DOCS: https://spacy.io/api/sentencizer#init """ if punct_chars: self.punct_chars = set(punct_chars) else: self.punct_chars = set(self.default_punct_chars) @classmethod def from_nlp(cls, nlp, **cfg): return cls(**cfg) def __call__(self, example): """Apply the sentencizer to a Doc and set Token.is_sent_start. example (Doc or Example): The document to process. RETURNS (Doc or Example): The processed Doc or Example. DOCS: https://spacy.io/api/sentencizer#call """ doc = self._get_doc(example) start = 0 seen_period = False for i, token in enumerate(doc): is_in_punct_chars = token.text in self.punct_chars token.is_sent_start = i == 0 if seen_period and not token.is_punct and not is_in_punct_chars: doc[start].is_sent_start = True start = token.i seen_period = False elif is_in_punct_chars: seen_period = True if start < len(doc): doc[start].is_sent_start = True if isinstance(example, Example): example.doc = doc return example return doc def pipe(self, stream, batch_size=128, n_threads=-1, as_example=False): for examples in util.minibatch(stream, size=batch_size): docs = [self._get_doc(ex) for ex in examples] predictions = self.predict(docs) if isinstance(predictions, tuple) and len(tuple) == 2: scores, tensors = predictions self.set_annotations(docs, scores, tensors=tensors) else: self.set_annotations(docs, predictions) if as_example: annotated_examples = [] for ex, doc in zip(examples, docs): ex.doc = doc annotated_examples.append(ex) yield from annotated_examples else: yield from docs def predict(self, docs): """Apply the pipeline's model to a batch of docs, without modifying them. """ if not any(len(doc) for doc in docs): # Handle cases where there are no tokens in any docs. guesses = [[] for doc in docs] return guesses guesses = [] for doc in docs: start = 0 seen_period = False doc_guesses = [False] * len(doc) doc_guesses[0] = True for i, token in enumerate(doc): is_in_punct_chars = token.text in self.punct_chars if seen_period and not token.is_punct and not is_in_punct_chars: doc_guesses[start] = True start = token.i seen_period = False elif is_in_punct_chars: seen_period = True if start < len(doc): doc_guesses[start] = True guesses.append(doc_guesses) return guesses def set_annotations(self, docs, batch_tag_ids, tensors=None): if isinstance(docs, Doc): docs = [docs] cdef Doc doc cdef int idx = 0 for i, doc in enumerate(docs): doc_tag_ids = batch_tag_ids[i] for j, tag_id in enumerate(doc_tag_ids): # Don't clobber existing sentence boundaries if doc.c[j].sent_start == 0: if tag_id: doc.c[j].sent_start = 1 else: doc.c[j].sent_start = -1 def to_bytes(self, **kwargs): """Serialize the sentencizer to a bytestring. RETURNS (bytes): The serialized object. DOCS: https://spacy.io/api/sentencizer#to_bytes """ return srsly.msgpack_dumps({"punct_chars": list(self.punct_chars)}) def from_bytes(self, bytes_data, **kwargs): """Load the sentencizer from a bytestring. bytes_data (bytes): The data to load. returns (Sentencizer): The loaded object. DOCS: https://spacy.io/api/sentencizer#from_bytes """ cfg = srsly.msgpack_loads(bytes_data) self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars)) return self def to_disk(self, path, exclude=tuple(), **kwargs): """Serialize the sentencizer to disk. DOCS: https://spacy.io/api/sentencizer#to_disk """ path = util.ensure_path(path) path = path.with_suffix(".json") srsly.write_json(path, {"punct_chars": list(self.punct_chars)}) def from_disk(self, path, exclude=tuple(), **kwargs): """Load the sentencizer from disk. DOCS: https://spacy.io/api/sentencizer#from_disk """ path = util.ensure_path(path) path = path.with_suffix(".json") cfg = srsly.read_json(path) self.punct_chars = set(cfg.get("punct_chars", self.default_punct_chars)) return self # Cython classes can't be decorated, so we need to add the factories here Language.factories["parser"] = lambda nlp, **cfg: DependencyParser.from_nlp(nlp, **cfg) Language.factories["ner"] = lambda nlp, **cfg: EntityRecognizer.from_nlp(nlp, **cfg) __all__ = ["Tagger", "DependencyParser", "EntityRecognizer", "Tensorizer", "TextCategorizer", "EntityLinker", "Sentencizer", "SentenceRecognizer"]