spaCy/spacy/pipeline.pyx

# 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

from .tokens.doc cimport Doc
from .syntax.parser cimport Parser
#from .syntax.beam_parser cimport BeamParser
from .syntax.ner cimport BiluoPushDown
from .syntax.arc_eager cimport ArcEager
from .tagger import Tagger
from .gold cimport GoldParse

from thinc.api import add, layerize, chain, clone, concatenate
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._classes.convolution import ExtractWindow
from thinc.neural._classes.resnet import Residual
from thinc.neural._classes.batchnorm import BatchNorm as BN

from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP
from ._ml import flatten, get_col, doc2feats



class TokenVectorEncoder(object):
    '''Assign position-sensitive vectors to tokens, using a CNN or RNN.'''
    def __init__(self, vocab, token_vector_width, **cfg):
        self.vocab = vocab
        self.doc2feats = doc2feats()
        self.model = self.build_model(vocab.lang, token_vector_width, **cfg)
        self.tagger = chain(
                        self.model,
                        Softmax(self.vocab.morphology.n_tags,
                                token_vector_width))

    def build_model(self, lang, width, embed_size=5000, **cfg):
        cols = self.doc2feats.cols
        with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
            lower = get_col(cols.index(LOWER))   >> (HashEmbed(width, embed_size)
                                                     +HashEmbed(width, embed_size))
            prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size//2)
            suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size//2)
            shape = get_col(cols.index(SHAPE))   >> HashEmbed(width, embed_size//2)

            tok2vec = (
                flatten
                >> (lower | prefix | suffix | shape )
                >> Maxout(width, pieces=3)
                >> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
                >> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
                >> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
                >> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
            )
        return tok2vec

    def pipe(self, docs):
        docs = list(docs)
        self.predict_tags(docs)
        for doc in docs:
            yield doc

    def __call__(self, doc):
        self.predict_tags([doc])

    def begin_update(self, feats, drop=0.):
        tokvecs, bp_tokvecs = self.model.begin_update(feats, drop=drop)
        return tokvecs, bp_tokvecs

    def predict_tags(self, docs, drop=0.):
        cdef Doc doc
        feats = self.doc2feats(docs)
        scores, finish_update = self.tagger.begin_update(feats, drop=drop)
        scores, _ = self.tagger.begin_update(feats, drop=drop)
        idx = 0
        guesses = scores.argmax(axis=1)
        if not isinstance(guesses, numpy.ndarray):
            guesses = guesses.get()
        for i, doc in enumerate(docs):
            tag_ids = guesses[idx:idx+len(doc)]
            for j, tag_id in enumerate(tag_ids):
                doc.vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
                idx += 1

    def update(self, docs_feats, golds, drop=0., sgd=None):
        cdef int i, j, idx
        cdef GoldParse gold
        docs, feats = docs_feats
        scores, finish_update = self.tagger.begin_update(feats, drop=drop)

        tag_index = {tag: i for i, tag in enumerate(docs[0].vocab.morphology.tag_names)}

        idx = 0
        correct = numpy.zeros((scores.shape[0],), dtype='i')
        for gold in golds:
            for tag in gold.tags:
                correct[idx] = tag_index[tag]
                idx += 1
        correct = self.model.ops.xp.array(correct)
        d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
        finish_update(d_scores, sgd)


cdef class EntityRecognizer(Parser):
    """
    Annotate named entities on Doc objects.
    """
    TransitionSystem = BiluoPushDown

    def add_label(self, label):
        Parser.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):
#        Parser.add_label(self, label)
#        if isinstance(label, basestring):
#            label = self.vocab.strings[label]
#        # Set label into serializer. Super hacky :(
#        for attr, freqs in self.vocab.serializer_freqs:
#            if attr == ENT_TYPE and label not in freqs:
#                freqs.append([label, 1])
#        self.vocab._serializer = None
#

cdef class DependencyParser(Parser):
    TransitionSystem = ArcEager

    def add_label(self, label):
        Parser.add_label(self, label)
        if isinstance(label, basestring):
            label = self.vocab.strings[label]

#
#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]
#        for attr, freqs in self.vocab.serializer_freqs:
#            if attr == DEP and label not in freqs:
#                freqs.append([label, 1])
#        # Super hacky :(
#        self.vocab._serializer = None
#

#__all__ = [Tagger, DependencyParser, EntityRecognizer, BeamDependencyParser, BeamEntityRecognizer]
__all__ = [Tagger, DependencyParser, EntityRecognizer]