mirror of
https://github.com/explosion/spaCy.git
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293 lines
9.3 KiB
Cython
293 lines
9.3 KiB
Cython
# cython: infer_types=True
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# cython: profile=True
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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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from thinc.neural import Model, Softmax
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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 thinc.api import add, layerize, chain, clone, concatenate
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from thinc.neural import Model, Maxout, Softmax, Affine
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from thinc.neural._classes.hash_embed import HashEmbed
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from thinc.neural.util import to_categorical
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from thinc.neural._classes.convolution import ExtractWindow
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from thinc.neural._classes.resnet import Residual
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from thinc.neural._classes.batchnorm import BatchNorm as BN
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from .tokens.doc cimport Doc
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from .syntax.parser cimport Parser as LinearParser
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from .syntax.nn_parser cimport Parser as NeuralParser
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from .syntax.parser import get_templates as get_feature_templates
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from .syntax.beam_parser cimport BeamParser
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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 .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP, POS
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from ._ml import Tok2Vec, flatten, get_col, doc2feats
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from .parts_of_speech import X
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class TokenVectorEncoder(object):
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'''Assign position-sensitive vectors to tokens, using a CNN or RNN.'''
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name = 'tok2vec'
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@classmethod
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def Model(cls, width=128, embed_size=5000, **cfg):
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width = util.env_opt('token_vector_width', width)
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embed_size = util.env_opt('embed_size', embed_size)
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return Tok2Vec(width, embed_size, preprocess=None)
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def __init__(self, vocab, model=True, **cfg):
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self.vocab = vocab
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self.doc2feats = doc2feats()
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self.model = model
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def __call__(self, docs, state=None):
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if isinstance(docs, Doc):
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docs = [docs]
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tokvecs = self.predict(docs)
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self.set_annotations(docs, tokvecs)
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state = {} if state is None else state
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state['tokvecs'] = tokvecs
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return state
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def pipe(self, stream, batch_size=128, n_threads=-1):
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for batch in cytoolz.partition_all(batch_size, stream):
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docs, states = zip(*batch)
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tokvecs = self.predict(docs)
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self.set_annotations(docs, tokvecs)
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for state in states:
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state['tokvecs'] = tokvecs
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yield from zip(docs, states)
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def predict(self, docs):
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feats = self.doc2feats(docs)
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tokvecs = self.model(feats)
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return tokvecs
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def set_annotations(self, docs, tokvecs):
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start = 0
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for doc in docs:
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doc.tensor = tokvecs[start : start + len(doc)]
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start += len(doc)
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def update(self, docs, golds, state=None,
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drop=0., sgd=None):
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if isinstance(docs, Doc):
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docs = [docs]
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golds = [golds]
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state = {} if state is None else state
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feats = self.doc2feats(docs)
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tokvecs, bp_tokvecs = self.model.begin_update(feats, drop=drop)
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state['feats'] = feats
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state['tokvecs'] = tokvecs
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state['bp_tokvecs'] = bp_tokvecs
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return state
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def get_loss(self, docs, golds, scores):
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raise NotImplementedError
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def begin_training(self, gold_tuples, pipeline=None):
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self.doc2feats = doc2feats()
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if self.model is True:
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self.model = self.Model()
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def use_params(self, params):
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with self.model.use_params(params):
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yield
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class NeuralTagger(object):
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name = 'nn_tagger'
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def __init__(self, vocab, model=True):
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self.vocab = vocab
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self.model = model
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def __call__(self, doc, state=None):
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assert state is not None
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assert 'tokvecs' in state
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tokvecs = state['tokvecs']
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tags = self.predict(tokvecs)
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self.set_annotations([doc], tags)
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return state
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def pipe(self, stream, batch_size=128, n_threads=-1):
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for batch in cytoolz.partition_all(batch_size, stream):
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docs, states = zip(*batch)
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tag_ids = self.predict(states[0]['tokvecs'])
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self.set_annotations(docs, tag_ids)
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for state in states:
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state['tag_ids'] = tag_ids
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yield from zip(docs, states)
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def predict(self, tokvecs):
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scores = self.model(tokvecs)
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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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return guesses
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def set_annotations(self, docs, batch_tag_ids):
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if isinstance(docs, Doc):
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docs = [docs]
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cdef Doc doc
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cdef int idx = 0
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cdef int i, j, tag_id
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cdef Vocab vocab = self.vocab
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for i, doc in enumerate(docs):
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doc_tag_ids = batch_tag_ids[idx:idx+len(doc)]
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for j, tag_id in enumerate(doc_tag_ids):
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vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
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idx += 1
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def update(self, docs, golds, state=None, drop=0., sgd=None):
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state = {} if state is None else state
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tokvecs = state['tokvecs']
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bp_tokvecs = state['bp_tokvecs']
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if self.model.nI is None:
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self.model.nI = tokvecs.shape[1]
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tag_scores, bp_tag_scores = self.model.begin_update(tokvecs, drop=drop)
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loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
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d_tokvecs = bp_tag_scores(d_tag_scores, sgd=sgd)
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bp_tokvecs(d_tokvecs, sgd=sgd)
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state['tag_scores'] = tag_scores
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state['tag_loss'] = loss
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return state
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def get_loss(self, docs, golds, scores):
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tag_index = {tag: i 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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for gold in golds:
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for tag in gold.tags:
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correct[idx] = tag_index[tag]
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idx += 1
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correct = self.model.ops.xp.array(correct, dtype='i')
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d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
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loss = (d_scores**2).sum()
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d_scores = self.model.ops.asarray(d_scores, dtype='f')
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return float(loss), d_scores
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def begin_training(self, gold_tuples, pipeline=None):
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orig_tag_map = dict(self.vocab.morphology.tag_map)
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new_tag_map = {}
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for raw_text, annots_brackets in gold_tuples:
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for annots, brackets in annots_brackets:
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ids, words, tags, heads, deps, ents = annots
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for tag in tags:
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if tag in orig_tag_map:
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new_tag_map[tag] = orig_tag_map[tag]
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else:
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new_tag_map[tag] = {POS: X}
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cdef Vocab vocab = self.vocab
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vocab.morphology = Morphology(vocab.strings, new_tag_map,
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vocab.morphology.lemmatizer)
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self.model = Softmax(self.vocab.morphology.n_tags)
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print("Tagging", self.model.nO, "tags")
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def use_params(self, params):
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with self.model.use_params(params):
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yield
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cdef class EntityRecognizer(LinearParser):
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"""
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Annotate named entities on Doc objects.
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"""
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TransitionSystem = BiluoPushDown
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feature_templates = get_feature_templates('ner')
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def add_label(self, label):
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LinearParser.add_label(self, label)
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if isinstance(label, basestring):
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label = self.vocab.strings[label]
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cdef class BeamEntityRecognizer(BeamParser):
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"""
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Annotate named entities on Doc objects.
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"""
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TransitionSystem = BiluoPushDown
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feature_templates = get_feature_templates('ner')
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def add_label(self, label):
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LinearParser.add_label(self, label)
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if isinstance(label, basestring):
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label = self.vocab.strings[label]
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cdef class DependencyParser(LinearParser):
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TransitionSystem = ArcEager
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feature_templates = get_feature_templates('basic')
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def add_label(self, label):
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LinearParser.add_label(self, label)
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if isinstance(label, basestring):
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label = self.vocab.strings[label]
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cdef class NeuralDependencyParser(NeuralParser):
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name = 'parser'
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TransitionSystem = ArcEager
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cdef class NeuralEntityRecognizer(NeuralParser):
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name = 'entity'
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TransitionSystem = BiluoPushDown
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nr_feature = 6
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def get_token_ids(self, states):
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cdef StateClass state
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cdef int n_tokens = 6
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ids = numpy.zeros((len(states), n_tokens), dtype='i', order='c')
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for i, state in enumerate(states):
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ids[i, 0] = state.c.B(0)-1
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ids[i, 1] = state.c.B(0)
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ids[i, 2] = state.c.B(1)
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ids[i, 3] = state.c.E(0)
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ids[i, 4] = state.c.E(0)-1
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ids[i, 5] = state.c.E(0)+1
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for j in range(6):
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if ids[i, j] >= state.c.length:
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ids[i, j] = -1
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if ids[i, j] != -1:
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ids[i, j] += state.c.offset
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return ids
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cdef class BeamDependencyParser(BeamParser):
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TransitionSystem = ArcEager
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feature_templates = get_feature_templates('basic')
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def add_label(self, label):
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Parser.add_label(self, label)
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if isinstance(label, basestring):
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label = self.vocab.strings[label]
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__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'BeamDependencyParser',
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'BeamEntityRecognizer', 'TokenVectorEnoder']
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