# cython: infer_types=True # cython: profile=True # coding: utf8 from __future__ import unicode_literals import numpy cimport numpy as np from collections import OrderedDict, defaultdict import srsly from thinc.api import chain from thinc.v2v import Affine, Maxout, Softmax from thinc.misc import LayerNorm from thinc.t2v import Pooling, max_pool, mean_pool from thinc.neural.util import to_categorical, copy_array from thinc.neural._classes.difference import Siamese, CauchySimilarity from .tokens.doc cimport Doc from .syntax.nn_parser cimport Parser from .syntax import nonproj from .syntax.ner cimport BiluoPushDown from .syntax.arc_eager cimport ArcEager from .morphology cimport Morphology from .vocab cimport Vocab from .syntax import nonproj from .matcher import Matcher from .matcher import Matcher, PhraseMatcher from .tokens.span import Span from .attrs import POS, ID from .parts_of_speech import X from ._ml import Tok2Vec, build_text_classifier, build_tagger_model from ._ml import build_simple_cnn_text_classifier from ._ml import link_vectors_to_models, zero_init, flatten from ._ml import create_default_optimizer from ._ml import masked_language_model from .errors import Errors, TempErrors from .compat import basestring_ from . import util class SentenceSegmenter(object): """A simple spaCy hook, to allow custom sentence boundary detection logic (that doesn't require the dependency parse). To change the sentence boundary detection strategy, pass a generator function `strategy` on initialization, or assign a new strategy to the .strategy attribute. Sentence detection strategies should be generators that take `Doc` objects and yield `Span` objects for each sentence. """ name = 'sentencizer' def __init__(self, vocab, strategy=None): self.vocab = vocab if strategy is None or strategy == 'on_punct': strategy = self.split_on_punct self.strategy = strategy def __call__(self, doc): doc.user_hooks['sents'] = self.strategy return doc @staticmethod def split_on_punct(doc): start = 0 seen_period = False for i, word in enumerate(doc): if seen_period and not word.is_punct: yield doc[start:word.i] start = word.i seen_period = False elif word.text in ['.', '!', '?']: seen_period = True if start < len(doc): yield doc[start:len(doc)] def merge_noun_chunks(doc): """Merge noun chunks into a single token. doc (Doc): The Doc object. RETURNS (Doc): The Doc object with merged noun chunks. """ if not doc.is_parsed: return doc spans = [(np.start_char, np.end_char, np.root.tag, np.root.dep) for np in doc.noun_chunks] for start, end, tag, dep in spans: doc.merge(start, end, tag=tag, dep=dep) return doc def merge_entities(doc): """Merge entities into a single token. doc (Doc): The Doc object. RETURNS (Doc): The Doc object with merged noun entities. """ spans = [(e.start_char, e.end_char, e.root.tag, e.root.dep, e.label) for e in doc.ents] for start, end, tag, dep, ent_type in spans: doc.merge(start, end, tag=tag, dep=dep, ent_type=ent_type) return doc def merge_subtokens(doc, label='subtok'): merger = Matcher(doc.vocab) merger.add('SUBTOK', None, [{'DEP': label, 'op': '+'}]) matches = merger(doc) spans = [doc[start:end+1] for _, start, end in matches] offsets = [(span.start_char, span.end_char) for span in spans] for start_char, end_char in offsets: doc.merge(start_char, end_char) return doc class EntityRuler(object): name = 'entity_ruler' def __init__(self, nlp, **cfg): """Initialise the entitiy ruler. If patterns are supplied here, they need to be a list of dictionaries with a `"label"` and `"pattern"` key. A pattern can either be a token pattern (list) or a phrase pattern (string). For example: `{'label': 'ORG', 'pattern': 'Apple'}`. nlp (Language): The shared nlp object to pass the vocab to the matchers and process phrase patterns. patterns (iterable): Optional patterns to load in. overwrite_ents (bool): If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. **cfg: Other config parameters. If pipeline component is loaded as part of a model pipeline, this will include all keyword arguments passed to `spacy.load`. RETURNS (EntityRuler): The newly constructed object. """ self.nlp = nlp self.overwrite = cfg.get('overwrite_ents', False) self.token_patterns = defaultdict(list) self.phrase_patterns = defaultdict(list) self.matcher = Matcher(nlp.vocab) self.phrase_matcher = PhraseMatcher(nlp.vocab) patterns = cfg.get('patterns') if patterns is not None: self.add_patterns(patterns) def __len__(self): """The number of all patterns added to the entity ruler.""" n_token_patterns = sum(len(p) for p in self.token_patterns.values()) n_phrase_patterns = sum(len(p) for p in self.phrase_patterns.values()) return n_token_patterns + n_phrase_patterns def __contains__(self, label): """Whether a label is present in the patterns.""" return label in self.token_patterns or label in self.phrase_patterns def __call__(self, doc): """Find matches in document and add them as entities. doc (Doc): The Doc object in the pipeline. RETURNS (Doc): The Doc with added entities, if available. """ matches = list(self.matcher(doc)) + list(self.phrase_matcher(doc)) matches = set([(m_id, start, end) for m_id, start, end in matches if start != end]) get_sort_key = lambda m: (m[2] - m[1], m[1]) matches = sorted(matches, key=get_sort_key, reverse=True) entities = list(doc.ents) new_entities = [] seen_tokens = set() for match_id, start, end in matches: if any(t.ent_type for t in doc[start:end]) and not self.overwrite: continue # check for end - 1 here because boundaries are inclusive if start not in seen_tokens and end - 1 not in seen_tokens: new_entities.append(Span(doc, start, end, label=match_id)) entities = [e for e in entities if not (e.start < end and e.end > start)] seen_tokens.update(range(start, end)) doc.ents = entities + new_entities return doc @property def labels(self): """All labels present in the match patterns. RETURNS (set): The string labels. """ all_labels = set(self.token_patterns.keys()) all_labels.update(self.phrase_patterns.keys()) return all_labels @property def patterns(self): """Get all patterns that were added to the entity ruler. RETURNS (list): The original patterns, one dictionary per pattern. """ all_patterns = [] for label, patterns in self.token_patterns.items(): for pattern in patterns: all_patterns.append({'label': label, 'pattern': pattern}) for label, patterns in self.phrase_patterns.items(): for pattern in patterns: all_patterns.append({'label': label, 'pattern': pattern.text}) return all_patterns def add_patterns(self, patterns): """Add patterns to the entitiy ruler. A pattern can either be a token pattern (list of dicts) or a phrase pattern (string). For example: {'label': 'ORG', 'pattern': 'Apple'} {'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]} patterns (list): The patterns to add. """ for entry in patterns: label = entry['label'] pattern = entry['pattern'] if isinstance(pattern, basestring_): self.phrase_patterns[label].append(self.nlp(pattern)) elif isinstance(pattern, list): self.token_patterns[label].append(pattern) else: raise ValueError(Errors.E097.format(pattern=pattern)) for label, patterns in self.token_patterns.items(): self.matcher.add(label, None, *patterns) for label, patterns in self.phrase_patterns.items(): self.phrase_matcher.add(label, None, *patterns) def from_bytes(self, patterns_bytes, **kwargs): """Load the entity ruler from a bytestring. patterns_bytes (bytes): The bytestring to load. **kwargs: Other config paramters, mostly for consistency. RETURNS (EntityRuler): The loaded entity ruler. """ patterns = srsly.msgpack_loads(patterns_bytes) self.add_patterns(patterns) return self def to_bytes(self, **kwargs): """Serialize the entity ruler patterns to a bytestring. RETURNS (bytes): The serialized patterns. """ return srsly.msgpack_dumps(self.patterns) def from_disk(self, path, **kwargs): """Load the entity ruler from a file. Expects a file containing newline-delimited JSON (JSONL) with one entry per line. path (unicode / Path): The JSONL file to load. **kwargs: Other config paramters, mostly for consistency. RETURNS (EntityRuler): The loaded entity ruler. """ path = util.ensure_path(path) path = path.with_suffix('.jsonl') patterns = srsly.read_jsonl(path) self.add_patterns(patterns) return self def to_disk(self, path, **kwargs): """Save the entity ruler patterns to a directory. The patterns will be saved as newline-delimited JSON (JSONL). path (unicode / Path): The JSONL file to load. **kwargs: Other config paramters, mostly for consistency. RETURNS (EntityRuler): The loaded entity ruler. """ path = util.ensure_path(path) path = path.with_suffix('.jsonl') srsly.write_jsonl(path, self.patterns) 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 def __init__(self, vocab, model=True, **cfg): """Create a new pipe instance.""" raise NotImplementedError def __call__(self, doc): """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() scores, tensors = self.predict([doc]) self.set_annotations([doc], scores, tensors=tensors) 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): """Apply the pipe to a stream of documents. Both __call__ and pipe should delegate to the `predict()` and `set_annotations()` methods. """ for docs in util.minibatch(stream, size=batch_size): docs = list(docs) scores, tensors = self.predict(docs) self.set_annotations(docs, scores, tensor=tensors) 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, docs, golds, drop=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(). """ self.require_model() raise NotImplementedError def rehearse(self, docs, sgd=None, losses=None, **config): pass def get_loss(self, docs, golds, scores): """Find the loss and gradient of loss for the batch of documents 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 pre-trained 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(self.model.ops, **self.cfg.get('optimizer', {})) def begin_training(self, get_gold_tuples=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) link_vectors_to_models(self.vocab) if sgd is None: sgd = self.create_optimizer() return sgd 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): """Serialize the pipe to a bytestring.""" serialize = OrderedDict() serialize['cfg'] = lambda: srsly.json_dumps(self.cfg) if self.model in (True, False, None): serialize['model'] = lambda: self.model else: serialize['model'] = self.model.to_bytes serialize['vocab'] = self.vocab.to_bytes return util.to_bytes(serialize, exclude) def from_bytes(self, bytes_data, **exclude): """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.name if self.model is True: self.model = self.Model(**self.cfg) self.model.from_bytes(b) deserialize = OrderedDict(( ('cfg', lambda b: self.cfg.update(srsly.json_loads(b))), ('vocab', lambda b: self.vocab.from_bytes(b)), ('model', load_model), )) util.from_bytes(bytes_data, deserialize, exclude) return self def to_disk(self, path, **exclude): """Serialize the pipe to disk.""" serialize = OrderedDict() 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()) util.to_disk(path, serialize, exclude) def from_disk(self, path, **exclude): """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.name if self.model is True: self.model = self.Model(**self.cfg) self.model.from_bytes(p.open('rb').read()) deserialize = OrderedDict(( ('cfg', lambda p: self.cfg.update(_load_cfg(p))), ('vocab', lambda p: self.vocab.from_disk(p)), ('model', load_model), )) util.from_disk(path, deserialize, exclude) return self def _load_cfg(path): if path.exists(): return srsly.read_json(path) else: return {} class Tensorizer(Pipe): """Pre-train position-sensitive vectors for tokens.""" name = 'tensorizer' @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.neural.Model` or similar instance. """ input_size = util.env_opt('token_vector_width', cfg.get('input_size', 96)) return zero_init(Affine(output_size, input_size, drop_factor=0.0)) 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` 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) self.cfg.setdefault('cnn_maxout_pieces', 3) def __call__(self, doc): """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. """ tokvecses = self.predict([doc]) self.set_annotations([doc], tokvecses) return doc def pipe(self, stream, batch_size=128, n_threads=-1): """Process `Doc` objects as a stream. stream (iterator): A sequence of `Doc` objects to process. batch_size (int): Number of `Doc` objects to group. n_threads (int): Number of threads. YIELDS (iterator): A sequence of `Doc` objects, in order of input. """ for docs in util.minibatch(stream, size=batch_size): docs = list(docs) tensors = self.predict(docs) self.set_annotations(docs, tensors) 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, docs, golds, state=None, drop=0., sgd=None, losses=None): """Update the model. docs (iterable): A batch of `Doc` objects. golds (iterable): A batch of `GoldParse` objects. drop (float): The droput rate. sgd (callable): An optimizer. RETURNS (dict): Results from the update. """ self.require_model() if isinstance(docs, Doc): docs = [docs] inputs = [] bp_inputs = [] for tok2vec in self.input_models: tensor, bp_tensor = tok2vec.begin_update(docs, drop=drop) inputs.append(tensor) bp_inputs.append(bp_tensor) inputs = self.model.ops.xp.hstack(inputs) scores, bp_scores = self.model.begin_update(inputs, drop=drop) loss, d_scores = self.get_loss(docs, golds, 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, sgd=sgd) if losses is not None: losses.setdefault(self.name, 0.) losses[self.name] += loss return loss def get_loss(self, docs, golds, prediction): ids = self.model.ops.flatten([doc.to_array(ID).ravel() for doc in docs]) 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, gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs): """Allocate models, pre-process training data and acquire an optimizer. gold_tuples (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) link_vectors_to_models(self.vocab) if sgd is None: sgd = self.create_optimizer() return sgd class Tagger(Pipe): name = 'tagger' def __init__(self, vocab, model=True, **cfg): self.vocab = vocab self.model = model self._rehearsal_model = None self.cfg = OrderedDict(sorted(cfg.items())) self.cfg.setdefault('cnn_maxout_pieces', 2) @property def labels(self): return self.vocab.morphology.tag_names @property def tok2vec(self): if self.model in (None, True, False): return None else: return chain(self.model.tok2vec, flatten) def __call__(self, doc): tags, tokvecs = self.predict([doc]) self.set_annotations([doc], tags, tensors=tokvecs) return doc def pipe(self, stream, batch_size=128, n_threads=-1): for docs in util.minibatch(stream, size=batch_size): docs = list(docs) tag_ids, tokvecs = self.predict(docs) self.set_annotations(docs, tag_ids, tensors=tokvecs) yield from docs def predict(self, docs): self.require_model() if not any(len(doc) for doc in docs): # Handle case where there are no tokens in any docs. n_labels = len(self.labels) guesses = [self.model.ops.allocate((0, n_labels)) for doc in docs] tokvecs = self.model.ops.allocate((0, self.model.tok2vec.nO)) return guesses, tokvecs tokvecs = self.model.tok2vec(docs) scores = self.model.softmax(tokvecs) 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, tokvecs def set_annotations(self, docs, batch_tag_ids, tensors=None): if isinstance(docs, Doc): docs = [docs] cdef Doc doc cdef int idx = 0 cdef Vocab vocab = self.vocab 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 and doc.c[j].pos == 0: # 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 idx += 1 if tensors is not None and len(tensors): if isinstance(doc.tensor, numpy.ndarray) \ and not isinstance(tensors[i], numpy.ndarray): doc.extend_tensor(tensors[i].get()) else: doc.extend_tensor(tensors[i]) doc.is_tagged = True def update(self, docs, golds, drop=0., sgd=None, losses=None): self.require_model() if losses is not None and self.name not in losses: losses[self.name] = 0. tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop) loss, d_tag_scores = self.get_loss(docs, golds, tag_scores) bp_tag_scores(d_tag_scores, sgd=sgd) if losses is not None: losses[self.name] += loss def rehearse(self, docs, 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 guesses, backprop = self.model.begin_update(docs, drop=drop) target = self._rehearsal_model(docs) gradient = guesses - target backprop(gradient, sgd=sgd) if losses is not None: losses.setdefault(self.name, 0.0) losses[self.name] += (gradient**2).sum() def get_loss(self, docs, golds, 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 gold in golds: 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, nb_classes=scores.shape[1]) d_scores *= self.model.ops.asarray(known_labels) loss = (d_scores**2).sum() d_scores = self.model.ops.unflatten(d_scores, [len(d) for d in docs]) return float(loss), d_scores def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs): orig_tag_map = dict(self.vocab.morphology.tag_map) new_tag_map = OrderedDict() for raw_text, annots_brackets in get_gold_tuples(): for annots, brackets in annots_brackets: ids, words, tags, heads, deps, ents = annots for tag in 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) link_vectors_to_models(self.vocab) if sgd is None: sgd = self.create_optimizer() return sgd @classmethod def Model(cls, n_tags, **cfg): if cfg.get('pretrained_dims') and not cfg.get('pretrained_vectors'): raise ValueError(TempErrors.T008) return build_tagger_model(n_tags, **cfg) def add_label(self, label, values=None): 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): serialize = OrderedDict() if self.model in (None, True, False): serialize['model'] = lambda: self.model else: serialize['model'] = self.model.to_bytes serialize['vocab'] = self.vocab.to_bytes serialize['cfg'] = lambda: srsly.json_dumps(self.cfg) tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items())) serialize['tag_map'] = lambda: srsly.msgpack_dumps(tag_map) return util.to_bytes(serialize, exclude) def from_bytes(self, bytes_data, **exclude): 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.name 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.vocab.morphology.n_tags, **self.cfg) self.model.from_bytes(b) 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 = OrderedDict(( ('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)), )) util.from_bytes(bytes_data, deserialize, exclude) return self def to_disk(self, path, **exclude): tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items())) serialize = OrderedDict(( ('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)) )) util.to_disk(path, serialize, exclude) def from_disk(self, path, **exclude): 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.name if self.model is True: self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg) with p.open('rb') as file_: self.model.from_bytes(file_.read()) 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 = OrderedDict(( ('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), )) util.from_disk(path, deserialize, exclude) return self class MultitaskObjective(Tagger): """Experimental: Assist training of a parser or tagger, by training a side-objective. """ name = 'nn_labeller' 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_gold_tuples=lambda: [], pipeline=None, tok2vec=None, sgd=None, **kwargs): gold_tuples = nonproj.preprocess_training_data(get_gold_tuples()) for raw_text, annots_brackets in gold_tuples: for annots, brackets in annots_brackets: ids, words, tags, heads, deps, ents = annots for i in range(len(ids)): label = self.make_label(i, words, tags, heads, deps, ents) 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) 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) softmax = Softmax(n_tags, token_vector_width*2) model = chain( tok2vec, LayerNorm(Maxout(token_vector_width*2, token_vector_width, pieces=3)), softmax ) model.tok2vec = tok2vec model.softmax = softmax 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, docs, golds, scores): if len(docs) != len(golds): raise ValueError(Errors.E077.format(value='loss', n_docs=len(docs), n_golds=len(golds))) cdef int idx = 0 correct = numpy.zeros((scores.shape[0],), dtype='i') guesses = scores.argmax(axis=1) for i, gold in enumerate(golds): for j in range(len(docs[i])): # Handes alignment for tokenization differences label = self.make_label(j, gold.words, gold.tags, gold.heads, gold.labels, gold.ents) 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, nb_classes=scores.shape[1]) loss = (d_scores**2).sum() return float(loss), d_scores @staticmethod def make_dep(i, words, tags, heads, deps, ents): if deps[i] is None or heads[i] is None: return None return deps[i] @staticmethod def make_tag(i, words, tags, heads, deps, ents): return tags[i] @staticmethod def make_ent(i, words, tags, heads, deps, ents): if ents is None: return None return ents[i] @staticmethod def make_dep_tag_offset(i, words, tags, heads, deps, ents): if deps[i] is None or heads[i] is None: return None offset = heads[i] - i offset = min(offset, 2) offset = max(offset, -2) return '%s-%s:%d' % (deps[i], tags[i], offset) @staticmethod def make_ent_tag(i, words, tags, heads, deps, ents): if ents is None or ents[i] is None: return None else: return '%s-%s' % (tags[i], ents[i]) @staticmethod def make_sent_start(target, words, tags, heads, deps, ents, 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. ''' 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( LayerNorm(Maxout(output_size, tok2vec.nO, pieces=3)), zero_init(Affine(output_size, output_size, drop_factor=0.0)) ) model = chain(tok2vec, output_layer) model = masked_language_model(vocab, model) model.tok2vec = tok2vec model.output_layer = output_layer return model def __init__(self, vocab, model=True, **cfg): self.vocab = vocab self.model = model self.cfg = cfg def set_annotations(self, docs, dep_ids, tensors=None): pass def begin_training(self, get_gold_tuples=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.allocate((5, self.model.tok2vec.nO)) 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, docs, 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([doc.to_array(ID).ravel() for doc in docs]) target = vectors[ids] gradient = (prediction - target) / prediction.shape[0] loss = (gradient**2).sum() return float(loss), gradient def update(self, docs, golds, drop=0., sgd=None, losses=None): pass def rehearse(self, docs, drop=0., sgd=None, losses=None): self.require_model() if losses is not None and self.name not in losses: losses[self.name] = 0. predictions, bp_predictions = self.model.begin_update(docs, drop=drop) loss, d_predictions = self.get_loss(docs, self.vocab.vectors.data, predictions) bp_predictions(d_predictions, sgd=sgd) if losses is not None: losses[self.name] += loss class SimilarityHook(Pipe): """ Experimental: A pipeline component to install a hook for supervised similarity into `Doc` objects. Requires a `Tensorizer` to pre-process documents. The similarity model can be any object obeying the Thinc `Model` interface. By default, the model concatenates the elementwise mean and elementwise max of the two tensors, and compares them using the Cauchy-like similarity function from Chen (2013): >>> similarity = 1. / (1. + (W * (vec1-vec2)**2).sum()) Where W is a vector of dimension weights, initialized to 1. """ name = 'similarity' def __init__(self, vocab, model=True, **cfg): self.vocab = vocab self.model = model self.cfg = dict(cfg) @classmethod def Model(cls, length): return Siamese(Pooling(max_pool, mean_pool), CauchySimilarity(length)) def __call__(self, doc): """Install similarity hook""" doc.user_hooks['similarity'] = self.predict return doc def pipe(self, docs, **kwargs): for doc in docs: yield self(doc) def predict(self, doc1, doc2): self.require_model() return self.model.predict([(doc1, doc2)]) def update(self, doc1_doc2, golds, sgd=None, drop=0.): self.require_model() sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop) def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs): """Allocate model, using width from tensorizer in pipeline. gold_tuples (iterable): Gold-standard training data. pipeline (list): The pipeline the model is part of. """ if self.model is True: self.model = self.Model(pipeline[0].model.nO) link_vectors_to_models(self.vocab) if sgd is None: sgd = self.create_optimizer() return sgd class TextCategorizer(Pipe): name = 'textcat' @classmethod def Model(cls, nr_class, **cfg): embed_size = util.env_opt("embed_size", 2000) if "token_vector_width" in cfg: token_vector_width = cfg["token_vector_width"] else: token_vector_width = util.env_opt("token_vector_width", 96) tok2vec = Tok2Vec(token_vector_width, embed_size, **cfg) return build_simple_cnn_text_classifier(tok2vec, nr_class, **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) @property def labels(self): return self.cfg.setdefault('labels', []) @labels.setter def labels(self, value): self.cfg['labels'] = value def __call__(self, doc): scores, tensors = self.predict([doc]) self.set_annotations([doc], scores, tensors=tensors) return doc def pipe(self, stream, batch_size=128, n_threads=-1): for docs in util.minibatch(stream, size=batch_size): docs = list(docs) scores, tensors = self.predict(docs) self.set_annotations(docs, scores, tensors=tensors) yield from docs def predict(self, docs): self.require_model() scores = self.model(docs) scores = self.model.ops.asarray(scores) tensors = [doc.tensor for doc in docs] 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, docs, golds, state=None, drop=0., sgd=None, losses=None): scores, bp_scores = self.model.begin_update(docs, drop=drop) loss, d_scores = self.get_loss(docs, golds, scores) bp_scores(d_scores, sgd=sgd) if losses is not None: losses.setdefault(self.name, 0.0) losses[self.name] += loss def rehearse(self, docs, drop=0., sgd=None, losses=None): if self._rehearsal_model is None: return scores, bp_scores = self.model.begin_update(docs, drop=drop) target = self._rehearsal_model(docs) gradient = scores - target bp_scores(gradient, sgd=sgd) if losses is not None: losses.setdefault(self.name, 0.0) losses[self.name] += (gradient**2).sum() def get_loss(self, docs, golds, scores): 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 = ((scores-truths)**2).sum(axis=1).mean() return float(mean_square_error), d_scores def add_label(self, label): 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( "Cannot currently add labels to pre-trained text classifier. " "Add labels before training begins. This functionality was " "available in previous versions, but had significant bugs that " "let to poor performance") #smaller = self.model._layers[-1] #larger = Affine(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.append(label) return 1 def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None, **kwargs): if pipeline and getattr(pipeline[0], 'name', None) == 'tensorizer': token_vector_width = pipeline[0].model.nO else: token_vector_width = 64 if self.model is True: self.cfg['pretrained_vectors'] = kwargs.get('pretrained_vectors') self.model = self.Model(len(self.labels), **self.cfg) link_vectors_to_models(self.vocab) if sgd is None: sgd = self.create_optimizer() return sgd cdef class DependencyParser(Parser): name = 'parser' TransitionSystem = ArcEager @property def postprocesses(self): return [nonproj.deprojectivize] 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_gold_tuples, pipeline, sgd=None, **cfg): for labeller in self._multitasks: tok2vec = self.model.tok2vec labeller.begin_training(get_gold_tuples, pipeline=pipeline, tok2vec=tok2vec, sgd=sgd) def __reduce__(self): return (DependencyParser, (self.vocab, self.moves, self.model), None, None) cdef class EntityRecognizer(Parser): name = 'ner' 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_gold_tuples, pipeline, sgd=None, **cfg): for labeller in self._multitasks: tok2vec = self.model.tok2vec labeller.begin_training(get_gold_tuples, 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 return [move.split('-')[1] for move in self.move_names if move[0] in ('B', 'I', 'L', 'U')] __all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'Tensorizer', 'TextCategorizer']