spaCy/spacy/pipeline.pyx
2017-07-25 19:41:11 +02:00

682 lines
24 KiB
Cython

# cython: infer_types=True
# cython: profile=True
# coding: utf8
from __future__ import unicode_literals
from thinc.api import chain, layerize, with_getitem
from thinc.neural import Model, Softmax
import numpy
cimport numpy as np
import cytoolz
import util
from collections import OrderedDict
import ujson
import msgpack
from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
from thinc.neural import Model, Maxout, Softmax, Affine
from thinc.neural._classes.hash_embed import HashEmbed
from thinc.neural.util import to_categorical
from thinc.neural.pooling import Pooling, max_pool, mean_pool
from thinc.neural._classes.difference import Siamese, CauchySimilarity
from thinc.neural._classes.convolution import ExtractWindow
from thinc.neural._classes.resnet import Residual
from thinc.neural._classes.batchnorm import BatchNorm as BN
from .tokens.doc cimport Doc
from .syntax.parser cimport Parser as LinearParser
from .syntax.nn_parser cimport Parser as NeuralParser
from .syntax.parser import get_templates as get_feature_templates
from .syntax.beam_parser cimport BeamParser
from .syntax.ner cimport BiluoPushDown
from .syntax.arc_eager cimport ArcEager
from .tagger import Tagger
from .syntax.stateclass cimport StateClass
from .gold cimport GoldParse
from .morphology cimport Morphology
from .vocab cimport Vocab
from .syntax import nonproj
from .compat import json_dumps
from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
from ._ml import rebatch, Tok2Vec, flatten, get_col, doc2feats
from ._ml import build_text_classifier
from .parts_of_speech import X
class BaseThincComponent(object):
name = None
@classmethod
def Model(cls, *shape, **kwargs):
raise NotImplementedError
def __init__(self, vocab, model=True, **cfg):
raise NotImplementedError
def __call__(self, doc):
scores = self.predict([doc])
self.set_annotations([doc], scores)
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 = self.predict(docs)
self.set_annotations(docs, scores)
yield from docs
def predict(self, docs):
raise NotImplementedError
def set_annotations(self, docs, scores):
raise NotImplementedError
def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
raise NotImplementedError
def get_loss(self, docs, golds, scores):
raise NotImplementedError
def begin_training(self, gold_tuples=tuple(), pipeline=None):
token_vector_width = pipeline[0].model.nO
if self.model is True:
self.model = self.Model(1, token_vector_width)
def use_params(self, params):
with self.model.use_params(params):
yield
def to_bytes(self, **exclude):
serialize = OrderedDict((
('model', lambda: self.model.to_bytes()),
('vocab', lambda: self.vocab.to_bytes())
))
return util.to_bytes(serialize, exclude)
def from_bytes(self, bytes_data, **exclude):
if self.model is True:
self.model = self.Model()
deserialize = OrderedDict((
('model', lambda b: self.model.from_bytes(b)),
('vocab', lambda b: self.vocab.from_bytes(b))
))
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, **exclude):
serialize = OrderedDict((
('model', lambda p: p.open('wb').write(self.model.to_bytes())),
('vocab', lambda p: self.vocab.to_disk(p)),
('cfg', lambda p: p.open('w').write(json_dumps(self.cfg)))
))
util.to_disk(path, serialize, exclude)
def from_disk(self, path, **exclude):
if self.model is True:
self.model = self.Model()
deserialize = OrderedDict((
('model', lambda p: self.model.from_bytes(p.open('rb').read())),
('vocab', lambda p: self.vocab.from_disk(p)),
('cfg', lambda p: self.cfg.update(_load_cfg(p)))
))
util.from_disk(path, deserialize, exclude)
return self
def _load_cfg(path):
if path.exists():
return ujson.load(path.open())
else:
return {}
class TokenVectorEncoder(BaseThincComponent):
"""Assign position-sensitive vectors to tokens, using a CNN or RNN."""
name = 'tensorizer'
@classmethod
def Model(cls, width=128, embed_size=7500, **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.
"""
width = util.env_opt('token_vector_width', width)
embed_size = util.env_opt('embed_size', embed_size)
return Tok2Vec(width, embed_size, preprocess=None)
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.doc2feats = doc2feats()
self.model = model
self.cfg = dict(cfg)
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)
tokvecses = self.predict(docs)
self.set_annotations(docs, tokvecses)
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 documents.
"""
feats = self.doc2feats(docs)
tokvecs = self.model(feats)
return tokvecs
def set_annotations(self, docs, tokvecses):
"""Set the tensor attribute for a batch of documents.
docs (iterable): A sequence of `Doc` objects.
tokvecs (object): Vector representation for each token in the documents.
"""
for doc, tokvecs in zip(docs, tokvecses):
assert tokvecs.shape[0] == len(doc)
doc.tensor = tokvecs
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]
feats = self.doc2feats(docs)
tokvecs, bp_tokvecs = self.model.begin_update(feats, drop=drop)
return tokvecs, bp_tokvecs
def get_loss(self, docs, golds, scores):
# TODO: implement
raise NotImplementedError
def begin_training(self, gold_tuples=tuple(), pipeline=None):
"""Allocate models, pre-process training data and acquire a trainer and
optimizer.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
"""
self.doc2feats = doc2feats()
if self.model is True:
self.model = self.Model()
class NeuralTagger(BaseThincComponent):
name = 'tagger'
def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
self.cfg = dict(cfg)
def __call__(self, doc):
tags = self.predict([doc.tensor])
self.set_annotations([doc], tags)
return doc
def pipe(self, stream, batch_size=128, n_threads=-1):
for docs in cytoolz.partition_all(batch_size, stream):
tokvecs = [d.tensor for d in docs]
tag_ids = self.predict(tokvecs)
self.set_annotations(docs, tag_ids)
yield from docs
def predict(self, tokvecs):
scores = self.model(tokvecs)
scores = self.model.ops.flatten(scores)
guesses = scores.argmax(axis=1)
if not isinstance(guesses, numpy.ndarray):
guesses = guesses.get()
guesses = self.model.ops.unflatten(guesses,
[tv.shape[0] for tv in tokvecs])
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
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 preset POS tags
if doc.c[j].tag == 0 and doc.c[j].pos == 0:
vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
idx += 1
doc.is_tagged = True
def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
docs, tokvecs = docs_tokvecs
if self.model.nI is None:
self.model.nI = tokvecs[0].shape[1]
tag_scores, bp_tag_scores = self.model.begin_update(tokvecs, drop=drop)
loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
return d_tokvecs
def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores)
tag_index = {tag: i for i, tag in enumerate(self.vocab.morphology.tag_names)}
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):
orig_tag_map = dict(self.vocab.morphology.tag_map)
new_tag_map = {}
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}
if 'SP' not in new_tag_map:
new_tag_map['SP'] = orig_tag_map.get('SP', {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)
token_vector_width = pipeline[0].model.nO
if self.model is True:
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
@classmethod
def Model(cls, n_tags, token_vector_width):
return with_flatten(
chain(Maxout(token_vector_width, token_vector_width),
Softmax(n_tags, token_vector_width)))
def use_params(self, params):
with self.model.use_params(params):
yield
def to_bytes(self, **exclude):
serialize = OrderedDict((
('model', lambda: self.model.to_bytes()),
('vocab', lambda: self.vocab.to_bytes()),
('tag_map', lambda: msgpack.dumps(self.vocab.morphology.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):
if self.model is True:
token_vector_width = util.env_opt('token_vector_width', 128)
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
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),
('model', lambda b: load_model(b)),
))
util.from_bytes(bytes_data, deserialize, exclude)
return self
def to_disk(self, path, **exclude):
serialize = OrderedDict((
('vocab', lambda p: self.vocab.to_disk(p)),
('tag_map', lambda p: p.open('wb').write(msgpack.dumps(
self.vocab.morphology.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):
if self.model is True:
token_vector_width = util.env_opt('token_vector_width', 128)
self.model = self.Model(self.vocab.morphology.n_tags, token_vector_width)
self.model.from_bytes(p.open('rb').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((
('vocab', lambda p: self.vocab.from_disk(p)),
('tag_map', load_tag_map),
('model', load_model),
('cfg', lambda p: self.cfg.update(_load_cfg(p)))
))
util.from_disk(path, deserialize, exclude)
return self
class NeuralLabeller(NeuralTagger):
name = 'nn_labeller'
def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
self.cfg = dict(cfg)
@property
def labels(self):
return self.cfg.get('labels', {})
@labels.setter
def labels(self, value):
self.cfg['labels'] = value
def set_annotations(self, docs, dep_ids):
pass
def begin_training(self, gold_tuples=tuple(), pipeline=None):
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 dep in deps:
if dep not in self.labels:
self.labels[dep] = len(self.labels)
token_vector_width = pipeline[0].model.nO
if self.model is True:
self.model = self.Model(len(self.labels), token_vector_width)
@classmethod
def Model(cls, n_tags, token_vector_width):
return with_flatten(
chain(Maxout(token_vector_width, token_vector_width),
Softmax(n_tags, token_vector_width)))
def get_loss(self, docs, golds, scores):
scores = self.model.ops.flatten(scores)
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.labels:
if tag is None or tag not in self.labels:
correct[idx] = guesses[idx]
else:
correct[idx] = self.labels[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
class SimilarityHook(BaseThincComponent):
"""
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.tensor, doc2.tensor)])
def update(self, doc1_tensor1_doc2_tensor2, golds, sgd=None, drop=0.):
doc1s, tensor1s, doc2s, tensor2s = doc1_tensor1_doc2_tensor2
sims, bp_sims = self.model.begin_update(zip(tensor1s, tensor2s),
drop=drop)
d_tensor1s, d_tensor2s = bp_sims(golds, sgd=sgd)
return d_tensor1s, d_tensor2s
def begin_training(self, _=tuple(), pipeline=None):
"""
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)
class TextCategorizer(BaseThincComponent):
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.get('labels', ['LABEL'])
@labels.setter
def labels(self, value):
self.cfg['labels'] = value
def __call__(self, doc):
scores = self.predict([doc])
self.set_annotations([doc], scores)
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 = self.predict(docs)
self.set_annotations(docs, scores)
yield from docs
def predict(self, docs):
scores = self.model(docs)
scores = self.model.ops.asarray(scores)
return scores
def set_annotations(self, docs, scores):
for i, doc in enumerate(docs):
for j, label in enumerate(self.labels):
doc.cats[label] = float(scores[i, j])
def update(self, docs_tensors, golds, state=None, drop=0., sgd=None, losses=None):
docs, tensors = docs_tensors
scores, bp_scores = self.model.begin_update(docs, drop=drop)
loss, d_scores = self.get_loss(docs, golds, scores)
d_tensors = bp_scores(d_scores, sgd=sgd)
if losses is not None:
losses.setdefault(self.name, 0.0)
losses[self.name] += loss
return d_tensors
def get_loss(self, docs, golds, scores):
truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
for i, gold in enumerate(golds):
for j, label in enumerate(self.labels):
truths[i, j] = label in gold.cats
truths = self.model.ops.asarray(truths)
d_scores = (scores-truths) / scores.shape[0]
mean_square_error = ((scores-truths)**2).sum(axis=1).mean()
return mean_square_error, d_scores
def begin_training(self, gold_tuples=tuple(), pipeline=None):
if pipeline:
token_vector_width = pipeline[0].model.nO
else:
token_vector_width = 64
if self.model is True:
self.model = self.Model(len(self.labels), token_vector_width)
cdef class EntityRecognizer(LinearParser):
"""Annotate named entities on Doc objects."""
TransitionSystem = BiluoPushDown
feature_templates = get_feature_templates('ner')
def add_label(self, label):
LinearParser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
cdef class BeamEntityRecognizer(BeamParser):
"""Annotate named entities on Doc objects."""
TransitionSystem = BiluoPushDown
feature_templates = get_feature_templates('ner')
def add_label(self, label):
LinearParser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
cdef class DependencyParser(LinearParser):
TransitionSystem = ArcEager
feature_templates = get_feature_templates('basic')
def add_label(self, label):
LinearParser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
cdef class NeuralDependencyParser(NeuralParser):
name = 'parser'
TransitionSystem = ArcEager
def __reduce__(self):
return (NeuralDependencyParser, (self.vocab, self.moves, self.model), None, None)
cdef class NeuralEntityRecognizer(NeuralParser):
name = 'ner'
TransitionSystem = BiluoPushDown
nr_feature = 6
def predict_confidences(self, docs):
tensors = [d.tensor for d in docs]
samples = []
for i in range(10):
states = self.parse_batch(docs, tensors, drop=0.3)
for state in states:
samples.append(self._get_entities(state))
def __reduce__(self):
return (NeuralEntityRecognizer, (self.vocab, self.moves, self.model), None, None)
cdef class BeamDependencyParser(BeamParser):
TransitionSystem = ArcEager
feature_templates = get_feature_templates('basic')
def add_label(self, label):
Parser.add_label(self, label)
if isinstance(label, basestring):
label = self.vocab.strings[label]
__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'BeamDependencyParser',
'BeamEntityRecognizer', 'TokenVectorEnoder']