You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
972 lines
35 KiB
972 lines
35 KiB
# cython: infer_types=True
|
|
# cython: profile=True
|
|
# coding: utf8
|
|
from __future__ import unicode_literals
|
|
|
|
import numpy
|
|
cimport numpy as np
|
|
import cytoolz
|
|
from collections import OrderedDict
|
|
import ujson
|
|
|
|
from .util import msgpack
|
|
from .util import msgpack_numpy
|
|
|
|
from thinc.api import chain
|
|
from thinc.v2v import Affine, SELU, Softmax
|
|
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 .compat import json_dumps
|
|
|
|
from .attrs import POS
|
|
from .parts_of_speech import X
|
|
from ._ml import Tok2Vec, build_text_classifier, build_tagger_model
|
|
from ._ml import link_vectors_to_models, zero_init, flatten
|
|
from ._ml import create_default_optimizer
|
|
from .errors import Errors, TempErrors
|
|
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 = 'sbd'
|
|
|
|
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
|
|
|
|
|
|
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.
|
|
"""
|
|
scores, tensors = self.predict([doc])
|
|
self.set_annotations([doc], scores, tensors=tensors)
|
|
return doc
|
|
|
|
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 cytoolz.partition_all(batch_size, stream):
|
|
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.
|
|
"""
|
|
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().
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
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, gold_tuples=tuple(), 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: 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(ujson.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: p.open('w').write(json_dumps(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():
|
|
with path.open() as file_:
|
|
return ujson.load(file_)
|
|
else:
|
|
return {}
|
|
|
|
|
|
class Tensorizer(Pipe):
|
|
"""Assign position-sensitive vectors to tokens, using a CNN or RNN."""
|
|
name = 'tensorizer'
|
|
|
|
@classmethod
|
|
def Model(cls, output_size=300, input_size=384, **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.
|
|
"""
|
|
model = chain(
|
|
SELU(output_size, input_size),
|
|
SELU(output_size, output_size),
|
|
zero_init(Affine(output_size, output_size)))
|
|
return model
|
|
|
|
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 cytoolz.partition_all(batch_size, stream):
|
|
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.
|
|
"""
|
|
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.
|
|
"""
|
|
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):
|
|
target = []
|
|
i = 0
|
|
for doc in docs:
|
|
vectors = self.model.ops.xp.vstack([w.vector for w in doc])
|
|
target.append(vectors)
|
|
target = self.model.ops.xp.vstack(target)
|
|
d_scores = (prediction - target) / prediction.shape[0]
|
|
loss = (d_scores**2).sum()
|
|
return loss, d_scores
|
|
|
|
def begin_training(self, gold_tuples=tuple(), 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.
|
|
"""
|
|
for name, model in pipeline:
|
|
if getattr(model, 'tok2vec', None):
|
|
self.input_models.append(model.tok2vec)
|
|
if self.model is True:
|
|
self.cfg['input_size'] = 384
|
|
self.cfg['output_size'] = 300
|
|
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.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 cytoolz.partition_all(batch_size, stream):
|
|
docs = list(docs)
|
|
tag_ids, tokvecs = self.predict(docs)
|
|
self.set_annotations(docs, tag_ids, tensors=tokvecs)
|
|
yield from docs
|
|
|
|
def predict(self, docs):
|
|
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:
|
|
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):
|
|
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 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)
|
|
for gold in golds:
|
|
for tag in gold.tags:
|
|
if tag is None:
|
|
correct[idx] = guesses[idx]
|
|
else:
|
|
correct[idx] = tag_index[tag]
|
|
idx += 1
|
|
correct = self.model.ops.xp.array(correct, dtype='i')
|
|
d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
|
|
d_scores /= d_scores.shape[0]
|
|
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, gold_tuples=tuple(), pipeline=None, sgd=None,
|
|
**kwargs):
|
|
orig_tag_map = dict(self.vocab.morphology.tag_map)
|
|
new_tag_map = OrderedDict()
|
|
for raw_text, annots_brackets in 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:
|
|
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: ujson.dumps(self.cfg)
|
|
tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items()))
|
|
serialize['tag_map'] = lambda: msgpack.dumps(
|
|
tag_map, use_bin_type=True, encoding='utf8')
|
|
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', 128))
|
|
self.model = self.Model(self.vocab.morphology.n_tags,
|
|
**self.cfg)
|
|
self.model.from_bytes(b)
|
|
|
|
def load_tag_map(b):
|
|
tag_map = msgpack.loads(b, encoding='utf8')
|
|
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(ujson.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: p.open('wb').write(msgpack.dumps(
|
|
tag_map, use_bin_type=True, encoding='utf8'))),
|
|
('model', lambda p: p.open('wb').write(self.model.to_bytes())),
|
|
('cfg', lambda p: p.open('w').write(json_dumps(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):
|
|
with p.open('rb') as file_:
|
|
tag_map = msgpack.loads(file_.read(), encoding='utf8')
|
|
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 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, gold_tuples=tuple(), pipeline=None, tok2vec=None,
|
|
sgd=None, **kwargs):
|
|
gold_tuples = nonproj.preprocess_training_data(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', 128)
|
|
softmax = Softmax(n_tags, token_vector_width)
|
|
model = chain(
|
|
tok2vec,
|
|
softmax
|
|
)
|
|
model.tok2vec = tok2vec
|
|
model.softmax = softmax
|
|
return model
|
|
|
|
def predict(self, docs):
|
|
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
|
|
gold_idx = gold.cand_to_gold[j]
|
|
if gold_idx is None:
|
|
idx += 1
|
|
continue
|
|
label = self.make_label(gold_idx, 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])
|
|
d_scores /= d_scores.shape[0]
|
|
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])
|
|
|
|
|
|
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):
|
|
return self.model.predict([(doc1, doc2)])
|
|
|
|
def update(self, doc1_doc2, golds, sgd=None, drop=0.):
|
|
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=1, width=64, **cfg):
|
|
return build_text_classifier(nr_class, width, **cfg)
|
|
|
|
def __init__(self, vocab, model=True, **cfg):
|
|
self.vocab = vocab
|
|
self.model = model
|
|
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 cytoolz.partition_all(batch_size, stream):
|
|
docs = list(docs)
|
|
scores, tensors = self.predict(docs)
|
|
self.set_annotations(docs, scores, tensors=tensors)
|
|
yield from docs
|
|
|
|
def predict(self, docs):
|
|
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 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 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):
|
|
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, gold_tuples=tuple(), 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), token_vector_width,
|
|
**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):
|
|
labeller = MultitaskObjective(self.vocab, target=target)
|
|
self._multitasks.append(labeller)
|
|
|
|
def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
|
|
for labeller in self._multitasks:
|
|
tok2vec = self.model[0]
|
|
labeller.begin_training(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):
|
|
labeller = MultitaskObjective(self.vocab, target=target)
|
|
self._multitasks.append(labeller)
|
|
|
|
def init_multitask_objectives(self, gold_tuples, pipeline, sgd=None, **cfg):
|
|
for labeller in self._multitasks:
|
|
tok2vec = self.model[0]
|
|
labeller.begin_training(gold_tuples, pipeline=pipeline,
|
|
tok2vec=tok2vec)
|
|
|
|
def __reduce__(self):
|
|
return (EntityRecognizer, (self.vocab, self.moves, self.model),
|
|
None, None)
|
|
|
|
|
|
__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'Tensorizer']
|