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Tidy up pipeline

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ines 2017-10-27 20:29:08 +02:00
parent b4d226a3f1
commit ba5e646219
1 changed files with 59 additions and 67 deletions
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spacy/pipeline.pyx
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@ -3,26 +3,17 @@
# coding: utf8
from __future__ import unicode_literals
from thinc.api import chain, layerize, with_getitem
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.v2v import Model, Maxout, Softmax, Affine, ReLu, SELU
from thinc.i2v import HashEmbed
from thinc.t2v import Pooling, max_pool, mean_pool, sum_pool
from thinc.t2t import ExtractWindow, ParametricAttention
from thinc.misc import Residual
from thinc.misc import BatchNorm as BN
from thinc.misc import LayerNorm as LN
from thinc.api import chain
from thinc.v2v import Softmax
from thinc.t2v import Pooling, max_pool, mean_pool
from thinc.neural.util import to_categorical
from thinc.neural._classes.difference import Siamese, CauchySimilarity
from .tokens.doc cimport Doc
@ -30,29 +21,23 @@ from .syntax.nn_parser cimport Parser
from .syntax import nonproj
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
from ._ml import build_text_classifier, build_tagger_model
from ._ml import link_vectors_to_models
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
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.
(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.
"""
@ -84,6 +69,10 @@ class SentenceSegmenter(object):
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
@ -149,8 +138,7 @@ class Pipe(object):
link_vectors_to_models(self.vocab)
def use_params(self, params):
"""Modify the pipe's model, to use the given parameter values.
"""
"""Modify the pipe's model, to use the given parameter values."""
with self.model.use_params(params):
yield
@ -235,8 +223,8 @@ class Tensorizer(Pipe):
"""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.
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.
@ -280,7 +268,7 @@ class Tensorizer(Pipe):
"""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.
RETURNS (object): Vector representations for each token in the docs.
"""
tokvecs = self.model(docs)
return tokvecs
@ -289,7 +277,7 @@ class Tensorizer(Pipe):
"""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.
tokvecs (object): Vector representation for each token in the docs.
"""
for doc, tokvecs in zip(docs, tokvecses):
assert tokvecs.shape[0] == len(doc)
@ -328,12 +316,14 @@ class Tensorizer(Pipe):
class Tagger(Pipe):
name = 'tagger'
def __init__(self, vocab, model=True, **cfg):
self.vocab = vocab
self.model = model
self.cfg = dict(cfg)
self.cfg.setdefault('cnn_maxout_pieces', 2)
self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
self.cfg.setdefault('pretrained_dims',
self.vocab.vectors.data.shape[1])
def __call__(self, doc):
tags = self.predict([doc])
@ -353,8 +343,7 @@ class Tagger(Pipe):
guesses = scores.argmax(axis=1)
if not isinstance(guesses, numpy.ndarray):
guesses = guesses.get()
guesses = self.model.ops.unflatten(guesses,
[len(d) for d in docs])
guesses = self.model.ops.unflatten(guesses, [len(d) for d in docs])
return guesses
def set_annotations(self, docs, batch_tag_ids):
@ -387,8 +376,8 @@ class Tagger(Pipe):
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)}
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)
@ -443,17 +432,18 @@ class Tagger(Pipe):
serialize['model'] = self.model.to_bytes
serialize['vocab'] = self.vocab.to_bytes
serialize['tag_map'] = lambda: msgpack.dumps(self.vocab.morphology.tag_map,
use_bin_type=True,
encoding='utf8')
serialize['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',
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 = self.Model(self.vocab.morphology.n_tags,
**self.cfg)
self.model.from_bytes(b)
def load_tag_map(b):
@ -509,11 +499,11 @@ class Tagger(Pipe):
class MultitaskObjective(Tagger):
'''Assist training of a parser or tagger, by training a side-objective.
Experimental
'''
"""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
@ -530,12 +520,12 @@ class MultitaskObjective(Tagger):
elif hasattr(target, '__call__'):
self.make_label = target
else:
raise ValueError(
"MultitaskObjective target should be function or one of "
"['dep', 'tag', 'ent', 'dep_tag_offset', 'ent_tag']")
raise ValueError("MultitaskObjective target should be function or "
"one of: dep, tag, ent, dep_tag_offset, ent_tag.")
self.cfg = dict(cfg)
self.cfg.setdefault('cnn_maxout_pieces', 2)
self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
self.cfg.setdefault('pretrained_dims',
self.vocab.vectors.data.shape[1])
@property
def labels(self):
@ -623,20 +613,19 @@ class MultitaskObjective(Tagger):
class SimilarityHook(Pipe):
"""
Experimental
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):
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())
>>> 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
@ -662,8 +651,7 @@ class SimilarityHook(Pipe):
sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop)
def begin_training(self, _=tuple(), pipeline=None):
"""
Allocate model, using width from tensorizer in pipeline.
"""Allocate model, using width from tensorizer in pipeline.
gold_tuples (iterable): Gold-standard training data.
pipeline (list): The pipeline the model is part of.
@ -763,12 +751,14 @@ cdef class DependencyParser(Parser):
for target in []:
labeller = MultitaskObjective(self.vocab, target=target)
tok2vec = self.model[0]
labeller.begin_training(gold_tuples, pipeline=pipeline, tok2vec=tok2vec)
labeller.begin_training(gold_tuples, pipeline=pipeline,
tok2vec=tok2vec)
pipeline.append(labeller)
self._multitasks.append(labeller)
def __reduce__(self):
return (DependencyParser, (self.vocab, self.moves, self.model), None, None)
return (DependencyParser, (self.vocab, self.moves, self.model),
None, None)
cdef class EntityRecognizer(Parser):
@ -781,12 +771,14 @@ cdef class EntityRecognizer(Parser):
for target in []:
labeller = MultitaskObjective(self.vocab, target=target)
tok2vec = self.model[0]
labeller.begin_training(gold_tuples, pipeline=pipeline, tok2vec=tok2vec)
labeller.begin_training(gold_tuples, pipeline=pipeline,
tok2vec=tok2vec)
pipeline.append(labeller)
self._multitasks.append(labeller)
def __reduce__(self):
return (EntityRecognizer, (self.vocab, self.moves, self.model), None, None)
return (EntityRecognizer, (self.vocab, self.moves, self.model),
None, None)
__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'Tensorizer']