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https://github.com/explosion/spaCy.git
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Tidy up pipeline
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ines
parent
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commit
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@ -3,26 +3,17 @@
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# coding: utf8
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from __future__ import unicode_literals
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from thinc.api import chain, layerize, with_getitem
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import numpy
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cimport numpy as np
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import cytoolz
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import util
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from collections import OrderedDict
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import ujson
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import msgpack
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from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
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from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu, SELU
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from thinc.i2v import HashEmbed
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from thinc.t2v import Pooling, max_pool, mean_pool, sum_pool
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from thinc.t2t import ExtractWindow, ParametricAttention
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from thinc.misc import Residual
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from thinc.misc import BatchNorm as BN
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from thinc.misc import LayerNorm as LN
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from thinc.api import chain
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from thinc.v2v import Softmax
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from thinc.t2v import Pooling, max_pool, mean_pool
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from thinc.neural.util import to_categorical
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from thinc.neural._classes.difference import Siamese, CauchySimilarity
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from .tokens.doc cimport Doc
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@ -30,29 +21,23 @@ from .syntax.nn_parser cimport Parser
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from .syntax import nonproj
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from .syntax.ner cimport BiluoPushDown
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from .syntax.arc_eager cimport ArcEager
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from .tagger import Tagger
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from .syntax.stateclass cimport StateClass
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from .gold cimport GoldParse
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from .morphology cimport Morphology
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from .vocab cimport Vocab
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from .syntax import nonproj
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from .compat import json_dumps
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from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
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from ._ml import rebatch, Tok2Vec, flatten
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from ._ml import build_text_classifier, build_tagger_model
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from ._ml import link_vectors_to_models
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from .attrs import POS
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from .parts_of_speech import X
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from ._ml import Tok2Vec, build_text_classifier, build_tagger_model
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from ._ml import link_vectors_to_models
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from . import util
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class SentenceSegmenter(object):
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"""A simple spaCy hook, to allow custom sentence boundary detection logic
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(that doesn't require the dependency parse).
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To change the sentence boundary detection strategy, pass a generator
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function `strategy` on initialization, or assign a new strategy to
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the .strategy attribute.
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(that doesn't require the dependency parse). To change the sentence
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boundary detection strategy, pass a generator function `strategy` on
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initialization, or assign a new strategy to the .strategy attribute.
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Sentence detection strategies should be generators that take `Doc` objects
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and yield `Span` objects for each sentence.
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"""
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@ -74,16 +59,20 @@ class SentenceSegmenter(object):
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seen_period = False
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for i, word in enumerate(doc):
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if seen_period and not word.is_punct:
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yield doc[start : word.i]
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yield doc[start:word.i]
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start = word.i
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seen_period = False
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elif word.text in ['.', '!', '?']:
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seen_period = True
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if start < len(doc):
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yield doc[start : len(doc)]
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yield doc[start:len(doc)]
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class Pipe(object):
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"""This class is not instantiated directly. Components inherit from it, and
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it defines the interface that components should follow to function as
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components in a spaCy analysis pipeline.
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"""
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name = None
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@classmethod
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@ -149,8 +138,7 @@ class Pipe(object):
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link_vectors_to_models(self.vocab)
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def use_params(self, params):
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"""Modify the pipe's model, to use the given parameter values.
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"""
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"""Modify the pipe's model, to use the given parameter values."""
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with self.model.use_params(params):
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yield
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@ -235,8 +223,8 @@ class Tensorizer(Pipe):
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"""Construct a new statistical model. Weights are not allocated on
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initialisation.
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vocab (Vocab): A `Vocab` instance. The model must share the same `Vocab`
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instance with the `Doc` objects it will process.
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vocab (Vocab): A `Vocab` instance. The model must share the same
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`Vocab` instance with the `Doc` objects it will process.
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model (Model): A `Model` instance or `True` allocate one later.
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**cfg: Config parameters.
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@ -280,7 +268,7 @@ class Tensorizer(Pipe):
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"""Return a single tensor for a batch of documents.
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docs (iterable): A sequence of `Doc` objects.
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RETURNS (object): Vector representations for each token in the documents.
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RETURNS (object): Vector representations for each token in the docs.
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"""
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tokvecs = self.model(docs)
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return tokvecs
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@ -289,7 +277,7 @@ class Tensorizer(Pipe):
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"""Set the tensor attribute for a batch of documents.
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docs (iterable): A sequence of `Doc` objects.
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tokvecs (object): Vector representation for each token in the documents.
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tokvecs (object): Vector representation for each token in the docs.
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"""
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for doc, tokvecs in zip(docs, tokvecses):
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assert tokvecs.shape[0] == len(doc)
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@ -328,12 +316,14 @@ class Tensorizer(Pipe):
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class Tagger(Pipe):
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name = 'tagger'
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def __init__(self, vocab, model=True, **cfg):
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self.vocab = vocab
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self.model = model
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self.cfg = dict(cfg)
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self.cfg.setdefault('cnn_maxout_pieces', 2)
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self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
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self.cfg.setdefault('pretrained_dims',
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self.vocab.vectors.data.shape[1])
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def __call__(self, doc):
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tags = self.predict([doc])
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@ -353,8 +343,7 @@ class Tagger(Pipe):
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guesses = scores.argmax(axis=1)
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if not isinstance(guesses, numpy.ndarray):
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guesses = guesses.get()
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guesses = self.model.ops.unflatten(guesses,
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[len(d) for d in docs])
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guesses = self.model.ops.unflatten(guesses, [len(d) for d in docs])
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return guesses
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def set_annotations(self, docs, batch_tag_ids):
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@ -387,8 +376,8 @@ class Tagger(Pipe):
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def get_loss(self, docs, golds, scores):
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scores = self.model.ops.flatten(scores)
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tag_index = {tag: i for i, tag in enumerate(self.vocab.morphology.tag_names)}
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tag_index = {tag: i
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for i, tag in enumerate(self.vocab.morphology.tag_names)}
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cdef int idx = 0
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correct = numpy.zeros((scores.shape[0],), dtype='i')
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guesses = scores.argmax(axis=1)
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@ -443,17 +432,18 @@ class Tagger(Pipe):
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serialize['model'] = self.model.to_bytes
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serialize['vocab'] = self.vocab.to_bytes
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serialize['tag_map'] = lambda: msgpack.dumps(self.vocab.morphology.tag_map,
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use_bin_type=True,
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encoding='utf8')
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serialize['tag_map'] = lambda: msgpack.dumps(
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self.vocab.morphology.tag_map, use_bin_type=True, encoding='utf8')
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return util.to_bytes(serialize, exclude)
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def from_bytes(self, bytes_data, **exclude):
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def load_model(b):
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if self.model is True:
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token_vector_width = util.env_opt('token_vector_width',
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self.cfg.get('token_vector_width', 128))
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self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
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token_vector_width = util.env_opt(
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'token_vector_width',
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self.cfg.get('token_vector_width', 128))
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self.model = self.Model(self.vocab.morphology.n_tags,
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**self.cfg)
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self.model.from_bytes(b)
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def load_tag_map(b):
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@ -509,11 +499,11 @@ class Tagger(Pipe):
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class MultitaskObjective(Tagger):
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'''Assist training of a parser or tagger, by training a side-objective.
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Experimental
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'''
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"""Experimental: Assist training of a parser or tagger, by training a
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side-objective.
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"""
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name = 'nn_labeller'
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def __init__(self, vocab, model=True, target='dep_tag_offset', **cfg):
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self.vocab = vocab
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self.model = model
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@ -530,12 +520,12 @@ class MultitaskObjective(Tagger):
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elif hasattr(target, '__call__'):
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self.make_label = target
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else:
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raise ValueError(
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"MultitaskObjective target should be function or one of "
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"['dep', 'tag', 'ent', 'dep_tag_offset', 'ent_tag']")
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raise ValueError("MultitaskObjective target should be function or "
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"one of: dep, tag, ent, dep_tag_offset, ent_tag.")
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self.cfg = dict(cfg)
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self.cfg.setdefault('cnn_maxout_pieces', 2)
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self.cfg.setdefault('pretrained_dims', self.vocab.vectors.data.shape[1])
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self.cfg.setdefault('pretrained_dims',
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self.vocab.vectors.data.shape[1])
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@property
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def labels(self):
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@ -623,20 +613,19 @@ class MultitaskObjective(Tagger):
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class SimilarityHook(Pipe):
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"""
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Experimental
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Experimental: A pipeline component to install a hook for supervised
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similarity into `Doc` objects. Requires a `Tensorizer` to pre-process
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documents. The similarity model can be any object obeying the Thinc `Model`
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interface. By default, the model concatenates the elementwise mean and
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elementwise max of the two tensors, and compares them using the
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Cauchy-like similarity function from Chen (2013):
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A pipeline component to install a hook for supervised similarity into
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Doc objects. Requires a Tensorizer to pre-process documents. The similarity
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model can be any object obeying the Thinc Model interface. By default,
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the model concatenates the elementwise mean and elementwise max of the two
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tensors, and compares them using the Cauchy-like similarity function
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from Chen (2013):
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similarity = 1. / (1. + (W * (vec1-vec2)**2).sum())
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>>> similarity = 1. / (1. + (W * (vec1-vec2)**2).sum())
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Where W is a vector of dimension weights, initialized to 1.
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"""
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name = 'similarity'
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def __init__(self, vocab, model=True, **cfg):
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self.vocab = vocab
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self.model = model
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@ -662,8 +651,7 @@ class SimilarityHook(Pipe):
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sims, bp_sims = self.model.begin_update(doc1_doc2, drop=drop)
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def begin_training(self, _=tuple(), pipeline=None):
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"""
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Allocate model, using width from tensorizer in pipeline.
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"""Allocate model, using width from tensorizer in pipeline.
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gold_tuples (iterable): Gold-standard training data.
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pipeline (list): The pipeline the model is part of.
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@ -763,12 +751,14 @@ cdef class DependencyParser(Parser):
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for target in []:
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labeller = MultitaskObjective(self.vocab, target=target)
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tok2vec = self.model[0]
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labeller.begin_training(gold_tuples, pipeline=pipeline, tok2vec=tok2vec)
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labeller.begin_training(gold_tuples, pipeline=pipeline,
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tok2vec=tok2vec)
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pipeline.append(labeller)
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self._multitasks.append(labeller)
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def __reduce__(self):
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return (DependencyParser, (self.vocab, self.moves, self.model), None, None)
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return (DependencyParser, (self.vocab, self.moves, self.model),
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None, None)
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cdef class EntityRecognizer(Parser):
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@ -781,12 +771,14 @@ cdef class EntityRecognizer(Parser):
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for target in []:
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labeller = MultitaskObjective(self.vocab, target=target)
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tok2vec = self.model[0]
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labeller.begin_training(gold_tuples, pipeline=pipeline, tok2vec=tok2vec)
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labeller.begin_training(gold_tuples, pipeline=pipeline,
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tok2vec=tok2vec)
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pipeline.append(labeller)
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self._multitasks.append(labeller)
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def __reduce__(self):
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return (EntityRecognizer, (self.vocab, self.moves, self.model), None, None)
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return (EntityRecognizer, (self.vocab, self.moves, self.model),
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None, None)
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__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'Tensorizer']
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