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, defaultdict
import ujson

from .util import msgpack
from .util import msgpack_numpy

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 .matcher import Matcher

from .matcher import Matcher, PhraseMatcher
from .tokens.span import Span
from .attrs import POS, ID
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 ._ml import create_default_optimizer
from .errors import Errors, TempErrors
from .compat import json_dumps, basestring_
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)]


def merge_noun_chunks(doc):
    """Merge noun chunks into a single token.

    doc (Doc): The Doc object.
    RETURNS (Doc): The Doc object with merged noun chunks.
    """
    if not doc.is_parsed:
        return doc
    spans = [(np.start_char, np.end_char, np.root.tag, np.root.dep)
             for np in doc.noun_chunks]
    for start, end, tag, dep in spans:
        doc.merge(start, end, tag=tag, dep=dep)
    return doc


def merge_entities(doc):
    """Merge entities into a single token.

    doc (Doc): The Doc object.
    RETURNS (Doc): The Doc object with merged noun entities.
    """
    spans = [(e.start_char, e.end_char, e.root.tag, e.root.dep, e.label)
             for e in doc.ents]
    for start, end, tag, dep, ent_type in spans:
        doc.merge(start, end, tag=tag, dep=dep, ent_type=ent_type)
    return doc


def merge_subtokens(doc, label='subtok'):
    merger = Matcher(doc.vocab)
    merger.add('SUBTOK', None, [{'DEP': label, 'op': '+'}])
    matches = merger(doc)
    spans = [doc[start:end+1] for _, start, end in matches]
    offsets = [(span.start_char, span.end_char) for span in spans]
    for start_char, end_char in offsets:
        doc.merge(start_char, end_char)
    return doc


class EntityRuler(object):
    name = 'entity_ruler'

    def __init__(self, nlp, **cfg):
        """Initialise the entitiy ruler. If patterns are supplied here, they
        need to be a list of dictionaries with a `"label"` and `"pattern"`
        key. A pattern can either be a token pattern (list) or a phrase pattern
        (string). For example: `{'label': 'ORG', 'pattern': 'Apple'}`.

        nlp (Language): The shared nlp object to pass the vocab to the matchers
            and process phrase patterns.
        patterns (iterable): Optional patterns to load in.
        overwrite_ents (bool): If existing entities are present, e.g. entities
            added by the model, overwrite them by matches if necessary.
        **cfg: Other config parameters. If pipeline component is loaded as part
            of a model pipeline, this will include all keyword arguments passed
            to `spacy.load`.
        RETURNS (EntityRuler): The newly constructed object.
        """
        self.nlp = nlp
        self.overwrite = cfg.get('overwrite_ents', False)
        self.token_patterns = defaultdict(list)
        self.phrase_patterns = defaultdict(list)
        self.matcher = Matcher(nlp.vocab)
        self.phrase_matcher = PhraseMatcher(nlp.vocab)
        patterns = cfg.get('patterns')
        if patterns is not None:
            self.add_patterns(patterns)

    def __len__(self):
        """The number of all patterns added to the entity ruler."""
        n_token_patterns = sum(len(p) for p in self.token_patterns.values())
        n_phrase_patterns = sum(len(p) for p in self.phrase_patterns.values())
        return n_token_patterns + n_phrase_patterns

    def __contains__(self, label):
        """Whether a label is present in the patterns."""
        return label in self.token_patterns or label in self.phrase_patterns

    def __call__(self, doc):
        """Find matches in document and add them as entities.

        doc (Doc): The Doc object in the pipeline.
        RETURNS (Doc): The Doc with added entities, if available.
        """
        matches = list(self.matcher(doc)) + list(self.phrase_matcher(doc))
        matches = set([(m_id, start, end) for m_id, start, end in matches
                       if start != end])
        get_sort_key = lambda m: (m[2] - m[1], m[1])
        matches = sorted(matches, key=get_sort_key, reverse=True)
        entities = list(doc.ents)
        new_entities = []
        seen_tokens = set()
        for match_id, start, end in matches:
            if any(t.ent_type for t in doc[start:end]) and not self.overwrite:
                continue
            # check for end - 1 here because boundaries are inclusive
            if start not in seen_tokens and end - 1 not in seen_tokens:
                new_entities.append(Span(doc, start, end, label=match_id))
                entities = [e for e in entities
                            if not (e.start < end and e.end > start)]
                seen_tokens.update(range(start, end))
        doc.ents = entities + new_entities
        return doc

    @property
    def labels(self):
        """All labels present in the match patterns.

        RETURNS (set): The string labels.
        """
        all_labels = set(self.token_patterns.keys())
        all_labels.update(self.phrase_patterns.keys())
        return all_labels

    @property
    def patterns(self):
        """Get all patterns that were added to the entity ruler.

        RETURNS (list): The original patterns, one dictionary per pattern.
        """
        all_patterns = []
        for label, patterns in self.token_patterns.items():
            for pattern in patterns:
                all_patterns.append({'label': label, 'pattern': pattern})
        for label, patterns in self.phrase_patterns.items():
            for pattern in patterns:
                all_patterns.append({'label': label, 'pattern': pattern.text})
        return all_patterns

    def add_patterns(self, patterns):
        """Add patterns to the entitiy ruler. A pattern can either be a token
        pattern (list of dicts) or a phrase pattern (string). For example:
        {'label': 'ORG', 'pattern': 'Apple'}
        {'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]}

        patterns (list): The patterns to add.
        """
        for entry in patterns:
            label = entry['label']
            pattern = entry['pattern']
            if isinstance(pattern, basestring_):
                self.phrase_patterns[label].append(self.nlp(pattern))
            elif isinstance(pattern, list):
                self.token_patterns[label].append(pattern)
            else:
                raise ValueError(Errors.E097.format(pattern=pattern))
        for label, patterns in self.token_patterns.items():
            self.matcher.add(label, None, *patterns)
        for label, patterns in self.phrase_patterns.items():
            self.phrase_matcher.add(label, None, *patterns)

    def from_bytes(self, patterns_bytes, **kwargs):
        """Load the entity ruler from a bytestring.

        patterns_bytes (bytes): The bytestring to load.
        **kwargs: Other config paramters, mostly for consistency.
        RETURNS (EntityRuler): The loaded entity ruler.
        """
        patterns = msgpack.loads(patterns_bytes, raw=False)
        self.add_patterns(patterns)
        return self

    def to_bytes(self, **kwargs):
        """Serialize the entity ruler patterns to a bytestring.

        RETURNS (bytes): The serialized patterns.
        """
        return msgpack.dumps(self.patterns, use_bin_type=True)

    def from_disk(self, path, **kwargs):
        """Load the entity ruler from a file. Expects a file containing
        newline-delimited JSON (JSONL) with one entry per line.

        path (unicode / Path): The JSONL file to load.
        **kwargs: Other config paramters, mostly for consistency.
        RETURNS (EntityRuler): The loaded entity ruler.
        """
        path = util.ensure_path(path)
        path = path.with_suffix('.jsonl')
        patterns = util.read_jsonl(path)
        self.add_patterns(patterns)
        return self

    def to_disk(self, path, **kwargs):
        """Save the entity ruler patterns to a directory. The patterns will be
        saved as newline-delimited JSON (JSONL).

        path (unicode / Path): The JSONL file to load.
        **kwargs: Other config paramters, mostly for consistency.
        RETURNS (EntityRuler): The loaded entity ruler.
        """
        path = util.ensure_path(path)
        path = path.with_suffix('.jsonl')
        data = [json_dumps(line, indent=0) for line in self.patterns]
        path.open('w').write('\n'.join(data))


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 create_optimizer(self):
        return create_default_optimizer(self.model.ops,
                                        **self.cfg.get('optimizer', {}))

    def begin_training(self, get_gold_tuples=lambda: [], pipeline=None, sgd=None,
                       **kwargs):
        """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)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd

    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):
            # TODO: Remove this once we don't have to handle previous models
            if self.cfg.get('pretrained_dims') and 'pretrained_vectors' not in self.cfg:
                self.cfg['pretrained_vectors'] = self.vocab.vectors.name
            if self.model is True:
                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):
            # TODO: Remove this once we don't have to handle previous models
            if self.cfg.get('pretrained_dims') and 'pretrained_vectors' not in self.cfg:
                self.cfg['pretrained_vectors'] = self.vocab.vectors.name
            if self.model is True:
                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():
        with path.open() as file_:
            return ujson.load(file_)
    else:
        return {}


class Tensorizer(Pipe):
    """Pre-train position-sensitive vectors for tokens."""
    name = 'tensorizer'

    @classmethod
    def Model(cls, output_size=300, **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.
        """
        input_size = util.env_opt('token_vector_width', cfg.get('input_size', 128))
        return zero_init(Affine(output_size, input_size, drop_factor=0.0))

    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.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):
            if tensor.shape[0] != len(doc):
                raise ValueError(Errors.E076.format(rows=tensor.shape[0], words=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):
        ids = self.model.ops.flatten([doc.to_array(ID).ravel() for doc in docs])
        target = self.vocab.vectors.data[ids]
        d_scores = (prediction - target) / prediction.shape[0]
        loss = (d_scores**2).sum()
        return loss, d_scores

    def begin_training(self, gold_tuples=lambda: [], pipeline=None, sgd=None,
                        **kwargs):
        """Allocate models, pre-process training data and acquire an
        optimizer.

        gold_tuples (iterable): Gold-standard training data.
        pipeline (list): The pipeline the model is part of.
        """
        if pipeline is not None:
            for name, model in pipeline:
                if getattr(model, 'tok2vec', None):
                    self.input_models.append(model.tok2vec)
        if self.model is True:
            self.model = self.Model(**self.cfg)
        link_vectors_to_models(self.vocab)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd


class Tagger(Pipe):
    name = 'tagger'

    def __init__(self, vocab, model=True, **cfg):
        self.vocab = vocab
        self.model = model
        self.cfg = OrderedDict(sorted(cfg.items()))
        self.cfg.setdefault('cnn_maxout_pieces', 2)

    @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):
        if not any(len(doc) for doc in docs):
            # Handle case where there are no tokens in any docs.
            n_labels = len(self.labels)
            guesses = [self.model.ops.allocate((0, n_labels)) for doc in docs]
            tokvecs = self.model.ops.allocate((0, self.model.tok2vec.nO))
            return guesses, tokvecs
        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:
                    # Don't clobber preset lemmas
                    lemma = doc.c[j].lemma
                    vocab.morphology.assign_tag_id(&doc.c[j], tag_id)
                    if lemma != 0 and lemma != doc.c[j].lex.orth:
                        doc.c[j].lemma = lemma
                idx += 1
            if tensors is not None and len(tensors):
                if isinstance(doc.tensor, numpy.ndarray) \
                and not isinstance(tensors[i], numpy.ndarray):
                    doc.extend_tensor(tensors[i].get())
                else:
                    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)
        known_labels = numpy.ones((scores.shape[0], 1), dtype='f')
        for gold in golds:
            for tag in gold.tags:
                if tag is None:
                    correct[idx] = guesses[idx]
                elif tag in tag_index:
                    correct[idx] = tag_index[tag]
                else:
                    correct[idx] = 0
                    known_labels[idx] = 0.
                idx += 1
        correct = self.model.ops.xp.array(correct, dtype='i')
        d_scores = scores - to_categorical(correct, nb_classes=scores.shape[1])
        d_scores *= self.model.ops.asarray(known_labels)
        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, get_gold_tuples=lambda: [], pipeline=None, sgd=None,
                       **kwargs):
        orig_tag_map = dict(self.vocab.morphology.tag_map)
        new_tag_map = OrderedDict()
        for raw_text, annots_brackets in get_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)
        self.cfg['pretrained_vectors'] = kwargs.get('pretrained_vectors')
        if self.model is True:
            self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
        link_vectors_to_models(self.vocab)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd

    @classmethod
    def Model(cls, n_tags, **cfg):
        if cfg.get('pretrained_dims') and not cfg.get('pretrained_vectors'):
            raise ValueError(TempErrors.T008)
        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(TempErrors.T003)
        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['cfg'] = lambda: ujson.dumps(self.cfg)
        tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items()))
        serialize['tag_map'] = lambda: msgpack.dumps(
            tag_map, use_bin_type=True)
        return util.to_bytes(serialize, exclude)

    def from_bytes(self, bytes_data, **exclude):
        def load_model(b):
            # TODO: Remove this once we don't have to handle previous models
            if self.cfg.get('pretrained_dims') and 'pretrained_vectors' not in self.cfg:
                self.cfg['pretrained_vectors'] = self.vocab.vectors.name

            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, raw=False)
            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):
        tag_map = OrderedDict(sorted(self.vocab.morphology.tag_map.items()))
        serialize = OrderedDict((
            ('vocab', lambda p: self.vocab.to_disk(p)),
            ('tag_map', lambda p: p.open('wb').write(msgpack.dumps(
                tag_map, use_bin_type=True))),
            ('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):
            # TODO: Remove this once we don't have to handle previous models
            if self.cfg.get('pretrained_dims') and 'pretrained_vectors' not in self.cfg:
                self.cfg['pretrained_vectors'] = self.vocab.vectors.name
            if self.model is True:
                self.model = self.Model(self.vocab.morphology.n_tags, **self.cfg)
            with p.open('rb') as file_:
                self.model.from_bytes(file_.read())

        def load_tag_map(p):
            with p.open('rb') as file_:
                tag_map = msgpack.loads(file_.read(), raw=False)
            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 target == 'sent_start':
            self.make_label = self.make_sent_start
        elif hasattr(target, '__call__'):
            self.make_label = target
        else:
            raise ValueError(Errors.E016)
        self.cfg = dict(cfg)
        self.cfg.setdefault('cnn_maxout_pieces', 2)

    @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, get_gold_tuples=lambda: [], pipeline=None, tok2vec=None,
                       sgd=None, **kwargs):
        gold_tuples = nonproj.preprocess_training_data(get_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 = self.Model(len(self.labels), tok2vec=tok2vec)
        link_vectors_to_models(self.vocab)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd

    @classmethod
    def Model(cls, n_tags, tok2vec=None, **cfg):
        token_vector_width = util.env_opt('token_vector_width', 128)
        softmax = Softmax(n_tags, token_vector_width)
        model = chain(
            tok2vec,
            softmax
        )
        model.tok2vec = tok2vec
        model.softmax = softmax
        return model

    def predict(self, docs):
        tokvecs = self.model.tok2vec(docs)
        scores = self.model.softmax(tokvecs)
        return tokvecs, scores

    def get_loss(self, docs, golds, scores):
        if len(docs) != len(golds):
            raise ValueError(Errors.E077.format(value='loss', n_docs=len(docs),
                                                n_golds=len(golds)))
        cdef int idx = 0
        correct = numpy.zeros((scores.shape[0],), dtype='i')
        guesses = scores.argmax(axis=1)
        for i, gold in enumerate(golds):
            for j in range(len(docs[i])):
                # Handes alignment for tokenization differences
                label = self.make_label(j, 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])
        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])

    @staticmethod
    def make_sent_start(target, words, tags, heads, deps, ents, cache=True, _cache={}):
        '''A multi-task objective for representing sentence boundaries,
        using BILU scheme. (O is impossible)

        The implementation of this method uses an internal cache that relies
        on the identity of the heads array, to avoid requiring a new piece
        of gold data. You can pass cache=False if you know the cache will
        do the wrong thing.
        '''
        assert len(words) == len(heads)
        assert target < len(words), (target, len(words))
        if cache:
            if id(heads) in _cache:
                return _cache[id(heads)][target]
            else:
                for key in list(_cache.keys()):
                    _cache.pop(key)
            sent_tags = ['I-SENT'] * len(words)
            _cache[id(heads)] = sent_tags
        else:
            sent_tags = ['I-SENT'] * len(words)

        def _find_root(child):
            seen = set([child])
            while child is not None and heads[child] != child:
                seen.add(child)
                child = heads[child]
            return child

        sentences = {}
        for i in range(len(words)):
            root = _find_root(i)
            if root is None:
                sent_tags[i] = None
            else:
                sentences.setdefault(root, []).append(i)
        for root, span in sorted(sentences.items()):
            if len(span) == 1:
                sent_tags[span[0]] = 'U-SENT'
            else:
                sent_tags[span[0]] = 'B-SENT'
                sent_tags[span[-1]] = 'L-SENT'
        return sent_tags[target]


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, sgd=None, **kwargs):
        """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)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd


class TextCategorizer(Pipe):
    name = 'textcat'

    @classmethod
    def Model(cls, nr_class, **cfg):
        return build_text_classifier(nr_class, **cfg)

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


    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', [])

    @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)
        tensors = [doc.tensor for doc in docs]
        return scores, tensors

    def set_annotations(self, docs, scores, tensors=None):
        for i, doc in enumerate(docs):
            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 float(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):
            # This functionality was available previously, but was broken.
            # The problem is that we resize the last layer, but the last layer
            # is actually just an ensemble. We're not resizing the child layers
            # -- a huge problem.
            raise ValueError(
                "Cannot currently add labels to pre-trained text classifier. "
                "Add labels before training begins. This functionality was "
                "available in previous versions, but had significant bugs that "
                "let to poor performance")
            #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, get_gold_tuples=lambda: [], pipeline=None, sgd=None,
                       **kwargs):
        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_vectors'] = kwargs.get('pretrained_vectors')
            self.model = self.Model(len(self.labels), **self.cfg)
            link_vectors_to_models(self.vocab)
        if sgd is None:
            sgd = self.create_optimizer()
        return sgd


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

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

    def add_multitask_objective(self, target):
        labeller = MultitaskObjective(self.vocab, target=target)
        self._multitasks.append(labeller)

    def init_multitask_objectives(self, get_gold_tuples, pipeline, sgd=None, **cfg):
        for labeller in self._multitasks:
            tok2vec = self.model.tok2vec
            labeller.begin_training(get_gold_tuples, pipeline=pipeline,
                                    tok2vec=tok2vec, sgd=sgd)

    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 add_multitask_objective(self, target):
        labeller = MultitaskObjective(self.vocab, target=target)
        self._multitasks.append(labeller)

    def init_multitask_objectives(self, get_gold_tuples, pipeline, sgd=None, **cfg):
        for labeller in self._multitasks:
            tok2vec = self.model.tok2vec
            labeller.begin_training(get_gold_tuples, pipeline=pipeline,
                                    tok2vec=tok2vec)

    def __reduce__(self):
        return (EntityRecognizer, (self.vocab, self.moves, self.model),
                None, None)

    @property
    def labels(self):
        # Get the labels from the model by looking at the available moves, e.g.
        # B-PERSON, I-PERSON, L-PERSON, U-PERSON
        return [move.split('-')[1] for move in self.move_names
                if move[0] in ('B', 'I', 'L', 'U')]


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