spaCy/spacy/pipeline.pyx

# cython: infer_types=True
# cython: profile=True
# coding: utf8
from __future__ import unicode_literals

import numpy
cimport numpy as np
import cytoolz
from collections import OrderedDict
import ujson
import msgpack

from thinc.api import chain
from thinc.v2v import Affine, SELU, Softmax
from thinc.t2v import Pooling, max_pool, mean_pool
from thinc.neural.util import to_categorical, copy_array
from thinc.neural._classes.difference import Siamese, CauchySimilarity

from .tokens.doc cimport Doc
from .syntax.nn_parser cimport Parser
from .syntax import nonproj
from .syntax.ner cimport BiluoPushDown
from .syntax.arc_eager cimport ArcEager
from .morphology cimport Morphology
from .vocab cimport Vocab
from .syntax import nonproj
from .compat import json_dumps

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, zero_init, flatten
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.
    Sentence detection strategies should be generators that take `Doc` objects
    and yield `Span` objects for each sentence.
    """
    name = 'sbd'

    def __init__(self, vocab, strategy=None):
        self.vocab = vocab
        if strategy is None or strategy == 'on_punct':
            strategy = self.split_on_punct
        self.strategy = strategy

    def __call__(self, doc):
        doc.user_hooks['sents'] = self.strategy
        return doc

    @staticmethod
    def split_on_punct(doc):
        start = 0
        seen_period = False
        for i, word in enumerate(doc):
            if seen_period and not word.is_punct:
                yield doc[start:word.i]
                start = word.i
                seen_period = False
            elif word.text in ['.', '!', '?']:
                seen_period = True
        if start < len(doc):
            yield doc[start:len(doc)]


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
    def Model(cls, *shape, **kwargs):
        """Initialize a model for the pipe."""
        raise NotImplementedError

    def __init__(self, vocab, model=True, **cfg):
        """Create a new pipe instance."""
        raise NotImplementedError

    def __call__(self, doc):
        """Apply the pipe to one document. The document is
        modified in-place, and returned.

        Both __call__ and pipe should delegate to the `predict()`
        and `set_annotations()` methods.
        """
        scores, tensors = self.predict([doc])
        self.set_annotations([doc], scores, tensors=tensors)
        return doc

    def pipe(self, stream, batch_size=128, n_threads=-1):
        """Apply the pipe to a stream of documents.

        Both __call__ and pipe should delegate to the `predict()`
        and `set_annotations()` methods.
        """
        for docs in cytoolz.partition_all(batch_size, stream):
            docs = list(docs)
            scores, tensors = self.predict(docs)
            self.set_annotations(docs, scores, tensor=tensors)
            yield from docs

    def predict(self, docs):
        """Apply the pipeline's model to a batch of docs, without
        modifying them.
        """
        raise NotImplementedError

    def set_annotations(self, docs, scores, tensors=None):
        """Modify a batch of documents, using pre-computed scores."""
        raise NotImplementedError

    def update(self, docs, golds, drop=0., sgd=None, losses=None):
        """Learn from a batch of documents and gold-standard information,
        updating the pipe's model.

        Delegates to predict() and get_loss().
        """
        raise NotImplementedError

    def get_loss(self, docs, golds, scores):
        """Find the loss and gradient of loss for the batch of
        documents and their predicted scores."""
        raise NotImplementedError

    def add_label(self, label):
        """Add an output label, to be predicted by the model.

        It's possible to extend pre-trained models with new labels,
        but care should be taken to avoid the "catastrophic forgetting"
        problem.
        """
        raise NotImplementedError

    def begin_training(self, gold_tuples=tuple(), pipeline=None):
        """Initialize the pipe for training, using data exampes if available.
        If no model has been initialized yet, the model is added."""
        if self.model is True:
            self.model = self.Model(**self.cfg)
        link_vectors_to_models(self.vocab)

    def use_params(self, params):
        """Modify the pipe's model, to use the given parameter values."""
        with self.model.use_params(params):
            yield

    def to_bytes(self, **exclude):
        """Serialize the pipe to a bytestring."""
        serialize = OrderedDict()
        serialize['cfg'] = lambda: json_dumps(self.cfg)
        if self.model in (True, False, None):
            serialize['model'] = lambda: self.model
        else:
            serialize['model'] = self.model.to_bytes
        serialize['vocab'] = self.vocab.to_bytes
        return util.to_bytes(serialize, exclude)

    def from_bytes(self, bytes_data, **exclude):
        """Load the pipe from a bytestring."""
        def load_model(b):
            if self.model is True:
                self.cfg['pretrained_dims'] = self.vocab.vectors_length
                self.model = self.Model(**self.cfg)
            self.model.from_bytes(b)

        deserialize = OrderedDict((
            ('cfg', lambda b: self.cfg.update(ujson.loads(b))),
            ('vocab', lambda b: self.vocab.from_bytes(b)),
            ('model', load_model),
        ))
        util.from_bytes(bytes_data, deserialize, exclude)
        return self

    def to_disk(self, path, **exclude):
        """Serialize the pipe to disk."""
        serialize = OrderedDict()
        serialize['cfg'] = lambda p: p.open('w').write(json_dumps(self.cfg))
        serialize['vocab'] = lambda p: self.vocab.to_disk(p)
        if self.model not in (None, True, False):
            serialize['model'] = lambda p: p.open('wb').write(self.model.to_bytes())
        util.to_disk(path, serialize, exclude)

    def from_disk(self, path, **exclude):
        """Load the pipe from disk."""
        def load_model(p):
            if self.model is True:
                self.cfg['pretrained_dims'] = self.vocab.vectors_length
                self.model = self.Model(**self.cfg)
            self.model.from_bytes(p.open('rb').read())

        deserialize = OrderedDict((
            ('cfg', lambda p: self.cfg.update(_load_cfg(p))),
            ('vocab', lambda p: self.vocab.from_disk(p)),
            ('model', load_model),
        ))
        util.from_disk(path, deserialize, exclude)
        return self


def _load_cfg(path):
    if path.exists():
        return ujson.load(path.open())
    else:
        return {}


class Tensorizer(Pipe):
    """Assign position-sensitive vectors to tokens, using a CNN or RNN."""
    name = 'tensorizer'

    @classmethod
    def Model(cls, output_size=300, input_size=384, **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.
        """
        model = chain(
                    SELU(output_size, input_size),
                    SELU(output_size, output_size),
                    zero_init(Affine(output_size, output_size)))
        return model

    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.model = model
        self.input_models = []
        self.cfg = dict(cfg)
        self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
        self.cfg.setdefault('cnn_maxout_pieces', 3)

    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)
            tensors = self.predict(docs)
            self.set_annotations(docs, tensors)
            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 docs.
        """
        inputs = self.model.ops.flatten([doc.tensor for doc in docs])
        outputs = self.model(inputs)
        return self.model.ops.unflatten(outputs, [len(d) for d in docs])

    def set_annotations(self, docs, tensors):
        """Set the tensor attribute for a batch of documents.

        docs (iterable): A sequence of `Doc` objects.
        tensors (object): Vector representation for each token in the docs.
        """
        for doc, tensor in zip(docs, tensors):
            assert tensor.shape[0] == len(doc)
            doc.tensor = tensor

    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]
        inputs = []
        bp_inputs = []
        for tok2vec in self.input_models:
            tensor, bp_tensor = tok2vec.begin_update(docs, drop=drop)
            inputs.append(tensor)
            bp_inputs.append(bp_tensor)
        inputs = self.model.ops.xp.hstack(inputs)
        scores, bp_scores = self.model.begin_update(inputs, drop=drop)
        loss, d_scores = self.get_loss(docs, golds, scores)
        d_inputs = bp_scores(d_scores, sgd=sgd)
        d_inputs = self.model.ops.xp.split(d_inputs, len(self.input_models), axis=1)
        for d_input, bp_input in zip(d_inputs, bp_inputs): 
            bp_input(d_input, sgd=sgd)
        if losses is not None:
            losses.setdefault(self.name, 0.)
            losses[self.name] += loss
        return loss

    def get_loss(self, docs, golds, prediction):
        target = []
        i = 0
        for doc in docs:
            vectors = self.model.ops.xp.vstack([w.vector for w in doc])
            target.append(vectors)
        target = self.model.ops.xp.vstack(target)
        d_scores = (prediction - target) / prediction.shape[0]
        loss = (d_scores**2).sum()
        return loss, d_scores

    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.
        """
        for name, model in pipeline:
            if getattr(model, 'tok2vec', None):
                self.input_models.append(model.tok2vec)
        if self.model is True:
            self.cfg['input_size'] = 384
            self.cfg['output_size'] = 300
            #self.cfg['pretrained_dims'] = self.vocab.vectors_length
            self.model = self.Model(**self.cfg)
        link_vectors_to_models(self.vocab)


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])

    @property
    def labels(self):
        return self.vocab.morphology.tag_names

    @property
    def tok2vec(self):
        if self.model in (None, True, False):
            return None
        else:
            return chain(self.model.tok2vec, flatten)

    def __call__(self, doc):
        tags, tokvecs = self.predict([doc])
        self.set_annotations([doc], tags, tensors=tokvecs)
        return doc

    def pipe(self, stream, batch_size=128, n_threads=-1):
        for docs in cytoolz.partition_all(batch_size, stream):
            docs = list(docs)
            tag_ids, tokvecs = self.predict(docs)
            self.set_annotations(docs, tag_ids, tensors=tokvecs)
            yield from docs

    def predict(self, docs):
        tokvecs = self.model.tok2vec(docs)
        scores = self.model.softmax(tokvecs)
        guesses = []
        for doc_scores in scores:
            doc_guesses = doc_scores.argmax(axis=1)
            if not isinstance(doc_guesses, numpy.ndarray):
                doc_guesses = doc_guesses.get()
            guesses.append(doc_guesses)
        return guesses, tokvecs

    def set_annotations(self, docs, batch_tag_ids, tensors=None):
        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]
            if hasattr(doc_tag_ids, 'get'):
                doc_tag_ids = doc_tag_ids.get()
            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
            if tensors is not None:
                doc.extend_tensor(tensors[i])
        doc.is_tagged = True

    def update(self, docs, golds, drop=0., sgd=None, losses=None):
        if losses is not None and self.name not in losses:
            losses[self.name] = 0.

        tag_scores, bp_tag_scores = self.model.begin_update(docs, drop=drop)
        loss, d_tag_scores = self.get_loss(docs, golds, tag_scores)
        bp_tag_scores(d_tag_scores, sgd=sgd)

        if losses is not None:
            losses[self.name] += loss

    def get_loss(self, docs, golds, scores):
        scores = self.model.ops.flatten(scores)
        tag_index = {tag: i for i, tag in enumerate(self.labels)}
        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}
        cdef Vocab vocab = self.vocab
        if new_tag_map:
            vocab.morphology = Morphology(vocab.strings, new_tag_map,
                                          vocab.morphology.lemmatizer,
                                          exc=vocab.morphology.exc)
        if self.model is True:
            self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
            self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
        link_vectors_to_models(self.vocab)

    @classmethod
    def Model(cls, n_tags, **cfg):
        return build_tagger_model(n_tags, **cfg)

    def add_label(self, label, values=None):
        if label in self.labels:
            return 0
        if self.model not in (True, False, None):
            # Here's how the model resizing will work, once the
            # neuron-to-tag mapping is no longer controlled by
            # the Morphology class, which sorts the tag names.
            # The sorting makes adding labels difficult.
            # smaller = self.model._layers[-1]
            # larger = Softmax(len(self.labels)+1, smaller.nI)
            # copy_array(larger.W[:smaller.nO], smaller.W)
            # copy_array(larger.b[:smaller.nO], smaller.b)
            # self.model._layers[-1] = larger
            raise ValueError(
                "Resizing pre-trained Tagger models is not "
                "currently supported.")
        tag_map = dict(self.vocab.morphology.tag_map)
        if values is None:
            values = {POS: "X"}
        tag_map[label] = values
        self.vocab.morphology = Morphology(
            self.vocab.strings, tag_map=tag_map,
            lemmatizer=self.vocab.morphology.lemmatizer,
            exc=self.vocab.morphology.exc)
        return 1

    def use_params(self, params):
        with self.model.use_params(params):
            yield

    def to_bytes(self, **exclude):
        serialize = OrderedDict()
        if self.model in (None, True, False):
            serialize['model'] = lambda: self.model
        else:
            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')
        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',
                    self.cfg.get('token_vector_width', 128))
                self.model = self.Model(self.vocab.morphology.n_tags,
                                        **self.cfg)
            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),
            ('cfg', lambda b: self.cfg.update(ujson.loads(b))),
            ('model', lambda b: load_model(b)),
        ))
        util.from_bytes(bytes_data, deserialize, exclude)
        return self

    def to_disk(self, path, **exclude):
        self.cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
        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:
                self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
            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((
            ('cfg', lambda p: self.cfg.update(_load_cfg(p))),
            ('vocab', lambda p: self.vocab.from_disk(p)),
            ('tag_map', load_tag_map),
            ('model', load_model),
        ))
        util.from_disk(path, deserialize, exclude)
        return self


class MultitaskObjective(Tagger):
    """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
        if target == 'dep':
            self.make_label = self.make_dep
        elif target == 'tag':
            self.make_label = self.make_tag
        elif target == 'ent':
            self.make_label = self.make_ent
        elif target == 'dep_tag_offset':
            self.make_label = self.make_dep_tag_offset
        elif target == 'ent_tag':
            self.make_label = self.make_ent_tag
        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.")
        self.cfg = dict(cfg)
        self.cfg.setdefault('cnn_maxout_pieces', 2)
        self.cfg.setdefault('pretrained_dims',
                            self.vocab.vectors.data.shape[1])

    @property
    def labels(self):
        return self.cfg.setdefault('labels', {})

    @labels.setter
    def labels(self, value):
        self.cfg['labels'] = value

    def set_annotations(self, docs, dep_ids, tensors=None):
        pass

    def begin_training(self, gold_tuples=tuple(), pipeline=None, tok2vec=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 i in range(len(ids)):
                    label = self.make_label(i, words, tags, heads, deps, ents)
                    if label is not None and label not in self.labels:
                        self.labels[label] = len(self.labels)
        if self.model is True:
            token_vector_width = util.env_opt('token_vector_width')
            self.model = chain(
                tok2vec,
                Softmax(len(self.labels), token_vector_width)
            )
        link_vectors_to_models(self.vocab)

    @classmethod
    def Model(cls, n_tags, tok2vec=None, **cfg):
        return build_tagger_model(n_tags, tok2vec=tok2vec, **cfg)

    def get_loss(self, docs, golds, scores):
        cdef int idx = 0
        correct = numpy.zeros((scores.shape[0],), dtype='i')
        guesses = scores.argmax(axis=1)
        for gold in golds:
            for i in range(len(gold.labels)):
                label = self.make_label(i, gold.words, gold.tags, gold.heads,
                                        gold.labels, gold.ents)
                if label is None or label not in self.labels:
                    correct[idx] = guesses[idx]
                else:
                    correct[idx] = self.labels[label]
                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()
        return float(loss), d_scores

    @staticmethod
    def make_dep(i, words, tags, heads, deps, ents):
        if deps[i] is None or heads[i] is None:
            return None
        return deps[i]

    @staticmethod
    def make_tag(i, words, tags, heads, deps, ents):
        return tags[i]

    @staticmethod
    def make_ent(i, words, tags, heads, deps, ents):
        if ents is None:
            return None
        return ents[i]

    @staticmethod
    def make_dep_tag_offset(i, words, tags, heads, deps, ents):
        if deps[i] is None or heads[i] is None:
            return None
        offset = heads[i] - i
        offset = min(offset, 2)
        offset = max(offset, -2)
        return '%s-%s:%d' % (deps[i], tags[i], offset)

    @staticmethod
    def make_ent_tag(i, words, tags, heads, deps, ents):
        if ents is None or ents[i] is None:
            return None
        else:
            return '%s-%s' % (tags[i], ents[i])


class SimilarityHook(Pipe):
    """
    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, doc2)])

    def update(self, doc1_doc2, golds, sgd=None, drop=0.):
        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.

        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)
            link_vectors_to_models(self.vocab)


class TextCategorizer(Pipe):
    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.setdefault('labels', ['LABEL'])

    @labels.setter
    def labels(self, value):
        self.cfg['labels'] = value

    def __call__(self, doc):
        scores, tensors = self.predict([doc])
        self.set_annotations([doc], scores, tensors=tensors)
        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, tensors = self.predict(docs)
            self.set_annotations(docs, scores, tensors=tensors)
            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, tensors=None):
        for i, doc in enumerate(docs):
            if tensors is not None:
                doc.extend_tensor(tensors[i])
            for j, label in enumerate(self.labels):
                doc.cats[label] = float(scores[i, j])

    def update(self, docs, golds, state=None, drop=0., sgd=None, losses=None):
        scores, bp_scores = self.model.begin_update(docs, drop=drop)
        loss, d_scores = self.get_loss(docs, golds, scores)
        bp_scores(d_scores, sgd=sgd)
        if losses is not None:
            losses.setdefault(self.name, 0.0)
            losses[self.name] += loss

    def get_loss(self, docs, golds, scores):
        truths = numpy.zeros((len(golds), len(self.labels)), dtype='f')
        not_missing = numpy.ones((len(golds), len(self.labels)), dtype='f')
        for i, gold in enumerate(golds):
            for j, label in enumerate(self.labels):
                if label in gold.cats:
                    truths[i, j] = gold.cats[label]
                else:
                    not_missing[i, j] = 0.
        truths = self.model.ops.asarray(truths)
        not_missing = self.model.ops.asarray(not_missing)
        d_scores = (scores-truths) / scores.shape[0]
        d_scores *= not_missing
        mean_square_error = ((scores-truths)**2).sum(axis=1).mean()
        return mean_square_error, d_scores

    def add_label(self, label):
        if label in self.labels:
            return 0
        if self.model not in (None, True, False):
            smaller = self.model._layers[-1]
            larger = Affine(len(self.labels)+1, smaller.nI)
            copy_array(larger.W[:smaller.nO], smaller.W)
            copy_array(larger.b[:smaller.nO], smaller.b)
            self.model._layers[-1] = larger
        self.labels.append(label)
        return 1

    def begin_training(self, gold_tuples=tuple(), pipeline=None):
        if pipeline and getattr(pipeline[0], 'name', None) == 'tensorizer':
            token_vector_width = pipeline[0].model.nO
        else:
            token_vector_width = 64
        if self.model is True:
            self.cfg['pretrained_dims'] = self.vocab.vectors_length
            self.model = self.Model(len(self.labels), token_vector_width,
                                    **self.cfg)
            link_vectors_to_models(self.vocab)


cdef class DependencyParser(Parser):
    name = 'parser'
    TransitionSystem = ArcEager

    @property
    def postprocesses(self):
        return [nonproj.deprojectivize]

    def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
        for target in []:
            labeller = MultitaskObjective(self.vocab, target=target)
            tok2vec = self.model[0]
            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)


cdef class EntityRecognizer(Parser):
    name = 'ner'
    TransitionSystem = BiluoPushDown

    nr_feature = 6

    def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
        for target in []:
            labeller = MultitaskObjective(self.vocab, target=target)
            tok2vec = self.model[0]
            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)


__all__ = ['Tagger', 'DependencyParser', 'EntityRecognizer', 'Tensorizer']